Split (1)

train · 5k rows

id int64 953M 3.35B	number int64 2.72k 7.75k	title stringlengths 1 290	state stringclasses 2 values	created_at timestamp[s]date 2021-07-26 12:21:17 2025-08-23 00:18:43	updated_at timestamp[s]date 2021-07-26 13:27:59 2025-08-23 12:34:39	closed_at timestamp[s]date 2021-07-26 13:27:59 2025-08-20 16:35:55 ⌀	html_url stringlengths 49 51	pull_request dict	user_login stringlengths 3 26	is_pull_request bool 2 classes	comments listlengths 0 30
2,383,151,220	7,015	add split argument to Generator	closed	2024-07-01T08:09:25	2024-07-26T09:37:51	2024-07-26T09:31:56	https://github.com/huggingface/datasets/pull/7015	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/7015", "html_url": "https://github.com/huggingface/datasets/pull/7015", "diff_url": "https://github.com/huggingface/datasets/pull/7015.diff", "patch_url": "https://github.com/huggingface/datasets/pull/7015.patch", "merged_at": "2024-07-26T09:31:55" }	piercus	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7015). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@albertvillanova thanks for the review, please take a look", "@albertvillanova please take a look", "Thank you again! Your PR is merged.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005267 / 0.011353 (-0.006086) \| 0.003711 / 0.011008 (-0.007297) \| 0.062288 / 0.038508 (0.023780) \| 0.031357 / 0.023109 (0.008248) \| 0.233592 / 0.275898 (-0.042306) \| 0.257722 / 0.323480 (-0.065758) \| 0.003124 / 0.007986 (-0.004861) \| 0.003335 / 0.004328 (-0.000994) \| 0.048594 / 0.004250 (0.044344) \| 0.043853 / 0.037052 (0.006801) \| 0.248589 / 0.258489 (-0.009900) \| 0.278474 / 0.293841 (-0.015367) \| 0.029573 / 0.128546 (-0.098973) \| 0.011779 / 0.075646 (-0.063868) \| 0.204989 / 0.419271 (-0.214282) \| 0.035734 / 0.043533 (-0.007799) \| 0.240064 / 0.255139 (-0.015075) \| 0.263105 / 0.283200 (-0.020094) \| 0.018764 / 0.141683 (-0.122919) \| 1.115705 / 1.452155 (-0.336449) \| 1.175457 / 1.492716 (-0.317260) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092664 / 0.018006 (0.074657) \| 0.297893 / 0.000490 (0.297403) \| 0.000217 / 0.000200 (0.000017) \| 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) \| 0.019056 / 0.037411 (-0.018355) \| 0.062472 / 0.014526 (0.047946) \| 0.073462 / 0.176557 (-0.103094) \| 0.119723 / 0.737135 (-0.617412) \| 0.074420 / 0.296338 (-0.221919) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283131 / 0.215209 (0.067922) \| 2.776694 / 2.077655 (0.699039) \| 1.455586 / 1.504120 (-0.048534) \| 1.323902 / 1.541195 (-0.217293) \| 1.333169 / 1.468490 (-0.135321) \| 0.723921 / 4.584777 (-3.860856) \| 2.385842 / 3.745712 (-1.359870) \| 2.926843 / 5.269862 (-2.343018) \| 1.896773 / 4.565676 (-2.668903) \| 0.079754 / 0.424275 (-0.344521) \| 0.005188 / 0.007607 (-0.002419) \| 0.342466 / 0.226044 (0.116421) \| 3.404204 / 2.268929 (1.135275) \| 1.856575 / 55.444624 (-53.588049) \| 1.554507 / 6.876477 (-5.321970) \| 1.564065 / 2.142072 (-0.578007) \| 0.810363 / 4.805227 (-3.994864) \| 0.135537 / 6.500664 (-6.365127) \| 0.041987 / 0.075469 (-0.033482) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.962288 / 1.841788 (-0.879500) \| 11.310837 / 8.074308 (3.236529) \| 9.630034 / 10.191392 (-0.561358) \| 0.131108 / 0.680424 (-0.549316) \| 0.015225 / 0.534201 (-0.518976) \| 0.304211 / 0.579283 (-0.275072) \| 0.272707 / 0.434364 (-0.161657) \| 0.341550 / 0.540337 (-0.198787) \| 0.444528 / 1.386936 (-0.942408) \|\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) \| 0.005665 / 0.011353 (-0.005688) \| 0.003916 / 0.011008 (-0.007092) \| 0.049946 / 0.038508 (0.011438) \| 0.031760 / 0.023109 (0.008651) \| 0.273826 / 0.275898 (-0.002072) \| 0.300193 / 0.323480 (-0.023287) \| 0.004350 / 0.007986 (-0.003635) \| 0.002749 / 0.004328 (-0.001579) \| 0.048451 / 0.004250 (0.044201) \| 0.039798 / 0.037052 (0.002746) \| 0.284570 / 0.258489 (0.026081) \| 0.318855 / 0.293841 (0.025014) \| 0.032724 / 0.128546 (-0.095822) \| 0.012103 / 0.075646 (-0.063543) \| 0.059857 / 0.419271 (-0.359414) \| 0.034185 / 0.043533 (-0.009348) \| 0.276079 / 0.255139 (0.020940) \| 0.294070 / 0.283200 (0.010871) \| 0.018168 / 0.141683 (-0.123515) \| 1.149681 / 1.452155 (-0.302473) \| 1.191349 / 1.492716 (-0.301367) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092676 / 0.018006 (0.074669) \| 0.304971 / 0.000490 (0.304481) \| 0.000203 / 0.000200 (0.000003) \| 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) \| 0.023110 / 0.037411 (-0.014301) \| 0.079117 / 0.014526 (0.064591) \| 0.087457 / 0.176557 (-0.089099) \| 0.128295 / 0.737135 (-0.608840) \| 0.089747 / 0.296338 (-0.206592) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.305158 / 0.215209 (0.089949) \| 2.992277 / 2.077655 (0.914623) \| 1.595369 / 1.504120 (0.091249) \| 1.462955 / 1.541195 (-0.078240) \| 1.476269 / 1.468490 (0.007779) \| 0.731652 / 4.584777 (-3.853124) \| 0.961053 / 3.745712 (-2.784659) \| 2.800259 / 5.269862 (-2.469602) \| 1.881249 / 4.565676 (-2.684428) \| 0.079503 / 0.424275 (-0.344772) \| 0.005252 / 0.007607 (-0.002355) \| 0.354921 / 0.226044 (0.128877) \| 3.495272 / 2.268929 (1.226343) \| 1.956419 / 55.444624 (-53.488205) \| 1.654941 / 6.876477 (-5.221536) \| 1.782506 / 2.142072 (-0.359567) \| 0.816487 / 4.805227 (-3.988741) \| 0.135870 / 6.500664 (-6.364794) \| 0.041114 / 0.075469 (-0.034355) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.050346 / 1.841788 (-0.791442) \| 12.510129 / 8.074308 (4.435821) \| 10.524835 / 10.191392 (0.333443) \| 0.152388 / 0.680424 (-0.528036) \| 0.016073 / 0.534201 (-0.518128) \| 0.301956 / 0.579283 (-0.277327) \| 0.126871 / 0.434364 (-0.307493) \| 0.339554 / 0.540337 (-0.200783) \| 0.435873 / 1.386936 (-0.951064) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ead089d949febdce415d79ef3802e188316c0b26 \"CML watermark\")\n" ]
2,382,985,847	7,014	Skip faiss tests on Windows to avoid running CI for 360 minutes	closed	2024-07-01T06:45:35	2024-07-01T07:16:36	2024-07-01T07:10:27	https://github.com/huggingface/datasets/pull/7014	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/7014", "html_url": "https://github.com/huggingface/datasets/pull/7014", "diff_url": "https://github.com/huggingface/datasets/pull/7014.diff", "patch_url": "https://github.com/huggingface/datasets/pull/7014.patch", "merged_at": "2024-07-01T07:10:27" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7014). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "The failing CI tests are unrelated to this PR.\r\n\r\nWe can see that now the integration tests on Windows finish in a reasonable amount of time, e.g. 8m 10s.", "<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) \| 0.005219 / 0.011353 (-0.006134) \| 0.003825 / 0.011008 (-0.007183) \| 0.063082 / 0.038508 (0.024574) \| 0.031258 / 0.023109 (0.008149) \| 0.232288 / 0.275898 (-0.043610) \| 0.261140 / 0.323480 (-0.062340) \| 0.003185 / 0.007986 (-0.004801) \| 0.002807 / 0.004328 (-0.001522) \| 0.049438 / 0.004250 (0.045188) \| 0.045112 / 0.037052 (0.008059) \| 0.245327 / 0.258489 (-0.013162) \| 0.277941 / 0.293841 (-0.015900) \| 0.029190 / 0.128546 (-0.099357) \| 0.012071 / 0.075646 (-0.063575) \| 0.204351 / 0.419271 (-0.214921) \| 0.036546 / 0.043533 (-0.006987) \| 0.235999 / 0.255139 (-0.019140) \| 0.269069 / 0.283200 (-0.014131) \| 0.019047 / 0.141683 (-0.122636) \| 1.117213 / 1.452155 (-0.334941) \| 1.202807 / 1.492716 (-0.289909) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096680 / 0.018006 (0.078674) \| 0.304513 / 0.000490 (0.304023) \| 0.000211 / 0.000200 (0.000011) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019526 / 0.037411 (-0.017885) \| 0.062239 / 0.014526 (0.047713) \| 0.073988 / 0.176557 (-0.102569) \| 0.122156 / 0.737135 (-0.614980) \| 0.075727 / 0.296338 (-0.220611) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284125 / 0.215209 (0.068916) \| 2.804235 / 2.077655 (0.726581) \| 1.463729 / 1.504120 (-0.040390) \| 1.337854 / 1.541195 (-0.203341) \| 1.340435 / 1.468490 (-0.128055) \| 0.711647 / 4.584777 (-3.873130) \| 2.365194 / 3.745712 (-1.380518) \| 2.839193 / 5.269862 (-2.430669) \| 1.909730 / 4.565676 (-2.655947) \| 0.077399 / 0.424275 (-0.346876) \| 0.005432 / 0.007607 (-0.002175) \| 0.332281 / 0.226044 (0.106236) \| 3.301854 / 2.268929 (1.032925) \| 1.836672 / 55.444624 (-53.607952) \| 1.511144 / 6.876477 (-5.365333) \| 1.624167 / 2.142072 (-0.517905) \| 0.803453 / 4.805227 (-4.001775) \| 0.132760 / 6.500664 (-6.367904) \| 0.042323 / 0.075469 (-0.033146) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.951576 / 1.841788 (-0.890212) \| 11.476809 / 8.074308 (3.402501) \| 9.208285 / 10.191392 (-0.983107) \| 0.131797 / 0.680424 (-0.548626) \| 0.014362 / 0.534201 (-0.519839) \| 0.316051 / 0.579283 (-0.263232) \| 0.269250 / 0.434364 (-0.165114) \| 0.366949 / 0.540337 (-0.173388) \| 0.471047 / 1.386936 (-0.915889) \|\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) \| 0.005905 / 0.011353 (-0.005448) \| 0.003892 / 0.011008 (-0.007116) \| 0.050513 / 0.038508 (0.012005) \| 0.030903 / 0.023109 (0.007794) \| 0.268835 / 0.275898 (-0.007063) \| 0.288825 / 0.323480 (-0.034655) \| 0.004372 / 0.007986 (-0.003614) \| 0.002805 / 0.004328 (-0.001523) \| 0.048497 / 0.004250 (0.044246) \| 0.040665 / 0.037052 (0.003613) \| 0.279842 / 0.258489 (0.021352) \| 0.310715 / 0.293841 (0.016874) \| 0.032133 / 0.128546 (-0.096413) \| 0.012288 / 0.075646 (-0.063358) \| 0.059719 / 0.419271 (-0.359552) \| 0.033825 / 0.043533 (-0.009708) \| 0.264670 / 0.255139 (0.009531) \| 0.283799 / 0.283200 (0.000599) \| 0.017968 / 0.141683 (-0.123715) \| 1.160578 / 1.452155 (-0.291577) \| 1.198602 / 1.492716 (-0.294115) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094388 / 0.018006 (0.076382) \| 0.301861 / 0.000490 (0.301371) \| 0.000212 / 0.000200 (0.000012) \| 0.000045 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022901 / 0.037411 (-0.014510) \| 0.076816 / 0.014526 (0.062290) \| 0.089203 / 0.176557 (-0.087354) \| 0.129040 / 0.737135 (-0.608096) \| 0.090758 / 0.296338 (-0.205580) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.301191 / 0.215209 (0.085982) \| 2.962887 / 2.077655 (0.885232) \| 1.607134 / 1.504120 (0.103014) \| 1.477817 / 1.541195 (-0.063377) \| 1.485984 / 1.468490 (0.017494) \| 0.717358 / 4.584777 (-3.867419) \| 0.976018 / 3.745712 (-2.769694) \| 2.951509 / 5.269862 (-2.318352) \| 1.910619 / 4.565676 (-2.655057) \| 0.078579 / 0.424275 (-0.345697) \| 0.005209 / 0.007607 (-0.002398) \| 0.345287 / 0.226044 (0.119243) \| 3.487012 / 2.268929 (1.218084) \| 1.938104 / 55.444624 (-53.506521) \| 1.639341 / 6.876477 (-5.237136) \| 1.617874 / 2.142072 (-0.524198) \| 0.793721 / 4.805227 (-4.011506) \| 0.136834 / 6.500664 (-6.363830) \| 0.041211 / 0.075469 (-0.034258) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.988106 / 1.841788 (-0.853682) \| 12.035176 / 8.074308 (3.960868) \| 10.594559 / 10.191392 (0.403167) \| 0.149917 / 0.680424 (-0.530507) \| 0.015913 / 0.534201 (-0.518288) \| 0.307658 / 0.579283 (-0.271625) \| 0.130645 / 0.434364 (-0.303719) \| 0.348450 / 0.540337 (-0.191887) \| 0.443559 / 1.386936 (-0.943377) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9af8dd3de7626183a9a9ec8973cebc672d690400 \"CML watermark\")\n" ]
2,382,976,738	7,013	CI is broken for faiss tests on Windows: node down: Not properly terminated	closed	2024-07-01T06:40:03	2024-07-01T07:10:28	2024-07-01T07:10:28	https://github.com/huggingface/datasets/issues/7013	null	albertvillanova	false	[]
2,380,934,047	7,012	Raise an error when a nested object is expected to be a mapping that displays the object	closed	2024-06-28T18:10:59	2024-07-11T02:06:16	2024-07-11T02:06:16	https://github.com/huggingface/datasets/pull/7012	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/7012", "html_url": "https://github.com/huggingface/datasets/pull/7012", "diff_url": "https://github.com/huggingface/datasets/pull/7012.diff", "patch_url": "https://github.com/huggingface/datasets/pull/7012.patch", "merged_at": null }	sebbyjp	true	[]
2,379,785,262	7,011	Re-enable raising error from huggingface-hub FutureWarning in CI	closed	2024-06-28T07:28:32	2024-06-28T12:25:25	2024-06-28T12:19:28	https://github.com/huggingface/datasets/pull/7011	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/7011", "html_url": "https://github.com/huggingface/datasets/pull/7011", "diff_url": "https://github.com/huggingface/datasets/pull/7011.diff", "patch_url": "https://github.com/huggingface/datasets/pull/7011.patch", "merged_at": "2024-06-28T12:19:28" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7011). 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) \| 0.005589 / 0.011353 (-0.005764) \| 0.003855 / 0.011008 (-0.007153) \| 0.063445 / 0.038508 (0.024937) \| 0.030815 / 0.023109 (0.007706) \| 0.244052 / 0.275898 (-0.031846) \| 0.269916 / 0.323480 (-0.053563) \| 0.003130 / 0.007986 (-0.004856) \| 0.003349 / 0.004328 (-0.000980) \| 0.049338 / 0.004250 (0.045088) \| 0.045314 / 0.037052 (0.008261) \| 0.250646 / 0.258489 (-0.007844) \| 0.295828 / 0.293841 (0.001987) \| 0.029808 / 0.128546 (-0.098738) \| 0.012299 / 0.075646 (-0.063347) \| 0.204946 / 0.419271 (-0.214325) \| 0.036387 / 0.043533 (-0.007146) \| 0.244316 / 0.255139 (-0.010823) \| 0.269308 / 0.283200 (-0.013892) \| 0.019226 / 0.141683 (-0.122457) \| 1.138739 / 1.452155 (-0.313416) \| 1.155265 / 1.492716 (-0.337451) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094085 / 0.018006 (0.076078) \| 0.299764 / 0.000490 (0.299275) \| 0.000205 / 0.000200 (0.000005) \| 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) \| 0.018361 / 0.037411 (-0.019050) \| 0.062665 / 0.014526 (0.048139) \| 0.075888 / 0.176557 (-0.100668) \| 0.120915 / 0.737135 (-0.616221) \| 0.075465 / 0.296338 (-0.220873) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279698 / 0.215209 (0.064489) \| 2.784544 / 2.077655 (0.706889) \| 1.498441 / 1.504120 (-0.005679) \| 1.379789 / 1.541195 (-0.161406) \| 1.388480 / 1.468490 (-0.080011) \| 0.724249 / 4.584777 (-3.860528) \| 2.343139 / 3.745712 (-1.402573) \| 2.816179 / 5.269862 (-2.453683) \| 1.908737 / 4.565676 (-2.656940) \| 0.077686 / 0.424275 (-0.346589) \| 0.005444 / 0.007607 (-0.002163) \| 0.344084 / 0.226044 (0.118039) \| 3.367548 / 2.268929 (1.098619) \| 1.849200 / 55.444624 (-53.595424) \| 1.556390 / 6.876477 (-5.320087) \| 1.672902 / 2.142072 (-0.469170) \| 0.795457 / 4.805227 (-4.009770) \| 0.133521 / 6.500664 (-6.367143) \| 0.042883 / 0.075469 (-0.032586) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.959094 / 1.841788 (-0.882694) \| 11.399783 / 8.074308 (3.325475) \| 9.075784 / 10.191392 (-1.115608) \| 0.142897 / 0.680424 (-0.537527) \| 0.014765 / 0.534201 (-0.519436) \| 0.302259 / 0.579283 (-0.277024) \| 0.261148 / 0.434364 (-0.173216) \| 0.340302 / 0.540337 (-0.200035) \| 0.459203 / 1.386936 (-0.927733) \|\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) \| 0.005821 / 0.011353 (-0.005532) \| 0.003964 / 0.011008 (-0.007044) \| 0.049904 / 0.038508 (0.011396) \| 0.031061 / 0.023109 (0.007952) \| 0.270002 / 0.275898 (-0.005896) \| 0.289489 / 0.323480 (-0.033991) \| 0.004477 / 0.007986 (-0.003509) \| 0.002800 / 0.004328 (-0.001528) \| 0.048029 / 0.004250 (0.043779) \| 0.040486 / 0.037052 (0.003434) \| 0.278442 / 0.258489 (0.019953) \| 0.312606 / 0.293841 (0.018765) \| 0.032920 / 0.128546 (-0.095626) \| 0.012572 / 0.075646 (-0.063075) \| 0.060589 / 0.419271 (-0.358682) \| 0.034147 / 0.043533 (-0.009386) \| 0.275282 / 0.255139 (0.020143) \| 0.314073 / 0.283200 (0.030873) \| 0.017555 / 0.141683 (-0.124128) \| 1.149974 / 1.452155 (-0.302181) \| 1.183715 / 1.492716 (-0.309002) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095616 / 0.018006 (0.077610) \| 0.302101 / 0.000490 (0.301611) \| 0.000201 / 0.000200 (0.000001) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022245 / 0.037411 (-0.015166) \| 0.076890 / 0.014526 (0.062364) \| 0.088471 / 0.176557 (-0.088085) \| 0.128364 / 0.737135 (-0.608771) \| 0.089907 / 0.296338 (-0.206431) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302662 / 0.215209 (0.087453) \| 2.979054 / 2.077655 (0.901399) \| 1.576534 / 1.504120 (0.072414) \| 1.443784 / 1.541195 (-0.097410) \| 1.476000 / 1.468490 (0.007510) \| 0.740580 / 4.584777 (-3.844197) \| 0.953349 / 3.745712 (-2.792363) \| 2.925619 / 5.269862 (-2.344243) \| 1.904701 / 4.565676 (-2.660975) \| 0.078404 / 0.424275 (-0.345872) \| 0.005179 / 0.007607 (-0.002429) \| 0.357217 / 0.226044 (0.131173) \| 3.494812 / 2.268929 (1.225884) \| 1.927345 / 55.444624 (-53.517280) \| 1.627162 / 6.876477 (-5.249315) \| 1.676748 / 2.142072 (-0.465324) \| 0.798826 / 4.805227 (-4.006401) \| 0.133617 / 6.500664 (-6.367047) \| 0.041229 / 0.075469 (-0.034240) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.017046 / 1.841788 (-0.824742) \| 12.045942 / 8.074308 (3.971634) \| 10.430383 / 10.191392 (0.238991) \| 0.144497 / 0.680424 (-0.535926) \| 0.015809 / 0.534201 (-0.518392) \| 0.304701 / 0.579283 (-0.274582) \| 0.126496 / 0.434364 (-0.307868) \| 0.340308 / 0.540337 (-0.200030) \| 0.434917 / 1.386936 (-0.952019) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#054e57a8468af9fff5b75c08d2d6adf3e05fa763 \"CML watermark\")\n" ]
2,379,777,480	7,010	Re-enable raising error from huggingface-hub FutureWarning in CI	closed	2024-06-28T07:23:40	2024-06-28T12:19:30	2024-06-28T12:19:29	https://github.com/huggingface/datasets/issues/7010	null	albertvillanova	false	[]
2,379,619,132	7,009	Support ruff 0.5.0 in CI	closed	2024-06-28T05:37:36	2024-06-28T07:17:26	2024-06-28T07:11:17	https://github.com/huggingface/datasets/pull/7009	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/7009", "html_url": "https://github.com/huggingface/datasets/pull/7009", "diff_url": "https://github.com/huggingface/datasets/pull/7009.diff", "patch_url": "https://github.com/huggingface/datasets/pull/7009.patch", "merged_at": "2024-06-28T07:11:17" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7009). 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) \| 0.005481 / 0.011353 (-0.005872) \| 0.003580 / 0.011008 (-0.007428) \| 0.062682 / 0.038508 (0.024174) \| 0.031125 / 0.023109 (0.008015) \| 0.239443 / 0.275898 (-0.036455) \| 0.262950 / 0.323480 (-0.060529) \| 0.003129 / 0.007986 (-0.004857) \| 0.003393 / 0.004328 (-0.000935) \| 0.048765 / 0.004250 (0.044514) \| 0.044363 / 0.037052 (0.007311) \| 0.248632 / 0.258489 (-0.009857) \| 0.285056 / 0.293841 (-0.008785) \| 0.029674 / 0.128546 (-0.098872) \| 0.011963 / 0.075646 (-0.063684) \| 0.204122 / 0.419271 (-0.215150) \| 0.035867 / 0.043533 (-0.007665) \| 0.245422 / 0.255139 (-0.009717) \| 0.267165 / 0.283200 (-0.016035) \| 0.018556 / 0.141683 (-0.123127) \| 1.132112 / 1.452155 (-0.320043) \| 1.173512 / 1.492716 (-0.319204) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092749 / 0.018006 (0.074743) \| 0.298946 / 0.000490 (0.298457) \| 0.000211 / 0.000200 (0.000011) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019496 / 0.037411 (-0.017915) \| 0.062209 / 0.014526 (0.047683) \| 0.074656 / 0.176557 (-0.101901) \| 0.121238 / 0.737135 (-0.615897) \| 0.075810 / 0.296338 (-0.220528) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278089 / 0.215209 (0.062880) \| 2.725602 / 2.077655 (0.647948) \| 1.413346 / 1.504120 (-0.090774) \| 1.290352 / 1.541195 (-0.250843) \| 1.306732 / 1.468490 (-0.161758) \| 0.713945 / 4.584777 (-3.870832) \| 2.380131 / 3.745712 (-1.365581) \| 2.804548 / 5.269862 (-2.465314) \| 1.896506 / 4.565676 (-2.669170) \| 0.078303 / 0.424275 (-0.345972) \| 0.005475 / 0.007607 (-0.002132) \| 0.340162 / 0.226044 (0.114117) \| 3.355732 / 2.268929 (1.086803) \| 1.776012 / 55.444624 (-53.668613) \| 1.507006 / 6.876477 (-5.369471) \| 1.607234 / 2.142072 (-0.534838) \| 0.796458 / 4.805227 (-4.008769) \| 0.135888 / 6.500664 (-6.364776) \| 0.042352 / 0.075469 (-0.033118) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.988337 / 1.841788 (-0.853450) \| 11.299311 / 8.074308 (3.225003) \| 9.166845 / 10.191392 (-1.024547) \| 0.140351 / 0.680424 (-0.540073) \| 0.013932 / 0.534201 (-0.520269) \| 0.302157 / 0.579283 (-0.277126) \| 0.259355 / 0.434364 (-0.175009) \| 0.339850 / 0.540337 (-0.200488) \| 0.465345 / 1.386936 (-0.921591) \|\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) \| 0.005707 / 0.011353 (-0.005646) \| 0.003846 / 0.011008 (-0.007162) \| 0.050100 / 0.038508 (0.011591) \| 0.031810 / 0.023109 (0.008701) \| 0.265120 / 0.275898 (-0.010778) \| 0.286635 / 0.323480 (-0.036845) \| 0.004329 / 0.007986 (-0.003657) \| 0.002757 / 0.004328 (-0.001571) \| 0.050864 / 0.004250 (0.046614) \| 0.039872 / 0.037052 (0.002820) \| 0.277675 / 0.258489 (0.019186) \| 0.310251 / 0.293841 (0.016410) \| 0.032458 / 0.128546 (-0.096088) \| 0.012072 / 0.075646 (-0.063574) \| 0.060539 / 0.419271 (-0.358733) \| 0.033772 / 0.043533 (-0.009761) \| 0.265992 / 0.255139 (0.010853) \| 0.286152 / 0.283200 (0.002953) \| 0.018210 / 0.141683 (-0.123473) \| 1.151461 / 1.452155 (-0.300694) \| 1.199998 / 1.492716 (-0.292718) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094109 / 0.018006 (0.076103) \| 0.298190 / 0.000490 (0.297701) \| 0.000199 / 0.000200 (-0.000001) \| 0.000045 / 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) \| 0.022431 / 0.037411 (-0.014980) \| 0.076319 / 0.014526 (0.061794) \| 0.090023 / 0.176557 (-0.086533) \| 0.128189 / 0.737135 (-0.608946) \| 0.089564 / 0.296338 (-0.206774) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298887 / 0.215209 (0.083678) \| 2.928580 / 2.077655 (0.850926) \| 1.565379 / 1.504120 (0.061259) \| 1.424704 / 1.541195 (-0.116490) \| 1.446336 / 1.468490 (-0.022154) \| 0.716348 / 4.584777 (-3.868429) \| 0.967465 / 3.745712 (-2.778247) \| 2.967318 / 5.269862 (-2.302544) \| 1.918878 / 4.565676 (-2.646798) \| 0.077167 / 0.424275 (-0.347108) \| 0.005271 / 0.007607 (-0.002336) \| 0.342376 / 0.226044 (0.116332) \| 3.386044 / 2.268929 (1.117115) \| 1.915308 / 55.444624 (-53.529316) \| 1.612729 / 6.876477 (-5.263748) \| 1.621278 / 2.142072 (-0.520794) \| 0.804639 / 4.805227 (-4.000589) \| 0.132596 / 6.500664 (-6.368069) \| 0.041075 / 0.075469 (-0.034394) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.996521 / 1.841788 (-0.845267) \| 12.328856 / 8.074308 (4.254548) \| 10.585154 / 10.191392 (0.393762) \| 0.131720 / 0.680424 (-0.548704) \| 0.016777 / 0.534201 (-0.517424) \| 0.300424 / 0.579283 (-0.278860) \| 0.128526 / 0.434364 (-0.305838) \| 0.339961 / 0.540337 (-0.200377) \| 0.441661 / 1.386936 (-0.945275) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a16477ddf8f96e590e9597225a5d180cce343f26 \"CML watermark\")\n" ]
2,379,591,141	7,008	Support ruff 0.5.0 in CI	closed	2024-06-28T05:11:26	2024-06-28T07:11:18	2024-06-28T07:11:18	https://github.com/huggingface/datasets/issues/7008	null	albertvillanova	false	[]
2,379,588,676	7,007	Fix CI by temporarily pinning ruff < 0.5.0	closed	2024-06-28T05:09:17	2024-06-28T05:31:21	2024-06-28T05:25:17	https://github.com/huggingface/datasets/pull/7007	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/7007", "html_url": "https://github.com/huggingface/datasets/pull/7007", "diff_url": "https://github.com/huggingface/datasets/pull/7007.diff", "patch_url": "https://github.com/huggingface/datasets/pull/7007.patch", "merged_at": "2024-06-28T05:25:17" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7007). 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) \| 0.005225 / 0.011353 (-0.006128) \| 0.003856 / 0.011008 (-0.007152) \| 0.063455 / 0.038508 (0.024947) \| 0.030184 / 0.023109 (0.007075) \| 0.248518 / 0.275898 (-0.027380) \| 0.270596 / 0.323480 (-0.052884) \| 0.003185 / 0.007986 (-0.004800) \| 0.002739 / 0.004328 (-0.001590) \| 0.049379 / 0.004250 (0.045129) \| 0.043190 / 0.037052 (0.006138) \| 0.257181 / 0.258489 (-0.001308) \| 0.283385 / 0.293841 (-0.010456) \| 0.029702 / 0.128546 (-0.098844) \| 0.012022 / 0.075646 (-0.063624) \| 0.204531 / 0.419271 (-0.214741) \| 0.035621 / 0.043533 (-0.007912) \| 0.257745 / 0.255139 (0.002606) \| 0.269033 / 0.283200 (-0.014167) \| 0.019283 / 0.141683 (-0.122400) \| 1.152477 / 1.452155 (-0.299678) \| 1.180929 / 1.492716 (-0.311788) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094520 / 0.018006 (0.076514) \| 0.299383 / 0.000490 (0.298893) \| 0.000224 / 0.000200 (0.000024) \| 0.000052 / 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) \| 0.019267 / 0.037411 (-0.018145) \| 0.062458 / 0.014526 (0.047933) \| 0.075743 / 0.176557 (-0.100814) \| 0.128564 / 0.737135 (-0.608572) \| 0.075549 / 0.296338 (-0.220789) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288809 / 0.215209 (0.073600) \| 2.854469 / 2.077655 (0.776814) \| 1.581731 / 1.504120 (0.077611) \| 1.460196 / 1.541195 (-0.080999) \| 1.485567 / 1.468490 (0.017077) \| 0.708824 / 4.584777 (-3.875953) \| 2.362389 / 3.745712 (-1.383323) \| 2.980804 / 5.269862 (-2.289057) \| 1.918788 / 4.565676 (-2.646888) \| 0.088389 / 0.424275 (-0.335886) \| 0.005266 / 0.007607 (-0.002341) \| 0.348598 / 0.226044 (0.122554) \| 3.443202 / 2.268929 (1.174273) \| 1.979311 / 55.444624 (-53.465314) \| 1.655774 / 6.876477 (-5.220702) \| 1.685538 / 2.142072 (-0.456535) \| 0.788695 / 4.805227 (-4.016532) \| 0.138403 / 6.500664 (-6.362261) \| 0.043288 / 0.075469 (-0.032181) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975874 / 1.841788 (-0.865913) \| 11.506991 / 8.074308 (3.432683) \| 9.640619 / 10.191392 (-0.550773) \| 0.131897 / 0.680424 (-0.548527) \| 0.014912 / 0.534201 (-0.519289) \| 0.304173 / 0.579283 (-0.275110) \| 0.262483 / 0.434364 (-0.171881) \| 0.342636 / 0.540337 (-0.197701) \| 0.440337 / 1.386936 (-0.946599) \|\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) \| 0.005961 / 0.011353 (-0.005392) \| 0.004023 / 0.011008 (-0.006985) \| 0.050230 / 0.038508 (0.011722) \| 0.033204 / 0.023109 (0.010095) \| 0.263462 / 0.275898 (-0.012436) \| 0.287517 / 0.323480 (-0.035963) \| 0.004432 / 0.007986 (-0.003553) \| 0.002938 / 0.004328 (-0.001390) \| 0.049297 / 0.004250 (0.045047) \| 0.041166 / 0.037052 (0.004113) \| 0.279220 / 0.258489 (0.020731) \| 0.312857 / 0.293841 (0.019016) \| 0.032567 / 0.128546 (-0.095979) \| 0.012566 / 0.075646 (-0.063080) \| 0.060579 / 0.419271 (-0.358692) \| 0.033760 / 0.043533 (-0.009773) \| 0.264219 / 0.255139 (0.009080) \| 0.282929 / 0.283200 (-0.000270) \| 0.017434 / 0.141683 (-0.124248) \| 1.148472 / 1.452155 (-0.303683) \| 1.247434 / 1.492716 (-0.245282) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004566 / 0.018006 (-0.013440) \| 0.296110 / 0.000490 (0.295621) \| 0.000219 / 0.000200 (0.000019) \| 0.000051 / 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) \| 0.022514 / 0.037411 (-0.014897) \| 0.076554 / 0.014526 (0.062029) \| 0.090427 / 0.176557 (-0.086130) \| 0.128611 / 0.737135 (-0.608524) \| 0.090748 / 0.296338 (-0.205590) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.315051 / 0.215209 (0.099842) \| 3.099662 / 2.077655 (1.022007) \| 1.706009 / 1.504120 (0.201889) \| 1.574637 / 1.541195 (0.033442) \| 1.592454 / 1.468490 (0.123964) \| 0.724699 / 4.584777 (-3.860078) \| 0.949954 / 3.745712 (-2.795758) \| 2.818915 / 5.269862 (-2.450946) \| 1.931290 / 4.565676 (-2.634386) \| 0.079308 / 0.424275 (-0.344967) \| 0.005414 / 0.007607 (-0.002193) \| 0.373547 / 0.226044 (0.147503) \| 3.742222 / 2.268929 (1.473293) \| 2.076239 / 55.444624 (-53.368385) \| 1.772359 / 6.876477 (-5.104118) \| 1.894369 / 2.142072 (-0.247703) \| 0.803650 / 4.805227 (-4.001578) \| 0.136995 / 6.500664 (-6.363669) \| 0.041565 / 0.075469 (-0.033905) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.989806 / 1.841788 (-0.851982) \| 12.151497 / 8.074308 (4.077189) \| 10.188075 / 10.191392 (-0.003317) \| 0.141194 / 0.680424 (-0.539230) \| 0.016351 / 0.534201 (-0.517850) \| 0.303242 / 0.579283 (-0.276041) \| 0.127446 / 0.434364 (-0.306918) \| 0.339806 / 0.540337 (-0.200532) \| 0.443527 / 1.386936 (-0.943409) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dd631431cb73c3ca434dfd6b115a6c30c5a665a5 \"CML watermark\")\n" ]
2,379,581,543	7,006	CI is broken after ruff-0.5.0: E721	closed	2024-06-28T05:03:28	2024-06-28T05:25:18	2024-06-28T05:25:18	https://github.com/huggingface/datasets/issues/7006	null	albertvillanova	false	[]
2,378,424,349	7,005	EmptyDatasetError: The directory at /metadata.jsonl doesn't contain any data files	closed	2024-06-27T15:08:26	2024-06-28T09:56:19	2024-06-28T09:56:19	https://github.com/huggingface/datasets/issues/7005	null	Aki1991	false	[ "Hi ! `data_dir=` is for directories, can you try using `data_files=` instead ?", "If you are trying to load your image dataset from a local folder, you should replace \"data_dir=path/to/jsonl/metadata.jsonl\" with the real folder path in your computer.\r\n\r\nhttps://huggingface.co/docs/datasets/en/image_load#imagefolder", "Ah yes. My bad. I was giving file name. I should have given the folder directory as the path. That solved my issue. Thank you @albertvillanova and @lhoestq. " ]
2,376,064,264	7,004	Fix WebDatasets KeyError for user-defined Features when a field is missing in an example	closed	2024-06-26T18:58:05	2024-06-29T00:15:49	2024-06-28T09:30:12	https://github.com/huggingface/datasets/pull/7004	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/7004", "html_url": "https://github.com/huggingface/datasets/pull/7004", "diff_url": "https://github.com/huggingface/datasets/pull/7004.diff", "patch_url": "https://github.com/huggingface/datasets/pull/7004.patch", "merged_at": "2024-06-28T09:30:12" }	ProGamerGov	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7004). 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) \| 0.005188 / 0.011353 (-0.006165) \| 0.003812 / 0.011008 (-0.007196) \| 0.062408 / 0.038508 (0.023900) \| 0.030734 / 0.023109 (0.007625) \| 0.236528 / 0.275898 (-0.039370) \| 0.267684 / 0.323480 (-0.055796) \| 0.003182 / 0.007986 (-0.004804) \| 0.004009 / 0.004328 (-0.000319) \| 0.048966 / 0.004250 (0.044715) \| 0.045259 / 0.037052 (0.008207) \| 0.250586 / 0.258489 (-0.007903) \| 0.287079 / 0.293841 (-0.006762) \| 0.029235 / 0.128546 (-0.099311) \| 0.012216 / 0.075646 (-0.063431) \| 0.203864 / 0.419271 (-0.215408) \| 0.036324 / 0.043533 (-0.007209) \| 0.245180 / 0.255139 (-0.009959) \| 0.270327 / 0.283200 (-0.012872) \| 0.018676 / 0.141683 (-0.123007) \| 1.115568 / 1.452155 (-0.336586) \| 1.183267 / 1.492716 (-0.309449) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094307 / 0.018006 (0.076301) \| 0.299071 / 0.000490 (0.298581) \| 0.000219 / 0.000200 (0.000019) \| 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) \| 0.018336 / 0.037411 (-0.019076) \| 0.062973 / 0.014526 (0.048447) \| 0.074137 / 0.176557 (-0.102420) \| 0.120553 / 0.737135 (-0.616582) \| 0.075411 / 0.296338 (-0.220927) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284751 / 0.215209 (0.069542) \| 2.789294 / 2.077655 (0.711640) \| 1.457789 / 1.504120 (-0.046331) \| 1.339140 / 1.541195 (-0.202055) \| 1.341685 / 1.468490 (-0.126805) \| 0.714928 / 4.584777 (-3.869849) \| 2.361197 / 3.745712 (-1.384516) \| 2.791569 / 5.269862 (-2.478293) \| 1.892261 / 4.565676 (-2.673416) \| 0.077954 / 0.424275 (-0.346321) \| 0.005454 / 0.007607 (-0.002153) \| 0.350766 / 0.226044 (0.124721) \| 3.416749 / 2.268929 (1.147820) \| 1.835377 / 55.444624 (-53.609247) \| 1.506456 / 6.876477 (-5.370020) \| 1.642728 / 2.142072 (-0.499344) \| 0.791543 / 4.805227 (-4.013684) \| 0.133102 / 6.500664 (-6.367562) \| 0.042572 / 0.075469 (-0.032897) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.977958 / 1.841788 (-0.863830) \| 11.438271 / 8.074308 (3.363963) \| 9.305719 / 10.191392 (-0.885673) \| 0.141239 / 0.680424 (-0.539185) \| 0.014330 / 0.534201 (-0.519871) \| 0.302201 / 0.579283 (-0.277082) \| 0.261688 / 0.434364 (-0.172676) \| 0.338752 / 0.540337 (-0.201586) \| 0.468466 / 1.386936 (-0.918470) \|\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) \| 0.005629 / 0.011353 (-0.005723) \| 0.003997 / 0.011008 (-0.007011) \| 0.050447 / 0.038508 (0.011939) \| 0.031694 / 0.023109 (0.008585) \| 0.277392 / 0.275898 (0.001494) \| 0.290440 / 0.323480 (-0.033040) \| 0.004403 / 0.007986 (-0.003583) \| 0.002851 / 0.004328 (-0.001478) \| 0.048593 / 0.004250 (0.044343) \| 0.040622 / 0.037052 (0.003570) \| 0.282640 / 0.258489 (0.024151) \| 0.309390 / 0.293841 (0.015549) \| 0.031453 / 0.128546 (-0.097094) \| 0.012424 / 0.075646 (-0.063223) \| 0.060311 / 0.419271 (-0.358960) \| 0.033195 / 0.043533 (-0.010338) \| 0.266867 / 0.255139 (0.011728) \| 0.281966 / 0.283200 (-0.001234) \| 0.018026 / 0.141683 (-0.123657) \| 1.136273 / 1.452155 (-0.315882) \| 1.141643 / 1.492716 (-0.351073) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095011 / 0.018006 (0.077005) \| 0.300571 / 0.000490 (0.300082) \| 0.000220 / 0.000200 (0.000020) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022903 / 0.037411 (-0.014508) \| 0.077130 / 0.014526 (0.062604) \| 0.089576 / 0.176557 (-0.086980) \| 0.127156 / 0.737135 (-0.609980) \| 0.090008 / 0.296338 (-0.206331) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289270 / 0.215209 (0.074061) \| 2.848239 / 2.077655 (0.770585) \| 1.546788 / 1.504120 (0.042668) \| 1.417275 / 1.541195 (-0.123920) \| 1.456214 / 1.468490 (-0.012276) \| 0.716688 / 4.584777 (-3.868088) \| 0.940242 / 3.745712 (-2.805470) \| 2.911956 / 5.269862 (-2.357906) \| 1.871358 / 4.565676 (-2.694318) \| 0.077553 / 0.424275 (-0.346722) \| 0.005279 / 0.007607 (-0.002328) \| 0.343338 / 0.226044 (0.117294) \| 3.368694 / 2.268929 (1.099766) \| 1.896765 / 55.444624 (-53.547859) \| 1.612414 / 6.876477 (-5.264063) \| 1.615934 / 2.142072 (-0.526138) \| 0.794016 / 4.805227 (-4.011212) \| 0.131821 / 6.500664 (-6.368843) \| 0.041495 / 0.075469 (-0.033975) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.003418 / 1.841788 (-0.838370) \| 12.073906 / 8.074308 (3.999598) \| 10.166291 / 10.191392 (-0.025101) \| 0.131224 / 0.680424 (-0.549200) \| 0.015246 / 0.534201 (-0.518955) \| 0.299835 / 0.579283 (-0.279448) \| 0.124308 / 0.434364 (-0.310056) \| 0.336414 / 0.540337 (-0.203924) \| 0.429569 / 1.386936 (-0.957367) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#83d28601fad73755b74314a9bc1e327005514d54 \"CML watermark\")\n", "@lhoestq Thank you!" ]
2,373,084,132	7,003	minor fix for bfloat16	closed	2024-06-25T16:10:04	2024-06-25T16:16:11	2024-06-25T16:10:10	https://github.com/huggingface/datasets/pull/7003	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/7003", "html_url": "https://github.com/huggingface/datasets/pull/7003", "diff_url": "https://github.com/huggingface/datasets/pull/7003.diff", "patch_url": "https://github.com/huggingface/datasets/pull/7003.patch", "merged_at": "2024-06-25T16:10:10" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7003). 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) \| 0.005633 / 0.011353 (-0.005720) \| 0.004366 / 0.011008 (-0.006642) \| 0.064081 / 0.038508 (0.025573) \| 0.031790 / 0.023109 (0.008681) \| 0.239270 / 0.275898 (-0.036628) \| 0.267424 / 0.323480 (-0.056055) \| 0.003229 / 0.007986 (-0.004756) \| 0.002849 / 0.004328 (-0.001479) \| 0.050147 / 0.004250 (0.045897) \| 0.046119 / 0.037052 (0.009066) \| 0.253506 / 0.258489 (-0.004983) \| 0.280464 / 0.293841 (-0.013377) \| 0.030561 / 0.128546 (-0.097985) \| 0.012258 / 0.075646 (-0.063388) \| 0.212222 / 0.419271 (-0.207049) \| 0.036695 / 0.043533 (-0.006838) \| 0.242141 / 0.255139 (-0.012998) \| 0.263014 / 0.283200 (-0.020186) \| 0.020008 / 0.141683 (-0.121675) \| 1.103701 / 1.452155 (-0.348453) \| 1.151641 / 1.492716 (-0.341076) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095884 / 0.018006 (0.077878) \| 0.300858 / 0.000490 (0.300368) \| 0.000209 / 0.000200 (0.000009) \| 0.000052 / 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) \| 0.018713 / 0.037411 (-0.018698) \| 0.063659 / 0.014526 (0.049134) \| 0.074588 / 0.176557 (-0.101968) \| 0.120779 / 0.737135 (-0.616356) \| 0.077768 / 0.296338 (-0.218570) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281680 / 0.215209 (0.066471) \| 2.754658 / 2.077655 (0.677003) \| 1.454036 / 1.504120 (-0.050084) \| 1.333153 / 1.541195 (-0.208042) \| 1.383616 / 1.468490 (-0.084874) \| 0.728933 / 4.584777 (-3.855844) \| 2.374989 / 3.745712 (-1.370723) \| 2.990824 / 5.269862 (-2.279038) \| 1.899065 / 4.565676 (-2.666612) \| 0.078657 / 0.424275 (-0.345619) \| 0.005162 / 0.007607 (-0.002445) \| 0.335883 / 0.226044 (0.109838) \| 3.323047 / 2.268929 (1.054119) \| 1.848290 / 55.444624 (-53.596335) \| 1.519510 / 6.876477 (-5.356966) \| 1.563608 / 2.142072 (-0.578465) \| 0.807890 / 4.805227 (-3.997337) \| 0.134517 / 6.500664 (-6.366147) \| 0.042208 / 0.075469 (-0.033262) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963634 / 1.841788 (-0.878154) \| 11.617868 / 8.074308 (3.543560) \| 9.804648 / 10.191392 (-0.386744) \| 0.142311 / 0.680424 (-0.538113) \| 0.013748 / 0.534201 (-0.520453) \| 0.300309 / 0.579283 (-0.278974) \| 0.268214 / 0.434364 (-0.166150) \| 0.342406 / 0.540337 (-0.197931) \| 0.430315 / 1.386936 (-0.956621) \|\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) \| 0.005533 / 0.011353 (-0.005820) \| 0.004208 / 0.011008 (-0.006800) \| 0.051732 / 0.038508 (0.013224) \| 0.031296 / 0.023109 (0.008187) \| 0.275091 / 0.275898 (-0.000807) \| 0.296889 / 0.323480 (-0.026591) \| 0.004363 / 0.007986 (-0.003623) \| 0.002807 / 0.004328 (-0.001522) \| 0.049727 / 0.004250 (0.045476) \| 0.039798 / 0.037052 (0.002746) \| 0.284379 / 0.258489 (0.025890) \| 0.317281 / 0.293841 (0.023440) \| 0.031286 / 0.128546 (-0.097261) \| 0.012384 / 0.075646 (-0.063263) \| 0.061619 / 0.419271 (-0.357652) \| 0.032974 / 0.043533 (-0.010559) \| 0.274313 / 0.255139 (0.019174) \| 0.296142 / 0.283200 (0.012943) \| 0.017391 / 0.141683 (-0.124291) \| 1.148369 / 1.452155 (-0.303786) \| 1.171539 / 1.492716 (-0.321177) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097309 / 0.018006 (0.079302) \| 0.304701 / 0.000490 (0.304212) \| 0.000208 / 0.000200 (0.000008) \| 0.000110 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022382 / 0.037411 (-0.015030) \| 0.077000 / 0.014526 (0.062474) \| 0.088165 / 0.176557 (-0.088392) \| 0.129060 / 0.737135 (-0.608075) \| 0.090128 / 0.296338 (-0.206211) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285308 / 0.215209 (0.070099) \| 2.816680 / 2.077655 (0.739025) \| 1.542418 / 1.504120 (0.038298) \| 1.418567 / 1.541195 (-0.122628) \| 1.447018 / 1.468490 (-0.021472) \| 0.737055 / 4.584777 (-3.847722) \| 0.968285 / 3.745712 (-2.777427) \| 2.880120 / 5.269862 (-2.389741) \| 1.921813 / 4.565676 (-2.643864) \| 0.079110 / 0.424275 (-0.345165) \| 0.005826 / 0.007607 (-0.001781) \| 0.336441 / 0.226044 (0.110397) \| 3.326384 / 2.268929 (1.057456) \| 1.929205 / 55.444624 (-53.515419) \| 1.618215 / 6.876477 (-5.258261) \| 1.769688 / 2.142072 (-0.372385) \| 0.808009 / 4.805227 (-3.997219) \| 0.136384 / 6.500664 (-6.364280) \| 0.041332 / 0.075469 (-0.034137) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.010884 / 1.841788 (-0.830903) \| 12.266118 / 8.074308 (4.191810) \| 10.287424 / 10.191392 (0.096032) \| 0.143172 / 0.680424 (-0.537251) \| 0.015798 / 0.534201 (-0.518403) \| 0.301604 / 0.579283 (-0.277679) \| 0.131079 / 0.434364 (-0.303285) \| 0.338396 / 0.540337 (-0.201941) \| 0.460721 / 1.386936 (-0.926215) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1e1d31387aa594b2e745c8ed8964962c134d203d \"CML watermark\")\n" ]
2,373,010,351	7,002	Fix dump of bfloat16 torch tensor	closed	2024-06-25T15:38:09	2024-06-25T16:10:16	2024-06-25T15:51:52	https://github.com/huggingface/datasets/pull/7002	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/7002", "html_url": "https://github.com/huggingface/datasets/pull/7002", "diff_url": "https://github.com/huggingface/datasets/pull/7002.diff", "patch_url": "https://github.com/huggingface/datasets/pull/7002.patch", "merged_at": "2024-06-25T15:51:52" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7002). 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) \| 0.005321 / 0.011353 (-0.006032) \| 0.003495 / 0.011008 (-0.007514) \| 0.065577 / 0.038508 (0.027069) \| 0.030876 / 0.023109 (0.007767) \| 0.255216 / 0.275898 (-0.020682) \| 0.265111 / 0.323480 (-0.058368) \| 0.003149 / 0.007986 (-0.004837) \| 0.004062 / 0.004328 (-0.000267) \| 0.051142 / 0.004250 (0.046891) \| 0.042460 / 0.037052 (0.005408) \| 0.270692 / 0.258489 (0.012203) \| 0.284957 / 0.293841 (-0.008884) \| 0.030143 / 0.128546 (-0.098403) \| 0.012148 / 0.075646 (-0.063498) \| 0.203706 / 0.419271 (-0.215565) \| 0.035948 / 0.043533 (-0.007584) \| 0.251391 / 0.255139 (-0.003748) \| 0.270908 / 0.283200 (-0.012292) \| 0.018496 / 0.141683 (-0.123187) \| 1.118567 / 1.452155 (-0.333587) \| 1.157695 / 1.492716 (-0.335021) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.135649 / 0.018006 (0.117643) \| 0.281489 / 0.000490 (0.281000) \| 0.000244 / 0.000200 (0.000044) \| 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) \| 0.018700 / 0.037411 (-0.018711) \| 0.062305 / 0.014526 (0.047779) \| 0.074968 / 0.176557 (-0.101589) \| 0.121490 / 0.737135 (-0.615645) \| 0.075585 / 0.296338 (-0.220754) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276929 / 0.215209 (0.061720) \| 2.733543 / 2.077655 (0.655888) \| 1.414585 / 1.504120 (-0.089535) \| 1.301975 / 1.541195 (-0.239220) \| 1.336698 / 1.468490 (-0.131792) \| 0.720650 / 4.584777 (-3.864127) \| 2.374796 / 3.745712 (-1.370917) \| 2.866534 / 5.269862 (-2.403327) \| 1.819607 / 4.565676 (-2.746069) \| 0.077914 / 0.424275 (-0.346361) \| 0.005146 / 0.007607 (-0.002461) \| 0.331722 / 0.226044 (0.105678) \| 3.290875 / 2.268929 (1.021946) \| 1.799806 / 55.444624 (-53.644818) \| 1.476816 / 6.876477 (-5.399660) \| 1.511441 / 2.142072 (-0.630631) \| 0.798043 / 4.805227 (-4.007185) \| 0.134577 / 6.500664 (-6.366087) \| 0.042055 / 0.075469 (-0.033415) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.967908 / 1.841788 (-0.873880) \| 11.215688 / 8.074308 (3.141380) \| 9.486403 / 10.191392 (-0.704989) \| 0.141864 / 0.680424 (-0.538560) \| 0.013462 / 0.534201 (-0.520739) \| 0.302601 / 0.579283 (-0.276682) \| 0.266870 / 0.434364 (-0.167494) \| 0.336963 / 0.540337 (-0.203375) \| 0.425374 / 1.386936 (-0.961562) \|\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) \| 0.005549 / 0.011353 (-0.005803) \| 0.003464 / 0.011008 (-0.007544) \| 0.051421 / 0.038508 (0.012913) \| 0.032320 / 0.023109 (0.009211) \| 0.269591 / 0.275898 (-0.006307) \| 0.292015 / 0.323480 (-0.031465) \| 0.004351 / 0.007986 (-0.003634) \| 0.002772 / 0.004328 (-0.001556) \| 0.048836 / 0.004250 (0.044586) \| 0.039501 / 0.037052 (0.002449) \| 0.282419 / 0.258489 (0.023930) \| 0.312289 / 0.293841 (0.018448) \| 0.031788 / 0.128546 (-0.096759) \| 0.012074 / 0.075646 (-0.063572) \| 0.060457 / 0.419271 (-0.358814) \| 0.033106 / 0.043533 (-0.010427) \| 0.270323 / 0.255139 (0.015184) \| 0.287855 / 0.283200 (0.004655) \| 0.017865 / 0.141683 (-0.123818) \| 1.130406 / 1.452155 (-0.321749) \| 1.178679 / 1.492716 (-0.314038) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093606 / 0.018006 (0.075600) \| 0.297328 / 0.000490 (0.296838) \| 0.000211 / 0.000200 (0.000011) \| 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) \| 0.022498 / 0.037411 (-0.014913) \| 0.076927 / 0.014526 (0.062401) \| 0.088013 / 0.176557 (-0.088544) \| 0.127279 / 0.737135 (-0.609857) \| 0.089424 / 0.296338 (-0.206914) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296441 / 0.215209 (0.081232) \| 2.913051 / 2.077655 (0.835396) \| 1.581816 / 1.504120 (0.077696) \| 1.451575 / 1.541195 (-0.089620) \| 1.458968 / 1.468490 (-0.009522) \| 0.727191 / 4.584777 (-3.857586) \| 0.954607 / 3.745712 (-2.791106) \| 2.824357 / 5.269862 (-2.445505) \| 1.886779 / 4.565676 (-2.678898) \| 0.079397 / 0.424275 (-0.344878) \| 0.005566 / 0.007607 (-0.002041) \| 0.351655 / 0.226044 (0.125611) \| 3.395790 / 2.268929 (1.126862) \| 1.886238 / 55.444624 (-53.558387) \| 1.615413 / 6.876477 (-5.261064) \| 1.723922 / 2.142072 (-0.418150) \| 0.807858 / 4.805227 (-3.997369) \| 0.132998 / 6.500664 (-6.367667) \| 0.040396 / 0.075469 (-0.035073) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.008527 / 1.841788 (-0.833261) \| 11.736104 / 8.074308 (3.661796) \| 10.283367 / 10.191392 (0.091975) \| 0.141386 / 0.680424 (-0.539038) \| 0.015722 / 0.534201 (-0.518479) \| 0.301785 / 0.579283 (-0.277498) \| 0.123073 / 0.434364 (-0.311291) \| 0.340478 / 0.540337 (-0.199859) \| 0.462936 / 1.386936 (-0.924000) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bfb0a414d68e945addf95a9419a8314c372e19ba \"CML watermark\")\n" ]
2,372,930,879	7,001	Datasetbuilder Local Download FileNotFoundError	open	2024-06-25T15:02:34	2024-06-25T15:21:19	null	https://github.com/huggingface/datasets/issues/7001	null	purefall	false	[ "Ok it seems the solution is to use the directory string without the trailing \"/\" which in my case as: \r\n\r\n`parquet_dir = \"~/data/Parquet\" `\r\n\r\nStill i think this is a weird behavior... " ]
2,372,887,585	7,000	IterableDataset: Unsupported ScalarType BFloat16	closed	2024-06-25T14:43:26	2024-06-25T16:04:00	2024-06-25T15:51:53	https://github.com/huggingface/datasets/issues/7000	null	stoical07	false	[ "@lhoestq Thank you for merging #6607, but unfortunately the issue persists for `IterableDataset` :pensive: ", "Hi ! I opened https://github.com/huggingface/datasets/pull/7002 to fix this bug", "Amazing, thank you so much @lhoestq! :pray:" ]
2,372,124,589	6,999	Remove tasks	closed	2024-06-25T09:06:16	2024-08-21T09:07:07	2024-08-21T09:01:18	https://github.com/huggingface/datasets/pull/6999	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6999", "html_url": "https://github.com/huggingface/datasets/pull/6999", "diff_url": "https://github.com/huggingface/datasets/pull/6999.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6999.patch", "merged_at": "2024-08-21T09:01:18" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6999). 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) \| 0.005330 / 0.011353 (-0.006023) \| 0.003946 / 0.011008 (-0.007062) \| 0.063530 / 0.038508 (0.025022) \| 0.030529 / 0.023109 (0.007419) \| 0.239364 / 0.275898 (-0.036534) \| 0.261683 / 0.323480 (-0.061797) \| 0.003197 / 0.007986 (-0.004789) \| 0.003485 / 0.004328 (-0.000844) \| 0.049575 / 0.004250 (0.045325) \| 0.046164 / 0.037052 (0.009112) \| 0.246129 / 0.258489 (-0.012360) \| 0.281365 / 0.293841 (-0.012476) \| 0.029480 / 0.128546 (-0.099066) \| 0.012450 / 0.075646 (-0.063196) \| 0.203696 / 0.419271 (-0.215575) \| 0.036539 / 0.043533 (-0.006994) \| 0.241664 / 0.255139 (-0.013475) \| 0.260930 / 0.283200 (-0.022270) \| 0.019931 / 0.141683 (-0.121752) \| 1.221075 / 1.452155 (-0.231080) \| 1.246315 / 1.492716 (-0.246402) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095061 / 0.018006 (0.077055) \| 0.304773 / 0.000490 (0.304283) \| 0.000208 / 0.000200 (0.000008) \| 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) \| 0.019032 / 0.037411 (-0.018380) \| 0.062521 / 0.014526 (0.047995) \| 0.075668 / 0.176557 (-0.100889) \| 0.121634 / 0.737135 (-0.615501) \| 0.075456 / 0.296338 (-0.220882) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291721 / 0.215209 (0.076512) \| 2.845445 / 2.077655 (0.767790) \| 1.450971 / 1.504120 (-0.053149) \| 1.334586 / 1.541195 (-0.206609) \| 1.358095 / 1.468490 (-0.110396) \| 0.729624 / 4.584777 (-3.855153) \| 2.411504 / 3.745712 (-1.334208) \| 2.858871 / 5.269862 (-2.410991) \| 1.893074 / 4.565676 (-2.672603) \| 0.079068 / 0.424275 (-0.345207) \| 0.005476 / 0.007607 (-0.002131) \| 0.329816 / 0.226044 (0.103771) \| 3.305361 / 2.268929 (1.036432) \| 1.799924 / 55.444624 (-53.644700) \| 1.512130 / 6.876477 (-5.364347) \| 1.635195 / 2.142072 (-0.506877) \| 0.801486 / 4.805227 (-4.003741) \| 0.134677 / 6.500664 (-6.365987) \| 0.042266 / 0.075469 (-0.033203) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969835 / 1.841788 (-0.871952) \| 11.421833 / 8.074308 (3.347524) \| 9.799120 / 10.191392 (-0.392272) \| 0.144888 / 0.680424 (-0.535536) \| 0.014191 / 0.534201 (-0.520010) \| 0.301037 / 0.579283 (-0.278246) \| 0.263329 / 0.434364 (-0.171034) \| 0.403013 / 0.540337 (-0.137324) \| 0.463805 / 1.386936 (-0.923131) \|\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) \| 0.005913 / 0.011353 (-0.005440) \| 0.003890 / 0.011008 (-0.007118) \| 0.049995 / 0.038508 (0.011487) \| 0.032497 / 0.023109 (0.009387) \| 0.269926 / 0.275898 (-0.005972) \| 0.295567 / 0.323480 (-0.027913) \| 0.004365 / 0.007986 (-0.003620) \| 0.002818 / 0.004328 (-0.001510) \| 0.049055 / 0.004250 (0.044805) \| 0.040683 / 0.037052 (0.003630) \| 0.283043 / 0.258489 (0.024554) \| 0.321072 / 0.293841 (0.027232) \| 0.032760 / 0.128546 (-0.095787) \| 0.012370 / 0.075646 (-0.063277) \| 0.061574 / 0.419271 (-0.357698) \| 0.033714 / 0.043533 (-0.009819) \| 0.276287 / 0.255139 (0.021148) \| 0.290078 / 0.283200 (0.006878) \| 0.017250 / 0.141683 (-0.124432) \| 1.165291 / 1.452155 (-0.286863) \| 1.213687 / 1.492716 (-0.279029) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096122 / 0.018006 (0.078115) \| 0.311954 / 0.000490 (0.311464) \| 0.000213 / 0.000200 (0.000013) \| 0.000052 / 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) \| 0.022142 / 0.037411 (-0.015270) \| 0.076470 / 0.014526 (0.061945) \| 0.088340 / 0.176557 (-0.088216) \| 0.128594 / 0.737135 (-0.608542) \| 0.089780 / 0.296338 (-0.206558) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298129 / 0.215209 (0.082920) \| 2.943735 / 2.077655 (0.866080) \| 1.574351 / 1.504120 (0.070231) \| 1.446688 / 1.541195 (-0.094506) \| 1.477714 / 1.468490 (0.009223) \| 0.722195 / 4.584777 (-3.862582) \| 0.967675 / 3.745712 (-2.778037) \| 2.803346 / 5.269862 (-2.466515) \| 1.895882 / 4.565676 (-2.669794) \| 0.079193 / 0.424275 (-0.345082) \| 0.005250 / 0.007607 (-0.002357) \| 0.350193 / 0.226044 (0.124149) \| 3.514562 / 2.268929 (1.245634) \| 1.962743 / 55.444624 (-53.481881) \| 1.677308 / 6.876477 (-5.199169) \| 1.811473 / 2.142072 (-0.330600) \| 0.796234 / 4.805227 (-4.008993) \| 0.131810 / 6.500664 (-6.368854) \| 0.041301 / 0.075469 (-0.034168) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.030700 / 1.841788 (-0.811088) \| 12.108809 / 8.074308 (4.034501) \| 10.426112 / 10.191392 (0.234720) \| 0.139829 / 0.680424 (-0.540595) \| 0.015133 / 0.534201 (-0.519068) \| 0.307782 / 0.579283 (-0.271501) \| 0.130554 / 0.434364 (-0.303810) \| 0.342728 / 0.540337 (-0.197610) \| 0.435426 / 1.386936 (-0.951510) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9ddea80a0bca8dcf4ed5ca58dbeda3e309cf5a84 \"CML watermark\")\n" ]
2,371,973,926	6,998	Fix tests using hf-internal-testing/librispeech_asr_dummy	closed	2024-06-25T07:59:44	2024-06-25T08:22:38	2024-06-25T08:13:42	https://github.com/huggingface/datasets/pull/6998	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6998", "html_url": "https://github.com/huggingface/datasets/pull/6998", "diff_url": "https://github.com/huggingface/datasets/pull/6998.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6998.patch", "merged_at": "2024-06-25T08:13:42" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6998). 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) \| 0.005396 / 0.011353 (-0.005957) \| 0.003974 / 0.011008 (-0.007034) \| 0.063490 / 0.038508 (0.024982) \| 0.030299 / 0.023109 (0.007189) \| 0.244489 / 0.275898 (-0.031409) \| 0.274116 / 0.323480 (-0.049364) \| 0.003187 / 0.007986 (-0.004798) \| 0.003433 / 0.004328 (-0.000896) \| 0.049313 / 0.004250 (0.045062) \| 0.043677 / 0.037052 (0.006624) \| 0.260198 / 0.258489 (0.001709) \| 0.283558 / 0.293841 (-0.010283) \| 0.029728 / 0.128546 (-0.098819) \| 0.011950 / 0.075646 (-0.063696) \| 0.204371 / 0.419271 (-0.214901) \| 0.035712 / 0.043533 (-0.007821) \| 0.252715 / 0.255139 (-0.002424) \| 0.268906 / 0.283200 (-0.014293) \| 0.021153 / 0.141683 (-0.120529) \| 1.125599 / 1.452155 (-0.326556) \| 1.163122 / 1.492716 (-0.329594) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095089 / 0.018006 (0.077083) \| 0.298576 / 0.000490 (0.298086) \| 0.000214 / 0.000200 (0.000014) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018567 / 0.037411 (-0.018844) \| 0.062337 / 0.014526 (0.047811) \| 0.074231 / 0.176557 (-0.102326) \| 0.120960 / 0.737135 (-0.616175) \| 0.076124 / 0.296338 (-0.220215) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286936 / 0.215209 (0.071727) \| 2.816656 / 2.077655 (0.739001) \| 1.486772 / 1.504120 (-0.017348) \| 1.373289 / 1.541195 (-0.167905) \| 1.392739 / 1.468490 (-0.075752) \| 0.708091 / 4.584777 (-3.876686) \| 2.410034 / 3.745712 (-1.335678) \| 2.912701 / 5.269862 (-2.357161) \| 1.850924 / 4.565676 (-2.714752) \| 0.078896 / 0.424275 (-0.345380) \| 0.005116 / 0.007607 (-0.002491) \| 0.332275 / 0.226044 (0.106231) \| 3.306562 / 2.268929 (1.037633) \| 1.853051 / 55.444624 (-53.591573) \| 1.556210 / 6.876477 (-5.320267) \| 1.558892 / 2.142072 (-0.583181) \| 0.789917 / 4.805227 (-4.015310) \| 0.133683 / 6.500664 (-6.366981) \| 0.042566 / 0.075469 (-0.032904) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.957050 / 1.841788 (-0.884738) \| 11.401462 / 8.074308 (3.327154) \| 9.782988 / 10.191392 (-0.408404) \| 0.142127 / 0.680424 (-0.538296) \| 0.014730 / 0.534201 (-0.519471) \| 0.302647 / 0.579283 (-0.276636) \| 0.264654 / 0.434364 (-0.169710) \| 0.341340 / 0.540337 (-0.198998) \| 0.425808 / 1.386936 (-0.961128) \|\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) \| 0.005679 / 0.011353 (-0.005674) \| 0.003513 / 0.011008 (-0.007495) \| 0.050135 / 0.038508 (0.011627) \| 0.031614 / 0.023109 (0.008505) \| 0.260064 / 0.275898 (-0.015834) \| 0.285816 / 0.323480 (-0.037664) \| 0.004428 / 0.007986 (-0.003558) \| 0.002816 / 0.004328 (-0.001512) \| 0.048441 / 0.004250 (0.044191) \| 0.039622 / 0.037052 (0.002570) \| 0.274940 / 0.258489 (0.016451) \| 0.311837 / 0.293841 (0.017996) \| 0.031439 / 0.128546 (-0.097107) \| 0.012056 / 0.075646 (-0.063590) \| 0.060109 / 0.419271 (-0.359163) \| 0.033123 / 0.043533 (-0.010409) \| 0.261563 / 0.255139 (0.006424) \| 0.282640 / 0.283200 (-0.000560) \| 0.017168 / 0.141683 (-0.124515) \| 1.127859 / 1.452155 (-0.324295) \| 1.182414 / 1.492716 (-0.310303) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095517 / 0.018006 (0.077510) \| 0.300578 / 0.000490 (0.300088) \| 0.000212 / 0.000200 (0.000012) \| 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) \| 0.022192 / 0.037411 (-0.015220) \| 0.076617 / 0.014526 (0.062091) \| 0.087405 / 0.176557 (-0.089151) \| 0.127011 / 0.737135 (-0.610124) \| 0.088706 / 0.296338 (-0.207632) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294260 / 0.215209 (0.079051) \| 2.872879 / 2.077655 (0.795224) \| 1.531374 / 1.504120 (0.027254) \| 1.399232 / 1.541195 (-0.141962) \| 1.400708 / 1.468490 (-0.067782) \| 0.714003 / 4.584777 (-3.870773) \| 0.943144 / 3.745712 (-2.802568) \| 2.833396 / 5.269862 (-2.436466) \| 1.890570 / 4.565676 (-2.675106) \| 0.077664 / 0.424275 (-0.346611) \| 0.005651 / 0.007607 (-0.001956) \| 0.349476 / 0.226044 (0.123431) \| 3.405768 / 2.268929 (1.136840) \| 1.869739 / 55.444624 (-53.574885) \| 1.575293 / 6.876477 (-5.301184) \| 1.692981 / 2.142072 (-0.449092) \| 0.795363 / 4.805227 (-4.009865) \| 0.131532 / 6.500664 (-6.369132) \| 0.041183 / 0.075469 (-0.034286) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.000821 / 1.841788 (-0.840967) \| 11.987795 / 8.074308 (3.913487) \| 10.147652 / 10.191392 (-0.043740) \| 0.141314 / 0.680424 (-0.539110) \| 0.015506 / 0.534201 (-0.518695) \| 0.305090 / 0.579283 (-0.274193) \| 0.123403 / 0.434364 (-0.310960) \| 0.346507 / 0.540337 (-0.193831) \| 0.471318 / 1.386936 (-0.915618) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#186b560eb2393c7d1913f4b3e76e9e04a081e09b \"CML watermark\")\n" ]
2,371,966,127	6,997	CI is broken for tests using hf-internal-testing/librispeech_asr_dummy	closed	2024-06-25T07:55:44	2024-06-25T08:13:43	2024-06-25T08:13:43	https://github.com/huggingface/datasets/issues/6997	null	albertvillanova	false	[]
2,371,841,671	6,996	Remove deprecated code	closed	2024-06-25T06:54:40	2024-08-21T09:42:52	2024-08-21T09:35:06	https://github.com/huggingface/datasets/pull/6996	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6996", "html_url": "https://github.com/huggingface/datasets/pull/6996", "diff_url": "https://github.com/huggingface/datasets/pull/6996.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6996.patch", "merged_at": "2024-08-21T09:35:06" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6996). 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) \| 0.005296 / 0.011353 (-0.006057) \| 0.003991 / 0.011008 (-0.007017) \| 0.063892 / 0.038508 (0.025384) \| 0.031185 / 0.023109 (0.008076) \| 0.248300 / 0.275898 (-0.027598) \| 0.270326 / 0.323480 (-0.053154) \| 0.004343 / 0.007986 (-0.003643) \| 0.002735 / 0.004328 (-0.001594) \| 0.049751 / 0.004250 (0.045501) \| 0.045629 / 0.037052 (0.008577) \| 0.257584 / 0.258489 (-0.000905) \| 0.284697 / 0.293841 (-0.009144) \| 0.029403 / 0.128546 (-0.099143) \| 0.012155 / 0.075646 (-0.063491) \| 0.215241 / 0.419271 (-0.204031) \| 0.036258 / 0.043533 (-0.007275) \| 0.246878 / 0.255139 (-0.008261) \| 0.268728 / 0.283200 (-0.014472) \| 0.018113 / 0.141683 (-0.123570) \| 1.130733 / 1.452155 (-0.321422) \| 1.205148 / 1.492716 (-0.287568) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095196 / 0.018006 (0.077189) \| 0.300741 / 0.000490 (0.300252) \| 0.000220 / 0.000200 (0.000020) \| 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) \| 0.018319 / 0.037411 (-0.019093) \| 0.062766 / 0.014526 (0.048240) \| 0.074748 / 0.176557 (-0.101809) \| 0.122177 / 0.737135 (-0.614959) \| 0.076652 / 0.296338 (-0.219687) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284508 / 0.215209 (0.069299) \| 2.838298 / 2.077655 (0.760643) \| 1.480098 / 1.504120 (-0.024022) \| 1.362882 / 1.541195 (-0.178313) \| 1.389036 / 1.468490 (-0.079454) \| 0.747485 / 4.584777 (-3.837292) \| 2.385333 / 3.745712 (-1.360379) \| 2.924148 / 5.269862 (-2.345713) \| 1.869061 / 4.565676 (-2.696616) \| 0.079909 / 0.424275 (-0.344366) \| 0.005173 / 0.007607 (-0.002434) \| 0.345694 / 0.226044 (0.119650) \| 3.430648 / 2.268929 (1.161719) \| 1.837108 / 55.444624 (-53.607516) \| 1.528498 / 6.876477 (-5.347979) \| 1.567128 / 2.142072 (-0.574944) \| 0.804615 / 4.805227 (-4.000612) \| 0.135361 / 6.500664 (-6.365303) \| 0.042195 / 0.075469 (-0.033274) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.986240 / 1.841788 (-0.855548) \| 11.428084 / 8.074308 (3.353776) \| 9.168227 / 10.191392 (-1.023165) \| 0.131917 / 0.680424 (-0.548507) \| 0.014324 / 0.534201 (-0.519877) \| 0.302188 / 0.579283 (-0.277095) \| 0.263790 / 0.434364 (-0.170574) \| 0.343799 / 0.540337 (-0.196539) \| 0.428518 / 1.386936 (-0.958418) \|\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) \| 0.005734 / 0.011353 (-0.005618) \| 0.003914 / 0.011008 (-0.007094) \| 0.050105 / 0.038508 (0.011596) \| 0.031748 / 0.023109 (0.008639) \| 0.266392 / 0.275898 (-0.009506) \| 0.301221 / 0.323480 (-0.022259) \| 0.004408 / 0.007986 (-0.003578) \| 0.002811 / 0.004328 (-0.001517) \| 0.049103 / 0.004250 (0.044853) \| 0.041030 / 0.037052 (0.003978) \| 0.281003 / 0.258489 (0.022513) \| 0.318086 / 0.293841 (0.024245) \| 0.032695 / 0.128546 (-0.095852) \| 0.012239 / 0.075646 (-0.063408) \| 0.060387 / 0.419271 (-0.358885) \| 0.034179 / 0.043533 (-0.009354) \| 0.266020 / 0.255139 (0.010881) \| 0.288551 / 0.283200 (0.005351) \| 0.018778 / 0.141683 (-0.122905) \| 1.214959 / 1.452155 (-0.237196) \| 1.268269 / 1.492716 (-0.224447) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095449 / 0.018006 (0.077443) \| 0.305733 / 0.000490 (0.305243) \| 0.000216 / 0.000200 (0.000016) \| 0.000052 / 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) \| 0.022565 / 0.037411 (-0.014847) \| 0.077266 / 0.014526 (0.062740) \| 0.089345 / 0.176557 (-0.087212) \| 0.128900 / 0.737135 (-0.608236) \| 0.089746 / 0.296338 (-0.206593) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298221 / 0.215209 (0.083012) \| 2.957671 / 2.077655 (0.880016) \| 1.584674 / 1.504120 (0.080554) \| 1.456906 / 1.541195 (-0.084288) \| 1.467609 / 1.468490 (-0.000881) \| 0.718726 / 4.584777 (-3.866051) \| 0.948157 / 3.745712 (-2.797555) \| 2.953559 / 5.269862 (-2.316303) \| 1.895182 / 4.565676 (-2.670494) \| 0.078380 / 0.424275 (-0.345895) \| 0.005640 / 0.007607 (-0.001968) \| 0.352978 / 0.226044 (0.126933) \| 3.436341 / 2.268929 (1.167413) \| 1.962418 / 55.444624 (-53.482206) \| 1.655444 / 6.876477 (-5.221033) \| 1.680082 / 2.142072 (-0.461990) \| 0.792920 / 4.805227 (-4.012307) \| 0.133518 / 6.500664 (-6.367146) \| 0.041123 / 0.075469 (-0.034346) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.022546 / 1.841788 (-0.819242) \| 12.076711 / 8.074308 (4.002402) \| 10.159920 / 10.191392 (-0.031472) \| 0.143709 / 0.680424 (-0.536715) \| 0.015499 / 0.534201 (-0.518702) \| 0.302096 / 0.579283 (-0.277187) \| 0.125202 / 0.434364 (-0.309162) \| 0.349499 / 0.540337 (-0.190839) \| 0.456019 / 1.386936 (-0.930917) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c3aaf2108e3fd77f92aed3b1dce0fd551daf9a0a \"CML watermark\")\n" ]
2,370,713,475	6,995	ImportError when importing datasets.load_dataset	closed	2024-06-24T17:07:22	2024-11-14T01:42:09	2024-06-25T06:11:37	https://github.com/huggingface/datasets/issues/6995	null	Leo-Lsc	false	[ "What is the version of your installed `huggingface-hub`:\r\n```python\r\nimport huggingface_hub\r\nprint(huggingface_hub.__version__)\r\n```\r\n\r\nIt seems you have a very old version of `huggingface-hub`, where `CommitInfo` was not still implemented. You need to update it:\r\n```\r\npip install -U huggingface-hub\r\n```\r\n\r\nNote that `CommitInfo` was implemented in huggingface-hub 0.10.0 and datasets requires \"huggingface-hub>=0.21.2\"", "The version of my huggingface-hub is 0.23.4.", "The error message says there is no CommitInfo in your installed huggingface-hub library:\r\n```\r\nImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (D:\\Anaconda3\\envs\\CS224S\\Lib\\site-packages\\huggingface_hub_init_.py)\r\n```\r\n\r\nAnd this is implemented since version 0.10.0:\r\n- https://github.com/huggingface/huggingface_hub/pull/1066", "I am getting the exact same issue when I `import datasets`. The version of my huggingface-hub is also 0.23.4. I dont see a solution in the comments. Not sure why is this issue closed?", "I closed the issue because the problem is not related to the `datasets` library.\r\n\r\nThe problem is with your local Python environment: it seems corrupted. You could try to remove it and regenerate it again.", "I have recreated my conda environment but still run into the same issue. Here is my environment:\r\n```\r\nconda create --name esm python=3.10\r\n conda activate esm\r\n conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia\r\n pip3 install -r requirements.txt\r\n```\r\nRequirements.txt\r\n```\r\naccelerate\r\ndatasets==2.20.0\r\npyfastx\r\ntransformers\r\nboto3\r\nhuggingface_hub==0.23.4\r\n```\r\n\r\nAnd then I get:\r\n```\r\n>>> import datasets\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/fsx/ubuntu/miniconda3/envs/esm2/lib/python3.10/site-packages/datasets/__init__.py\", line 17, in <module>\r\n from .arrow_dataset import Dataset\r\n File \"/fsx/ubuntu/miniconda3/envs/esm2/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 63, in <module>\r\n from huggingface_hub import (\r\nImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (/fsx/ubuntu/miniconda3/envs/esm2/lib/python3.10/site-packages/huggingface_hub/__init__.py)\r\n>>>\r\n```\r\n\r\n", "You can check:\r\n```\r\n>>> import huggingface_hub\r\n>>> print(huggingface_hub.__version__)\r\n```", "This is what I see:\r\n```\r\n>>> import huggingface_hub\r\n>>> print(huggingface_hub.__version__)\r\n0.23.4\r\n```", "Installing `chardet` makes it work for some reason" ]
2,370,491,689	6,994	Fix incorrect rank value in data splitting	closed	2024-06-24T15:07:47	2024-06-26T04:37:35	2024-06-25T16:19:17	https://github.com/huggingface/datasets/pull/6994	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6994", "html_url": "https://github.com/huggingface/datasets/pull/6994", "diff_url": "https://github.com/huggingface/datasets/pull/6994.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6994.patch", "merged_at": "2024-06-25T16:19:17" }	yzhangcs	true	[ "Sure~", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6994). 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) \| 0.005538 / 0.011353 (-0.005815) \| 0.003997 / 0.011008 (-0.007011) \| 0.063444 / 0.038508 (0.024935) \| 0.032552 / 0.023109 (0.009442) \| 0.266574 / 0.275898 (-0.009324) \| 0.282841 / 0.323480 (-0.040639) \| 0.004279 / 0.007986 (-0.003706) \| 0.002788 / 0.004328 (-0.001540) \| 0.049226 / 0.004250 (0.044976) \| 0.044688 / 0.037052 (0.007636) \| 0.275464 / 0.258489 (0.016975) \| 0.305278 / 0.293841 (0.011437) \| 0.030097 / 0.128546 (-0.098450) \| 0.012237 / 0.075646 (-0.063410) \| 0.205526 / 0.419271 (-0.213745) \| 0.036145 / 0.043533 (-0.007388) \| 0.267395 / 0.255139 (0.012256) \| 0.289149 / 0.283200 (0.005949) \| 0.019044 / 0.141683 (-0.122639) \| 1.162294 / 1.452155 (-0.289861) \| 1.183642 / 1.492716 (-0.309074) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.139125 / 0.018006 (0.121119) \| 0.301743 / 0.000490 (0.301253) \| 0.000260 / 0.000200 (0.000061) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019494 / 0.037411 (-0.017917) \| 0.063078 / 0.014526 (0.048552) \| 0.076989 / 0.176557 (-0.099567) \| 0.121363 / 0.737135 (-0.615773) \| 0.080040 / 0.296338 (-0.216298) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284401 / 0.215209 (0.069192) \| 2.805397 / 2.077655 (0.727742) \| 1.555609 / 1.504120 (0.051489) \| 1.405662 / 1.541195 (-0.135533) \| 1.459492 / 1.468490 (-0.008999) \| 0.718376 / 4.584777 (-3.866401) \| 2.395918 / 3.745712 (-1.349794) \| 2.976753 / 5.269862 (-2.293108) \| 1.883938 / 4.565676 (-2.681738) \| 0.078867 / 0.424275 (-0.345408) \| 0.005207 / 0.007607 (-0.002400) \| 0.335178 / 0.226044 (0.109133) \| 3.313414 / 2.268929 (1.044485) \| 1.856929 / 55.444624 (-53.587696) \| 1.565319 / 6.876477 (-5.311158) \| 1.592723 / 2.142072 (-0.549350) \| 0.793621 / 4.805227 (-4.011606) \| 0.134208 / 6.500664 (-6.366456) \| 0.042853 / 0.075469 (-0.032616) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981553 / 1.841788 (-0.860235) \| 11.810438 / 8.074308 (3.736130) \| 9.529874 / 10.191392 (-0.661518) \| 0.142216 / 0.680424 (-0.538207) \| 0.014303 / 0.534201 (-0.519898) \| 0.304600 / 0.579283 (-0.274684) \| 0.261869 / 0.434364 (-0.172495) \| 0.347301 / 0.540337 (-0.193036) \| 0.437395 / 1.386936 (-0.949541) \|\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) \| 0.005881 / 0.011353 (-0.005472) \| 0.004039 / 0.011008 (-0.006969) \| 0.050241 / 0.038508 (0.011733) \| 0.032670 / 0.023109 (0.009561) \| 0.264940 / 0.275898 (-0.010959) \| 0.287105 / 0.323480 (-0.036374) \| 0.004844 / 0.007986 (-0.003142) \| 0.002867 / 0.004328 (-0.001462) \| 0.048083 / 0.004250 (0.043833) \| 0.040965 / 0.037052 (0.003913) \| 0.274390 / 0.258489 (0.015901) \| 0.312107 / 0.293841 (0.018266) \| 0.031714 / 0.128546 (-0.096832) \| 0.012603 / 0.075646 (-0.063043) \| 0.060698 / 0.419271 (-0.358573) \| 0.033130 / 0.043533 (-0.010402) \| 0.264444 / 0.255139 (0.009305) \| 0.282797 / 0.283200 (-0.000403) \| 0.027872 / 0.141683 (-0.113811) \| 1.139026 / 1.452155 (-0.313129) \| 1.181431 / 1.492716 (-0.311285) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097314 / 0.018006 (0.079308) \| 0.301326 / 0.000490 (0.300836) \| 0.000215 / 0.000200 (0.000015) \| 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) \| 0.023394 / 0.037411 (-0.014018) \| 0.076270 / 0.014526 (0.061744) \| 0.089065 / 0.176557 (-0.087491) \| 0.129996 / 0.737135 (-0.607139) \| 0.089642 / 0.296338 (-0.206697) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295390 / 0.215209 (0.080181) \| 2.877849 / 2.077655 (0.800194) \| 1.537129 / 1.504120 (0.033009) \| 1.409441 / 1.541195 (-0.131754) \| 1.432468 / 1.468490 (-0.036023) \| 0.718054 / 4.584777 (-3.866722) \| 0.930872 / 3.745712 (-2.814841) \| 2.841028 / 5.269862 (-2.428834) \| 1.921990 / 4.565676 (-2.643686) \| 0.077638 / 0.424275 (-0.346637) \| 0.005494 / 0.007607 (-0.002113) \| 0.336331 / 0.226044 (0.110287) \| 3.330490 / 2.268929 (1.061561) \| 1.887994 / 55.444624 (-53.556630) \| 1.593332 / 6.876477 (-5.283144) \| 1.726956 / 2.142072 (-0.415116) \| 0.783612 / 4.805227 (-4.021615) \| 0.129926 / 6.500664 (-6.370738) \| 0.040792 / 0.075469 (-0.034677) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980274 / 1.841788 (-0.861514) \| 12.193871 / 8.074308 (4.119563) \| 10.348934 / 10.191392 (0.157542) \| 0.141584 / 0.680424 (-0.538840) \| 0.015737 / 0.534201 (-0.518464) \| 0.300725 / 0.579283 (-0.278558) \| 0.127190 / 0.434364 (-0.307174) \| 0.341142 / 0.540337 (-0.199196) \| 0.459523 / 1.386936 (-0.927413) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#637246baf96f07b19b193ed101f34b65cb35cffb \"CML watermark\")\n" ]
2,370,444,104	6,993	less script docs	closed	2024-06-24T14:45:28	2024-07-08T13:10:53	2024-06-27T09:31:21	https://github.com/huggingface/datasets/pull/6993	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6993", "html_url": "https://github.com/huggingface/datasets/pull/6993", "diff_url": "https://github.com/huggingface/datasets/pull/6993.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6993.patch", "merged_at": "2024-06-27T09:31:21" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6993). 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) \| 0.005810 / 0.011353 (-0.005543) \| 0.003984 / 0.011008 (-0.007024) \| 0.064347 / 0.038508 (0.025839) \| 0.031943 / 0.023109 (0.008834) \| 0.252596 / 0.275898 (-0.023302) \| 0.274032 / 0.323480 (-0.049448) \| 0.003494 / 0.007986 (-0.004492) \| 0.002817 / 0.004328 (-0.001511) \| 0.050132 / 0.004250 (0.045881) \| 0.048008 / 0.037052 (0.010955) \| 0.249037 / 0.258489 (-0.009452) \| 0.288526 / 0.293841 (-0.005315) \| 0.031038 / 0.128546 (-0.097509) \| 0.012542 / 0.075646 (-0.063104) \| 0.205682 / 0.419271 (-0.213590) \| 0.038022 / 0.043533 (-0.005511) \| 0.259001 / 0.255139 (0.003862) \| 0.267455 / 0.283200 (-0.015744) \| 0.021980 / 0.141683 (-0.119703) \| 1.123996 / 1.452155 (-0.328159) \| 1.173801 / 1.492716 (-0.318915) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.102827 / 0.018006 (0.084821) \| 0.317210 / 0.000490 (0.316720) \| 0.000222 / 0.000200 (0.000022) \| 0.000052 / 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) \| 0.019483 / 0.037411 (-0.017928) \| 0.064098 / 0.014526 (0.049572) \| 0.076219 / 0.176557 (-0.100337) \| 0.122898 / 0.737135 (-0.614237) \| 0.080657 / 0.296338 (-0.215681) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278378 / 0.215209 (0.063169) \| 2.792314 / 2.077655 (0.714659) \| 1.516439 / 1.504120 (0.012319) \| 1.374052 / 1.541195 (-0.167143) \| 1.370848 / 1.468490 (-0.097642) \| 0.756002 / 4.584777 (-3.828775) \| 2.349581 / 3.745712 (-1.396131) \| 2.994094 / 5.269862 (-2.275768) \| 1.904242 / 4.565676 (-2.661435) \| 0.078769 / 0.424275 (-0.345506) \| 0.005103 / 0.007607 (-0.002505) \| 0.336331 / 0.226044 (0.110287) \| 3.329502 / 2.268929 (1.060574) \| 1.863545 / 55.444624 (-53.581079) \| 1.554690 / 6.876477 (-5.321787) \| 1.588448 / 2.142072 (-0.553624) \| 0.787322 / 4.805227 (-4.017905) \| 0.138345 / 6.500664 (-6.362320) \| 0.042228 / 0.075469 (-0.033241) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.968607 / 1.841788 (-0.873181) \| 11.972076 / 8.074308 (3.897768) \| 9.927608 / 10.191392 (-0.263784) \| 0.141666 / 0.680424 (-0.538758) \| 0.014591 / 0.534201 (-0.519610) \| 0.301995 / 0.579283 (-0.277288) \| 0.274360 / 0.434364 (-0.160004) \| 0.338396 / 0.540337 (-0.201941) \| 0.431081 / 1.386936 (-0.955855) \|\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) \| 0.006122 / 0.011353 (-0.005231) \| 0.004201 / 0.011008 (-0.006807) \| 0.050204 / 0.038508 (0.011695) \| 0.033222 / 0.023109 (0.010113) \| 0.274357 / 0.275898 (-0.001542) \| 0.296238 / 0.323480 (-0.027242) \| 0.004542 / 0.007986 (-0.003444) \| 0.002880 / 0.004328 (-0.001449) \| 0.049103 / 0.004250 (0.044852) \| 0.042294 / 0.037052 (0.005242) \| 0.286459 / 0.258489 (0.027970) \| 0.324988 / 0.293841 (0.031147) \| 0.032084 / 0.128546 (-0.096462) \| 0.012329 / 0.075646 (-0.063318) \| 0.060261 / 0.419271 (-0.359010) \| 0.034130 / 0.043533 (-0.009403) \| 0.271432 / 0.255139 (0.016293) \| 0.306251 / 0.283200 (0.023051) \| 0.019744 / 0.141683 (-0.121939) \| 1.153483 / 1.452155 (-0.298672) \| 1.209126 / 1.492716 (-0.283591) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004737 / 0.018006 (-0.013270) \| 0.313458 / 0.000490 (0.312968) \| 0.000216 / 0.000200 (0.000017) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022472 / 0.037411 (-0.014939) \| 0.076725 / 0.014526 (0.062199) \| 0.091356 / 0.176557 (-0.085201) \| 0.132427 / 0.737135 (-0.604708) \| 0.091072 / 0.296338 (-0.205266) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294414 / 0.215209 (0.079205) \| 2.913695 / 2.077655 (0.836040) \| 1.567309 / 1.504120 (0.063189) \| 1.448664 / 1.541195 (-0.092531) \| 1.466386 / 1.468490 (-0.002105) \| 0.718605 / 4.584777 (-3.866172) \| 0.951963 / 3.745712 (-2.793749) \| 2.812565 / 5.269862 (-2.457297) \| 1.886483 / 4.565676 (-2.679193) \| 0.077912 / 0.424275 (-0.346363) \| 0.005371 / 0.007607 (-0.002236) \| 0.349528 / 0.226044 (0.123484) \| 3.431049 / 2.268929 (1.162121) \| 1.920210 / 55.444624 (-53.524414) \| 1.637927 / 6.876477 (-5.238549) \| 1.767502 / 2.142072 (-0.374570) \| 0.808672 / 4.805227 (-3.996555) \| 0.134261 / 6.500664 (-6.366403) \| 0.041295 / 0.075469 (-0.034174) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.023454 / 1.841788 (-0.818334) \| 12.433731 / 8.074308 (4.359423) \| 10.413191 / 10.191392 (0.221799) \| 0.156813 / 0.680424 (-0.523611) \| 0.015446 / 0.534201 (-0.518755) \| 0.301935 / 0.579283 (-0.277348) \| 0.133655 / 0.434364 (-0.300709) \| 0.340296 / 0.540337 (-0.200041) \| 0.466314 / 1.386936 (-0.920622) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6cf563fd57807e923a29ebbe327fecb4ef969877 \"CML watermark\")\n", "Hi @lhoestq,\r\n\r\nI was confused by `legacy` prefix added to the [image data loading](https://huggingface.co/docs/datasets/main/en/image_dataset#legacy-loading-script) script section. I have a custom image dataset and have looked through the documentation to find something similar but can't find a good alternative What is now the recommend way to create a custom image dataset then? I want the HF format but will not host it on the hub.\r\n\r\nApologies in advance if this is the wrong place to ask such questions...", "We stopped making new features for datasets with scripts for obvious security reasons, that's why they are marked as \"legacy\". What is blocking you from hosting on HF ?", "Hi, thanks for the prompt answer :) I am working on proprietary datasets for the company where I am employed. We want to keep the data in-house but would like to investigate the use of the HF ecosystem.", "I see ! Note that it's possible to have private repos on HF (+ dataset viewer) and you can even choose the storage region, if it can help" ]
2,367,890,622	6,992	Dataset with streaming doesn't work with proxy	open	2024-06-22T16:12:08	2024-06-25T15:43:05	null	https://github.com/huggingface/datasets/issues/6992	null	YHL04	false	[ "Hi ! can you try updating `datasets` and `huggingface_hub` ?\r\n\r\n```\r\npip install -U datasets huggingface_hub\r\n```" ]
2,367,711,094	6,991	Unblock NumPy 2.0	closed	2024-06-22T09:19:53	2024-12-25T17:57:34	2024-07-12T12:04:53	https://github.com/huggingface/datasets/pull/6991	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6991", "html_url": "https://github.com/huggingface/datasets/pull/6991", "diff_url": "https://github.com/huggingface/datasets/pull/6991.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6991.patch", "merged_at": "2024-07-12T12:04:53" }	NeilGirdhar	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6991). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@albertvillanova Any chance we could get this in before the next release? Everything depending on HuggingFace has their NumPy upgrade blocked.", "The incompatible libraries are:\r\n- faiss-cpu 1.8.0.post1 requires numpy<2.0,>=1.0, but you have numpy 2.0.0 which is incompatible.\r\n- tensorflow 2.16.2 requires numpy<2.0.0,>=1.23.5; python_version <= \"3.11\", but you have numpy 2.0.0 which is incompatible.\r\n- transformers 4.42.3 requires numpy<2.0,>=1.17, but you have numpy 2.0.0 which is incompatible.", "Why is it installing numpy 2 if the dependencies don't support it?", "For me, I'm getting:\r\n```\r\n❯ uv pip install --system \"datasets[tests] @ .\"\r\nFound existing alias for \"uv pip install\". You should use: \"pipi\"\r\nResolved 119 packages in 934ms\r\n Built datasets @ file:///Users/neil/src/datasets\r\nPrepared 1 package in 1.28s\r\nUninstalled 1 package in 10ms\r\nInstalled 2 packages in 17ms\r\n - datasets==2.20.1.dev0 (from file:///Users/neil/src/datasets)\r\n + datasets==2.20.1.dev0 (from file:///Users/neil/src/datasets)\r\n + numpy==1.26.4\r\n```", "Which version on Python do you have?", "3.12.4 I'll try on 3.10 now.", "Please, note that I obtained the previous incompatible libraries in my local environment, by forcing the update of numpy.", "In the Python 3.10 CI, the situation is different:\r\n- for example, they install an older version of tensorflow (2.14.0), where probably the constraint on numpy was not yet implemented. See the details: https://github.com/huggingface/datasets/actions/runs/9879100332/job/27306903343?pr=6991\r\n```\r\n> uv pip install --system \"datasets[tests] @ .\"\r\n...\r\n + faiss-cpu==1.8.0\r\n...\r\n + numpy==2.0.0\r\n...\r\n + tensorflow==2.14.0\r\n```\r\n\r\nSee, CI installs:\r\n- faiss-cpu 1.8.0 instead of 1.8.0.post1\r\n- tensorflow 2.14.0 instead of 2.16.2\r\n- transformers 4.41.2 instead of 4.42.3", "~~The main point is that we cannot support numpy 2.0 until tensorflow and faiss do.~~\r\n\r\nAlternatively, we should ignore/select tests depending on the installed versions.", "> Alternatively, we should ignore/select tests depending on the installed versions.\r\n\r\nThat works.\r\n\r\nAlternatively, you could depend on tensorflow >= 2.16.2 (etc.) for the tests?", "Yes, I was thinking of a workaround solution.\r\n\r\nThe issue I see is that our CI will not test numpy 2.0 indeed.", "> The issue I see is that our CI will not test numpy 2.0 indeed.\r\n\r\nRight, that's the advantage of the test skipping you wanted, I see your point.\r\n\r\nThing is, it won't be long before tensorflow supports numpy 2.0, and then the situation is resolved and your tests test numpy 2.0. Do you really want to invest a lot of effort into testing numpy 2.0 for a few months benefit?", "Without testing Numpy 2.0, we do not know if there are some other parts in the code broken.", "> Without testing Numpy 2.0, we do not know if there are some other parts in the code broken.\r\n\r\nYes, you're right. I understand you're point, but you could say this for anything that your test dependencies don't support.\r\n\r\nI guess the solution is to write tests that don't depend on tensorflow, etc., but still use numpy. You could write some Jax tests for example.\r\n\r\nThat said, blocking numpy 2 isn't a good solution in my opinion. These dependencies are extremely late in supporting Numpy 2. They were supposed to be testing against preview releases over three months ago. I don't think the world should have to wait for them.", "> I guess the solution is to write tests that don't depend on tensorflow, etc., but still use numpy.\r\nThat is my point. What we cannot do is just blindly support Numpy 2.0 without knowing its consequences. We need to test it:\r\n- to know if our core code works with it\r\n- to know what optional libraries are incompatible\r\n\r\nFor example, while testing locally, I have discovered that librosa is also incompatible with numpy-2.0, due to its dependency on soxr:\r\n- https://github.com/dofuuz/python-soxr/issues/28", "While testing locally, I have also discovered that pytorch does not support Numpy 2.0 on Windows platforms:\r\n- https://github.com/pytorch/pytorch/issues/128860", "I am adding Numpy 2.0 tests to your PR if you don't mind, before merging this PR.", "Awesome, thank you! Please let me know if I need to do anything.", "Now we test numpy 2.0 in the `test_py310_numpy2` CI tests: https://github.com/huggingface/datasets/actions/runs/9907254874/job/27370545495?pr=6991\r\n```\r\n + numpy==2.0.0\r\n```", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005709 / 0.011353 (-0.005643) \| 0.003947 / 0.011008 (-0.007061) \| 0.064407 / 0.038508 (0.025899) \| 0.029903 / 0.023109 (0.006794) \| 0.244838 / 0.275898 (-0.031060) \| 0.268894 / 0.323480 (-0.054586) \| 0.003200 / 0.007986 (-0.004786) \| 0.002867 / 0.004328 (-0.001461) \| 0.050016 / 0.004250 (0.045765) \| 0.047682 / 0.037052 (0.010629) \| 0.252186 / 0.258489 (-0.006303) \| 0.292050 / 0.293841 (-0.001791) \| 0.030277 / 0.128546 (-0.098270) \| 0.012283 / 0.075646 (-0.063364) \| 0.205875 / 0.419271 (-0.213397) \| 0.037202 / 0.043533 (-0.006331) \| 0.246045 / 0.255139 (-0.009094) \| 0.272422 / 0.283200 (-0.010777) \| 0.020572 / 0.141683 (-0.121111) \| 1.114343 / 1.452155 (-0.337812) \| 1.169909 / 1.492716 (-0.322808) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096612 / 0.018006 (0.078605) \| 0.303025 / 0.000490 (0.302535) \| 0.000210 / 0.000200 (0.000010) \| 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) \| 0.019292 / 0.037411 (-0.018119) \| 0.062548 / 0.014526 (0.048023) \| 0.076027 / 0.176557 (-0.100530) \| 0.121752 / 0.737135 (-0.615383) \| 0.076608 / 0.296338 (-0.219730) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283900 / 0.215209 (0.068691) \| 2.829829 / 2.077655 (0.752174) \| 1.428934 / 1.504120 (-0.075186) \| 1.316796 / 1.541195 (-0.224399) \| 1.330012 / 1.468490 (-0.138478) \| 0.702245 / 4.584777 (-3.882532) \| 2.380454 / 3.745712 (-1.365259) \| 2.882881 / 5.269862 (-2.386980) \| 1.920345 / 4.565676 (-2.645332) \| 0.077860 / 0.424275 (-0.346415) \| 0.005295 / 0.007607 (-0.002312) \| 0.336968 / 0.226044 (0.110924) \| 3.327808 / 2.268929 (1.058879) \| 1.781958 / 55.444624 (-53.662666) \| 1.489412 / 6.876477 (-5.387065) \| 1.634829 / 2.142072 (-0.507243) \| 0.787985 / 4.805227 (-4.017243) \| 0.134397 / 6.500664 (-6.366267) \| 0.042906 / 0.075469 (-0.032563) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.967647 / 1.841788 (-0.874141) \| 11.714541 / 8.074308 (3.640233) \| 9.350228 / 10.191392 (-0.841164) \| 0.142675 / 0.680424 (-0.537749) \| 0.014609 / 0.534201 (-0.519592) \| 0.301970 / 0.579283 (-0.277314) \| 0.262350 / 0.434364 (-0.172014) \| 0.342933 / 0.540337 (-0.197404) \| 0.437321 / 1.386936 (-0.949615) \|\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) \| 0.005622 / 0.011353 (-0.005731) \| 0.003958 / 0.011008 (-0.007050) \| 0.050667 / 0.038508 (0.012159) \| 0.032842 / 0.023109 (0.009733) \| 0.252292 / 0.275898 (-0.023606) \| 0.280602 / 0.323480 (-0.042878) \| 0.004313 / 0.007986 (-0.003673) \| 0.002870 / 0.004328 (-0.001458) \| 0.049549 / 0.004250 (0.045299) \| 0.040448 / 0.037052 (0.003396) \| 0.270264 / 0.258489 (0.011775) \| 0.302988 / 0.293841 (0.009147) \| 0.030840 / 0.128546 (-0.097707) \| 0.012131 / 0.075646 (-0.063515) \| 0.060061 / 0.419271 (-0.359211) \| 0.033025 / 0.043533 (-0.010507) \| 0.251909 / 0.255139 (-0.003230) \| 0.275511 / 0.283200 (-0.007689) \| 0.018399 / 0.141683 (-0.123284) \| 1.160744 / 1.452155 (-0.291411) \| 1.188265 / 1.492716 (-0.304452) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097719 / 0.018006 (0.079712) \| 0.304389 / 0.000490 (0.303899) \| 0.000217 / 0.000200 (0.000017) \| 0.000045 / 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) \| 0.022964 / 0.037411 (-0.014447) \| 0.076897 / 0.014526 (0.062372) \| 0.088930 / 0.176557 (-0.087626) \| 0.128926 / 0.737135 (-0.608209) \| 0.091049 / 0.296338 (-0.205290) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285670 / 0.215209 (0.070461) \| 2.806071 / 2.077655 (0.728416) \| 1.527161 / 1.504120 (0.023041) \| 1.410291 / 1.541195 (-0.130903) \| 1.427071 / 1.468490 (-0.041419) \| 0.705527 / 4.584777 (-3.879250) \| 0.926915 / 3.745712 (-2.818797) \| 2.893078 / 5.269862 (-2.376784) \| 1.907113 / 4.565676 (-2.658564) \| 0.077326 / 0.424275 (-0.346949) \| 0.005182 / 0.007607 (-0.002425) \| 0.332282 / 0.226044 (0.106237) \| 3.312889 / 2.268929 (1.043960) \| 1.853839 / 55.444624 (-53.590785) \| 1.592013 / 6.876477 (-5.284464) \| 1.620234 / 2.142072 (-0.521838) \| 0.776894 / 4.805227 (-4.028333) \| 0.132411 / 6.500664 (-6.368253) \| 0.041430 / 0.075469 (-0.034039) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.003468 / 1.841788 (-0.838320) \| 12.472251 / 8.074308 (4.397943) \| 10.603243 / 10.191392 (0.411851) \| 0.132561 / 0.680424 (-0.547863) \| 0.015790 / 0.534201 (-0.518411) \| 0.306724 / 0.579283 (-0.272559) \| 0.125812 / 0.434364 (-0.308552) \| 0.343782 / 0.540337 (-0.196555) \| 0.445915 / 1.386936 (-0.941021) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dfc2b1b14ab8f32730d2bc36c8016ecefbcbabd1 \"CML watermark\")\n" ]
2,366,660,785	6,990	Problematic rank after calling `split_dataset_by_node` twice	closed	2024-06-21T14:25:26	2024-06-25T16:19:19	2024-06-25T16:19:19	https://github.com/huggingface/datasets/issues/6990	null	yzhangcs	false	[ "ah yes good catch ! feel free to open a PR with your suggested fix" ]
2,365,556,449	6,989	cache in nfs error	open	2024-06-21T02:09:22	2025-01-29T11:44:04	null	https://github.com/huggingface/datasets/issues/6989	null	simplew2011	false	[ "Hey @simplew2011 I am curious if you know of a workaround, or possible implications of letting the code run?" ]
2,364,129,918	6,988	[`feat`] Move dataset card creation to method for easier overriding	open	2024-06-20T10:47:57	2024-06-21T16:04:58	null	https://github.com/huggingface/datasets/pull/6988	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6988", "html_url": "https://github.com/huggingface/datasets/pull/6988", "diff_url": "https://github.com/huggingface/datasets/pull/6988.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6988.patch", "merged_at": null }	tomaarsen	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6988). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "`Dataset` objects are not made to be subclassed, so I don't think going in that direction is a good idea. In particular there is absolutely no test to make sure it works well, and nothing in the internal has been made to anticipate this use case.\r\n\r\nI'd suggest to use a separate function to push changes to the Dataset card, and call it after `push_to_hub()`. This way people can also use a similar logic with other tools that `datasets`. You can also use composition instead of subclassing.", "Would you consider an alternative where a Dataset instance carries a dataset card template which can be updated?\n\nI don't want to burden my users with having to call another method after `push_to_hub` themselves. If you're not a fan of the template approach above either, then I'll likely subclass `push_to_hub` to once again download the just-uploaded-but-empty dataset card, update it, and reupload it. It'll just be a bit more requests than necessary, but not a big deal overall.\n\n- Tom Aarsen ", "Actually I find the idea of overriding `_create_dataset_card` better than implementing a templating logic. My main concern is that if we go in that direction we better make sure that subclasses of `Dataset` are working well. \r\n\r\nWell if it's been working fine on your side why not, but make sure you test correctly features that could not work because of subclassing (e.g. I'm pretty sure `map()` won't return your subclass of `Dataset`). Or at least the ones that matter for your lib.\r\n\r\nIf it sounds good to you I'm fine with merging your addition to let you override the dataset card.", "> e.g. I'm pretty sure map() won't return your subclass of Dataset\r\n\r\nI understand that there's limitations such as this one. The subclass doesn't have to be robust - I'd just like some simple automatic dataset card generation options directly after generating the dataset. This can be removed if the user does additional steps before pushing the model, e.g. mapping, filtering, saving to disk and uploading the loaded dataset, etc.\r\n\r\n> If it sounds good to you I'm fine with merging your addition to let you override the dataset card.\r\n\r\nThat would be quite useful for me! I appreciate it.\r\n\r\nI'm not very sure what the test failures are caused by, I believe the only change in behaviour is that\r\n```python\r\n DatasetInfosDict({config_name: info_to_dump}).to_dataset_card_data(dataset_card_data)\r\n MetadataConfigs({config_name: metadata_config_to_dump}).to_dataset_card_data(dataset_card_data)\r\n```\r\nare not called when `dataset_card` was already defined. Unless these have side-effects other than updating `dataset_card_data`, it shouldn't be any different than `main`.\r\n\r\n- Tom Aarsen", "Let's try to have this PR merged then !\r\n\r\nIMO your current implementation can be improved since you path both the dataset card data and the dataset card itself, which is redundant. Also I anticipate the failures in the CI to come from your default implementation which doesn't correspond to what it was doing before\r\n\r\n> Unless these have side-effects other than updating dataset_card_data, it shouldn't be any different than main.\r\n\r\nIndeed the dataset_card_data is the value from attribute of the dataset_card from a few lines before your changes, so yes it modifies the dataset_card object too." ]
2,363,728,190	6,987	Remove beam	closed	2024-06-20T07:27:14	2024-06-26T19:41:55	2024-06-26T19:35:42	https://github.com/huggingface/datasets/pull/6987	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6987", "html_url": "https://github.com/huggingface/datasets/pull/6987", "diff_url": "https://github.com/huggingface/datasets/pull/6987.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6987.patch", "merged_at": "2024-06-26T19:35:42" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6987). 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) \| 0.005931 / 0.011353 (-0.005422) \| 0.004127 / 0.011008 (-0.006881) \| 0.063854 / 0.038508 (0.025346) \| 0.034687 / 0.023109 (0.011577) \| 0.251397 / 0.275898 (-0.024501) \| 0.280348 / 0.323480 (-0.043132) \| 0.005008 / 0.007986 (-0.002977) \| 0.002930 / 0.004328 (-0.001398) \| 0.050703 / 0.004250 (0.046452) \| 0.047109 / 0.037052 (0.010057) \| 0.258525 / 0.258489 (0.000035) \| 0.288759 / 0.293841 (-0.005081) \| 0.030547 / 0.128546 (-0.097999) \| 0.102184 / 0.075646 (0.026537) \| 0.207934 / 0.419271 (-0.211338) \| 0.036477 / 0.043533 (-0.007056) \| 0.338160 / 0.255139 (0.083021) \| 0.310735 / 0.283200 (0.027535) \| 0.018637 / 0.141683 (-0.123045) \| 1.228539 / 1.452155 (-0.223616) \| 1.168004 / 1.492716 (-0.324713) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098355 / 0.018006 (0.080348) \| 0.302310 / 0.000490 (0.301820) \| 0.000215 / 0.000200 (0.000015) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019607 / 0.037411 (-0.017804) \| 0.063795 / 0.014526 (0.049269) \| 0.075029 / 0.176557 (-0.101528) \| 0.121293 / 0.737135 (-0.615842) \| 0.076480 / 0.296338 (-0.219858) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285285 / 0.215209 (0.070076) \| 2.747455 / 2.077655 (0.669801) \| 1.454190 / 1.504120 (-0.049929) \| 1.330777 / 1.541195 (-0.210418) \| 1.358292 / 1.468490 (-0.110198) \| 0.724991 / 4.584777 (-3.859786) \| 2.374889 / 3.745712 (-1.370823) \| 2.985868 / 5.269862 (-2.283994) \| 1.921521 / 4.565676 (-2.644156) \| 0.078589 / 0.424275 (-0.345686) \| 0.005104 / 0.007607 (-0.002503) \| 0.333898 / 0.226044 (0.107853) \| 3.317702 / 2.268929 (1.048773) \| 1.887161 / 55.444624 (-53.557463) \| 1.510700 / 6.876477 (-5.365777) \| 1.544175 / 2.142072 (-0.597898) \| 0.804262 / 4.805227 (-4.000965) \| 0.134015 / 6.500664 (-6.366649) \| 0.042819 / 0.075469 (-0.032650) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.012142 / 1.841788 (-0.829645) \| 11.861780 / 8.074308 (3.787472) \| 9.797285 / 10.191392 (-0.394107) \| 0.142114 / 0.680424 (-0.538310) \| 0.013984 / 0.534201 (-0.520217) \| 0.302412 / 0.579283 (-0.276871) \| 0.265060 / 0.434364 (-0.169304) \| 0.337510 / 0.540337 (-0.202828) \| 0.432197 / 1.386936 (-0.954739) \|\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) \| 0.005920 / 0.011353 (-0.005433) \| 0.003991 / 0.011008 (-0.007017) \| 0.049874 / 0.038508 (0.011366) \| 0.033771 / 0.023109 (0.010662) \| 0.264789 / 0.275898 (-0.011109) \| 0.287554 / 0.323480 (-0.035926) \| 0.004341 / 0.007986 (-0.003644) \| 0.002888 / 0.004328 (-0.001441) \| 0.049383 / 0.004250 (0.045133) \| 0.040757 / 0.037052 (0.003704) \| 0.286067 / 0.258489 (0.027578) \| 0.311105 / 0.293841 (0.017264) \| 0.031482 / 0.128546 (-0.097064) \| 0.012358 / 0.075646 (-0.063288) \| 0.060298 / 0.419271 (-0.358973) \| 0.033237 / 0.043533 (-0.010296) \| 0.265804 / 0.255139 (0.010665) \| 0.281273 / 0.283200 (-0.001927) \| 0.017879 / 0.141683 (-0.123804) \| 1.154059 / 1.452155 (-0.298096) \| 1.156758 / 1.492716 (-0.335958) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004677 / 0.018006 (-0.013329) \| 0.300768 / 0.000490 (0.300278) \| 0.000212 / 0.000200 (0.000013) \| 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) \| 0.023032 / 0.037411 (-0.014379) \| 0.077498 / 0.014526 (0.062973) \| 0.089134 / 0.176557 (-0.087422) \| 0.129691 / 0.737135 (-0.607444) \| 0.091372 / 0.296338 (-0.204967) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290823 / 0.215209 (0.075613) \| 2.873159 / 2.077655 (0.795504) \| 1.563361 / 1.504120 (0.059241) \| 1.447048 / 1.541195 (-0.094147) \| 1.490473 / 1.468490 (0.021983) \| 0.715642 / 4.584777 (-3.869135) \| 0.996223 / 3.745712 (-2.749489) \| 2.861466 / 5.269862 (-2.408396) \| 1.915581 / 4.565676 (-2.650096) \| 0.077892 / 0.424275 (-0.346383) \| 0.005463 / 0.007607 (-0.002144) \| 0.339670 / 0.226044 (0.113626) \| 3.412830 / 2.268929 (1.143902) \| 1.908676 / 55.444624 (-53.535949) \| 1.625358 / 6.876477 (-5.251119) \| 1.769437 / 2.142072 (-0.372635) \| 0.792505 / 4.805227 (-4.012722) \| 0.133007 / 6.500664 (-6.367657) \| 0.041305 / 0.075469 (-0.034164) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.986882 / 1.841788 (-0.854905) \| 12.368101 / 8.074308 (4.293793) \| 10.367439 / 10.191392 (0.176047) \| 0.141248 / 0.680424 (-0.539176) \| 0.016144 / 0.534201 (-0.518057) \| 0.300962 / 0.579283 (-0.278321) \| 0.126863 / 0.434364 (-0.307501) \| 0.341107 / 0.540337 (-0.199230) \| 0.439819 / 1.386936 (-0.947117) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2754625d45e153bd9758af40e65e7545321fc2a \"CML watermark\")\n" ]
2,362,584,179	6,986	Add large_list type support in string_to_arrow	closed	2024-06-19T14:54:25	2024-08-12T14:43:48	2024-08-12T14:43:47	https://github.com/huggingface/datasets/pull/6986	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6986", "html_url": "https://github.com/huggingface/datasets/pull/6986", "diff_url": "https://github.com/huggingface/datasets/pull/6986.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6986.patch", "merged_at": null }	arthasking123	true	[ "@albertvillanova @KennethEnevoldsen" ]
2,362,378,276	6,985	AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'	closed	2024-06-19T13:22:28	2025-03-14T18:47:53	2024-06-25T05:40:51	https://github.com/huggingface/datasets/issues/6985	null	firmai	false	[ "Please note that the error is raised just at import:\r\n```python\r\nimport pyarrow.parquet as pq\r\n```\r\n\r\nTherefore it must be caused by some problem with your pyarrow installation. I would recommend you uninstall and install pyarrow again.\r\n\r\nI also see that it seems you use conda to install pyarrow. Please note that pyarrow offers 3 different packages in conda-forge: https://arrow.apache.org/docs/python/install.html#using-conda\r\n```\r\nconda install -c conda-forge pyarrow\r\n```\r\n> While the pyarrow [conda-forge](https://conda-forge.org/) package is the right choice for most users, both a minimal and maximal variant of the package exist, either of which may be better for your use case. See [Differences between conda-forge packages](https://arrow.apache.org/docs/python/install.html#python-conda-differences).\r\n\r\nPlease, make sure you install the right one: I guess it is either `pyarrow` (or `pyarrow-all`).", "I have same issue, please downgrade pyarrow==15.0.2, it seem datasets library need to be fix", "It is not a problem with the `datasets` library: we support latest version of `pyarrow` and our Continuous Integration tests are using pyarrow 16.1.0 without any problem.\r\n\r\nThe error reported here is raised when importing pyarrow.parquet:\r\n```\r\n---> 29 import pyarrow.parquet as pq\r\n```\r\n```\r\nFile /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/__init__.py:20\r\n 1 # Licensed to the Apache Software Foundation (ASF) under one\r\n 2 # or more contributor license agreements. See the NOTICE file\r\n 3 # distributed with this work for additional information\r\n (...)\r\n 17 \r\n 18 # flake8: noqa\r\n---> 20 from .core import *\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/core.py:33\r\n 30 import pyarrow as pa\r\n 32 try:\r\n---> 33 import pyarrow._parquet as _parquet\r\n 34 except ImportError as exc:\r\n 35 raise ImportError(\r\n 36 \"The pyarrow installation is not built with support \"\r\n 37 f\"for the Parquet file format ({str(exc)})\"\r\n 38 ) from None\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/pyarrow/_parquet.pyx:1, in init pyarrow._parquet()\r\n\r\nAttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'\r\n```\r\n\r\nThis can only be explained if pyarrow was not properly installed. \r\n\r\nIf the user just installed `pyarrow-core` from conda-forge, then its parquet subpackage is not installed and cannot be imported. You can check pyarrow docs:\r\n- Differences between conda-forge packages: https://arrow.apache.org/docs/python/install.html#python-conda-differences\r\n> The `pyarrow-core` package includes the following functionality:\r\n> ...\r\n> The `pyarrow` package adds the following:\r\n> ...\r\n> Parquet (i.e., `pyarrow.parquet`)", "I'm still seeing the same issue on datasets version 2.20.0. I installed pyarrow version 17.0.0 with `pip install`. Downgrading to pyarrow==15.0.2 also did not resolve the issue.", "@RenaLu As of UTC time 07/27/2024 23:20:00, I hit the same issue and reinstalling `pyarrow==15.0.2` resolved the issue for me. You may want to check if your `pyarrow` is successfully downgraded.", "I can confirm @albertvillanova's [analysis & suggestion](https://github.com/huggingface/datasets/issues/6985#issuecomment-2188022888) - `pip uninstall pyarrow` followed by `pip install pyarrow` solved it for me. \r\n\r\nI suspect this is because pyarrow was initially installed as a pandas extra `pandas[...,parquet,...]`, then pip-upgrading pyarrow resulted in the issue.\r\n\r\n@RenaLu did you uninstall pyarrow between changing versions?", "After trying all the above combinations and failing, running the following in the notebook fixed the error!!\r\n`!conda install -c conda-forge -y datasets pyarrow libparquet`\r\nNote : Uninstall any existing dataset and pyarrow installations in the env before executing the above.", "If on colab, remember to restart the runtime so the new pyarrow is imported. I also upgraded pip which is recommended in pyarrow's installation instructions.", "fixed doing this: !pip install --upgrade datasets\r\n\r\n!pip show pyarrow\r\n!pip show datasets\r\n!pip uninstall -y pyarrow\r\n!pip install pyarrow --no-cache-dir\r\n!pip install pyarrow\r\n!pip install transformers\r\n!pip install --upgrade datasets\r\n!pip install datasets\r\n! pip install pyarrow\r\n! pip install pyarrow.parquet\r\n!pip install transformers\r\n\r\n# Import necessary libraries\r\nfrom datasets import load_dataset\r\nimport pyarrow.parquet as pq\r\nimport pyarrow.lib as lib\r\nimport pandas as pd\r\nfrom transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments\r\n", "but now i cant run test, so i remove it, ERROR: Could not find a version that satisfies the requirement pyarrow.parquet (from versions: none)\r\nERROR: No matching distribution found for pyarrow.parquet will still running but will tell you this", "I have the same question right now, python3.12 and transformers4.44.2, I have not fixed it", "I did most of the suggestions above and I still got the error, but after restarting my computer the error was fixed", "how to fix this, still have this error. ", "have we figured out what causes it?\n" ]
2,362,143,554	6,984	Convert polars DataFrame back to datasets	closed	2024-06-19T11:38:48	2024-08-12T14:43:46	2024-08-12T14:43:46	https://github.com/huggingface/datasets/issues/6984	null	ljw20180420	false	[ "Hi ! Thanks for reporting :)\r\n\r\nWe don't support `large_list` yet, though it should be added to `Sequence` IMO (maybe with a parameter `large=True` ?)" ]
2,361,806,201	6,983	Remove metrics	closed	2024-06-19T09:08:55	2024-06-28T06:57:38	2024-06-28T06:51:30	https://github.com/huggingface/datasets/pull/6983	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6983", "html_url": "https://github.com/huggingface/datasets/pull/6983", "diff_url": "https://github.com/huggingface/datasets/pull/6983.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6983.patch", "merged_at": "2024-06-28T06:51:30" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6983). 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) \| 0.005566 / 0.011353 (-0.005787) \| 0.003977 / 0.011008 (-0.007031) \| 0.063250 / 0.038508 (0.024742) \| 0.030907 / 0.023109 (0.007798) \| 0.244989 / 0.275898 (-0.030909) \| 0.272139 / 0.323480 (-0.051341) \| 0.004332 / 0.007986 (-0.003653) \| 0.002960 / 0.004328 (-0.001368) \| 0.050147 / 0.004250 (0.045896) \| 0.044740 / 0.037052 (0.007688) \| 0.256947 / 0.258489 (-0.001542) \| 0.290372 / 0.293841 (-0.003469) \| 0.030444 / 0.128546 (-0.098102) \| 0.012675 / 0.075646 (-0.062971) \| 0.203852 / 0.419271 (-0.215420) \| 0.036977 / 0.043533 (-0.006556) \| 0.244401 / 0.255139 (-0.010738) \| 0.270020 / 0.283200 (-0.013179) \| 0.018177 / 0.141683 (-0.123506) \| 1.122189 / 1.452155 (-0.329966) \| 1.176688 / 1.492716 (-0.316028) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.100721 / 0.018006 (0.082715) \| 0.311824 / 0.000490 (0.311335) \| 0.000222 / 0.000200 (0.000022) \| 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) \| 0.020039 / 0.037411 (-0.017373) \| 0.062084 / 0.014526 (0.047558) \| 0.074317 / 0.176557 (-0.102240) \| 0.123935 / 0.737135 (-0.613200) \| 0.076186 / 0.296338 (-0.220153) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284827 / 0.215209 (0.069618) \| 2.782727 / 2.077655 (0.705072) \| 1.417624 / 1.504120 (-0.086496) \| 1.294476 / 1.541195 (-0.246718) \| 1.332658 / 1.468490 (-0.135832) \| 0.724820 / 4.584777 (-3.859957) \| 2.384546 / 3.745712 (-1.361166) \| 2.866759 / 5.269862 (-2.403103) \| 1.930756 / 4.565676 (-2.634921) \| 0.083090 / 0.424275 (-0.341185) \| 0.005566 / 0.007607 (-0.002041) \| 0.340117 / 0.226044 (0.114072) \| 3.342417 / 2.268929 (1.073488) \| 1.807842 / 55.444624 (-53.636782) \| 1.511647 / 6.876477 (-5.364830) \| 1.653893 / 2.142072 (-0.488179) \| 0.803983 / 4.805227 (-4.001244) \| 0.136205 / 6.500664 (-6.364459) \| 0.042815 / 0.075469 (-0.032654) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.962346 / 1.841788 (-0.879442) \| 11.792239 / 8.074308 (3.717931) \| 9.236256 / 10.191392 (-0.955136) \| 0.143200 / 0.680424 (-0.537224) \| 0.015050 / 0.534201 (-0.519151) \| 0.304623 / 0.579283 (-0.274660) \| 0.266417 / 0.434364 (-0.167947) \| 0.341213 / 0.540337 (-0.199124) \| 0.454258 / 1.386936 (-0.932678) \|\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) \| 0.005917 / 0.011353 (-0.005436) \| 0.004005 / 0.011008 (-0.007003) \| 0.049781 / 0.038508 (0.011273) \| 0.033310 / 0.023109 (0.010200) \| 0.271881 / 0.275898 (-0.004017) \| 0.296855 / 0.323480 (-0.026625) \| 0.004479 / 0.007986 (-0.003507) \| 0.002818 / 0.004328 (-0.001510) \| 0.048213 / 0.004250 (0.043962) \| 0.043480 / 0.037052 (0.006428) \| 0.285963 / 0.258489 (0.027473) \| 0.317304 / 0.293841 (0.023463) \| 0.031619 / 0.128546 (-0.096928) \| 0.012312 / 0.075646 (-0.063335) \| 0.059904 / 0.419271 (-0.359368) \| 0.033152 / 0.043533 (-0.010381) \| 0.274198 / 0.255139 (0.019059) \| 0.290469 / 0.283200 (0.007269) \| 0.019424 / 0.141683 (-0.122258) \| 1.133669 / 1.452155 (-0.318485) \| 1.194427 / 1.492716 (-0.298290) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.101561 / 0.018006 (0.083555) \| 0.312617 / 0.000490 (0.312127) \| 0.000216 / 0.000200 (0.000016) \| 0.000045 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023705 / 0.037411 (-0.013706) \| 0.076781 / 0.014526 (0.062255) \| 0.089922 / 0.176557 (-0.086634) \| 0.129182 / 0.737135 (-0.607953) \| 0.092022 / 0.296338 (-0.204317) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300977 / 0.215209 (0.085768) \| 2.909088 / 2.077655 (0.831433) \| 1.592821 / 1.504120 (0.088701) \| 1.466627 / 1.541195 (-0.074568) \| 1.497558 / 1.468490 (0.029068) \| 0.720986 / 4.584777 (-3.863791) \| 0.958039 / 3.745712 (-2.787673) \| 3.023413 / 5.269862 (-2.246448) \| 1.933245 / 4.565676 (-2.632432) \| 0.080500 / 0.424275 (-0.343775) \| 0.005243 / 0.007607 (-0.002364) \| 0.361259 / 0.226044 (0.135215) \| 3.447317 / 2.268929 (1.178389) \| 1.938234 / 55.444624 (-53.506390) \| 1.671563 / 6.876477 (-5.204913) \| 1.674647 / 2.142072 (-0.467425) \| 0.790606 / 4.805227 (-4.014621) \| 0.133312 / 6.500664 (-6.367352) \| 0.041241 / 0.075469 (-0.034228) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.996167 / 1.841788 (-0.845621) \| 12.460877 / 8.074308 (4.386569) \| 10.608415 / 10.191392 (0.417023) \| 0.134076 / 0.680424 (-0.546348) \| 0.016166 / 0.534201 (-0.518035) \| 0.301218 / 0.579283 (-0.278065) \| 0.128979 / 0.434364 (-0.305385) \| 0.336453 / 0.540337 (-0.203884) \| 0.435561 / 1.386936 (-0.951375) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#70e7355b7125fb792107ef5128ee3ad15cbec26c \"CML watermark\")\n" ]
2,361,661,469	6,982	cannot split dataset when using load_dataset	closed	2024-06-19T08:07:16	2024-07-08T06:20:16	2024-07-08T06:20:16	https://github.com/huggingface/datasets/issues/6982	null	cybest0608	false	[ "it seems the bug will happened in all windows system, I tried it in windows8.1, 10, 11 and all of them failed. But it won't happened in the Linux(Ubuntu and Centos7) and Mac (both my virtual and physical machine). I still don't know what the problem is. May be related to the path? I cannot run the split file in my windows server which created in Linux (even I replace the path in the arrow document)....work for it for a week but still cannot fix it .....upset", "Have you properly logged in? Are you using the a valid token?\r\n\r\nNote that this dataset is gated and you must follow the right procedure to be able to access it. You can find more info in the docs: https://huggingface.co/docs/hub/datasets-gated#access-gated-datasets-as-a-user", "> Have you properly logged in? Are you using the a valid token?\r\n> \r\n> Note that this dataset is gated and you must follow the right procedure to be able to access it. You can find more info in the docs: https://huggingface.co/docs/hub/datasets-gated#access-gated-datasets-as-a-user\r\n\r\nI finally found it what happened. It is not about the logging. When I copy the dataset from its original path (C:/Users/cybes/.cache/huggingface/datasets/downloads/extracted/XXX/cv-corpus-7.0-2021-07-21) to the desktop and load each tsv in it one by one , when I load the test spilt, the following warning occurs:\r\n\"ArrowInvalid: Failed to parse string: 'Benchmark' as a scalar of type double\"\r\n\r\nThen I manually deleted them in the \"segment\", the error won't happen anymore, even I replace the original path with these revised tsv and use the previous loading method (common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"train\", trust_remote_code=True)). It can work properly." ]
2,361,520,022	6,981	Update docs on trust_remote_code defaults to False	closed	2024-06-19T07:12:21	2024-06-19T14:32:59	2024-06-19T14:26:37	https://github.com/huggingface/datasets/pull/6981	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6981", "html_url": "https://github.com/huggingface/datasets/pull/6981", "diff_url": "https://github.com/huggingface/datasets/pull/6981.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6981.patch", "merged_at": "2024-06-19T14:26:37" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6981). 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) \| 0.005578 / 0.011353 (-0.005775) \| 0.003946 / 0.011008 (-0.007062) \| 0.063317 / 0.038508 (0.024808) \| 0.031878 / 0.023109 (0.008769) \| 0.312571 / 0.275898 (0.036673) \| 0.281415 / 0.323480 (-0.042065) \| 0.004139 / 0.007986 (-0.003846) \| 0.002730 / 0.004328 (-0.001598) \| 0.049539 / 0.004250 (0.045289) \| 0.045056 / 0.037052 (0.008003) \| 0.263820 / 0.258489 (0.005330) \| 0.297817 / 0.293841 (0.003976) \| 0.029490 / 0.128546 (-0.099056) \| 0.012467 / 0.075646 (-0.063179) \| 0.204607 / 0.419271 (-0.214664) \| 0.036305 / 0.043533 (-0.007228) \| 0.244102 / 0.255139 (-0.011037) \| 0.267855 / 0.283200 (-0.015345) \| 0.019794 / 0.141683 (-0.121889) \| 1.130784 / 1.452155 (-0.321371) \| 1.172507 / 1.492716 (-0.320209) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092430 / 0.018006 (0.074424) \| 0.296460 / 0.000490 (0.295970) \| 0.000210 / 0.000200 (0.000010) \| 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) \| 0.019467 / 0.037411 (-0.017944) \| 0.062850 / 0.014526 (0.048324) \| 0.074067 / 0.176557 (-0.102490) \| 0.123280 / 0.737135 (-0.613856) \| 0.077036 / 0.296338 (-0.219302) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282687 / 0.215209 (0.067478) \| 2.786715 / 2.077655 (0.709060) \| 1.492028 / 1.504120 (-0.012092) \| 1.373603 / 1.541195 (-0.167592) \| 1.405004 / 1.468490 (-0.063486) \| 0.714408 / 4.584777 (-3.870369) \| 2.376785 / 3.745712 (-1.368927) \| 2.916150 / 5.269862 (-2.353712) \| 1.921184 / 4.565676 (-2.644493) \| 0.078354 / 0.424275 (-0.345921) \| 0.005236 / 0.007607 (-0.002371) \| 0.334647 / 0.226044 (0.108603) \| 3.262069 / 2.268929 (0.993140) \| 1.858300 / 55.444624 (-53.586324) \| 1.572968 / 6.876477 (-5.303509) \| 1.659145 / 2.142072 (-0.482927) \| 0.779546 / 4.805227 (-4.025681) \| 0.132623 / 6.500664 (-6.368041) \| 0.042423 / 0.075469 (-0.033046) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985516 / 1.841788 (-0.856271) \| 12.001321 / 8.074308 (3.927013) \| 9.927011 / 10.191392 (-0.264381) \| 0.142645 / 0.680424 (-0.537779) \| 0.013808 / 0.534201 (-0.520393) \| 0.303422 / 0.579283 (-0.275861) \| 0.262666 / 0.434364 (-0.171698) \| 0.339369 / 0.540337 (-0.200969) \| 0.431028 / 1.386936 (-0.955908) \|\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) \| 0.005848 / 0.011353 (-0.005505) \| 0.003971 / 0.011008 (-0.007037) \| 0.050746 / 0.038508 (0.012238) \| 0.031554 / 0.023109 (0.008445) \| 0.277678 / 0.275898 (0.001780) \| 0.300776 / 0.323480 (-0.022704) \| 0.004428 / 0.007986 (-0.003558) \| 0.002773 / 0.004328 (-0.001555) \| 0.049882 / 0.004250 (0.045632) \| 0.039833 / 0.037052 (0.002780) \| 0.289143 / 0.258489 (0.030654) \| 0.321425 / 0.293841 (0.027584) \| 0.031701 / 0.128546 (-0.096845) \| 0.012687 / 0.075646 (-0.062960) \| 0.060650 / 0.419271 (-0.358621) \| 0.033318 / 0.043533 (-0.010215) \| 0.277019 / 0.255139 (0.021880) \| 0.292345 / 0.283200 (0.009145) \| 0.018520 / 0.141683 (-0.123163) \| 1.143933 / 1.452155 (-0.308222) \| 1.183913 / 1.492716 (-0.308803) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094467 / 0.018006 (0.076461) \| 0.298822 / 0.000490 (0.298332) \| 0.000201 / 0.000200 (0.000001) \| 0.000045 / 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) \| 0.022811 / 0.037411 (-0.014601) \| 0.078084 / 0.014526 (0.063558) \| 0.089079 / 0.176557 (-0.087477) \| 0.130229 / 0.737135 (-0.606906) \| 0.090851 / 0.296338 (-0.205487) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294981 / 0.215209 (0.079772) \| 2.908294 / 2.077655 (0.830639) \| 1.591281 / 1.504120 (0.087161) \| 1.446032 / 1.541195 (-0.095162) \| 1.469441 / 1.468490 (0.000951) \| 0.726477 / 4.584777 (-3.858300) \| 0.983086 / 3.745712 (-2.762626) \| 2.892715 / 5.269862 (-2.377147) \| 1.974092 / 4.565676 (-2.591584) \| 0.079500 / 0.424275 (-0.344775) \| 0.005497 / 0.007607 (-0.002110) \| 0.342220 / 0.226044 (0.116176) \| 3.414508 / 2.268929 (1.145579) \| 1.941550 / 55.444624 (-53.503074) \| 1.645268 / 6.876477 (-5.231209) \| 1.805909 / 2.142072 (-0.336163) \| 0.814483 / 4.805227 (-3.990744) \| 0.135867 / 6.500664 (-6.364797) \| 0.041718 / 0.075469 (-0.033751) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.999751 / 1.841788 (-0.842036) \| 12.488263 / 8.074308 (4.413954) \| 10.867040 / 10.191392 (0.675648) \| 0.143999 / 0.680424 (-0.536425) \| 0.015496 / 0.534201 (-0.518705) \| 0.302170 / 0.579283 (-0.277113) \| 0.123753 / 0.434364 (-0.310611) \| 0.340424 / 0.540337 (-0.199913) \| 0.458339 / 1.386936 (-0.928597) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a6ccf944e42c1a84de81bf326accab9999b86c90 \"CML watermark\")\n" ]
2,360,909,930	6,980	Support NumPy 2.0	closed	2024-06-18T23:30:22	2024-07-12T12:04:54	2024-07-12T12:04:53	https://github.com/huggingface/datasets/issues/6980	null	NeilGirdhar	false	[]
2,360,175,363	6,979	How can I load partial parquet files only?	closed	2024-06-18T15:44:16	2024-06-21T17:09:32	2024-06-21T13:32:50	https://github.com/huggingface/datasets/issues/6979	null	lucasjinreal	false	[ "Hello,\r\n\r\nHave you tried loading the dataset in streaming mode? [Documentation](https://huggingface.co/docs/datasets/v2.20.0/stream)\r\n\r\nThis way you wouldn't have to load it all. Also, let's be nice to Parquet, it's a really nice technology and we don't need to be mean :)", "I have downloaded part of it, just want to know how to load part of it, stream mode is not work for me since my network (in china) not stable, I don't want do it all again and again.\r\n\r\nJust curious, doesn't there a way to load part of it?", "Could you convert the IterableDataset to a Dataset after taking the first 100 rows with `.take`? This way, you would have a local copy of the first 100 rows on your system and thus won't need to download. Would that work?\r\n\r\nHere is a [SO question](https://stackoverflow.com/questions/76227219/can-i-convert-an-iterabledataset-to-dataset) detailing how to do the conversion.", "I mean, the parquet is like:\r\n\r\n00000-0143554\r\n00001-0143554\r\n00002-0143554\r\n...\r\n00100-0143554\r\n...\r\n09100-0143554\r\n\r\nI just downloaded the first 9900 part of it. \r\n\r\nI can not load with load_dataset, it throw an error says my file is not same as parquet all amount.\r\n\r\nHow could I load the only I have? \r\n\r\n( I really don't want downlaod them all, cause, I don't need all, and pulus, its huge.... )\r\n\r\nAs I said, I have donwloaded about 9999... It's not about stream... I just wnat to konw how to load offline... part....", "Hi, @lucasjinreal.\r\n\r\nI am not sure of understanding your issue. What is the error message and stack trace you get? What version of `datasets` are you using? Could you provide a reproducible example?\r\n\r\nWithout knowing all those details, I would naively say that you can load whatever number of Parquet files by using the \"parquet\" loader: https://huggingface.co/docs/datasets/loading#parquet\r\n```python\r\nds = load_dataset(\"parquet\", data_files=\"data/train-001-of-00314.parquet\", split=\"train\")\r\n```", "@albertvillanova Not sure you have tested with this or not, but I have tried,\r\n\r\nthe only error I got is it still laodding all parquet with a progress bar maxium to the whole number 014354, and it loads my 0 - 000999 part, then throws an error.\r\n\r\nSays Numinfo is not same.\r\n\r\nI am so confused,", "Yes, my code snippet works.\n\nCould you copy-paste your code and the output? Otherwise we are not able to know what the issue is.", "@albertvillanova Hi, thanks for the tracing of the issue.\r\n\r\nThis is the output:\r\n\r\n```\r\nython get_llava_recap_cc3m.py\r\nGenerating train split: 3%\|███▋ \| 101910/3199866 [00:16<08:30, 6065.67 examples/s]\r\nTraceback (most recent call last):\r\n File \"get_llava_recap_cc3m.py\", line 31, in <module>\r\n dataset = load_dataset(\"llava-recap-cc3m/\", data_files=\"data/train-0000-of-00314.parquet\")\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/load.py\", line 2582, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 1005, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 1118, in _download_and_prepare\r\n verify_splits(self.info.splits, split_dict)\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/info_utils.py\", line 101, in verify_splits\r\n raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=156885281898.75, num_examples=3199866, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=4994080770, num_examples=101910, shard_lengths=[10191, 10291, 10291, 10291, 10291, 10191, 10191, 10291, 10291, 9591], dataset_name='llava-recap-cc3m')}]\r\n```\r\n\r\nthis is my code:\r\n\r\n```\r\ndataset = load_dataset(\"llava-recap-cc3m/\", data_files=\"data/train-0000-of-00314.parquet\")\r\n```\r\n\r\nMy situation and requirements:\r\n\r\n00314 is all, but I downlaode about 150, half of it, as you can see, i used `0000-of-00314.` which should be at most 99 file being loaded.\r\n\r\nBut it just fail.\r\n\r\nCan u understand my issue now?\r\n\r\nIf so, then do not suggest me with stream, Just want to know, is there a way to load part if it...... and please don't say you can not replicate my issue when you have downloaded them all, my english is not good, but I think all situations and all prerequists I have addressed already.\r\n\r\n", "I see you did not use the \"parquet\" loader as I suggested in my code snippet above: https://github.com/huggingface/datasets/issues/6979#issuecomment-2182031415\r\nPlease try passing \"parquet\" instead of \"llava-recap-cc3m/\" to `load_dataset`, and the complete path to data files in `data_files`:\r\n```python\r\nload_dataset(\"parquet\", data_files=\"llava-recap-cc3m/data/train-001-of-00314.parquet\")\r\n```", "Let me explain that you get the error because of this content within the `dataset_info` YAML tag in the `llava-recap-cc3m/README.md`:\r\n```\r\n - name: train\r\n num_bytes: 156885281898.75\r\n num_examples: 3199866\r\n```\r\n\r\nBy default, if there is that content in the README file, `load_dataset` performs a basic check to verify it the generated number of examples matches the expected one and raises a `NonMatchingSplitsSizesError` if that is not the case. \r\n\r\nYou can avoid this basic check by passing `verification_mode=\"no_checks\"`:\r\n```python\r\nload_dataset(\"llava-recap-cc3m/\", data_files=\"data/train-0000-of-00314.parquet\", verification_mode=\"no_checks\")\r\n```", "And please, next time you have an issue, please fill the Bug template issue with all the necessary information: https://github.com/huggingface/datasets/issues/new?assignees=&labels=&projects=&template=bug-report.yml\r\n\r\nOtherwise it is very difficult for us to understand the underlying problem and to propose a pertinent solution.", "thank u albert!\r\n\r\nIt solved my issue!" ]
2,359,511,469	6,978	Fix regression for pandas < 2.0.0 in JSON loader	closed	2024-06-18T10:26:34	2024-06-19T06:23:24	2024-06-19T05:50:18	https://github.com/huggingface/datasets/pull/6978	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6978", "html_url": "https://github.com/huggingface/datasets/pull/6978", "diff_url": "https://github.com/huggingface/datasets/pull/6978.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6978.patch", "merged_at": "2024-06-19T05:50:18" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6978). 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) \| 0.005144 / 0.011353 (-0.006209) \| 0.003500 / 0.011008 (-0.007509) \| 0.063670 / 0.038508 (0.025162) \| 0.031793 / 0.023109 (0.008683) \| 0.239611 / 0.275898 (-0.036287) \| 0.276681 / 0.323480 (-0.046799) \| 0.004148 / 0.007986 (-0.003838) \| 0.002713 / 0.004328 (-0.001615) \| 0.048832 / 0.004250 (0.044582) \| 0.043066 / 0.037052 (0.006014) \| 0.256835 / 0.258489 (-0.001655) \| 0.292224 / 0.293841 (-0.001617) \| 0.027530 / 0.128546 (-0.101017) \| 0.010509 / 0.075646 (-0.065137) \| 0.203370 / 0.419271 (-0.215901) \| 0.035643 / 0.043533 (-0.007890) \| 0.252161 / 0.255139 (-0.002978) \| 0.271883 / 0.283200 (-0.011316) \| 0.018658 / 0.141683 (-0.123024) \| 1.081676 / 1.452155 (-0.370479) \| 1.142146 / 1.492716 (-0.350571) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093484 / 0.018006 (0.075477) \| 0.298607 / 0.000490 (0.298117) \| 0.000220 / 0.000200 (0.000020) \| 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) \| 0.019021 / 0.037411 (-0.018390) \| 0.062471 / 0.014526 (0.047946) \| 0.075393 / 0.176557 (-0.101163) \| 0.121040 / 0.737135 (-0.616095) \| 0.077613 / 0.296338 (-0.218726) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294857 / 0.215209 (0.079648) \| 2.931143 / 2.077655 (0.853489) \| 1.510866 / 1.504120 (0.006746) \| 1.379574 / 1.541195 (-0.161621) \| 1.352358 / 1.468490 (-0.116133) \| 0.561670 / 4.584777 (-4.023107) \| 2.378434 / 3.745712 (-1.367278) \| 2.713203 / 5.269862 (-2.556658) \| 1.706416 / 4.565676 (-2.859260) \| 0.062355 / 0.424275 (-0.361920) \| 0.004971 / 0.007607 (-0.002636) \| 0.336498 / 0.226044 (0.110453) \| 3.316464 / 2.268929 (1.047535) \| 1.833035 / 55.444624 (-53.611589) \| 1.532808 / 6.876477 (-5.343668) \| 1.537323 / 2.142072 (-0.604749) \| 0.639430 / 4.805227 (-4.165798) \| 0.115808 / 6.500664 (-6.384856) \| 0.043545 / 0.075469 (-0.031924) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974428 / 1.841788 (-0.867360) \| 11.368914 / 8.074308 (3.294606) \| 9.754488 / 10.191392 (-0.436904) \| 0.146277 / 0.680424 (-0.534146) \| 0.013917 / 0.534201 (-0.520284) \| 0.286809 / 0.579283 (-0.292474) \| 0.267144 / 0.434364 (-0.167219) \| 0.326161 / 0.540337 (-0.214177) \| 0.418059 / 1.386936 (-0.968877) \|\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) \| 0.005341 / 0.011353 (-0.006012) \| 0.003460 / 0.011008 (-0.007548) \| 0.050135 / 0.038508 (0.011627) \| 0.032014 / 0.023109 (0.008905) \| 0.259835 / 0.275898 (-0.016063) \| 0.286275 / 0.323480 (-0.037205) \| 0.004350 / 0.007986 (-0.003636) \| 0.002800 / 0.004328 (-0.001529) \| 0.049358 / 0.004250 (0.045107) \| 0.040182 / 0.037052 (0.003130) \| 0.278352 / 0.258489 (0.019863) \| 0.307869 / 0.293841 (0.014028) \| 0.029151 / 0.128546 (-0.099395) \| 0.010091 / 0.075646 (-0.065555) \| 0.058814 / 0.419271 (-0.360458) \| 0.033150 / 0.043533 (-0.010383) \| 0.263594 / 0.255139 (0.008455) \| 0.284065 / 0.283200 (0.000866) \| 0.017968 / 0.141683 (-0.123714) \| 1.145605 / 1.452155 (-0.306550) \| 1.196884 / 1.492716 (-0.295832) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094045 / 0.018006 (0.076039) \| 0.299031 / 0.000490 (0.298541) \| 0.000210 / 0.000200 (0.000011) \| 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) \| 0.022510 / 0.037411 (-0.014901) \| 0.077478 / 0.014526 (0.062953) \| 0.087746 / 0.176557 (-0.088811) \| 0.129311 / 0.737135 (-0.607825) \| 0.089921 / 0.296338 (-0.206418) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290279 / 0.215209 (0.075070) \| 2.880725 / 2.077655 (0.803070) \| 1.541262 / 1.504120 (0.037142) \| 1.424475 / 1.541195 (-0.116719) \| 1.436397 / 1.468490 (-0.032093) \| 0.578237 / 4.584777 (-4.006540) \| 0.965249 / 3.745712 (-2.780463) \| 2.682534 / 5.269862 (-2.587327) \| 1.732859 / 4.565676 (-2.832817) \| 0.065523 / 0.424275 (-0.358752) \| 0.005466 / 0.007607 (-0.002141) \| 0.343985 / 0.226044 (0.117940) \| 3.397463 / 2.268929 (1.128534) \| 1.929370 / 55.444624 (-53.515255) \| 1.605135 / 6.876477 (-5.271342) \| 1.753926 / 2.142072 (-0.388146) \| 0.659929 / 4.805227 (-4.145298) \| 0.118093 / 6.500664 (-6.382571) \| 0.041252 / 0.075469 (-0.034217) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.009177 / 1.841788 (-0.832610) \| 11.959624 / 8.074308 (3.885316) \| 10.484672 / 10.191392 (0.293280) \| 0.142085 / 0.680424 (-0.538339) \| 0.015955 / 0.534201 (-0.518245) \| 0.283649 / 0.579283 (-0.295634) \| 0.125681 / 0.434364 (-0.308683) \| 0.320490 / 0.540337 (-0.219847) \| 0.440353 / 1.386936 (-0.946583) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e47a746bcda4b97db2467542b76d3215b3569ff0 \"CML watermark\")\n", "Maybe a patch release will be needed with this fix." ]
2,359,295,045	6,977	load json file error with v2.20.0	closed	2024-06-18T08:41:01	2024-06-18T10:06:10	2024-06-18T10:06:09	https://github.com/huggingface/datasets/issues/6977	null	xiaoyaolangzhi	false	[ "Thanks for reporting, @xiaoyaolangzhi.\r\n\r\nIndeed, we are currently requiring `pandas` >= 2.0.0.\r\n\r\nYou will need to update pandas in your local environment:\r\n```\r\npip install -U pandas\r\n``` ", "Thank you very much." ]
2,357,107,203	6,976	Ensure compatibility with numpy 2.0.0	closed	2024-06-17T11:29:22	2024-06-19T14:30:32	2024-06-19T14:04:34	https://github.com/huggingface/datasets/pull/6976	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6976", "html_url": "https://github.com/huggingface/datasets/pull/6976", "diff_url": "https://github.com/huggingface/datasets/pull/6976.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6976.patch", "merged_at": "2024-06-19T14:04:34" }	KennethEnevoldsen	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6976). 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) \| 0.005361 / 0.011353 (-0.005992) \| 0.003983 / 0.011008 (-0.007025) \| 0.062865 / 0.038508 (0.024357) \| 0.029880 / 0.023109 (0.006771) \| 0.261465 / 0.275898 (-0.014433) \| 0.269791 / 0.323480 (-0.053689) \| 0.004198 / 0.007986 (-0.003788) \| 0.002942 / 0.004328 (-0.001387) \| 0.049002 / 0.004250 (0.044751) \| 0.043232 / 0.037052 (0.006180) \| 0.328774 / 0.258489 (0.070285) \| 0.297308 / 0.293841 (0.003467) \| 0.030552 / 0.128546 (-0.097994) \| 0.012632 / 0.075646 (-0.063015) \| 0.204156 / 0.419271 (-0.215116) \| 0.036014 / 0.043533 (-0.007519) \| 0.241224 / 0.255139 (-0.013915) \| 0.268358 / 0.283200 (-0.014842) \| 0.019227 / 0.141683 (-0.122456) \| 1.114515 / 1.452155 (-0.337639) \| 1.147029 / 1.492716 (-0.345688) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094925 / 0.018006 (0.076919) \| 0.301548 / 0.000490 (0.301059) \| 0.000211 / 0.000200 (0.000011) \| 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) \| 0.018875 / 0.037411 (-0.018536) \| 0.062824 / 0.014526 (0.048298) \| 0.075657 / 0.176557 (-0.100900) \| 0.121926 / 0.737135 (-0.615209) \| 0.077102 / 0.296338 (-0.219236) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286018 / 0.215209 (0.070808) \| 2.832222 / 2.077655 (0.754567) \| 1.462629 / 1.504120 (-0.041491) \| 1.354746 / 1.541195 (-0.186449) \| 1.339504 / 1.468490 (-0.128986) \| 0.718381 / 4.584777 (-3.866396) \| 2.401456 / 3.745712 (-1.344256) \| 3.013518 / 5.269862 (-2.256343) \| 1.944892 / 4.565676 (-2.620784) \| 0.078793 / 0.424275 (-0.345482) \| 0.005219 / 0.007607 (-0.002388) \| 0.349551 / 0.226044 (0.123507) \| 3.417844 / 2.268929 (1.148916) \| 1.830669 / 55.444624 (-53.613956) \| 1.502134 / 6.876477 (-5.374343) \| 1.529242 / 2.142072 (-0.612830) \| 0.793732 / 4.805227 (-4.011495) \| 0.133571 / 6.500664 (-6.367093) \| 0.042588 / 0.075469 (-0.032881) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.988167 / 1.841788 (-0.853620) \| 11.926728 / 8.074308 (3.852420) \| 9.806971 / 10.191392 (-0.384421) \| 0.173951 / 0.680424 (-0.506473) \| 0.015308 / 0.534201 (-0.518893) \| 0.310768 / 0.579283 (-0.268515) \| 0.268261 / 0.434364 (-0.166103) \| 0.342962 / 0.540337 (-0.197375) \| 0.431255 / 1.386936 (-0.955681) \|\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) \| 0.005680 / 0.011353 (-0.005673) \| 0.004231 / 0.011008 (-0.006778) \| 0.051009 / 0.038508 (0.012501) \| 0.031431 / 0.023109 (0.008322) \| 0.268582 / 0.275898 (-0.007316) \| 0.287942 / 0.323480 (-0.035538) \| 0.004442 / 0.007986 (-0.003543) \| 0.002818 / 0.004328 (-0.001511) \| 0.050241 / 0.004250 (0.045991) \| 0.039933 / 0.037052 (0.002881) \| 0.285814 / 0.258489 (0.027325) \| 0.316082 / 0.293841 (0.022241) \| 0.032416 / 0.128546 (-0.096130) \| 0.012398 / 0.075646 (-0.063248) \| 0.060779 / 0.419271 (-0.358493) \| 0.033706 / 0.043533 (-0.009827) \| 0.273915 / 0.255139 (0.018776) \| 0.289752 / 0.283200 (0.006553) \| 0.017859 / 0.141683 (-0.123824) \| 1.150224 / 1.452155 (-0.301930) \| 1.197467 / 1.492716 (-0.295250) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093810 / 0.018006 (0.075803) \| 0.302529 / 0.000490 (0.302039) \| 0.000221 / 0.000200 (0.000021) \| 0.000047 / 0.000054 (-0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022903 / 0.037411 (-0.014508) \| 0.077445 / 0.014526 (0.062919) \| 0.089335 / 0.176557 (-0.087222) \| 0.130848 / 0.737135 (-0.606287) \| 0.091106 / 0.296338 (-0.205232) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294194 / 0.215209 (0.078985) \| 2.886983 / 2.077655 (0.809328) \| 1.557768 / 1.504120 (0.053648) \| 1.424467 / 1.541195 (-0.116727) \| 1.440625 / 1.468490 (-0.027865) \| 0.724793 / 4.584777 (-3.859984) \| 0.985216 / 3.745712 (-2.760496) \| 2.856826 / 5.269862 (-2.413036) \| 1.911638 / 4.565676 (-2.654039) \| 0.080350 / 0.424275 (-0.343925) \| 0.005616 / 0.007607 (-0.001991) \| 0.348713 / 0.226044 (0.122668) \| 3.414764 / 2.268929 (1.145835) \| 1.925056 / 55.444624 (-53.519568) \| 1.635752 / 6.876477 (-5.240725) \| 1.761117 / 2.142072 (-0.380955) \| 0.808309 / 4.805227 (-3.996918) \| 0.136893 / 6.500664 (-6.363771) \| 0.042116 / 0.075469 (-0.033354) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.004740 / 1.841788 (-0.837048) \| 12.495859 / 8.074308 (4.421550) \| 10.681233 / 10.191392 (0.489841) \| 0.133320 / 0.680424 (-0.547104) \| 0.015943 / 0.534201 (-0.518258) \| 0.304869 / 0.579283 (-0.274414) \| 0.128616 / 0.434364 (-0.305748) \| 0.345930 / 0.540337 (-0.194407) \| 0.457434 / 1.386936 (-0.929502) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#84d9dea52098c9403efb43d5b542dd6d45000bec \"CML watermark\")\n" ]
2,357,003,959	6,975	Set temporary numpy upper version < 2.0.0 to fix CI	closed	2024-06-17T10:36:54	2024-06-17T12:49:53	2024-06-17T12:43:56	https://github.com/huggingface/datasets/pull/6975	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6975", "html_url": "https://github.com/huggingface/datasets/pull/6975", "diff_url": "https://github.com/huggingface/datasets/pull/6975.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6975.patch", "merged_at": "2024-06-17T12:43:56" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6975). 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) \| 0.005168 / 0.011353 (-0.006185) \| 0.003720 / 0.011008 (-0.007288) \| 0.063347 / 0.038508 (0.024839) \| 0.031474 / 0.023109 (0.008364) \| 0.243233 / 0.275898 (-0.032665) \| 0.276695 / 0.323480 (-0.046785) \| 0.004109 / 0.007986 (-0.003877) \| 0.002689 / 0.004328 (-0.001639) \| 0.049522 / 0.004250 (0.045271) \| 0.043477 / 0.037052 (0.006425) \| 0.258578 / 0.258489 (0.000088) \| 0.288134 / 0.293841 (-0.005707) \| 0.027836 / 0.128546 (-0.100710) \| 0.010677 / 0.075646 (-0.064969) \| 0.206412 / 0.419271 (-0.212860) \| 0.036204 / 0.043533 (-0.007329) \| 0.250588 / 0.255139 (-0.004551) \| 0.272354 / 0.283200 (-0.010846) \| 0.018359 / 0.141683 (-0.123324) \| 1.118867 / 1.452155 (-0.333288) \| 1.157318 / 1.492716 (-0.335399) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092927 / 0.018006 (0.074921) \| 0.298252 / 0.000490 (0.297762) \| 0.000228 / 0.000200 (0.000028) \| 0.000042 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018824 / 0.037411 (-0.018588) \| 0.069304 / 0.014526 (0.054778) \| 0.075094 / 0.176557 (-0.101462) \| 0.122546 / 0.737135 (-0.614590) \| 0.076453 / 0.296338 (-0.219885) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287131 / 0.215209 (0.071922) \| 2.838945 / 2.077655 (0.761291) \| 1.473578 / 1.504120 (-0.030542) \| 1.351214 / 1.541195 (-0.189981) \| 1.354924 / 1.468490 (-0.113566) \| 0.577092 / 4.584777 (-4.007685) \| 2.348072 / 3.745712 (-1.397640) \| 2.762130 / 5.269862 (-2.507732) \| 1.725195 / 4.565676 (-2.840482) \| 0.063596 / 0.424275 (-0.360679) \| 0.004921 / 0.007607 (-0.002686) \| 0.335422 / 0.226044 (0.109377) \| 3.340398 / 2.268929 (1.071469) \| 1.789390 / 55.444624 (-53.655234) \| 1.516247 / 6.876477 (-5.360229) \| 1.529653 / 2.142072 (-0.612420) \| 0.643547 / 4.805227 (-4.161680) \| 0.116491 / 6.500664 (-6.384173) \| 0.042404 / 0.075469 (-0.033065) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.959839 / 1.841788 (-0.881948) \| 11.269778 / 8.074308 (3.195470) \| 9.574898 / 10.191392 (-0.616494) \| 0.128979 / 0.680424 (-0.551444) \| 0.013901 / 0.534201 (-0.520300) \| 0.280778 / 0.579283 (-0.298505) \| 0.256511 / 0.434364 (-0.177853) \| 0.319361 / 0.540337 (-0.220977) \| 0.411803 / 1.386936 (-0.975133) \|\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) \| 0.005453 / 0.011353 (-0.005899) \| 0.003478 / 0.011008 (-0.007530) \| 0.050055 / 0.038508 (0.011547) \| 0.031415 / 0.023109 (0.008306) \| 0.275057 / 0.275898 (-0.000841) \| 0.296690 / 0.323480 (-0.026789) \| 0.004253 / 0.007986 (-0.003732) \| 0.002777 / 0.004328 (-0.001551) \| 0.049553 / 0.004250 (0.045303) \| 0.039843 / 0.037052 (0.002791) \| 0.286938 / 0.258489 (0.028449) \| 0.318579 / 0.293841 (0.024738) \| 0.029773 / 0.128546 (-0.098774) \| 0.010404 / 0.075646 (-0.065242) \| 0.057915 / 0.419271 (-0.361356) \| 0.033486 / 0.043533 (-0.010047) \| 0.273293 / 0.255139 (0.018154) \| 0.293155 / 0.283200 (0.009955) \| 0.017843 / 0.141683 (-0.123839) \| 1.131130 / 1.452155 (-0.321024) \| 1.167412 / 1.492716 (-0.325304) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092553 / 0.018006 (0.074547) \| 0.298888 / 0.000490 (0.298399) \| 0.000201 / 0.000200 (0.000001) \| 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) \| 0.022646 / 0.037411 (-0.014765) \| 0.076921 / 0.014526 (0.062395) \| 0.089238 / 0.176557 (-0.087318) \| 0.128793 / 0.737135 (-0.608342) \| 0.089190 / 0.296338 (-0.207148) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292552 / 0.215209 (0.077343) \| 2.884277 / 2.077655 (0.806622) \| 1.568798 / 1.504120 (0.064678) \| 1.441819 / 1.541195 (-0.099375) \| 1.435766 / 1.468490 (-0.032724) \| 0.572435 / 4.584777 (-4.012342) \| 0.957387 / 3.745712 (-2.788326) \| 2.650843 / 5.269862 (-2.619019) \| 1.727424 / 4.565676 (-2.838252) \| 0.063470 / 0.424275 (-0.360805) \| 0.005314 / 0.007607 (-0.002293) \| 0.345881 / 0.226044 (0.119836) \| 3.395463 / 2.268929 (1.126535) \| 1.921340 / 55.444624 (-53.523285) \| 1.621563 / 6.876477 (-5.254914) \| 1.742561 / 2.142072 (-0.399512) \| 0.639948 / 4.805227 (-4.165279) \| 0.116091 / 6.500664 (-6.384573) \| 0.041218 / 0.075469 (-0.034251) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.991506 / 1.841788 (-0.850281) \| 11.897462 / 8.074308 (3.823154) \| 10.083008 / 10.191392 (-0.108384) \| 0.140626 / 0.680424 (-0.539798) \| 0.015454 / 0.534201 (-0.518747) \| 0.283856 / 0.579283 (-0.295427) \| 0.125935 / 0.434364 (-0.308429) \| 0.323884 / 0.540337 (-0.216454) \| 0.438348 / 1.386936 (-0.948588) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e59582adc7fcb53a86a8ca8eda7e04a4e7b25bd2 \"CML watermark\")\n" ]
2,355,517,362	6,973	IndexError during training with Squad dataset and T5-small model	closed	2024-06-16T07:53:54	2024-07-01T11:25:40	2024-07-01T11:25:40	https://github.com/huggingface/datasets/issues/6973	null	ramtunguturi36	false	[ "add remove_unused_columns=False to training_args\r\nhttps://github.com/huggingface/datasets/issues/6535#issuecomment-1874024704", "Closing this issue because it was a reported and fixed in transformers." ]
2,353,531,912	6,972	Fix webdataset pickling	closed	2024-06-14T14:43:02	2024-06-14T15:43:43	2024-06-14T15:37:35	https://github.com/huggingface/datasets/pull/6972	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6972", "html_url": "https://github.com/huggingface/datasets/pull/6972", "diff_url": "https://github.com/huggingface/datasets/pull/6972.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6972.patch", "merged_at": "2024-06-14T15:37:35" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6972). 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) \| 0.005195 / 0.011353 (-0.006157) \| 0.003734 / 0.011008 (-0.007275) \| 0.063087 / 0.038508 (0.024579) \| 0.031467 / 0.023109 (0.008358) \| 0.245183 / 0.275898 (-0.030715) \| 0.280071 / 0.323480 (-0.043409) \| 0.003205 / 0.007986 (-0.004780) \| 0.003311 / 0.004328 (-0.001018) \| 0.049967 / 0.004250 (0.045717) \| 0.044927 / 0.037052 (0.007875) \| 0.262244 / 0.258489 (0.003755) \| 0.284549 / 0.293841 (-0.009292) \| 0.027595 / 0.128546 (-0.100952) \| 0.010521 / 0.075646 (-0.065126) \| 0.206928 / 0.419271 (-0.212343) \| 0.036179 / 0.043533 (-0.007354) \| 0.254256 / 0.255139 (-0.000883) \| 0.272733 / 0.283200 (-0.010467) \| 0.020456 / 0.141683 (-0.121226) \| 1.118527 / 1.452155 (-0.333628) \| 1.152741 / 1.492716 (-0.339975) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096642 / 0.018006 (0.078636) \| 0.306981 / 0.000490 (0.306491) \| 0.000220 / 0.000200 (0.000020) \| 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) \| 0.019031 / 0.037411 (-0.018380) \| 0.063960 / 0.014526 (0.049435) \| 0.074428 / 0.176557 (-0.102129) \| 0.121226 / 0.737135 (-0.615909) \| 0.077111 / 0.296338 (-0.219228) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279830 / 0.215209 (0.064621) \| 2.748243 / 2.077655 (0.670588) \| 1.481554 / 1.504120 (-0.022566) \| 1.355015 / 1.541195 (-0.186180) \| 1.379655 / 1.468490 (-0.088835) \| 0.560378 / 4.584777 (-4.024399) \| 2.407241 / 3.745712 (-1.338471) \| 2.837090 / 5.269862 (-2.432771) \| 1.767084 / 4.565676 (-2.798593) \| 0.063517 / 0.424275 (-0.360758) \| 0.005024 / 0.007607 (-0.002584) \| 0.334845 / 0.226044 (0.108800) \| 3.290712 / 2.268929 (1.021783) \| 1.836923 / 55.444624 (-53.607702) \| 1.543671 / 6.876477 (-5.332806) \| 1.582319 / 2.142072 (-0.559754) \| 0.637689 / 4.805227 (-4.167538) \| 0.119515 / 6.500664 (-6.381149) \| 0.042191 / 0.075469 (-0.033278) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980018 / 1.841788 (-0.861770) \| 11.620211 / 8.074308 (3.545903) \| 9.697799 / 10.191392 (-0.493593) \| 0.131733 / 0.680424 (-0.548691) \| 0.014007 / 0.534201 (-0.520193) \| 0.286046 / 0.579283 (-0.293237) \| 0.264776 / 0.434364 (-0.169588) \| 0.325041 / 0.540337 (-0.215296) \| 0.452740 / 1.386936 (-0.934196) \|\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) \| 0.005603 / 0.011353 (-0.005750) \| 0.003810 / 0.011008 (-0.007199) \| 0.050773 / 0.038508 (0.012265) \| 0.032601 / 0.023109 (0.009492) \| 0.268035 / 0.275898 (-0.007863) \| 0.292614 / 0.323480 (-0.030866) \| 0.005076 / 0.007986 (-0.002910) \| 0.004487 / 0.004328 (0.000159) \| 0.049988 / 0.004250 (0.045737) \| 0.040258 / 0.037052 (0.003205) \| 0.284145 / 0.258489 (0.025656) \| 0.318291 / 0.293841 (0.024450) \| 0.029672 / 0.128546 (-0.098875) \| 0.010534 / 0.075646 (-0.065113) \| 0.059020 / 0.419271 (-0.360252) \| 0.033451 / 0.043533 (-0.010082) \| 0.270220 / 0.255139 (0.015081) \| 0.290500 / 0.283200 (0.007300) \| 0.017123 / 0.141683 (-0.124560) \| 1.130870 / 1.452155 (-0.321285) \| 1.160038 / 1.492716 (-0.332678) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097045 / 0.018006 (0.079039) \| 0.314573 / 0.000490 (0.314083) \| 0.000203 / 0.000200 (0.000003) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022396 / 0.037411 (-0.015015) \| 0.079393 / 0.014526 (0.064867) \| 0.088460 / 0.176557 (-0.088097) \| 0.128050 / 0.737135 (-0.609085) \| 0.093070 / 0.296338 (-0.203268) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293858 / 0.215209 (0.078649) \| 2.819956 / 2.077655 (0.742301) \| 1.540181 / 1.504120 (0.036061) \| 1.419671 / 1.541195 (-0.121524) \| 1.441594 / 1.468490 (-0.026897) \| 0.565200 / 4.584777 (-4.019577) \| 0.963967 / 3.745712 (-2.781745) \| 2.752137 / 5.269862 (-2.517725) \| 1.779239 / 4.565676 (-2.786438) \| 0.063787 / 0.424275 (-0.360488) \| 0.005344 / 0.007607 (-0.002263) \| 0.344283 / 0.226044 (0.118239) \| 3.353263 / 2.268929 (1.084334) \| 1.898678 / 55.444624 (-53.545947) \| 1.607868 / 6.876477 (-5.268609) \| 1.781938 / 2.142072 (-0.360134) \| 0.652119 / 4.805227 (-4.153108) \| 0.117883 / 6.500664 (-6.382781) \| 0.048811 / 0.075469 (-0.026658) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.013154 / 1.841788 (-0.828634) \| 12.421963 / 8.074308 (4.347655) \| 10.352056 / 10.191392 (0.160664) \| 0.143784 / 0.680424 (-0.536640) \| 0.016370 / 0.534201 (-0.517831) \| 0.283668 / 0.579283 (-0.295615) \| 0.127070 / 0.434364 (-0.307294) \| 0.326199 / 0.540337 (-0.214138) \| 0.432776 / 1.386936 (-0.954160) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5e72fb13b4824dcb27aedb807e4e28c420dec244 \"CML watermark\")\n" ]
2,351,830,856	6,971	packaging: Remove useless dependencies	closed	2024-06-13T18:43:43	2024-06-14T14:03:34	2024-06-14T13:57:24	https://github.com/huggingface/datasets/pull/6971	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6971", "html_url": "https://github.com/huggingface/datasets/pull/6971", "diff_url": "https://github.com/huggingface/datasets/pull/6971.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6971.patch", "merged_at": "2024-06-14T13:57:24" }	daskol	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6971). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@HuggingFaceDocBuilderDev There is no doc for this change. Call a human.", "Haha it was me who triggered the CI for your PR", "<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) \| 0.005051 / 0.011353 (-0.006302) \| 0.004831 / 0.011008 (-0.006178) \| 0.063006 / 0.038508 (0.024498) \| 0.031589 / 0.023109 (0.008480) \| 0.296202 / 0.275898 (0.020304) \| 0.274274 / 0.323480 (-0.049205) \| 0.003199 / 0.007986 (-0.004786) \| 0.002768 / 0.004328 (-0.001561) \| 0.049422 / 0.004250 (0.045172) \| 0.045174 / 0.037052 (0.008121) \| 0.263814 / 0.258489 (0.005325) \| 0.288125 / 0.293841 (-0.005716) \| 0.027641 / 0.128546 (-0.100905) \| 0.010439 / 0.075646 (-0.065207) \| 0.203075 / 0.419271 (-0.216196) \| 0.036259 / 0.043533 (-0.007274) \| 0.245159 / 0.255139 (-0.009980) \| 0.268897 / 0.283200 (-0.014303) \| 0.019493 / 0.141683 (-0.122190) \| 1.108330 / 1.452155 (-0.343824) \| 1.155835 / 1.492716 (-0.336881) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096860 / 0.018006 (0.078854) \| 0.309428 / 0.000490 (0.308938) \| 0.000197 / 0.000200 (-0.000003) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019275 / 0.037411 (-0.018136) \| 0.062623 / 0.014526 (0.048098) \| 0.073871 / 0.176557 (-0.102686) \| 0.120410 / 0.737135 (-0.616726) \| 0.075766 / 0.296338 (-0.220572) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279876 / 0.215209 (0.064667) \| 2.742429 / 2.077655 (0.664774) \| 1.414368 / 1.504120 (-0.089752) \| 1.293194 / 1.541195 (-0.248001) \| 1.318043 / 1.468490 (-0.150447) \| 0.570904 / 4.584777 (-4.013873) \| 2.384386 / 3.745712 (-1.361326) \| 2.757953 / 5.269862 (-2.511908) \| 1.728766 / 4.565676 (-2.836910) \| 0.062699 / 0.424275 (-0.361576) \| 0.004951 / 0.007607 (-0.002656) \| 0.332222 / 0.226044 (0.106177) \| 3.407429 / 2.268929 (1.138500) \| 1.777136 / 55.444624 (-53.667488) \| 1.521269 / 6.876477 (-5.355207) \| 1.544814 / 2.142072 (-0.597258) \| 0.646249 / 4.805227 (-4.158978) \| 0.117032 / 6.500664 (-6.383632) \| 0.042274 / 0.075469 (-0.033195) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.016249 / 1.841788 (-0.825539) \| 11.794003 / 8.074308 (3.719695) \| 9.871925 / 10.191392 (-0.319467) \| 0.133694 / 0.680424 (-0.546730) \| 0.014904 / 0.534201 (-0.519297) \| 0.287453 / 0.579283 (-0.291831) \| 0.271802 / 0.434364 (-0.162561) \| 0.324711 / 0.540337 (-0.215626) \| 0.411812 / 1.386936 (-0.975124) \|\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) \| 0.005376 / 0.011353 (-0.005977) \| 0.003631 / 0.011008 (-0.007377) \| 0.050154 / 0.038508 (0.011646) \| 0.033665 / 0.023109 (0.010556) \| 0.279062 / 0.275898 (0.003164) \| 0.298899 / 0.323480 (-0.024581) \| 0.004388 / 0.007986 (-0.003598) \| 0.002810 / 0.004328 (-0.001518) \| 0.049032 / 0.004250 (0.044781) \| 0.040531 / 0.037052 (0.003478) \| 0.287220 / 0.258489 (0.028731) \| 0.319060 / 0.293841 (0.025219) \| 0.029473 / 0.128546 (-0.099073) \| 0.010317 / 0.075646 (-0.065329) \| 0.058483 / 0.419271 (-0.360789) \| 0.033359 / 0.043533 (-0.010174) \| 0.276404 / 0.255139 (0.021265) \| 0.295013 / 0.283200 (0.011813) \| 0.019372 / 0.141683 (-0.122311) \| 1.172624 / 1.452155 (-0.279531) \| 1.176815 / 1.492716 (-0.315902) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097347 / 0.018006 (0.079341) \| 0.306959 / 0.000490 (0.306469) \| 0.000200 / 0.000200 (-0.000000) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022776 / 0.037411 (-0.014635) \| 0.077865 / 0.014526 (0.063340) \| 0.088806 / 0.176557 (-0.087751) \| 0.130448 / 0.737135 (-0.606687) \| 0.090973 / 0.296338 (-0.205365) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.301168 / 0.215209 (0.085959) \| 2.957634 / 2.077655 (0.879979) \| 1.556999 / 1.504120 (0.052879) \| 1.413940 / 1.541195 (-0.127255) \| 1.427970 / 1.468490 (-0.040520) \| 0.587653 / 4.584777 (-3.997124) \| 0.951295 / 3.745712 (-2.794417) \| 2.691004 / 5.269862 (-2.578858) \| 1.755826 / 4.565676 (-2.809851) \| 0.064883 / 0.424275 (-0.359392) \| 0.005379 / 0.007607 (-0.002228) \| 0.353790 / 0.226044 (0.127745) \| 3.457747 / 2.268929 (1.188818) \| 1.891884 / 55.444624 (-53.552740) \| 1.616619 / 6.876477 (-5.259858) \| 1.736167 / 2.142072 (-0.405906) \| 0.669257 / 4.805227 (-4.135970) \| 0.119620 / 6.500664 (-6.381044) \| 0.041390 / 0.075469 (-0.034080) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.008851 / 1.841788 (-0.832937) \| 13.151216 / 8.074308 (5.076908) \| 10.398371 / 10.191392 (0.206979) \| 0.143420 / 0.680424 (-0.537004) \| 0.015759 / 0.534201 (-0.518442) \| 0.293068 / 0.579283 (-0.286215) \| 0.131449 / 0.434364 (-0.302914) \| 0.334715 / 0.540337 (-0.205623) \| 0.445824 / 1.386936 (-0.941112) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#087671dcaf817c906a8649404c07b0440e2732ea \"CML watermark\")\n" ]
2,351,380,029	6,970	Set dev version	closed	2024-06-13T14:59:45	2024-06-13T15:06:18	2024-06-13T14:59:56	https://github.com/huggingface/datasets/pull/6970	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6970", "html_url": "https://github.com/huggingface/datasets/pull/6970", "diff_url": "https://github.com/huggingface/datasets/pull/6970.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6970.patch", "merged_at": "2024-06-13T14:59:56" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6970). 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) \| 0.005450 / 0.011353 (-0.005902) \| 0.003911 / 0.011008 (-0.007098) \| 0.063467 / 0.038508 (0.024959) \| 0.031029 / 0.023109 (0.007920) \| 0.247916 / 0.275898 (-0.027982) \| 0.274737 / 0.323480 (-0.048743) \| 0.003255 / 0.007986 (-0.004731) \| 0.002842 / 0.004328 (-0.001487) \| 0.049617 / 0.004250 (0.045366) \| 0.046689 / 0.037052 (0.009637) \| 0.255152 / 0.258489 (-0.003337) \| 0.288630 / 0.293841 (-0.005211) \| 0.028174 / 0.128546 (-0.100372) \| 0.010773 / 0.075646 (-0.064873) \| 0.202119 / 0.419271 (-0.217153) \| 0.035914 / 0.043533 (-0.007619) \| 0.248197 / 0.255139 (-0.006942) \| 0.273508 / 0.283200 (-0.009691) \| 0.020626 / 0.141683 (-0.121057) \| 1.125668 / 1.452155 (-0.326487) \| 1.156678 / 1.492716 (-0.336038) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098294 / 0.018006 (0.080288) \| 0.306661 / 0.000490 (0.306172) \| 0.000227 / 0.000200 (0.000027) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019118 / 0.037411 (-0.018293) \| 0.063086 / 0.014526 (0.048560) \| 0.077735 / 0.176557 (-0.098822) \| 0.123159 / 0.737135 (-0.613976) \| 0.077228 / 0.296338 (-0.219111) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280031 / 0.215209 (0.064822) \| 2.762524 / 2.077655 (0.684870) \| 1.444571 / 1.504120 (-0.059549) \| 1.330590 / 1.541195 (-0.210604) \| 1.371937 / 1.468490 (-0.096553) \| 0.563847 / 4.584777 (-4.020930) \| 2.369908 / 3.745712 (-1.375804) \| 2.827441 / 5.269862 (-2.442420) \| 1.749864 / 4.565676 (-2.815812) \| 0.063996 / 0.424275 (-0.360279) \| 0.005060 / 0.007607 (-0.002547) \| 0.326067 / 0.226044 (0.100023) \| 3.270170 / 2.268929 (1.001242) \| 1.785164 / 55.444624 (-53.659460) \| 1.560432 / 6.876477 (-5.316045) \| 1.587005 / 2.142072 (-0.555068) \| 0.645714 / 4.805227 (-4.159513) \| 0.119975 / 6.500664 (-6.380689) \| 0.043962 / 0.075469 (-0.031507) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.979003 / 1.841788 (-0.862785) \| 11.988701 / 8.074308 (3.914393) \| 9.788564 / 10.191392 (-0.402828) \| 0.142644 / 0.680424 (-0.537780) \| 0.014924 / 0.534201 (-0.519277) \| 0.285942 / 0.579283 (-0.293341) \| 0.264086 / 0.434364 (-0.170278) \| 0.343360 / 0.540337 (-0.196977) \| 0.413467 / 1.386936 (-0.973469) \|\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) \| 0.005818 / 0.011353 (-0.005535) \| 0.003726 / 0.011008 (-0.007283) \| 0.050936 / 0.038508 (0.012428) \| 0.032000 / 0.023109 (0.008890) \| 0.273282 / 0.275898 (-0.002616) \| 0.293889 / 0.323480 (-0.029591) \| 0.004287 / 0.007986 (-0.003699) \| 0.002797 / 0.004328 (-0.001531) \| 0.049088 / 0.004250 (0.044838) \| 0.040235 / 0.037052 (0.003183) \| 0.280240 / 0.258489 (0.021751) \| 0.315749 / 0.293841 (0.021908) \| 0.029986 / 0.128546 (-0.098560) \| 0.010440 / 0.075646 (-0.065206) \| 0.058935 / 0.419271 (-0.360336) \| 0.033198 / 0.043533 (-0.010335) \| 0.274321 / 0.255139 (0.019182) \| 0.288039 / 0.283200 (0.004840) \| 0.018865 / 0.141683 (-0.122818) \| 1.114915 / 1.452155 (-0.337240) \| 1.180548 / 1.492716 (-0.312169) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095028 / 0.018006 (0.077022) \| 0.304797 / 0.000490 (0.304307) \| 0.000221 / 0.000200 (0.000021) \| 0.000056 / 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) \| 0.022556 / 0.037411 (-0.014855) \| 0.076839 / 0.014526 (0.062313) \| 0.090255 / 0.176557 (-0.086302) \| 0.128748 / 0.737135 (-0.608387) \| 0.091718 / 0.296338 (-0.204621) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296061 / 0.215209 (0.080852) \| 2.851376 / 2.077655 (0.773722) \| 1.548084 / 1.504120 (0.043964) \| 1.428589 / 1.541195 (-0.112606) \| 1.467244 / 1.468490 (-0.001246) \| 0.583533 / 4.584777 (-4.001244) \| 0.967436 / 3.745712 (-2.778277) \| 2.774775 / 5.269862 (-2.495087) \| 1.800435 / 4.565676 (-2.765242) \| 0.063998 / 0.424275 (-0.360277) \| 0.005420 / 0.007607 (-0.002187) \| 0.346353 / 0.226044 (0.120308) \| 3.383885 / 2.268929 (1.114956) \| 1.902914 / 55.444624 (-53.541710) \| 1.599545 / 6.876477 (-5.276932) \| 1.772754 / 2.142072 (-0.369318) \| 0.651804 / 4.805227 (-4.153423) \| 0.120380 / 6.500664 (-6.380284) \| 0.043311 / 0.075469 (-0.032159) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.004414 / 1.841788 (-0.837374) \| 12.356077 / 8.074308 (4.281769) \| 10.513420 / 10.191392 (0.322028) \| 0.132419 / 0.680424 (-0.548005) \| 0.015470 / 0.534201 (-0.518731) \| 0.284883 / 0.579283 (-0.294400) \| 0.130763 / 0.434364 (-0.303601) \| 0.320068 / 0.540337 (-0.220270) \| 0.430284 / 1.386936 (-0.956652) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#574791e0a0cf57ba761f679a054b9e89e4a3ee22 \"CML watermark\")\n" ]
2,351,351,436	6,969	Release: 2.20.0	closed	2024-06-13T14:48:20	2024-06-13T15:04:39	2024-06-13T14:55:53	https://github.com/huggingface/datasets/pull/6969	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6969", "html_url": "https://github.com/huggingface/datasets/pull/6969", "diff_url": "https://github.com/huggingface/datasets/pull/6969.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6969.patch", "merged_at": "2024-06-13T14:55:53" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6969). 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) \| 0.005414 / 0.011353 (-0.005939) \| 0.003936 / 0.011008 (-0.007073) \| 0.064129 / 0.038508 (0.025621) \| 0.032985 / 0.023109 (0.009875) \| 0.244051 / 0.275898 (-0.031847) \| 0.273500 / 0.323480 (-0.049980) \| 0.003227 / 0.007986 (-0.004759) \| 0.002858 / 0.004328 (-0.001470) \| 0.049212 / 0.004250 (0.044962) \| 0.046432 / 0.037052 (0.009380) \| 0.249543 / 0.258489 (-0.008946) \| 0.297339 / 0.293841 (0.003498) \| 0.027880 / 0.128546 (-0.100666) \| 0.010582 / 0.075646 (-0.065065) \| 0.202345 / 0.419271 (-0.216927) \| 0.036402 / 0.043533 (-0.007131) \| 0.253157 / 0.255139 (-0.001982) \| 0.283355 / 0.283200 (0.000155) \| 0.021907 / 0.141683 (-0.119776) \| 1.174431 / 1.452155 (-0.277723) \| 1.172103 / 1.492716 (-0.320613) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097942 / 0.018006 (0.079936) \| 0.307114 / 0.000490 (0.306624) \| 0.000230 / 0.000200 (0.000030) \| 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) \| 0.019149 / 0.037411 (-0.018262) \| 0.064283 / 0.014526 (0.049758) \| 0.075643 / 0.176557 (-0.100913) \| 0.122531 / 0.737135 (-0.614604) \| 0.077360 / 0.296338 (-0.218978) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291790 / 0.215209 (0.076581) \| 2.869234 / 2.077655 (0.791580) \| 1.550266 / 1.504120 (0.046146) \| 1.392392 / 1.541195 (-0.148802) \| 1.375700 / 1.468490 (-0.092790) \| 0.574963 / 4.584777 (-4.009814) \| 2.444746 / 3.745712 (-1.300966) \| 2.920602 / 5.269862 (-2.349259) \| 1.812720 / 4.565676 (-2.752957) \| 0.064811 / 0.424275 (-0.359464) \| 0.005163 / 0.007607 (-0.002444) \| 0.341306 / 0.226044 (0.115261) \| 3.443177 / 2.268929 (1.174249) \| 1.843510 / 55.444624 (-53.601115) \| 1.534023 / 6.876477 (-5.342454) \| 1.603575 / 2.142072 (-0.538498) \| 0.656923 / 4.805227 (-4.148304) \| 0.120338 / 6.500664 (-6.380326) \| 0.042958 / 0.075469 (-0.032511) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975993 / 1.841788 (-0.865795) \| 11.942335 / 8.074308 (3.868027) \| 9.964277 / 10.191392 (-0.227115) \| 0.131247 / 0.680424 (-0.549176) \| 0.014166 / 0.534201 (-0.520035) \| 0.283994 / 0.579283 (-0.295290) \| 0.267516 / 0.434364 (-0.166848) \| 0.328363 / 0.540337 (-0.211974) \| 0.412204 / 1.386936 (-0.974732) \|\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) \| 0.005867 / 0.011353 (-0.005486) \| 0.003860 / 0.011008 (-0.007148) \| 0.050247 / 0.038508 (0.011739) \| 0.033819 / 0.023109 (0.010710) \| 0.264840 / 0.275898 (-0.011058) \| 0.291253 / 0.323480 (-0.032227) \| 0.004481 / 0.007986 (-0.003504) \| 0.002880 / 0.004328 (-0.001449) \| 0.048528 / 0.004250 (0.044278) \| 0.041720 / 0.037052 (0.004667) \| 0.280467 / 0.258489 (0.021978) \| 0.315244 / 0.293841 (0.021404) \| 0.030569 / 0.128546 (-0.097977) \| 0.010494 / 0.075646 (-0.065152) \| 0.058652 / 0.419271 (-0.360620) \| 0.034181 / 0.043533 (-0.009352) \| 0.266466 / 0.255139 (0.011327) \| 0.292038 / 0.283200 (0.008838) \| 0.018501 / 0.141683 (-0.123182) \| 1.115965 / 1.452155 (-0.336189) \| 1.162753 / 1.492716 (-0.329963) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.101301 / 0.018006 (0.083295) \| 0.296812 / 0.000490 (0.296322) \| 0.000212 / 0.000200 (0.000012) \| 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) \| 0.023662 / 0.037411 (-0.013749) \| 0.080678 / 0.014526 (0.066153) \| 0.089867 / 0.176557 (-0.086689) \| 0.130803 / 0.737135 (-0.606332) \| 0.091479 / 0.296338 (-0.204860) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286028 / 0.215209 (0.070819) \| 2.780072 / 2.077655 (0.702418) \| 1.520146 / 1.504120 (0.016026) \| 1.372952 / 1.541195 (-0.168243) \| 1.428734 / 1.468490 (-0.039756) \| 0.571484 / 4.584777 (-4.013293) \| 0.969643 / 3.745712 (-2.776069) \| 2.788157 / 5.269862 (-2.481705) \| 1.841166 / 4.565676 (-2.724511) \| 0.063311 / 0.424275 (-0.360964) \| 0.005320 / 0.007607 (-0.002287) \| 0.333341 / 0.226044 (0.107296) \| 3.295141 / 2.268929 (1.026213) \| 1.865537 / 55.444624 (-53.579088) \| 1.584655 / 6.876477 (-5.291821) \| 1.747417 / 2.142072 (-0.394655) \| 0.634549 / 4.805227 (-4.170678) \| 0.116373 / 6.500664 (-6.384291) \| 0.041567 / 0.075469 (-0.033902) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.023086 / 1.841788 (-0.818702) \| 13.091905 / 8.074308 (5.017597) \| 10.572164 / 10.191392 (0.380772) \| 0.142208 / 0.680424 (-0.538216) \| 0.015692 / 0.534201 (-0.518509) \| 0.284309 / 0.579283 (-0.294974) \| 0.126467 / 0.434364 (-0.307897) \| 0.322719 / 0.540337 (-0.217618) \| 0.439952 / 1.386936 (-0.946985) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98fdc9e78e6d057ca66e58a37f49d6618aab8130 \"CML watermark\")\n" ]
2,351,331,417	6,968	Use `HF_HUB_OFFLINE` instead of `HF_DATASETS_OFFLINE`	closed	2024-06-13T14:39:40	2024-06-13T17:31:37	2024-06-13T17:25:37	https://github.com/huggingface/datasets/pull/6968	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6968", "html_url": "https://github.com/huggingface/datasets/pull/6968", "diff_url": "https://github.com/huggingface/datasets/pull/6968.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6968.patch", "merged_at": "2024-06-13T17:25:37" }	Wauplin	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6968). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Oops, sorry for the style issue. Fixed in https://github.com/huggingface/datasets/pull/6968/commits/a4e2b28fa647b28190ae2615d7271e6ac63c8499.\r\n\r\nRegarding docs, I can't find mentions of `HF_DATASETS_OFFLINE` anywhere else in `datasets`/`hub-docs`. Once this is merged and released, I'm planning to update some `transformers` docs that briefly mention it.", "<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) \| 0.005173 / 0.011353 (-0.006180) \| 0.003485 / 0.011008 (-0.007524) \| 0.063867 / 0.038508 (0.025359) \| 0.031338 / 0.023109 (0.008229) \| 0.242093 / 0.275898 (-0.033805) \| 0.266606 / 0.323480 (-0.056874) \| 0.003069 / 0.007986 (-0.004916) \| 0.003307 / 0.004328 (-0.001022) \| 0.051059 / 0.004250 (0.046808) \| 0.044396 / 0.037052 (0.007344) \| 0.254896 / 0.258489 (-0.003593) \| 0.282835 / 0.293841 (-0.011006) \| 0.027548 / 0.128546 (-0.100998) \| 0.010520 / 0.075646 (-0.065126) \| 0.201701 / 0.419271 (-0.217570) \| 0.035613 / 0.043533 (-0.007920) \| 0.240955 / 0.255139 (-0.014184) \| 0.271902 / 0.283200 (-0.011298) \| 0.019826 / 0.141683 (-0.121857) \| 1.116994 / 1.452155 (-0.335161) \| 1.162886 / 1.492716 (-0.329831) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093683 / 0.018006 (0.075677) \| 0.297970 / 0.000490 (0.297480) \| 0.000211 / 0.000200 (0.000011) \| 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) \| 0.018952 / 0.037411 (-0.018459) \| 0.062710 / 0.014526 (0.048184) \| 0.073641 / 0.176557 (-0.102916) \| 0.121200 / 0.737135 (-0.615935) \| 0.075723 / 0.296338 (-0.220616) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286056 / 0.215209 (0.070847) \| 2.811424 / 2.077655 (0.733770) \| 1.448045 / 1.504120 (-0.056075) \| 1.338309 / 1.541195 (-0.202885) \| 1.328371 / 1.468490 (-0.140119) \| 0.557282 / 4.584777 (-4.027495) \| 2.362235 / 3.745712 (-1.383477) \| 2.732108 / 5.269862 (-2.537754) \| 1.730911 / 4.565676 (-2.834765) \| 0.061689 / 0.424275 (-0.362586) \| 0.004947 / 0.007607 (-0.002660) \| 0.346700 / 0.226044 (0.120656) \| 3.355989 / 2.268929 (1.087060) \| 1.828078 / 55.444624 (-53.616546) \| 1.511531 / 6.876477 (-5.364946) \| 1.535897 / 2.142072 (-0.606175) \| 0.630276 / 4.805227 (-4.174951) \| 0.115808 / 6.500664 (-6.384857) \| 0.042199 / 0.075469 (-0.033270) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969203 / 1.841788 (-0.872584) \| 11.282997 / 8.074308 (3.208689) \| 9.538914 / 10.191392 (-0.652478) \| 0.140072 / 0.680424 (-0.540352) \| 0.014021 / 0.534201 (-0.520180) \| 0.283784 / 0.579283 (-0.295499) \| 0.255973 / 0.434364 (-0.178391) \| 0.320284 / 0.540337 (-0.220053) \| 0.412689 / 1.386936 (-0.974247) \|\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) \| 0.005201 / 0.011353 (-0.006152) \| 0.003312 / 0.011008 (-0.007697) \| 0.050044 / 0.038508 (0.011536) \| 0.033610 / 0.023109 (0.010501) \| 0.266429 / 0.275898 (-0.009469) \| 0.287782 / 0.323480 (-0.035698) \| 0.004316 / 0.007986 (-0.003670) \| 0.002696 / 0.004328 (-0.001633) \| 0.049667 / 0.004250 (0.045417) \| 0.040244 / 0.037052 (0.003192) \| 0.278870 / 0.258489 (0.020381) \| 0.311415 / 0.293841 (0.017574) \| 0.029150 / 0.128546 (-0.099396) \| 0.010046 / 0.075646 (-0.065600) \| 0.058527 / 0.419271 (-0.360744) \| 0.032871 / 0.043533 (-0.010662) \| 0.266582 / 0.255139 (0.011443) \| 0.286157 / 0.283200 (0.002957) \| 0.017197 / 0.141683 (-0.124486) \| 1.120944 / 1.452155 (-0.331211) \| 1.161111 / 1.492716 (-0.331606) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092679 / 0.018006 (0.074672) \| 0.299195 / 0.000490 (0.298705) \| 0.000204 / 0.000200 (0.000004) \| 0.000048 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022212 / 0.037411 (-0.015199) \| 0.076734 / 0.014526 (0.062208) \| 0.088326 / 0.176557 (-0.088230) \| 0.128209 / 0.737135 (-0.608926) \| 0.088807 / 0.296338 (-0.207531) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291782 / 0.215209 (0.076573) \| 2.882990 / 2.077655 (0.805335) \| 1.601638 / 1.504120 (0.097518) \| 1.457560 / 1.541195 (-0.083635) \| 1.470517 / 1.468490 (0.002027) \| 0.565738 / 4.584777 (-4.019039) \| 0.949235 / 3.745712 (-2.796478) \| 2.661927 / 5.269862 (-2.607934) \| 1.722178 / 4.565676 (-2.843498) \| 0.063680 / 0.424275 (-0.360595) \| 0.005339 / 0.007607 (-0.002268) \| 0.344280 / 0.226044 (0.118235) \| 3.432998 / 2.268929 (1.164070) \| 1.985516 / 55.444624 (-53.459108) \| 1.651826 / 6.876477 (-5.224651) \| 1.764541 / 2.142072 (-0.377531) \| 0.640219 / 4.805227 (-4.165008) \| 0.116541 / 6.500664 (-6.384124) \| 0.041237 / 0.075469 (-0.034232) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.013927 / 1.841788 (-0.827861) \| 11.876661 / 8.074308 (3.802353) \| 10.264144 / 10.191392 (0.072752) \| 0.131151 / 0.680424 (-0.549273) \| 0.015774 / 0.534201 (-0.518427) \| 0.284948 / 0.579283 (-0.294335) \| 0.125924 / 0.434364 (-0.308439) \| 0.319845 / 0.540337 (-0.220493) \| 0.431978 / 1.386936 (-0.954958) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#68f67741ffde68c98d0a2f59ac4d8e3a7bc03065 \"CML watermark\")\n" ]
2,349,146,398	6,967	Method to load Laion400m	open	2024-06-12T16:04:04	2024-06-12T16:04:04	null	https://github.com/huggingface/datasets/issues/6967	null	humanely	false	[]
2,348,934,466	6,966	Remove underlines between badges	closed	2024-06-12T14:32:11	2024-06-19T14:16:21	2024-06-19T14:10:11	https://github.com/huggingface/datasets/pull/6966	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6966", "html_url": "https://github.com/huggingface/datasets/pull/6966", "diff_url": "https://github.com/huggingface/datasets/pull/6966.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6966.patch", "merged_at": "2024-06-19T14:10:11" }	andrewhong04	true	[ "<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) \| 0.005326 / 0.011353 (-0.006027) \| 0.003448 / 0.011008 (-0.007560) \| 0.062516 / 0.038508 (0.024008) \| 0.030222 / 0.023109 (0.007113) \| 0.237006 / 0.275898 (-0.038892) \| 0.258224 / 0.323480 (-0.065256) \| 0.003191 / 0.007986 (-0.004795) \| 0.002768 / 0.004328 (-0.001560) \| 0.048754 / 0.004250 (0.044504) \| 0.043694 / 0.037052 (0.006641) \| 0.248832 / 0.258489 (-0.009657) \| 0.272217 / 0.293841 (-0.021624) \| 0.029684 / 0.128546 (-0.098862) \| 0.011997 / 0.075646 (-0.063650) \| 0.204047 / 0.419271 (-0.215225) \| 0.035944 / 0.043533 (-0.007589) \| 0.242094 / 0.255139 (-0.013045) \| 0.258897 / 0.283200 (-0.024303) \| 0.019228 / 0.141683 (-0.122455) \| 1.110193 / 1.452155 (-0.341961) \| 1.166780 / 1.492716 (-0.325937) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097162 / 0.018006 (0.079156) \| 0.303148 / 0.000490 (0.302659) \| 0.000229 / 0.000200 (0.000029) \| 0.000050 / 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) \| 0.019981 / 0.037411 (-0.017431) \| 0.062669 / 0.014526 (0.048144) \| 0.074801 / 0.176557 (-0.101756) \| 0.120509 / 0.737135 (-0.616626) \| 0.075957 / 0.296338 (-0.220382) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279527 / 0.215209 (0.064318) \| 2.722749 / 2.077655 (0.645094) \| 1.441770 / 1.504120 (-0.062350) \| 1.312172 / 1.541195 (-0.229023) \| 1.329418 / 1.468490 (-0.139072) \| 0.723939 / 4.584777 (-3.860838) \| 2.359146 / 3.745712 (-1.386566) \| 2.963445 / 5.269862 (-2.306416) \| 1.881974 / 4.565676 (-2.683702) \| 0.078189 / 0.424275 (-0.346086) \| 0.005249 / 0.007607 (-0.002358) \| 0.334508 / 0.226044 (0.108463) \| 3.271961 / 2.268929 (1.003032) \| 1.817365 / 55.444624 (-53.627259) \| 1.522755 / 6.876477 (-5.353721) \| 1.514203 / 2.142072 (-0.627870) \| 0.803486 / 4.805227 (-4.001741) \| 0.134189 / 6.500664 (-6.366475) \| 0.042761 / 0.075469 (-0.032708) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971126 / 1.841788 (-0.870662) \| 11.367159 / 8.074308 (3.292851) \| 9.520174 / 10.191392 (-0.671218) \| 0.142705 / 0.680424 (-0.537719) \| 0.014586 / 0.534201 (-0.519615) \| 0.300869 / 0.579283 (-0.278414) \| 0.263161 / 0.434364 (-0.171203) \| 0.336403 / 0.540337 (-0.203935) \| 0.436088 / 1.386936 (-0.950848) \|\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) \| 0.005800 / 0.011353 (-0.005553) \| 0.003906 / 0.011008 (-0.007103) \| 0.050197 / 0.038508 (0.011689) \| 0.031348 / 0.023109 (0.008238) \| 0.265636 / 0.275898 (-0.010262) \| 0.286550 / 0.323480 (-0.036930) \| 0.004502 / 0.007986 (-0.003484) \| 0.002828 / 0.004328 (-0.001501) \| 0.049668 / 0.004250 (0.045417) \| 0.039552 / 0.037052 (0.002499) \| 0.279091 / 0.258489 (0.020602) \| 0.309987 / 0.293841 (0.016146) \| 0.032104 / 0.128546 (-0.096442) \| 0.011989 / 0.075646 (-0.063657) \| 0.059875 / 0.419271 (-0.359397) \| 0.033446 / 0.043533 (-0.010087) \| 0.265256 / 0.255139 (0.010117) \| 0.285649 / 0.283200 (0.002449) \| 0.018330 / 0.141683 (-0.123353) \| 1.140073 / 1.452155 (-0.312081) \| 1.194538 / 1.492716 (-0.298178) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093692 / 0.018006 (0.075685) \| 0.301422 / 0.000490 (0.300932) \| 0.000216 / 0.000200 (0.000016) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022844 / 0.037411 (-0.014568) \| 0.077129 / 0.014526 (0.062603) \| 0.087948 / 0.176557 (-0.088608) \| 0.129905 / 0.737135 (-0.607230) \| 0.089872 / 0.296338 (-0.206466) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293135 / 0.215209 (0.077926) \| 2.880280 / 2.077655 (0.802626) \| 1.554250 / 1.504120 (0.050130) \| 1.428005 / 1.541195 (-0.113190) \| 1.520863 / 1.468490 (0.052373) \| 0.759903 / 4.584777 (-3.824874) \| 0.959674 / 3.745712 (-2.786038) \| 2.848914 / 5.269862 (-2.420948) \| 1.900355 / 4.565676 (-2.665322) \| 0.079434 / 0.424275 (-0.344841) \| 0.005487 / 0.007607 (-0.002121) \| 0.344837 / 0.226044 (0.118793) \| 3.401730 / 2.268929 (1.132802) \| 1.887526 / 55.444624 (-53.557098) \| 1.596821 / 6.876477 (-5.279655) \| 1.732190 / 2.142072 (-0.409882) \| 0.800929 / 4.805227 (-4.004299) \| 0.132763 / 6.500664 (-6.367901) \| 0.041185 / 0.075469 (-0.034284) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.994396 / 1.841788 (-0.847391) \| 12.488692 / 8.074308 (4.414384) \| 10.365952 / 10.191392 (0.174560) \| 0.142951 / 0.680424 (-0.537472) \| 0.015448 / 0.534201 (-0.518753) \| 0.305577 / 0.579283 (-0.273706) \| 0.126897 / 0.434364 (-0.307467) \| 0.340784 / 0.540337 (-0.199554) \| 0.461955 / 1.386936 (-0.924981) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1d65718438ac4bc401468e57d5358e69012ed0c8 \"CML watermark\")\n" ]
2,348,653,895	6,965	Improve skip take shuffling and distributed	closed	2024-06-12T12:30:27	2024-06-24T15:22:21	2024-06-24T15:16:16	https://github.com/huggingface/datasets/pull/6965	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6965", "html_url": "https://github.com/huggingface/datasets/pull/6965", "diff_url": "https://github.com/huggingface/datasets/pull/6965.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6965.patch", "merged_at": "2024-06-24T15:16:16" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6965). 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) \| 0.005879 / 0.011353 (-0.005474) \| 0.004144 / 0.011008 (-0.006865) \| 0.063327 / 0.038508 (0.024819) \| 0.032577 / 0.023109 (0.009468) \| 0.242936 / 0.275898 (-0.032962) \| 0.269882 / 0.323480 (-0.053598) \| 0.003339 / 0.007986 (-0.004647) \| 0.002901 / 0.004328 (-0.001428) \| 0.049163 / 0.004250 (0.044912) \| 0.047072 / 0.037052 (0.010019) \| 0.261120 / 0.258489 (0.002631) \| 0.287857 / 0.293841 (-0.005984) \| 0.029688 / 0.128546 (-0.098858) \| 0.012702 / 0.075646 (-0.062944) \| 0.204040 / 0.419271 (-0.215231) \| 0.036012 / 0.043533 (-0.007521) \| 0.244210 / 0.255139 (-0.010929) \| 0.267600 / 0.283200 (-0.015599) \| 0.019627 / 0.141683 (-0.122056) \| 1.103770 / 1.452155 (-0.348385) \| 1.197710 / 1.492716 (-0.295006) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.101683 / 0.018006 (0.083677) \| 0.311825 / 0.000490 (0.311335) \| 0.000236 / 0.000200 (0.000036) \| 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) \| 0.019642 / 0.037411 (-0.017769) \| 0.061618 / 0.014526 (0.047092) \| 0.075237 / 0.176557 (-0.101320) \| 0.122250 / 0.737135 (-0.614886) \| 0.076087 / 0.296338 (-0.220251) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285120 / 0.215209 (0.069911) \| 2.811527 / 2.077655 (0.733872) \| 1.457961 / 1.504120 (-0.046159) \| 1.333819 / 1.541195 (-0.207376) \| 1.387863 / 1.468490 (-0.080627) \| 0.730828 / 4.584777 (-3.853949) \| 2.417224 / 3.745712 (-1.328488) \| 2.994842 / 5.269862 (-2.275020) \| 1.922079 / 4.565676 (-2.643598) \| 0.087486 / 0.424275 (-0.336789) \| 0.005211 / 0.007607 (-0.002396) \| 0.335585 / 0.226044 (0.109541) \| 3.297664 / 2.268929 (1.028735) \| 1.809391 / 55.444624 (-53.635233) \| 1.501646 / 6.876477 (-5.374831) \| 1.567573 / 2.142072 (-0.574500) \| 0.800816 / 4.805227 (-4.004411) \| 0.134204 / 6.500664 (-6.366460) \| 0.043156 / 0.075469 (-0.032313) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.982955 / 1.841788 (-0.858833) \| 12.256850 / 8.074308 (4.182542) \| 9.821500 / 10.191392 (-0.369892) \| 0.143739 / 0.680424 (-0.536685) \| 0.014425 / 0.534201 (-0.519776) \| 0.302718 / 0.579283 (-0.276565) \| 0.267746 / 0.434364 (-0.166618) \| 0.340036 / 0.540337 (-0.200301) \| 0.436211 / 1.386936 (-0.950725) \|\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) \| 0.006136 / 0.011353 (-0.005217) \| 0.004125 / 0.011008 (-0.006883) \| 0.050341 / 0.038508 (0.011833) \| 0.034547 / 0.023109 (0.011438) \| 0.270237 / 0.275898 (-0.005661) \| 0.294503 / 0.323480 (-0.028977) \| 0.004528 / 0.007986 (-0.003458) \| 0.003103 / 0.004328 (-0.001225) \| 0.048817 / 0.004250 (0.044566) \| 0.041301 / 0.037052 (0.004249) \| 0.279461 / 0.258489 (0.020972) \| 0.319376 / 0.293841 (0.025535) \| 0.032733 / 0.128546 (-0.095813) \| 0.012426 / 0.075646 (-0.063221) \| 0.060543 / 0.419271 (-0.358729) \| 0.034015 / 0.043533 (-0.009518) \| 0.267387 / 0.255139 (0.012248) \| 0.288590 / 0.283200 (0.005390) \| 0.019697 / 0.141683 (-0.121986) \| 1.145994 / 1.452155 (-0.306161) \| 1.198122 / 1.492716 (-0.294595) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099091 / 0.018006 (0.081085) \| 0.313767 / 0.000490 (0.313277) \| 0.000220 / 0.000200 (0.000020) \| 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) \| 0.023219 / 0.037411 (-0.014192) \| 0.083609 / 0.014526 (0.069084) \| 0.089529 / 0.176557 (-0.087028) \| 0.131025 / 0.737135 (-0.606110) \| 0.091947 / 0.296338 (-0.204391) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283711 / 0.215209 (0.068502) \| 2.811702 / 2.077655 (0.734047) \| 1.577720 / 1.504120 (0.073600) \| 1.415700 / 1.541195 (-0.125495) \| 1.436097 / 1.468490 (-0.032393) \| 0.732090 / 4.584777 (-3.852687) \| 0.990552 / 3.745712 (-2.755160) \| 2.887319 / 5.269862 (-2.382543) \| 1.923707 / 4.565676 (-2.641969) \| 0.079361 / 0.424275 (-0.344915) \| 0.005520 / 0.007607 (-0.002087) \| 0.336684 / 0.226044 (0.110639) \| 3.325342 / 2.268929 (1.056413) \| 1.911853 / 55.444624 (-53.532771) \| 1.621450 / 6.876477 (-5.255027) \| 1.807964 / 2.142072 (-0.334109) \| 0.813958 / 4.805227 (-3.991269) \| 0.137564 / 6.500664 (-6.363100) \| 0.043151 / 0.075469 (-0.032318) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.002775 / 1.841788 (-0.839013) \| 12.526367 / 8.074308 (4.452058) \| 10.426992 / 10.191392 (0.235600) \| 0.134902 / 0.680424 (-0.545522) \| 0.016726 / 0.534201 (-0.517475) \| 0.303549 / 0.579283 (-0.275734) \| 0.129334 / 0.434364 (-0.305030) \| 0.339254 / 0.540337 (-0.201084) \| 0.456845 / 1.386936 (-0.930091) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5464b32ce03739431235c13f314732201abcfac \"CML watermark\")\n" ]
2,344,973,229	6,964	Fix resuming arrow format	closed	2024-06-10T22:40:33	2024-06-14T15:04:49	2024-06-14T14:58:37	https://github.com/huggingface/datasets/pull/6964	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6964", "html_url": "https://github.com/huggingface/datasets/pull/6964", "diff_url": "https://github.com/huggingface/datasets/pull/6964.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6964.patch", "merged_at": "2024-06-14T14:58:37" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6964). 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) \| 0.005735 / 0.011353 (-0.005618) \| 0.003746 / 0.011008 (-0.007263) \| 0.063115 / 0.038508 (0.024606) \| 0.033557 / 0.023109 (0.010447) \| 0.247599 / 0.275898 (-0.028299) \| 0.275310 / 0.323480 (-0.048170) \| 0.004203 / 0.007986 (-0.003783) \| 0.002770 / 0.004328 (-0.001558) \| 0.050951 / 0.004250 (0.046700) \| 0.046609 / 0.037052 (0.009557) \| 0.256237 / 0.258489 (-0.002252) \| 0.292050 / 0.293841 (-0.001791) \| 0.027991 / 0.128546 (-0.100556) \| 0.010367 / 0.075646 (-0.065279) \| 0.202295 / 0.419271 (-0.216977) \| 0.037287 / 0.043533 (-0.006246) \| 0.250330 / 0.255139 (-0.004809) \| 0.281250 / 0.283200 (-0.001950) \| 0.018832 / 0.141683 (-0.122851) \| 1.117303 / 1.452155 (-0.334852) \| 1.141593 / 1.492716 (-0.351123) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097318 / 0.018006 (0.079312) \| 0.304853 / 0.000490 (0.304364) \| 0.000220 / 0.000200 (0.000020) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020353 / 0.037411 (-0.017058) \| 0.065497 / 0.014526 (0.050971) \| 0.076205 / 0.176557 (-0.100351) \| 0.122471 / 0.737135 (-0.614665) \| 0.079522 / 0.296338 (-0.216816) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282604 / 0.215209 (0.067395) \| 2.743198 / 2.077655 (0.665543) \| 1.480436 / 1.504120 (-0.023684) \| 1.373935 / 1.541195 (-0.167260) \| 1.388901 / 1.468490 (-0.079589) \| 0.571961 / 4.584777 (-4.012816) \| 2.431790 / 3.745712 (-1.313922) \| 2.942126 / 5.269862 (-2.327736) \| 1.857361 / 4.565676 (-2.708316) \| 0.063535 / 0.424275 (-0.360740) \| 0.005039 / 0.007607 (-0.002568) \| 0.331726 / 0.226044 (0.105682) \| 3.282504 / 2.268929 (1.013576) \| 1.852303 / 55.444624 (-53.592321) \| 1.506665 / 6.876477 (-5.369812) \| 1.577524 / 2.142072 (-0.564548) \| 0.646267 / 4.805227 (-4.158960) \| 0.118706 / 6.500664 (-6.381958) \| 0.043437 / 0.075469 (-0.032033) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.978073 / 1.841788 (-0.863714) \| 12.028575 / 8.074308 (3.954267) \| 10.066303 / 10.191392 (-0.125090) \| 0.131763 / 0.680424 (-0.548661) \| 0.016479 / 0.534201 (-0.517722) \| 0.286012 / 0.579283 (-0.293271) \| 0.266824 / 0.434364 (-0.167540) \| 0.328452 / 0.540337 (-0.211885) \| 0.414562 / 1.386936 (-0.972374) \|\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) \| 0.005943 / 0.011353 (-0.005409) \| 0.003992 / 0.011008 (-0.007016) \| 0.051159 / 0.038508 (0.012651) \| 0.033805 / 0.023109 (0.010695) \| 0.268425 / 0.275898 (-0.007474) \| 0.295662 / 0.323480 (-0.027818) \| 0.004473 / 0.007986 (-0.003512) \| 0.002910 / 0.004328 (-0.001418) \| 0.048595 / 0.004250 (0.044345) \| 0.043724 / 0.037052 (0.006671) \| 0.280552 / 0.258489 (0.022063) \| 0.319052 / 0.293841 (0.025211) \| 0.031269 / 0.128546 (-0.097278) \| 0.010976 / 0.075646 (-0.064671) \| 0.060128 / 0.419271 (-0.359144) \| 0.034198 / 0.043533 (-0.009335) \| 0.269664 / 0.255139 (0.014525) \| 0.292249 / 0.283200 (0.009049) \| 0.019950 / 0.141683 (-0.121733) \| 1.143073 / 1.452155 (-0.309082) \| 1.188553 / 1.492716 (-0.304164) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095188 / 0.018006 (0.077182) \| 0.300207 / 0.000490 (0.299717) \| 0.000205 / 0.000200 (0.000005) \| 0.000051 / 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) \| 0.023610 / 0.037411 (-0.013802) \| 0.082868 / 0.014526 (0.068342) \| 0.089059 / 0.176557 (-0.087498) \| 0.131735 / 0.737135 (-0.605401) \| 0.091467 / 0.296338 (-0.204872) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302497 / 0.215209 (0.087287) \| 2.985794 / 2.077655 (0.908140) \| 1.590783 / 1.504120 (0.086663) \| 1.468819 / 1.541195 (-0.072375) \| 1.503115 / 1.468490 (0.034625) \| 0.575109 / 4.584777 (-4.009668) \| 0.972370 / 3.745712 (-2.773342) \| 2.727976 / 5.269862 (-2.541886) \| 1.793438 / 4.565676 (-2.772238) \| 0.068840 / 0.424275 (-0.355435) \| 0.005440 / 0.007607 (-0.002167) \| 0.351843 / 0.226044 (0.125799) \| 3.523108 / 2.268929 (1.254180) \| 1.928576 / 55.444624 (-53.516049) \| 1.627939 / 6.876477 (-5.248538) \| 1.837618 / 2.142072 (-0.304454) \| 0.669351 / 4.805227 (-4.135876) \| 0.121822 / 6.500664 (-6.378842) \| 0.042056 / 0.075469 (-0.033413) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.020081 / 1.841788 (-0.821707) \| 13.417448 / 8.074308 (5.343140) \| 10.974516 / 10.191392 (0.783124) \| 0.135240 / 0.680424 (-0.545184) \| 0.017581 / 0.534201 (-0.516620) \| 0.289080 / 0.579283 (-0.290203) \| 0.127679 / 0.434364 (-0.306685) \| 0.331818 / 0.540337 (-0.208520) \| 0.453143 / 1.386936 (-0.933793) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef2fb358433678b322d275c0bdee3239fa6485b2 \"CML watermark\")\n" ]
2,344,269,477	6,963	[Streaming] retry on requests errors	closed	2024-06-10T15:51:56	2024-06-28T09:53:11	2024-06-28T09:46:52	https://github.com/huggingface/datasets/pull/6963	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6963", "html_url": "https://github.com/huggingface/datasets/pull/6963", "diff_url": "https://github.com/huggingface/datasets/pull/6963.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6963.patch", "merged_at": "2024-06-28T09:46:52" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6963). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "ci failures are r-unrelated to this PR, merging", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005532 / 0.011353 (-0.005821) \| 0.004018 / 0.011008 (-0.006991) \| 0.064685 / 0.038508 (0.026177) \| 0.031303 / 0.023109 (0.008194) \| 0.254670 / 0.275898 (-0.021228) \| 0.271357 / 0.323480 (-0.052123) \| 0.003372 / 0.007986 (-0.004614) \| 0.004153 / 0.004328 (-0.000175) \| 0.050381 / 0.004250 (0.046131) \| 0.046837 / 0.037052 (0.009784) \| 0.253166 / 0.258489 (-0.005323) \| 0.294257 / 0.293841 (0.000416) \| 0.029746 / 0.128546 (-0.098800) \| 0.012519 / 0.075646 (-0.063127) \| 0.208822 / 0.419271 (-0.210449) \| 0.036925 / 0.043533 (-0.006608) \| 0.247636 / 0.255139 (-0.007503) \| 0.269102 / 0.283200 (-0.014097) \| 0.019021 / 0.141683 (-0.122662) \| 1.138825 / 1.452155 (-0.313330) \| 1.203301 / 1.492716 (-0.289415) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095950 / 0.018006 (0.077944) \| 0.303347 / 0.000490 (0.302857) \| 0.000221 / 0.000200 (0.000022) \| 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) \| 0.019014 / 0.037411 (-0.018397) \| 0.062220 / 0.014526 (0.047694) \| 0.074811 / 0.176557 (-0.101745) \| 0.122917 / 0.737135 (-0.614218) \| 0.075765 / 0.296338 (-0.220574) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288359 / 0.215209 (0.073150) \| 2.849491 / 2.077655 (0.771837) \| 1.479448 / 1.504120 (-0.024672) \| 1.350560 / 1.541195 (-0.190635) \| 1.366079 / 1.468490 (-0.102411) \| 0.733609 / 4.584777 (-3.851168) \| 2.416014 / 3.745712 (-1.329698) \| 2.954834 / 5.269862 (-2.315028) \| 1.985703 / 4.565676 (-2.579974) \| 0.080589 / 0.424275 (-0.343686) \| 0.005581 / 0.007607 (-0.002026) \| 0.343706 / 0.226044 (0.117661) \| 3.416257 / 2.268929 (1.147329) \| 1.865937 / 55.444624 (-53.578687) \| 1.545911 / 6.876477 (-5.330566) \| 1.711004 / 2.142072 (-0.431069) \| 0.821231 / 4.805227 (-3.983996) \| 0.138865 / 6.500664 (-6.361799) \| 0.046466 / 0.075469 (-0.029003) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965632 / 1.841788 (-0.876155) \| 11.812101 / 8.074308 (3.737792) \| 9.399156 / 10.191392 (-0.792236) \| 0.143325 / 0.680424 (-0.537099) \| 0.014824 / 0.534201 (-0.519377) \| 0.306143 / 0.579283 (-0.273140) \| 0.264063 / 0.434364 (-0.170301) \| 0.347820 / 0.540337 (-0.192517) \| 0.476818 / 1.386936 (-0.910118) \|\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) \| 0.005978 / 0.011353 (-0.005375) \| 0.004482 / 0.011008 (-0.006526) \| 0.053788 / 0.038508 (0.015280) \| 0.033963 / 0.023109 (0.010853) \| 0.267258 / 0.275898 (-0.008640) \| 0.290916 / 0.323480 (-0.032563) \| 0.004485 / 0.007986 (-0.003500) \| 0.002876 / 0.004328 (-0.001453) \| 0.048637 / 0.004250 (0.044386) \| 0.042050 / 0.037052 (0.004997) \| 0.278607 / 0.258489 (0.020118) \| 0.315411 / 0.293841 (0.021570) \| 0.032059 / 0.128546 (-0.096487) \| 0.012851 / 0.075646 (-0.062795) \| 0.061672 / 0.419271 (-0.357600) \| 0.034545 / 0.043533 (-0.008988) \| 0.262068 / 0.255139 (0.006929) \| 0.291197 / 0.283200 (0.007997) \| 0.019092 / 0.141683 (-0.122591) \| 1.108690 / 1.452155 (-0.343464) \| 1.161025 / 1.492716 (-0.331691) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096775 / 0.018006 (0.078768) \| 0.306825 / 0.000490 (0.306335) \| 0.000210 / 0.000200 (0.000010) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023160 / 0.037411 (-0.014251) \| 0.078794 / 0.014526 (0.064268) \| 0.088954 / 0.176557 (-0.087602) \| 0.129488 / 0.737135 (-0.607648) \| 0.091239 / 0.296338 (-0.205099) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292911 / 0.215209 (0.077702) \| 2.910802 / 2.077655 (0.833148) \| 1.569310 / 1.504120 (0.065191) \| 1.433807 / 1.541195 (-0.107388) \| 1.478619 / 1.468490 (0.010129) \| 0.720982 / 4.584777 (-3.863795) \| 0.972104 / 3.745712 (-2.773608) \| 3.026941 / 5.269862 (-2.242921) \| 1.919170 / 4.565676 (-2.646506) \| 0.079292 / 0.424275 (-0.344983) \| 0.005227 / 0.007607 (-0.002380) \| 0.345363 / 0.226044 (0.119319) \| 3.416149 / 2.268929 (1.147221) \| 1.938377 / 55.444624 (-53.506248) \| 1.626037 / 6.876477 (-5.250440) \| 1.644405 / 2.142072 (-0.497668) \| 0.802485 / 4.805227 (-4.002742) \| 0.135114 / 6.500664 (-6.365550) \| 0.042015 / 0.075469 (-0.033454) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.014812 / 1.841788 (-0.826976) \| 12.583844 / 8.074308 (4.509536) \| 10.522495 / 10.191392 (0.331103) \| 0.143336 / 0.680424 (-0.537088) \| 0.015843 / 0.534201 (-0.518357) \| 0.306556 / 0.579283 (-0.272727) \| 0.129654 / 0.434364 (-0.304710) \| 0.340442 / 0.540337 (-0.199896) \| 0.445220 / 1.386936 (-0.941716) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5cab892dcd26fb51938634e13e300c6611ab66e0 \"CML watermark\")\n" ]
2,343,394,378	6,962	fix(ci): remove unnecessary permissions	closed	2024-06-10T09:28:02	2024-06-11T08:31:52	2024-06-11T08:25:47	https://github.com/huggingface/datasets/pull/6962	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6962", "html_url": "https://github.com/huggingface/datasets/pull/6962", "diff_url": "https://github.com/huggingface/datasets/pull/6962.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6962.patch", "merged_at": "2024-06-11T08:25:47" }	McPatate	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6962). 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) \| 0.005520 / 0.011353 (-0.005833) \| 0.003989 / 0.011008 (-0.007019) \| 0.064786 / 0.038508 (0.026278) \| 0.031075 / 0.023109 (0.007966) \| 0.241619 / 0.275898 (-0.034279) \| 0.275341 / 0.323480 (-0.048139) \| 0.003139 / 0.007986 (-0.004847) \| 0.002820 / 0.004328 (-0.001508) \| 0.049766 / 0.004250 (0.045515) \| 0.045047 / 0.037052 (0.007995) \| 0.251906 / 0.258489 (-0.006583) \| 0.285889 / 0.293841 (-0.007952) \| 0.028297 / 0.128546 (-0.100249) \| 0.010683 / 0.075646 (-0.064963) \| 0.206467 / 0.419271 (-0.212805) \| 0.036267 / 0.043533 (-0.007266) \| 0.250720 / 0.255139 (-0.004419) \| 0.268565 / 0.283200 (-0.014635) \| 0.020394 / 0.141683 (-0.121289) \| 1.114283 / 1.452155 (-0.337872) \| 1.163884 / 1.492716 (-0.328833) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.112698 / 0.018006 (0.094692) \| 0.302740 / 0.000490 (0.302251) \| 0.000209 / 0.000200 (0.000009) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019337 / 0.037411 (-0.018075) \| 0.062854 / 0.014526 (0.048328) \| 0.077088 / 0.176557 (-0.099468) \| 0.120926 / 0.737135 (-0.616209) \| 0.075594 / 0.296338 (-0.220744) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290787 / 0.215209 (0.075578) \| 2.867894 / 2.077655 (0.790239) \| 1.490043 / 1.504120 (-0.014076) \| 1.356383 / 1.541195 (-0.184812) \| 1.400229 / 1.468490 (-0.068261) \| 0.582076 / 4.584777 (-4.002701) \| 2.398270 / 3.745712 (-1.347442) \| 2.856459 / 5.269862 (-2.413403) \| 1.815545 / 4.565676 (-2.750131) \| 0.063259 / 0.424275 (-0.361016) \| 0.005056 / 0.007607 (-0.002551) \| 0.347699 / 0.226044 (0.121655) \| 3.466511 / 2.268929 (1.197582) \| 1.862096 / 55.444624 (-53.582528) \| 1.532324 / 6.876477 (-5.344152) \| 1.599411 / 2.142072 (-0.542661) \| 0.657350 / 4.805227 (-4.147878) \| 0.118981 / 6.500664 (-6.381683) \| 0.042224 / 0.075469 (-0.033245) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965649 / 1.841788 (-0.876139) \| 11.896501 / 8.074308 (3.822193) \| 9.873923 / 10.191392 (-0.317469) \| 0.141165 / 0.680424 (-0.539258) \| 0.013885 / 0.534201 (-0.520316) \| 0.291464 / 0.579283 (-0.287819) \| 0.273153 / 0.434364 (-0.161211) \| 0.324395 / 0.540337 (-0.215942) \| 0.422040 / 1.386936 (-0.964897) \|\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) \| 0.005640 / 0.011353 (-0.005713) \| 0.004035 / 0.011008 (-0.006973) \| 0.050831 / 0.038508 (0.012323) \| 0.032841 / 0.023109 (0.009732) \| 0.272226 / 0.275898 (-0.003672) \| 0.297880 / 0.323480 (-0.025599) \| 0.004397 / 0.007986 (-0.003588) \| 0.002762 / 0.004328 (-0.001566) \| 0.049887 / 0.004250 (0.045637) \| 0.040372 / 0.037052 (0.003320) \| 0.286337 / 0.258489 (0.027848) \| 0.320015 / 0.293841 (0.026174) \| 0.029992 / 0.128546 (-0.098554) \| 0.010781 / 0.075646 (-0.064865) \| 0.059391 / 0.419271 (-0.359880) \| 0.034410 / 0.043533 (-0.009123) \| 0.273024 / 0.255139 (0.017885) \| 0.288953 / 0.283200 (0.005754) \| 0.018072 / 0.141683 (-0.123611) \| 1.125742 / 1.452155 (-0.326413) \| 1.175233 / 1.492716 (-0.317483) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093470 / 0.018006 (0.075463) \| 0.313248 / 0.000490 (0.312758) \| 0.000324 / 0.000200 (0.000124) \| 0.000081 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023529 / 0.037411 (-0.013882) \| 0.077305 / 0.014526 (0.062779) \| 0.088916 / 0.176557 (-0.087640) \| 0.128792 / 0.737135 (-0.608344) \| 0.090141 / 0.296338 (-0.206197) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291110 / 0.215209 (0.075901) \| 2.848118 / 2.077655 (0.770464) \| 1.581664 / 1.504120 (0.077544) \| 1.446390 / 1.541195 (-0.094804) \| 1.452594 / 1.468490 (-0.015896) \| 0.571213 / 4.584777 (-4.013564) \| 0.976382 / 3.745712 (-2.769330) \| 2.756192 / 5.269862 (-2.513670) \| 1.770274 / 4.565676 (-2.795403) \| 0.064513 / 0.424275 (-0.359763) \| 0.005334 / 0.007607 (-0.002273) \| 0.347380 / 0.226044 (0.121335) \| 3.424800 / 2.268929 (1.155871) \| 1.942374 / 55.444624 (-53.502250) \| 1.636069 / 6.876477 (-5.240407) \| 1.795327 / 2.142072 (-0.346745) \| 0.658942 / 4.805227 (-4.146285) \| 0.119542 / 6.500664 (-6.381123) \| 0.041826 / 0.075469 (-0.033643) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.007230 / 1.841788 (-0.834558) \| 12.293084 / 8.074308 (4.218776) \| 10.618104 / 10.191392 (0.426712) \| 0.133691 / 0.680424 (-0.546733) \| 0.015725 / 0.534201 (-0.518476) \| 0.288860 / 0.579283 (-0.290423) \| 0.130546 / 0.434364 (-0.303818) \| 0.327279 / 0.540337 (-0.213059) \| 0.428768 / 1.386936 (-0.958168) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#af3acfdfcf76bb980dbac871540e30c2cade0cf9 \"CML watermark\")\n" ]
2,342,022,418	6,961	Manual downloads should count as downloads	open	2024-06-09T04:52:06	2024-06-13T16:05:00	null	https://github.com/huggingface/datasets/issues/6961	null	umarbutler	false	[ "We're unlikely to add more features/support for datasets with python loading scripts, which include datasets with manual download. Sorry for the inconvenience" ]
2,340,791,685	6,960	feat(ci): add trufflehog secrets detection	closed	2024-06-07T16:18:23	2024-06-08T14:58:27	2024-06-08T14:52:18	https://github.com/huggingface/datasets/pull/6960	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6960", "html_url": "https://github.com/huggingface/datasets/pull/6960", "diff_url": "https://github.com/huggingface/datasets/pull/6960.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6960.patch", "merged_at": "2024-06-08T14:52:18" }	McPatate	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6960). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Yes!", "<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) \| 0.005007 / 0.011353 (-0.006346) \| 0.003603 / 0.011008 (-0.007405) \| 0.062719 / 0.038508 (0.024211) \| 0.029327 / 0.023109 (0.006217) \| 0.250360 / 0.275898 (-0.025538) \| 0.265095 / 0.323480 (-0.058385) \| 0.004205 / 0.007986 (-0.003781) \| 0.002713 / 0.004328 (-0.001616) \| 0.049209 / 0.004250 (0.044958) \| 0.045162 / 0.037052 (0.008110) \| 0.260439 / 0.258489 (0.001950) \| 0.287778 / 0.293841 (-0.006063) \| 0.027458 / 0.128546 (-0.101088) \| 0.010169 / 0.075646 (-0.065477) \| 0.199487 / 0.419271 (-0.219784) \| 0.036584 / 0.043533 (-0.006949) \| 0.254523 / 0.255139 (-0.000616) \| 0.269902 / 0.283200 (-0.013298) \| 0.017138 / 0.141683 (-0.124545) \| 1.099285 / 1.452155 (-0.352869) \| 1.150878 / 1.492716 (-0.341839) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092868 / 0.018006 (0.074862) \| 0.300421 / 0.000490 (0.299932) \| 0.000213 / 0.000200 (0.000013) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018810 / 0.037411 (-0.018601) \| 0.062341 / 0.014526 (0.047815) \| 0.074779 / 0.176557 (-0.101777) \| 0.120641 / 0.737135 (-0.616494) \| 0.075020 / 0.296338 (-0.221318) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277782 / 0.215209 (0.062573) \| 2.716427 / 2.077655 (0.638772) \| 1.434204 / 1.504120 (-0.069916) \| 1.335990 / 1.541195 (-0.205205) \| 1.336636 / 1.468490 (-0.131854) \| 0.557562 / 4.584777 (-4.027215) \| 2.323517 / 3.745712 (-1.422196) \| 2.647937 / 5.269862 (-2.621925) \| 1.728735 / 4.565676 (-2.836941) \| 0.061888 / 0.424275 (-0.362387) \| 0.004981 / 0.007607 (-0.002627) \| 0.329429 / 0.226044 (0.103385) \| 3.324708 / 2.268929 (1.055779) \| 1.832641 / 55.444624 (-53.611983) \| 1.514386 / 6.876477 (-5.362091) \| 1.656912 / 2.142072 (-0.485160) \| 0.630706 / 4.805227 (-4.174521) \| 0.116250 / 6.500664 (-6.384414) \| 0.042598 / 0.075469 (-0.032871) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969217 / 1.841788 (-0.872570) \| 11.232580 / 8.074308 (3.158272) \| 9.541306 / 10.191392 (-0.650086) \| 0.139544 / 0.680424 (-0.540880) \| 0.014441 / 0.534201 (-0.519760) \| 0.285834 / 0.579283 (-0.293449) \| 0.261950 / 0.434364 (-0.172414) \| 0.325449 / 0.540337 (-0.214889) \| 0.415501 / 1.386936 (-0.971435) \|\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) \| 0.005422 / 0.011353 (-0.005931) \| 0.003528 / 0.011008 (-0.007480) \| 0.049582 / 0.038508 (0.011074) \| 0.032683 / 0.023109 (0.009574) \| 0.277309 / 0.275898 (0.001411) \| 0.298598 / 0.323480 (-0.024882) \| 0.004325 / 0.007986 (-0.003661) \| 0.002741 / 0.004328 (-0.001588) \| 0.047933 / 0.004250 (0.043683) \| 0.040778 / 0.037052 (0.003726) \| 0.287492 / 0.258489 (0.029003) \| 0.311408 / 0.293841 (0.017567) \| 0.029482 / 0.128546 (-0.099064) \| 0.010630 / 0.075646 (-0.065016) \| 0.057745 / 0.419271 (-0.361526) \| 0.033501 / 0.043533 (-0.010031) \| 0.279880 / 0.255139 (0.024741) \| 0.297421 / 0.283200 (0.014221) \| 0.017907 / 0.141683 (-0.123776) \| 1.152221 / 1.452155 (-0.299934) \| 1.189332 / 1.492716 (-0.303385) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094464 / 0.018006 (0.076457) \| 0.300769 / 0.000490 (0.300279) \| 0.000196 / 0.000200 (-0.000004) \| 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) \| 0.022232 / 0.037411 (-0.015179) \| 0.076626 / 0.014526 (0.062100) \| 0.087807 / 0.176557 (-0.088750) \| 0.128847 / 0.737135 (-0.608288) \| 0.092135 / 0.296338 (-0.204203) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299013 / 0.215209 (0.083804) \| 2.929788 / 2.077655 (0.852133) \| 1.614185 / 1.504120 (0.110065) \| 1.486720 / 1.541195 (-0.054475) \| 1.492473 / 1.468490 (0.023983) \| 0.563699 / 4.584777 (-4.021078) \| 0.928820 / 3.745712 (-2.816892) \| 2.597271 / 5.269862 (-2.672590) \| 1.716534 / 4.565676 (-2.849142) \| 0.062568 / 0.424275 (-0.361707) \| 0.005168 / 0.007607 (-0.002439) \| 0.353781 / 0.226044 (0.127737) \| 3.493732 / 2.268929 (1.224803) \| 2.018343 / 55.444624 (-53.426282) \| 1.694516 / 6.876477 (-5.181961) \| 1.796950 / 2.142072 (-0.345123) \| 0.634846 / 4.805227 (-4.170382) \| 0.115230 / 6.500664 (-6.385434) \| 0.040816 / 0.075469 (-0.034654) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.986212 / 1.841788 (-0.855575) \| 11.954392 / 8.074308 (3.880084) \| 10.299670 / 10.191392 (0.108278) \| 0.128358 / 0.680424 (-0.552066) \| 0.016313 / 0.534201 (-0.517888) \| 0.289621 / 0.579283 (-0.289662) \| 0.124708 / 0.434364 (-0.309656) \| 0.325269 / 0.540337 (-0.215068) \| 0.415133 / 1.386936 (-0.971803) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#97513be330114a8aa07e5199ec252ac662aeb76d \"CML watermark\")\n" ]
2,340,229,908	6,959	Better error handling in `dataset_module_factory`	closed	2024-06-07T11:24:15	2024-06-10T07:33:53	2024-06-10T07:27:43	https://github.com/huggingface/datasets/pull/6959	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6959", "html_url": "https://github.com/huggingface/datasets/pull/6959", "diff_url": "https://github.com/huggingface/datasets/pull/6959.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6959.patch", "merged_at": "2024-06-10T07:27:43" }	Wauplin	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6959). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Test should be fixed by https://github.com/huggingface/datasets/pull/6959/commits/ef8f7cee79ffb070d9b5190f21128fc523b3d3ee (tested locally). Let's see what CI says :crossed_fingers: ", "<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) \| 0.005678 / 0.011353 (-0.005675) \| 0.004119 / 0.011008 (-0.006889) \| 0.063901 / 0.038508 (0.025393) \| 0.032071 / 0.023109 (0.008961) \| 0.243182 / 0.275898 (-0.032716) \| 0.280709 / 0.323480 (-0.042770) \| 0.004195 / 0.007986 (-0.003791) \| 0.002810 / 0.004328 (-0.001518) \| 0.048722 / 0.004250 (0.044472) \| 0.049381 / 0.037052 (0.012328) \| 0.257816 / 0.258489 (-0.000673) \| 0.288460 / 0.293841 (-0.005381) \| 0.028518 / 0.128546 (-0.100029) \| 0.010775 / 0.075646 (-0.064871) \| 0.203149 / 0.419271 (-0.216122) \| 0.038792 / 0.043533 (-0.004741) \| 0.248502 / 0.255139 (-0.006637) \| 0.268251 / 0.283200 (-0.014949) \| 0.019536 / 0.141683 (-0.122147) \| 1.133935 / 1.452155 (-0.318220) \| 1.182855 / 1.492716 (-0.309862) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097531 / 0.018006 (0.079525) \| 0.303612 / 0.000490 (0.303122) \| 0.000222 / 0.000200 (0.000022) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019670 / 0.037411 (-0.017741) \| 0.063439 / 0.014526 (0.048913) \| 0.075119 / 0.176557 (-0.101438) \| 0.122419 / 0.737135 (-0.614717) \| 0.076965 / 0.296338 (-0.219374) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286780 / 0.215209 (0.071571) \| 2.811860 / 2.077655 (0.734206) \| 1.485165 / 1.504120 (-0.018954) \| 1.373296 / 1.541195 (-0.167898) \| 1.412700 / 1.468490 (-0.055790) \| 0.566442 / 4.584777 (-4.018335) \| 2.382616 / 3.745712 (-1.363096) \| 2.677214 / 5.269862 (-2.592647) \| 1.760073 / 4.565676 (-2.805603) \| 0.062673 / 0.424275 (-0.361602) \| 0.005050 / 0.007607 (-0.002557) \| 0.341701 / 0.226044 (0.115657) \| 3.321182 / 2.268929 (1.052253) \| 1.811715 / 55.444624 (-53.632909) \| 1.554986 / 6.876477 (-5.321491) \| 1.727448 / 2.142072 (-0.414624) \| 0.642193 / 4.805227 (-4.163034) \| 0.117878 / 6.500664 (-6.382786) \| 0.042814 / 0.075469 (-0.032655) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985894 / 1.841788 (-0.855894) \| 12.195975 / 8.074308 (4.121667) \| 9.890180 / 10.191392 (-0.301212) \| 0.142638 / 0.680424 (-0.537786) \| 0.015207 / 0.534201 (-0.518994) \| 0.283140 / 0.579283 (-0.296143) \| 0.266016 / 0.434364 (-0.168348) \| 0.325518 / 0.540337 (-0.214820) \| 0.418994 / 1.386936 (-0.967942) \|\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) \| 0.005978 / 0.011353 (-0.005374) \| 0.003915 / 0.011008 (-0.007093) \| 0.051592 / 0.038508 (0.013084) \| 0.033338 / 0.023109 (0.010229) \| 0.267925 / 0.275898 (-0.007973) \| 0.296011 / 0.323480 (-0.027469) \| 0.004503 / 0.007986 (-0.003483) \| 0.002854 / 0.004328 (-0.001475) \| 0.049958 / 0.004250 (0.045707) \| 0.041708 / 0.037052 (0.004656) \| 0.287185 / 0.258489 (0.028696) \| 0.322715 / 0.293841 (0.028874) \| 0.030088 / 0.128546 (-0.098458) \| 0.010709 / 0.075646 (-0.064938) \| 0.059736 / 0.419271 (-0.359536) \| 0.034294 / 0.043533 (-0.009239) \| 0.264316 / 0.255139 (0.009177) \| 0.285471 / 0.283200 (0.002272) \| 0.019197 / 0.141683 (-0.122486) \| 1.135571 / 1.452155 (-0.316583) \| 1.190019 / 1.492716 (-0.302698) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099251 / 0.018006 (0.081245) \| 0.305357 / 0.000490 (0.304867) \| 0.000215 / 0.000200 (0.000015) \| 0.000045 / 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) \| 0.023206 / 0.037411 (-0.014205) \| 0.077835 / 0.014526 (0.063310) \| 0.090242 / 0.176557 (-0.086315) \| 0.131208 / 0.737135 (-0.605928) \| 0.091726 / 0.296338 (-0.204612) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292487 / 0.215209 (0.077278) \| 2.837044 / 2.077655 (0.759389) \| 1.553155 / 1.504120 (0.049035) \| 1.433645 / 1.541195 (-0.107550) \| 1.476702 / 1.468490 (0.008212) \| 0.561926 / 4.584777 (-4.022851) \| 0.954630 / 3.745712 (-2.791082) \| 2.752286 / 5.269862 (-2.517575) \| 1.782746 / 4.565676 (-2.782931) \| 0.062984 / 0.424275 (-0.361291) \| 0.005056 / 0.007607 (-0.002551) \| 0.341700 / 0.226044 (0.115656) \| 3.343726 / 2.268929 (1.074798) \| 1.953390 / 55.444624 (-53.491234) \| 1.616989 / 6.876477 (-5.259488) \| 1.785104 / 2.142072 (-0.356969) \| 0.643465 / 4.805227 (-4.161763) \| 0.115905 / 6.500664 (-6.384759) \| 0.041678 / 0.075469 (-0.033791) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.000237 / 1.841788 (-0.841550) \| 12.633517 / 8.074308 (4.559208) \| 10.553485 / 10.191392 (0.362092) \| 0.143188 / 0.680424 (-0.537236) \| 0.016020 / 0.534201 (-0.518181) \| 0.286739 / 0.579283 (-0.292544) \| 0.128488 / 0.434364 (-0.305876) \| 0.321932 / 0.540337 (-0.218405) \| 0.418635 / 1.386936 (-0.968301) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9510252f03fded02b8cc87ca6dfa3195d17594ba \"CML watermark\")\n" ]
2,337,476,383	6,958	My Private Dataset doesn't exist on the Hub or cannot be accessed	closed	2024-06-06T06:52:19	2024-07-01T11:27:46	2024-07-01T11:27:46	https://github.com/huggingface/datasets/issues/6958	null	wangguan1995	false	[ "I can load public dataset, but for my private dataset it fails", "https://huggingface.co/docs/datasets/upload_dataset", "I have checked the API HTTP link. Repository Not Found for url: https://huggingface.co/api/datasets/xxx/xxx.\r\n\r\n![image](https://github.com/huggingface/datasets/assets/39621324/4aceef59-0c65-4161-9665-676d25d73225)\r\n\r\nIt just works fine.", "It seems that everything is in a mass huh....\r\n\r\n![image](https://github.com/huggingface/datasets/assets/39621324/fb2fe12c-4f0a-4bf6-9656-63ba50347b10)\r\n", "https://huggingface.co/datasets/rajpurkar/squad/blob/main/squad.py fails again", "https://github.com/huggingface/datasets/blob/main/templates/new_dataset_script.py#L81 can not use this, too complex. I just need a def to load my file to a dict", "I am facing the same issue. Did you find a fix?", "You should authenticate to be able to access private or gated repos: https://huggingface.co/docs/hub/datasets-gated#access-gated-datasets-as-a-user" ]
2,335,559,400	6,957	Fix typos in docs	closed	2024-06-05T10:46:47	2024-06-05T13:01:07	2024-06-05T12:43:26	https://github.com/huggingface/datasets/pull/6957	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6957", "html_url": "https://github.com/huggingface/datasets/pull/6957", "diff_url": "https://github.com/huggingface/datasets/pull/6957.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6957.patch", "merged_at": "2024-06-05T12:43:26" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6957). 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) \| 0.005371 / 0.011353 (-0.005982) \| 0.003834 / 0.011008 (-0.007174) \| 0.063032 / 0.038508 (0.024524) \| 0.031623 / 0.023109 (0.008514) \| 0.250008 / 0.275898 (-0.025890) \| 0.273998 / 0.323480 (-0.049482) \| 0.004114 / 0.007986 (-0.003871) \| 0.002821 / 0.004328 (-0.001508) \| 0.049470 / 0.004250 (0.045220) \| 0.046586 / 0.037052 (0.009534) \| 0.276807 / 0.258489 (0.018318) \| 0.288607 / 0.293841 (-0.005234) \| 0.027427 / 0.128546 (-0.101119) \| 0.010634 / 0.075646 (-0.065012) \| 0.202451 / 0.419271 (-0.216821) \| 0.036346 / 0.043533 (-0.007187) \| 0.250426 / 0.255139 (-0.004713) \| 0.274104 / 0.283200 (-0.009096) \| 0.018461 / 0.141683 (-0.123222) \| 1.120326 / 1.452155 (-0.331829) \| 1.157635 / 1.492716 (-0.335081) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.102287 / 0.018006 (0.084281) \| 0.313145 / 0.000490 (0.312655) \| 0.000255 / 0.000200 (0.000055) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019494 / 0.037411 (-0.017917) \| 0.063252 / 0.014526 (0.048727) \| 0.075318 / 0.176557 (-0.101239) \| 0.122194 / 0.737135 (-0.614942) \| 0.076837 / 0.296338 (-0.219501) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284098 / 0.215209 (0.068889) \| 2.822301 / 2.077655 (0.744647) \| 1.490185 / 1.504120 (-0.013935) \| 1.366723 / 1.541195 (-0.174472) \| 1.398832 / 1.468490 (-0.069658) \| 0.563661 / 4.584777 (-4.021116) \| 2.385129 / 3.745712 (-1.360583) \| 2.689823 / 5.269862 (-2.580039) \| 1.731271 / 4.565676 (-2.834405) \| 0.063351 / 0.424275 (-0.360924) \| 0.004974 / 0.007607 (-0.002633) \| 0.332163 / 0.226044 (0.106119) \| 3.314906 / 2.268929 (1.045977) \| 1.811331 / 55.444624 (-53.633294) \| 1.513357 / 6.876477 (-5.363120) \| 1.718454 / 2.142072 (-0.423618) \| 0.639663 / 4.805227 (-4.165564) \| 0.120377 / 6.500664 (-6.380287) \| 0.043254 / 0.075469 (-0.032215) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.978534 / 1.841788 (-0.863253) \| 11.622313 / 8.074308 (3.548005) \| 9.608732 / 10.191392 (-0.582660) \| 0.131339 / 0.680424 (-0.549085) \| 0.015226 / 0.534201 (-0.518975) \| 0.287317 / 0.579283 (-0.291966) \| 0.266647 / 0.434364 (-0.167717) \| 0.324243 / 0.540337 (-0.216094) \| 0.442025 / 1.386936 (-0.944911) \|\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) \| 0.005673 / 0.011353 (-0.005680) \| 0.003722 / 0.011008 (-0.007286) \| 0.049483 / 0.038508 (0.010975) \| 0.033308 / 0.023109 (0.010199) \| 0.261912 / 0.275898 (-0.013986) \| 0.291151 / 0.323480 (-0.032329) \| 0.004389 / 0.007986 (-0.003596) \| 0.002762 / 0.004328 (-0.001567) \| 0.048970 / 0.004250 (0.044719) \| 0.041509 / 0.037052 (0.004457) \| 0.273288 / 0.258489 (0.014798) \| 0.308351 / 0.293841 (0.014510) \| 0.029958 / 0.128546 (-0.098589) \| 0.010500 / 0.075646 (-0.065146) \| 0.058253 / 0.419271 (-0.361019) \| 0.033820 / 0.043533 (-0.009713) \| 0.261089 / 0.255139 (0.005950) \| 0.282179 / 0.283200 (-0.001021) \| 0.018543 / 0.141683 (-0.123140) \| 1.121303 / 1.452155 (-0.330852) \| 1.166141 / 1.492716 (-0.326575) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099209 / 0.018006 (0.081203) \| 0.316920 / 0.000490 (0.316430) \| 0.000216 / 0.000200 (0.000016) \| 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) \| 0.023339 / 0.037411 (-0.014072) \| 0.077127 / 0.014526 (0.062602) \| 0.088160 / 0.176557 (-0.088396) \| 0.129449 / 0.737135 (-0.607686) \| 0.093159 / 0.296338 (-0.203180) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281262 / 0.215209 (0.066053) \| 2.797504 / 2.077655 (0.719850) \| 1.513354 / 1.504120 (0.009234) \| 1.383034 / 1.541195 (-0.158161) \| 1.395202 / 1.468490 (-0.073288) \| 0.563180 / 4.584777 (-4.021597) \| 0.979330 / 3.745712 (-2.766383) \| 2.674008 / 5.269862 (-2.595853) \| 1.762174 / 4.565676 (-2.803502) \| 0.062333 / 0.424275 (-0.361942) \| 0.004991 / 0.007607 (-0.002616) \| 0.336043 / 0.226044 (0.109999) \| 3.313500 / 2.268929 (1.044571) \| 1.848083 / 55.444624 (-53.596541) \| 1.554723 / 6.876477 (-5.321754) \| 1.743485 / 2.142072 (-0.398587) \| 0.657117 / 4.805227 (-4.148111) \| 0.115736 / 6.500664 (-6.384928) \| 0.040527 / 0.075469 (-0.034942) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005876 / 1.841788 (-0.835911) \| 12.525895 / 8.074308 (4.451587) \| 10.492961 / 10.191392 (0.301569) \| 0.143443 / 0.680424 (-0.536981) \| 0.016652 / 0.534201 (-0.517548) \| 0.288236 / 0.579283 (-0.291047) \| 0.131401 / 0.434364 (-0.302963) \| 0.322885 / 0.540337 (-0.217452) \| 0.416048 / 1.386936 (-0.970888) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6548e0e282aeeda7bfb18beafbc65ebecd780c63 \"CML watermark\")\n" ]
2,333,940,021	6,956	update docs on N-dim arrays	closed	2024-06-04T16:32:19	2024-06-04T16:46:34	2024-06-04T16:40:27	https://github.com/huggingface/datasets/pull/6956	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6956", "html_url": "https://github.com/huggingface/datasets/pull/6956", "diff_url": "https://github.com/huggingface/datasets/pull/6956.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6956.patch", "merged_at": "2024-06-04T16:40:27" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6956). 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) \| 0.005348 / 0.011353 (-0.006005) \| 0.003785 / 0.011008 (-0.007223) \| 0.061674 / 0.038508 (0.023166) \| 0.032127 / 0.023109 (0.009017) \| 0.247095 / 0.275898 (-0.028803) \| 0.276466 / 0.323480 (-0.047014) \| 0.004197 / 0.007986 (-0.003789) \| 0.002734 / 0.004328 (-0.001594) \| 0.049604 / 0.004250 (0.045354) \| 0.048553 / 0.037052 (0.011500) \| 0.253230 / 0.258489 (-0.005259) \| 0.286954 / 0.293841 (-0.006887) \| 0.028181 / 0.128546 (-0.100365) \| 0.010602 / 0.075646 (-0.065044) \| 0.200719 / 0.419271 (-0.218552) \| 0.037278 / 0.043533 (-0.006254) \| 0.251565 / 0.255139 (-0.003574) \| 0.269026 / 0.283200 (-0.014174) \| 0.017632 / 0.141683 (-0.124050) \| 1.136216 / 1.452155 (-0.315939) \| 1.181158 / 1.492716 (-0.311559) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004892 / 0.018006 (-0.013114) \| 0.312921 / 0.000490 (0.312431) \| 0.000247 / 0.000200 (0.000047) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019303 / 0.037411 (-0.018108) \| 0.062699 / 0.014526 (0.048174) \| 0.075227 / 0.176557 (-0.101329) \| 0.122919 / 0.737135 (-0.614217) \| 0.076506 / 0.296338 (-0.219833) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277299 / 0.215209 (0.062090) \| 2.754771 / 2.077655 (0.677116) \| 1.457164 / 1.504120 (-0.046956) \| 1.318878 / 1.541195 (-0.222317) \| 1.374245 / 1.468490 (-0.094245) \| 0.566253 / 4.584777 (-4.018524) \| 2.352589 / 3.745712 (-1.393123) \| 2.764263 / 5.269862 (-2.505599) \| 1.843141 / 4.565676 (-2.722535) \| 0.063996 / 0.424275 (-0.360279) \| 0.005045 / 0.007607 (-0.002562) \| 0.336703 / 0.226044 (0.110658) \| 3.342538 / 2.268929 (1.073609) \| 1.836664 / 55.444624 (-53.607960) \| 1.528901 / 6.876477 (-5.347576) \| 1.769562 / 2.142072 (-0.372511) \| 0.674192 / 4.805227 (-4.131035) \| 0.122421 / 6.500664 (-6.378243) \| 0.043714 / 0.075469 (-0.031756) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.989432 / 1.841788 (-0.852356) \| 12.178341 / 8.074308 (4.104033) \| 9.730838 / 10.191392 (-0.460554) \| 0.146751 / 0.680424 (-0.533673) \| 0.014720 / 0.534201 (-0.519481) \| 0.285821 / 0.579283 (-0.293462) \| 0.266474 / 0.434364 (-0.167889) \| 0.327886 / 0.540337 (-0.212451) \| 0.455672 / 1.386936 (-0.931264) \|\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) \| 0.005691 / 0.011353 (-0.005662) \| 0.004089 / 0.011008 (-0.006919) \| 0.049878 / 0.038508 (0.011370) \| 0.033578 / 0.023109 (0.010469) \| 0.268295 / 0.275898 (-0.007603) \| 0.288918 / 0.323480 (-0.034561) \| 0.005092 / 0.007986 (-0.002894) \| 0.002916 / 0.004328 (-0.001412) \| 0.049489 / 0.004250 (0.045239) \| 0.042495 / 0.037052 (0.005442) \| 0.276253 / 0.258489 (0.017764) \| 0.313321 / 0.293841 (0.019480) \| 0.029386 / 0.128546 (-0.099160) \| 0.010926 / 0.075646 (-0.064720) \| 0.071747 / 0.419271 (-0.347525) \| 0.033642 / 0.043533 (-0.009891) \| 0.264950 / 0.255139 (0.009811) \| 0.282962 / 0.283200 (-0.000238) \| 0.018878 / 0.141683 (-0.122805) \| 1.170685 / 1.452155 (-0.281470) \| 1.198321 / 1.492716 (-0.294396) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.100422 / 0.018006 (0.082415) \| 0.311750 / 0.000490 (0.311260) \| 0.000235 / 0.000200 (0.000035) \| 0.000063 / 0.000054 (0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023093 / 0.037411 (-0.014318) \| 0.076934 / 0.014526 (0.062408) \| 0.088959 / 0.176557 (-0.087598) \| 0.129511 / 0.737135 (-0.607624) \| 0.090151 / 0.296338 (-0.206187) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.301646 / 0.215209 (0.086437) \| 2.961780 / 2.077655 (0.884126) \| 1.656051 / 1.504120 (0.151931) \| 1.533154 / 1.541195 (-0.008041) \| 1.585152 / 1.468490 (0.116662) \| 0.582157 / 4.584777 (-4.002620) \| 0.954881 / 3.745712 (-2.790831) \| 2.813174 / 5.269862 (-2.456688) \| 1.842840 / 4.565676 (-2.722837) \| 0.065598 / 0.424275 (-0.358677) \| 0.005306 / 0.007607 (-0.002301) \| 0.359610 / 0.226044 (0.133565) \| 3.575320 / 2.268929 (1.306391) \| 2.015327 / 55.444624 (-53.429297) \| 1.734086 / 6.876477 (-5.142391) \| 1.919081 / 2.142072 (-0.222991) \| 0.671178 / 4.805227 (-4.134049) \| 0.120109 / 6.500664 (-6.380555) \| 0.042353 / 0.075469 (-0.033116) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.011726 / 1.841788 (-0.830062) \| 13.007806 / 8.074308 (4.933498) \| 10.632486 / 10.191392 (0.441094) \| 0.148535 / 0.680424 (-0.531889) \| 0.015988 / 0.534201 (-0.518213) \| 0.290023 / 0.579283 (-0.289260) \| 0.130685 / 0.434364 (-0.303679) \| 0.322912 / 0.540337 (-0.217425) \| 0.420596 / 1.386936 (-0.966340) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#336512dcba4fdb4c349d5ecb632b6ced80e038d5 \"CML watermark\")\n" ]
2,333,802,815	6,955	Fix small typo	closed	2024-06-04T15:19:02	2024-06-05T10:18:56	2024-06-04T15:20:55	https://github.com/huggingface/datasets/pull/6955	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6955", "html_url": "https://github.com/huggingface/datasets/pull/6955", "diff_url": "https://github.com/huggingface/datasets/pull/6955.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6955.patch", "merged_at": "2024-06-04T15:20:55" }	marcenacp	true	[ "<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) \| 0.005507 / 0.011353 (-0.005845) \| 0.003757 / 0.011008 (-0.007251) \| 0.063274 / 0.038508 (0.024766) \| 0.029720 / 0.023109 (0.006610) \| 0.247974 / 0.275898 (-0.027924) \| 0.272283 / 0.323480 (-0.051197) \| 0.004186 / 0.007986 (-0.003799) \| 0.002820 / 0.004328 (-0.001508) \| 0.049070 / 0.004250 (0.044820) \| 0.050026 / 0.037052 (0.012973) \| 0.256501 / 0.258489 (-0.001988) \| 0.297082 / 0.293841 (0.003241) \| 0.028549 / 0.128546 (-0.099997) \| 0.010361 / 0.075646 (-0.065285) \| 0.213202 / 0.419271 (-0.206070) \| 0.038117 / 0.043533 (-0.005416) \| 0.258878 / 0.255139 (0.003739) \| 0.282980 / 0.283200 (-0.000220) \| 0.018911 / 0.141683 (-0.122772) \| 1.118857 / 1.452155 (-0.333298) \| 1.157763 / 1.492716 (-0.334953) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004499 / 0.018006 (-0.013507) \| 0.310445 / 0.000490 (0.309956) \| 0.000218 / 0.000200 (0.000018) \| 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) \| 0.019275 / 0.037411 (-0.018137) \| 0.063257 / 0.014526 (0.048731) \| 0.075833 / 0.176557 (-0.100724) \| 0.122323 / 0.737135 (-0.614812) \| 0.079046 / 0.296338 (-0.217292) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292811 / 0.215209 (0.077602) \| 2.903501 / 2.077655 (0.825846) \| 1.592434 / 1.504120 (0.088314) \| 1.450833 / 1.541195 (-0.090362) \| 1.481285 / 1.468490 (0.012795) \| 0.570150 / 4.584777 (-4.014627) \| 2.388618 / 3.745712 (-1.357094) \| 2.699322 / 5.269862 (-2.570540) \| 1.781405 / 4.565676 (-2.784272) \| 0.063451 / 0.424275 (-0.360824) \| 0.004979 / 0.007607 (-0.002628) \| 0.353346 / 0.226044 (0.127302) \| 3.541217 / 2.268929 (1.272289) \| 1.972335 / 55.444624 (-53.472289) \| 1.634780 / 6.876477 (-5.241697) \| 1.815944 / 2.142072 (-0.326128) \| 0.651559 / 4.805227 (-4.153669) \| 0.118398 / 6.500664 (-6.382266) \| 0.041962 / 0.075469 (-0.033507) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971435 / 1.841788 (-0.870352) \| 11.843740 / 8.074308 (3.769431) \| 9.716333 / 10.191392 (-0.475059) \| 0.145923 / 0.680424 (-0.534501) \| 0.015073 / 0.534201 (-0.519128) \| 0.293307 / 0.579283 (-0.285976) \| 0.265505 / 0.434364 (-0.168859) \| 0.327578 / 0.540337 (-0.212760) \| 0.436409 / 1.386936 (-0.950527) \|\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) \| 0.005647 / 0.011353 (-0.005706) \| 0.003669 / 0.011008 (-0.007339) \| 0.050234 / 0.038508 (0.011726) \| 0.033033 / 0.023109 (0.009924) \| 0.269303 / 0.275898 (-0.006595) \| 0.282472 / 0.323480 (-0.041008) \| 0.004283 / 0.007986 (-0.003703) \| 0.002821 / 0.004328 (-0.001507) \| 0.050887 / 0.004250 (0.046637) \| 0.041618 / 0.037052 (0.004565) \| 0.277628 / 0.258489 (0.019139) \| 0.310539 / 0.293841 (0.016698) \| 0.030036 / 0.128546 (-0.098511) \| 0.010401 / 0.075646 (-0.065245) \| 0.058845 / 0.419271 (-0.360427) \| 0.033676 / 0.043533 (-0.009857) \| 0.261148 / 0.255139 (0.006009) \| 0.295232 / 0.283200 (0.012032) \| 0.018603 / 0.141683 (-0.123080) \| 1.132182 / 1.452155 (-0.319972) \| 1.173763 / 1.492716 (-0.318953) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.100594 / 0.018006 (0.082588) \| 0.308101 / 0.000490 (0.307611) \| 0.000217 / 0.000200 (0.000017) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023040 / 0.037411 (-0.014371) \| 0.080676 / 0.014526 (0.066150) \| 0.094687 / 0.176557 (-0.081870) \| 0.129780 / 0.737135 (-0.607356) \| 0.092241 / 0.296338 (-0.204097) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294799 / 0.215209 (0.079590) \| 2.957570 / 2.077655 (0.879915) \| 1.576795 / 1.504120 (0.072675) \| 1.446869 / 1.541195 (-0.094326) \| 1.463133 / 1.468490 (-0.005357) \| 0.568511 / 4.584777 (-4.016266) \| 1.011502 / 3.745712 (-2.734211) \| 2.759571 / 5.269862 (-2.510291) \| 1.771738 / 4.565676 (-2.793939) \| 0.064104 / 0.424275 (-0.360171) \| 0.005160 / 0.007607 (-0.002448) \| 0.347554 / 0.226044 (0.121510) \| 3.463905 / 2.268929 (1.194976) \| 1.931843 / 55.444624 (-53.512781) \| 1.622765 / 6.876477 (-5.253712) \| 1.809146 / 2.142072 (-0.332926) \| 0.653388 / 4.805227 (-4.151839) \| 0.122703 / 6.500664 (-6.377961) \| 0.041680 / 0.075469 (-0.033790) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.000428 / 1.841788 (-0.841359) \| 12.503003 / 8.074308 (4.428695) \| 10.434802 / 10.191392 (0.243410) \| 0.144684 / 0.680424 (-0.535740) \| 0.015988 / 0.534201 (-0.518213) \| 0.287179 / 0.579283 (-0.292104) \| 0.124811 / 0.434364 (-0.309553) \| 0.327855 / 0.540337 (-0.212482) \| 0.425144 / 1.386936 (-0.961792) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7170067f819222153fcd45682db61279bdfe673 \"CML watermark\")\n" ]
2,333,530,558	6,954	Remove default `trust_remote_code=True`	closed	2024-06-04T13:22:56	2024-06-17T16:32:24	2024-06-07T12:20:29	https://github.com/huggingface/datasets/pull/6954	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6954", "html_url": "https://github.com/huggingface/datasets/pull/6954", "diff_url": "https://github.com/huggingface/datasets/pull/6954.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6954.patch", "merged_at": "2024-06-07T12:20:29" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6954). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "yay! 🎉 ", "<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) \| 0.004881 / 0.011353 (-0.006472) \| 0.003246 / 0.011008 (-0.007762) \| 0.062496 / 0.038508 (0.023988) \| 0.030760 / 0.023109 (0.007651) \| 0.241500 / 0.275898 (-0.034398) \| 0.272073 / 0.323480 (-0.051407) \| 0.004123 / 0.007986 (-0.003863) \| 0.002796 / 0.004328 (-0.001533) \| 0.049015 / 0.004250 (0.044764) \| 0.047095 / 0.037052 (0.010043) \| 0.257002 / 0.258489 (-0.001487) \| 0.287602 / 0.293841 (-0.006239) \| 0.027281 / 0.128546 (-0.101265) \| 0.010132 / 0.075646 (-0.065514) \| 0.203699 / 0.419271 (-0.215572) \| 0.036553 / 0.043533 (-0.006980) \| 0.246221 / 0.255139 (-0.008918) \| 0.268137 / 0.283200 (-0.015062) \| 0.017260 / 0.141683 (-0.124423) \| 1.100677 / 1.452155 (-0.351478) \| 1.148367 / 1.492716 (-0.344349) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.102519 / 0.018006 (0.084513) \| 0.301929 / 0.000490 (0.301439) \| 0.000223 / 0.000200 (0.000023) \| 0.000046 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018590 / 0.037411 (-0.018821) \| 0.061615 / 0.014526 (0.047089) \| 0.074579 / 0.176557 (-0.101978) \| 0.121415 / 0.737135 (-0.615720) \| 0.075696 / 0.296338 (-0.220642) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283842 / 0.215209 (0.068633) \| 2.788321 / 2.077655 (0.710666) \| 1.481376 / 1.504120 (-0.022743) \| 1.356064 / 1.541195 (-0.185131) \| 1.380592 / 1.468490 (-0.087898) \| 0.575577 / 4.584777 (-4.009199) \| 2.471858 / 3.745712 (-1.273854) \| 2.760769 / 5.269862 (-2.509093) \| 1.808638 / 4.565676 (-2.757038) \| 0.064930 / 0.424275 (-0.359345) \| 0.005056 / 0.007607 (-0.002551) \| 0.337794 / 0.226044 (0.111750) \| 3.359444 / 2.268929 (1.090515) \| 1.829540 / 55.444624 (-53.615084) \| 1.518660 / 6.876477 (-5.357817) \| 1.671612 / 2.142072 (-0.470460) \| 0.664286 / 4.805227 (-4.140941) \| 0.119593 / 6.500664 (-6.381071) \| 0.042519 / 0.075469 (-0.032950) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.993152 / 1.841788 (-0.848636) \| 11.733054 / 8.074308 (3.658746) \| 9.746734 / 10.191392 (-0.444658) \| 0.143026 / 0.680424 (-0.537398) \| 0.014900 / 0.534201 (-0.519301) \| 0.292243 / 0.579283 (-0.287040) \| 0.261301 / 0.434364 (-0.173063) \| 0.330838 / 0.540337 (-0.209500) \| 0.523719 / 1.386936 (-0.863217) \|\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) \| 0.005707 / 0.011353 (-0.005646) \| 0.003523 / 0.011008 (-0.007485) \| 0.052265 / 0.038508 (0.013757) \| 0.034296 / 0.023109 (0.011187) \| 0.266589 / 0.275898 (-0.009309) \| 0.288441 / 0.323480 (-0.035039) \| 0.004507 / 0.007986 (-0.003478) \| 0.002745 / 0.004328 (-0.001583) \| 0.049417 / 0.004250 (0.045167) \| 0.042679 / 0.037052 (0.005627) \| 0.278518 / 0.258489 (0.020029) \| 0.328751 / 0.293841 (0.034911) \| 0.029530 / 0.128546 (-0.099016) \| 0.010373 / 0.075646 (-0.065274) \| 0.058207 / 0.419271 (-0.361064) \| 0.033434 / 0.043533 (-0.010099) \| 0.267902 / 0.255139 (0.012763) \| 0.288192 / 0.283200 (0.004993) \| 0.018866 / 0.141683 (-0.122817) \| 1.132734 / 1.452155 (-0.319421) \| 1.172879 / 1.492716 (-0.319837) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097787 / 0.018006 (0.079780) \| 0.305509 / 0.000490 (0.305019) \| 0.000268 / 0.000200 (0.000068) \| 0.000060 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023230 / 0.037411 (-0.014181) \| 0.076637 / 0.014526 (0.062111) \| 0.088386 / 0.176557 (-0.088171) \| 0.131079 / 0.737135 (-0.606057) \| 0.091142 / 0.296338 (-0.205197) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295586 / 0.215209 (0.080377) \| 2.872090 / 2.077655 (0.794435) \| 1.538152 / 1.504120 (0.034032) \| 1.405695 / 1.541195 (-0.135500) \| 1.421058 / 1.468490 (-0.047432) \| 0.561179 / 4.584777 (-4.023598) \| 0.943954 / 3.745712 (-2.801758) \| 2.684381 / 5.269862 (-2.585481) \| 1.757457 / 4.565676 (-2.808220) \| 0.062903 / 0.424275 (-0.361372) \| 0.004998 / 0.007607 (-0.002610) \| 0.370290 / 0.226044 (0.144245) \| 3.374988 / 2.268929 (1.106059) \| 1.899282 / 55.444624 (-53.545342) \| 1.598787 / 6.876477 (-5.277690) \| 1.735371 / 2.142072 (-0.406702) \| 0.647367 / 4.805227 (-4.157860) \| 0.116975 / 6.500664 (-6.383689) \| 0.040811 / 0.075469 (-0.034658) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.996380 / 1.841788 (-0.845408) \| 12.225657 / 8.074308 (4.151349) \| 10.291221 / 10.191392 (0.099829) \| 0.142791 / 0.680424 (-0.537633) \| 0.016087 / 0.534201 (-0.518114) \| 0.299978 / 0.579283 (-0.279305) \| 0.149444 / 0.434364 (-0.284920) \| 0.321354 / 0.540337 (-0.218984) \| 0.414492 / 1.386936 (-0.972444) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a2dc287cbef5311cf1a32ad4e3685f4052db227c \"CML watermark\")\n", "@lhoestq Thanks for the PR, Is there a way to detect if `trust_remote_code=True` will be required for loading the dataset, without loading it? It would be great if you could please point me to the relevant documentation.", "You can check the presence of a python loading script in the repository.\r\n\r\nIf there is a .py file named after the repository name, then it requires trust_remote_code.", "Thanks @lhoestq for the reference." ]
2,333,366,120	6,953	Remove canonical datasets from docs	closed	2024-06-04T12:09:03	2024-07-01T11:31:25	2024-07-01T11:31:25	https://github.com/huggingface/datasets/issues/6953	null	albertvillanova	false	[ "Canonical datasets are no longer mentioned in the docs." ]
2,333,320,411	6,952	Move info_utils errors to exceptions module	closed	2024-06-04T11:48:32	2024-06-10T14:09:59	2024-06-10T14:03:55	https://github.com/huggingface/datasets/pull/6952	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6952", "html_url": "https://github.com/huggingface/datasets/pull/6952", "diff_url": "https://github.com/huggingface/datasets/pull/6952.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6952.patch", "merged_at": "2024-06-10T14:03:55" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6952). 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) \| 0.005232 / 0.011353 (-0.006121) \| 0.003744 / 0.011008 (-0.007264) \| 0.064089 / 0.038508 (0.025581) \| 0.032409 / 0.023109 (0.009300) \| 0.255886 / 0.275898 (-0.020013) \| 0.276033 / 0.323480 (-0.047447) \| 0.004165 / 0.007986 (-0.003821) \| 0.002741 / 0.004328 (-0.001588) \| 0.052145 / 0.004250 (0.047894) \| 0.043863 / 0.037052 (0.006811) \| 0.258844 / 0.258489 (0.000355) \| 0.290108 / 0.293841 (-0.003733) \| 0.027390 / 0.128546 (-0.101156) \| 0.010543 / 0.075646 (-0.065103) \| 0.206936 / 0.419271 (-0.212335) \| 0.036778 / 0.043533 (-0.006755) \| 0.254331 / 0.255139 (-0.000808) \| 0.279037 / 0.283200 (-0.004163) \| 0.018564 / 0.141683 (-0.123119) \| 1.112765 / 1.452155 (-0.339390) \| 1.160099 / 1.492716 (-0.332617) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092148 / 0.018006 (0.074142) \| 0.297156 / 0.000490 (0.296667) \| 0.000211 / 0.000200 (0.000011) \| 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) \| 0.018797 / 0.037411 (-0.018615) \| 0.062992 / 0.014526 (0.048466) \| 0.076361 / 0.176557 (-0.100195) \| 0.121168 / 0.737135 (-0.615968) \| 0.075845 / 0.296338 (-0.220494) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293842 / 0.215209 (0.078633) \| 2.880720 / 2.077655 (0.803065) \| 1.477779 / 1.504120 (-0.026341) \| 1.345136 / 1.541195 (-0.196059) \| 1.352153 / 1.468490 (-0.116337) \| 0.574722 / 4.584777 (-4.010055) \| 2.373925 / 3.745712 (-1.371787) \| 2.750704 / 5.269862 (-2.519157) \| 1.725979 / 4.565676 (-2.839697) \| 0.063006 / 0.424275 (-0.361269) \| 0.005019 / 0.007607 (-0.002588) \| 0.341228 / 0.226044 (0.115184) \| 3.352576 / 2.268929 (1.083647) \| 1.821363 / 55.444624 (-53.623261) \| 1.529441 / 6.876477 (-5.347036) \| 1.543401 / 2.142072 (-0.598671) \| 0.634282 / 4.805227 (-4.170945) \| 0.115565 / 6.500664 (-6.385099) \| 0.042514 / 0.075469 (-0.032956) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.987532 / 1.841788 (-0.854255) \| 11.483853 / 8.074308 (3.409545) \| 9.565657 / 10.191392 (-0.625735) \| 0.141247 / 0.680424 (-0.539176) \| 0.015026 / 0.534201 (-0.519175) \| 0.299905 / 0.579283 (-0.279378) \| 0.267667 / 0.434364 (-0.166697) \| 0.320661 / 0.540337 (-0.219676) \| 0.427368 / 1.386936 (-0.959568) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005448 / 0.011353 (-0.005905) \| 0.003726 / 0.011008 (-0.007283) \| 0.049776 / 0.038508 (0.011268) \| 0.032733 / 0.023109 (0.009624) \| 0.261387 / 0.275898 (-0.014511) \| 0.280087 / 0.323480 (-0.043393) \| 0.004351 / 0.007986 (-0.003634) \| 0.002842 / 0.004328 (-0.001487) \| 0.049440 / 0.004250 (0.045190) \| 0.039585 / 0.037052 (0.002533) \| 0.266331 / 0.258489 (0.007842) \| 0.299643 / 0.293841 (0.005802) \| 0.029649 / 0.128546 (-0.098897) \| 0.010381 / 0.075646 (-0.065265) \| 0.058596 / 0.419271 (-0.360676) \| 0.033271 / 0.043533 (-0.010262) \| 0.251070 / 0.255139 (-0.004069) \| 0.272850 / 0.283200 (-0.010349) \| 0.016728 / 0.141683 (-0.124955) \| 1.146952 / 1.452155 (-0.305202) \| 1.182602 / 1.492716 (-0.310114) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091673 / 0.018006 (0.073667) \| 0.297228 / 0.000490 (0.296738) \| 0.000197 / 0.000200 (-0.000003) \| 0.000051 / 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) \| 0.023174 / 0.037411 (-0.014237) \| 0.078866 / 0.014526 (0.064341) \| 0.088436 / 0.176557 (-0.088121) \| 0.129650 / 0.737135 (-0.607485) \| 0.091100 / 0.296338 (-0.205238) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293882 / 0.215209 (0.078673) \| 2.882667 / 2.077655 (0.805012) \| 1.562949 / 1.504120 (0.058829) \| 1.435104 / 1.541195 (-0.106090) \| 1.450815 / 1.468490 (-0.017675) \| 0.584090 / 4.584777 (-4.000687) \| 0.984176 / 3.745712 (-2.761536) \| 2.668740 / 5.269862 (-2.601121) \| 1.766993 / 4.565676 (-2.798683) \| 0.064710 / 0.424275 (-0.359565) \| 0.005329 / 0.007607 (-0.002278) \| 0.346008 / 0.226044 (0.119964) \| 3.414576 / 2.268929 (1.145647) \| 1.911388 / 55.444624 (-53.533236) \| 1.660357 / 6.876477 (-5.216120) \| 1.818628 / 2.142072 (-0.323444) \| 0.659585 / 4.805227 (-4.145643) \| 0.116980 / 6.500664 (-6.383684) \| 0.041364 / 0.075469 (-0.034105) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005659 / 1.841788 (-0.836129) \| 12.023761 / 8.074308 (3.949453) \| 10.351086 / 10.191392 (0.159694) \| 0.143261 / 0.680424 (-0.537162) \| 0.016143 / 0.534201 (-0.518058) \| 0.287793 / 0.579283 (-0.291490) \| 0.123698 / 0.434364 (-0.310666) \| 0.325241 / 0.540337 (-0.215097) \| 0.418772 / 1.386936 (-0.968164) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#37a603679f451826cfafd8aae00738b01dcb9d58 \"CML watermark\")\n" ]
2,333,231,042	6,951	load_dataset() should load all subsets, if no specific subset is specified	closed	2024-06-04T11:02:33	2024-11-26T08:32:18	2024-07-01T11:33:10	https://github.com/huggingface/datasets/issues/6951	null	windmaple	false	[ "@xianbaoqian ", "Feel free to open a PR in `m-a-p/COIG-CQIA` to define a default subset. Currently there is no default.\r\n\r\nYou can find some documentation at https://huggingface.co/docs/hub/datasets-manual-configuration#multiple-configurations", "@lhoestq \r\n\r\nWhilst having a default subset readily available (e.g. `all`) by the dataset author is an ideal solution, it is not always the reality.\r\n\r\nWithout the ability to fork the dataset, this can be problematic.\r\n\r\nAs far as I know, it is not possible at all to specify multiple subsets in a generalized programmatic way without hard coding subset names for a specific dataset.\r\n\r\nEven the ability to fetch subset names and loop over them would be sufficient.", "Please note that each subset can have different feature columns, thus making it impossible to load them all into a unique Dataset instance.\r\n\r\nThat is why subsets were created: to support different but related datasets to coexist in a single dataset repository.\r\n\r\nIf you would like to programmatically get the list of subset names, you can use `datasets.get_dataset_config_names`: https://huggingface.co/docs/datasets/v2.20.0/en/load_hub#configurations", "found a better method in another link that can not only obtain the subset but also get the corresponding split\r\nhttps://huggingface.co/docs/dataset-viewer/splits" ]
2,333,005,974	6,950	`Dataset.with_format` behaves inconsistently with documentation	closed	2024-06-04T09:18:32	2024-06-25T08:05:49	2024-06-25T08:05:49	https://github.com/huggingface/datasets/issues/6950	null	iansheng	false	[ "Hi ! It seems the documentation was outdated in this paragraph\r\n\r\nI fixed it here: https://github.com/huggingface/datasets/pull/6956", "Fixed." ]
2,332,336,573	6,949	load_dataset error	closed	2024-06-04T01:24:45	2024-07-01T11:33:46	2024-07-01T11:33:46	https://github.com/huggingface/datasets/issues/6949	null	frederichen01	false	[ "Hi, @lion-ops.\r\n\r\nIn our Continuous Integration we have many tests on loading JSON files and all of them work properly.\r\n\r\nCould you please share your \"train.json\" file, so that we can try to reproduce the issue you have? ", "> Hi, @lion-ops.\r\n> \r\n> In our Continuous Integration we have many tests on loading JSON files and all of them work properly.\r\n> \r\n> Could you please share your \"train.json\" file, so that we can try to reproduce the issue you have?\r\n\r\nThank you for your reply. I can load it normally in another server. Is it possible that the disk of my server is a network disk in the LAN, so it will be downloaded from the LAN and get stuck?" ]
2,331,758,300	6,948	to_tf_dataset: Visible devices cannot be modified after being initialized	open	2024-06-03T18:10:57	2024-06-03T18:10:57	null	https://github.com/huggingface/datasets/issues/6948	null	logasja	false	[]
2,331,114,055	6,947	FileNotFoundError：error when loading C4 dataset	closed	2024-06-03T13:06:33	2024-06-25T06:21:28	2024-06-25T06:21:28	https://github.com/huggingface/datasets/issues/6947	null	W-215	false	[ "same problem here", "Hello,\r\n\r\nAre you sure you are really using datasets version 2.19.2? We just made the patch release yesterday specifically to fix this issue:\r\n- #6925\r\n\r\nI can't reproduce the error:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset('allenai/c4', data_files={'validation': 'en/c4-validation.00003-of-00008.json.gz'}, split='validation')\r\nDownloading readme: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 41.1k/41.1k [00:00<00:00, 596kB/s]\r\nDownloading data: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 40.7M/40.7M [00:04<00:00, 8.50MB/s]\r\nGenerating validation split: 45576 examples [00:01, 44956.75 examples/s]\r\n\r\nIn [3]: ds\r\nOut[3]: \r\nDataset({\r\n features: ['text', 'timestamp', 'url'],\r\n num_rows: 45576\r\n})\r\n```", "> Hello,\r\n> \r\n> Are you sure you are really using datasets version 2.19.2? We just made the patch release yesterday specifically to fix this issue:\r\n> \r\n> * [Fix NonMatchingSplitsSizesError/ExpectedMoreSplits when passing data_dir/data_files in no-code Hub datasets #6925](https://github.com/huggingface/datasets/pull/6925)\r\n> \r\n> I can't reproduce the error:\r\n> \r\n> ```python\r\n> In [1]: from datasets import load_dataset\r\n> \r\n> In [2]: ds = load_dataset('allenai/c4', data_files={'validation': 'en/c4-validation.00003-of-00008.json.gz'}, split='validation')\r\n> Downloading readme: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 41.1k/41.1k [00:00<00:00, 596kB/s]\r\n> Downloading data: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 40.7M/40.7M [00:04<00:00, 8.50MB/s]\r\n> Generating validation split: 45576 examples [00:01, 44956.75 examples/s]\r\n> \r\n> In [3]: ds\r\n> Out[3]: \r\n> Dataset({\r\n> features: ['text', 'timestamp', 'url'],\r\n> num_rows: 45576\r\n> })\r\n> ```\r\nThank you for your reply,ExpectedMoreSplits was encountered in datasets version 2.12.2. After I updated the version, that is, datasets version 2.19.2, I encountered the FileNotFoundError problem mentioned above.", "That might be due to a corrupted cache.\r\n\r\nPlease, retry loading the dataset passing: `download_mode=\"force_redownload\"`\r\n```python\r\nds = load_dataset('allenai/c4', data_files={'validation': 'en/c4-validation.00003-of-00008.json.gz'}, split='validation', download_mode=\"force_redownload\")\r\n```\r\n\r\nIt the above command does not fix the issue, then you will need to fix the cache manually, by removing the corresponding directory inside `~/.cache/huggingface/`.\r\n", "> That might be due to a corrupted cache.\r\n> \r\n> Please, retry loading the dataset passing: `download_mode=\"force_redownload\"`\r\n> \r\n> ```python\r\n> ds = load_dataset('allenai/c4', data_files={'validation': 'en/c4-validation.00003-of-00008.json.gz'}, split='validation', download_mode=\"force_redownload\")\r\n> ```\r\n> \r\n> It the above command does not fix the issue, then you will need to fix the cache manually, by removing the corresponding directory inside `~/.cache/huggingface/`.\r\n\r\nThe two methods you mentioned above can not solve this problem, but the command line interface shows Downloading readme: 41.1kB [00:00, 281kB/s], and then FileNotFoundError appears. It is worth noting that I have no problem loading other datasets with the initial method, such as wikitext datasets", "> The two methods you mentioned above can not solve this problem, but the command line interface shows Downloading readme: 41.1kB [00:00, 281kB/s], and then FileNotFoundError appears.\r\n\r\nSame issue encountered.\r\n", "I really think the issue is caused by a corrupted cache, between versions 2.12.0 (there does not exist 2.12.2 version) and 2.19.2.\r\n\r\nAre you sure you removed all the corresponding corrupted directories within the cache?\r\n\r\nYou can easily check if the issue is caused by a corrupted cache by removing the entire cache:\r\n```shell\r\nmv ~/.cache/huggingface ~/.cache/huggingface.bak\r\n```\r\nand then reloading the dataset:\r\n```python\r\nds = load_dataset('allenai/c4', data_files={'validation': 'en/c4-validation.00003-of-00008.json.gz'}, split='validation', download_mode=\"force_redownload\")\r\n```", "@albertvillanova Thanks for the reply. I tried removing the entire cache and reloading the dataset as you suggest. However, the same issue still exists. \r\n\r\nAs a test, I switch to a new platform, which (is a Windows system and) hasn't downloaded huggingface dataset before, and the dataset is loaded successfully. So I think \"a corrupted cache\" explanation makes sense. I wonder, besides `~/.cache/huggingface`, is there any other directory that may save the cache thing?\r\n\r\nAs a side note, I am using `datasets==2.20.0` and proxy `export HF_ENDPOINT=https://hf-mirror.com`.", "Ho @ZhangGe6,\r\n\r\nAs far as I know, that directory is the only one where the cache is saved, unless you configured another one. You can check it:\r\n```python\r\nimport datasets.config\r\n\r\nprint(datasets.config.HF_CACHE_HOME)\r\n# ~/.cache/huggingface\r\n\r\nprint(datasets.config.HF_DATASETS_CACHE)\r\n# ~/.cache/huggingface/datasets\r\n\r\nprint(datasets.config.HF_MODULES_CACHE)\r\n# ~/.cache/huggingface/modules\r\n\r\nprint(datasets.config.DOWNLOADED_DATASETS_PATH)\r\n# ~/.cache/huggingface/datasets/downloads\r\n\r\nprint(datasets.config.EXTRACTED_DATASETS_PATH)\r\n# ~/.cache/huggingface/datasets/downloads/extracted\r\n```\r\n\r\nAdditionally, `datasets` uses `huggingface_hub`, but its cache directory should also be inside `~/.cache/huggingface`, unless you configured another one. You can check it:\r\n```python\r\nimport huggingface_hub.constants\r\n\r\nprint(huggingface_hub.constants.HF_HOME)\r\n# ~/.cache/huggingface\r\n\r\nprint(huggingface_hub.constants.HF_HUB_CACHE)\r\n# ~/.cache/huggingface/hub\r\n```", "@albertvillanova I checked the directories you listed, and find that they are the same as the ones you provided. I am going to find more clues and will update what I find here.", "I've had a similar problem, and for some reason decreasing the number of workers in the dataloader solved it", "Same issue.\r\n", "Hi folks. Finally, I find it is a network issue that causes huggingface hub unreachable (in China).\r\n\r\nTo run the following script \r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('allenai/c4', data_files={'validation': 'en/c4-validation.00003-of-00008.json.gz'}, split='validation', download_mode=\"force_redownload\")\r\n```\r\nWithout setting `export HF_ENDPOINT=https://hf-mirror.com`, I get the following error log\r\n```bash\r\nTraceback (most recent call last):\r\n File \".\\demo.py\", line 8, in <module>\r\n ds = load_dataset('allenai/c4', data_files={'validation': 'en/c4-validation.00003-of-00008.json.gz'}, split='validation', download_mode=\"force_redownload\")\r\n File \"D:\\SoftwareInstall\\Python\\lib\\site-packages\\datasets\\load.py\", line 2594, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"D:\\SoftwareInstall\\Python\\lib\\site-packages\\datasets\\load.py\", line 2266, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n File \"D:\\SoftwareInstall\\Python\\lib\\site-packages\\datasets\\load.py\", line 1914, in dataset_module_factory\r\n raise e1 from None\r\n File \"D:\\SoftwareInstall\\Python\\lib\\site-packages\\datasets\\load.py\", line 1845, in dataset_module_factory\r\n raise ConnectionError(f\"Couldn't reach '{path}' on the Hub ({e.__class__.__name__})\") from e\r\nConnectionError: Couldn't reach 'allenai/c4' on the Hub (ConnectionError)\r\n```\r\nAfter setting `export HF_ENDPOINT=https://hf-mirror.com`, I get the following error, which is exactly the same as what we are debugging in this issue\r\n```bash\r\nDownloading readme: 41.1kB [00:00, 41.1MB/s]\r\nTraceback (most recent call last):\r\n File \".\\demo.py\", line 8, in <module>\r\n ds = load_dataset('allenai/c4', data_files={'validation': 'en/c4-validation.00003-of-00008.json.gz'}, split='validation', download_mode=\"force_redownload\")\r\n File \"D:\\SoftwareInstall\\Python\\lib\\site-packages\\datasets\\load.py\", line 2594, in loa builder_instance = load_dataset_builder(\r\n File \"D:\\SoftwareInstall\\Python\\lib\\site-packages\\datasets\\load.py\", line 2266, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n raise FileNotFoundError(\r\nFileNotFoundError: Couldn't find a dataset script at C:\\Users\\ZhangGe\\Desktop\\allenai\\c4\\c4.py or any data file in the same directory. Couldn't find 'allenai/c4' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/allenai/c4@1588ec454eed extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.geoparquet', '.gpq', '.arrow', '.txt', '.tar', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', \r\n'.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns',pm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', \r\n'.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip']\r\n```\r\n\r\nUsing a proxy software that avoids the internet access restrictions imposed by China, I can download the dataset using the same script\r\n```bash\r\nDownloading readme: 100%\|███████████████████████████████████████████\| 41.1k/41.1k [00:00<00:00, 312kB/s] \r\nDownloading data: 100%\|████████████████████████████████████████████\| 40.7M/40.7M [00:19<00:00, 2.07MB/s] \r\nGenerating validation split: 45576 examples [00:00, 54883.48 examples/s]\r\n```\r\nSo `allenai/c4` is still unreachable even after setting `export HF_ENDPOINT=https://hf-mirror.com`.", "I have created an issue to inform the maintainers of `hf-mirror`：https://github.com/padeoe/hf-mirror-site/issues/30", "Thanks for the investigation: so finally it is an issue with the specific endpoint you are using.\r\n\r\nYou properly opened an issue in their repo, so they can fix it.\r\n\r\nI am closing this issue here." ]
2,330,276,848	6,946	Re-enable import sorting disabled by flake8:noqa directive when using ruff linter	closed	2024-06-03T06:24:47	2024-06-04T10:00:08	2024-06-04T09:54:23	https://github.com/huggingface/datasets/pull/6946	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6946", "html_url": "https://github.com/huggingface/datasets/pull/6946", "diff_url": "https://github.com/huggingface/datasets/pull/6946.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6946.patch", "merged_at": "2024-06-04T09:54:23" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6946). 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) \| 0.004847 / 0.011353 (-0.006506) \| 0.003199 / 0.011008 (-0.007810) \| 0.060677 / 0.038508 (0.022169) \| 0.030544 / 0.023109 (0.007435) \| 0.240870 / 0.275898 (-0.035028) \| 0.261320 / 0.323480 (-0.062160) \| 0.002816 / 0.007986 (-0.005170) \| 0.002483 / 0.004328 (-0.001845) \| 0.048527 / 0.004250 (0.044277) \| 0.045496 / 0.037052 (0.008444) \| 0.251296 / 0.258489 (-0.007193) \| 0.285746 / 0.293841 (-0.008095) \| 0.025076 / 0.128546 (-0.103470) \| 0.009417 / 0.075646 (-0.066229) \| 0.191361 / 0.419271 (-0.227911) \| 0.033778 / 0.043533 (-0.009755) \| 0.235581 / 0.255139 (-0.019558) \| 0.261069 / 0.283200 (-0.022131) \| 0.018255 / 0.141683 (-0.123428) \| 1.098437 / 1.452155 (-0.353718) \| 1.127124 / 1.492716 (-0.365592) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004479 / 0.018006 (-0.013527) \| 0.283706 / 0.000490 (0.283216) \| 0.000214 / 0.000200 (0.000014) \| 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) \| 0.018364 / 0.037411 (-0.019048) \| 0.058398 / 0.014526 (0.043872) \| 0.073056 / 0.176557 (-0.103501) \| 0.117147 / 0.737135 (-0.619989) \| 0.073683 / 0.296338 (-0.222656) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.265121 / 0.215209 (0.049912) \| 2.636981 / 2.077655 (0.559327) \| 1.380192 / 1.504120 (-0.123928) \| 1.270779 / 1.541195 (-0.270416) \| 1.295729 / 1.468490 (-0.172762) \| 0.523768 / 4.584777 (-4.061009) \| 2.295720 / 3.745712 (-1.449992) \| 2.519211 / 5.269862 (-2.750650) \| 1.618712 / 4.565676 (-2.946965) \| 0.058321 / 0.424275 (-0.365954) \| 0.004492 / 0.007607 (-0.003115) \| 0.316101 / 0.226044 (0.090057) \| 3.169913 / 2.268929 (0.900984) \| 1.793412 / 55.444624 (-53.651213) \| 1.473784 / 6.876477 (-5.402693) \| 1.565325 / 2.142072 (-0.576748) \| 0.592734 / 4.805227 (-4.212493) \| 0.109333 / 6.500664 (-6.391331) \| 0.039063 / 0.075469 (-0.036406) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.935504 / 1.841788 (-0.906284) \| 10.865520 / 8.074308 (2.791212) \| 9.219337 / 10.191392 (-0.972055) \| 0.135284 / 0.680424 (-0.545140) \| 0.013664 / 0.534201 (-0.520537) \| 0.271601 / 0.579283 (-0.307682) \| 0.260456 / 0.434364 (-0.173908) \| 0.302931 / 0.540337 (-0.237406) \| 0.414643 / 1.386936 (-0.972293) \|\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) \| 0.004801 / 0.011353 (-0.006552) \| 0.003092 / 0.011008 (-0.007917) \| 0.046471 / 0.038508 (0.007963) \| 0.031337 / 0.023109 (0.008228) \| 0.258920 / 0.275898 (-0.016978) \| 0.269842 / 0.323480 (-0.053638) \| 0.003976 / 0.007986 (-0.004009) \| 0.002661 / 0.004328 (-0.001668) \| 0.045676 / 0.004250 (0.041426) \| 0.038199 / 0.037052 (0.001146) \| 0.277382 / 0.258489 (0.018893) \| 0.289351 / 0.293841 (-0.004490) \| 0.028452 / 0.128546 (-0.100094) \| 0.009737 / 0.075646 (-0.065910) \| 0.055201 / 0.419271 (-0.364071) \| 0.032686 / 0.043533 (-0.010847) \| 0.259617 / 0.255139 (0.004478) \| 0.277163 / 0.283200 (-0.006037) \| 0.017825 / 0.141683 (-0.123858) \| 1.102797 / 1.452155 (-0.349357) \| 1.105018 / 1.492716 (-0.387699) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094844 / 0.018006 (0.076838) \| 0.290519 / 0.000490 (0.290029) \| 0.000211 / 0.000200 (0.000012) \| 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) \| 0.021917 / 0.037411 (-0.015494) \| 0.075278 / 0.014526 (0.060753) \| 0.085971 / 0.176557 (-0.090586) \| 0.127072 / 0.737135 (-0.610063) \| 0.088244 / 0.296338 (-0.208095) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276704 / 0.215209 (0.061495) \| 2.736960 / 2.077655 (0.659305) \| 1.519634 / 1.504120 (0.015514) \| 1.403026 / 1.541195 (-0.138168) \| 1.418465 / 1.468490 (-0.050025) \| 0.552425 / 4.584777 (-4.032352) \| 0.955244 / 3.745712 (-2.790468) \| 2.556563 / 5.269862 (-2.713298) \| 1.705095 / 4.565676 (-2.860582) \| 0.061212 / 0.424275 (-0.363063) \| 0.004707 / 0.007607 (-0.002900) \| 0.326284 / 0.226044 (0.100239) \| 3.253911 / 2.268929 (0.984983) \| 1.868649 / 55.444624 (-53.575976) \| 1.598697 / 6.876477 (-5.277780) \| 1.682617 / 2.142072 (-0.459455) \| 0.606379 / 4.805227 (-4.198848) \| 0.114126 / 6.500664 (-6.386538) \| 0.038869 / 0.075469 (-0.036601) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.966354 / 1.841788 (-0.875433) \| 11.575918 / 8.074308 (3.501609) \| 9.816597 / 10.191392 (-0.374795) \| 0.141492 / 0.680424 (-0.538932) \| 0.015375 / 0.534201 (-0.518826) \| 0.276027 / 0.579283 (-0.303256) \| 0.118979 / 0.434364 (-0.315385) \| 0.313467 / 0.540337 (-0.226870) \| 0.403539 / 1.386936 (-0.983397) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b59c75856d765e60b66a5216062102d001c6612 \"CML watermark\")\n" ]
2,330,224,869	6,945	Update yanked version of minimum requests requirement	closed	2024-06-03T05:45:50	2024-06-18T07:36:15	2024-06-03T06:09:43	https://github.com/huggingface/datasets/pull/6945	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6945", "html_url": "https://github.com/huggingface/datasets/pull/6945", "diff_url": "https://github.com/huggingface/datasets/pull/6945.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6945.patch", "merged_at": "2024-06-03T06:09:43" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6945). 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) \| 0.005725 / 0.011353 (-0.005627) \| 0.003788 / 0.011008 (-0.007220) \| 0.063059 / 0.038508 (0.024551) \| 0.031364 / 0.023109 (0.008255) \| 0.259209 / 0.275898 (-0.016689) \| 0.278805 / 0.323480 (-0.044675) \| 0.003032 / 0.007986 (-0.004953) \| 0.002633 / 0.004328 (-0.001696) \| 0.049804 / 0.004250 (0.045554) \| 0.046717 / 0.037052 (0.009665) \| 0.267246 / 0.258489 (0.008757) \| 0.299271 / 0.293841 (0.005430) \| 0.027687 / 0.128546 (-0.100860) \| 0.010524 / 0.075646 (-0.065123) \| 0.201736 / 0.419271 (-0.217536) \| 0.036192 / 0.043533 (-0.007341) \| 0.264492 / 0.255139 (0.009353) \| 0.280809 / 0.283200 (-0.002391) \| 0.018187 / 0.141683 (-0.123496) \| 1.170751 / 1.452155 (-0.281404) \| 1.223450 / 1.492716 (-0.269266) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096610 / 0.018006 (0.078604) \| 0.297122 / 0.000490 (0.296632) \| 0.000211 / 0.000200 (0.000011) \| 0.000046 / 0.000054 (-0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018380 / 0.037411 (-0.019031) \| 0.062214 / 0.014526 (0.047688) \| 0.075833 / 0.176557 (-0.100723) \| 0.121825 / 0.737135 (-0.615310) \| 0.075475 / 0.296338 (-0.220864) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275601 / 0.215209 (0.060392) \| 2.698014 / 2.077655 (0.620359) \| 1.434043 / 1.504120 (-0.070077) \| 1.313217 / 1.541195 (-0.227978) \| 1.339014 / 1.468490 (-0.129476) \| 0.566703 / 4.584777 (-4.018074) \| 2.367794 / 3.745712 (-1.377918) \| 2.660787 / 5.269862 (-2.609074) \| 1.738503 / 4.565676 (-2.827174) \| 0.061693 / 0.424275 (-0.362582) \| 0.004978 / 0.007607 (-0.002629) \| 0.334719 / 0.226044 (0.108675) \| 3.300889 / 2.268929 (1.031960) \| 1.764493 / 55.444624 (-53.680131) \| 1.475956 / 6.876477 (-5.400521) \| 1.635988 / 2.142072 (-0.506084) \| 0.643906 / 4.805227 (-4.161321) \| 0.118002 / 6.500664 (-6.382662) \| 0.042593 / 0.075469 (-0.032876) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.953511 / 1.841788 (-0.888276) \| 11.489727 / 8.074308 (3.415419) \| 9.775017 / 10.191392 (-0.416375) \| 0.139864 / 0.680424 (-0.540560) \| 0.014219 / 0.534201 (-0.519982) \| 0.284389 / 0.579283 (-0.294894) \| 0.264250 / 0.434364 (-0.170113) \| 0.323471 / 0.540337 (-0.216866) \| 0.415189 / 1.386936 (-0.971747) \|\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) \| 0.005437 / 0.011353 (-0.005916) \| 0.003710 / 0.011008 (-0.007298) \| 0.049940 / 0.038508 (0.011432) \| 0.032565 / 0.023109 (0.009456) \| 0.266374 / 0.275898 (-0.009524) \| 0.288069 / 0.323480 (-0.035411) \| 0.004140 / 0.007986 (-0.003845) \| 0.002669 / 0.004328 (-0.001660) \| 0.049646 / 0.004250 (0.045395) \| 0.040926 / 0.037052 (0.003874) \| 0.278805 / 0.258489 (0.020316) \| 0.311396 / 0.293841 (0.017555) \| 0.029363 / 0.128546 (-0.099183) \| 0.010260 / 0.075646 (-0.065386) \| 0.058222 / 0.419271 (-0.361049) \| 0.033063 / 0.043533 (-0.010470) \| 0.266798 / 0.255139 (0.011659) \| 0.283091 / 0.283200 (-0.000109) \| 0.017904 / 0.141683 (-0.123779) \| 1.139531 / 1.452155 (-0.312624) \| 1.163909 / 1.492716 (-0.328808) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089063 / 0.018006 (0.071057) \| 0.296757 / 0.000490 (0.296268) \| 0.000202 / 0.000200 (0.000002) \| 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) \| 0.022843 / 0.037411 (-0.014568) \| 0.076032 / 0.014526 (0.061507) \| 0.087545 / 0.176557 (-0.089012) \| 0.128870 / 0.737135 (-0.608266) \| 0.089359 / 0.296338 (-0.206980) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285213 / 0.215209 (0.070004) \| 2.854950 / 2.077655 (0.777295) \| 1.539311 / 1.504120 (0.035191) \| 1.413753 / 1.541195 (-0.127442) \| 1.440819 / 1.468490 (-0.027671) \| 0.564734 / 4.584777 (-4.020043) \| 0.944924 / 3.745712 (-2.800788) \| 2.703612 / 5.269862 (-2.566249) \| 1.749429 / 4.565676 (-2.816247) \| 0.063239 / 0.424275 (-0.361036) \| 0.005024 / 0.007607 (-0.002583) \| 0.340866 / 0.226044 (0.114821) \| 3.359511 / 2.268929 (1.090582) \| 1.895794 / 55.444624 (-53.548831) \| 1.606613 / 6.876477 (-5.269864) \| 1.756539 / 2.142072 (-0.385533) \| 0.646553 / 4.805227 (-4.158675) \| 0.121278 / 6.500664 (-6.379386) \| 0.041066 / 0.075469 (-0.034403) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005548 / 1.841788 (-0.836240) \| 12.080103 / 8.074308 (4.005794) \| 10.444822 / 10.191392 (0.253430) \| 0.145024 / 0.680424 (-0.535400) \| 0.015287 / 0.534201 (-0.518914) \| 0.288567 / 0.579283 (-0.290716) \| 0.118034 / 0.434364 (-0.316330) \| 0.333474 / 0.540337 (-0.206864) \| 0.421716 / 1.386936 (-0.965220) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3d95159dbd918009e1ff710dba0cd15d96d4264e \"CML watermark\")\n", "@albertvillanova could I ask why we should use latest `requests` here? we are using `docker` and `datasets` in the same time. However, docker requires requests<2.32.0.", "Hi @pingsutw,\r\n\r\nWe updated the minimum required `requests` version for security reasons: https://www.cve.org/CVERecord?id=CVE-2024-35195\r\n- affected versions < 2.32.0 \r\n\r\nLatest version of `docker` should normally support `requests` >= 2.32.0: https://github.com/docker/docker-py/releases/tag/7.1.0\r\n> Fixed an issue due to an update in the [requests](https://github.com/psf/requests) package breaking docker-py by applying the https://github.com/psf/requests/pull/6710\r\n- https://github.com/docker/docker-py/pull/3257\r\n\r\nI guess you need to update your `docker` library as well:\r\n```\r\npip install -U docker\r\n```", "> I guess you need to update your docker library as well:\r\n\r\nThank you! it works for me 👍 " ]
2,330,207,120	6,944	Set dev version	closed	2024-06-03T05:29:59	2024-06-03T05:37:51	2024-06-03T05:31:47	https://github.com/huggingface/datasets/pull/6944	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6944", "html_url": "https://github.com/huggingface/datasets/pull/6944", "diff_url": "https://github.com/huggingface/datasets/pull/6944.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6944.patch", "merged_at": "2024-06-03T05:31:46" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6944). 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) \| 0.005150 / 0.011353 (-0.006203) \| 0.003663 / 0.011008 (-0.007346) \| 0.062832 / 0.038508 (0.024324) \| 0.031928 / 0.023109 (0.008819) \| 0.246455 / 0.275898 (-0.029443) \| 0.272121 / 0.323480 (-0.051359) \| 0.004220 / 0.007986 (-0.003765) \| 0.002756 / 0.004328 (-0.001573) \| 0.050071 / 0.004250 (0.045821) \| 0.046074 / 0.037052 (0.009022) \| 0.259676 / 0.258489 (0.001187) \| 0.290674 / 0.293841 (-0.003167) \| 0.027822 / 0.128546 (-0.100724) \| 0.010791 / 0.075646 (-0.064855) \| 0.202827 / 0.419271 (-0.216445) \| 0.037057 / 0.043533 (-0.006476) \| 0.256128 / 0.255139 (0.000989) \| 0.269422 / 0.283200 (-0.013777) \| 0.017395 / 0.141683 (-0.124288) \| 1.125919 / 1.452155 (-0.326236) \| 1.177708 / 1.492716 (-0.315008) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098466 / 0.018006 (0.080460) \| 0.305508 / 0.000490 (0.305018) \| 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) \| 0.018866 / 0.037411 (-0.018545) \| 0.062079 / 0.014526 (0.047553) \| 0.074670 / 0.176557 (-0.101886) \| 0.121025 / 0.737135 (-0.616111) \| 0.075883 / 0.296338 (-0.220455) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291880 / 0.215209 (0.076671) \| 2.874064 / 2.077655 (0.796409) \| 1.477040 / 1.504120 (-0.027080) \| 1.356198 / 1.541195 (-0.184997) \| 1.354676 / 1.468490 (-0.113814) \| 0.559731 / 4.584777 (-4.025046) \| 2.362746 / 3.745712 (-1.382966) \| 2.678838 / 5.269862 (-2.591024) \| 1.752633 / 4.565676 (-2.813044) \| 0.064023 / 0.424275 (-0.360252) \| 0.005035 / 0.007607 (-0.002572) \| 0.354807 / 0.226044 (0.128762) \| 3.424463 / 2.268929 (1.155534) \| 1.810476 / 55.444624 (-53.634149) \| 1.519031 / 6.876477 (-5.357446) \| 1.693957 / 2.142072 (-0.448116) \| 0.647987 / 4.805227 (-4.157240) \| 0.118993 / 6.500664 (-6.381671) \| 0.042186 / 0.075469 (-0.033283) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.982565 / 1.841788 (-0.859223) \| 11.645075 / 8.074308 (3.570767) \| 9.588360 / 10.191392 (-0.603032) \| 0.142369 / 0.680424 (-0.538055) \| 0.014025 / 0.534201 (-0.520176) \| 0.285668 / 0.579283 (-0.293616) \| 0.265825 / 0.434364 (-0.168539) \| 0.323371 / 0.540337 (-0.216966) \| 0.421227 / 1.386936 (-0.965709) \|\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) \| 0.005587 / 0.011353 (-0.005766) \| 0.003664 / 0.011008 (-0.007345) \| 0.050411 / 0.038508 (0.011903) \| 0.033268 / 0.023109 (0.010159) \| 0.266631 / 0.275898 (-0.009267) \| 0.291135 / 0.323480 (-0.032345) \| 0.004275 / 0.007986 (-0.003710) \| 0.002822 / 0.004328 (-0.001506) \| 0.049349 / 0.004250 (0.045099) \| 0.040653 / 0.037052 (0.003601) \| 0.282641 / 0.258489 (0.024152) \| 0.315460 / 0.293841 (0.021619) \| 0.029343 / 0.128546 (-0.099203) \| 0.010606 / 0.075646 (-0.065040) \| 0.058783 / 0.419271 (-0.360489) \| 0.033205 / 0.043533 (-0.010327) \| 0.266805 / 0.255139 (0.011666) \| 0.288907 / 0.283200 (0.005707) \| 0.017817 / 0.141683 (-0.123866) \| 1.128132 / 1.452155 (-0.324023) \| 1.175120 / 1.492716 (-0.317597) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095653 / 0.018006 (0.077647) \| 0.304825 / 0.000490 (0.304335) \| 0.000212 / 0.000200 (0.000012) \| 0.000045 / 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) \| 0.022766 / 0.037411 (-0.014645) \| 0.076598 / 0.014526 (0.062072) \| 0.088314 / 0.176557 (-0.088242) \| 0.127888 / 0.737135 (-0.609247) \| 0.090391 / 0.296338 (-0.205947) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293384 / 0.215209 (0.078175) \| 2.883742 / 2.077655 (0.806087) \| 1.533868 / 1.504120 (0.029748) \| 1.391964 / 1.541195 (-0.149231) \| 1.423732 / 1.468490 (-0.044759) \| 0.575457 / 4.584777 (-4.009320) \| 0.970860 / 3.745712 (-2.774852) \| 2.711405 / 5.269862 (-2.558457) \| 1.774468 / 4.565676 (-2.791208) \| 0.064611 / 0.424275 (-0.359664) \| 0.005120 / 0.007607 (-0.002487) \| 0.343892 / 0.226044 (0.117847) \| 3.362579 / 2.268929 (1.093650) \| 1.880200 / 55.444624 (-53.564424) \| 1.587435 / 6.876477 (-5.289042) \| 1.756464 / 2.142072 (-0.385609) \| 0.661469 / 4.805227 (-4.143759) \| 0.119030 / 6.500664 (-6.381634) \| 0.041704 / 0.075469 (-0.033765) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.025008 / 1.841788 (-0.816780) \| 12.146244 / 8.074308 (4.071936) \| 10.397267 / 10.191392 (0.205875) \| 0.145917 / 0.680424 (-0.534507) \| 0.015779 / 0.534201 (-0.518422) \| 0.287122 / 0.579283 (-0.292161) \| 0.125464 / 0.434364 (-0.308900) \| 0.323315 / 0.540337 (-0.217023) \| 0.416761 / 1.386936 (-0.970175) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e2d15a6b1871f3998986853298e4338d72891491 \"CML watermark\")\n" ]
2,330,176,890	6,943	Release 2.19.2	closed	2024-06-03T05:01:50	2024-06-03T05:17:41	2024-06-03T05:17:40	https://github.com/huggingface/datasets/pull/6943	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6943", "html_url": "https://github.com/huggingface/datasets/pull/6943", "diff_url": "https://github.com/huggingface/datasets/pull/6943.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6943.patch", "merged_at": "2024-06-03T05:17:40" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6943). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]
2,329,562,382	6,942	Import sorting is disabled by flake8 noqa directive after switching to ruff linter	closed	2024-06-02T09:43:34	2024-06-04T09:54:24	2024-06-04T09:54:24	https://github.com/huggingface/datasets/issues/6942	null	albertvillanova	false	[]
2,328,930,165	6,941	Supporting FFCV: Fast Forward Computer Vision	open	2024-06-01T05:34:52	2024-06-01T05:34:52	null	https://github.com/huggingface/datasets/issues/6941	null	Luciennnnnnn	false	[]
2,328,637,831	6,940	Enable Sharding to Equal Sized Shards	open	2024-05-31T21:55:50	2024-06-01T07:34:12	null	https://github.com/huggingface/datasets/issues/6940	null	yuvalkirstain	false	[]
2,328,059,386	6,939	ExpectedMoreSplits error when using data_dir	closed	2024-05-31T15:08:42	2024-05-31T17:10:39	2024-05-31T17:10:39	https://github.com/huggingface/datasets/issues/6939	null	albertvillanova	false	[]
2,327,568,281	6,938	Fix expected splits when passing data_files or dir	closed	2024-05-31T11:04:22	2024-05-31T15:28:03	2024-05-31T15:28:02	https://github.com/huggingface/datasets/pull/6938	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6938", "html_url": "https://github.com/huggingface/datasets/pull/6938", "diff_url": "https://github.com/huggingface/datasets/pull/6938.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6938.patch", "merged_at": null }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6938). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "fix is included in https://github.com/huggingface/datasets/pull/6925" ]
2,327,212,611	6,937	JSON loader implicitly coerces floats to integers	open	2024-05-31T08:09:12	2025-06-24T05:49:20	null	https://github.com/huggingface/datasets/issues/6937	null	albertvillanova	false	[ "Hi @albertvillanova, I'd like to work on this issue if it's still open!\n\nFrom what I see, the float-to-int coercion happens during JSON parsing, possibly due to recent `pandas` behavior. I'll investigate the loading logic inside `json.py` and ensure float values like `[0.0, 1.0, 2.0]` retain their type throughout the loading pipeline — either by patching pandas usage or manually casting using `pyarrow`.\n\nWill share a draft fix soon!\n" ]
2,326,119,853	6,936	save_to_disk() freezes when saving on s3 bucket with multiprocessing	open	2024-05-30T16:48:39	2025-02-06T22:12:52	null	https://github.com/huggingface/datasets/issues/6936	null	ycattan	false	[ "I got the same issue. Any updates so far for this issue?", "Same here. Any updates?", "+1, experiencing this as well" ]
2,325,612,022	6,935	Support for pathlib.Path in datasets 2.19.0	open	2024-05-30T12:53:36	2025-01-14T11:50:22	null	https://github.com/huggingface/datasets/issues/6935	null	lamyiowce	false	[ "+1 I just noticed this when I tried to update `datasets` today.", "The same issue, I also get error." ]
2,325,341,717	6,934	Revert ci user	closed	2024-05-30T10:45:26	2024-05-31T10:25:08	2024-05-30T10:45:37	https://github.com/huggingface/datasets/pull/6934	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6934", "html_url": "https://github.com/huggingface/datasets/pull/6934", "diff_url": "https://github.com/huggingface/datasets/pull/6934.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6934.patch", "merged_at": "2024-05-30T10:45:37" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6934). 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) \| 0.005218 / 0.011353 (-0.006135) \| 0.003313 / 0.011008 (-0.007695) \| 0.062992 / 0.038508 (0.024484) \| 0.029621 / 0.023109 (0.006512) \| 0.244421 / 0.275898 (-0.031477) \| 0.267178 / 0.323480 (-0.056302) \| 0.002986 / 0.007986 (-0.005000) \| 0.002607 / 0.004328 (-0.001721) \| 0.049149 / 0.004250 (0.044898) \| 0.045362 / 0.037052 (0.008310) \| 0.252862 / 0.258489 (-0.005627) \| 0.286326 / 0.293841 (-0.007515) \| 0.027888 / 0.128546 (-0.100658) \| 0.010295 / 0.075646 (-0.065352) \| 0.205525 / 0.419271 (-0.213746) \| 0.036696 / 0.043533 (-0.006837) \| 0.248716 / 0.255139 (-0.006423) \| 0.263803 / 0.283200 (-0.019397) \| 0.016926 / 0.141683 (-0.124757) \| 1.123093 / 1.452155 (-0.329062) \| 1.155434 / 1.492716 (-0.337282) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092349 / 0.018006 (0.074343) \| 0.298154 / 0.000490 (0.297664) \| 0.000213 / 0.000200 (0.000013) \| 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) \| 0.018496 / 0.037411 (-0.018915) \| 0.061983 / 0.014526 (0.047457) \| 0.075043 / 0.176557 (-0.101514) \| 0.120678 / 0.737135 (-0.616457) \| 0.074917 / 0.296338 (-0.221422) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290558 / 0.215209 (0.075349) \| 2.842635 / 2.077655 (0.764981) \| 1.485761 / 1.504120 (-0.018359) \| 1.346948 / 1.541195 (-0.194247) \| 1.352424 / 1.468490 (-0.116066) \| 0.564567 / 4.584777 (-4.020210) \| 2.393583 / 3.745712 (-1.352129) \| 2.654061 / 5.269862 (-2.615800) \| 1.729154 / 4.565676 (-2.836523) \| 0.064652 / 0.424275 (-0.359623) \| 0.004973 / 0.007607 (-0.002634) \| 0.334924 / 0.226044 (0.108879) \| 3.330518 / 2.268929 (1.061590) \| 1.773848 / 55.444624 (-53.670776) \| 1.513796 / 6.876477 (-5.362681) \| 1.676492 / 2.142072 (-0.465580) \| 0.650551 / 4.805227 (-4.154677) \| 0.118423 / 6.500664 (-6.382241) \| 0.042700 / 0.075469 (-0.032769) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.943394 / 1.841788 (-0.898394) \| 11.235766 / 8.074308 (3.161458) \| 9.896586 / 10.191392 (-0.294806) \| 0.130174 / 0.680424 (-0.550249) \| 0.014148 / 0.534201 (-0.520053) \| 0.284002 / 0.579283 (-0.295281) \| 0.261354 / 0.434364 (-0.173010) \| 0.320839 / 0.540337 (-0.219499) \| 0.422399 / 1.386936 (-0.964537) \|\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) \| 0.005496 / 0.011353 (-0.005857) \| 0.003603 / 0.011008 (-0.007406) \| 0.050104 / 0.038508 (0.011596) \| 0.032939 / 0.023109 (0.009830) \| 0.265643 / 0.275898 (-0.010255) \| 0.291819 / 0.323480 (-0.031661) \| 0.004273 / 0.007986 (-0.003713) \| 0.002715 / 0.004328 (-0.001613) \| 0.049191 / 0.004250 (0.044941) \| 0.040782 / 0.037052 (0.003730) \| 0.276562 / 0.258489 (0.018072) \| 0.314307 / 0.293841 (0.020466) \| 0.029878 / 0.128546 (-0.098669) \| 0.010134 / 0.075646 (-0.065513) \| 0.058686 / 0.419271 (-0.360585) \| 0.033562 / 0.043533 (-0.009971) \| 0.265961 / 0.255139 (0.010822) \| 0.282009 / 0.283200 (-0.001191) \| 0.018956 / 0.141683 (-0.122727) \| 1.149668 / 1.452155 (-0.302487) \| 1.192242 / 1.492716 (-0.300474) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089449 / 0.018006 (0.071443) \| 0.300346 / 0.000490 (0.299856) \| 0.000198 / 0.000200 (-0.000001) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022094 / 0.037411 (-0.015317) \| 0.075987 / 0.014526 (0.061461) \| 0.088191 / 0.176557 (-0.088365) \| 0.127698 / 0.737135 (-0.609437) \| 0.089642 / 0.296338 (-0.206696) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299127 / 0.215209 (0.083918) \| 2.961219 / 2.077655 (0.883565) \| 1.589108 / 1.504120 (0.084988) \| 1.464060 / 1.541195 (-0.077135) \| 1.475249 / 1.468490 (0.006759) \| 0.569041 / 4.584777 (-4.015736) \| 0.966965 / 3.745712 (-2.778747) \| 2.653049 / 5.269862 (-2.616813) \| 1.733650 / 4.565676 (-2.832026) \| 0.062537 / 0.424275 (-0.361738) \| 0.005003 / 0.007607 (-0.002605) \| 0.353345 / 0.226044 (0.127301) \| 3.432888 / 2.268929 (1.163960) \| 1.953217 / 55.444624 (-53.491407) \| 1.651995 / 6.876477 (-5.224482) \| 1.764549 / 2.142072 (-0.377523) \| 0.647255 / 4.805227 (-4.157973) \| 0.116827 / 6.500664 (-6.383837) \| 0.040765 / 0.075469 (-0.034704) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985490 / 1.841788 (-0.856298) \| 11.965147 / 8.074308 (3.890839) \| 10.488286 / 10.191392 (0.296894) \| 0.142134 / 0.680424 (-0.538290) \| 0.015415 / 0.534201 (-0.518786) \| 0.289864 / 0.579283 (-0.289419) \| 0.122778 / 0.434364 (-0.311586) \| 0.328691 / 0.540337 (-0.211647) \| 0.422677 / 1.386936 (-0.964259) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#456f790d2c2e9181bc305ab3d54fe2ca58742b9b \"CML watermark\")\n", "There was an incident in hub-ci that invalidated our token. It's been fixed so I reverted this change" ]
2,325,300,800	6,933	update ci user	closed	2024-05-30T10:23:02	2024-05-30T10:30:54	2024-05-30T10:23:12	https://github.com/huggingface/datasets/pull/6933	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6933", "html_url": "https://github.com/huggingface/datasets/pull/6933", "diff_url": "https://github.com/huggingface/datasets/pull/6933.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6933.patch", "merged_at": "2024-05-30T10:23:12" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6933). 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) \| 0.004937 / 0.011353 (-0.006416) \| 0.003706 / 0.011008 (-0.007302) \| 0.062627 / 0.038508 (0.024119) \| 0.031372 / 0.023109 (0.008263) \| 0.246616 / 0.275898 (-0.029282) \| 0.272196 / 0.323480 (-0.051284) \| 0.004129 / 0.007986 (-0.003856) \| 0.002766 / 0.004328 (-0.001562) \| 0.049975 / 0.004250 (0.045725) \| 0.045098 / 0.037052 (0.008046) \| 0.261802 / 0.258489 (0.003313) \| 0.290088 / 0.293841 (-0.003753) \| 0.027082 / 0.128546 (-0.101465) \| 0.010442 / 0.075646 (-0.065205) \| 0.201795 / 0.419271 (-0.217477) \| 0.037081 / 0.043533 (-0.006452) \| 0.249500 / 0.255139 (-0.005639) \| 0.268800 / 0.283200 (-0.014399) \| 0.017556 / 0.141683 (-0.124127) \| 1.137201 / 1.452155 (-0.314953) \| 1.186993 / 1.492716 (-0.305723) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097426 / 0.018006 (0.079419) \| 0.303653 / 0.000490 (0.303163) \| 0.000235 / 0.000200 (0.000035) \| 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) \| 0.020206 / 0.037411 (-0.017206) \| 0.063673 / 0.014526 (0.049147) \| 0.076173 / 0.176557 (-0.100383) \| 0.122459 / 0.737135 (-0.614676) \| 0.076958 / 0.296338 (-0.219380) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282146 / 0.215209 (0.066937) \| 2.785682 / 2.077655 (0.708027) \| 1.468847 / 1.504120 (-0.035273) \| 1.346731 / 1.541195 (-0.194464) \| 1.378459 / 1.468490 (-0.090031) \| 0.564961 / 4.584777 (-4.019816) \| 2.400095 / 3.745712 (-1.345617) \| 2.658285 / 5.269862 (-2.611577) \| 1.747873 / 4.565676 (-2.817803) \| 0.063763 / 0.424275 (-0.360512) \| 0.004969 / 0.007607 (-0.002638) \| 0.337764 / 0.226044 (0.111720) \| 3.309568 / 2.268929 (1.040639) \| 1.812516 / 55.444624 (-53.632109) \| 1.521519 / 6.876477 (-5.354957) \| 1.690091 / 2.142072 (-0.451982) \| 0.640922 / 4.805227 (-4.164305) \| 0.119291 / 6.500664 (-6.381373) \| 0.042195 / 0.075469 (-0.033274) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965327 / 1.841788 (-0.876461) \| 11.538832 / 8.074308 (3.464523) \| 9.594644 / 10.191392 (-0.596748) \| 0.144687 / 0.680424 (-0.535737) \| 0.014049 / 0.534201 (-0.520152) \| 0.296873 / 0.579283 (-0.282410) \| 0.269281 / 0.434364 (-0.165083) \| 0.325091 / 0.540337 (-0.215246) \| 0.420917 / 1.386936 (-0.966019) \|\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) \| 0.005239 / 0.011353 (-0.006114) \| 0.003168 / 0.011008 (-0.007840) \| 0.049301 / 0.038508 (0.010793) \| 0.032248 / 0.023109 (0.009139) \| 0.266463 / 0.275898 (-0.009435) \| 0.293311 / 0.323480 (-0.030168) \| 0.004185 / 0.007986 (-0.003800) \| 0.002681 / 0.004328 (-0.001647) \| 0.048644 / 0.004250 (0.044393) \| 0.040366 / 0.037052 (0.003314) \| 0.280345 / 0.258489 (0.021856) \| 0.312745 / 0.293841 (0.018904) \| 0.029616 / 0.128546 (-0.098930) \| 0.010001 / 0.075646 (-0.065646) \| 0.057365 / 0.419271 (-0.361906) \| 0.033189 / 0.043533 (-0.010344) \| 0.267601 / 0.255139 (0.012462) \| 0.285647 / 0.283200 (0.002448) \| 0.017119 / 0.141683 (-0.124564) \| 1.139776 / 1.452155 (-0.312378) \| 1.172451 / 1.492716 (-0.320266) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095462 / 0.018006 (0.077455) \| 0.303009 / 0.000490 (0.302519) \| 0.000227 / 0.000200 (0.000027) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023026 / 0.037411 (-0.014385) \| 0.077905 / 0.014526 (0.063380) \| 0.087275 / 0.176557 (-0.089282) \| 0.127355 / 0.737135 (-0.609780) \| 0.088940 / 0.296338 (-0.207399) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298267 / 0.215209 (0.083058) \| 2.894679 / 2.077655 (0.817024) \| 1.568663 / 1.504120 (0.064543) \| 1.438342 / 1.541195 (-0.102853) \| 1.456110 / 1.468490 (-0.012380) \| 0.556337 / 4.584777 (-4.028440) \| 0.969795 / 3.745712 (-2.775917) \| 2.667348 / 5.269862 (-2.602513) \| 1.767169 / 4.565676 (-2.798507) \| 0.060969 / 0.424275 (-0.363306) \| 0.005009 / 0.007607 (-0.002598) \| 0.343299 / 0.226044 (0.117255) \| 3.396529 / 2.268929 (1.127601) \| 1.889816 / 55.444624 (-53.554808) \| 1.635077 / 6.876477 (-5.241400) \| 1.795238 / 2.142072 (-0.346835) \| 0.631876 / 4.805227 (-4.173352) \| 0.115483 / 6.500664 (-6.385181) \| 0.041772 / 0.075469 (-0.033697) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.008423 / 1.841788 (-0.833364) \| 12.432488 / 8.074308 (4.358180) \| 10.418002 / 10.191392 (0.226610) \| 0.142395 / 0.680424 (-0.538029) \| 0.015718 / 0.534201 (-0.518483) \| 0.281917 / 0.579283 (-0.297366) \| 0.132619 / 0.434364 (-0.301745) \| 0.318500 / 0.540337 (-0.221838) \| 0.410798 / 1.386936 (-0.976138) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3d6cd158d2e3bb9030fea7c5a9580b9d34d721ac \"CML watermark\")\n" ]
2,324,729,267	6,932	Update dataset_dict.py	closed	2024-05-30T05:22:35	2024-06-04T12:56:20	2024-06-04T12:50:13	https://github.com/huggingface/datasets/pull/6932	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6932", "html_url": "https://github.com/huggingface/datasets/pull/6932", "diff_url": "https://github.com/huggingface/datasets/pull/6932.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6932.patch", "merged_at": "2024-06-04T12:50:13" }	Arunprakash-A	true	[ "thanks !", "<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) \| 0.005050 / 0.011353 (-0.006303) \| 0.003786 / 0.011008 (-0.007222) \| 0.062406 / 0.038508 (0.023898) \| 0.029459 / 0.023109 (0.006349) \| 0.262388 / 0.275898 (-0.013510) \| 0.274119 / 0.323480 (-0.049361) \| 0.004085 / 0.007986 (-0.003901) \| 0.002754 / 0.004328 (-0.001574) \| 0.048779 / 0.004250 (0.044529) \| 0.046187 / 0.037052 (0.009135) \| 0.263513 / 0.258489 (0.005024) \| 0.294260 / 0.293841 (0.000419) \| 0.027391 / 0.128546 (-0.101155) \| 0.010567 / 0.075646 (-0.065080) \| 0.200225 / 0.419271 (-0.219046) \| 0.036165 / 0.043533 (-0.007367) \| 0.251757 / 0.255139 (-0.003382) \| 0.268271 / 0.283200 (-0.014928) \| 0.018446 / 0.141683 (-0.123237) \| 1.125787 / 1.452155 (-0.326368) \| 1.163172 / 1.492716 (-0.329544) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004428 / 0.018006 (-0.013578) \| 0.301730 / 0.000490 (0.301241) \| 0.000215 / 0.000200 (0.000015) \| 0.000045 / 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) \| 0.019424 / 0.037411 (-0.017987) \| 0.062269 / 0.014526 (0.047743) \| 0.074289 / 0.176557 (-0.102268) \| 0.121069 / 0.737135 (-0.616067) \| 0.076485 / 0.296338 (-0.219853) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277315 / 0.215209 (0.062106) \| 2.742027 / 2.077655 (0.664372) \| 1.472970 / 1.504120 (-0.031150) \| 1.350065 / 1.541195 (-0.191130) \| 1.378806 / 1.468490 (-0.089684) \| 0.567742 / 4.584777 (-4.017035) \| 2.376752 / 3.745712 (-1.368960) \| 2.662459 / 5.269862 (-2.607402) \| 1.750396 / 4.565676 (-2.815280) \| 0.063589 / 0.424275 (-0.360686) \| 0.004987 / 0.007607 (-0.002620) \| 0.326441 / 0.226044 (0.100397) \| 3.224125 / 2.268929 (0.955197) \| 1.801623 / 55.444624 (-53.643001) \| 1.534712 / 6.876477 (-5.341765) \| 1.652365 / 2.142072 (-0.489708) \| 0.647624 / 4.805227 (-4.157603) \| 0.117161 / 6.500664 (-6.383504) \| 0.041908 / 0.075469 (-0.033561) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.954879 / 1.841788 (-0.886909) \| 11.571875 / 8.074308 (3.497567) \| 9.489146 / 10.191392 (-0.702246) \| 0.141630 / 0.680424 (-0.538794) \| 0.014764 / 0.534201 (-0.519437) \| 0.285003 / 0.579283 (-0.294280) \| 0.266138 / 0.434364 (-0.168226) \| 0.323527 / 0.540337 (-0.216810) \| 0.419658 / 1.386936 (-0.967278) \|\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) \| 0.005359 / 0.011353 (-0.005994) \| 0.003615 / 0.011008 (-0.007393) \| 0.050692 / 0.038508 (0.012184) \| 0.033632 / 0.023109 (0.010522) \| 0.273614 / 0.275898 (-0.002284) \| 0.303780 / 0.323480 (-0.019700) \| 0.004171 / 0.007986 (-0.003814) \| 0.002687 / 0.004328 (-0.001642) \| 0.050002 / 0.004250 (0.045751) \| 0.040824 / 0.037052 (0.003772) \| 0.287759 / 0.258489 (0.029270) \| 0.324144 / 0.293841 (0.030303) \| 0.029101 / 0.128546 (-0.099445) \| 0.010244 / 0.075646 (-0.065402) \| 0.059599 / 0.419271 (-0.359672) \| 0.033146 / 0.043533 (-0.010387) \| 0.276592 / 0.255139 (0.021453) \| 0.293670 / 0.283200 (0.010470) \| 0.018270 / 0.141683 (-0.123413) \| 1.126216 / 1.452155 (-0.325939) \| 1.155658 / 1.492716 (-0.337058) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093537 / 0.018006 (0.075530) \| 0.302706 / 0.000490 (0.302216) \| 0.000216 / 0.000200 (0.000016) \| 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) \| 0.023118 / 0.037411 (-0.014293) \| 0.076995 / 0.014526 (0.062469) \| 0.089476 / 0.176557 (-0.087080) \| 0.130705 / 0.737135 (-0.606430) \| 0.090258 / 0.296338 (-0.206081) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285920 / 0.215209 (0.070710) \| 2.830581 / 2.077655 (0.752927) \| 1.561695 / 1.504120 (0.057575) \| 1.522791 / 1.541195 (-0.018403) \| 1.429875 / 1.468490 (-0.038615) \| 0.566683 / 4.584777 (-4.018094) \| 0.957157 / 3.745712 (-2.788555) \| 2.663718 / 5.269862 (-2.606143) \| 1.748885 / 4.565676 (-2.816791) \| 0.063697 / 0.424275 (-0.360578) \| 0.004996 / 0.007607 (-0.002611) \| 0.340042 / 0.226044 (0.113998) \| 3.352792 / 2.268929 (1.083863) \| 1.907189 / 55.444624 (-53.537435) \| 1.608177 / 6.876477 (-5.268300) \| 1.775438 / 2.142072 (-0.366634) \| 0.645264 / 4.805227 (-4.159963) \| 0.116441 / 6.500664 (-6.384223) \| 0.040671 / 0.075469 (-0.034798) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005050 / 1.841788 (-0.836738) \| 12.040057 / 8.074308 (3.965749) \| 10.213560 / 10.191392 (0.022168) \| 0.138383 / 0.680424 (-0.542041) \| 0.015409 / 0.534201 (-0.518792) \| 0.283509 / 0.579283 (-0.295774) \| 0.125501 / 0.434364 (-0.308863) \| 0.318816 / 0.540337 (-0.221521) \| 0.415454 / 1.386936 (-0.971482) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cbb29cea0e21dc0eb8f7de01d0c6ed5718d6ce4e \"CML watermark\")\n" ]
2,323,457,525	6,931	[WebDataset] Support compressed files	closed	2024-05-29T14:19:06	2024-05-29T16:33:18	2024-05-29T16:24:21	https://github.com/huggingface/datasets/pull/6931	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6931", "html_url": "https://github.com/huggingface/datasets/pull/6931", "diff_url": "https://github.com/huggingface/datasets/pull/6931.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6931.patch", "merged_at": "2024-05-29T16:24:21" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6931). 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) \| 0.005362 / 0.011353 (-0.005991) \| 0.003969 / 0.011008 (-0.007039) \| 0.063390 / 0.038508 (0.024882) \| 0.030814 / 0.023109 (0.007705) \| 0.246891 / 0.275898 (-0.029007) \| 0.271047 / 0.323480 (-0.052432) \| 0.004036 / 0.007986 (-0.003950) \| 0.002732 / 0.004328 (-0.001597) \| 0.049466 / 0.004250 (0.045216) \| 0.047227 / 0.037052 (0.010175) \| 0.255978 / 0.258489 (-0.002511) \| 0.297956 / 0.293841 (0.004115) \| 0.028641 / 0.128546 (-0.099905) \| 0.010510 / 0.075646 (-0.065136) \| 0.204268 / 0.419271 (-0.215004) \| 0.037093 / 0.043533 (-0.006440) \| 0.247287 / 0.255139 (-0.007852) \| 0.263830 / 0.283200 (-0.019370) \| 0.018335 / 0.141683 (-0.123348) \| 1.116074 / 1.452155 (-0.336081) \| 1.182589 / 1.492716 (-0.310128) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094435 / 0.018006 (0.076429) \| 0.310422 / 0.000490 (0.309932) \| 0.000215 / 0.000200 (0.000015) \| 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) \| 0.019220 / 0.037411 (-0.018192) \| 0.062090 / 0.014526 (0.047564) \| 0.074511 / 0.176557 (-0.102046) \| 0.121825 / 0.737135 (-0.615310) \| 0.075406 / 0.296338 (-0.220933) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281185 / 0.215209 (0.065976) \| 2.770157 / 2.077655 (0.692502) \| 1.472095 / 1.504120 (-0.032025) \| 1.339342 / 1.541195 (-0.201853) \| 1.374621 / 1.468490 (-0.093869) \| 0.566607 / 4.584777 (-4.018170) \| 2.357642 / 3.745712 (-1.388070) \| 2.735034 / 5.269862 (-2.534827) \| 1.782779 / 4.565676 (-2.782897) \| 0.063046 / 0.424275 (-0.361229) \| 0.005015 / 0.007607 (-0.002592) \| 0.336690 / 0.226044 (0.110646) \| 3.360955 / 2.268929 (1.092027) \| 1.804424 / 55.444624 (-53.640200) \| 1.517334 / 6.876477 (-5.359143) \| 1.665254 / 2.142072 (-0.476818) \| 0.627185 / 4.805227 (-4.178042) \| 0.114388 / 6.500664 (-6.386276) \| 0.041788 / 0.075469 (-0.033681) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975270 / 1.841788 (-0.866517) \| 11.647633 / 8.074308 (3.573325) \| 9.872873 / 10.191392 (-0.318519) \| 0.141744 / 0.680424 (-0.538680) \| 0.014524 / 0.534201 (-0.519677) \| 0.286697 / 0.579283 (-0.292586) \| 0.266837 / 0.434364 (-0.167527) \| 0.328513 / 0.540337 (-0.211825) \| 0.424676 / 1.386936 (-0.962260) \|\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) \| 0.005654 / 0.011353 (-0.005699) \| 0.004058 / 0.011008 (-0.006950) \| 0.051030 / 0.038508 (0.012522) \| 0.033085 / 0.023109 (0.009976) \| 0.307532 / 0.275898 (0.031634) \| 0.335672 / 0.323480 (0.012192) \| 0.004244 / 0.007986 (-0.003742) \| 0.002842 / 0.004328 (-0.001487) \| 0.050131 / 0.004250 (0.045880) \| 0.040709 / 0.037052 (0.003656) \| 0.319514 / 0.258489 (0.061025) \| 0.357153 / 0.293841 (0.063312) \| 0.029014 / 0.128546 (-0.099532) \| 0.010999 / 0.075646 (-0.064648) \| 0.058789 / 0.419271 (-0.360482) \| 0.033284 / 0.043533 (-0.010249) \| 0.310783 / 0.255139 (0.055644) \| 0.331466 / 0.283200 (0.048266) \| 0.018998 / 0.141683 (-0.122685) \| 1.138822 / 1.452155 (-0.313332) \| 1.180731 / 1.492716 (-0.311985) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095725 / 0.018006 (0.077719) \| 0.302788 / 0.000490 (0.302298) \| 0.000206 / 0.000200 (0.000006) \| 0.000056 / 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) \| 0.023247 / 0.037411 (-0.014164) \| 0.077619 / 0.014526 (0.063093) \| 0.090489 / 0.176557 (-0.086067) \| 0.132033 / 0.737135 (-0.605102) \| 0.090964 / 0.296338 (-0.205374) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297912 / 0.215209 (0.082703) \| 2.954107 / 2.077655 (0.876452) \| 1.591155 / 1.504120 (0.087035) \| 1.469217 / 1.541195 (-0.071978) \| 1.513315 / 1.468490 (0.044825) \| 0.562728 / 4.584777 (-4.022049) \| 0.960093 / 3.745712 (-2.785620) \| 2.852106 / 5.269862 (-2.417756) \| 1.861668 / 4.565676 (-2.704009) \| 0.063530 / 0.424275 (-0.360745) \| 0.005194 / 0.007607 (-0.002413) \| 0.351116 / 0.226044 (0.125072) \| 3.498787 / 2.268929 (1.229859) \| 1.952223 / 55.444624 (-53.492401) \| 1.696208 / 6.876477 (-5.180269) \| 1.861650 / 2.142072 (-0.280422) \| 0.653494 / 4.805227 (-4.151733) \| 0.123797 / 6.500664 (-6.376868) \| 0.042696 / 0.075469 (-0.032773) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.006657 / 1.841788 (-0.835131) \| 12.659771 / 8.074308 (4.585463) \| 10.672140 / 10.191392 (0.480748) \| 0.143726 / 0.680424 (-0.536698) \| 0.015895 / 0.534201 (-0.518306) \| 0.285952 / 0.579283 (-0.293331) \| 0.126078 / 0.434364 (-0.308286) \| 0.325943 / 0.540337 (-0.214395) \| 0.410774 / 1.386936 (-0.976162) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#88d53d1ae762bec6736fffb000e6540e52bf1998 \"CML watermark\")\n" ]
2,323,225,922	6,930	ValueError: Couldn't infer the same data file format for all splits. Got {'train': ('json', {}), 'validation': (None, {})}	open	2024-05-29T12:40:05	2024-07-23T06:25:24	null	https://github.com/huggingface/datasets/issues/6930	null	Polarisamoon	false	[ "How do you solve it ?\r\n", "> How do you solve it ?\r\n\r\nPlease check your Python environment and dataset version. I have just resolved the issue, which was caused by a Python environment switching error\r\n" ]
2,322,980,077	6,929	Avoid downloading the whole dataset when only README.me has been touched on hub.	open	2024-05-29T10:36:06	2024-05-29T20:51:56	null	https://github.com/huggingface/datasets/issues/6929	null	zinc75	false	[ "you're right, we're tackling this here: https://github.com/huggingface/dataset-viewer/issues/2757", "@severo : great !" ]
2,322,267,727	6,928	Update process.mdx: Code Listings Fixes	closed	2024-05-29T03:17:07	2024-06-04T13:08:19	2024-06-04T12:55:00	https://github.com/huggingface/datasets/pull/6928	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6928", "html_url": "https://github.com/huggingface/datasets/pull/6928", "diff_url": "https://github.com/huggingface/datasets/pull/6928.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6928.patch", "merged_at": "2024-06-04T12:55:00" }	FadyMorris	true	[ "<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) \| 0.005062 / 0.011353 (-0.006291) \| 0.003410 / 0.011008 (-0.007598) \| 0.062241 / 0.038508 (0.023733) \| 0.030294 / 0.023109 (0.007185) \| 0.249249 / 0.275898 (-0.026649) \| 0.267718 / 0.323480 (-0.055761) \| 0.003047 / 0.007986 (-0.004938) \| 0.002661 / 0.004328 (-0.001668) \| 0.049142 / 0.004250 (0.044892) \| 0.047929 / 0.037052 (0.010877) \| 0.255262 / 0.258489 (-0.003227) \| 0.286241 / 0.293841 (-0.007600) \| 0.027064 / 0.128546 (-0.101482) \| 0.010374 / 0.075646 (-0.065273) \| 0.201454 / 0.419271 (-0.217818) \| 0.036586 / 0.043533 (-0.006947) \| 0.255200 / 0.255139 (0.000061) \| 0.267660 / 0.283200 (-0.015539) \| 0.018621 / 0.141683 (-0.123062) \| 1.159821 / 1.452155 (-0.292334) \| 1.171597 / 1.492716 (-0.321120) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004752 / 0.018006 (-0.013254) \| 0.295427 / 0.000490 (0.294937) \| 0.000225 / 0.000200 (0.000025) \| 0.000052 / 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) \| 0.018914 / 0.037411 (-0.018497) \| 0.061180 / 0.014526 (0.046654) \| 0.073649 / 0.176557 (-0.102907) \| 0.120142 / 0.737135 (-0.616993) \| 0.074754 / 0.296338 (-0.221585) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286637 / 0.215209 (0.071428) \| 2.807941 / 2.077655 (0.730287) \| 1.473577 / 1.504120 (-0.030542) \| 1.353112 / 1.541195 (-0.188083) \| 1.363020 / 1.468490 (-0.105470) \| 0.567745 / 4.584777 (-4.017032) \| 2.384887 / 3.745712 (-1.360826) \| 2.685132 / 5.269862 (-2.584730) \| 1.755922 / 4.565676 (-2.809755) \| 0.062296 / 0.424275 (-0.361979) \| 0.004941 / 0.007607 (-0.002666) \| 0.346752 / 0.226044 (0.120707) \| 3.378623 / 2.268929 (1.109694) \| 1.809070 / 55.444624 (-53.635555) \| 1.531490 / 6.876477 (-5.344986) \| 1.687954 / 2.142072 (-0.454119) \| 0.639917 / 4.805227 (-4.165310) \| 0.118455 / 6.500664 (-6.382209) \| 0.043072 / 0.075469 (-0.032397) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.977154 / 1.841788 (-0.864634) \| 11.380127 / 8.074308 (3.305819) \| 9.621632 / 10.191392 (-0.569760) \| 0.141768 / 0.680424 (-0.538655) \| 0.014120 / 0.534201 (-0.520081) \| 0.285073 / 0.579283 (-0.294210) \| 0.264801 / 0.434364 (-0.169563) \| 0.322357 / 0.540337 (-0.217981) \| 0.431192 / 1.386936 (-0.955744) \|\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) \| 0.005162 / 0.011353 (-0.006191) \| 0.003499 / 0.011008 (-0.007509) \| 0.049667 / 0.038508 (0.011159) \| 0.032473 / 0.023109 (0.009363) \| 0.259988 / 0.275898 (-0.015910) \| 0.285723 / 0.323480 (-0.037757) \| 0.004197 / 0.007986 (-0.003789) \| 0.002710 / 0.004328 (-0.001618) \| 0.049235 / 0.004250 (0.044984) \| 0.040440 / 0.037052 (0.003387) \| 0.276791 / 0.258489 (0.018302) \| 0.311990 / 0.293841 (0.018149) \| 0.029217 / 0.128546 (-0.099329) \| 0.010217 / 0.075646 (-0.065429) \| 0.057844 / 0.419271 (-0.361427) \| 0.032799 / 0.043533 (-0.010734) \| 0.260705 / 0.255139 (0.005566) \| 0.280439 / 0.283200 (-0.002761) \| 0.018682 / 0.141683 (-0.123001) \| 1.135946 / 1.452155 (-0.316208) \| 1.163144 / 1.492716 (-0.329572) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097968 / 0.018006 (0.079961) \| 0.309276 / 0.000490 (0.308786) \| 0.000214 / 0.000200 (0.000014) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022623 / 0.037411 (-0.014788) \| 0.075471 / 0.014526 (0.060945) \| 0.087928 / 0.176557 (-0.088629) \| 0.129537 / 0.737135 (-0.607599) \| 0.089376 / 0.296338 (-0.206963) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298223 / 0.215209 (0.083014) \| 2.940462 / 2.077655 (0.862807) \| 1.586024 / 1.504120 (0.081904) \| 1.451161 / 1.541195 (-0.090034) \| 1.457707 / 1.468490 (-0.010783) \| 0.571172 / 4.584777 (-4.013604) \| 0.961591 / 3.745712 (-2.784121) \| 2.661258 / 5.269862 (-2.608604) \| 1.755172 / 4.565676 (-2.810504) \| 0.063430 / 0.424275 (-0.360845) \| 0.005034 / 0.007607 (-0.002573) \| 0.352356 / 0.226044 (0.126312) \| 3.454986 / 2.268929 (1.186057) \| 1.967375 / 55.444624 (-53.477249) \| 1.638465 / 6.876477 (-5.238012) \| 1.774098 / 2.142072 (-0.367975) \| 0.650094 / 4.805227 (-4.155134) \| 0.117377 / 6.500664 (-6.383287) \| 0.041229 / 0.075469 (-0.034240) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.014356 / 1.841788 (-0.827432) \| 12.175823 / 8.074308 (4.101515) \| 10.657486 / 10.191392 (0.466094) \| 0.145080 / 0.680424 (-0.535344) \| 0.015563 / 0.534201 (-0.518638) \| 0.287093 / 0.579283 (-0.292190) \| 0.127164 / 0.434364 (-0.307200) \| 0.318518 / 0.540337 (-0.221820) \| 0.415333 / 1.386936 (-0.971603) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#372078f617d9210c7f073c22f5f6f4fbee52c67f \"CML watermark\")\n" ]
2,322,260,725	6,927	Update process.mdx: Minor Code Listings Updates and Fixes	closed	2024-05-29T03:09:01	2024-05-29T03:12:46	2024-05-29T03:12:46	https://github.com/huggingface/datasets/pull/6927	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6927", "html_url": "https://github.com/huggingface/datasets/pull/6927", "diff_url": "https://github.com/huggingface/datasets/pull/6927.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6927.patch", "merged_at": null }	FadyMorris	true	[]
2,322,164,287	6,926	Update process.mdx: Fix code listing in Shard section	closed	2024-05-29T01:25:55	2024-05-29T03:11:20	2024-05-29T03:11:08	https://github.com/huggingface/datasets/pull/6926	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6926", "html_url": "https://github.com/huggingface/datasets/pull/6926", "diff_url": "https://github.com/huggingface/datasets/pull/6926.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6926.patch", "merged_at": null }	FadyMorris	true	[]
2,321,084,967	6,925	Fix NonMatchingSplitsSizesError/ExpectedMoreSplits when passing data_dir/data_files in no-code Hub datasets	closed	2024-05-28T13:33:38	2024-11-07T20:41:58	2024-05-31T17:10:37	https://github.com/huggingface/datasets/pull/6925	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6925", "html_url": "https://github.com/huggingface/datasets/pull/6925", "diff_url": "https://github.com/huggingface/datasets/pull/6925.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6925.patch", "merged_at": "2024-05-31T17:10:37" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6925). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Do you think this is worth making a patch release for?\r\nCC: @huggingface/datasets", "I will add some regression tests before merging.\r\n\r\nAnd I will make a patch release afterwards.", "<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) \| 0.004959 / 0.011353 (-0.006394) \| 0.003654 / 0.011008 (-0.007354) \| 0.064087 / 0.038508 (0.025579) \| 0.031942 / 0.023109 (0.008833) \| 0.236830 / 0.275898 (-0.039068) \| 0.265359 / 0.323480 (-0.058121) \| 0.003108 / 0.007986 (-0.004878) \| 0.002824 / 0.004328 (-0.001504) \| 0.049102 / 0.004250 (0.044852) \| 0.046070 / 0.037052 (0.009017) \| 0.248830 / 0.258489 (-0.009659) \| 0.283900 / 0.293841 (-0.009941) \| 0.027799 / 0.128546 (-0.100747) \| 0.010572 / 0.075646 (-0.065074) \| 0.223595 / 0.419271 (-0.195677) \| 0.036951 / 0.043533 (-0.006582) \| 0.238813 / 0.255139 (-0.016326) \| 0.253841 / 0.283200 (-0.029359) \| 0.018471 / 0.141683 (-0.123212) \| 1.131969 / 1.452155 (-0.320186) \| 1.173763 / 1.492716 (-0.318954) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095504 / 0.018006 (0.077498) \| 0.301469 / 0.000490 (0.300979) \| 0.000212 / 0.000200 (0.000012) \| 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) \| 0.019194 / 0.037411 (-0.018217) \| 0.062313 / 0.014526 (0.047787) \| 0.075852 / 0.176557 (-0.100704) \| 0.121996 / 0.737135 (-0.615140) \| 0.076416 / 0.296338 (-0.219923) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292465 / 0.215209 (0.077256) \| 2.910234 / 2.077655 (0.832579) \| 1.479672 / 1.504120 (-0.024448) \| 1.332281 / 1.541195 (-0.208913) \| 1.354095 / 1.468490 (-0.114395) \| 0.573438 / 4.584777 (-4.011339) \| 2.382406 / 3.745712 (-1.363307) \| 2.708289 / 5.269862 (-2.561572) \| 1.739665 / 4.565676 (-2.826011) \| 0.063514 / 0.424275 (-0.360761) \| 0.005008 / 0.007607 (-0.002599) \| 0.350070 / 0.226044 (0.124025) \| 3.475837 / 2.268929 (1.206909) \| 1.804639 / 55.444624 (-53.639985) \| 1.520472 / 6.876477 (-5.356005) \| 1.658061 / 2.142072 (-0.484011) \| 0.648495 / 4.805227 (-4.156732) \| 0.118394 / 6.500664 (-6.382270) \| 0.042557 / 0.075469 (-0.032912) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.960772 / 1.841788 (-0.881016) \| 11.451629 / 8.074308 (3.377321) \| 9.613331 / 10.191392 (-0.578061) \| 0.130259 / 0.680424 (-0.550164) \| 0.015828 / 0.534201 (-0.518373) \| 0.287581 / 0.579283 (-0.291702) \| 0.266517 / 0.434364 (-0.167847) \| 0.327334 / 0.540337 (-0.213003) \| 0.427881 / 1.386936 (-0.959055) \|\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) \| 0.005364 / 0.011353 (-0.005989) \| 0.003723 / 0.011008 (-0.007285) \| 0.049990 / 0.038508 (0.011482) \| 0.032023 / 0.023109 (0.008913) \| 0.258609 / 0.275898 (-0.017289) \| 0.281250 / 0.323480 (-0.042230) \| 0.004222 / 0.007986 (-0.003764) \| 0.002799 / 0.004328 (-0.001529) \| 0.049546 / 0.004250 (0.045296) \| 0.040298 / 0.037052 (0.003246) \| 0.273552 / 0.258489 (0.015063) \| 0.304042 / 0.293841 (0.010201) \| 0.030116 / 0.128546 (-0.098430) \| 0.010792 / 0.075646 (-0.064855) \| 0.058427 / 0.419271 (-0.360845) \| 0.033415 / 0.043533 (-0.010118) \| 0.258794 / 0.255139 (0.003655) \| 0.275304 / 0.283200 (-0.007896) \| 0.017944 / 0.141683 (-0.123739) \| 1.109291 / 1.452155 (-0.342864) \| 1.156627 / 1.492716 (-0.336090) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096700 / 0.018006 (0.078693) \| 0.301108 / 0.000490 (0.300618) \| 0.000208 / 0.000200 (0.000008) \| 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) \| 0.022632 / 0.037411 (-0.014779) \| 0.075813 / 0.014526 (0.061287) \| 0.090302 / 0.176557 (-0.086254) \| 0.130375 / 0.737135 (-0.606760) \| 0.089710 / 0.296338 (-0.206629) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297091 / 0.215209 (0.081882) \| 2.910379 / 2.077655 (0.832725) \| 1.570460 / 1.504120 (0.066340) \| 1.441619 / 1.541195 (-0.099576) \| 1.442417 / 1.468490 (-0.026073) \| 0.570034 / 4.584777 (-4.014743) \| 0.952613 / 3.745712 (-2.793099) \| 2.659274 / 5.269862 (-2.610588) \| 1.751013 / 4.565676 (-2.814663) \| 0.064639 / 0.424275 (-0.359636) \| 0.005145 / 0.007607 (-0.002462) \| 0.347478 / 0.226044 (0.121434) \| 3.443862 / 2.268929 (1.174933) \| 1.897246 / 55.444624 (-53.547379) \| 1.609267 / 6.876477 (-5.267210) \| 1.755116 / 2.142072 (-0.386956) \| 0.658982 / 4.805227 (-4.146245) \| 0.117000 / 6.500664 (-6.383664) \| 0.041453 / 0.075469 (-0.034016) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005843 / 1.841788 (-0.835944) \| 12.101306 / 8.074308 (4.026998) \| 10.370706 / 10.191392 (0.179314) \| 0.139374 / 0.680424 (-0.541050) \| 0.015605 / 0.534201 (-0.518596) \| 0.286978 / 0.579283 (-0.292305) \| 0.122951 / 0.434364 (-0.311413) \| 0.331729 / 0.540337 (-0.208609) \| 0.422088 / 1.386936 (-0.964848) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#157585f964b1c7f675860af0d21712555b34aabc \"CML watermark\")\n", "I'm hitting this error now, using Spaces. Here's what an attempt to just get the 'validation' split is doing:\r\n\r\ncode:\r\n\r\n```\r\n\r\n \| import os\r\n \| from huggingface_hub import HfApi\r\n \| from datasets import Dataset, load_dataset, DownloadConfig\r\n \| \r\n \| \r\n \| GATED_IMAGENET = os.environ.get(\"GATED_IMAGENET\")\r\n \| api = HfApi(token=GATED_IMAGENET)\r\n \|\r\n \| ds = load_dataset('datacomp/imagenet-1k-random0.0', token=GATED_IMAGENET, data_files={'validation': 'data/val'}, split='validation', trust_remote_code=True)\r\n\r\n\r\n```\r\n\r\nlog:\r\n\r\n```\r\nGenerating validation split: 0%\| \| 0/50000 [00:00<?, ? examples/s]\r\nGenerating validation split: 12%\|█▏ \| 6172/50000 [00:01<00:07, 5804.90 examples/s]\r\nGenerating validation split: 25%\|██▌ \| 12716/50000 [00:02<00:06, 6167.77 examples/s]\r\nGenerating validation split: 38%\|███▊ \| 19060/50000 [00:03<00:04, 6218.99 examples/s]\r\nGenerating validation split: 51%\|█████ \| 25603/50000 [00:04<00:03, 6126.35 examples/s]\r\nGenerating validation split: 64%\|██████▍ \| 32145/50000 [00:05<00:02, 6166.95 examples/s]\r\nGenerating validation split: 77%\|███████▋ \| 38716/50000 [00:06<00:01, 6272.66 examples/s]\r\nGenerating validation split: 90%\|█████████ \| 45158/50000 [00:07<00:00, 6307.44 examples/s]\r\nGenerating validation split: 100%\|██████████\| 50000/50000 [00:08<00:00, 6212.19 examples/s]\r\nTraceback (most recent call last):\r\n File \"/home/user/app/app.py\", line 12, in <module>\r\n ds = load_dataset('datacomp/imagenet-1k-random0.0', token=GATED_IMAGENET, data_files={'validation': 'data/val'}, split='validation', trust_remote_code=True)\r\n File \"/usr/local/lib/python3.10/site-packages/datasets/load.py\", line 2154, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/usr/local/lib/python3.10/site-packages/datasets/builder.py\", line 924, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/usr/local/lib/python3.10/site-packages/datasets/builder.py\", line 1018, in _download_and_prepare\r\n verify_splits(self.info.splits, split_dict)\r\n File \"/usr/local/lib/python3.10/site-packages/datasets/utils/info_utils.py\", line 68, in verify_splits\r\n raise ExpectedMoreSplitsError(str(set(expected_splits) - set(recorded_splits)))\r\ndatasets.exceptions.ExpectedMoreSplitsError: {'train', 'test'}\r\n```", "Hi Meg ! Thanks for reporting, I'll see how I can fix this. In the meantime feel free to pass `verification_mode=\"no_checks\"` to `load_dataset`" ]
2,320,531,015	6,924	Caching map result of DatasetDict.	open	2024-05-28T09:07:41	2025-07-28T12:57:34	null	https://github.com/huggingface/datasets/issues/6924	null	MostHumble	false	[ "Has to do with sharding as far as I am aware, num_proc splits the dataset in shards and each process picks up a shard.", "@dimidagd why wouldn't you redistribute the processes (e.g. param_num_process to sequentially fill fingerprint_num_process) to make things work out? \n\nPerhaps I'm missing something important.", "I assume data has to be split between the processes somehow, and the decision to split it to num_processes shards is very convenient :). As I understand, the idea behind individual shard caching is being able to recover part of the processing if one of the processes fails. It would make sense to do a hierarchical cache checking though, where irregardless of the amount of processes, there is a check on whether the entire .map result has been cached or not (basically num_proc=1). Downside is more disk space used to cache both the shards and the entire dataset. " ]
2,319,292,872	6,923	Export Parquet Tablet Audio-Set is null bytes in Arrow	open	2024-05-27T14:27:57	2024-05-27T14:27:57	null	https://github.com/huggingface/datasets/issues/6923	null	anioji	false	[]
2,318,602,059	6,922	Remove torchaudio remnants from code	closed	2024-05-27T08:45:07	2024-05-27T09:08:19	2024-05-27T08:59:21	https://github.com/huggingface/datasets/pull/6922	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6922", "html_url": "https://github.com/huggingface/datasets/pull/6922", "diff_url": "https://github.com/huggingface/datasets/pull/6922.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6922.patch", "merged_at": "2024-05-27T08:59:21" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6922). 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) \| 0.005525 / 0.011353 (-0.005828) \| 0.004013 / 0.011008 (-0.006996) \| 0.063931 / 0.038508 (0.025423) \| 0.033857 / 0.023109 (0.010748) \| 0.250910 / 0.275898 (-0.024988) \| 0.278289 / 0.323480 (-0.045191) \| 0.004289 / 0.007986 (-0.003697) \| 0.002800 / 0.004328 (-0.001529) \| 0.050127 / 0.004250 (0.045877) \| 0.048901 / 0.037052 (0.011848) \| 0.260628 / 0.258489 (0.002139) \| 0.293904 / 0.293841 (0.000063) \| 0.028339 / 0.128546 (-0.100207) \| 0.010879 / 0.075646 (-0.064767) \| 0.203618 / 0.419271 (-0.215654) \| 0.036241 / 0.043533 (-0.007292) \| 0.250481 / 0.255139 (-0.004657) \| 0.274274 / 0.283200 (-0.008926) \| 0.018912 / 0.141683 (-0.122771) \| 1.146785 / 1.452155 (-0.305370) \| 1.199795 / 1.492716 (-0.292921) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095571 / 0.018006 (0.077564) \| 0.302961 / 0.000490 (0.302471) \| 0.000217 / 0.000200 (0.000017) \| 0.000109 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020121 / 0.037411 (-0.017290) \| 0.063231 / 0.014526 (0.048705) \| 0.075434 / 0.176557 (-0.101122) \| 0.123994 / 0.737135 (-0.613141) \| 0.076479 / 0.296338 (-0.219860) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277816 / 0.215209 (0.062607) \| 2.775481 / 2.077655 (0.697826) \| 1.454881 / 1.504120 (-0.049239) \| 1.339055 / 1.541195 (-0.202140) \| 1.347810 / 1.468490 (-0.120681) \| 0.572802 / 4.584777 (-4.011975) \| 2.357490 / 3.745712 (-1.388222) \| 2.822548 / 5.269862 (-2.447313) \| 1.746538 / 4.565676 (-2.819138) \| 0.066159 / 0.424275 (-0.358116) \| 0.005037 / 0.007607 (-0.002570) \| 0.329256 / 0.226044 (0.103212) \| 3.277511 / 2.268929 (1.008582) \| 1.807855 / 55.444624 (-53.636769) \| 1.505507 / 6.876477 (-5.370970) \| 1.634237 / 2.142072 (-0.507835) \| 0.643999 / 4.805227 (-4.161229) \| 0.117494 / 6.500664 (-6.383170) \| 0.042634 / 0.075469 (-0.032835) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.977689 / 1.841788 (-0.864098) \| 12.261836 / 8.074308 (4.187528) \| 9.871541 / 10.191392 (-0.319851) \| 0.147293 / 0.680424 (-0.533130) \| 0.015134 / 0.534201 (-0.519067) \| 0.287677 / 0.579283 (-0.291606) \| 0.264622 / 0.434364 (-0.169742) \| 0.330511 / 0.540337 (-0.209826) \| 0.467618 / 1.386936 (-0.919318) \|\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) \| 0.005690 / 0.011353 (-0.005663) \| 0.003801 / 0.011008 (-0.007207) \| 0.051817 / 0.038508 (0.013309) \| 0.033355 / 0.023109 (0.010246) \| 0.264416 / 0.275898 (-0.011482) \| 0.288494 / 0.323480 (-0.034986) \| 0.004246 / 0.007986 (-0.003740) \| 0.002814 / 0.004328 (-0.001515) \| 0.050547 / 0.004250 (0.046297) \| 0.042977 / 0.037052 (0.005925) \| 0.276884 / 0.258489 (0.018395) \| 0.303758 / 0.293841 (0.009917) \| 0.029412 / 0.128546 (-0.099134) \| 0.010697 / 0.075646 (-0.064949) \| 0.059497 / 0.419271 (-0.359775) \| 0.033670 / 0.043533 (-0.009862) \| 0.261311 / 0.255139 (0.006172) \| 0.286478 / 0.283200 (0.003278) \| 0.019386 / 0.141683 (-0.122297) \| 1.155943 / 1.452155 (-0.296211) \| 1.198512 / 1.492716 (-0.294205) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092954 / 0.018006 (0.074948) \| 0.294144 / 0.000490 (0.293655) \| 0.000213 / 0.000200 (0.000013) \| 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) \| 0.023013 / 0.037411 (-0.014398) \| 0.077161 / 0.014526 (0.062635) \| 0.089957 / 0.176557 (-0.086600) \| 0.129305 / 0.737135 (-0.607831) \| 0.091006 / 0.296338 (-0.205333) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294091 / 0.215209 (0.078882) \| 2.885395 / 2.077655 (0.807741) \| 1.555658 / 1.504120 (0.051538) \| 1.423276 / 1.541195 (-0.117919) \| 1.476485 / 1.468490 (0.007995) \| 0.569507 / 4.584777 (-4.015270) \| 0.979221 / 3.745712 (-2.766491) \| 2.818503 / 5.269862 (-2.451358) \| 1.871938 / 4.565676 (-2.693739) \| 0.064342 / 0.424275 (-0.359933) \| 0.005495 / 0.007607 (-0.002112) \| 0.351451 / 0.226044 (0.125407) \| 3.516078 / 2.268929 (1.247149) \| 1.928351 / 55.444624 (-53.516273) \| 1.625362 / 6.876477 (-5.251115) \| 1.813756 / 2.142072 (-0.328317) \| 0.657642 / 4.805227 (-4.147585) \| 0.117893 / 6.500664 (-6.382771) \| 0.042009 / 0.075469 (-0.033460) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.032893 / 1.841788 (-0.808894) \| 12.983400 / 8.074308 (4.909092) \| 10.747204 / 10.191392 (0.555812) \| 0.133163 / 0.680424 (-0.547261) \| 0.015875 / 0.534201 (-0.518326) \| 0.312592 / 0.579283 (-0.266691) \| 0.124780 / 0.434364 (-0.309584) \| 0.350735 / 0.540337 (-0.189603) \| 0.447130 / 1.386936 (-0.939806) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#048c789607af0370c1f2337248897956f7a91617 \"CML watermark\")\n" ]
2,318,394,398	6,921	Support fsspec 2024.5.0	closed	2024-05-27T07:00:59	2024-05-27T08:07:16	2024-05-27T08:01:08	https://github.com/huggingface/datasets/pull/6921	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6921", "html_url": "https://github.com/huggingface/datasets/pull/6921", "diff_url": "https://github.com/huggingface/datasets/pull/6921.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6921.patch", "merged_at": "2024-05-27T08:01:08" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6921). 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) \| 0.005252 / 0.011353 (-0.006100) \| 0.003752 / 0.011008 (-0.007257) \| 0.064034 / 0.038508 (0.025526) \| 0.031205 / 0.023109 (0.008096) \| 0.248903 / 0.275898 (-0.026995) \| 0.275808 / 0.323480 (-0.047671) \| 0.003135 / 0.007986 (-0.004851) \| 0.002635 / 0.004328 (-0.001693) \| 0.049869 / 0.004250 (0.045619) \| 0.047602 / 0.037052 (0.010549) \| 0.259738 / 0.258489 (0.001249) \| 0.296131 / 0.293841 (0.002290) \| 0.027467 / 0.128546 (-0.101080) \| 0.010449 / 0.075646 (-0.065197) \| 0.201369 / 0.419271 (-0.217903) \| 0.036317 / 0.043533 (-0.007216) \| 0.244347 / 0.255139 (-0.010792) \| 0.267597 / 0.283200 (-0.015602) \| 0.019930 / 0.141683 (-0.121753) \| 1.149012 / 1.452155 (-0.303143) \| 1.188083 / 1.492716 (-0.304633) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095190 / 0.018006 (0.077184) \| 0.300705 / 0.000490 (0.300215) \| 0.000222 / 0.000200 (0.000022) \| 0.000051 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019297 / 0.037411 (-0.018115) \| 0.063183 / 0.014526 (0.048657) \| 0.075094 / 0.176557 (-0.101463) \| 0.123556 / 0.737135 (-0.613579) \| 0.076721 / 0.296338 (-0.219618) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284136 / 0.215209 (0.068927) \| 2.814041 / 2.077655 (0.736387) \| 1.471038 / 1.504120 (-0.033082) \| 1.344002 / 1.541195 (-0.197193) \| 1.353875 / 1.468490 (-0.114615) \| 0.599495 / 4.584777 (-3.985282) \| 2.394491 / 3.745712 (-1.351221) \| 2.781734 / 5.269862 (-2.488128) \| 1.729829 / 4.565676 (-2.835848) \| 0.064194 / 0.424275 (-0.360081) \| 0.005022 / 0.007607 (-0.002585) \| 0.343384 / 0.226044 (0.117340) \| 3.357067 / 2.268929 (1.088139) \| 1.816323 / 55.444624 (-53.628301) \| 1.549405 / 6.876477 (-5.327072) \| 1.594394 / 2.142072 (-0.547679) \| 0.660650 / 4.805227 (-4.144578) \| 0.120271 / 6.500664 (-6.380393) \| 0.042422 / 0.075469 (-0.033047) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975776 / 1.841788 (-0.866011) \| 11.828093 / 8.074308 (3.753784) \| 9.384164 / 10.191392 (-0.807228) \| 0.140761 / 0.680424 (-0.539663) \| 0.014038 / 0.534201 (-0.520163) \| 0.284904 / 0.579283 (-0.294379) \| 0.263430 / 0.434364 (-0.170934) \| 0.320856 / 0.540337 (-0.219482) \| 0.419199 / 1.386936 (-0.967737) \|\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) \| 0.005672 / 0.011353 (-0.005681) \| 0.003667 / 0.011008 (-0.007341) \| 0.049989 / 0.038508 (0.011481) \| 0.033115 / 0.023109 (0.010006) \| 0.269808 / 0.275898 (-0.006090) \| 0.293286 / 0.323480 (-0.030193) \| 0.004238 / 0.007986 (-0.003748) \| 0.002722 / 0.004328 (-0.001606) \| 0.049516 / 0.004250 (0.045265) \| 0.042076 / 0.037052 (0.005024) \| 0.282182 / 0.258489 (0.023693) \| 0.310817 / 0.293841 (0.016976) \| 0.029824 / 0.128546 (-0.098722) \| 0.010516 / 0.075646 (-0.065130) \| 0.058223 / 0.419271 (-0.361049) \| 0.033263 / 0.043533 (-0.010270) \| 0.268769 / 0.255139 (0.013630) \| 0.288308 / 0.283200 (0.005108) \| 0.018531 / 0.141683 (-0.123151) \| 1.136806 / 1.452155 (-0.315349) \| 1.192636 / 1.492716 (-0.300080) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096583 / 0.018006 (0.078577) \| 0.303678 / 0.000490 (0.303188) \| 0.000211 / 0.000200 (0.000011) \| 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) \| 0.022741 / 0.037411 (-0.014670) \| 0.075799 / 0.014526 (0.061273) \| 0.089930 / 0.176557 (-0.086626) \| 0.129093 / 0.737135 (-0.608042) \| 0.089672 / 0.296338 (-0.206666) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292789 / 0.215209 (0.077580) \| 2.860137 / 2.077655 (0.782483) \| 1.566678 / 1.504120 (0.062558) \| 1.437756 / 1.541195 (-0.103439) \| 1.472347 / 1.468490 (0.003857) \| 0.566814 / 4.584777 (-4.017963) \| 0.963918 / 3.745712 (-2.781794) \| 2.717199 / 5.269862 (-2.552663) \| 1.763612 / 4.565676 (-2.802064) \| 0.063601 / 0.424275 (-0.360674) \| 0.005308 / 0.007607 (-0.002299) \| 0.363111 / 0.226044 (0.137066) \| 3.458222 / 2.268929 (1.189293) \| 1.939185 / 55.444624 (-53.505440) \| 1.659552 / 6.876477 (-5.216925) \| 1.801006 / 2.142072 (-0.341067) \| 0.648884 / 4.805227 (-4.156343) \| 0.116259 / 6.500664 (-6.384405) \| 0.041384 / 0.075469 (-0.034085) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.001594 / 1.841788 (-0.840194) \| 12.371125 / 8.074308 (4.296817) \| 10.489763 / 10.191392 (0.298371) \| 0.132500 / 0.680424 (-0.547924) \| 0.014742 / 0.534201 (-0.519459) \| 0.282258 / 0.579283 (-0.297026) \| 0.122755 / 0.434364 (-0.311608) \| 0.346068 / 0.540337 (-0.194269) \| 0.424943 / 1.386936 (-0.961994) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#df445c20346a34c08e7e039e4ec1a302eef3a69c \"CML watermark\")\n" ]
2,317,648,021	6,920	[WebDataset] Add `.pth` support for torch tensors	closed	2024-05-26T11:12:07	2024-05-27T09:11:17	2024-05-27T09:04:54	https://github.com/huggingface/datasets/pull/6920	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6920", "html_url": "https://github.com/huggingface/datasets/pull/6920", "diff_url": "https://github.com/huggingface/datasets/pull/6920.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6920.patch", "merged_at": "2024-05-27T09:04:54" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6920). 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) \| 0.005643 / 0.011353 (-0.005710) \| 0.003810 / 0.011008 (-0.007198) \| 0.065896 / 0.038508 (0.027388) \| 0.031692 / 0.023109 (0.008583) \| 0.258297 / 0.275898 (-0.017601) \| 0.294555 / 0.323480 (-0.028925) \| 0.004403 / 0.007986 (-0.003583) \| 0.002857 / 0.004328 (-0.001472) \| 0.049848 / 0.004250 (0.045597) \| 0.049719 / 0.037052 (0.012666) \| 0.266393 / 0.258489 (0.007904) \| 0.306214 / 0.293841 (0.012373) \| 0.028283 / 0.128546 (-0.100264) \| 0.010450 / 0.075646 (-0.065196) \| 0.203064 / 0.419271 (-0.216208) \| 0.036535 / 0.043533 (-0.006998) \| 0.247839 / 0.255139 (-0.007300) \| 0.270538 / 0.283200 (-0.012661) \| 0.018748 / 0.141683 (-0.122935) \| 1.117478 / 1.452155 (-0.334677) \| 1.162575 / 1.492716 (-0.330141) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.101074 / 0.018006 (0.083068) \| 0.304321 / 0.000490 (0.303831) \| 0.000270 / 0.000200 (0.000070) \| 0.000045 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019036 / 0.037411 (-0.018376) \| 0.064496 / 0.014526 (0.049970) \| 0.076848 / 0.176557 (-0.099709) \| 0.122979 / 0.737135 (-0.614156) \| 0.078008 / 0.296338 (-0.218330) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287009 / 0.215209 (0.071800) \| 2.839084 / 2.077655 (0.761429) \| 1.495977 / 1.504120 (-0.008143) \| 1.379147 / 1.541195 (-0.162047) \| 1.413170 / 1.468490 (-0.055320) \| 0.616408 / 4.584777 (-3.968369) \| 2.419183 / 3.745712 (-1.326529) \| 2.905720 / 5.269862 (-2.364142) \| 1.801634 / 4.565676 (-2.764043) \| 0.064034 / 0.424275 (-0.360241) \| 0.005098 / 0.007607 (-0.002509) \| 0.341732 / 0.226044 (0.115688) \| 3.365262 / 2.268929 (1.096334) \| 1.844335 / 55.444624 (-53.600289) \| 1.561450 / 6.876477 (-5.315027) \| 1.646254 / 2.142072 (-0.495819) \| 0.654993 / 4.805227 (-4.150234) \| 0.119837 / 6.500664 (-6.380827) \| 0.043375 / 0.075469 (-0.032094) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.000352 / 1.841788 (-0.841435) \| 12.765122 / 8.074308 (4.690813) \| 9.818879 / 10.191392 (-0.372513) \| 0.133986 / 0.680424 (-0.546438) \| 0.014065 / 0.534201 (-0.520136) \| 0.295859 / 0.579283 (-0.283424) \| 0.268497 / 0.434364 (-0.165867) \| 0.330909 / 0.540337 (-0.209429) \| 0.449218 / 1.386936 (-0.937718) \|\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) \| 0.005646 / 0.011353 (-0.005707) \| 0.003926 / 0.011008 (-0.007082) \| 0.050437 / 0.038508 (0.011929) \| 0.031828 / 0.023109 (0.008719) \| 0.268218 / 0.275898 (-0.007680) \| 0.292987 / 0.323480 (-0.030493) \| 0.004353 / 0.007986 (-0.003633) \| 0.002933 / 0.004328 (-0.001395) \| 0.050357 / 0.004250 (0.046107) \| 0.042988 / 0.037052 (0.005935) \| 0.281627 / 0.258489 (0.023138) \| 0.305664 / 0.293841 (0.011824) \| 0.030162 / 0.128546 (-0.098385) \| 0.010856 / 0.075646 (-0.064790) \| 0.059528 / 0.419271 (-0.359744) \| 0.033800 / 0.043533 (-0.009733) \| 0.268200 / 0.255139 (0.013061) \| 0.284982 / 0.283200 (0.001782) \| 0.019105 / 0.141683 (-0.122578) \| 1.171714 / 1.452155 (-0.280441) \| 1.205690 / 1.492716 (-0.287026) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.100979 / 0.018006 (0.082973) \| 0.314691 / 0.000490 (0.314201) \| 0.000217 / 0.000200 (0.000017) \| 0.000050 / 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) \| 0.023816 / 0.037411 (-0.013596) \| 0.081749 / 0.014526 (0.067223) \| 0.090118 / 0.176557 (-0.086438) \| 0.131615 / 0.737135 (-0.605520) \| 0.091821 / 0.296338 (-0.204517) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.301222 / 0.215209 (0.086013) \| 2.835310 / 2.077655 (0.757655) \| 1.562396 / 1.504120 (0.058276) \| 1.432365 / 1.541195 (-0.108830) \| 1.468358 / 1.468490 (-0.000132) \| 0.561300 / 4.584777 (-4.023477) \| 0.962294 / 3.745712 (-2.783419) \| 2.799705 / 5.269862 (-2.470157) \| 1.803035 / 4.565676 (-2.762642) \| 0.064104 / 0.424275 (-0.360171) \| 0.005480 / 0.007607 (-0.002127) \| 0.342519 / 0.226044 (0.116475) \| 3.406286 / 2.268929 (1.137357) \| 1.966962 / 55.444624 (-53.477663) \| 1.654664 / 6.876477 (-5.221813) \| 1.829303 / 2.142072 (-0.312769) \| 0.650932 / 4.805227 (-4.154295) \| 0.119211 / 6.500664 (-6.381453) \| 0.043739 / 0.075469 (-0.031730) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.006657 / 1.841788 (-0.835130) \| 12.915348 / 8.074308 (4.841040) \| 10.808156 / 10.191392 (0.616764) \| 0.132664 / 0.680424 (-0.547760) \| 0.015574 / 0.534201 (-0.518627) \| 0.284525 / 0.579283 (-0.294758) \| 0.122322 / 0.434364 (-0.312042) \| 0.326826 / 0.540337 (-0.213511) \| 0.416593 / 1.386936 (-0.970343) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#15ffefe5be194790a50af88ae1236a51b0ac95e6 \"CML watermark\")\n" ]
2,315,618,993	6,919	Invalid YAML in README.md: unknown tag !<tag:yaml.org,2002:python/tuple>	open	2024-05-24T14:59:45	2024-05-24T14:59:45	null	https://github.com/huggingface/datasets/issues/6919	null	juanqui	false	[]
2,315,322,738	6,918	NonMatchingSplitsSizesError when using data_dir	closed	2024-05-24T12:43:39	2024-05-31T17:10:38	2024-05-31T17:10:38	https://github.com/huggingface/datasets/issues/6918	null	srehaag	false	[ "Thanks for reporting, @srehaag.\r\n\r\nWe are investigating this issue.", "I confirm there is a bug for data-based Hub datasets when the user passes `data_dir`, which was introduced by PR:\r\n- #6714" ]
2,314,683,663	6,917	WinError 32 The process cannot access the file during load_dataset	open	2024-05-24T07:54:51	2024-05-24T07:54:51	null	https://github.com/huggingface/datasets/issues/6917	null	elwe-2808	false	[]
2,311,675,564	6,916	```push_to_hub()``` - Prevent Automatic Generation of Splits	closed	2024-05-22T23:52:15	2024-05-23T00:07:53	2024-05-23T00:07:53	https://github.com/huggingface/datasets/issues/6916	null	jetlime	false	[]
2,310,564,961	6,915	Validate config name and data_files in packaged modules	closed	2024-05-22T13:36:33	2024-06-06T09:32:10	2024-06-06T09:24:35	https://github.com/huggingface/datasets/pull/6915	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6915", "html_url": "https://github.com/huggingface/datasets/pull/6915", "diff_url": "https://github.com/huggingface/datasets/pull/6915.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6915.patch", "merged_at": "2024-06-06T09:24:35" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6915). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I pushed a change that fixes 2.15 cache reloading (I fixed the packaged module hash), feel free to merge if this change is fine for you", "Something weird happened in GitHub: I just merged this PR to main, See: https://github.com/huggingface/datasets/commit/5bbbf1b19766e31a6905f3e82bf3aa3f9f84a982\r\n\r\nHowever this PR still appears as Open...\r\n\r\nIf I retry to merge this PR, an error appears: \"Merge attempt failed: Merge already in progress\"\r\n![Screenshot from 2024-06-06 06-29-22](https://github.com/huggingface/datasets/assets/8515462/5fe87442-cc5d-4e9b-b60e-fdfbab830c81)\r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005543 / 0.011353 (-0.005810) \| 0.004059 / 0.011008 (-0.006949) \| 0.064678 / 0.038508 (0.026170) \| 0.032615 / 0.023109 (0.009506) \| 0.245883 / 0.275898 (-0.030015) \| 0.273545 / 0.323480 (-0.049935) \| 0.004268 / 0.007986 (-0.003718) \| 0.003160 / 0.004328 (-0.001168) \| 0.051982 / 0.004250 (0.047731) \| 0.051186 / 0.037052 (0.014134) \| 0.254009 / 0.258489 (-0.004480) \| 0.289594 / 0.293841 (-0.004247) \| 0.028459 / 0.128546 (-0.100087) \| 0.011061 / 0.075646 (-0.064585) \| 0.203571 / 0.419271 (-0.215700) \| 0.038049 / 0.043533 (-0.005484) \| 0.243700 / 0.255139 (-0.011439) \| 0.264816 / 0.283200 (-0.018383) \| 0.019556 / 0.141683 (-0.122127) \| 1.114395 / 1.452155 (-0.337759) \| 1.168915 / 1.492716 (-0.323802) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098814 / 0.018006 (0.080808) \| 0.308218 / 0.000490 (0.307728) \| 0.000221 / 0.000200 (0.000022) \| 0.000047 / 0.000054 (-0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019660 / 0.037411 (-0.017752) \| 0.070542 / 0.014526 (0.056017) \| 0.078906 / 0.176557 (-0.097650) \| 0.126658 / 0.737135 (-0.610477) \| 0.080427 / 0.296338 (-0.215911) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280686 / 0.215209 (0.065477) \| 2.767480 / 2.077655 (0.689825) \| 1.455325 / 1.504120 (-0.048795) \| 1.336677 / 1.541195 (-0.204518) \| 1.380359 / 1.468490 (-0.088131) \| 0.576310 / 4.584777 (-4.008467) \| 2.431829 / 3.745712 (-1.313883) \| 2.815266 / 5.269862 (-2.454595) \| 1.908962 / 4.565676 (-2.656714) \| 0.065306 / 0.424275 (-0.358969) \| 0.005229 / 0.007607 (-0.002378) \| 0.336018 / 0.226044 (0.109973) \| 3.349283 / 2.268929 (1.080355) \| 1.814696 / 55.444624 (-53.629929) \| 1.520969 / 6.876477 (-5.355508) \| 1.735322 / 2.142072 (-0.406751) \| 0.661513 / 4.805227 (-4.143714) \| 0.121465 / 6.500664 (-6.379199) \| 0.044505 / 0.075469 (-0.030964) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.989204 / 1.841788 (-0.852584) \| 12.608414 / 8.074308 (4.534106) \| 10.133358 / 10.191392 (-0.058034) \| 0.133986 / 0.680424 (-0.546438) \| 0.014332 / 0.534201 (-0.519869) \| 0.293207 / 0.579283 (-0.286076) \| 0.265657 / 0.434364 (-0.168707) \| 0.325972 / 0.540337 (-0.214365) \| 0.478103 / 1.386936 (-0.908833) \|\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) \| 0.006070 / 0.011353 (-0.005283) \| 0.004122 / 0.011008 (-0.006886) \| 0.050572 / 0.038508 (0.012064) \| 0.033732 / 0.023109 (0.010623) \| 0.271282 / 0.275898 (-0.004616) \| 0.296247 / 0.323480 (-0.027233) \| 0.004400 / 0.007986 (-0.003585) \| 0.002914 / 0.004328 (-0.001415) \| 0.049332 / 0.004250 (0.045082) \| 0.042213 / 0.037052 (0.005161) \| 0.281230 / 0.258489 (0.022741) \| 0.315514 / 0.293841 (0.021673) \| 0.030864 / 0.128546 (-0.097682) \| 0.011185 / 0.075646 (-0.064461) \| 0.059227 / 0.419271 (-0.360044) \| 0.034006 / 0.043533 (-0.009527) \| 0.270059 / 0.255139 (0.014920) \| 0.284014 / 0.283200 (0.000814) \| 0.019502 / 0.141683 (-0.122181) \| 1.143650 / 1.452155 (-0.308505) \| 1.190968 / 1.492716 (-0.301749) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.100502 / 0.018006 (0.082496) \| 0.307863 / 0.000490 (0.307373) \| 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) \| 0.023442 / 0.037411 (-0.013969) \| 0.080185 / 0.014526 (0.065659) \| 0.089372 / 0.176557 (-0.087185) \| 0.131030 / 0.737135 (-0.606105) \| 0.091174 / 0.296338 (-0.205165) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304187 / 0.215209 (0.088978) \| 3.043055 / 2.077655 (0.965400) \| 1.629578 / 1.504120 (0.125459) \| 1.533762 / 1.541195 (-0.007432) \| 1.546134 / 1.468490 (0.077643) \| 0.577739 / 4.584777 (-4.007038) \| 0.986310 / 3.745712 (-2.759402) \| 2.791650 / 5.269862 (-2.478212) \| 1.841190 / 4.565676 (-2.724487) \| 0.064943 / 0.424275 (-0.359333) \| 0.005251 / 0.007607 (-0.002356) \| 0.355009 / 0.226044 (0.128965) \| 3.560935 / 2.268929 (1.292007) \| 1.991995 / 55.444624 (-53.452629) \| 1.708796 / 6.876477 (-5.167681) \| 1.917721 / 2.142072 (-0.224351) \| 0.667667 / 4.805227 (-4.137561) \| 0.119956 / 6.500664 (-6.380708) \| 0.042069 / 0.075469 (-0.033400) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.006242 / 1.841788 (-0.835546) \| 13.321644 / 8.074308 (5.247336) \| 10.712409 / 10.191392 (0.521017) \| 0.134036 / 0.680424 (-0.546388) \| 0.017645 / 0.534201 (-0.516555) \| 0.289077 / 0.579283 (-0.290206) \| 0.131356 / 0.434364 (-0.303007) \| 0.333062 / 0.540337 (-0.207275) \| 0.425327 / 1.386936 (-0.961609) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#09ebf5190afbd017f3ca24ef444be2d933411eed \"CML watermark\")\n", "Indeed, the merge commit is: https://github.com/huggingface/datasets/commit/5bbbf1b19766e31a6905f3e82bf3aa3f9f84a982\r\n\r\nThe following commit is just empty: https://github.com/huggingface/datasets/commit/09ebf5190afbd017f3ca24ef444be2d933411eed" ]

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