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
2,163,799,868	6,707	Silence ruff deprecation messages	closed	2024-03-01T16:52:29	2024-03-01T17:32:14	2024-03-01T17:25:46	https://github.com/huggingface/datasets/pull/6707	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6707", "html_url": "https://github.com/huggingface/datasets/pull/6707", "diff_url": "https://github.com/huggingface/datasets/pull/6707.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6707.patch", "merged_at": "2024-03-01T17:25:46" }	mariosasko	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6707). 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) \| 0.004728 / 0.011353 (-0.006624) \| 0.002941 / 0.011008 (-0.008067) \| 0.058270 / 0.038508 (0.019762) \| 0.027418 / 0.023109 (0.004309) \| 0.224993 / 0.275898 (-0.050905) \| 0.243103 / 0.323480 (-0.080377) \| 0.004668 / 0.007986 (-0.003318) \| 0.002499 / 0.004328 (-0.001829) \| 0.045020 / 0.004250 (0.040770) \| 0.038006 / 0.037052 (0.000953) \| 0.240807 / 0.258489 (-0.017682) \| 0.264554 / 0.293841 (-0.029287) \| 0.027018 / 0.128546 (-0.101529) \| 0.009866 / 0.075646 (-0.065780) \| 0.196578 / 0.419271 (-0.222694) \| 0.034536 / 0.043533 (-0.008997) \| 0.236535 / 0.255139 (-0.018604) \| 0.248879 / 0.283200 (-0.034321) \| 0.017140 / 0.141683 (-0.124543) \| 1.046927 / 1.452155 (-0.405228) \| 1.121209 / 1.492716 (-0.371507) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088267 / 0.018006 (0.070261) \| 0.279774 / 0.000490 (0.279284) \| 0.000214 / 0.000200 (0.000014) \| 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) \| 0.016521 / 0.037411 (-0.020890) \| 0.056499 / 0.014526 (0.041974) \| 0.067264 / 0.176557 (-0.109293) \| 0.117270 / 0.737135 (-0.619865) \| 0.069284 / 0.296338 (-0.227055) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.260679 / 0.215209 (0.045470) \| 2.608971 / 2.077655 (0.531316) \| 1.363139 / 1.504120 (-0.140981) \| 1.262128 / 1.541195 (-0.279067) \| 1.273619 / 1.468490 (-0.194871) \| 0.523417 / 4.584777 (-4.061360) \| 2.291145 / 3.745712 (-1.454567) \| 2.540603 / 5.269862 (-2.729258) \| 1.599090 / 4.565676 (-2.966586) \| 0.058170 / 0.424275 (-0.366105) \| 0.004556 / 0.007607 (-0.003051) \| 0.308361 / 0.226044 (0.082316) \| 3.069269 / 2.268929 (0.800340) \| 1.698064 / 55.444624 (-53.746560) \| 1.426631 / 6.876477 (-5.449846) \| 1.463913 / 2.142072 (-0.678160) \| 0.595234 / 4.805227 (-4.209993) \| 0.107202 / 6.500664 (-6.393462) \| 0.038183 / 0.075469 (-0.037286) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.905999 / 1.841788 (-0.935789) \| 10.828492 / 8.074308 (2.754184) \| 8.705635 / 10.191392 (-1.485757) \| 0.121203 / 0.680424 (-0.559221) \| 0.013789 / 0.534201 (-0.520412) \| 0.268172 / 0.579283 (-0.311111) \| 0.254277 / 0.434364 (-0.180086) \| 0.310280 / 0.540337 (-0.230057) \| 0.410490 / 1.386936 (-0.976446) \|\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) \| 0.004696 / 0.011353 (-0.006657) \| 0.002982 / 0.011008 (-0.008026) \| 0.045333 / 0.038508 (0.006825) \| 0.027483 / 0.023109 (0.004374) \| 0.253438 / 0.275898 (-0.022460) \| 0.272657 / 0.323480 (-0.050823) \| 0.004060 / 0.007986 (-0.003926) \| 0.002574 / 0.004328 (-0.001754) \| 0.045462 / 0.004250 (0.041212) \| 0.041260 / 0.037052 (0.004208) \| 0.267919 / 0.258489 (0.009430) \| 0.290935 / 0.293841 (-0.002906) \| 0.026674 / 0.128546 (-0.101873) \| 0.009370 / 0.075646 (-0.066276) \| 0.053543 / 0.419271 (-0.365729) \| 0.047390 / 0.043533 (0.003857) \| 0.255774 / 0.255139 (0.000635) \| 0.273909 / 0.283200 (-0.009291) \| 0.017252 / 0.141683 (-0.124431) \| 1.064298 / 1.452155 (-0.387857) \| 1.125374 / 1.492716 (-0.367342) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091506 / 0.018006 (0.073499) \| 0.298570 / 0.000490 (0.298080) \| 0.000741 / 0.000200 (0.000541) \| 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) \| 0.020232 / 0.037411 (-0.017179) \| 0.069640 / 0.014526 (0.055114) \| 0.081360 / 0.176557 (-0.095197) \| 0.116955 / 0.737135 (-0.620180) \| 0.080920 / 0.296338 (-0.215418) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283000 / 0.215209 (0.067791) \| 2.802526 / 2.077655 (0.724871) \| 1.534631 / 1.504120 (0.030511) \| 1.407260 / 1.541195 (-0.133935) \| 1.409111 / 1.468490 (-0.059379) \| 0.534892 / 4.584777 (-4.049885) \| 2.350516 / 3.745712 (-1.395196) \| 2.550444 / 5.269862 (-2.719418) \| 1.661747 / 4.565676 (-2.903930) \| 0.060978 / 0.424275 (-0.363297) \| 0.005300 / 0.007607 (-0.002308) \| 0.367418 / 0.226044 (0.141373) \| 3.338046 / 2.268929 (1.069117) \| 1.883914 / 55.444624 (-53.560710) \| 1.638561 / 6.876477 (-5.237916) \| 1.751547 / 2.142072 (-0.390526) \| 0.633318 / 4.805227 (-4.171910) \| 0.114971 / 6.500664 (-6.385693) \| 0.040202 / 0.075469 (-0.035267) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.962053 / 1.841788 (-0.879735) \| 11.600643 / 8.074308 (3.526334) \| 9.526461 / 10.191392 (-0.664931) \| 0.123909 / 0.680424 (-0.556515) \| 0.015944 / 0.534201 (-0.518257) \| 0.271542 / 0.579283 (-0.307741) \| 0.254366 / 0.434364 (-0.179998) \| 0.300499 / 0.540337 (-0.239838) \| 0.409122 / 1.386936 (-0.977814) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0acb27347a3c03efde612023235201a777e08e72 \"CML watermark\")\n" ]
2,163,783,123	6,706	Update ruff	closed	2024-03-01T16:44:58	2024-03-01T17:02:13	2024-03-01T16:52:17	https://github.com/huggingface/datasets/pull/6706	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6706", "html_url": "https://github.com/huggingface/datasets/pull/6706", "diff_url": "https://github.com/huggingface/datasets/pull/6706.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6706.patch", "merged_at": "2024-03-01T16:52:17" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6706). 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) \| 0.005014 / 0.011353 (-0.006339) \| 0.003324 / 0.011008 (-0.007685) \| 0.062501 / 0.038508 (0.023993) \| 0.027633 / 0.023109 (0.004524) \| 0.245693 / 0.275898 (-0.030205) \| 0.271963 / 0.323480 (-0.051517) \| 0.003062 / 0.007986 (-0.004923) \| 0.002646 / 0.004328 (-0.001683) \| 0.049020 / 0.004250 (0.044769) \| 0.042381 / 0.037052 (0.005328) \| 0.269729 / 0.258489 (0.011240) \| 0.289052 / 0.293841 (-0.004789) \| 0.027138 / 0.128546 (-0.101408) \| 0.010246 / 0.075646 (-0.065400) \| 0.205378 / 0.419271 (-0.213893) \| 0.035792 / 0.043533 (-0.007741) \| 0.247204 / 0.255139 (-0.007935) \| 0.271805 / 0.283200 (-0.011394) \| 0.019541 / 0.141683 (-0.122142) \| 1.129335 / 1.452155 (-0.322820) \| 1.174088 / 1.492716 (-0.318629) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091340 / 0.018006 (0.073334) \| 0.300037 / 0.000490 (0.299547) \| 0.000214 / 0.000200 (0.000014) \| 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) \| 0.018360 / 0.037411 (-0.019051) \| 0.061239 / 0.014526 (0.046713) \| 0.072304 / 0.176557 (-0.104253) \| 0.118883 / 0.737135 (-0.618253) \| 0.073562 / 0.296338 (-0.222777) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284478 / 0.215209 (0.069269) \| 2.761819 / 2.077655 (0.684165) \| 1.443757 / 1.504120 (-0.060363) \| 1.315221 / 1.541195 (-0.225974) \| 1.333930 / 1.468490 (-0.134560) \| 0.581470 / 4.584777 (-4.003307) \| 2.422530 / 3.745712 (-1.323183) \| 2.869898 / 5.269862 (-2.399963) \| 1.789159 / 4.565676 (-2.776517) \| 0.063708 / 0.424275 (-0.360567) \| 0.004922 / 0.007607 (-0.002685) \| 0.337352 / 0.226044 (0.111307) \| 3.290192 / 2.268929 (1.021263) \| 1.840192 / 55.444624 (-53.604432) \| 1.543008 / 6.876477 (-5.333469) \| 1.548947 / 2.142072 (-0.593125) \| 0.655129 / 4.805227 (-4.150098) \| 0.119010 / 6.500664 (-6.381654) \| 0.042583 / 0.075469 (-0.032886) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981333 / 1.841788 (-0.860455) \| 11.349564 / 8.074308 (3.275256) \| 9.397603 / 10.191392 (-0.793789) \| 0.142151 / 0.680424 (-0.538273) \| 0.013850 / 0.534201 (-0.520351) \| 0.286323 / 0.579283 (-0.292960) \| 0.265223 / 0.434364 (-0.169141) \| 0.335322 / 0.540337 (-0.205015) \| 0.441727 / 1.386936 (-0.945209) \|\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) \| 0.005134 / 0.011353 (-0.006219) \| 0.003216 / 0.011008 (-0.007792) \| 0.049401 / 0.038508 (0.010893) \| 0.031509 / 0.023109 (0.008400) \| 0.262211 / 0.275898 (-0.013687) \| 0.284814 / 0.323480 (-0.038665) \| 0.004165 / 0.007986 (-0.003821) \| 0.002693 / 0.004328 (-0.001636) \| 0.048088 / 0.004250 (0.043838) \| 0.043609 / 0.037052 (0.006557) \| 0.271126 / 0.258489 (0.012637) \| 0.301374 / 0.293841 (0.007533) \| 0.028891 / 0.128546 (-0.099655) \| 0.009911 / 0.075646 (-0.065735) \| 0.057334 / 0.419271 (-0.361938) \| 0.050936 / 0.043533 (0.007403) \| 0.258883 / 0.255139 (0.003744) \| 0.282884 / 0.283200 (-0.000315) \| 0.017475 / 0.141683 (-0.124208) \| 1.167562 / 1.452155 (-0.284593) \| 1.214081 / 1.492716 (-0.278636) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096890 / 0.018006 (0.078884) \| 0.315819 / 0.000490 (0.315329) \| 0.000218 / 0.000200 (0.000018) \| 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) \| 0.021775 / 0.037411 (-0.015637) \| 0.075816 / 0.014526 (0.061290) \| 0.086992 / 0.176557 (-0.089564) \| 0.125816 / 0.737135 (-0.611319) \| 0.090343 / 0.296338 (-0.205995) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295204 / 0.215209 (0.079995) \| 2.903129 / 2.077655 (0.825475) \| 1.629838 / 1.504120 (0.125718) \| 1.531862 / 1.541195 (-0.009332) \| 1.504614 / 1.468490 (0.036123) \| 0.572910 / 4.584777 (-4.011867) \| 2.482555 / 3.745712 (-1.263157) \| 2.637259 / 5.269862 (-2.632603) \| 1.733049 / 4.565676 (-2.832628) \| 0.063239 / 0.424275 (-0.361036) \| 0.005037 / 0.007607 (-0.002570) \| 0.346657 / 0.226044 (0.120612) \| 3.446469 / 2.268929 (1.177540) \| 2.017864 / 55.444624 (-53.426761) \| 1.688704 / 6.876477 (-5.187773) \| 1.790813 / 2.142072 (-0.351259) \| 0.660769 / 4.805227 (-4.144458) \| 0.115582 / 6.500664 (-6.385082) \| 0.040111 / 0.075469 (-0.035358) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.058089 / 1.841788 (-0.783699) \| 11.998171 / 8.074308 (3.923863) \| 10.459128 / 10.191392 (0.267736) \| 0.149653 / 0.680424 (-0.530771) \| 0.015015 / 0.534201 (-0.519186) \| 0.289973 / 0.579283 (-0.289310) \| 0.274217 / 0.434364 (-0.160147) \| 0.351057 / 0.540337 (-0.189281) \| 0.434295 / 1.386936 (-0.952641) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cb33582eb2262cec5ec6f238b50a7d043ab3ca94 \"CML watermark\")\n" ]
2,163,768,640	6,705	Fix data_files when passing data_dir	closed	2024-03-01T16:38:53	2024-03-01T18:59:06	2024-03-01T18:52:49	https://github.com/huggingface/datasets/pull/6705	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6705", "html_url": "https://github.com/huggingface/datasets/pull/6705", "diff_url": "https://github.com/huggingface/datasets/pull/6705.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6705.patch", "merged_at": "2024-03-01T18:52:49" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6705). 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) \| 0.005014 / 0.011353 (-0.006339) \| 0.003371 / 0.011008 (-0.007637) \| 0.063622 / 0.038508 (0.025114) \| 0.026551 / 0.023109 (0.003442) \| 0.244602 / 0.275898 (-0.031296) \| 0.269981 / 0.323480 (-0.053499) \| 0.003959 / 0.007986 (-0.004027) \| 0.002678 / 0.004328 (-0.001650) \| 0.049421 / 0.004250 (0.045170) \| 0.039926 / 0.037052 (0.002873) \| 0.256609 / 0.258489 (-0.001881) \| 0.281934 / 0.293841 (-0.011907) \| 0.027794 / 0.128546 (-0.100752) \| 0.010130 / 0.075646 (-0.065516) \| 0.207471 / 0.419271 (-0.211800) \| 0.035423 / 0.043533 (-0.008110) \| 0.246987 / 0.255139 (-0.008152) \| 0.265413 / 0.283200 (-0.017787) \| 0.018287 / 0.141683 (-0.123396) \| 1.117550 / 1.452155 (-0.334604) \| 1.151713 / 1.492716 (-0.341003) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095632 / 0.018006 (0.077626) \| 0.304315 / 0.000490 (0.303825) \| 0.000214 / 0.000200 (0.000014) \| 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) \| 0.018591 / 0.037411 (-0.018820) \| 0.062081 / 0.014526 (0.047555) \| 0.075137 / 0.176557 (-0.101420) \| 0.119116 / 0.737135 (-0.618020) \| 0.075254 / 0.296338 (-0.221085) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286161 / 0.215209 (0.070952) \| 2.793824 / 2.077655 (0.716169) \| 1.492523 / 1.504120 (-0.011597) \| 1.372158 / 1.541195 (-0.169037) \| 1.385921 / 1.468490 (-0.082569) \| 0.568700 / 4.584777 (-4.016077) \| 2.340451 / 3.745712 (-1.405262) \| 2.712022 / 5.269862 (-2.557840) \| 1.712479 / 4.565676 (-2.853197) \| 0.060906 / 0.424275 (-0.363369) \| 0.004909 / 0.007607 (-0.002698) \| 0.338227 / 0.226044 (0.112182) \| 3.331329 / 2.268929 (1.062400) \| 1.845646 / 55.444624 (-53.598978) \| 1.559384 / 6.876477 (-5.317093) \| 1.577683 / 2.142072 (-0.564390) \| 0.629367 / 4.805227 (-4.175860) \| 0.118645 / 6.500664 (-6.382019) \| 0.041517 / 0.075469 (-0.033952) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.962237 / 1.841788 (-0.879551) \| 11.232566 / 8.074308 (3.158258) \| 9.627141 / 10.191392 (-0.564251) \| 0.129732 / 0.680424 (-0.550692) \| 0.013701 / 0.534201 (-0.520500) \| 0.291869 / 0.579283 (-0.287414) \| 0.269298 / 0.434364 (-0.165066) \| 0.342502 / 0.540337 (-0.197835) \| 0.455891 / 1.386936 (-0.931045) \|\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) \| 0.005256 / 0.011353 (-0.006097) \| 0.003419 / 0.011008 (-0.007589) \| 0.049681 / 0.038508 (0.011173) \| 0.029566 / 0.023109 (0.006457) \| 0.268010 / 0.275898 (-0.007888) \| 0.293721 / 0.323480 (-0.029759) \| 0.004249 / 0.007986 (-0.003737) \| 0.002643 / 0.004328 (-0.001685) \| 0.048758 / 0.004250 (0.044508) \| 0.044294 / 0.037052 (0.007241) \| 0.279584 / 0.258489 (0.021095) \| 0.311150 / 0.293841 (0.017309) \| 0.029443 / 0.128546 (-0.099103) \| 0.010314 / 0.075646 (-0.065333) \| 0.057770 / 0.419271 (-0.361501) \| 0.050953 / 0.043533 (0.007420) \| 0.268283 / 0.255139 (0.013144) \| 0.289155 / 0.283200 (0.005956) \| 0.017742 / 0.141683 (-0.123941) \| 1.163963 / 1.452155 (-0.288192) \| 1.200580 / 1.492716 (-0.292136) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096365 / 0.018006 (0.078359) \| 0.307257 / 0.000490 (0.306767) \| 0.000265 / 0.000200 (0.000065) \| 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) \| 0.021862 / 0.037411 (-0.015550) \| 0.075502 / 0.014526 (0.060976) \| 0.087800 / 0.176557 (-0.088756) \| 0.125468 / 0.737135 (-0.611667) \| 0.088207 / 0.296338 (-0.208132) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.324184 / 0.215209 (0.108975) \| 3.198442 / 2.077655 (1.120787) \| 1.862801 / 1.504120 (0.358682) \| 1.728637 / 1.541195 (0.187443) \| 1.727997 / 1.468490 (0.259507) \| 0.571590 / 4.584777 (-4.013187) \| 2.448661 / 3.745712 (-1.297051) \| 2.665943 / 5.269862 (-2.603919) \| 1.731718 / 4.565676 (-2.833958) \| 0.063644 / 0.424275 (-0.360631) \| 0.004989 / 0.007607 (-0.002619) \| 0.364543 / 0.226044 (0.138498) \| 3.615859 / 2.268929 (1.346930) \| 2.131637 / 55.444624 (-53.312987) \| 1.857317 / 6.876477 (-5.019159) \| 1.992813 / 2.142072 (-0.149260) \| 0.654662 / 4.805227 (-4.150565) \| 0.117631 / 6.500664 (-6.383034) \| 0.040934 / 0.075469 (-0.034535) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.013802 / 1.841788 (-0.827985) \| 11.899873 / 8.074308 (3.825565) \| 10.291297 / 10.191392 (0.099905) \| 0.155245 / 0.680424 (-0.525179) \| 0.014449 / 0.534201 (-0.519752) \| 0.286331 / 0.579283 (-0.292952) \| 0.273111 / 0.434364 (-0.161253) \| 0.321182 / 0.540337 (-0.219155) \| 0.433406 / 1.386936 (-0.953530) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e5406f9a9a453f2c0614c2ee26975e5973edc278 \"CML watermark\")\n" ]
2,163,752,391	6,704	Improve default patterns resolution	closed	2024-03-01T16:31:25	2024-04-23T09:43:09	2024-03-15T15:22:03	https://github.com/huggingface/datasets/pull/6704	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6704", "html_url": "https://github.com/huggingface/datasets/pull/6704", "diff_url": "https://github.com/huggingface/datasets/pull/6704.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6704.patch", "merged_at": "2024-03-15T15:22:03" }	mariosasko	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6704). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Awesome !\r\n\r\nNote that it can still create duplicates if a path matches several dir patterns, e.g.\r\n\r\n```\r\ndata/train-train/data/txt\r\n```\r\nmatches two dir patterns:\r\n```\r\n*/{keyword}[{sep}]/\r\n/[{sep}]{keyword}/\r\n```\r\n\r\nPS: feel free to update your branch, I just updated ruff on `main`", "Yes, I didn't mention that case on purpose 🙂. One solution would be deprecating the `/[{sep}]{keyword}/**` pattern (and eventually removing it). This way, the directory patterns would align more with the filename ones. Or do you think this is too big of a breaking change?", "I think it's too big of a breaking change yes :/ (and would make the docs / logic more complex for users to get imo) Though I think your approach is already a nice step in the right direction", "These changes to the `resolve_pattern` function lead to 20-30x faster local file resolution in my benchmarks.", "Nice ! Though since `fsspec` caches the filesystem, is there a risk when adding new files and reloading a dataset ?\r\n\r\n\r\n```python\r\nwith open(\"my/local/dir/0000.txt\", \"w\") as f:\r\n f.write(\"Hello there\")\r\nd1 = load_dataset(\"my/local/dir\")\r\nwith open(\"my/local/dir/0001.txt\", \"w\") as f:\r\n f.write(\"General Kenobi\")\r\nd2 = load_dataset(\"my/local/dir\")\r\nassert list(d1) != list(d2)\r\n```", "Yes. But I think I have a solution for this.", "I'm not satisfied with the context manager approach...\r\n\r\nA clean solution would require a bigger rewrite of the resolution logic (e.g., merging `get_data_patterns` and `DataFilesDict.from_patterns` into a `get_data_files` function that would build the `DataFilesDict` by matching the paths using `fs.find` and `fsspec.utils.glob_translate` (available in `fsspec>=2023.12.0`))\r\n\r\nThe current changes make the local resolution 2-3x faster, which is good enough for now, I think.", "<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) \| 0.004888 / 0.011353 (-0.006465) \| 0.003267 / 0.011008 (-0.007742) \| 0.065117 / 0.038508 (0.026609) \| 0.029416 / 0.023109 (0.006306) \| 0.232021 / 0.275898 (-0.043877) \| 0.258053 / 0.323480 (-0.065427) \| 0.003971 / 0.007986 (-0.004014) \| 0.002550 / 0.004328 (-0.001779) \| 0.049126 / 0.004250 (0.044876) \| 0.040620 / 0.037052 (0.003568) \| 0.253437 / 0.258489 (-0.005052) \| 0.273583 / 0.293841 (-0.020258) \| 0.026775 / 0.128546 (-0.101771) \| 0.010073 / 0.075646 (-0.065573) \| 0.219089 / 0.419271 (-0.200183) \| 0.035047 / 0.043533 (-0.008486) \| 0.247661 / 0.255139 (-0.007478) \| 0.258674 / 0.283200 (-0.024525) \| 0.018428 / 0.141683 (-0.123255) \| 1.130394 / 1.452155 (-0.321761) \| 1.173167 / 1.492716 (-0.319549) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092581 / 0.018006 (0.074574) \| 0.303657 / 0.000490 (0.303167) \| 0.000215 / 0.000200 (0.000015) \| 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) \| 0.018640 / 0.037411 (-0.018771) \| 0.062032 / 0.014526 (0.047506) \| 0.073982 / 0.176557 (-0.102575) \| 0.121499 / 0.737135 (-0.615636) \| 0.076780 / 0.296338 (-0.219559) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279411 / 0.215209 (0.064202) \| 2.737977 / 2.077655 (0.660322) \| 1.454135 / 1.504120 (-0.049985) \| 1.343144 / 1.541195 (-0.198051) \| 1.339876 / 1.468490 (-0.128614) \| 0.567306 / 4.584777 (-4.017471) \| 2.372569 / 3.745712 (-1.373143) \| 2.716810 / 5.269862 (-2.553052) \| 1.697895 / 4.565676 (-2.867782) \| 0.061804 / 0.424275 (-0.362471) \| 0.004986 / 0.007607 (-0.002622) \| 0.332721 / 0.226044 (0.106676) \| 3.274572 / 2.268929 (1.005644) \| 1.789900 / 55.444624 (-53.654725) \| 1.536346 / 6.876477 (-5.340131) \| 1.551940 / 2.142072 (-0.590132) \| 0.634539 / 4.805227 (-4.170688) \| 0.115860 / 6.500664 (-6.384805) \| 0.041737 / 0.075469 (-0.033732) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.024469 / 1.841788 (-0.817319) \| 11.327496 / 8.074308 (3.253188) \| 9.265855 / 10.191392 (-0.925537) \| 0.142200 / 0.680424 (-0.538224) \| 0.013945 / 0.534201 (-0.520256) \| 0.289670 / 0.579283 (-0.289614) \| 0.269240 / 0.434364 (-0.165124) \| 0.324748 / 0.540337 (-0.215590) \| 0.421393 / 1.386936 (-0.965543) \|\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) \| 0.005284 / 0.011353 (-0.006069) \| 0.003351 / 0.011008 (-0.007658) \| 0.049973 / 0.038508 (0.011465) \| 0.030257 / 0.023109 (0.007148) \| 0.273660 / 0.275898 (-0.002238) \| 0.300328 / 0.323480 (-0.023152) \| 0.004133 / 0.007986 (-0.003852) \| 0.002614 / 0.004328 (-0.001715) \| 0.048055 / 0.004250 (0.043804) \| 0.044731 / 0.037052 (0.007678) \| 0.290257 / 0.258489 (0.031768) \| 0.321243 / 0.293841 (0.027402) \| 0.029542 / 0.128546 (-0.099004) \| 0.010074 / 0.075646 (-0.065573) \| 0.057944 / 0.419271 (-0.361327) \| 0.051267 / 0.043533 (0.007734) \| 0.276278 / 0.255139 (0.021139) \| 0.302464 / 0.283200 (0.019264) \| 0.018231 / 0.141683 (-0.123452) \| 1.140782 / 1.452155 (-0.311373) \| 1.182991 / 1.492716 (-0.309725) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092325 / 0.018006 (0.074319) \| 0.302610 / 0.000490 (0.302121) \| 0.000202 / 0.000200 (0.000002) \| 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) \| 0.021458 / 0.037411 (-0.015954) \| 0.074883 / 0.014526 (0.060357) \| 0.085747 / 0.176557 (-0.090809) \| 0.125506 / 0.737135 (-0.611629) \| 0.086921 / 0.296338 (-0.209417) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290485 / 0.215209 (0.075276) \| 2.853898 / 2.077655 (0.776243) \| 1.615606 / 1.504120 (0.111486) \| 1.491797 / 1.541195 (-0.049397) \| 1.515981 / 1.468490 (0.047491) \| 0.566760 / 4.584777 (-4.018017) \| 2.462593 / 3.745712 (-1.283119) \| 2.765516 / 5.269862 (-2.504345) \| 1.755078 / 4.565676 (-2.810598) \| 0.063614 / 0.424275 (-0.360661) \| 0.005040 / 0.007607 (-0.002567) \| 0.347957 / 0.226044 (0.121912) \| 3.464258 / 2.268929 (1.195330) \| 1.992273 / 55.444624 (-53.452351) \| 1.699147 / 6.876477 (-5.177330) \| 1.868438 / 2.142072 (-0.273635) \| 0.660756 / 4.805227 (-4.144471) \| 0.118142 / 6.500664 (-6.382522) \| 0.041974 / 0.075469 (-0.033495) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.012206 / 1.841788 (-0.829581) \| 12.343735 / 8.074308 (4.269427) \| 10.321975 / 10.191392 (0.130583) \| 0.140007 / 0.680424 (-0.540417) \| 0.015755 / 0.534201 (-0.518446) \| 0.291978 / 0.579283 (-0.287305) \| 0.278792 / 0.434364 (-0.155572) \| 0.325366 / 0.540337 (-0.214972) \| 0.439403 / 1.386936 (-0.947533) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d1d3c06a651c6ad5142f331cb5dc0008ddcade33 \"CML watermark\")\n", "This change is breaking in \r\n\r\nhttps://github.com/huggingface/datasets/blob/f96e74d5c633cd5435dd526adb4a74631eb05c43/src/datasets/arrow_dataset.py#L1515\r\n\r\nwhen the input is `pathlib.Path`. The issue is that `url_to_fs` expects a `str` and cannot deal with `Path`. `get_fs_token_paths` converts to `str` so it is not a problem", "I opened https://github.com/huggingface/datasets/pull/6828 to add proper Path support to save_to_disk / load_from_disk" ]
2,163,250,590	6,703	Unable to load dataset that was saved with `save_to_disk`	closed	2024-03-01T11:59:56	2024-03-04T13:46:20	2024-03-04T13:46:20	https://github.com/huggingface/datasets/issues/6703	null	casper-hansen	false	[ "`save_to_disk` uses a special serialization that can only be read using `load_from_disk`.\r\n\r\nContrary to `load_dataset`, `load_from_disk` directly loads Arrow files and uses the dataset directory as cache.\r\n\r\nOn the other hand `load_dataset` does a conversion step to get Arrow files from the raw data files (could be in JSON, CSV, Parquet etc.) and caches them in the `datasets` cache directory (default is `~/.cache/huggingface/datasets`). We haven't implemented any logic in `load_dataset` to support datasets saved with `save_to_disk` because they don't use the same cache.\r\n\r\nEDIT: note that you can save your dataset in Parquet format locally using `.to.parquet()` (make sure to shard in multiple files your dataset if it's multiple GBs - you can use `.shard()` + `.to_parquet()` to do that) and you'll be able to reload it using `load_dataset`", "@lhoestq, so is it correctly understood that if I run `to_parquet()` and then `save_to_disk()`, I can load it with `load_dataset`? If yes, then it would resolve this issue (and should probably be documented somewhere 😄)", "Here is an example:\r\n```python\r\nds.to_parquet(\"my/local/dir/data.parquet\")\r\n\r\n# later\r\nds = load_dataset(\"my/local/dir\")\r\n```\r\n\r\nand for bigger datasets:\r\n```python\r\nnum_shards = 1024 # set number of files to save (e.g. try to have files smaller than 5GB)\r\nfor shard_idx in num_shards:\r\n shard = ds.shard(index=shard_idx, num_shards=num_shards)\r\n shard.to_parquet(f\"my/local/dir/{shard_idx:05d}.parquet\") # 00000.parquet to 01023.parquet\r\n\r\n# later\r\nds = load_dataset(\"my/local/dir\")\r\n```\r\n\r\n\r\nI hope this helps :)", "Thanks for helping out! Does this approach work with `s3fs`? e.g. something like this:\r\n\r\n```python\r\nimport s3fs\r\ns3 = s3fs.S3FileSystem(anon=True)\r\nwith s3.open('mybucket/new-file.parquet', 'w') as f:\r\n ds.to_parquet(f)\r\n```\r\n\r\nThis is instead of `save_to_disk` to save to an S3 bucket.\r\n\r\nOtherwise, I am not sure how to make this work when saving the dataset to an S3 bucket. Would `dataset.set_format(\"arrow\")` work as a replacement?", "`load_dataset` does't support S3 buckets unfortunately :/", "> `load_dataset` does't support S3 buckets unfortunately :/\r\n\r\nI am aware but I have some code that downloads it to disk before using that method. The most important part is to store it in a format that load_dataset is compatible with. ", "Feel free to use Parquet then :)", "I ended up with this. Not ideal to save to local disk, but it works and loads via `load_datasets` after downloading from S3 with another method.\r\n\r\n```python\r\nwith tempfile.TemporaryDirectory() as dir:\r\n dataset_nbytes = ds._estimate_nbytes()\r\n max_shard_size_local = convert_file_size_to_int(max_shard_size)\r\n num_shards = int(dataset_nbytes / max_shard_size_local) + 1\r\n\r\n for shard_idx in range(num_shards):\r\n shard = ds.shard(index=shard_idx, num_shards=num_shards)\r\n shard.to_parquet(f\"{dir}/{shard_idx:05d}.parquet\")\r\n \r\n fs.upload(\r\n lpath=dir,\r\n rpath=s3_path,\r\n recursive=True,\r\n )\r\n```" ]
2,161,938,484	6,702	Push samples to dataset on hub without having the dataset locally	closed	2024-02-29T19:17:12	2024-03-08T21:08:38	2024-03-08T21:08:38	https://github.com/huggingface/datasets/issues/6702	null	jbdel	false	[ "Hi ! For now I would recommend creating a new Parquet file using `dataset_new.to_parquet()` and upload it to HF using `huggingface_hub` every time you get a new batch of data. You can name the Parquet files `0000.parquet`, `0001.parquet`, etc.\r\n\r\nThough maybe make sure to not upload one file per sample since that would be inefficient. You can buffer your data and upload when you have enough new samples for example", "This is excellent, thanks!" ]
2,161,448,017	6,701	Base parquet batch_size on parquet row group size	closed	2024-02-29T14:53:01	2024-02-29T15:15:18	2024-02-29T15:08:55	https://github.com/huggingface/datasets/pull/6701	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6701", "html_url": "https://github.com/huggingface/datasets/pull/6701", "diff_url": "https://github.com/huggingface/datasets/pull/6701.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6701.patch", "merged_at": "2024-02-29T15:08:55" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6701). 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) \| 0.005490 / 0.011353 (-0.005863) \| 0.003709 / 0.011008 (-0.007299) \| 0.064192 / 0.038508 (0.025684) \| 0.029581 / 0.023109 (0.006472) \| 0.251086 / 0.275898 (-0.024812) \| 0.267306 / 0.323480 (-0.056174) \| 0.003074 / 0.007986 (-0.004912) \| 0.003340 / 0.004328 (-0.000988) \| 0.048820 / 0.004250 (0.044569) \| 0.045370 / 0.037052 (0.008318) \| 0.260384 / 0.258489 (0.001895) \| 0.284558 / 0.293841 (-0.009283) \| 0.027732 / 0.128546 (-0.100814) \| 0.010661 / 0.075646 (-0.064986) \| 0.213403 / 0.419271 (-0.205868) \| 0.036283 / 0.043533 (-0.007250) \| 0.250107 / 0.255139 (-0.005032) \| 0.265220 / 0.283200 (-0.017980) \| 0.021021 / 0.141683 (-0.120661) \| 1.112058 / 1.452155 (-0.340096) \| 1.169039 / 1.492716 (-0.323678) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095008 / 0.018006 (0.077002) \| 0.303509 / 0.000490 (0.303019) \| 0.000233 / 0.000200 (0.000033) \| 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) \| 0.018224 / 0.037411 (-0.019187) \| 0.061366 / 0.014526 (0.046841) \| 0.073584 / 0.176557 (-0.102972) \| 0.119869 / 0.737135 (-0.617266) \| 0.074228 / 0.296338 (-0.222111) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288147 / 0.215209 (0.072938) \| 2.824419 / 2.077655 (0.746764) \| 1.478530 / 1.504120 (-0.025590) \| 1.350127 / 1.541195 (-0.191067) \| 1.349622 / 1.468490 (-0.118868) \| 0.568058 / 4.584777 (-4.016719) \| 2.377494 / 3.745712 (-1.368218) \| 2.720767 / 5.269862 (-2.549094) \| 1.710763 / 4.565676 (-2.854914) \| 0.061498 / 0.424275 (-0.362778) \| 0.004893 / 0.007607 (-0.002715) \| 0.335633 / 0.226044 (0.109588) \| 3.380646 / 2.268929 (1.111717) \| 1.802436 / 55.444624 (-53.642188) \| 1.562737 / 6.876477 (-5.313739) \| 1.566267 / 2.142072 (-0.575806) \| 0.629058 / 4.805227 (-4.176169) \| 0.116307 / 6.500664 (-6.384357) \| 0.042174 / 0.075469 (-0.033295) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.950945 / 1.841788 (-0.890842) \| 11.279009 / 8.074308 (3.204701) \| 9.433251 / 10.191392 (-0.758141) \| 0.138964 / 0.680424 (-0.541460) \| 0.014155 / 0.534201 (-0.520046) \| 0.284065 / 0.579283 (-0.295218) \| 0.263301 / 0.434364 (-0.171063) \| 0.331932 / 0.540337 (-0.208406) \| 0.441656 / 1.386936 (-0.945280) \|\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) \| 0.005132 / 0.011353 (-0.006221) \| 0.003484 / 0.011008 (-0.007524) \| 0.049040 / 0.038508 (0.010532) \| 0.030254 / 0.023109 (0.007145) \| 0.277141 / 0.275898 (0.001243) \| 0.295242 / 0.323480 (-0.028238) \| 0.004295 / 0.007986 (-0.003690) \| 0.002632 / 0.004328 (-0.001696) \| 0.048540 / 0.004250 (0.044290) \| 0.044787 / 0.037052 (0.007734) \| 0.287736 / 0.258489 (0.029247) \| 0.313146 / 0.293841 (0.019305) \| 0.029340 / 0.128546 (-0.099206) \| 0.010204 / 0.075646 (-0.065442) \| 0.059058 / 0.419271 (-0.360214) \| 0.051033 / 0.043533 (0.007500) \| 0.274086 / 0.255139 (0.018947) \| 0.293048 / 0.283200 (0.009848) \| 0.019573 / 0.141683 (-0.122110) \| 1.174032 / 1.452155 (-0.278123) \| 1.227107 / 1.492716 (-0.265609) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094896 / 0.018006 (0.076890) \| 0.303519 / 0.000490 (0.303029) \| 0.000223 / 0.000200 (0.000023) \| 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) \| 0.021495 / 0.037411 (-0.015917) \| 0.074234 / 0.014526 (0.059708) \| 0.086212 / 0.176557 (-0.090345) \| 0.125052 / 0.737135 (-0.612084) \| 0.087464 / 0.296338 (-0.208874) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297098 / 0.215209 (0.081889) \| 2.970944 / 2.077655 (0.893289) \| 1.650101 / 1.504120 (0.145981) \| 1.532694 / 1.541195 (-0.008501) \| 1.513652 / 1.468490 (0.045162) \| 0.559614 / 4.584777 (-4.025163) \| 2.404848 / 3.745712 (-1.340865) \| 2.627851 / 5.269862 (-2.642011) \| 1.707550 / 4.565676 (-2.858127) \| 0.061821 / 0.424275 (-0.362454) \| 0.005012 / 0.007607 (-0.002595) \| 0.342462 / 0.226044 (0.116417) \| 3.401703 / 2.268929 (1.132774) \| 1.991632 / 55.444624 (-53.452993) \| 1.737706 / 6.876477 (-5.138771) \| 1.837457 / 2.142072 (-0.304616) \| 0.638845 / 4.805227 (-4.166383) \| 0.114773 / 6.500664 (-6.385891) \| 0.040175 / 0.075469 (-0.035294) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.038286 / 1.841788 (-0.803501) \| 11.885757 / 8.074308 (3.811448) \| 10.061530 / 10.191392 (-0.129862) \| 0.140824 / 0.680424 (-0.539600) \| 0.015080 / 0.534201 (-0.519121) \| 0.287992 / 0.579283 (-0.291291) \| 0.273498 / 0.434364 (-0.160866) \| 0.326478 / 0.540337 (-0.213860) \| 0.426900 / 1.386936 (-0.960036) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b02be21047087c5ffc11cf1c072a5aceab517eba \"CML watermark\")\n" ]
2,158,871,038	6,700	remove_columns is not in-place but the doc shows it is in-place	closed	2024-02-28T12:36:22	2024-04-02T17:15:28	2024-04-02T17:15:28	https://github.com/huggingface/datasets/issues/6700	null	shelfofclub	false	[ "Good catch! I've opened a PR with a fix in the `transformers` repo.", "@mariosasko Thanks!\r\n\r\nWill the doc of `datasets` be updated?\r\n\r\nI find some possible mistakes in doc about whether `remove_columns` is in-place.\r\n1. [You can also remove a column using map() with remove_columns but the present method is in-place (doesn’t copy the data to a new dataset) and is thus faster.](https://huggingface.co/docs/datasets/v2.17.1/en/package_reference/main_classes#datasets.Dataset.remove_columns)\r\n2. [You can also remove a column using Dataset.map() with remove_columns but the present method is in-place (doesn’t copy the data to a new dataset) and is thus faster.](https://huggingface.co/docs/datasets/v2.17.1/en/package_reference/main_classes#datasets.DatasetDict.remove_columns)\r\n3. [🤗 Datasets also has a remove_columns() function which is faster because it doesn’t copy the data of the remaining columns.](https://huggingface.co/docs/datasets/v2.17.1/en/process#map)", "I've linked a PR that will fix the usage in the `datasets` docs." ]
2,158,152,341	6,699	`Dataset` unexpected changed dict data and may cause error	open	2024-02-28T05:30:10	2024-02-28T19:14:36	null	https://github.com/huggingface/datasets/issues/6699	null	scruel	false	[ "If `test.jsonl` contains more lines like:\r\n```\r\n{\"id\": 0, \"indexs\": {\"-1\": [0, 10]}}\r\n{\"id\": 1, \"indexs\": {\"-1\": [0, 10]}}\r\n{\"id\": 2, \"indexs\": {\"-2\": [0, 10]}}\r\n...\r\n{\"id\": n, \"indexs\": {\"-9999\": [0, 10]}}\r\n```\r\n\r\n`Dataset.from_json` will just raise an error:\r\n```\r\nAn error occurred while generating the dataset\r\nTypeError: Couldn't cast array of type\r\nstruct<-5942: list<item: int64>, -5943: list<item: int64>, -5944: list<item: int64>, -5945: list<item: int64>, -5946: list<item: int64>, -5947: list<item: int64>, -5948: list<item: int64>, -5949: list<item: int64>: ...\r\nto\r\n{... '-5312': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '-5313': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/runpy.py\", line 198, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/runpy.py\", line 88, in _run_code\r\n exec(code, run_globals)\r\n File \"/home/scruel/.vscode-server/extensions/ms-python.debugpy-2024.0.0-linux-x64/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py\", line 39, in <module>\r\n cli.main()\r\n File \"/home/scruel/.vscode-server/extensions/ms-python.debugpy-2024.0.0-linux-x64/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 430, in main\r\n run()\r\n File \"/home/scruel/.vscode-server/extensions/ms-python.debugpy-2024.0.0-linux-x64/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 284, in run_file\r\n runpy.run_path(target, run_name=\"__main__\")\r\n File \"/home/scruel/.vscode-server/extensions/ms-python.debugpy-2024.0.0-linux-x64/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 321, in run_path\r\n return _run_module_code(code, init_globals, run_name,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/scruel/.vscode-server/extensions/ms-python.debugpy-2024.0.0-linux-x64/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 135, in _run_module_code\r\n _run_code(code, mod_globals, init_globals,\r\n File \"/home/scruel/.vscode-server/extensions/ms-python.debugpy-2024.0.0-linux-x64/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 124, in _run_code\r\n exec(code, run_globals)\r\n File \"/home/scruel/Code/Python/Working/llm-memory/data_reader.py\", line 120, in <module>\r\n reader = SnippetReader(jsonl_path, npy_path)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/scruel/Code/Python/Working/llm-memory/data_reader.py\", line 85, in __init__\r\n self._dataset = Dataset.from_json(jsonl_path, features=)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/site-packages/datasets/arrow_dataset.py\", line 1130, in from_json\r\n ).read()\r\n ^^^^^^\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/site-packages/datasets/io/json.py\", line 59, in read\r\n self.builder.download_and_prepare(\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/site-packages/datasets/builder.py\", line 1005, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/site-packages/datasets/builder.py\", line 1100, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/site-packages/datasets/builder.py\", line 1860, in _prepare_split\r\n for job_id, done, content in self._prepare_split_single(\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/site-packages/datasets/builder.py\", line 2016, in _prepare_split_single\r\n raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\ndatasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset\r\n```", "Hi! Our JSON parser expects all examples/rows to share the same set of columns (applies to nested columns, too), hence the error. \r\n\r\nTo read the `index` column, we would have to manually cast the input to PyArrow's `pa.map_` type, but this requires a more thorough investigation, as `pa.map_` has limited support in PyArrow." ]
2,157,752,392	6,698	Faster `xlistdir`	closed	2024-02-27T22:55:08	2024-02-27T23:44:49	2024-02-27T23:38:14	https://github.com/huggingface/datasets/pull/6698	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6698", "html_url": "https://github.com/huggingface/datasets/pull/6698", "diff_url": "https://github.com/huggingface/datasets/pull/6698.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6698.patch", "merged_at": "2024-02-27T23:38:14" }	mariosasko	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6698). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "CI failure is unrelated to the changes.", "<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) \| 0.005499 / 0.011353 (-0.005854) \| 0.003824 / 0.011008 (-0.007184) \| 0.064230 / 0.038508 (0.025722) \| 0.028962 / 0.023109 (0.005853) \| 0.283540 / 0.275898 (0.007642) \| 0.300774 / 0.323480 (-0.022706) \| 0.003405 / 0.007986 (-0.004581) \| 0.002796 / 0.004328 (-0.001532) \| 0.049834 / 0.004250 (0.045584) \| 0.045924 / 0.037052 (0.008872) \| 0.274818 / 0.258489 (0.016328) \| 0.306189 / 0.293841 (0.012348) \| 0.028304 / 0.128546 (-0.100242) \| 0.011496 / 0.075646 (-0.064150) \| 0.208236 / 0.419271 (-0.211036) \| 0.035720 / 0.043533 (-0.007813) \| 0.261190 / 0.255139 (0.006051) \| 0.281545 / 0.283200 (-0.001655) \| 0.019388 / 0.141683 (-0.122295) \| 1.134999 / 1.452155 (-0.317156) \| 1.203053 / 1.492716 (-0.289663) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096007 / 0.018006 (0.078000) \| 0.316958 / 0.000490 (0.316469) \| 0.000210 / 0.000200 (0.000010) \| 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) \| 0.018330 / 0.037411 (-0.019081) \| 0.063299 / 0.014526 (0.048773) \| 0.073833 / 0.176557 (-0.102723) \| 0.122285 / 0.737135 (-0.614850) \| 0.077352 / 0.296338 (-0.218987) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304487 / 0.215209 (0.089278) \| 3.017666 / 2.077655 (0.940012) \| 1.664292 / 1.504120 (0.160172) \| 1.448446 / 1.541195 (-0.092748) \| 1.435612 / 1.468490 (-0.032878) \| 0.569704 / 4.584777 (-4.015073) \| 2.362015 / 3.745712 (-1.383698) \| 2.910380 / 5.269862 (-2.359481) \| 1.814560 / 4.565676 (-2.751116) \| 0.063986 / 0.424275 (-0.360289) \| 0.005022 / 0.007607 (-0.002585) \| 0.363528 / 0.226044 (0.137483) \| 3.641940 / 2.268929 (1.373011) \| 1.961589 / 55.444624 (-53.483035) \| 1.603683 / 6.876477 (-5.272793) \| 1.663144 / 2.142072 (-0.478928) \| 0.645628 / 4.805227 (-4.159599) \| 0.118759 / 6.500664 (-6.381905) \| 0.042631 / 0.075469 (-0.032838) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985648 / 1.841788 (-0.856140) \| 13.082558 / 8.074308 (5.008250) \| 9.909811 / 10.191392 (-0.281581) \| 0.131340 / 0.680424 (-0.549083) \| 0.013983 / 0.534201 (-0.520218) \| 0.289869 / 0.579283 (-0.289414) \| 0.271775 / 0.434364 (-0.162589) \| 0.334853 / 0.540337 (-0.205485) \| 0.457017 / 1.386936 (-0.929919) \|\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) \| 0.005580 / 0.011353 (-0.005773) \| 0.003788 / 0.011008 (-0.007221) \| 0.049401 / 0.038508 (0.010893) \| 0.030372 / 0.023109 (0.007263) \| 0.278554 / 0.275898 (0.002655) \| 0.302462 / 0.323480 (-0.021018) \| 0.004412 / 0.007986 (-0.003573) \| 0.002825 / 0.004328 (-0.001504) \| 0.047826 / 0.004250 (0.043576) \| 0.047903 / 0.037052 (0.010851) \| 0.293098 / 0.258489 (0.034609) \| 0.322777 / 0.293841 (0.028936) \| 0.030010 / 0.128546 (-0.098536) \| 0.011187 / 0.075646 (-0.064459) \| 0.057639 / 0.419271 (-0.361632) \| 0.059693 / 0.043533 (0.016160) \| 0.280288 / 0.255139 (0.025149) \| 0.294022 / 0.283200 (0.010823) \| 0.019635 / 0.141683 (-0.122048) \| 1.154733 / 1.452155 (-0.297422) \| 1.200808 / 1.492716 (-0.291908) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099682 / 0.018006 (0.081676) \| 0.319521 / 0.000490 (0.319031) \| 0.000224 / 0.000200 (0.000024) \| 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) \| 0.022042 / 0.037411 (-0.015370) \| 0.078842 / 0.014526 (0.064317) \| 0.088715 / 0.176557 (-0.087841) \| 0.126832 / 0.737135 (-0.610303) \| 0.089217 / 0.296338 (-0.207122) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300099 / 0.215209 (0.084890) \| 2.907746 / 2.077655 (0.830092) \| 1.619418 / 1.504120 (0.115298) \| 1.495693 / 1.541195 (-0.045501) \| 1.544956 / 1.468490 (0.076466) \| 0.556652 / 4.584777 (-4.028124) \| 2.414408 / 3.745712 (-1.331304) \| 2.737227 / 5.269862 (-2.532635) \| 1.763187 / 4.565676 (-2.802490) \| 0.062207 / 0.424275 (-0.362069) \| 0.005076 / 0.007607 (-0.002531) \| 0.349880 / 0.226044 (0.123836) \| 3.425355 / 2.268929 (1.156427) \| 1.972094 / 55.444624 (-53.472531) \| 1.710650 / 6.876477 (-5.165827) \| 1.902218 / 2.142072 (-0.239855) \| 0.640699 / 4.805227 (-4.164529) \| 0.117879 / 6.500664 (-6.382785) \| 0.042412 / 0.075469 (-0.033057) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.030131 / 1.841788 (-0.811656) \| 12.750637 / 8.074308 (4.676329) \| 10.352636 / 10.191392 (0.161244) \| 0.141139 / 0.680424 (-0.539285) \| 0.015343 / 0.534201 (-0.518858) \| 0.294931 / 0.579283 (-0.284352) \| 0.275237 / 0.434364 (-0.159127) \| 0.336669 / 0.540337 (-0.203668) \| 0.429945 / 1.386936 (-0.956991) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9c424fa517a1b8517c89545f979e0c8c7d90c3e3 \"CML watermark\")\n" ]
2,157,322,224	6,697	Unable to Load Dataset in Kaggle	closed	2024-02-27T18:19:34	2024-02-29T17:32:42	2024-02-29T17:32:41	https://github.com/huggingface/datasets/issues/6697	null	vrunm	false	[ "FWIW, I run `load_dataset(\"llm-blender/mix-instruct\")` and it ran successfully.\r\nCan you clear your cache and try again?\r\n\r\n\r\n### Environment Info\r\n\r\n- `datasets` version: 2.17.0\r\n- Platform: Linux-6.2.6-76060206-generic-x86_64-with-glibc2.35\r\n- Python version: 3.9.13\r\n- `huggingface_hub` version: 0.20.3\r\n- PyArrow version: 15.0.0\r\n- Pandas version: 1.5.3\r\n- `fsspec` version: 2023.10.0", "It is working on the Kaggle GPU instance but gives this same error when running on the CPU instance. Still to run it on Kaggle you require to install the latest versions of datasets and transformers.", "This error means that `fsspec>=2023.12.0` is installed, which is incompatible with the current releases (the next `datasets` release will be the first to support it). In the meantime, downgrading `fsspec` (`pip install fsspec<=2023.12.0`) should fix the issue.", "@mariosasko Thanks I got it to work with installing that version of fsspec." ]
2,154,161,357	6,696	Make JSON builder support an array of strings	closed	2024-02-26T13:18:31	2024-02-28T06:45:23	2024-02-28T06:39:12	https://github.com/huggingface/datasets/pull/6696	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6696", "html_url": "https://github.com/huggingface/datasets/pull/6696", "diff_url": "https://github.com/huggingface/datasets/pull/6696.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6696.patch", "merged_at": "2024-02-28T06:39:12" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6696). 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) \| 0.005057 / 0.011353 (-0.006296) \| 0.003665 / 0.011008 (-0.007343) \| 0.063217 / 0.038508 (0.024709) \| 0.028789 / 0.023109 (0.005679) \| 0.233597 / 0.275898 (-0.042301) \| 0.254792 / 0.323480 (-0.068687) \| 0.003065 / 0.007986 (-0.004921) \| 0.002686 / 0.004328 (-0.001642) \| 0.050182 / 0.004250 (0.045932) \| 0.042204 / 0.037052 (0.005151) \| 0.254262 / 0.258489 (-0.004227) \| 0.277099 / 0.293841 (-0.016742) \| 0.027564 / 0.128546 (-0.100982) \| 0.010768 / 0.075646 (-0.064878) \| 0.207302 / 0.419271 (-0.211969) \| 0.035737 / 0.043533 (-0.007796) \| 0.242388 / 0.255139 (-0.012751) \| 0.259833 / 0.283200 (-0.023367) \| 0.019833 / 0.141683 (-0.121850) \| 1.135928 / 1.452155 (-0.316227) \| 1.162851 / 1.492716 (-0.329865) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089209 / 0.018006 (0.071202) \| 0.300493 / 0.000490 (0.300003) \| 0.000216 / 0.000200 (0.000016) \| 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) \| 0.017968 / 0.037411 (-0.019444) \| 0.061773 / 0.014526 (0.047247) \| 0.073835 / 0.176557 (-0.102722) \| 0.118592 / 0.737135 (-0.618544) \| 0.073606 / 0.296338 (-0.222732) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287858 / 0.215209 (0.072649) \| 2.822917 / 2.077655 (0.745262) \| 1.485259 / 1.504120 (-0.018861) \| 1.355922 / 1.541195 (-0.185273) \| 1.364008 / 1.468490 (-0.104482) \| 0.557713 / 4.584777 (-4.027064) \| 2.378972 / 3.745712 (-1.366741) \| 2.737218 / 5.269862 (-2.532643) \| 1.718317 / 4.565676 (-2.847359) \| 0.062362 / 0.424275 (-0.361913) \| 0.004992 / 0.007607 (-0.002615) \| 0.350765 / 0.226044 (0.124721) \| 3.387579 / 2.268929 (1.118650) \| 1.860408 / 55.444624 (-53.584216) \| 1.569355 / 6.876477 (-5.307122) \| 1.593013 / 2.142072 (-0.549059) \| 0.639325 / 4.805227 (-4.165902) \| 0.121769 / 6.500664 (-6.378895) \| 0.042148 / 0.075469 (-0.033322) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.990594 / 1.841788 (-0.851194) \| 11.460904 / 8.074308 (3.386596) \| 9.438691 / 10.191392 (-0.752701) \| 0.141884 / 0.680424 (-0.538540) \| 0.013725 / 0.534201 (-0.520476) \| 0.288847 / 0.579283 (-0.290436) \| 0.278815 / 0.434364 (-0.155549) \| 0.337108 / 0.540337 (-0.203229) \| 0.441659 / 1.386936 (-0.945277) \|\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) \| 0.005265 / 0.011353 (-0.006088) \| 0.003734 / 0.011008 (-0.007274) \| 0.049365 / 0.038508 (0.010857) \| 0.030483 / 0.023109 (0.007373) \| 0.275085 / 0.275898 (-0.000813) \| 0.296004 / 0.323480 (-0.027475) \| 0.004964 / 0.007986 (-0.003022) \| 0.002542 / 0.004328 (-0.001787) \| 0.048734 / 0.004250 (0.044483) \| 0.044098 / 0.037052 (0.007046) \| 0.292517 / 0.258489 (0.034028) \| 0.319992 / 0.293841 (0.026151) \| 0.029552 / 0.128546 (-0.098994) \| 0.010669 / 0.075646 (-0.064977) \| 0.058887 / 0.419271 (-0.360385) \| 0.051163 / 0.043533 (0.007630) \| 0.277266 / 0.255139 (0.022127) \| 0.295347 / 0.283200 (0.012147) \| 0.018403 / 0.141683 (-0.123280) \| 1.151979 / 1.452155 (-0.300176) \| 1.204583 / 1.492716 (-0.288134) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091157 / 0.018006 (0.073151) \| 0.300109 / 0.000490 (0.299619) \| 0.000211 / 0.000200 (0.000011) \| 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) \| 0.021521 / 0.037411 (-0.015890) \| 0.074954 / 0.014526 (0.060428) \| 0.087010 / 0.176557 (-0.089546) \| 0.125853 / 0.737135 (-0.611282) \| 0.087877 / 0.296338 (-0.208461) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297890 / 0.215209 (0.082681) \| 2.912159 / 2.077655 (0.834504) \| 1.619311 / 1.504120 (0.115192) \| 1.501726 / 1.541195 (-0.039468) \| 1.494143 / 1.468490 (0.025652) \| 0.566744 / 4.584777 (-4.018033) \| 2.497594 / 3.745712 (-1.248118) \| 2.631403 / 5.269862 (-2.638459) \| 1.727896 / 4.565676 (-2.837780) \| 0.065937 / 0.424275 (-0.358339) \| 0.005023 / 0.007607 (-0.002585) \| 0.345747 / 0.226044 (0.119702) \| 3.417615 / 2.268929 (1.148686) \| 1.949970 / 55.444624 (-53.494654) \| 1.680019 / 6.876477 (-5.196457) \| 1.789879 / 2.142072 (-0.352193) \| 0.648053 / 4.805227 (-4.157174) \| 0.117408 / 6.500664 (-6.383256) \| 0.040681 / 0.075469 (-0.034788) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.012535 / 1.841788 (-0.829252) \| 11.935819 / 8.074308 (3.861511) \| 10.241452 / 10.191392 (0.050060) \| 0.130956 / 0.680424 (-0.549468) \| 0.015396 / 0.534201 (-0.518805) \| 0.289166 / 0.579283 (-0.290117) \| 0.274149 / 0.434364 (-0.160215) \| 0.325844 / 0.540337 (-0.214493) \| 0.424919 / 1.386936 (-0.962017) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cb834d9c63ab8cb14725ae8e4fc2da8672892a6d \"CML watermark\")\n" ]
2,154,075,509	6,695	Support JSON file with an array of strings	closed	2024-02-26T12:35:11	2024-03-08T14:16:25	2024-02-28T06:39:13	https://github.com/huggingface/datasets/issues/6695	null	albertvillanova	false	[ "https://huggingface.co/datasets/CausalLM/Refined-Anime-Text/discussions/1 has been fixed, but how can we check if there are other datasets with the same error, in datasets-server's database? I don't know how to get the list of erroneous cache entries, since we only copied `Error code: JobManagerCrashedError`, but not the traceback in `details`... Do you remember the error message, or the underlying exception, we had?" ]
2,153,086,984	6,694	__add__ for Dataset, IterableDataset	open	2024-02-26T01:46:55	2024-02-29T16:52:58	null	https://github.com/huggingface/datasets/pull/6694	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6694", "html_url": "https://github.com/huggingface/datasets/pull/6694", "diff_url": "https://github.com/huggingface/datasets/pull/6694.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6694.patch", "merged_at": null }	oh-gnues-iohc	true	[ "Hi! You can find a reason why we are against this feature in https://github.com/huggingface/datasets/issues/3449. \r\n\r\n> It's too cumbersome to write this command every time we perform a dataset merging operation\r\n\r\nExplicit is better than implicit, so this isn't a good enough reason. \r\n\r\nThanks for the effort nonetheless :)!" ]
2,152,887,712	6,693	Update the print message for chunked_dataset in process.mdx	closed	2024-02-25T18:37:07	2024-02-25T19:57:12	2024-02-25T19:51:02	https://github.com/huggingface/datasets/pull/6693	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6693", "html_url": "https://github.com/huggingface/datasets/pull/6693", "diff_url": "https://github.com/huggingface/datasets/pull/6693.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6693.patch", "merged_at": "2024-02-25T19:51:02" }	gzbfgjf2	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6693). 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) \| 0.005069 / 0.011353 (-0.006284) \| 0.003682 / 0.011008 (-0.007326) \| 0.063733 / 0.038508 (0.025225) \| 0.030377 / 0.023109 (0.007268) \| 0.242962 / 0.275898 (-0.032936) \| 0.262865 / 0.323480 (-0.060615) \| 0.004760 / 0.007986 (-0.003225) \| 0.002772 / 0.004328 (-0.001557) \| 0.049094 / 0.004250 (0.044843) \| 0.041093 / 0.037052 (0.004041) \| 0.260423 / 0.258489 (0.001934) \| 0.283908 / 0.293841 (-0.009933) \| 0.027409 / 0.128546 (-0.101138) \| 0.010548 / 0.075646 (-0.065098) \| 0.208637 / 0.419271 (-0.210634) \| 0.035386 / 0.043533 (-0.008147) \| 0.242352 / 0.255139 (-0.012787) \| 0.264201 / 0.283200 (-0.018999) \| 0.017822 / 0.141683 (-0.123860) \| 1.140792 / 1.452155 (-0.311363) \| 1.166782 / 1.492716 (-0.325934) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094727 / 0.018006 (0.076720) \| 0.308548 / 0.000490 (0.308059) \| 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) \| 0.018106 / 0.037411 (-0.019305) \| 0.062057 / 0.014526 (0.047531) \| 0.073821 / 0.176557 (-0.102735) \| 0.121269 / 0.737135 (-0.615867) \| 0.074062 / 0.296338 (-0.222277) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282978 / 0.215209 (0.067768) \| 2.788626 / 2.077655 (0.710971) \| 1.479756 / 1.504120 (-0.024364) \| 1.360620 / 1.541195 (-0.180575) \| 1.363996 / 1.468490 (-0.104494) \| 0.571646 / 4.584777 (-4.013131) \| 2.430630 / 3.745712 (-1.315083) \| 2.783909 / 5.269862 (-2.485953) \| 1.744617 / 4.565676 (-2.821060) \| 0.062771 / 0.424275 (-0.361504) \| 0.004978 / 0.007607 (-0.002629) \| 0.347929 / 0.226044 (0.121884) \| 3.368837 / 2.268929 (1.099908) \| 1.855635 / 55.444624 (-53.588990) \| 1.581555 / 6.876477 (-5.294922) \| 1.589888 / 2.142072 (-0.552184) \| 0.655821 / 4.805227 (-4.149406) \| 0.118990 / 6.500664 (-6.381674) \| 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) \| 0.991099 / 1.841788 (-0.850688) \| 11.627919 / 8.074308 (3.553611) \| 9.554180 / 10.191392 (-0.637212) \| 0.140541 / 0.680424 (-0.539882) \| 0.014264 / 0.534201 (-0.519937) \| 0.288465 / 0.579283 (-0.290818) \| 0.266400 / 0.434364 (-0.167964) \| 0.324400 / 0.540337 (-0.215938) \| 0.423158 / 1.386936 (-0.963778) \|\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) \| 0.005588 / 0.011353 (-0.005765) \| 0.003784 / 0.011008 (-0.007224) \| 0.049961 / 0.038508 (0.011453) \| 0.031215 / 0.023109 (0.008105) \| 0.280859 / 0.275898 (0.004961) \| 0.306416 / 0.323480 (-0.017063) \| 0.004310 / 0.007986 (-0.003676) \| 0.002884 / 0.004328 (-0.001445) \| 0.049662 / 0.004250 (0.045412) \| 0.046611 / 0.037052 (0.009559) \| 0.293353 / 0.258489 (0.034864) \| 0.327839 / 0.293841 (0.033998) \| 0.050784 / 0.128546 (-0.077763) \| 0.010890 / 0.075646 (-0.064757) \| 0.059612 / 0.419271 (-0.359659) \| 0.033175 / 0.043533 (-0.010358) \| 0.281085 / 0.255139 (0.025946) \| 0.302746 / 0.283200 (0.019547) \| 0.019201 / 0.141683 (-0.122481) \| 1.126722 / 1.452155 (-0.325433) \| 1.225678 / 1.492716 (-0.267038) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094335 / 0.018006 (0.076329) \| 0.304774 / 0.000490 (0.304285) \| 0.000207 / 0.000200 (0.000007) \| 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) \| 0.021648 / 0.037411 (-0.015763) \| 0.077920 / 0.014526 (0.063394) \| 0.087125 / 0.176557 (-0.089432) \| 0.125481 / 0.737135 (-0.611654) \| 0.089415 / 0.296338 (-0.206924) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304955 / 0.215209 (0.089746) \| 2.992587 / 2.077655 (0.914932) \| 1.654609 / 1.504120 (0.150490) \| 1.509114 / 1.541195 (-0.032081) \| 1.530906 / 1.468490 (0.062416) \| 0.572092 / 4.584777 (-4.012685) \| 2.477902 / 3.745712 (-1.267810) \| 2.731363 / 5.269862 (-2.538498) \| 1.750000 / 4.565676 (-2.815677) \| 0.063662 / 0.424275 (-0.360613) \| 0.005008 / 0.007607 (-0.002600) \| 0.353066 / 0.226044 (0.127022) \| 3.528309 / 2.268929 (1.259380) \| 2.009238 / 55.444624 (-53.435387) \| 1.717792 / 6.876477 (-5.158685) \| 1.861699 / 2.142072 (-0.280373) \| 0.667392 / 4.805227 (-4.137835) \| 0.119197 / 6.500664 (-6.381467) \| 0.041131 / 0.075469 (-0.034338) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.032182 / 1.841788 (-0.809605) \| 12.042613 / 8.074308 (3.968305) \| 10.256293 / 10.191392 (0.064901) \| 0.141180 / 0.680424 (-0.539244) \| 0.015005 / 0.534201 (-0.519196) \| 0.290081 / 0.579283 (-0.289202) \| 0.281081 / 0.434364 (-0.153283) \| 0.331425 / 0.540337 (-0.208912) \| 0.418674 / 1.386936 (-0.968262) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ad5b221c01a183a66cbf52a6d708f94e0cff0b53 \"CML watermark\")\n" ]
2,152,270,987	6,692	Enhancement: Enable loading TSV files in load_dataset()	closed	2024-02-24T11:38:59	2024-02-26T15:33:50	2024-02-26T07:14:03	https://github.com/huggingface/datasets/pull/6692	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6692", "html_url": "https://github.com/huggingface/datasets/pull/6692", "diff_url": "https://github.com/huggingface/datasets/pull/6692.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6692.patch", "merged_at": null }	harsh1504660	true	[ "Hi @harsh1504660,\r\n\r\nThanks for your work, but this functionality already exists. See my comment in the corresponding issue: https://github.com/huggingface/datasets/issues/6691#issuecomment-1963449923\r\n\r\nNext time you would like to contribute, I would suggest you take on an issue that is previously validated by one of the maintainers. Thanks anyway." ]
2,152,134,041	6,691	load_dataset() does not support tsv	closed	2024-02-24T05:56:04	2024-02-26T07:15:07	2024-02-26T07:09:35	https://github.com/huggingface/datasets/issues/6691	null	dipsivenkatesh	false	[ "#self-assign", "Hi @dipsivenkatesh,\r\n\r\nPlease note that this functionality is already implemented. Our CSV builder uses `pandas.read_csv` under the hood, and you can pass the parameter `delimiter=\"\\t\"` to read TSV files.\r\n\r\nSee the list of CSV config parameters in our docs: https://huggingface.co/docs/datasets/package_reference/loading_methods#datasets.packaged_modules.csv.CsvConfig" ]
2,150,800,065	6,690	Add function to convert a script-dataset to Parquet	closed	2024-02-23T10:28:20	2024-04-12T15:27:05	2024-04-12T15:27:05	https://github.com/huggingface/datasets/issues/6690	null	albertvillanova	false	[]
2,149,581,147	6,689	.load_dataset() method defaults to zstandard	closed	2024-02-22T17:39:27	2024-03-07T14:54:16	2024-03-07T14:54:15	https://github.com/huggingface/datasets/issues/6689	null	ElleLeonne	false	[ "The dataset is made of JSON files compressed using zstandard, as you can see here: https://huggingface.co/datasets/cerebras/SlimPajama-627B/tree/main/test/chunk1\r\n\r\nThat's why it asks for zstandard to be installed.\r\n\r\nThough I'm intrigued that you manage to load the dataset without zstandard installed. Maybe `pyarrow` that we use to load JSON data under the hood got support for zstandard at one point.", "> The dataset is made of JSON files compressed using zstandard, as you can see here: https://huggingface.co/datasets/cerebras/SlimPajama-627B/tree/main/test/chunk1\r\n> \r\n> That's why it asks for zstandard to be installed.\r\n> \r\n> Though I'm intrigued that you manage to load the dataset without zstandard installed. Maybe `pyarrow` that we use to load JSON data under the hood got support for zstandard at one point.\r\n\r\nQuestion, then.\r\n\r\nWhen I loaded this dataset back in October, it downloaded all the files, and then loaded into memory just fine.\r\n\r\nNOW, it has to sit there and unpack all these zstd files (3.6TB worth). Further, when they're in my harddrive, they're regular json files. It's only when looking at the LFS, or when the loading script runs, that I get asked to install zstd.\r\n\r\nMy question is, is this normal? As far as I can tell, there's no reason the dataset or the loading methods should have changed between then and now. Was my old behavior flawed, and the new behavior correct?\r\n\r\nI mean, I got it working eventually, but it was pulling teeth, and it still doesn't load right, as I had to unpack each chunk separately, so there's no clean mapping between the chunks and the broader dataset.", "The `ZstdExtractor` has been added 3 years ago and we haven't touched it since then. Same for the JSON loader.\r\n\r\n`zstandard` is required as soon as you try to load a file with the `.zstd` extension or if a file starts with the Zstandard magic number `b\"\\x28\\xb5\\x2f\\xfd\"` (used to recognize Zstandard files).\r\n\r\nNote that the extraction only has to happen once - if you reload the dataset it will be reloaded from your cache directly.\r\n\r\nNot sure what happened between October and now unfortunately", "Understood, thank you for clarifying that for me.\r\n\r\nI'll look into how best to collate my stack of batches w/o creating duplicate arrow tables for each one." ]
2,148,609,859	6,688	Tensor type (e.g. from `return_tensors`) ignored in map	open	2024-02-22T09:27:57	2024-02-22T15:56:21	null	https://github.com/huggingface/datasets/issues/6688	null	srossi93	false	[ "Hi, this is expected behavior since all the tensors are converted to Arrow data (the storage type behind a Dataset).\r\n\r\nTo get pytorch tensors back, you can set the dataset format to \"torch\":\r\n\r\n```python\r\nds = ds.with_format(\"torch\")\r\n```", "Thanks. Just one additional question. During the pipeline `<framework> -> arrow -> <framework>`, does `.with_format` zero-copies the tensors or is it a deep copy? And is this behavior framework-dependent?\r\n\r\nThanks again.", "We do zero-copy Arrow <-> NumPy <-> PyTorch when the output dtype matches the original dtype, but for other frameworks it depends. For example JAX doesn't allow zero-copy NumPy -> JAX at all IIRC.\r\n\r\nCurrently tokenized data are formatted using a copy though, since tokens are stored as int32 and returned as int64 torch tensors." ]
2,148,554,178	6,687	fsspec: support fsspec>=2023.12.0 glob changes	closed	2024-02-22T08:59:32	2024-03-04T12:59:42	2024-02-29T15:12:17	https://github.com/huggingface/datasets/pull/6687	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6687", "html_url": "https://github.com/huggingface/datasets/pull/6687", "diff_url": "https://github.com/huggingface/datasets/pull/6687.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6687.patch", "merged_at": "2024-02-29T15:12:17" }	pmrowla	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6687). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Looking into the CI failure, this PR is incompatible with `huggingface-hub>=0.20.0`. It looks like there were several changes made to HfFileSystem in 0.20.0, @lhoestq any ideas on what the issue might be in particular?\r\n\r\na bisect indicates that it's related to https://github.com/huggingface/huggingface_hub/pull/1815", "It looks like huggingface-hub's `HfFileSystem.glob` is broken for exact string matches (that don't contain glob wildcards) when combining `huggingface-hub>=0.20.0` and `fsspec>=2023.12.0`.\r\n\r\nI did a quick test with huggingface-hub `main`, and adding this test case to `tests/test_hf_filesystem::HfFileSystemTests::test_glob` (https://github.com/huggingface/huggingface_hub/blob/main/tests/test_hf_file_system.py) passes with `fsspec==2023.10.0` and fails with `fsspec==2023.12.0`\r\n```python\r\n self.assertEqual(\r\n sorted(self.hffs.glob(self.hf_path + \"/.gitattributes\")),\r\n sorted([self.hf_path + \"/.gitattributes\"]),\r\n )\r\n\r\n```\r\n\r\nthe `hffs.glob()` call with a pattern that does not contain any wildcards returns an empty list:\r\n```\r\nE AssertionError: Lists differ: [] != ['datasets/__DUMMY_TRANSFORMERS_USER__/rep[35 chars]tes']\r\nE\r\nE Second list contains 1 additional elements.\r\nE First extra element 0:\r\nE 'datasets/__DUMMY_TRANSFORMERS_USER__/repo-7d0ae9-17091013467064/.gitattributes'\r\nE\r\nE - []\r\nE + ['datasets/__DUMMY_TRANSFORMERS_USER__/repo-7d0ae9-17091013467064/.gitattributes']\r\n```\r\n(and with the compatible/passing older fsspec versions the glob call returns the single exact file match as expected)\r\n\r\nSo it looks like the CI failure here isn't directly related to this PR. The failing patterns that don't contain any `*` wildcards are generated by `datasets` with or without this PR, but now that this PR installs the incompatible fsspec version, the underlying `HfFileSystem.glob()` call ends up failing.", "I just opened https://github.com/huggingface/huggingface_hub/pull/2056 to fix this.\r\n\r\nDo you mind if I continue this PR to run the CI against `huggingface_hub@main` until the fix is released ?\r\n\r\nEDIT: the fix has been released in `huggingface_hub` 0.21.2 - I removed my commits that were using `huggingface_hub@main`", "I just added two additional patterns to cover cases like `test-data/xxx.csv` and `data-test/xxx.csv`", "<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) \| 0.005461 / 0.011353 (-0.005892) \| 0.003861 / 0.011008 (-0.007148) \| 0.063252 / 0.038508 (0.024744) \| 0.031474 / 0.023109 (0.008364) \| 0.250321 / 0.275898 (-0.025577) \| 0.275198 / 0.323480 (-0.048282) \| 0.003275 / 0.007986 (-0.004710) \| 0.002874 / 0.004328 (-0.001454) \| 0.049499 / 0.004250 (0.045248) \| 0.045334 / 0.037052 (0.008282) \| 0.266347 / 0.258489 (0.007858) \| 0.308974 / 0.293841 (0.015133) \| 0.027742 / 0.128546 (-0.100804) \| 0.010274 / 0.075646 (-0.065373) \| 0.207516 / 0.419271 (-0.211755) \| 0.036538 / 0.043533 (-0.006995) \| 0.247949 / 0.255139 (-0.007190) \| 0.268986 / 0.283200 (-0.014214) \| 0.019842 / 0.141683 (-0.121841) \| 1.117547 / 1.452155 (-0.334607) \| 1.175813 / 1.492716 (-0.316903) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.103661 / 0.018006 (0.085655) \| 0.331023 / 0.000490 (0.330534) \| 0.000240 / 0.000200 (0.000040) \| 0.000041 / 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) \| 0.019767 / 0.037411 (-0.017645) \| 0.061500 / 0.014526 (0.046974) \| 0.075899 / 0.176557 (-0.100658) \| 0.122240 / 0.737135 (-0.614895) \| 0.074621 / 0.296338 (-0.221717) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287501 / 0.215209 (0.072292) \| 2.794737 / 2.077655 (0.717082) \| 1.505362 / 1.504120 (0.001242) \| 1.379481 / 1.541195 (-0.161713) \| 1.394836 / 1.468490 (-0.073654) \| 0.545803 / 4.584777 (-4.038974) \| 2.364167 / 3.745712 (-1.381545) \| 2.800923 / 5.269862 (-2.468939) \| 1.723910 / 4.565676 (-2.841766) \| 0.061270 / 0.424275 (-0.363005) \| 0.005006 / 0.007607 (-0.002601) \| 0.334952 / 0.226044 (0.108908) \| 3.367122 / 2.268929 (1.098194) \| 1.839822 / 55.444624 (-53.604803) \| 1.553774 / 6.876477 (-5.322703) \| 1.583585 / 2.142072 (-0.558488) \| 0.624680 / 4.805227 (-4.180547) \| 0.116364 / 6.500664 (-6.384300) \| 0.042412 / 0.075469 (-0.033057) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975207 / 1.841788 (-0.866580) \| 11.843126 / 8.074308 (3.768818) \| 9.418537 / 10.191392 (-0.772855) \| 0.130648 / 0.680424 (-0.549775) \| 0.013747 / 0.534201 (-0.520454) \| 0.288195 / 0.579283 (-0.291088) \| 0.269861 / 0.434364 (-0.164503) \| 0.326732 / 0.540337 (-0.213606) \| 0.441256 / 1.386936 (-0.945680) \|\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) \| 0.005185 / 0.011353 (-0.006168) \| 0.003836 / 0.011008 (-0.007172) \| 0.050057 / 0.038508 (0.011549) \| 0.030929 / 0.023109 (0.007820) \| 0.263558 / 0.275898 (-0.012340) \| 0.284553 / 0.323480 (-0.038927) \| 0.004331 / 0.007986 (-0.003655) \| 0.002815 / 0.004328 (-0.001513) \| 0.050187 / 0.004250 (0.045936) \| 0.048431 / 0.037052 (0.011379) \| 0.271005 / 0.258489 (0.012515) \| 0.304749 / 0.293841 (0.010908) \| 0.029286 / 0.128546 (-0.099260) \| 0.010598 / 0.075646 (-0.065048) \| 0.058111 / 0.419271 (-0.361160) \| 0.053665 / 0.043533 (0.010132) \| 0.257574 / 0.255139 (0.002436) \| 0.285802 / 0.283200 (0.002602) \| 0.018917 / 0.141683 (-0.122766) \| 1.206517 / 1.452155 (-0.245638) \| 1.220572 / 1.492716 (-0.272144) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.122466 / 0.018006 (0.104460) \| 0.567887 / 0.000490 (0.567397) \| 0.000321 / 0.000200 (0.000121) \| 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) \| 0.022120 / 0.037411 (-0.015292) \| 0.075456 / 0.014526 (0.060931) \| 0.086385 / 0.176557 (-0.090171) \| 0.126045 / 0.737135 (-0.611091) \| 0.087502 / 0.296338 (-0.208837) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304847 / 0.215209 (0.089638) \| 3.008095 / 2.077655 (0.930441) \| 1.726178 / 1.504120 (0.222058) \| 1.592332 / 1.541195 (0.051138) \| 1.603714 / 1.468490 (0.135224) \| 0.576875 / 4.584777 (-4.007902) \| 2.450884 / 3.745712 (-1.294828) \| 2.719073 / 5.269862 (-2.550789) \| 1.775261 / 4.565676 (-2.790415) \| 0.063144 / 0.424275 (-0.361131) \| 0.005122 / 0.007607 (-0.002485) \| 0.350004 / 0.226044 (0.123960) \| 3.467146 / 2.268929 (1.198218) \| 2.062907 / 55.444624 (-53.381717) \| 1.798793 / 6.876477 (-5.077684) \| 1.921204 / 2.142072 (-0.220868) \| 0.651832 / 4.805227 (-4.153396) \| 0.122326 / 6.500664 (-6.378338) \| 0.041396 / 0.075469 (-0.034073) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.024859 / 1.841788 (-0.816928) \| 12.569744 / 8.074308 (4.495436) \| 10.448487 / 10.191392 (0.257095) \| 0.131529 / 0.680424 (-0.548895) \| 0.014853 / 0.534201 (-0.519348) \| 0.287683 / 0.579283 (-0.291600) \| 0.289814 / 0.434364 (-0.144550) \| 0.323935 / 0.540337 (-0.216403) \| 0.425208 / 1.386936 (-0.961728) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ba71e92c59c9bd9d1ee6168691977f0c4728ed6e \"CML watermark\")\n", "> EDIT: the fix has been released in `huggingface_hub` 0.21.2 - I removed my commits that were using `huggingface_hub@main`\r\n\r\nPlease note that people using `huggingface_hub` < 0.21.2 and latest `fsspec` will have issues when using `datasets`:\r\n- https://github.com/huggingface/lighteval/actions/runs/8139147047/job/22241658122?pr=86\r\n- https://github.com/huggingface/lighteval/pull/84\r\n\r\nCC: @clefourrier \r\n" ]
2,147,795,103	6,686	Question: Is there any way for uploading a large image dataset?	open	2024-02-21T22:07:21	2024-05-02T03:44:59	null	https://github.com/huggingface/datasets/issues/6686	null	zhjohnchan	false	[ "```\r\nimport pandas as pd\r\nfrom datasets import Dataset, Image\r\n\r\n# Read the CSV file\r\ndata = pd.read_csv(\"XXXX.csv\")\r\n\r\n# Create a Hugging Face Dataset\r\ndataset = Dataset.from_pandas(data)\r\ndataset = dataset.cast_column(\"file_name\", Image())\r\n\r\n# Upload to Hugging Face Hub (make sure authentication is set up)\r\ndataset.push_to_hub(\"XXXXX\"\")\r\n```\r\n\r\nstuck in \"Casting the dataset\r\n![截屏2024-05-02 11 44 50](https://github.com/huggingface/datasets/assets/48406770/dc012dc5-16f6-4fd5-9e02-1b705c552c5b)\r\n\"\r\n" ]
2,145,570,006	6,685	Updated Quickstart Notebook link	closed	2024-02-21T01:04:18	2024-03-12T21:31:04	2024-02-25T18:48:08	https://github.com/huggingface/datasets/pull/6685	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6685", "html_url": "https://github.com/huggingface/datasets/pull/6685", "diff_url": "https://github.com/huggingface/datasets/pull/6685.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6685.patch", "merged_at": "2024-02-25T18:48:08" }	Codeblockz	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6685). 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) \| 0.005386 / 0.011353 (-0.005967) \| 0.003707 / 0.011008 (-0.007301) \| 0.062661 / 0.038508 (0.024153) \| 0.029058 / 0.023109 (0.005949) \| 0.249669 / 0.275898 (-0.026230) \| 0.280996 / 0.323480 (-0.042484) \| 0.004041 / 0.007986 (-0.003945) \| 0.002713 / 0.004328 (-0.001616) \| 0.047914 / 0.004250 (0.043664) \| 0.042014 / 0.037052 (0.004961) \| 0.265209 / 0.258489 (0.006720) \| 0.297320 / 0.293841 (0.003479) \| 0.028323 / 0.128546 (-0.100223) \| 0.010844 / 0.075646 (-0.064802) \| 0.205895 / 0.419271 (-0.213377) \| 0.035997 / 0.043533 (-0.007536) \| 0.245069 / 0.255139 (-0.010070) \| 0.266159 / 0.283200 (-0.017040) \| 0.017590 / 0.141683 (-0.124093) \| 1.132046 / 1.452155 (-0.320109) \| 1.177496 / 1.492716 (-0.315220) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.105441 / 0.018006 (0.087435) \| 0.301321 / 0.000490 (0.300831) \| 0.000211 / 0.000200 (0.000011) \| 0.000064 / 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) \| 0.018687 / 0.037411 (-0.018724) \| 0.061221 / 0.014526 (0.046695) \| 0.072556 / 0.176557 (-0.104001) \| 0.119641 / 0.737135 (-0.617495) \| 0.073781 / 0.296338 (-0.222557) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284564 / 0.215209 (0.069354) \| 2.795786 / 2.077655 (0.718131) \| 1.437059 / 1.504120 (-0.067061) \| 1.309319 / 1.541195 (-0.231876) \| 1.315849 / 1.468490 (-0.152641) \| 0.578571 / 4.584777 (-4.006206) \| 2.350754 / 3.745712 (-1.394958) \| 2.758499 / 5.269862 (-2.511362) \| 1.705545 / 4.565676 (-2.860131) \| 0.063660 / 0.424275 (-0.360615) \| 0.005506 / 0.007607 (-0.002101) \| 0.334915 / 0.226044 (0.108871) \| 3.295922 / 2.268929 (1.026994) \| 1.796513 / 55.444624 (-53.648111) \| 1.488113 / 6.876477 (-5.388364) \| 1.523042 / 2.142072 (-0.619031) \| 0.648169 / 4.805227 (-4.157058) \| 0.119321 / 6.500664 (-6.381343) \| 0.041932 / 0.075469 (-0.033537) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.982432 / 1.841788 (-0.859356) \| 11.344780 / 8.074308 (3.270472) \| 9.627219 / 10.191392 (-0.564173) \| 0.142590 / 0.680424 (-0.537834) \| 0.013899 / 0.534201 (-0.520302) \| 0.286335 / 0.579283 (-0.292948) \| 0.266552 / 0.434364 (-0.167812) \| 0.320361 / 0.540337 (-0.219977) \| 0.420303 / 1.386936 (-0.966633) \|\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) \| 0.005251 / 0.011353 (-0.006102) \| 0.003515 / 0.011008 (-0.007494) \| 0.049344 / 0.038508 (0.010836) \| 0.032055 / 0.023109 (0.008945) \| 0.280653 / 0.275898 (0.004755) \| 0.303989 / 0.323480 (-0.019491) \| 0.004402 / 0.007986 (-0.003584) \| 0.002758 / 0.004328 (-0.001570) \| 0.050947 / 0.004250 (0.046697) \| 0.044405 / 0.037052 (0.007353) \| 0.292856 / 0.258489 (0.034367) \| 0.325307 / 0.293841 (0.031466) \| 0.047720 / 0.128546 (-0.080827) \| 0.010589 / 0.075646 (-0.065057) \| 0.057728 / 0.419271 (-0.361543) \| 0.033842 / 0.043533 (-0.009691) \| 0.285443 / 0.255139 (0.030304) \| 0.300013 / 0.283200 (0.016814) \| 0.017444 / 0.141683 (-0.124238) \| 1.152880 / 1.452155 (-0.299275) \| 1.200670 / 1.492716 (-0.292046) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092355 / 0.018006 (0.074349) \| 0.307907 / 0.000490 (0.307418) \| 0.000226 / 0.000200 (0.000026) \| 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) \| 0.021624 / 0.037411 (-0.015787) \| 0.075855 / 0.014526 (0.061329) \| 0.087109 / 0.176557 (-0.089447) \| 0.124859 / 0.737135 (-0.612276) \| 0.088933 / 0.296338 (-0.207406) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294213 / 0.215209 (0.079004) \| 2.893146 / 2.077655 (0.815491) \| 1.595061 / 1.504120 (0.090942) \| 1.480959 / 1.541195 (-0.060236) \| 1.528277 / 1.468490 (0.059787) \| 0.570273 / 4.584777 (-4.014504) \| 2.412948 / 3.745712 (-1.332764) \| 2.675009 / 5.269862 (-2.594852) \| 1.724005 / 4.565676 (-2.841671) \| 0.063359 / 0.424275 (-0.360916) \| 0.005008 / 0.007607 (-0.002599) \| 0.346570 / 0.226044 (0.120526) \| 3.456566 / 2.268929 (1.187637) \| 1.973109 / 55.444624 (-53.471515) \| 1.657562 / 6.876477 (-5.218915) \| 1.790086 / 2.142072 (-0.351986) \| 0.655277 / 4.805227 (-4.149950) \| 0.117985 / 6.500664 (-6.382679) \| 0.041128 / 0.075469 (-0.034342) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.001428 / 1.841788 (-0.840360) \| 11.953458 / 8.074308 (3.879150) \| 10.188439 / 10.191392 (-0.002953) \| 0.140863 / 0.680424 (-0.539561) \| 0.015278 / 0.534201 (-0.518923) \| 0.288193 / 0.579283 (-0.291090) \| 0.281732 / 0.434364 (-0.152632) \| 0.328034 / 0.540337 (-0.212304) \| 0.414571 / 1.386936 (-0.972365) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#531f35e688f81ec6b4c9044856a89a6b48142bd8 \"CML watermark\")\n" ]
2,144,092,388	6,684	Improve error message for gated datasets on load	closed	2024-02-20T10:51:27	2024-02-20T15:40:52	2024-02-20T15:33:56	https://github.com/huggingface/datasets/pull/6684	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6684", "html_url": "https://github.com/huggingface/datasets/pull/6684", "diff_url": "https://github.com/huggingface/datasets/pull/6684.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6684.patch", "merged_at": "2024-02-20T15:33:56" }	lewtun	true	[ "Thank you ! Should we also add the link to the dataset page ?", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6684). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> Thank you ! Should we also add the link to the dataset page ?\r\n\r\nGood idea! Done in https://github.com/huggingface/datasets/pull/6684/commits/4ab55210dca1815b6c2f23901598bfb29fc92a47", "Looks like a test is failing: `test_load_dataset_cached_local_script `.\r\n\r\nApparently your new message is also shown for datasets that don't exist, which is maybe not ideal", "Ah let me take a look!", "> Looks like a test is failing: `test_load_dataset_cached_local_script `.\r\n> \r\n> Apparently your new message is also shown for datasets that don't exist, which is maybe not ideal\r\n\r\nFixed by reverting the error message root + added a small clarifying part", "<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) \| 0.005634 / 0.011353 (-0.005719) \| 0.003786 / 0.011008 (-0.007222) \| 0.064245 / 0.038508 (0.025737) \| 0.031228 / 0.023109 (0.008119) \| 0.248162 / 0.275898 (-0.027736) \| 0.273454 / 0.323480 (-0.050026) \| 0.003176 / 0.007986 (-0.004809) \| 0.002814 / 0.004328 (-0.001515) \| 0.049234 / 0.004250 (0.044984) \| 0.046075 / 0.037052 (0.009023) \| 0.262410 / 0.258489 (0.003921) \| 0.290597 / 0.293841 (-0.003244) \| 0.028545 / 0.128546 (-0.100001) \| 0.010881 / 0.075646 (-0.064766) \| 0.212098 / 0.419271 (-0.207173) \| 0.036406 / 0.043533 (-0.007127) \| 0.244571 / 0.255139 (-0.010568) \| 0.269537 / 0.283200 (-0.013663) \| 0.019574 / 0.141683 (-0.122109) \| 1.120369 / 1.452155 (-0.331785) \| 1.170188 / 1.492716 (-0.322529) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.108088 / 0.018006 (0.090082) \| 0.299836 / 0.000490 (0.299346) \| 0.000204 / 0.000200 (0.000004) \| 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) \| 0.020881 / 0.037411 (-0.016531) \| 0.065290 / 0.014526 (0.050764) \| 0.074283 / 0.176557 (-0.102274) \| 0.122189 / 0.737135 (-0.614947) \| 0.077772 / 0.296338 (-0.218566) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278329 / 0.215209 (0.063120) \| 2.709885 / 2.077655 (0.632230) \| 1.428824 / 1.504120 (-0.075296) \| 1.314338 / 1.541195 (-0.226857) \| 1.349445 / 1.468490 (-0.119045) \| 0.571863 / 4.584777 (-4.012914) \| 2.358306 / 3.745712 (-1.387407) \| 2.873498 / 5.269862 (-2.396364) \| 1.779897 / 4.565676 (-2.785779) \| 0.062828 / 0.424275 (-0.361447) \| 0.005416 / 0.007607 (-0.002191) \| 0.337645 / 0.226044 (0.111601) \| 3.328868 / 2.268929 (1.059940) \| 1.793387 / 55.444624 (-53.651238) \| 1.539201 / 6.876477 (-5.337276) \| 1.589552 / 2.142072 (-0.552520) \| 0.645454 / 4.805227 (-4.159773) \| 0.116966 / 6.500664 (-6.383698) \| 0.043339 / 0.075469 (-0.032130) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.995743 / 1.841788 (-0.846045) \| 12.096551 / 8.074308 (4.022243) \| 10.214299 / 10.191392 (0.022907) \| 0.133025 / 0.680424 (-0.547399) \| 0.014393 / 0.534201 (-0.519808) \| 0.289018 / 0.579283 (-0.290266) \| 0.267879 / 0.434364 (-0.166485) \| 0.324362 / 0.540337 (-0.215976) \| 0.425596 / 1.386936 (-0.961340) \|\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) \| 0.005739 / 0.011353 (-0.005614) \| 0.003992 / 0.011008 (-0.007017) \| 0.051362 / 0.038508 (0.012854) \| 0.031707 / 0.023109 (0.008598) \| 0.274807 / 0.275898 (-0.001091) \| 0.298897 / 0.323480 (-0.024583) \| 0.004363 / 0.007986 (-0.003622) \| 0.002862 / 0.004328 (-0.001466) \| 0.050462 / 0.004250 (0.046212) \| 0.048158 / 0.037052 (0.011106) \| 0.282759 / 0.258489 (0.024270) \| 0.317766 / 0.293841 (0.023926) \| 0.060245 / 0.128546 (-0.068301) \| 0.011279 / 0.075646 (-0.064367) \| 0.061175 / 0.419271 (-0.358097) \| 0.035876 / 0.043533 (-0.007656) \| 0.273963 / 0.255139 (0.018824) \| 0.288788 / 0.283200 (0.005589) \| 0.019690 / 0.141683 (-0.121992) \| 1.167074 / 1.452155 (-0.285080) \| 1.206344 / 1.492716 (-0.286372) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091211 / 0.018006 (0.073205) \| 0.299295 / 0.000490 (0.298805) \| 0.000216 / 0.000200 (0.000016) \| 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) \| 0.022718 / 0.037411 (-0.014693) \| 0.079483 / 0.014526 (0.064957) \| 0.087437 / 0.176557 (-0.089120) \| 0.126977 / 0.737135 (-0.610159) \| 0.089678 / 0.296338 (-0.206660) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294719 / 0.215209 (0.079510) \| 2.864505 / 2.077655 (0.786851) \| 1.583993 / 1.504120 (0.079873) \| 1.455079 / 1.541195 (-0.086115) \| 1.504080 / 1.468490 (0.035590) \| 0.569040 / 4.584777 (-4.015737) \| 2.423472 / 3.745712 (-1.322240) \| 2.742848 / 5.269862 (-2.527014) \| 1.785244 / 4.565676 (-2.780432) \| 0.062655 / 0.424275 (-0.361620) \| 0.005027 / 0.007607 (-0.002580) \| 0.343863 / 0.226044 (0.117818) \| 3.376286 / 2.268929 (1.107358) \| 1.933846 / 55.444624 (-53.510779) \| 1.667316 / 6.876477 (-5.209161) \| 1.815621 / 2.142072 (-0.326451) \| 0.639378 / 4.805227 (-4.165850) \| 0.116514 / 6.500664 (-6.384150) \| 0.042191 / 0.075469 (-0.033279) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.007103 / 1.841788 (-0.834685) \| 12.791193 / 8.074308 (4.716885) \| 10.870575 / 10.191392 (0.679183) \| 0.131040 / 0.680424 (-0.549384) \| 0.016510 / 0.534201 (-0.517691) \| 0.288372 / 0.579283 (-0.290911) \| 0.275574 / 0.434364 (-0.158790) \| 0.327801 / 0.540337 (-0.212536) \| 0.415942 / 1.386936 (-0.970994) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b775390900132834e5edf487f5cbbf1299af1d88 \"CML watermark\")\n" ]
2,142,751,955	6,683	Fix imagefolder dataset url	closed	2024-02-19T16:26:51	2024-02-19T17:24:25	2024-02-19T17:18:10	https://github.com/huggingface/datasets/pull/6683	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6683", "html_url": "https://github.com/huggingface/datasets/pull/6683", "diff_url": "https://github.com/huggingface/datasets/pull/6683.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6683.patch", "merged_at": "2024-02-19T17:18:10" }	mariosasko	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6683). 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) \| 0.005501 / 0.011353 (-0.005851) \| 0.003907 / 0.011008 (-0.007101) \| 0.063524 / 0.038508 (0.025016) \| 0.031773 / 0.023109 (0.008664) \| 0.244672 / 0.275898 (-0.031226) \| 0.293342 / 0.323480 (-0.030138) \| 0.004091 / 0.007986 (-0.003895) \| 0.002837 / 0.004328 (-0.001491) \| 0.049181 / 0.004250 (0.044930) \| 0.044515 / 0.037052 (0.007462) \| 0.263932 / 0.258489 (0.005443) \| 0.288412 / 0.293841 (-0.005429) \| 0.028338 / 0.128546 (-0.100208) \| 0.010865 / 0.075646 (-0.064781) \| 0.207979 / 0.419271 (-0.211293) \| 0.036149 / 0.043533 (-0.007384) \| 0.250674 / 0.255139 (-0.004465) \| 0.263232 / 0.283200 (-0.019968) \| 0.017919 / 0.141683 (-0.123763) \| 1.127794 / 1.452155 (-0.324360) \| 1.172071 / 1.492716 (-0.320645) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090435 / 0.018006 (0.072429) \| 0.300041 / 0.000490 (0.299552) \| 0.000217 / 0.000200 (0.000018) \| 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) \| 0.018986 / 0.037411 (-0.018426) \| 0.064872 / 0.014526 (0.050346) \| 0.074738 / 0.176557 (-0.101818) \| 0.121577 / 0.737135 (-0.615558) \| 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) \| 0.279471 / 0.215209 (0.064262) \| 2.743066 / 2.077655 (0.665411) \| 1.429511 / 1.504120 (-0.074609) \| 1.315391 / 1.541195 (-0.225804) \| 1.371255 / 1.468490 (-0.097235) \| 0.570708 / 4.584777 (-4.014069) \| 2.373047 / 3.745712 (-1.372666) \| 2.813198 / 5.269862 (-2.456663) \| 1.768928 / 4.565676 (-2.796749) \| 0.066031 / 0.424275 (-0.358244) \| 0.005074 / 0.007607 (-0.002533) \| 0.333484 / 0.226044 (0.107440) \| 3.295002 / 2.268929 (1.026074) \| 1.796089 / 55.444624 (-53.648535) \| 1.521849 / 6.876477 (-5.354627) \| 1.604417 / 2.142072 (-0.537655) \| 0.645235 / 4.805227 (-4.159992) \| 0.119226 / 6.500664 (-6.381439) \| 0.043275 / 0.075469 (-0.032194) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.986350 / 1.841788 (-0.855438) \| 11.921886 / 8.074308 (3.847578) \| 9.878841 / 10.191392 (-0.312551) \| 0.141072 / 0.680424 (-0.539352) \| 0.014514 / 0.534201 (-0.519687) \| 0.304060 / 0.579283 (-0.275223) \| 0.267844 / 0.434364 (-0.166520) \| 0.324881 / 0.540337 (-0.215457) \| 0.421426 / 1.386936 (-0.965510) \|\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) \| 0.005322 / 0.011353 (-0.006030) \| 0.003942 / 0.011008 (-0.007066) \| 0.050629 / 0.038508 (0.012121) \| 0.031176 / 0.023109 (0.008066) \| 0.279627 / 0.275898 (0.003729) \| 0.302667 / 0.323480 (-0.020813) \| 0.004281 / 0.007986 (-0.003705) \| 0.002900 / 0.004328 (-0.001428) \| 0.048168 / 0.004250 (0.043918) \| 0.046094 / 0.037052 (0.009042) \| 0.290714 / 0.258489 (0.032224) \| 0.321336 / 0.293841 (0.027496) \| 0.047934 / 0.128546 (-0.080612) \| 0.010773 / 0.075646 (-0.064873) \| 0.059439 / 0.419271 (-0.359832) \| 0.033644 / 0.043533 (-0.009889) \| 0.273710 / 0.255139 (0.018571) \| 0.295144 / 0.283200 (0.011944) \| 0.018115 / 0.141683 (-0.123568) \| 1.150302 / 1.452155 (-0.301853) \| 1.197304 / 1.492716 (-0.295412) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090262 / 0.018006 (0.072255) \| 0.300727 / 0.000490 (0.300238) \| 0.000228 / 0.000200 (0.000028) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022706 / 0.037411 (-0.014706) \| 0.077420 / 0.014526 (0.062894) \| 0.089119 / 0.176557 (-0.087437) \| 0.126760 / 0.737135 (-0.610375) \| 0.090702 / 0.296338 (-0.205637) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296558 / 0.215209 (0.081349) \| 2.865311 / 2.077655 (0.787656) \| 1.587355 / 1.504120 (0.083235) \| 1.491660 / 1.541195 (-0.049534) \| 1.513604 / 1.468490 (0.045114) \| 0.565209 / 4.584777 (-4.019568) \| 2.450648 / 3.745712 (-1.295064) \| 2.709941 / 5.269862 (-2.559921) \| 1.775032 / 4.565676 (-2.790645) \| 0.063767 / 0.424275 (-0.360508) \| 0.005047 / 0.007607 (-0.002560) \| 0.347406 / 0.226044 (0.121361) \| 3.416671 / 2.268929 (1.147743) \| 1.949653 / 55.444624 (-53.494971) \| 1.669885 / 6.876477 (-5.206592) \| 1.848125 / 2.142072 (-0.293947) \| 0.648179 / 4.805227 (-4.157048) \| 0.116374 / 6.500664 (-6.384290) \| 0.041816 / 0.075469 (-0.033653) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.007009 / 1.841788 (-0.834779) \| 12.749964 / 8.074308 (4.675656) \| 10.765890 / 10.191392 (0.574498) \| 0.141743 / 0.680424 (-0.538681) \| 0.016077 / 0.534201 (-0.518124) \| 0.293275 / 0.579283 (-0.286008) \| 0.277064 / 0.434364 (-0.157300) \| 0.327039 / 0.540337 (-0.213299) \| 0.421784 / 1.386936 (-0.965152) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f807cd4c733a3616011a3f7f53a9fa56f7d5f685 \"CML watermark\")\n" ]
2,142,000,800	6,682	Update GitHub Actions to Node 20	closed	2024-02-19T10:10:50	2024-02-28T07:02:40	2024-02-28T06:56:34	https://github.com/huggingface/datasets/pull/6682	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6682", "html_url": "https://github.com/huggingface/datasets/pull/6682", "diff_url": "https://github.com/huggingface/datasets/pull/6682.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6682.patch", "merged_at": "2024-02-28T06:56:34" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6682). 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) \| 0.005292 / 0.011353 (-0.006060) \| 0.003354 / 0.011008 (-0.007654) \| 0.063150 / 0.038508 (0.024642) \| 0.028616 / 0.023109 (0.005507) \| 0.242267 / 0.275898 (-0.033631) \| 0.267305 / 0.323480 (-0.056175) \| 0.003041 / 0.007986 (-0.004944) \| 0.003346 / 0.004328 (-0.000982) \| 0.048268 / 0.004250 (0.044018) \| 0.042070 / 0.037052 (0.005018) \| 0.256526 / 0.258489 (-0.001963) \| 0.279744 / 0.293841 (-0.014097) \| 0.027862 / 0.128546 (-0.100684) \| 0.010786 / 0.075646 (-0.064861) \| 0.206998 / 0.419271 (-0.212273) \| 0.035503 / 0.043533 (-0.008030) \| 0.248454 / 0.255139 (-0.006685) \| 0.265639 / 0.283200 (-0.017561) \| 0.019590 / 0.141683 (-0.122093) \| 1.134445 / 1.452155 (-0.317709) \| 1.194956 / 1.492716 (-0.297761) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090987 / 0.018006 (0.072981) \| 0.301907 / 0.000490 (0.301418) \| 0.000213 / 0.000200 (0.000013) \| 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) \| 0.018324 / 0.037411 (-0.019088) \| 0.061492 / 0.014526 (0.046966) \| 0.074166 / 0.176557 (-0.102391) \| 0.119990 / 0.737135 (-0.617145) \| 0.074554 / 0.296338 (-0.221785) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279646 / 0.215209 (0.064437) \| 2.773819 / 2.077655 (0.696164) \| 1.436460 / 1.504120 (-0.067660) \| 1.310303 / 1.541195 (-0.230892) \| 1.315328 / 1.468490 (-0.153162) \| 0.558328 / 4.584777 (-4.026449) \| 2.383819 / 3.745712 (-1.361893) \| 2.735034 / 5.269862 (-2.534827) \| 1.724413 / 4.565676 (-2.841263) \| 0.061476 / 0.424275 (-0.362799) \| 0.004899 / 0.007607 (-0.002708) \| 0.333195 / 0.226044 (0.107151) \| 3.228900 / 2.268929 (0.959971) \| 1.787315 / 55.444624 (-53.657309) \| 1.526949 / 6.876477 (-5.349527) \| 1.539816 / 2.142072 (-0.602257) \| 0.636926 / 4.805227 (-4.168302) \| 0.117533 / 6.500664 (-6.383131) \| 0.041859 / 0.075469 (-0.033610) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.964637 / 1.841788 (-0.877151) \| 11.296021 / 8.074308 (3.221713) \| 9.375436 / 10.191392 (-0.815956) \| 0.140330 / 0.680424 (-0.540094) \| 0.013638 / 0.534201 (-0.520563) \| 0.287046 / 0.579283 (-0.292237) \| 0.265054 / 0.434364 (-0.169310) \| 0.331548 / 0.540337 (-0.208790) \| 0.438418 / 1.386936 (-0.948518) \|\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) \| 0.005284 / 0.011353 (-0.006069) \| 0.003853 / 0.011008 (-0.007155) \| 0.049301 / 0.038508 (0.010793) \| 0.030477 / 0.023109 (0.007368) \| 0.278507 / 0.275898 (0.002609) \| 0.298245 / 0.323480 (-0.025235) \| 0.004225 / 0.007986 (-0.003761) \| 0.002736 / 0.004328 (-0.001593) \| 0.049345 / 0.004250 (0.045094) \| 0.045141 / 0.037052 (0.008088) \| 0.290992 / 0.258489 (0.032503) \| 0.317430 / 0.293841 (0.023589) \| 0.029623 / 0.128546 (-0.098924) \| 0.010351 / 0.075646 (-0.065295) \| 0.058027 / 0.419271 (-0.361244) \| 0.051306 / 0.043533 (0.007773) \| 0.279947 / 0.255139 (0.024808) \| 0.296916 / 0.283200 (0.013717) \| 0.018859 / 0.141683 (-0.122823) \| 1.153484 / 1.452155 (-0.298670) \| 1.189141 / 1.492716 (-0.303575) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091030 / 0.018006 (0.073024) \| 0.301305 / 0.000490 (0.300815) \| 0.000230 / 0.000200 (0.000030) \| 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) \| 0.021801 / 0.037411 (-0.015611) \| 0.075162 / 0.014526 (0.060636) \| 0.086455 / 0.176557 (-0.090102) \| 0.125431 / 0.737135 (-0.611705) \| 0.087797 / 0.296338 (-0.208542) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295950 / 0.215209 (0.080741) \| 2.895839 / 2.077655 (0.818184) \| 1.603121 / 1.504120 (0.099001) \| 1.482162 / 1.541195 (-0.059033) \| 1.474231 / 1.468490 (0.005741) \| 0.571370 / 4.584777 (-4.013407) \| 2.466864 / 3.745712 (-1.278848) \| 2.607279 / 5.269862 (-2.662582) \| 1.723106 / 4.565676 (-2.842571) \| 0.062068 / 0.424275 (-0.362208) \| 0.004958 / 0.007607 (-0.002649) \| 0.345213 / 0.226044 (0.119168) \| 3.403916 / 2.268929 (1.134987) \| 1.935538 / 55.444624 (-53.509086) \| 1.658930 / 6.876477 (-5.217547) \| 1.767611 / 2.142072 (-0.374461) \| 0.645780 / 4.805227 (-4.159447) \| 0.116077 / 6.500664 (-6.384587) \| 0.040774 / 0.075469 (-0.034695) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.025952 / 1.841788 (-0.815836) \| 11.935970 / 8.074308 (3.861662) \| 9.935799 / 10.191392 (-0.255593) \| 0.131081 / 0.680424 (-0.549343) \| 0.016010 / 0.534201 (-0.518191) \| 0.285476 / 0.579283 (-0.293807) \| 0.274928 / 0.434364 (-0.159435) \| 0.325788 / 0.540337 (-0.214550) \| 0.421666 / 1.386936 (-0.965270) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7a79064b7a2255c0d6950dc998509ecefb893689 \"CML watermark\")\n" ]
2,141,985,239	6,681	Update release instructions	closed	2024-02-19T10:03:08	2024-02-28T07:23:49	2024-02-28T07:17:22	https://github.com/huggingface/datasets/pull/6681	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6681", "html_url": "https://github.com/huggingface/datasets/pull/6681", "diff_url": "https://github.com/huggingface/datasets/pull/6681.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6681.patch", "merged_at": "2024-02-28T07:17:22" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6681). 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) \| 0.005410 / 0.011353 (-0.005943) \| 0.003862 / 0.011008 (-0.007146) \| 0.063457 / 0.038508 (0.024949) \| 0.030081 / 0.023109 (0.006972) \| 0.250657 / 0.275898 (-0.025241) \| 0.275483 / 0.323480 (-0.047997) \| 0.004048 / 0.007986 (-0.003938) \| 0.002818 / 0.004328 (-0.001511) \| 0.048940 / 0.004250 (0.044689) \| 0.043397 / 0.037052 (0.006345) \| 0.262160 / 0.258489 (0.003671) \| 0.294154 / 0.293841 (0.000313) \| 0.030028 / 0.128546 (-0.098519) \| 0.010789 / 0.075646 (-0.064857) \| 0.209665 / 0.419271 (-0.209606) \| 0.035297 / 0.043533 (-0.008236) \| 0.253169 / 0.255139 (-0.001970) \| 0.271775 / 0.283200 (-0.011424) \| 0.018332 / 0.141683 (-0.123351) \| 1.152420 / 1.452155 (-0.299735) \| 1.262767 / 1.492716 (-0.229949) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089990 / 0.018006 (0.071984) \| 0.298552 / 0.000490 (0.298062) \| 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) \| 0.018414 / 0.037411 (-0.018997) \| 0.061566 / 0.014526 (0.047040) \| 0.075360 / 0.176557 (-0.101196) \| 0.123470 / 0.737135 (-0.613665) \| 0.075215 / 0.296338 (-0.221124) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279563 / 0.215209 (0.064354) \| 2.725212 / 2.077655 (0.647557) \| 1.446413 / 1.504120 (-0.057707) \| 1.321665 / 1.541195 (-0.219529) \| 1.352475 / 1.468490 (-0.116015) \| 0.568440 / 4.584777 (-4.016337) \| 2.393217 / 3.745712 (-1.352495) \| 2.793150 / 5.269862 (-2.476711) \| 1.764316 / 4.565676 (-2.801360) \| 0.063157 / 0.424275 (-0.361118) \| 0.005117 / 0.007607 (-0.002491) \| 0.333310 / 0.226044 (0.107265) \| 3.291000 / 2.268929 (1.022071) \| 1.824654 / 55.444624 (-53.619971) \| 1.558681 / 6.876477 (-5.317795) \| 1.580558 / 2.142072 (-0.561514) \| 0.649831 / 4.805227 (-4.155396) \| 0.118674 / 6.500664 (-6.381990) \| 0.042247 / 0.075469 (-0.033222) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976552 / 1.841788 (-0.865236) \| 11.847361 / 8.074308 (3.773053) \| 9.490786 / 10.191392 (-0.700606) \| 0.141643 / 0.680424 (-0.538781) \| 0.013653 / 0.534201 (-0.520548) \| 0.284345 / 0.579283 (-0.294938) \| 0.268314 / 0.434364 (-0.166050) \| 0.339586 / 0.540337 (-0.200751) \| 0.445239 / 1.386936 (-0.941697) \|\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) \| 0.005754 / 0.011353 (-0.005599) \| 0.004038 / 0.011008 (-0.006970) \| 0.050027 / 0.038508 (0.011519) \| 0.033598 / 0.023109 (0.010488) \| 0.286514 / 0.275898 (0.010616) \| 0.302493 / 0.323480 (-0.020986) \| 0.004254 / 0.007986 (-0.003731) \| 0.002827 / 0.004328 (-0.001502) \| 0.050433 / 0.004250 (0.046182) \| 0.046106 / 0.037052 (0.009054) \| 0.301522 / 0.258489 (0.043033) \| 0.325784 / 0.293841 (0.031943) \| 0.030014 / 0.128546 (-0.098532) \| 0.010891 / 0.075646 (-0.064756) \| 0.059899 / 0.419271 (-0.359373) \| 0.057252 / 0.043533 (0.013719) \| 0.280276 / 0.255139 (0.025137) \| 0.295632 / 0.283200 (0.012433) \| 0.019060 / 0.141683 (-0.122622) \| 1.141423 / 1.452155 (-0.310731) \| 1.226960 / 1.492716 (-0.265757) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091919 / 0.018006 (0.073913) \| 0.300769 / 0.000490 (0.300279) \| 0.000220 / 0.000200 (0.000020) \| 0.000053 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022467 / 0.037411 (-0.014945) \| 0.075342 / 0.014526 (0.060816) \| 0.087988 / 0.176557 (-0.088569) \| 0.128304 / 0.737135 (-0.608831) \| 0.089058 / 0.296338 (-0.207280) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294662 / 0.215209 (0.079453) \| 2.887743 / 2.077655 (0.810088) \| 1.591756 / 1.504120 (0.087636) \| 1.469249 / 1.541195 (-0.071945) \| 1.495639 / 1.468490 (0.027149) \| 0.575507 / 4.584777 (-4.009270) \| 2.449674 / 3.745712 (-1.296038) \| 2.737217 / 5.269862 (-2.532645) \| 1.783066 / 4.565676 (-2.782610) \| 0.063388 / 0.424275 (-0.360887) \| 0.005044 / 0.007607 (-0.002563) \| 0.344807 / 0.226044 (0.118763) \| 3.410845 / 2.268929 (1.141916) \| 1.967452 / 55.444624 (-53.477173) \| 1.699884 / 6.876477 (-5.176593) \| 1.862466 / 2.142072 (-0.279607) \| 0.663714 / 4.805227 (-4.141513) \| 0.118356 / 6.500664 (-6.382308) \| 0.041176 / 0.075469 (-0.034293) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.013523 / 1.841788 (-0.828264) \| 12.498866 / 8.074308 (4.424558) \| 10.382595 / 10.191392 (0.191203) \| 0.141757 / 0.680424 (-0.538667) \| 0.015992 / 0.534201 (-0.518209) \| 0.295639 / 0.579283 (-0.283644) \| 0.278382 / 0.434364 (-0.155982) \| 0.330351 / 0.540337 (-0.209986) \| 0.431293 / 1.386936 (-0.955643) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf1aaa32eddd73076cf6600125661df4a32cb20a \"CML watermark\")\n" ]
2,141,979,527	6,680	Set dev version	closed	2024-02-19T10:00:31	2024-02-19T10:06:43	2024-02-19T10:00:40	https://github.com/huggingface/datasets/pull/6680	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6680", "html_url": "https://github.com/huggingface/datasets/pull/6680", "diff_url": "https://github.com/huggingface/datasets/pull/6680.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6680.patch", "merged_at": "2024-02-19T10:00:40" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6680). 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) \| 0.004981 / 0.011353 (-0.006372) \| 0.003030 / 0.011008 (-0.007978) \| 0.059862 / 0.038508 (0.021354) \| 0.030595 / 0.023109 (0.007486) \| 0.262638 / 0.275898 (-0.013260) \| 0.276287 / 0.323480 (-0.047193) \| 0.003955 / 0.007986 (-0.004030) \| 0.002667 / 0.004328 (-0.001661) \| 0.047827 / 0.004250 (0.043576) \| 0.041170 / 0.037052 (0.004118) \| 0.252494 / 0.258489 (-0.005995) \| 0.277493 / 0.293841 (-0.016348) \| 0.027269 / 0.128546 (-0.101277) \| 0.010380 / 0.075646 (-0.065266) \| 0.204404 / 0.419271 (-0.214867) \| 0.035251 / 0.043533 (-0.008282) \| 0.244368 / 0.255139 (-0.010771) \| 0.258003 / 0.283200 (-0.025197) \| 0.016751 / 0.141683 (-0.124932) \| 1.134108 / 1.452155 (-0.318047) \| 1.159969 / 1.492716 (-0.332748) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.087011 / 0.018006 (0.069004) \| 0.295577 / 0.000490 (0.295087) \| 0.000213 / 0.000200 (0.000013) \| 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) \| 0.017993 / 0.037411 (-0.019419) \| 0.061690 / 0.014526 (0.047164) \| 0.071791 / 0.176557 (-0.104765) \| 0.118282 / 0.737135 (-0.618853) \| 0.073453 / 0.296338 (-0.222885) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284764 / 0.215209 (0.069555) \| 2.771791 / 2.077655 (0.694136) \| 1.469614 / 1.504120 (-0.034506) \| 1.334096 / 1.541195 (-0.207099) \| 1.339995 / 1.468490 (-0.128495) \| 0.562740 / 4.584777 (-4.022037) \| 2.390219 / 3.745712 (-1.355493) \| 2.679776 / 5.269862 (-2.590086) \| 1.684397 / 4.565676 (-2.881279) \| 0.062137 / 0.424275 (-0.362138) \| 0.004934 / 0.007607 (-0.002673) \| 0.336257 / 0.226044 (0.110212) \| 3.256330 / 2.268929 (0.987401) \| 1.801520 / 55.444624 (-53.643105) \| 1.520662 / 6.876477 (-5.355815) \| 1.537023 / 2.142072 (-0.605049) \| 0.644360 / 4.805227 (-4.160867) \| 0.115603 / 6.500664 (-6.385061) \| 0.040601 / 0.075469 (-0.034868) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.982992 / 1.841788 (-0.858796) \| 11.002182 / 8.074308 (2.927873) \| 9.564671 / 10.191392 (-0.626721) \| 0.137682 / 0.680424 (-0.542742) \| 0.013936 / 0.534201 (-0.520265) \| 0.285898 / 0.579283 (-0.293385) \| 0.264426 / 0.434364 (-0.169938) \| 0.321615 / 0.540337 (-0.218723) \| 0.420216 / 1.386936 (-0.966720) \|\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) \| 0.003165 / 0.011008 (-0.007844) \| 0.048176 / 0.038508 (0.009668) \| 0.030680 / 0.023109 (0.007571) \| 0.258176 / 0.275898 (-0.017722) \| 0.282342 / 0.323480 (-0.041138) \| 0.004218 / 0.007986 (-0.003767) \| 0.002616 / 0.004328 (-0.001713) \| 0.047253 / 0.004250 (0.043003) \| 0.044178 / 0.037052 (0.007126) \| 0.276942 / 0.258489 (0.018453) \| 0.312353 / 0.293841 (0.018512) \| 0.046714 / 0.128546 (-0.081832) \| 0.009892 / 0.075646 (-0.065755) \| 0.056123 / 0.419271 (-0.363149) \| 0.032691 / 0.043533 (-0.010842) \| 0.268781 / 0.255139 (0.013642) \| 0.285921 / 0.283200 (0.002722) \| 0.016050 / 0.141683 (-0.125633) \| 1.138058 / 1.452155 (-0.314096) \| 1.193405 / 1.492716 (-0.299311) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089280 / 0.018006 (0.071273) \| 0.288425 / 0.000490 (0.287935) \| 0.000201 / 0.000200 (0.000001) \| 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) \| 0.021536 / 0.037411 (-0.015875) \| 0.075157 / 0.014526 (0.060631) \| 0.088943 / 0.176557 (-0.087613) \| 0.125191 / 0.737135 (-0.611945) \| 0.087991 / 0.296338 (-0.208348) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285103 / 0.215209 (0.069894) \| 2.791798 / 2.077655 (0.714144) \| 1.518104 / 1.504120 (0.013984) \| 1.388690 / 1.541195 (-0.152505) \| 1.409896 / 1.468490 (-0.058594) \| 0.554077 / 4.584777 (-4.030700) \| 2.396994 / 3.745712 (-1.348718) \| 2.596801 / 5.269862 (-2.673060) \| 1.683761 / 4.565676 (-2.881915) \| 0.061209 / 0.424275 (-0.363066) \| 0.004735 / 0.007607 (-0.002873) \| 0.337566 / 0.226044 (0.111522) \| 3.258183 / 2.268929 (0.989254) \| 1.886185 / 55.444624 (-53.558439) \| 1.599148 / 6.876477 (-5.277329) \| 1.726867 / 2.142072 (-0.415206) \| 0.642784 / 4.805227 (-4.162444) \| 0.114947 / 6.500664 (-6.385717) \| 0.040450 / 0.075469 (-0.035019) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.001316 / 1.841788 (-0.840472) \| 11.695367 / 8.074308 (3.621058) \| 9.854870 / 10.191392 (-0.336522) \| 0.136462 / 0.680424 (-0.543961) \| 0.016708 / 0.534201 (-0.517493) \| 0.286421 / 0.579283 (-0.292862) \| 0.270773 / 0.434364 (-0.163591) \| 0.322947 / 0.540337 (-0.217390) \| 0.416772 / 1.386936 (-0.970164) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6ba542847314bd349301937e59c3de04ce13aa5e \"CML watermark\")\n" ]
2,141,953,981	6,679	Node.js 16 GitHub Actions are deprecated	closed	2024-02-19T09:47:37	2024-02-28T06:56:35	2024-02-28T06:56:35	https://github.com/huggingface/datasets/issues/6679	null	albertvillanova	false	[]
2,141,902,154	6,678	Release: 2.17.1	closed	2024-02-19T09:24:29	2024-02-19T10:03:00	2024-02-19T09:56:52	https://github.com/huggingface/datasets/pull/6678	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6678", "html_url": "https://github.com/huggingface/datasets/pull/6678", "diff_url": "https://github.com/huggingface/datasets/pull/6678.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6678.patch", "merged_at": "2024-02-19T09:56:52" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6678). 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) \| 0.005070 / 0.011353 (-0.006283) \| 0.003685 / 0.011008 (-0.007323) \| 0.063191 / 0.038508 (0.024683) \| 0.030506 / 0.023109 (0.007397) \| 0.258033 / 0.275898 (-0.017865) \| 0.269790 / 0.323480 (-0.053690) \| 0.004180 / 0.007986 (-0.003805) \| 0.002811 / 0.004328 (-0.001517) \| 0.048718 / 0.004250 (0.044467) \| 0.043473 / 0.037052 (0.006421) \| 0.267306 / 0.258489 (0.008817) \| 0.290315 / 0.293841 (-0.003526) \| 0.027402 / 0.128546 (-0.101144) \| 0.010782 / 0.075646 (-0.064864) \| 0.207243 / 0.419271 (-0.212029) \| 0.035637 / 0.043533 (-0.007896) \| 0.264032 / 0.255139 (0.008893) \| 0.270450 / 0.283200 (-0.012749) \| 0.017407 / 0.141683 (-0.124276) \| 1.107481 / 1.452155 (-0.344674) \| 1.163187 / 1.492716 (-0.329529) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095065 / 0.018006 (0.077059) \| 0.305169 / 0.000490 (0.304680) \| 0.000221 / 0.000200 (0.000021) \| 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) \| 0.017706 / 0.037411 (-0.019706) \| 0.061431 / 0.014526 (0.046905) \| 0.073541 / 0.176557 (-0.103016) \| 0.117326 / 0.737135 (-0.619809) \| 0.074368 / 0.296338 (-0.221971) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284533 / 0.215209 (0.069324) \| 2.775230 / 2.077655 (0.697575) \| 1.455196 / 1.504120 (-0.048924) \| 1.357651 / 1.541195 (-0.183544) \| 1.337477 / 1.468490 (-0.131013) \| 0.567439 / 4.584777 (-4.017338) \| 2.380612 / 3.745712 (-1.365100) \| 2.792305 / 5.269862 (-2.477556) \| 1.726501 / 4.565676 (-2.839176) \| 0.061729 / 0.424275 (-0.362546) \| 0.004928 / 0.007607 (-0.002679) \| 0.331989 / 0.226044 (0.105944) \| 3.301704 / 2.268929 (1.032776) \| 1.805107 / 55.444624 (-53.639518) \| 1.500434 / 6.876477 (-5.376043) \| 1.535548 / 2.142072 (-0.606524) \| 0.639490 / 4.805227 (-4.165737) \| 0.115876 / 6.500664 (-6.384788) \| 0.041895 / 0.075469 (-0.033574) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.993584 / 1.841788 (-0.848203) \| 11.596680 / 8.074308 (3.522371) \| 9.631726 / 10.191392 (-0.559666) \| 0.141153 / 0.680424 (-0.539271) \| 0.014077 / 0.534201 (-0.520124) \| 0.288237 / 0.579283 (-0.291046) \| 0.261213 / 0.434364 (-0.173151) \| 0.323897 / 0.540337 (-0.216441) \| 0.420350 / 1.386936 (-0.966586) \|\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) \| 0.005275 / 0.011353 (-0.006078) \| 0.003739 / 0.011008 (-0.007269) \| 0.049801 / 0.038508 (0.011293) \| 0.030544 / 0.023109 (0.007435) \| 0.264835 / 0.275898 (-0.011063) \| 0.297738 / 0.323480 (-0.025742) \| 0.004487 / 0.007986 (-0.003499) \| 0.002835 / 0.004328 (-0.001493) \| 0.048091 / 0.004250 (0.043841) \| 0.044375 / 0.037052 (0.007322) \| 0.286538 / 0.258489 (0.028049) \| 0.319561 / 0.293841 (0.025720) \| 0.047925 / 0.128546 (-0.080621) \| 0.010816 / 0.075646 (-0.064831) \| 0.057940 / 0.419271 (-0.361331) \| 0.033588 / 0.043533 (-0.009945) \| 0.270075 / 0.255139 (0.014936) \| 0.290441 / 0.283200 (0.007242) \| 0.017173 / 0.141683 (-0.124509) \| 1.164686 / 1.452155 (-0.287469) \| 1.213205 / 1.492716 (-0.279511) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093408 / 0.018006 (0.075402) \| 0.305525 / 0.000490 (0.305036) \| 0.000235 / 0.000200 (0.000035) \| 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) \| 0.021605 / 0.037411 (-0.015806) \| 0.075479 / 0.014526 (0.060953) \| 0.085990 / 0.176557 (-0.090567) \| 0.124783 / 0.737135 (-0.612352) \| 0.089108 / 0.296338 (-0.207230) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.306222 / 0.215209 (0.091013) \| 2.987282 / 2.077655 (0.909627) \| 1.664714 / 1.504120 (0.160594) \| 1.523136 / 1.541195 (-0.018059) \| 1.534112 / 1.468490 (0.065622) \| 0.566347 / 4.584777 (-4.018430) \| 2.438641 / 3.745712 (-1.307071) \| 2.669048 / 5.269862 (-2.600814) \| 1.732935 / 4.565676 (-2.832741) \| 0.063460 / 0.424275 (-0.360815) \| 0.004973 / 0.007607 (-0.002634) \| 0.366233 / 0.226044 (0.140189) \| 3.553578 / 2.268929 (1.284649) \| 1.984343 / 55.444624 (-53.460281) \| 1.711038 / 6.876477 (-5.165439) \| 1.857346 / 2.142072 (-0.284726) \| 0.651077 / 4.805227 (-4.154150) \| 0.118670 / 6.500664 (-6.381994) \| 0.041839 / 0.075469 (-0.033631) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.008230 / 1.841788 (-0.833558) \| 12.047403 / 8.074308 (3.973095) \| 10.039053 / 10.191392 (-0.152339) \| 0.141640 / 0.680424 (-0.538784) \| 0.014758 / 0.534201 (-0.519443) \| 0.285016 / 0.579283 (-0.294267) \| 0.275461 / 0.434364 (-0.158903) \| 0.325535 / 0.540337 (-0.214803) \| 0.415871 / 1.386936 (-0.971065) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d2268261bf0fb3eed8faae6bc1fa20a25b4382c \"CML watermark\")\n" ]
2,141,244,167	6,677	Pass through information about location of cache directory.	closed	2024-02-18T23:48:57	2024-02-28T18:57:39	2024-02-28T18:51:15	https://github.com/huggingface/datasets/pull/6677	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6677", "html_url": "https://github.com/huggingface/datasets/pull/6677", "diff_url": "https://github.com/huggingface/datasets/pull/6677.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6677.patch", "merged_at": "2024-02-28T18:51:15" }	stridge-cruxml	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6677). 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) \| 0.007162 / 0.011353 (-0.004191) \| 0.004125 / 0.011008 (-0.006883) \| 0.064011 / 0.038508 (0.025503) \| 0.031795 / 0.023109 (0.008686) \| 0.248761 / 0.275898 (-0.027137) \| 0.275130 / 0.323480 (-0.048350) \| 0.003138 / 0.007986 (-0.004847) \| 0.002736 / 0.004328 (-0.001592) \| 0.050515 / 0.004250 (0.046264) \| 0.044787 / 0.037052 (0.007735) \| 0.261997 / 0.258489 (0.003507) \| 0.292170 / 0.293841 (-0.001671) \| 0.028122 / 0.128546 (-0.100424) \| 0.010780 / 0.075646 (-0.064866) \| 0.208805 / 0.419271 (-0.210467) \| 0.036362 / 0.043533 (-0.007171) \| 0.251599 / 0.255139 (-0.003540) \| 0.271200 / 0.283200 (-0.012000) \| 0.020215 / 0.141683 (-0.121468) \| 1.133352 / 1.452155 (-0.318803) \| 1.185240 / 1.492716 (-0.307477) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089990 / 0.018006 (0.071984) \| 0.298099 / 0.000490 (0.297609) \| 0.000221 / 0.000200 (0.000021) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018432 / 0.037411 (-0.018980) \| 0.062641 / 0.014526 (0.048115) \| 0.075210 / 0.176557 (-0.101346) \| 0.122239 / 0.737135 (-0.614897) \| 0.078914 / 0.296338 (-0.217424) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287682 / 0.215209 (0.072473) \| 2.815030 / 2.077655 (0.737375) \| 1.499512 / 1.504120 (-0.004607) \| 1.370210 / 1.541195 (-0.170985) \| 1.381944 / 1.468490 (-0.086546) \| 0.571645 / 4.584777 (-4.013132) \| 2.377773 / 3.745712 (-1.367939) \| 2.757206 / 5.269862 (-2.512655) \| 1.717159 / 4.565676 (-2.848518) \| 0.063038 / 0.424275 (-0.361237) \| 0.004913 / 0.007607 (-0.002694) \| 0.340854 / 0.226044 (0.114810) \| 3.348087 / 2.268929 (1.079159) \| 1.843123 / 55.444624 (-53.601502) \| 1.569714 / 6.876477 (-5.306763) \| 1.593791 / 2.142072 (-0.548281) \| 0.642865 / 4.805227 (-4.162362) \| 0.116933 / 6.500664 (-6.383731) \| 0.041891 / 0.075469 (-0.033578) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976453 / 1.841788 (-0.865334) \| 12.229986 / 8.074308 (4.155678) \| 9.617912 / 10.191392 (-0.573480) \| 0.141292 / 0.680424 (-0.539132) \| 0.013732 / 0.534201 (-0.520469) \| 0.291424 / 0.579283 (-0.287859) \| 0.264748 / 0.434364 (-0.169616) \| 0.345262 / 0.540337 (-0.195075) \| 0.445126 / 1.386936 (-0.941810) \|\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) \| 0.005286 / 0.011353 (-0.006067) \| 0.003749 / 0.011008 (-0.007259) \| 0.049070 / 0.038508 (0.010562) \| 0.031779 / 0.023109 (0.008670) \| 0.275636 / 0.275898 (-0.000262) \| 0.296956 / 0.323480 (-0.026524) \| 0.004278 / 0.007986 (-0.003708) \| 0.002702 / 0.004328 (-0.001626) \| 0.049658 / 0.004250 (0.045408) \| 0.046025 / 0.037052 (0.008973) \| 0.293238 / 0.258489 (0.034749) \| 0.316676 / 0.293841 (0.022835) \| 0.029277 / 0.128546 (-0.099269) \| 0.010096 / 0.075646 (-0.065550) \| 0.059861 / 0.419271 (-0.359411) \| 0.054310 / 0.043533 (0.010778) \| 0.275025 / 0.255139 (0.019886) \| 0.292995 / 0.283200 (0.009796) \| 0.018448 / 0.141683 (-0.123235) \| 1.150805 / 1.452155 (-0.301350) \| 1.178310 / 1.492716 (-0.314406) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092644 / 0.018006 (0.074638) \| 0.297979 / 0.000490 (0.297489) \| 0.000207 / 0.000200 (0.000007) \| 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) \| 0.021758 / 0.037411 (-0.015654) \| 0.076734 / 0.014526 (0.062208) \| 0.088522 / 0.176557 (-0.088034) \| 0.126190 / 0.737135 (-0.610945) \| 0.090466 / 0.296338 (-0.205873) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.305355 / 0.215209 (0.090146) \| 2.978927 / 2.077655 (0.901272) \| 1.612312 / 1.504120 (0.108192) \| 1.485829 / 1.541195 (-0.055366) \| 1.513303 / 1.468490 (0.044813) \| 0.592368 / 4.584777 (-3.992409) \| 2.448529 / 3.745712 (-1.297183) \| 2.713460 / 5.269862 (-2.556402) \| 1.803859 / 4.565676 (-2.761817) \| 0.065630 / 0.424275 (-0.358645) \| 0.005072 / 0.007607 (-0.002535) \| 0.358340 / 0.226044 (0.132295) \| 3.528516 / 2.268929 (1.259588) \| 1.977901 / 55.444624 (-53.466723) \| 1.692526 / 6.876477 (-5.183950) \| 1.858405 / 2.142072 (-0.283668) \| 0.676169 / 4.805227 (-4.129059) \| 0.121136 / 6.500664 (-6.379528) \| 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) \| 1.011801 / 1.841788 (-0.829987) \| 12.496459 / 8.074308 (4.422151) \| 10.465659 / 10.191392 (0.274267) \| 0.154121 / 0.680424 (-0.526302) \| 0.016796 / 0.534201 (-0.517405) \| 0.288908 / 0.579283 (-0.290376) \| 0.274328 / 0.434364 (-0.160036) \| 0.322366 / 0.540337 (-0.217971) \| 0.423498 / 1.386936 (-0.963438) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#52b9273b5ddbcadfdb512a693bc813b21e863b1b \"CML watermark\")\n" ]
2,140,648,619	6,676	Can't Read List of JSON Files Properly	open	2024-02-17T22:58:15	2024-03-02T20:47:22	null	https://github.com/huggingface/datasets/issues/6676	null	lordsoffallen	false	[ "Found the issue, if there are other files in the directory, it gets caught into this `` so essentially it should be `.json`. Could we possibly to check for list of files to make sure the pattern matches json files and raise error if not?", "I don't think we should filter for `.json` as this might silently remove desired files for many users. And this could be a major breaking change for many organizations.\r\n\r\nYou could do the globbing yourself which would keep the code clean.\r\n\r\n```python\r\nfrom glob import glob\r\n\r\nDataset.from_json(glob('folder/.json'))\r\n```", "I think it should still be fine to log a warning message in case the folder contains different files? I also don't get why would this be breaking as in the end using `from_FILE_TYPE` should be able to read a specific file type only. Maybe some other use case I am not aware of but since globbing or this case not mentioned anywhere in the doc, I spent quite a bit of time trying to figure out where the issue was. Just making sure it's clear for users." ]
2,139,640,381	6,675	Allow image model (color conversion) to be specified as part of datasets Image() decode	closed	2024-02-16T23:43:20	2024-03-18T15:41:34	2024-03-18T15:41:34	https://github.com/huggingface/datasets/issues/6675	null	rwightman	false	[ "It would be a great addition indeed :)\r\n\r\nThis can be implemented the same way we have `sampling_rate` for Audio(): we just add a new parameter to the Image() type and take this parameter into account in `Image.decode_example`\r\n\r\nEDIT: adding an example of how it can be used:\r\n\r\n```python\r\nds = ds.cast_column(\"image\", Image(mode=...))\r\n```" ]
2,139,595,576	6,674	Depprcated Overview.ipynb Link to new Quickstart Notebook invalid	closed	2024-02-16T22:51:35	2024-02-25T18:48:09	2024-02-25T18:48:09	https://github.com/huggingface/datasets/issues/6674	null	Codeblockz	false	[ "Good catch! Feel free to open a PR to fix the link." ]
2,139,522,827	6,673	IterableDataset `set_epoch` is ignored when DataLoader `persistent_workers=True`	closed	2024-02-16T21:38:12	2024-07-01T17:45:31	2024-07-01T17:45:31	https://github.com/huggingface/datasets/issues/6673	null	rwightman	false	[]
2,138,732,288	6,672	Remove deprecated verbose parameter from CSV builder	closed	2024-02-16T14:26:21	2024-02-19T09:26:34	2024-02-19T09:20:22	https://github.com/huggingface/datasets/pull/6672	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6672", "html_url": "https://github.com/huggingface/datasets/pull/6672", "diff_url": "https://github.com/huggingface/datasets/pull/6672.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6672.patch", "merged_at": "2024-02-19T09:20:22" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6672). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I am merging this PR (so that it is included in the next patch release) to remove the deprecation warning raised by the CSV builder from pandas 2.2.0.", "<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) \| 0.005374 / 0.011353 (-0.005979) \| 0.003833 / 0.011008 (-0.007175) \| 0.063465 / 0.038508 (0.024957) \| 0.029564 / 0.023109 (0.006455) \| 0.252759 / 0.275898 (-0.023139) \| 0.274726 / 0.323480 (-0.048754) \| 0.004014 / 0.007986 (-0.003971) \| 0.002754 / 0.004328 (-0.001574) \| 0.049351 / 0.004250 (0.045101) \| 0.041858 / 0.037052 (0.004806) \| 0.269023 / 0.258489 (0.010534) \| 0.290670 / 0.293841 (-0.003171) \| 0.028435 / 0.128546 (-0.100111) \| 0.010988 / 0.075646 (-0.064658) \| 0.207447 / 0.419271 (-0.211824) \| 0.035945 / 0.043533 (-0.007588) \| 0.257336 / 0.255139 (0.002197) \| 0.267310 / 0.283200 (-0.015890) \| 0.018575 / 0.141683 (-0.123108) \| 1.144515 / 1.452155 (-0.307640) \| 1.214614 / 1.492716 (-0.278102) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.103527 / 0.018006 (0.085521) \| 0.310607 / 0.000490 (0.310117) \| 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) \| 0.018597 / 0.037411 (-0.018814) \| 0.063176 / 0.014526 (0.048650) \| 0.073553 / 0.176557 (-0.103003) \| 0.120648 / 0.737135 (-0.616487) \| 0.075625 / 0.296338 (-0.220713) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289148 / 0.215209 (0.073939) \| 2.798351 / 2.077655 (0.720696) \| 1.487909 / 1.504120 (-0.016211) \| 1.369945 / 1.541195 (-0.171250) \| 1.378889 / 1.468490 (-0.089602) \| 0.569825 / 4.584777 (-4.014952) \| 2.413309 / 3.745712 (-1.332403) \| 2.795668 / 5.269862 (-2.474193) \| 1.757748 / 4.565676 (-2.807929) \| 0.064686 / 0.424275 (-0.359589) \| 0.005027 / 0.007607 (-0.002580) \| 0.341835 / 0.226044 (0.115791) \| 3.349915 / 2.268929 (1.080987) \| 1.864253 / 55.444624 (-53.580371) \| 1.595788 / 6.876477 (-5.280688) \| 1.666127 / 2.142072 (-0.475945) \| 0.665239 / 4.805227 (-4.139989) \| 0.120563 / 6.500664 (-6.380101) \| 0.043649 / 0.075469 (-0.031820) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.988543 / 1.841788 (-0.853244) \| 11.973275 / 8.074308 (3.898967) \| 9.685401 / 10.191392 (-0.505991) \| 0.141416 / 0.680424 (-0.539008) \| 0.014328 / 0.534201 (-0.519873) \| 0.287063 / 0.579283 (-0.292220) \| 0.266284 / 0.434364 (-0.168080) \| 0.324643 / 0.540337 (-0.215694) \| 0.423845 / 1.386936 (-0.963091) \|\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) \| 0.005430 / 0.011353 (-0.005923) \| 0.003770 / 0.011008 (-0.007239) \| 0.050879 / 0.038508 (0.012371) \| 0.031929 / 0.023109 (0.008819) \| 0.297739 / 0.275898 (0.021841) \| 0.319380 / 0.323480 (-0.004100) \| 0.004348 / 0.007986 (-0.003637) \| 0.002783 / 0.004328 (-0.001545) \| 0.050024 / 0.004250 (0.045774) \| 0.045209 / 0.037052 (0.008157) \| 0.307608 / 0.258489 (0.049119) \| 0.338168 / 0.293841 (0.044327) \| 0.051712 / 0.128546 (-0.076834) \| 0.011092 / 0.075646 (-0.064554) \| 0.059830 / 0.419271 (-0.359441) \| 0.033894 / 0.043533 (-0.009638) \| 0.295278 / 0.255139 (0.040139) \| 0.310749 / 0.283200 (0.027550) \| 0.018676 / 0.141683 (-0.123007) \| 1.201086 / 1.452155 (-0.251069) \| 1.258214 / 1.492716 (-0.234502) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094079 / 0.018006 (0.076073) \| 0.304657 / 0.000490 (0.304168) \| 0.000225 / 0.000200 (0.000026) \| 0.000057 / 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) \| 0.021969 / 0.037411 (-0.015442) \| 0.075749 / 0.014526 (0.061223) \| 0.087878 / 0.176557 (-0.088679) \| 0.126022 / 0.737135 (-0.611114) \| 0.089466 / 0.296338 (-0.206873) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286415 / 0.215209 (0.071206) \| 2.831867 / 2.077655 (0.754212) \| 1.584119 / 1.504120 (0.079999) \| 1.468454 / 1.541195 (-0.072740) \| 1.495831 / 1.468490 (0.027341) \| 0.579569 / 4.584777 (-4.005208) \| 2.477248 / 3.745712 (-1.268464) \| 2.830536 / 5.269862 (-2.439325) \| 1.820188 / 4.565676 (-2.745488) \| 0.064408 / 0.424275 (-0.359867) \| 0.005156 / 0.007607 (-0.002451) \| 0.342391 / 0.226044 (0.116347) \| 3.424380 / 2.268929 (1.155452) \| 1.993110 / 55.444624 (-53.451514) \| 1.702971 / 6.876477 (-5.173506) \| 1.844281 / 2.142072 (-0.297792) \| 0.668208 / 4.805227 (-4.137020) \| 0.120306 / 6.500664 (-6.380358) \| 0.042127 / 0.075469 (-0.033342) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.019118 / 1.841788 (-0.822670) \| 12.418330 / 8.074308 (4.344022) \| 10.474226 / 10.191392 (0.282834) \| 0.148510 / 0.680424 (-0.531914) \| 0.015107 / 0.534201 (-0.519094) \| 0.289488 / 0.579283 (-0.289795) \| 0.278149 / 0.434364 (-0.156215) \| 0.334655 / 0.540337 (-0.205682) \| 0.419127 / 1.386936 (-0.967809) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#58733d2824192fc748cc8730cf77c33be5ded2ea \"CML watermark\")\n" ]
2,138,727,870	6,671	CSV builder raises deprecation warning on verbose parameter	closed	2024-02-16T14:23:46	2024-02-19T09:20:23	2024-02-19T09:20:23	https://github.com/huggingface/datasets/issues/6671	null	albertvillanova	false	[]
2,138,372,958	6,670	ValueError	closed	2024-02-16T11:05:17	2024-02-17T04:26:34	2024-02-16T14:43:53	https://github.com/huggingface/datasets/issues/6670	null	prashanth19bolukonda	false	[ "Hi @prashanth19bolukonda,\r\n\r\nYou have to restart the notebook runtime session after the installation of `datasets`.\r\n\r\nDuplicate of:\r\n- #5923", "Thank you soo much\r\n\r\nOn Fri, Feb 16, 2024 at 8:14 PM Albert Villanova del Moral <\r\n*@.> wrote:\r\n\r\n> Closed #6670 <https://github.com/huggingface/datasets/issues/6670> as\r\n> completed.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6670#event-11829788289>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/A2Y44YDQOBUFUWMR4C5O3QTYT5WDJAVCNFSM6AAAAABDL24S5SVHI2DSMVQWIX3LMV45UABCJFZXG5LFIV3GK3TUJZXXI2LGNFRWC5DJN5XDWMJRHAZDSNZYHAZDQOI>\r\n> .\r\n> You are receiving this because you were mentioned.Message ID:\r\n> @.*>\r\n>\r\n" ]
2,138,322,662	6,669	attribute error when writing trainer.train()	closed	2024-02-16T10:40:49	2024-03-01T10:58:00	2024-02-29T17:25:17	https://github.com/huggingface/datasets/issues/6669	null	prashanth19bolukonda	false	[ "Hi! Kaggle notebooks use an outdated version of `datasets`, so you should update the `datasets` installation (with `!pip install -U datasets`) to avoid the error.", "Thank you for your response\r\n\r\nOn Thu, Feb 29, 2024 at 10:55 PM Mario Šaško *@.>\r\nwrote:\r\n\r\n> Closed #6669 <https://github.com/huggingface/datasets/issues/6669> as\r\n> completed.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6669#event-11969246964>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/A2Y44YG2RRVMYONNKPLBVE3YV5SAPAVCNFSM6AAAAABDLZ3BTSVHI2DSMVQWIX3LMV45UABCJFZXG5LFIV3GK3TUJZXXI2LGNFRWC5DJN5XDWMJRHE3DSMRUGY4TMNA>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> @.*>\r\n>\r\n" ]
2,137,859,935	6,668	Chapter 6 - Issue Loading `cnn_dailymail` dataset	open	2024-02-16T04:40:56	2024-02-16T04:40:56	null	https://github.com/huggingface/datasets/issues/6668	null	hariravichandran	false	[]
2,137,769,552	6,667	Default config for squad is incorrect	open	2024-02-16T02:36:55	2024-02-23T09:10:00	null	https://github.com/huggingface/datasets/issues/6667	null	kiddyboots216	false	[ "you can try: pip install datasets==2.16.1" ]
2,136,136,425	6,665	Allow SplitDict setitem to replace existing SplitInfo	closed	2024-02-15T10:17:08	2024-03-01T16:02:46	2024-03-01T15:56:38	https://github.com/huggingface/datasets/pull/6665	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6665", "html_url": "https://github.com/huggingface/datasets/pull/6665", "diff_url": "https://github.com/huggingface/datasets/pull/6665.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6665.patch", "merged_at": "2024-03-01T15:56:38" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6665). 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) \| 0.004968 / 0.011353 (-0.006385) \| 0.003732 / 0.011008 (-0.007276) \| 0.063672 / 0.038508 (0.025164) \| 0.027066 / 0.023109 (0.003957) \| 0.253306 / 0.275898 (-0.022592) \| 0.283382 / 0.323480 (-0.040098) \| 0.004217 / 0.007986 (-0.003768) \| 0.002865 / 0.004328 (-0.001464) \| 0.048672 / 0.004250 (0.044421) \| 0.040740 / 0.037052 (0.003688) \| 0.271848 / 0.258489 (0.013359) \| 0.293162 / 0.293841 (-0.000679) \| 0.027410 / 0.128546 (-0.101136) \| 0.010605 / 0.075646 (-0.065042) \| 0.210545 / 0.419271 (-0.208726) \| 0.036085 / 0.043533 (-0.007447) \| 0.259807 / 0.255139 (0.004668) \| 0.274056 / 0.283200 (-0.009144) \| 0.018812 / 0.141683 (-0.122871) \| 1.116687 / 1.452155 (-0.335468) \| 1.164276 / 1.492716 (-0.328440) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092874 / 0.018006 (0.074868) \| 0.355897 / 0.000490 (0.355407) \| 0.000224 / 0.000200 (0.000024) \| 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) \| 0.018461 / 0.037411 (-0.018950) \| 0.062061 / 0.014526 (0.047535) \| 0.072353 / 0.176557 (-0.104203) \| 0.119162 / 0.737135 (-0.617974) \| 0.082974 / 0.296338 (-0.213364) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291631 / 0.215209 (0.076422) \| 2.861495 / 2.077655 (0.783841) \| 1.496753 / 1.504120 (-0.007367) \| 1.371164 / 1.541195 (-0.170031) \| 1.415473 / 1.468490 (-0.053018) \| 0.566778 / 4.584777 (-4.017999) \| 2.376209 / 3.745712 (-1.369503) \| 2.812326 / 5.269862 (-2.457535) \| 1.765640 / 4.565676 (-2.800037) \| 0.063274 / 0.424275 (-0.361001) \| 0.004933 / 0.007607 (-0.002674) \| 0.342345 / 0.226044 (0.116301) \| 3.407487 / 2.268929 (1.138558) \| 1.856646 / 55.444624 (-53.587978) \| 1.590284 / 6.876477 (-5.286193) \| 1.610068 / 2.142072 (-0.532004) \| 0.656007 / 4.805227 (-4.149220) \| 0.118310 / 6.500664 (-6.382354) \| 0.042596 / 0.075469 (-0.032873) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.991392 / 1.841788 (-0.850395) \| 11.612397 / 8.074308 (3.538089) \| 9.627836 / 10.191392 (-0.563556) \| 0.130575 / 0.680424 (-0.549848) \| 0.014152 / 0.534201 (-0.520049) \| 0.289736 / 0.579283 (-0.289548) \| 0.260041 / 0.434364 (-0.174323) \| 0.339730 / 0.540337 (-0.200608) \| 0.447529 / 1.386936 (-0.939407) \|\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) \| 0.005315 / 0.011353 (-0.006038) \| 0.003955 / 0.011008 (-0.007053) \| 0.049618 / 0.038508 (0.011110) \| 0.030404 / 0.023109 (0.007295) \| 0.258727 / 0.275898 (-0.017171) \| 0.282020 / 0.323480 (-0.041460) \| 0.004356 / 0.007986 (-0.003629) \| 0.002866 / 0.004328 (-0.001462) \| 0.049122 / 0.004250 (0.044872) \| 0.045534 / 0.037052 (0.008482) \| 0.269560 / 0.258489 (0.011071) \| 0.301225 / 0.293841 (0.007384) \| 0.029786 / 0.128546 (-0.098761) \| 0.010433 / 0.075646 (-0.065213) \| 0.058222 / 0.419271 (-0.361049) \| 0.052968 / 0.043533 (0.009435) \| 0.256605 / 0.255139 (0.001467) \| 0.279899 / 0.283200 (-0.003300) \| 0.018233 / 0.141683 (-0.123450) \| 1.164060 / 1.452155 (-0.288095) \| 1.218049 / 1.492716 (-0.274667) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093646 / 0.018006 (0.075639) \| 0.288804 / 0.000490 (0.288314) \| 0.000224 / 0.000200 (0.000024) \| 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) \| 0.022193 / 0.037411 (-0.015219) \| 0.075507 / 0.014526 (0.060981) \| 0.086091 / 0.176557 (-0.090465) \| 0.127433 / 0.737135 (-0.609703) \| 0.087064 / 0.296338 (-0.209274) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292459 / 0.215209 (0.077250) \| 2.842430 / 2.077655 (0.764776) \| 1.505824 / 1.504120 (0.001704) \| 1.377052 / 1.541195 (-0.164143) \| 1.408757 / 1.468490 (-0.059733) \| 0.571705 / 4.584777 (-4.013072) \| 2.459798 / 3.745712 (-1.285914) \| 2.714826 / 5.269862 (-2.555035) \| 1.782064 / 4.565676 (-2.783613) \| 0.063113 / 0.424275 (-0.361162) \| 0.005099 / 0.007607 (-0.002509) \| 0.343624 / 0.226044 (0.117579) \| 3.415806 / 2.268929 (1.146878) \| 1.853253 / 55.444624 (-53.591371) \| 1.584392 / 6.876477 (-5.292084) \| 1.720384 / 2.142072 (-0.421689) \| 0.646637 / 4.805227 (-4.158590) \| 0.118072 / 6.500664 (-6.382593) \| 0.041362 / 0.075469 (-0.034107) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.020086 / 1.841788 (-0.821701) \| 12.303980 / 8.074308 (4.229672) \| 10.322869 / 10.191392 (0.131477) \| 0.140959 / 0.680424 (-0.539465) \| 0.015372 / 0.534201 (-0.518829) \| 0.288552 / 0.579283 (-0.290731) \| 0.278243 / 0.434364 (-0.156121) \| 0.328399 / 0.540337 (-0.211939) \| 0.433618 / 1.386936 (-0.953318) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9469092d88ff7bb4d3f7fe6c2de0109ca458b5da \"CML watermark\")\n" ]
2,135,483,978	6,664	Revert the changes in `arrow_writer.py` from #6636	closed	2024-02-15T01:47:33	2024-02-16T14:02:39	2024-02-16T02:31:11	https://github.com/huggingface/datasets/pull/6664	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6664", "html_url": "https://github.com/huggingface/datasets/pull/6664", "diff_url": "https://github.com/huggingface/datasets/pull/6664.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6664.patch", "merged_at": "2024-02-16T02:31:11" }	bryant1410	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6664). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> Hi! We can't revert this as the \"reverted\" implementation has quadratic time complexity. Instead, let's fix it:\r\n\r\nI agree, but it's the implementation we have had so far. Why don't we:\r\n1. Release a hotfix ASAP (since would be doing a revert, we know it works as before) so people can continue using this library fine since AFAIU right now mostly writing examples for people is broken.\r\n2. Then, focus on still applying the performance improvement and release again", "The fix is straightforward, so one patch release (after this PR is merged) is enough.\r\n\r\nBtw, let's also add a test to `tests/test_arrow_writer.py` to avoid this issue in the future.", "> Btw, let's also add a test to tests/test_arrow_writer.py to avoid this issue in the future.\r\n\r\nWould you mind adding such test, as you're more familiar with the codebase?", "<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) \| 0.005083 / 0.011353 (-0.006270) \| 0.003697 / 0.011008 (-0.007311) \| 0.063302 / 0.038508 (0.024794) \| 0.028866 / 0.023109 (0.005757) \| 0.249987 / 0.275898 (-0.025911) \| 0.270803 / 0.323480 (-0.052677) \| 0.004096 / 0.007986 (-0.003890) \| 0.002752 / 0.004328 (-0.001577) \| 0.049156 / 0.004250 (0.044906) \| 0.042936 / 0.037052 (0.005884) \| 0.266907 / 0.258489 (0.008418) \| 0.291462 / 0.293841 (-0.002379) \| 0.027703 / 0.128546 (-0.100844) \| 0.011006 / 0.075646 (-0.064641) \| 0.206238 / 0.419271 (-0.213033) \| 0.035446 / 0.043533 (-0.008087) \| 0.248923 / 0.255139 (-0.006216) \| 0.264141 / 0.283200 (-0.019058) \| 0.017545 / 0.141683 (-0.124138) \| 1.157145 / 1.452155 (-0.295009) \| 1.199007 / 1.492716 (-0.293710) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092741 / 0.018006 (0.074734) \| 0.299057 / 0.000490 (0.298567) \| 0.000211 / 0.000200 (0.000011) \| 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) \| 0.017936 / 0.037411 (-0.019475) \| 0.061552 / 0.014526 (0.047026) \| 0.072938 / 0.176557 (-0.103618) \| 0.118192 / 0.737135 (-0.618944) \| 0.074589 / 0.296338 (-0.221750) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287186 / 0.215209 (0.071977) \| 2.795694 / 2.077655 (0.718039) \| 1.474386 / 1.504120 (-0.029734) \| 1.359065 / 1.541195 (-0.182130) \| 1.375295 / 1.468490 (-0.093196) \| 0.569448 / 4.584777 (-4.015329) \| 2.374428 / 3.745712 (-1.371284) \| 2.770198 / 5.269862 (-2.499663) \| 1.716346 / 4.565676 (-2.849330) \| 0.063173 / 0.424275 (-0.361102) \| 0.005031 / 0.007607 (-0.002576) \| 0.333197 / 0.226044 (0.107153) \| 3.271739 / 2.268929 (1.002811) \| 1.826406 / 55.444624 (-53.618218) \| 1.554537 / 6.876477 (-5.321939) \| 1.565927 / 2.142072 (-0.576146) \| 0.649796 / 4.805227 (-4.155431) \| 0.118371 / 6.500664 (-6.382293) \| 0.042536 / 0.075469 (-0.032933) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969882 / 1.841788 (-0.871906) \| 11.638201 / 8.074308 (3.563893) \| 9.759370 / 10.191392 (-0.432022) \| 0.128069 / 0.680424 (-0.552355) \| 0.013493 / 0.534201 (-0.520708) \| 0.287324 / 0.579283 (-0.291959) \| 0.267542 / 0.434364 (-0.166821) \| 0.320072 / 0.540337 (-0.220265) \| 0.421132 / 1.386936 (-0.965804) \|\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) \| 0.003746 / 0.011008 (-0.007262) \| 0.050149 / 0.038508 (0.011641) \| 0.034382 / 0.023109 (0.011273) \| 0.289802 / 0.275898 (0.013904) \| 0.314993 / 0.323480 (-0.008487) \| 0.004488 / 0.007986 (-0.003498) \| 0.002786 / 0.004328 (-0.001542) \| 0.047987 / 0.004250 (0.043737) \| 0.046589 / 0.037052 (0.009537) \| 0.301420 / 0.258489 (0.042931) \| 0.335384 / 0.293841 (0.041543) \| 0.050701 / 0.128546 (-0.077845) \| 0.010987 / 0.075646 (-0.064660) \| 0.058292 / 0.419271 (-0.360979) \| 0.033973 / 0.043533 (-0.009560) \| 0.288923 / 0.255139 (0.033784) \| 0.306263 / 0.283200 (0.023064) \| 0.018856 / 0.141683 (-0.122827) \| 1.160721 / 1.452155 (-0.291433) \| 1.208151 / 1.492716 (-0.284565) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092633 / 0.018006 (0.074626) \| 0.300353 / 0.000490 (0.299864) \| 0.000219 / 0.000200 (0.000019) \| 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) \| 0.022257 / 0.037411 (-0.015154) \| 0.075417 / 0.014526 (0.060892) \| 0.087289 / 0.176557 (-0.089268) \| 0.125416 / 0.737135 (-0.611720) \| 0.088751 / 0.296338 (-0.207588) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286477 / 0.215209 (0.071268) \| 2.801931 / 2.077655 (0.724277) \| 1.553034 / 1.504120 (0.048914) \| 1.426152 / 1.541195 (-0.115043) \| 1.443824 / 1.468490 (-0.024666) \| 0.563298 / 4.584777 (-4.021479) \| 2.428968 / 3.745712 (-1.316744) \| 2.685964 / 5.269862 (-2.583897) \| 1.752304 / 4.565676 (-2.813372) \| 0.064174 / 0.424275 (-0.360101) \| 0.005079 / 0.007607 (-0.002528) \| 0.344899 / 0.226044 (0.118855) \| 3.372528 / 2.268929 (1.103600) \| 1.900723 / 55.444624 (-53.543901) \| 1.623721 / 6.876477 (-5.252756) \| 1.781009 / 2.142072 (-0.361064) \| 0.655229 / 4.805227 (-4.149998) \| 0.116050 / 6.500664 (-6.384614) \| 0.040374 / 0.075469 (-0.035095) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.004714 / 1.841788 (-0.837074) \| 12.108179 / 8.074308 (4.033871) \| 10.233447 / 10.191392 (0.042055) \| 0.141438 / 0.680424 (-0.538986) \| 0.015387 / 0.534201 (-0.518814) \| 0.288068 / 0.579283 (-0.291216) \| 0.277025 / 0.434364 (-0.157339) \| 0.331714 / 0.540337 (-0.208623) \| 0.424209 / 1.386936 (-0.962727) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bdebf1922663c30744efb8869c86b28f102b84dd \"CML watermark\")\n" ]
2,135,480,811	6,663	`write_examples_on_file` and `write_batch` are broken in `ArrowWriter`	closed	2024-02-15T01:43:27	2024-02-16T09:25:00	2024-02-16T09:25:00	https://github.com/huggingface/datasets/issues/6663	null	bryant1410	false	[ "Thanks for reporting! I've left some comments on the PR on how to fix this recent change rather than reverting it.", "> Thanks for reporting! I've left some comments on the PR on how to fix this recent change rather than reverting it.\r\n\r\nI feel that'd be good, but it'd be great to release a hotfix ASAP (a revert is a fast thing to do) so people can continue using this library and then focus on still applying the improvement.", "Fixed by #6664 " ]
2,132,425,812	6,662	fix: show correct package name to install biopython	closed	2024-02-13T14:15:04	2024-03-01T17:49:48	2024-03-01T17:43:39	https://github.com/huggingface/datasets/pull/6662	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6662", "html_url": "https://github.com/huggingface/datasets/pull/6662", "diff_url": "https://github.com/huggingface/datasets/pull/6662.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6662.patch", "merged_at": "2024-03-01T17:43:39" }	BioGeek	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6662). 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) \| 0.005135 / 0.011353 (-0.006218) \| 0.003666 / 0.011008 (-0.007342) \| 0.062660 / 0.038508 (0.024152) \| 0.028656 / 0.023109 (0.005546) \| 0.249601 / 0.275898 (-0.026297) \| 0.265745 / 0.323480 (-0.057735) \| 0.002935 / 0.007986 (-0.005051) \| 0.002606 / 0.004328 (-0.001723) \| 0.048774 / 0.004250 (0.044523) \| 0.043643 / 0.037052 (0.006591) \| 0.263114 / 0.258489 (0.004625) \| 0.284596 / 0.293841 (-0.009245) \| 0.027818 / 0.128546 (-0.100728) \| 0.010726 / 0.075646 (-0.064921) \| 0.205900 / 0.419271 (-0.213371) \| 0.035646 / 0.043533 (-0.007887) \| 0.245599 / 0.255139 (-0.009540) \| 0.267706 / 0.283200 (-0.015493) \| 0.018441 / 0.141683 (-0.123242) \| 1.143365 / 1.452155 (-0.308790) \| 1.191823 / 1.492716 (-0.300893) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089703 / 0.018006 (0.071696) \| 0.298073 / 0.000490 (0.297583) \| 0.000209 / 0.000200 (0.000009) \| 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) \| 0.018068 / 0.037411 (-0.019343) \| 0.061416 / 0.014526 (0.046890) \| 0.075989 / 0.176557 (-0.100567) \| 0.120765 / 0.737135 (-0.616370) \| 0.075476 / 0.296338 (-0.220863) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284043 / 0.215209 (0.068834) \| 2.770282 / 2.077655 (0.692627) \| 1.473040 / 1.504120 (-0.031080) \| 1.349064 / 1.541195 (-0.192131) \| 1.362783 / 1.468490 (-0.105708) \| 0.560765 / 4.584777 (-4.024012) \| 2.357731 / 3.745712 (-1.387981) \| 2.745771 / 5.269862 (-2.524090) \| 1.726764 / 4.565676 (-2.838913) \| 0.061212 / 0.424275 (-0.363063) \| 0.004902 / 0.007607 (-0.002705) \| 0.336963 / 0.226044 (0.110919) \| 3.324519 / 2.268929 (1.055591) \| 1.825826 / 55.444624 (-53.618798) \| 1.548811 / 6.876477 (-5.327666) \| 1.570618 / 2.142072 (-0.571454) \| 0.642411 / 4.805227 (-4.162816) \| 0.116068 / 6.500664 (-6.384596) \| 0.042433 / 0.075469 (-0.033036) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.988402 / 1.841788 (-0.853386) \| 11.509601 / 8.074308 (3.435293) \| 9.555338 / 10.191392 (-0.636054) \| 0.138728 / 0.680424 (-0.541696) \| 0.014107 / 0.534201 (-0.520094) \| 0.285465 / 0.579283 (-0.293818) \| 0.263086 / 0.434364 (-0.171278) \| 0.327469 / 0.540337 (-0.212869) \| 0.444799 / 1.386936 (-0.942137) \|\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 (-0.005993) \| 0.003605 / 0.011008 (-0.007403) \| 0.049734 / 0.038508 (0.011226) \| 0.029792 / 0.023109 (0.006683) \| 0.276384 / 0.275898 (0.000486) \| 0.297915 / 0.323480 (-0.025564) \| 0.004949 / 0.007986 (-0.003036) \| 0.002713 / 0.004328 (-0.001616) \| 0.049499 / 0.004250 (0.045249) \| 0.044969 / 0.037052 (0.007917) \| 0.284558 / 0.258489 (0.026069) \| 0.315170 / 0.293841 (0.021329) \| 0.029457 / 0.128546 (-0.099089) \| 0.010573 / 0.075646 (-0.065073) \| 0.058191 / 0.419271 (-0.361080) \| 0.051461 / 0.043533 (0.007928) \| 0.270744 / 0.255139 (0.015605) \| 0.291664 / 0.283200 (0.008465) \| 0.018607 / 0.141683 (-0.123076) \| 1.158799 / 1.452155 (-0.293355) \| 1.210509 / 1.492716 (-0.282208) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090277 / 0.018006 (0.072270) \| 0.298748 / 0.000490 (0.298258) \| 0.000228 / 0.000200 (0.000028) \| 0.000060 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021850 / 0.037411 (-0.015561) \| 0.075433 / 0.014526 (0.060907) \| 0.087171 / 0.176557 (-0.089386) \| 0.125828 / 0.737135 (-0.611308) \| 0.090343 / 0.296338 (-0.205996) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297267 / 0.215209 (0.082058) \| 2.865234 / 2.077655 (0.787579) \| 1.595024 / 1.504120 (0.090904) \| 1.476100 / 1.541195 (-0.065094) \| 1.494896 / 1.468490 (0.026406) \| 0.569086 / 4.584777 (-4.015691) \| 2.401976 / 3.745712 (-1.343736) \| 2.676091 / 5.269862 (-2.593771) \| 1.742087 / 4.565676 (-2.823590) \| 0.065161 / 0.424275 (-0.359114) \| 0.005006 / 0.007607 (-0.002602) \| 0.342302 / 0.226044 (0.116257) \| 3.450571 / 2.268929 (1.181643) \| 1.928754 / 55.444624 (-53.515871) \| 1.672823 / 6.876477 (-5.203653) \| 1.798830 / 2.142072 (-0.343243) \| 0.648730 / 4.805227 (-4.156498) \| 0.116433 / 6.500664 (-6.384231) \| 0.040683 / 0.075469 (-0.034786) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.006158 / 1.841788 (-0.835630) \| 12.200093 / 8.074308 (4.125785) \| 10.180691 / 10.191392 (-0.010701) \| 0.146620 / 0.680424 (-0.533804) \| 0.015621 / 0.534201 (-0.518580) \| 0.287956 / 0.579283 (-0.291327) \| 0.277231 / 0.434364 (-0.157133) \| 0.323815 / 0.540337 (-0.216522) \| 0.429655 / 1.386936 (-0.957281) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#273e16f9a21d6eaba1fd40fbdf0c05e66642c5a7 \"CML watermark\")\n" ]
2,132,296,267	6,661	Import error on Google Colab	closed	2024-02-13T13:12:40	2024-02-25T16:37:54	2024-02-14T08:04:47	https://github.com/huggingface/datasets/issues/6661	null	kithogue	false	[ "Hi! This can happen if an incompatible `pyarrow` version (`pyarrow<12.0.0`) has been imported before the `datasets` installation and the Colab session hasn't been restarted afterward. To avoid the error, go to \"Runtime -> Restart session\" after `!pip install -U datasets` and before `import datasets`, or insert the `import os; os.kill(os.getpid(), 9)` cell between `!pip install -U datasets` and `import datasets` to do the same programmatically.", "One possible cause might be the one pointed out by @mariosasko above, and you get the following warning on Colab:\r\n```\r\nWARNING: The following packages were previously imported in this runtime:\r\n [pyarrow]\r\nYou must restart the runtime in order to use newly installed versions.\r\n```\r\n\r\nOn the other hand, if the old version of `pyarrow` is not previously imported (before the installation of `datasets`), the reported issue here is not reproducible: `datasets` can be installed, imported and used on Colab.", "Duplicate of:\r\n- #5923", "Google Colab now pre-installs PyArrow 14.0.2, making this issue unlikely to happen. So, I'm unpinning it." ]
2,131,977,011	6,660	Automatic Conversion for uint16/uint32 to Compatible PyTorch Dtypes	closed	2024-02-13T10:24:33	2024-03-01T19:01:57	2024-03-01T18:52:37	https://github.com/huggingface/datasets/pull/6660	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6660", "html_url": "https://github.com/huggingface/datasets/pull/6660", "diff_url": "https://github.com/huggingface/datasets/pull/6660.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6660.patch", "merged_at": "2024-03-01T18:52:37" }	mohalisad	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6660). 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) \| 0.004995 / 0.011353 (-0.006357) \| 0.003230 / 0.011008 (-0.007779) \| 0.062836 / 0.038508 (0.024328) \| 0.026684 / 0.023109 (0.003575) \| 0.249286 / 0.275898 (-0.026612) \| 0.272936 / 0.323480 (-0.050544) \| 0.003952 / 0.007986 (-0.004033) \| 0.002708 / 0.004328 (-0.001620) \| 0.055346 / 0.004250 (0.051095) \| 0.040023 / 0.037052 (0.002971) \| 0.263350 / 0.258489 (0.004860) \| 0.294727 / 0.293841 (0.000886) \| 0.027280 / 0.128546 (-0.101266) \| 0.010273 / 0.075646 (-0.065373) \| 0.206035 / 0.419271 (-0.213236) \| 0.035715 / 0.043533 (-0.007818) \| 0.255474 / 0.255139 (0.000335) \| 0.273960 / 0.283200 (-0.009240) \| 0.018563 / 0.141683 (-0.123120) \| 1.140013 / 1.452155 (-0.312142) \| 1.188655 / 1.492716 (-0.304062) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091895 / 0.018006 (0.073888) \| 0.284621 / 0.000490 (0.284131) \| 0.000213 / 0.000200 (0.000013) \| 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) \| 0.018610 / 0.037411 (-0.018801) \| 0.061554 / 0.014526 (0.047028) \| 0.072454 / 0.176557 (-0.104103) \| 0.120283 / 0.737135 (-0.616853) \| 0.073744 / 0.296338 (-0.222595) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288850 / 0.215209 (0.073641) \| 2.836761 / 2.077655 (0.759107) \| 1.533407 / 1.504120 (0.029287) \| 1.409394 / 1.541195 (-0.131801) \| 1.421667 / 1.468490 (-0.046823) \| 0.566183 / 4.584777 (-4.018594) \| 2.390670 / 3.745712 (-1.355043) \| 2.732031 / 5.269862 (-2.537831) \| 1.730886 / 4.565676 (-2.834791) \| 0.064280 / 0.424275 (-0.359995) \| 0.004959 / 0.007607 (-0.002648) \| 0.342664 / 0.226044 (0.116619) \| 3.398969 / 2.268929 (1.130040) \| 1.887354 / 55.444624 (-53.557270) \| 1.572955 / 6.876477 (-5.303522) \| 1.596179 / 2.142072 (-0.545894) \| 0.645844 / 4.805227 (-4.159383) \| 0.118050 / 6.500664 (-6.382614) \| 0.042158 / 0.075469 (-0.033311) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.959170 / 1.841788 (-0.882617) \| 11.276491 / 8.074308 (3.202183) \| 9.471198 / 10.191392 (-0.720194) \| 0.128346 / 0.680424 (-0.552078) \| 0.013851 / 0.534201 (-0.520350) \| 0.286125 / 0.579283 (-0.293158) \| 0.266915 / 0.434364 (-0.167449) \| 0.332811 / 0.540337 (-0.207526) \| 0.444780 / 1.386936 (-0.942156) \|\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 (-0.005687) \| 0.003267 / 0.011008 (-0.007741) \| 0.050238 / 0.038508 (0.011730) \| 0.032882 / 0.023109 (0.009773) \| 0.269320 / 0.275898 (-0.006578) \| 0.293140 / 0.323480 (-0.030340) \| 0.004127 / 0.007986 (-0.003858) \| 0.002728 / 0.004328 (-0.001601) \| 0.049360 / 0.004250 (0.045109) \| 0.043764 / 0.037052 (0.006712) \| 0.291211 / 0.258489 (0.032722) \| 0.319745 / 0.293841 (0.025904) \| 0.029371 / 0.128546 (-0.099175) \| 0.010212 / 0.075646 (-0.065434) \| 0.059064 / 0.419271 (-0.360207) \| 0.051148 / 0.043533 (0.007615) \| 0.276698 / 0.255139 (0.021559) \| 0.292329 / 0.283200 (0.009129) \| 0.018349 / 0.141683 (-0.123334) \| 1.150816 / 1.452155 (-0.301338) \| 1.184292 / 1.492716 (-0.308425) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091646 / 0.018006 (0.073640) \| 0.301737 / 0.000490 (0.301247) \| 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) \| 0.021529 / 0.037411 (-0.015883) \| 0.075596 / 0.014526 (0.061070) \| 0.087912 / 0.176557 (-0.088645) \| 0.125240 / 0.737135 (-0.611895) \| 0.088035 / 0.296338 (-0.208303) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.305097 / 0.215209 (0.089888) \| 2.979612 / 2.077655 (0.901957) \| 1.647009 / 1.504120 (0.142889) \| 1.520251 / 1.541195 (-0.020944) \| 1.513361 / 1.468490 (0.044870) \| 0.571733 / 4.584777 (-4.013044) \| 2.415587 / 3.745712 (-1.330125) \| 2.615983 / 5.269862 (-2.653879) \| 1.732637 / 4.565676 (-2.833039) \| 0.062830 / 0.424275 (-0.361445) \| 0.004972 / 0.007607 (-0.002635) \| 0.348559 / 0.226044 (0.122515) \| 3.450567 / 2.268929 (1.181639) \| 1.970743 / 55.444624 (-53.473882) \| 1.702232 / 6.876477 (-5.174245) \| 1.799592 / 2.142072 (-0.342480) \| 0.649477 / 4.805227 (-4.155751) \| 0.115940 / 6.500664 (-6.384724) \| 0.040364 / 0.075469 (-0.035105) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.000014 / 1.841788 (-0.841773) \| 11.937886 / 8.074308 (3.863578) \| 10.169478 / 10.191392 (-0.021914) \| 0.153359 / 0.680424 (-0.527064) \| 0.015205 / 0.534201 (-0.518996) \| 0.287812 / 0.579283 (-0.291471) \| 0.278688 / 0.434364 (-0.155676) \| 0.322831 / 0.540337 (-0.217507) \| 0.425631 / 1.386936 (-0.961305) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6e176efbed29374e7c2cd33da64aeeae3c11ca0f \"CML watermark\")\n" ]
2,129,229,810	6,659	Change default compression argument for JsonDatasetWriter	closed	2024-02-11T23:49:07	2024-03-01T17:51:50	2024-03-01T17:44:55	https://github.com/huggingface/datasets/pull/6659	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6659", "html_url": "https://github.com/huggingface/datasets/pull/6659", "diff_url": "https://github.com/huggingface/datasets/pull/6659.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6659.patch", "merged_at": "2024-03-01T17:44:55" }	Rexhaif	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6659). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Can someone check this out?", "<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) \| 0.005008 / 0.011353 (-0.006345) \| 0.003267 / 0.011008 (-0.007741) \| 0.064140 / 0.038508 (0.025632) \| 0.027419 / 0.023109 (0.004309) \| 0.246692 / 0.275898 (-0.029206) \| 0.271303 / 0.323480 (-0.052177) \| 0.004127 / 0.007986 (-0.003859) \| 0.002698 / 0.004328 (-0.001631) \| 0.050415 / 0.004250 (0.046165) \| 0.040323 / 0.037052 (0.003271) \| 0.265738 / 0.258489 (0.007249) \| 0.291556 / 0.293841 (-0.002285) \| 0.027924 / 0.128546 (-0.100622) \| 0.010206 / 0.075646 (-0.065441) \| 0.207106 / 0.419271 (-0.212165) \| 0.036087 / 0.043533 (-0.007446) \| 0.250412 / 0.255139 (-0.004727) \| 0.269014 / 0.283200 (-0.014186) \| 0.018102 / 0.141683 (-0.123581) \| 1.135137 / 1.452155 (-0.317018) \| 1.177718 / 1.492716 (-0.314998) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095557 / 0.018006 (0.077550) \| 0.306235 / 0.000490 (0.305745) \| 0.000214 / 0.000200 (0.000014) \| 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) \| 0.018217 / 0.037411 (-0.019194) \| 0.060993 / 0.014526 (0.046467) \| 0.072748 / 0.176557 (-0.103808) \| 0.119357 / 0.737135 (-0.617778) \| 0.073719 / 0.296338 (-0.222619) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295924 / 0.215209 (0.080715) \| 2.901071 / 2.077655 (0.823417) \| 1.497316 / 1.504120 (-0.006804) \| 1.371232 / 1.541195 (-0.169962) \| 1.395643 / 1.468490 (-0.072847) \| 0.577548 / 4.584777 (-4.007229) \| 2.383813 / 3.745712 (-1.361899) \| 2.764451 / 5.269862 (-2.505411) \| 1.733074 / 4.565676 (-2.832602) \| 0.063730 / 0.424275 (-0.360545) \| 0.004933 / 0.007607 (-0.002674) \| 0.347135 / 0.226044 (0.121090) \| 3.390814 / 2.268929 (1.121885) \| 1.849454 / 55.444624 (-53.595170) \| 1.561801 / 6.876477 (-5.314675) \| 1.587818 / 2.142072 (-0.554254) \| 0.652061 / 4.805227 (-4.153166) \| 0.117195 / 6.500664 (-6.383469) \| 0.041922 / 0.075469 (-0.033548) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.949050 / 1.841788 (-0.892738) \| 11.353664 / 8.074308 (3.279355) \| 9.261581 / 10.191392 (-0.929811) \| 0.140374 / 0.680424 (-0.540050) \| 0.014254 / 0.534201 (-0.519946) \| 0.288124 / 0.579283 (-0.291159) \| 0.262888 / 0.434364 (-0.171476) \| 0.330774 / 0.540337 (-0.209564) \| 0.444777 / 1.386936 (-0.942159) \|\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) \| 0.003418 / 0.011008 (-0.007591) \| 0.049764 / 0.038508 (0.011256) \| 0.029336 / 0.023109 (0.006226) \| 0.278570 / 0.275898 (0.002672) \| 0.300676 / 0.323480 (-0.022804) \| 0.004292 / 0.007986 (-0.003694) \| 0.002745 / 0.004328 (-0.001584) \| 0.049194 / 0.004250 (0.044943) \| 0.044036 / 0.037052 (0.006984) \| 0.299258 / 0.258489 (0.040769) \| 0.324451 / 0.293841 (0.030610) \| 0.029777 / 0.128546 (-0.098769) \| 0.010426 / 0.075646 (-0.065221) \| 0.057267 / 0.419271 (-0.362004) \| 0.051276 / 0.043533 (0.007743) \| 0.278012 / 0.255139 (0.022873) \| 0.297099 / 0.283200 (0.013899) \| 0.018340 / 0.141683 (-0.123343) \| 1.179255 / 1.452155 (-0.272899) \| 1.231536 / 1.492716 (-0.261180) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092546 / 0.018006 (0.074540) \| 0.299959 / 0.000490 (0.299469) \| 0.000220 / 0.000200 (0.000020) \| 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) \| 0.021657 / 0.037411 (-0.015755) \| 0.075440 / 0.014526 (0.060914) \| 0.086246 / 0.176557 (-0.090310) \| 0.126511 / 0.737135 (-0.610624) \| 0.091303 / 0.296338 (-0.205036) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294775 / 0.215209 (0.079566) \| 2.868973 / 2.077655 (0.791319) \| 1.666971 / 1.504120 (0.162851) \| 1.545680 / 1.541195 (0.004486) \| 1.559983 / 1.468490 (0.091493) \| 0.572191 / 4.584777 (-4.012586) \| 2.429317 / 3.745712 (-1.316395) \| 2.673334 / 5.269862 (-2.596527) \| 1.758114 / 4.565676 (-2.807563) \| 0.063766 / 0.424275 (-0.360509) \| 0.005070 / 0.007607 (-0.002537) \| 0.345488 / 0.226044 (0.119443) \| 3.464525 / 2.268929 (1.195596) \| 1.975717 / 55.444624 (-53.468908) \| 1.686671 / 6.876477 (-5.189806) \| 1.825434 / 2.142072 (-0.316638) \| 0.655853 / 4.805227 (-4.149374) \| 0.116372 / 6.500664 (-6.384292) \| 0.040647 / 0.075469 (-0.034822) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.014080 / 1.841788 (-0.827707) \| 12.038496 / 8.074308 (3.964188) \| 10.354536 / 10.191392 (0.163144) \| 0.130285 / 0.680424 (-0.550139) \| 0.015514 / 0.534201 (-0.518687) \| 0.284743 / 0.579283 (-0.294540) \| 0.280275 / 0.434364 (-0.154088) \| 0.321175 / 0.540337 (-0.219162) \| 0.425840 / 1.386936 (-0.961096) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4bb6c6d46a171c4fa1b65167cb81998e2f863892 \"CML watermark\")\n" ]
2,129,158,371	6,658	[Resumable IterableDataset] Add IterableDataset state_dict	closed	2024-02-11T20:35:52	2024-10-01T10:19:38	2024-06-03T19:15:39	https://github.com/huggingface/datasets/pull/6658	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6658", "html_url": "https://github.com/huggingface/datasets/pull/6658", "diff_url": "https://github.com/huggingface/datasets/pull/6658.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6658.patch", "merged_at": "2024-06-03T19:15:39" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6658). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "would be nice to have this feature in the new dataset release!", "Before finalising this this I'd like to make sure this philosophy makes sense for other libs like `accelerate` for example.\r\n\r\ncc @muellerzr I'd love your feedback on this one\r\ncc @LysandreJik also if you think other people should take a look", "> One design question though: what's the logic behind self._state_dict rather than having it all be state_dict?\r\n\r\nThe `_state_dict` is the internal object that is updated in-place while you iterate on the dataset.\r\n\r\nWe need to copy it every time the user accesses it.\r\n\r\nOtherwise we would get\r\n```python\r\nstate_dict = ds.state_dict()\r\nfor x in ds:\r\n assert ds.state_dict() == state_dict # and actually `assert ds.state_dict() is state_dict`\r\n```\r\n\r\nThe state is updated in-place since it's made of dictionaries that are shared with the steps in the IterableDataset pipeline.", "What do you think of making it a full property with a docstring explicitly stating users shouldn’t call/modify it directly?\r\n\r\nI can imagine some exploratory users getting curious", "I don't think users read docstrings of properties that often. What about explaining the logic in the `.state_dict()` docstring ? This also feels aligned with the way `.state_dict()` and `.load_state_dict()` works in pytorch (you should use load_state_dict to load a modified copy of the state dict)", "Sure, I can agree with that!", "Just a small note mentioning returns a copy of the state dict should be enough imo", "looking forward as well for this PR to be merge", "> I don't think users read docstrings of properties that often. What about explaining the logic in the `.state_dict()` docstring ? This also feels aligned with the way `.state_dict()` and `.load_state_dict()` works in pytorch (you should use load_state_dict to load a modified copy of the state dict)\r\n\r\nHi, I'm experimenting with LLM pretraining using your code. I found that the time of resuming an iterable dataset can be reduced to 5% (my streaming process includes tokenization), but I'm not sure if I'm using it correctly. Could you help me check it? Thanks.\r\n\r\n```\r\nclass CustomTrainer(Trainer):\r\n def _save_rng_state(self, output_dir):\r\n super()._save_rng_state(output_dir)\r\n if self.args.should_save:\r\n with open(os.path.join(output_dir, f'iterable_data_state_dict.json'), 'w', encoding='utf-8') as fo:\r\n json.dump(self.train_dataset.state_dict(), fo, ensure_ascii=False)\r\n```\r\n\r\n```\r\n dataset = <A IterableDataset constructed by (interleave, map(tokenization))>\r\n lask_ckpt_iterable_data_state_dict_file_path = os.path.join(training_args.resume_from_checkpoint, f'iterable_data_state_dict.json')\r\n if os.path.exists(lask_ckpt_iterable_data_state_dict_file_path) and finetuning_args.load_iteratable_state_dict:\r\n if not training_args.ignore_data_skip:\r\n raise ValueError(f'Found `iterable_data_state_dict_file_path`: `{lask_ckpt_iterable_data_state_dict_file_path}`. Please set `ignore_data_skip`=True to skip tokenization.')\r\n with open(lask_ckpt_iterable_data_state_dict_file_path) as f:\r\n lask_ckpt_iterable_data_state_dict = json.load(f)\r\n dataset.load_state_dict(lask_ckpt_iterable_data_state_dict)\r\n logger.info(f'Loading `iterable_data_state_dict` from {lask_ckpt_iterable_data_state_dict_file_path}')\r\n```\r\n", "it sounds good to me :)", "@lhoestq Hi, if I set `prefetch`, does this dataset work well?", "It does work well if you prefetch and then resume from a state, but you might lose the samples that were in the prefetch buffer of the DataLoader (which could be acceptable in some circumstances).\r\n\r\nFortunately we're about to ship an integration with the new StatefulDataLoader from torchdata which can help on this matter :)", "yeah, what I meant is that prefetch might drop a few data entries. really looking forward to the new StatefulDataLoader. ：)", "<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) \| 0.005788 / 0.011353 (-0.005564) \| 0.004036 / 0.011008 (-0.006972) \| 0.064720 / 0.038508 (0.026212) \| 0.034990 / 0.023109 (0.011881) \| 0.245488 / 0.275898 (-0.030410) \| 0.272596 / 0.323480 (-0.050884) \| 0.003170 / 0.007986 (-0.004815) \| 0.002867 / 0.004328 (-0.001461) \| 0.049961 / 0.004250 (0.045711) \| 0.050951 / 0.037052 (0.013899) \| 0.257757 / 0.258489 (-0.000732) \| 0.292957 / 0.293841 (-0.000884) \| 0.027739 / 0.128546 (-0.100807) \| 0.010942 / 0.075646 (-0.064705) \| 0.205153 / 0.419271 (-0.214118) \| 0.037892 / 0.043533 (-0.005641) \| 0.247536 / 0.255139 (-0.007603) \| 0.267239 / 0.283200 (-0.015960) \| 0.021490 / 0.141683 (-0.120193) \| 1.107306 / 1.452155 (-0.344848) \| 1.144675 / 1.492716 (-0.348041) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.103212 / 0.018006 (0.085205) \| 0.315174 / 0.000490 (0.314684) \| 0.000229 / 0.000200 (0.000029) \| 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) \| 0.019771 / 0.037411 (-0.017641) \| 0.064033 / 0.014526 (0.049507) \| 0.076751 / 0.176557 (-0.099805) \| 0.122615 / 0.737135 (-0.614521) \| 0.078490 / 0.296338 (-0.217848) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286236 / 0.215209 (0.071027) \| 2.841469 / 2.077655 (0.763814) \| 1.514079 / 1.504120 (0.009959) \| 1.393792 / 1.541195 (-0.147403) \| 1.432741 / 1.468490 (-0.035749) \| 0.571003 / 4.584777 (-4.013774) \| 2.369031 / 3.745712 (-1.376681) \| 2.825246 / 5.269862 (-2.444616) \| 1.858524 / 4.565676 (-2.707153) \| 0.065366 / 0.424275 (-0.358909) \| 0.005107 / 0.007607 (-0.002500) \| 0.341010 / 0.226044 (0.114965) \| 3.443894 / 2.268929 (1.174966) \| 1.879192 / 55.444624 (-53.565433) \| 1.603046 / 6.876477 (-5.273431) \| 1.807639 / 2.142072 (-0.334433) \| 0.646726 / 4.805227 (-4.158502) \| 0.119409 / 6.500664 (-6.381255) \| 0.044564 / 0.075469 (-0.030905) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971026 / 1.841788 (-0.870762) \| 12.593884 / 8.074308 (4.519576) \| 10.305243 / 10.191392 (0.113851) \| 0.132018 / 0.680424 (-0.548406) \| 0.014387 / 0.534201 (-0.519814) \| 0.288597 / 0.579283 (-0.290686) \| 0.267373 / 0.434364 (-0.166991) \| 0.325626 / 0.540337 (-0.214711) \| 0.488808 / 1.386936 (-0.898128) \|\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) \| 0.005991 / 0.011353 (-0.005362) \| 0.004028 / 0.011008 (-0.006980) \| 0.051951 / 0.038508 (0.013443) \| 0.036870 / 0.023109 (0.013761) \| 0.263777 / 0.275898 (-0.012122) \| 0.290914 / 0.323480 (-0.032566) \| 0.004594 / 0.007986 (-0.003392) \| 0.002971 / 0.004328 (-0.001357) \| 0.049699 / 0.004250 (0.045449) \| 0.044939 / 0.037052 (0.007887) \| 0.275055 / 0.258489 (0.016566) \| 0.316244 / 0.293841 (0.022403) \| 0.030501 / 0.128546 (-0.098045) \| 0.011197 / 0.075646 (-0.064449) \| 0.058718 / 0.419271 (-0.360554) \| 0.034926 / 0.043533 (-0.008607) \| 0.259172 / 0.255139 (0.004033) \| 0.280127 / 0.283200 (-0.003072) \| 0.019775 / 0.141683 (-0.121908) \| 1.169468 / 1.452155 (-0.282687) \| 1.178098 / 1.492716 (-0.314619) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.101633 / 0.018006 (0.083626) \| 0.314684 / 0.000490 (0.314194) \| 0.000224 / 0.000200 (0.000024) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024071 / 0.037411 (-0.013341) \| 0.079894 / 0.014526 (0.065368) \| 0.090915 / 0.176557 (-0.085642) \| 0.132397 / 0.737135 (-0.604738) \| 0.091919 / 0.296338 (-0.204419) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296237 / 0.215209 (0.081028) \| 2.891752 / 2.077655 (0.814097) \| 1.551937 / 1.504120 (0.047817) \| 1.414179 / 1.541195 (-0.127016) \| 1.450192 / 1.468490 (-0.018298) \| 0.556272 / 4.584777 (-4.028504) \| 0.952374 / 3.745712 (-2.793339) \| 2.709450 / 5.269862 (-2.560411) \| 1.771251 / 4.565676 (-2.794426) \| 0.061873 / 0.424275 (-0.362402) \| 0.005058 / 0.007607 (-0.002549) \| 0.344790 / 0.226044 (0.118746) \| 3.398982 / 2.268929 (1.130053) \| 1.905832 / 55.444624 (-53.538792) \| 1.632357 / 6.876477 (-5.244120) \| 1.822913 / 2.142072 (-0.319160) \| 0.643426 / 4.805227 (-4.161802) \| 0.117321 / 6.500664 (-6.383343) \| 0.042107 / 0.075469 (-0.033363) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974921 / 1.841788 (-0.866867) \| 12.497801 / 8.074308 (4.423493) \| 11.216174 / 10.191392 (1.024782) \| 0.135288 / 0.680424 (-0.545136) \| 0.016731 / 0.534201 (-0.517470) \| 0.287987 / 0.579283 (-0.291296) \| 0.130246 / 0.434364 (-0.304117) \| 0.323282 / 0.540337 (-0.217055) \| 0.414595 / 1.386936 (-0.972341) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#43fd659eacab37b50abfe7f1b4efe2564055990c \"CML watermark\")\n", "@lhoestq Hello, I'm wondering if there are any solutions to work with shuffle now. I've noticed the caveats in docs, \r\n> examples from shuffle buffers are lost when resuming and the buffers are refilled with new data ", "Hi ! I haven't experimented with implementing state_dict for the shuffle buffer. Not sure if this is a good idea to add this, given a shuffle buffer can be quite big and poses serialization challenges.\r\n\r\nIt shouldn't be difficult to experiment with a simple implementation in `BufferShuffledExamplesIterable` though", "@lhoestq thank you for your quick response! I'll try it :}", "@lhoestq Hi, just revise the `BufferShuffledExamplesIterable` and it works\r\n```py\r\n\r\nclass BufferShuffledExamplesIterable(datasets.iterable_dataset.BufferShuffledExamplesIterable):\r\n\r\n def __init__(self, args, kwargs):\r\n super().__init__(args, kwargs)\r\n\r\n def _init_state_dict(self) -> dict:\r\n self._state_dict = self.ex_iterable._init_state_dict()\r\n self._state_dict['mem_buffer'] = ([],)\r\n self._state_dict['gloabl_example_index'] = 0\r\n return self._state_dict\r\n\r\n def __iter__(self):\r\n buffer_size = self.buffer_size\r\n rng = deepcopy(self.generator)\r\n indices_iterator = self._iter_random_indices(rng, buffer_size)\r\n # this is the shuffle buffer that we keep in memory\r\n mem_buffer = self._state_dict['mem_buffer'][0]\r\n gloabl_example_index_start = self._state_dict[\"gloabl_example_index\"] if self._state_dict else 0\r\n # skip already consumed ones\r\n for i in range(gloabl_example_index_start):\r\n _ = next(indices_iterator)\r\n for x in self.ex_iterable:\r\n if len(mem_buffer) == buffer_size: # if the buffer is full, pick and example from it\r\n i = next(indices_iterator)\r\n if self._state_dict:\r\n self._state_dict['gloabl_example_index'] += 1\r\n yield mem_buffer[i]\r\n mem_buffer[i] = x # replace the picked example by a new one\r\n else: # otherwise, keep filling the buffer\r\n mem_buffer.append(x)\r\n # when we run out of examples, we shuffle the remaining examples in the buffer and yield them\r\n rng.shuffle(mem_buffer)\r\n yield from mem_buffer\r\n\r\n def shuffle_data_sources(self, generator: np.random.Generator) -> BufferShuffledExamplesIterable:\r\n \"\"\"Shuffle the wrapped examples iterable as well as the shuffling buffer.\"\"\"\r\n return BufferShuffledExamplesIterable(\r\n self.ex_iterable.shuffle_data_sources(generator), buffer_size=self.buffer_size, generator=generator\r\n )\r\n\r\n def shard_data_sources(self, worker_id: int, num_workers: int) -> BufferShuffledExamplesIterable:\r\n \"\"\"Keep only the requested shard.\"\"\"\r\n return BufferShuffledExamplesIterable(\r\n self.ex_iterable.shard_data_sources(worker_id, num_workers),\r\n buffer_size=self.buffer_size,\r\n generator=self.generator,\r\n )\r\n\r\n def load_state_dict(self, state_dict: dict) -> dict:\r\n def _inner_load_state_dict(state, new_state):\r\n if new_state is not None and isinstance(state, dict):\r\n for key in state:\r\n state[key] = _inner_load_state_dict(state[key], new_state[key])\r\n return state\r\n elif new_state is not None and isinstance(state, list):\r\n for i in range(len(state)):\r\n state[i] = _inner_load_state_dict(state[i], new_state[i])\r\n return state\r\n return new_state\r\n\r\n return _inner_load_state_dict(self._state_dict, state_dict)\r\n```\r\n\r\nI've noticed that it uses significantly more RAM than the original version and experiences a considerable decrease in GPU utilization. Could you offer some suggestions to address this issue?\r\n\r\nor is it prohibited** to maintain sth except for simple indices that small enough for each worker 😢 \r\n\r\n", "Some ExamplesIterable copy and store old versions of the state_dict of parent ExamplesIterable. It is the case for example for batched `map()` (state_dict of beginning of the batch) or `interleave_dataset()` (state_dict of the previous step since it buffers one example to know if the iterable is exhausted).\r\n\r\nCopying a shuffle buffer takes some RAM and some time, which can slow down the data loading pipeline.\r\nMaybe the examples in the shuffle buffer shouldn't not be copied (only do a shallow copy of the list), this would surely help." ]
2,129,147,085	6,657	Release not pushed to conda channel	closed	2024-02-11T20:05:17	2024-03-06T15:06:22	2024-03-06T15:06:22	https://github.com/huggingface/datasets/issues/6657	null	atulsaurav	false	[ "Thanks for reporting, @atulsaurav.\r\n\r\nWe are investigating the issue. ", "I can't fix this issue because I do not appear as a team member of the huggingface datasets project: https://anaconda.org/huggingface/datasets\r\n\r\n@lhoestq could you please add `datasets` team members to the corresponding Anaconda project?\r\n\r\nOnce this done, I could recreate and update the Anaconda token, as mentioned above it seems the current one has expired.", "I think @LysandreJik has access ?", "FYI it failed for 2.18.0 too: https://github.com/huggingface/datasets/actions/runs/8117132330/job/22188677936", "We updated the token and I re-ran the conda releases :)" ]
2,127,338,377	6,656	Error when loading a big local json file	open	2024-02-09T15:14:21	2024-11-29T10:06:57	null	https://github.com/huggingface/datasets/issues/6656	null	Riccorl	false	[ "I get similar when dealing with a large jsonl file (6k lines), \r\n\r\n> TypeError: Couldn't cast array of type timestamp[us] to null\r\n\r\nYet when I split it into 1k lines, files, load_dataset works fine!\r\n\r\nhttps://github.com/huggingface/course/issues/692\r\n\r\n", "What's the proposed solution? :-)" ]
2,127,020,042	6,655	Cannot load the dataset go_emotions	open	2024-02-09T12:15:39	2024-02-12T09:35:55	null	https://github.com/huggingface/datasets/issues/6655	null	arame	false	[ "Thanks for reporting, @arame.\r\n\r\nI guess you have an old version of `transformers` (that submodule is present in `transformers` since version 3.0.1, since nearly 4 years ago). If you update it, the error should disappear:\r\n```shell\r\npip install -U transformers\r\n```\r\n\r\nOn the other hand, I am wondering: does it make sense to use `transformers` in this case, even if we don't need it to load the `go_emotions` dataset (already converted to Parquet files)?\r\n- Maybe @mariosasko can give some insight, as he included these code lines:\r\n - #6454\r\n\r\nhttps://github.com/huggingface/datasets/blob/9751fb14594d354e952f0ebdfaf31cb203b011e7/src/datasets/utils/_dill.py#L60-L63\r\n", "The linked code lazily registers a custom reducer for `transformers.PreTrainedTokenizerBase` only if `transformers` have already been imported (imports are expensive, so we check `sys.modules`).\r\n\r\nHowever, the logic does not account for `transformers<3`, so we should add a version check to fix that.", "> The linked code lazily registers a custom reducer for `transformers.PreTrainedTokenizerBase` only if `transformers` have already been imported (imports are expensive, so we check `sys.modules`).\r\n> \r\n> However, the logic does not account for `transformers<3`, so we should add a version check to fix that.\r\n\r\nThank you for that Mario. Would this fix solve the problem and do you have any idea when it will be done? \r\nI tried the pip install suggested by Albert and it made no difference.", "I tried running the code today and the problem appears to be fixed." ]
2,126,939,358	6,654	Batched dataset map throws exception that cannot cast fixed length array to Sequence	closed	2024-02-09T11:23:19	2024-02-12T08:26:53	2024-02-12T08:26:53	https://github.com/huggingface/datasets/issues/6654	null	keesjandevries	false	[ "Hi ! This issue has been fixed by https://github.com/huggingface/datasets/pull/6283\r\n\r\nCan you try again with the new release 2.17.0 ?\r\n\r\n```\r\npip install -U datasets\r\n```\r\n\r\n", "Amazing! It's indeed fixed now. Thanks!" ]
2,126,831,929	6,653	Set dev version	closed	2024-02-09T10:12:02	2024-02-09T10:18:20	2024-02-09T10:12:12	https://github.com/huggingface/datasets/pull/6653	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6653", "html_url": "https://github.com/huggingface/datasets/pull/6653", "diff_url": "https://github.com/huggingface/datasets/pull/6653.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6653.patch", "merged_at": "2024-02-09T10:12:12" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6653). 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) \| 0.005076 / 0.011353 (-0.006277) \| 0.003424 / 0.011008 (-0.007584) \| 0.064195 / 0.038508 (0.025687) \| 0.031742 / 0.023109 (0.008633) \| 0.244774 / 0.275898 (-0.031124) \| 0.268529 / 0.323480 (-0.054951) \| 0.003970 / 0.007986 (-0.004016) \| 0.002657 / 0.004328 (-0.001672) \| 0.048847 / 0.004250 (0.044597) \| 0.042196 / 0.037052 (0.005144) \| 0.266044 / 0.258489 (0.007555) \| 0.282400 / 0.293841 (-0.011441) \| 0.027617 / 0.128546 (-0.100929) \| 0.010400 / 0.075646 (-0.065246) \| 0.205910 / 0.419271 (-0.213362) \| 0.035820 / 0.043533 (-0.007713) \| 0.247750 / 0.255139 (-0.007389) \| 0.267318 / 0.283200 (-0.015882) \| 0.017980 / 0.141683 (-0.123703) \| 1.107263 / 1.452155 (-0.344892) \| 1.173208 / 1.492716 (-0.319509) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095830 / 0.018006 (0.077824) \| 0.293891 / 0.000490 (0.293401) \| 0.000257 / 0.000200 (0.000057) \| 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) \| 0.018138 / 0.037411 (-0.019273) \| 0.061631 / 0.014526 (0.047105) \| 0.073038 / 0.176557 (-0.103519) \| 0.118317 / 0.737135 (-0.618818) \| 0.074190 / 0.296338 (-0.222148) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287026 / 0.215209 (0.071817) \| 2.786137 / 2.077655 (0.708482) \| 1.472575 / 1.504120 (-0.031544) \| 1.346919 / 1.541195 (-0.194276) \| 1.388535 / 1.468490 (-0.079955) \| 0.565731 / 4.584777 (-4.019046) \| 2.382573 / 3.745712 (-1.363139) \| 2.736926 / 5.269862 (-2.532935) \| 1.716517 / 4.565676 (-2.849159) \| 0.062168 / 0.424275 (-0.362108) \| 0.004924 / 0.007607 (-0.002683) \| 0.341897 / 0.226044 (0.115853) \| 3.355715 / 2.268929 (1.086787) \| 1.837014 / 55.444624 (-53.607611) \| 1.532063 / 6.876477 (-5.344414) \| 1.548193 / 2.142072 (-0.593880) \| 0.634995 / 4.805227 (-4.170232) \| 0.115622 / 6.500664 (-6.385042) \| 0.042252 / 0.075469 (-0.033217) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.970713 / 1.841788 (-0.871075) \| 11.727576 / 8.074308 (3.653268) \| 9.806524 / 10.191392 (-0.384868) \| 0.127622 / 0.680424 (-0.552802) \| 0.014140 / 0.534201 (-0.520061) \| 0.286832 / 0.579283 (-0.292451) \| 0.266556 / 0.434364 (-0.167808) \| 0.325940 / 0.540337 (-0.214398) \| 0.421839 / 1.386936 (-0.965097) \|\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) \| 0.005495 / 0.011353 (-0.005858) \| 0.003676 / 0.011008 (-0.007332) \| 0.054361 / 0.038508 (0.015853) \| 0.030743 / 0.023109 (0.007633) \| 0.277200 / 0.275898 (0.001302) \| 0.313459 / 0.323480 (-0.010021) \| 0.004316 / 0.007986 (-0.003670) \| 0.002750 / 0.004328 (-0.001578) \| 0.049491 / 0.004250 (0.045241) \| 0.044268 / 0.037052 (0.007215) \| 0.292529 / 0.258489 (0.034039) \| 0.326524 / 0.293841 (0.032683) \| 0.048040 / 0.128546 (-0.080507) \| 0.010390 / 0.075646 (-0.065256) \| 0.058459 / 0.419271 (-0.360813) \| 0.033765 / 0.043533 (-0.009768) \| 0.276003 / 0.255139 (0.020864) \| 0.297299 / 0.283200 (0.014099) \| 0.018532 / 0.141683 (-0.123151) \| 1.157639 / 1.452155 (-0.294515) \| 1.220492 / 1.492716 (-0.272225) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093903 / 0.018006 (0.075897) \| 0.303005 / 0.000490 (0.302515) \| 0.000224 / 0.000200 (0.000024) \| 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) \| 0.021580 / 0.037411 (-0.015831) \| 0.076176 / 0.014526 (0.061650) \| 0.086998 / 0.176557 (-0.089558) \| 0.124148 / 0.737135 (-0.612987) \| 0.088613 / 0.296338 (-0.207725) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300623 / 0.215209 (0.085414) \| 2.911876 / 2.077655 (0.834221) \| 1.588398 / 1.504120 (0.084278) \| 1.471251 / 1.541195 (-0.069944) \| 1.505528 / 1.468490 (0.037038) \| 0.570635 / 4.584777 (-4.014142) \| 2.485769 / 3.745712 (-1.259943) \| 2.785355 / 5.269862 (-2.484507) \| 1.752944 / 4.565676 (-2.812732) \| 0.063146 / 0.424275 (-0.361129) \| 0.004980 / 0.007607 (-0.002627) \| 0.354577 / 0.226044 (0.128532) \| 3.477181 / 2.268929 (1.208253) \| 1.951906 / 55.444624 (-53.492718) \| 1.677169 / 6.876477 (-5.199307) \| 1.686338 / 2.142072 (-0.455735) \| 0.637156 / 4.805227 (-4.168071) \| 0.117732 / 6.500664 (-6.382932) \| 0.041091 / 0.075469 (-0.034378) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.010071 / 1.841788 (-0.831717) \| 12.172242 / 8.074308 (4.097934) \| 10.422811 / 10.191392 (0.231419) \| 0.137185 / 0.680424 (-0.543239) \| 0.014643 / 0.534201 (-0.519558) \| 0.287248 / 0.579283 (-0.292035) \| 0.272779 / 0.434364 (-0.161585) \| 0.331761 / 0.540337 (-0.208576) \| 0.417266 / 1.386936 (-0.969670) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9751fb14594d354e952f0ebdfaf31cb203b011e7 \"CML watermark\")\n" ]
2,126,760,798	6,652	Release: 2.17.0	closed	2024-02-09T09:25:01	2024-02-09T10:11:48	2024-02-09T10:05:35	https://github.com/huggingface/datasets/pull/6652	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6652", "html_url": "https://github.com/huggingface/datasets/pull/6652", "diff_url": "https://github.com/huggingface/datasets/pull/6652.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6652.patch", "merged_at": "2024-02-09T10:05:35" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6652). 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) \| 0.005207 / 0.011353 (-0.006145) \| 0.003785 / 0.011008 (-0.007223) \| 0.064221 / 0.038508 (0.025713) \| 0.028981 / 0.023109 (0.005872) \| 0.246215 / 0.275898 (-0.029683) \| 0.268058 / 0.323480 (-0.055422) \| 0.004028 / 0.007986 (-0.003958) \| 0.002804 / 0.004328 (-0.001525) \| 0.048878 / 0.004250 (0.044627) \| 0.042641 / 0.037052 (0.005589) \| 0.255590 / 0.258489 (-0.002899) \| 0.287377 / 0.293841 (-0.006464) \| 0.027772 / 0.128546 (-0.100774) \| 0.010637 / 0.075646 (-0.065009) \| 0.211526 / 0.419271 (-0.207746) \| 0.035789 / 0.043533 (-0.007744) \| 0.243042 / 0.255139 (-0.012097) \| 0.268369 / 0.283200 (-0.014830) \| 0.017907 / 0.141683 (-0.123776) \| 1.138829 / 1.452155 (-0.313326) \| 1.175732 / 1.492716 (-0.316984) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094205 / 0.018006 (0.076199) \| 0.304317 / 0.000490 (0.303827) \| 0.000206 / 0.000200 (0.000006) \| 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) \| 0.018424 / 0.037411 (-0.018987) \| 0.061719 / 0.014526 (0.047193) \| 0.073471 / 0.176557 (-0.103085) \| 0.121577 / 0.737135 (-0.615558) \| 0.075134 / 0.296338 (-0.221204) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275178 / 0.215209 (0.059969) \| 2.689222 / 2.077655 (0.611568) \| 1.396680 / 1.504120 (-0.107439) \| 1.278782 / 1.541195 (-0.262413) \| 1.326632 / 1.468490 (-0.141858) \| 0.566915 / 4.584777 (-4.017862) \| 2.365928 / 3.745712 (-1.379784) \| 2.785435 / 5.269862 (-2.484427) \| 1.745131 / 4.565676 (-2.820546) \| 0.062798 / 0.424275 (-0.361477) \| 0.005107 / 0.007607 (-0.002500) \| 0.330441 / 0.226044 (0.104396) \| 3.266265 / 2.268929 (0.997337) \| 1.792588 / 55.444624 (-53.652036) \| 1.516021 / 6.876477 (-5.360455) \| 1.562750 / 2.142072 (-0.579323) \| 0.652964 / 4.805227 (-4.152264) \| 0.117813 / 6.500664 (-6.382852) \| 0.042372 / 0.075469 (-0.033097) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.010107 / 1.841788 (-0.831680) \| 11.819910 / 8.074308 (3.745602) \| 9.701673 / 10.191392 (-0.489719) \| 0.178165 / 0.680424 (-0.502259) \| 0.014438 / 0.534201 (-0.519763) \| 0.297733 / 0.579283 (-0.281550) \| 0.264914 / 0.434364 (-0.169450) \| 0.324531 / 0.540337 (-0.215806) \| 0.430207 / 1.386936 (-0.956729) \|\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) \| 0.003870 / 0.011008 (-0.007138) \| 0.050379 / 0.038508 (0.011871) \| 0.031238 / 0.023109 (0.008129) \| 0.276839 / 0.275898 (0.000941) \| 0.299488 / 0.323480 (-0.023992) \| 0.005143 / 0.007986 (-0.002842) \| 0.002725 / 0.004328 (-0.001604) \| 0.048184 / 0.004250 (0.043934) \| 0.046232 / 0.037052 (0.009180) \| 0.287058 / 0.258489 (0.028569) \| 0.322659 / 0.293841 (0.028818) \| 0.047598 / 0.128546 (-0.080949) \| 0.011116 / 0.075646 (-0.064530) \| 0.058252 / 0.419271 (-0.361019) \| 0.033404 / 0.043533 (-0.010128) \| 0.277650 / 0.255139 (0.022511) \| 0.295610 / 0.283200 (0.012410) \| 0.018124 / 0.141683 (-0.123559) \| 1.135052 / 1.452155 (-0.317103) \| 1.194261 / 1.492716 (-0.298456) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095595 / 0.018006 (0.077588) \| 0.306408 / 0.000490 (0.305918) \| 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) \| 0.022027 / 0.037411 (-0.015385) \| 0.076224 / 0.014526 (0.061698) \| 0.087441 / 0.176557 (-0.089116) \| 0.126636 / 0.737135 (-0.610499) \| 0.089442 / 0.296338 (-0.206896) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291315 / 0.215209 (0.076106) \| 2.835304 / 2.077655 (0.757650) \| 1.581102 / 1.504120 (0.076982) \| 1.463046 / 1.541195 (-0.078149) \| 1.481982 / 1.468490 (0.013492) \| 0.559989 / 4.584777 (-4.024788) \| 2.385262 / 3.745712 (-1.360450) \| 2.773478 / 5.269862 (-2.496383) \| 1.744427 / 4.565676 (-2.821249) \| 0.062687 / 0.424275 (-0.361589) \| 0.005149 / 0.007607 (-0.002458) \| 0.374600 / 0.226044 (0.148555) \| 3.376507 / 2.268929 (1.107579) \| 1.935290 / 55.444624 (-53.509334) \| 1.663227 / 6.876477 (-5.213250) \| 1.678987 / 2.142072 (-0.463085) \| 0.638970 / 4.805227 (-4.166258) \| 0.120000 / 6.500664 (-6.380664) \| 0.040862 / 0.075469 (-0.034608) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.008795 / 1.841788 (-0.832993) \| 12.275084 / 8.074308 (4.200776) \| 10.340088 / 10.191392 (0.148696) \| 0.136454 / 0.680424 (-0.543970) \| 0.014404 / 0.534201 (-0.519797) \| 0.289478 / 0.579283 (-0.289805) \| 0.279243 / 0.434364 (-0.155121) \| 0.330992 / 0.540337 (-0.209346) \| 0.422043 / 1.386936 (-0.964893) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#70633576ecf1f3f5e5cdfd8c9189246b3604f4b6 \"CML watermark\")\n" ]
2,126,649,626	6,651	Slice splits support for datasets.load_from_disk	open	2024-02-09T08:00:21	2024-06-14T14:42:46	null	https://github.com/huggingface/datasets/issues/6651	null	mhorlacher	false	[]
2,125,680,991	6,650	AttributeError: 'InMemoryTable' object has no attribute '_batches'	open	2024-02-08T17:11:26	2024-02-21T00:34:41	null	https://github.com/huggingface/datasets/issues/6650	null	matsuobasho	false	[ "Hi! Does running the following code also return the same error on your machine? \r\n\r\n```python\r\nimport copy\r\nimport pyarrow as pa\r\nfrom datasets.table import InMemoryTable\r\n\r\ncopy.deepcopy(InMemoryTable(pa.table({\"a\": [1, 2, 3], \"b\": [\"foo\", \"bar\", \"foobar\"]})))\r\n```", "No, it doesn't, it runs fine. But what's really strange is that the error just went away after I reran the data prep script for conversion from csv to a datasets object. I realize that's not very helpful since the problem isn't reproducible. ", "Feel free to close the issue then :)." ]
2,124,940,213	6,649	Minor multi gpu doc improvement	closed	2024-02-08T11:17:24	2024-02-08T11:23:35	2024-02-08T11:17:35	https://github.com/huggingface/datasets/pull/6649	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6649", "html_url": "https://github.com/huggingface/datasets/pull/6649", "diff_url": "https://github.com/huggingface/datasets/pull/6649.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6649.patch", "merged_at": "2024-02-08T11:17:35" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6649). 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) \| 0.005197 / 0.011353 (-0.006156) \| 0.003469 / 0.011008 (-0.007539) \| 0.062306 / 0.038508 (0.023798) \| 0.028417 / 0.023109 (0.005308) \| 0.241147 / 0.275898 (-0.034751) \| 0.270910 / 0.323480 (-0.052569) \| 0.003053 / 0.007986 (-0.004933) \| 0.003343 / 0.004328 (-0.000985) \| 0.048044 / 0.004250 (0.043794) \| 0.043738 / 0.037052 (0.006686) \| 0.259274 / 0.258489 (0.000785) \| 0.282522 / 0.293841 (-0.011319) \| 0.027807 / 0.128546 (-0.100739) \| 0.010413 / 0.075646 (-0.065234) \| 0.206322 / 0.419271 (-0.212950) \| 0.035770 / 0.043533 (-0.007763) \| 0.243465 / 0.255139 (-0.011674) \| 0.261596 / 0.283200 (-0.021604) \| 0.018613 / 0.141683 (-0.123070) \| 1.115509 / 1.452155 (-0.336645) \| 1.189403 / 1.492716 (-0.303314) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.086075 / 0.018006 (0.068069) \| 0.296140 / 0.000490 (0.295650) \| 0.000198 / 0.000200 (-0.000002) \| 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) \| 0.018238 / 0.037411 (-0.019173) \| 0.061783 / 0.014526 (0.047257) \| 0.072014 / 0.176557 (-0.104543) \| 0.118746 / 0.737135 (-0.618389) \| 0.073279 / 0.296338 (-0.223060) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278281 / 0.215209 (0.063072) \| 2.772209 / 2.077655 (0.694555) \| 1.404503 / 1.504120 (-0.099617) \| 1.274753 / 1.541195 (-0.266441) \| 1.304394 / 1.468490 (-0.164096) \| 0.556903 / 4.584777 (-4.027874) \| 2.335428 / 3.745712 (-1.410284) \| 2.712255 / 5.269862 (-2.557606) \| 1.722252 / 4.565676 (-2.843425) \| 0.061268 / 0.424275 (-0.363007) \| 0.005029 / 0.007607 (-0.002578) \| 0.326112 / 0.226044 (0.100067) \| 3.207917 / 2.268929 (0.938988) \| 1.743513 / 55.444624 (-53.701111) \| 1.476418 / 6.876477 (-5.400059) \| 1.489776 / 2.142072 (-0.652297) \| 0.628181 / 4.805227 (-4.177046) \| 0.115959 / 6.500664 (-6.384706) \| 0.041854 / 0.075469 (-0.033615) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969039 / 1.841788 (-0.872749) \| 11.178646 / 8.074308 (3.104338) \| 9.639716 / 10.191392 (-0.551676) \| 0.139750 / 0.680424 (-0.540674) \| 0.014230 / 0.534201 (-0.519971) \| 0.285318 / 0.579283 (-0.293965) \| 0.260788 / 0.434364 (-0.173576) \| 0.324183 / 0.540337 (-0.216154) \| 0.416326 / 1.386936 (-0.970610) \|\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) \| 0.005149 / 0.011353 (-0.006204) \| 0.003469 / 0.011008 (-0.007539) \| 0.049761 / 0.038508 (0.011253) \| 0.030723 / 0.023109 (0.007614) \| 0.271562 / 0.275898 (-0.004336) \| 0.297843 / 0.323480 (-0.025637) \| 0.004296 / 0.007986 (-0.003690) \| 0.002704 / 0.004328 (-0.001624) \| 0.048890 / 0.004250 (0.044640) \| 0.044776 / 0.037052 (0.007723) \| 0.285490 / 0.258489 (0.027001) \| 0.312888 / 0.293841 (0.019047) \| 0.046239 / 0.128546 (-0.082307) \| 0.010238 / 0.075646 (-0.065408) \| 0.057968 / 0.419271 (-0.361304) \| 0.033295 / 0.043533 (-0.010238) \| 0.274320 / 0.255139 (0.019181) \| 0.296199 / 0.283200 (0.012999) \| 0.017856 / 0.141683 (-0.123827) \| 1.147532 / 1.452155 (-0.304622) \| 1.211647 / 1.492716 (-0.281070) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089655 / 0.018006 (0.071649) \| 0.297275 / 0.000490 (0.296785) \| 0.000207 / 0.000200 (0.000007) \| 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) \| 0.021739 / 0.037411 (-0.015672) \| 0.075041 / 0.014526 (0.060515) \| 0.085754 / 0.176557 (-0.090802) \| 0.124512 / 0.737135 (-0.612623) \| 0.086926 / 0.296338 (-0.209412) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290306 / 0.215209 (0.075097) \| 2.847404 / 2.077655 (0.769749) \| 1.606175 / 1.504120 (0.102055) \| 1.483220 / 1.541195 (-0.057974) \| 1.514551 / 1.468490 (0.046061) \| 0.559332 / 4.584777 (-4.025445) \| 2.403089 / 3.745712 (-1.342624) \| 2.715179 / 5.269862 (-2.554683) \| 1.688340 / 4.565676 (-2.877337) \| 0.062057 / 0.424275 (-0.362218) \| 0.004955 / 0.007607 (-0.002652) \| 0.338909 / 0.226044 (0.112865) \| 3.356882 / 2.268929 (1.087954) \| 1.942259 / 55.444624 (-53.502366) \| 1.675195 / 6.876477 (-5.201282) \| 1.688158 / 2.142072 (-0.453914) \| 0.637270 / 4.805227 (-4.167957) \| 0.114314 / 6.500664 (-6.386350) \| 0.040677 / 0.075469 (-0.034792) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.022126 / 1.841788 (-0.819661) \| 11.783359 / 8.074308 (3.709051) \| 10.247652 / 10.191392 (0.056260) \| 0.138188 / 0.680424 (-0.542236) \| 0.014850 / 0.534201 (-0.519351) \| 0.287414 / 0.579283 (-0.291869) \| 0.274393 / 0.434364 (-0.159971) \| 0.327255 / 0.540337 (-0.213082) \| 0.416355 / 1.386936 (-0.970581) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#727a952367966a98b759d54f333b1e2c28cfd4d4 \"CML watermark\")\n" ]
2,124,813,589	6,648	Document usage of hfh cli instead of git	closed	2024-02-08T10:24:56	2024-02-08T13:57:41	2024-02-08T13:51:39	https://github.com/huggingface/datasets/pull/6648	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6648", "html_url": "https://github.com/huggingface/datasets/pull/6648", "diff_url": "https://github.com/huggingface/datasets/pull/6648.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6648.patch", "merged_at": "2024-02-08T13:51:39" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6648). 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) \| 0.004951 / 0.011353 (-0.006402) \| 0.003187 / 0.011008 (-0.007821) \| 0.062959 / 0.038508 (0.024451) \| 0.028037 / 0.023109 (0.004928) \| 0.241374 / 0.275898 (-0.034524) \| 0.262792 / 0.323480 (-0.060688) \| 0.004132 / 0.007986 (-0.003854) \| 0.002766 / 0.004328 (-0.001563) \| 0.051416 / 0.004250 (0.047165) \| 0.040957 / 0.037052 (0.003904) \| 0.260760 / 0.258489 (0.002271) \| 0.282018 / 0.293841 (-0.011823) \| 0.027689 / 0.128546 (-0.100857) \| 0.010433 / 0.075646 (-0.065214) \| 0.211598 / 0.419271 (-0.207674) \| 0.035447 / 0.043533 (-0.008086) \| 0.244333 / 0.255139 (-0.010806) \| 0.263192 / 0.283200 (-0.020008) \| 0.016816 / 0.141683 (-0.124867) \| 1.103188 / 1.452155 (-0.348967) \| 1.179093 / 1.492716 (-0.313623) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092412 / 0.018006 (0.074406) \| 0.301226 / 0.000490 (0.300736) \| 0.000208 / 0.000200 (0.000008) \| 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) \| 0.018146 / 0.037411 (-0.019265) \| 0.061447 / 0.014526 (0.046921) \| 0.072162 / 0.176557 (-0.104394) \| 0.118965 / 0.737135 (-0.618170) \| 0.073756 / 0.296338 (-0.222583) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285361 / 0.215209 (0.070152) \| 2.776928 / 2.077655 (0.699273) \| 1.506859 / 1.504120 (0.002739) \| 1.379119 / 1.541195 (-0.162075) \| 1.401798 / 1.468490 (-0.066692) \| 0.572512 / 4.584777 (-4.012265) \| 2.403793 / 3.745712 (-1.341919) \| 2.740496 / 5.269862 (-2.529366) \| 1.714611 / 4.565676 (-2.851065) \| 0.063496 / 0.424275 (-0.360780) \| 0.005009 / 0.007607 (-0.002598) \| 0.342438 / 0.226044 (0.116393) \| 3.368129 / 2.268929 (1.099200) \| 1.831200 / 55.444624 (-53.613424) \| 1.553611 / 6.876477 (-5.322866) \| 1.578116 / 2.142072 (-0.563956) \| 0.653034 / 4.805227 (-4.152193) \| 0.117724 / 6.500664 (-6.382940) \| 0.041188 / 0.075469 (-0.034282) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972520 / 1.841788 (-0.869268) \| 11.186297 / 8.074308 (3.111989) \| 9.485829 / 10.191392 (-0.705563) \| 0.139715 / 0.680424 (-0.540708) \| 0.013705 / 0.534201 (-0.520496) \| 0.287384 / 0.579283 (-0.291899) \| 0.266784 / 0.434364 (-0.167580) \| 0.320789 / 0.540337 (-0.219548) \| 0.417484 / 1.386936 (-0.969452) \|\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) \| 0.005570 / 0.011353 (-0.005783) \| 0.003416 / 0.011008 (-0.007592) \| 0.051160 / 0.038508 (0.012652) \| 0.031082 / 0.023109 (0.007973) \| 0.279336 / 0.275898 (0.003438) \| 0.300529 / 0.323480 (-0.022951) \| 0.004320 / 0.007986 (-0.003666) \| 0.002781 / 0.004328 (-0.001548) \| 0.049642 / 0.004250 (0.045391) \| 0.044379 / 0.037052 (0.007327) \| 0.293797 / 0.258489 (0.035308) \| 0.317844 / 0.293841 (0.024003) \| 0.049697 / 0.128546 (-0.078849) \| 0.010624 / 0.075646 (-0.065023) \| 0.058834 / 0.419271 (-0.360437) \| 0.033869 / 0.043533 (-0.009664) \| 0.280547 / 0.255139 (0.025408) \| 0.300685 / 0.283200 (0.017486) \| 0.017010 / 0.141683 (-0.124673) \| 1.172277 / 1.452155 (-0.279878) \| 1.205359 / 1.492716 (-0.287358) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092914 / 0.018006 (0.074907) \| 0.303561 / 0.000490 (0.303071) \| 0.000219 / 0.000200 (0.000019) \| 0.000054 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022379 / 0.037411 (-0.015032) \| 0.075460 / 0.014526 (0.060934) \| 0.085795 / 0.176557 (-0.090762) \| 0.124776 / 0.737135 (-0.612360) \| 0.088260 / 0.296338 (-0.208079) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302873 / 0.215209 (0.087664) \| 2.936173 / 2.077655 (0.858519) \| 1.589251 / 1.504120 (0.085131) \| 1.477552 / 1.541195 (-0.063643) \| 1.479322 / 1.468490 (0.010832) \| 0.570481 / 4.584777 (-4.014296) \| 2.434137 / 3.745712 (-1.311575) \| 2.774012 / 5.269862 (-2.495849) \| 1.718103 / 4.565676 (-2.847574) \| 0.061951 / 0.424275 (-0.362324) \| 0.004992 / 0.007607 (-0.002615) \| 0.352250 / 0.226044 (0.126205) \| 3.457417 / 2.268929 (1.188488) \| 1.934587 / 55.444624 (-53.510037) \| 1.646904 / 6.876477 (-5.229573) \| 1.669429 / 2.142072 (-0.472643) \| 0.649665 / 4.805227 (-4.155562) \| 0.116630 / 6.500664 (-6.384034) \| 0.040669 / 0.075469 (-0.034800) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.011488 / 1.841788 (-0.830300) \| 11.866394 / 8.074308 (3.792086) \| 10.144588 / 10.191392 (-0.046804) \| 0.129931 / 0.680424 (-0.550493) \| 0.014885 / 0.534201 (-0.519316) \| 0.287463 / 0.579283 (-0.291821) \| 0.280754 / 0.434364 (-0.153610) \| 0.330139 / 0.540337 (-0.210199) \| 0.414653 / 1.386936 (-0.972283) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#585275b8deaebd1bdcbd3725fa63172395791c73 \"CML watermark\")\n" ]
2,123,397,569	6,647	Update loading.mdx to include "jsonl" file loading.	open	2024-02-07T16:18:08	2024-02-08T15:34:17	null	https://github.com/huggingface/datasets/pull/6647	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6647", "html_url": "https://github.com/huggingface/datasets/pull/6647", "diff_url": "https://github.com/huggingface/datasets/pull/6647.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6647.patch", "merged_at": null }	mosheber	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6647). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> Thanks for adding the explicit loading command.\r\n> \r\n> However, I would move it just below, where we present the JSON-Lines example.\r\n> \r\n> * Maybe adding that this format is called JSON-Lines\r\n> * Add the example after the JSON-Lines data example\r\n> \r\n> https://github.com/huggingface/datasets/blob/14d9afbb7ae1b787c450261ca0ff374551993031/docs/source/loading.mdx#L135-L138\r\n\r\nThank you @albertvillanova for the feedback! I moved the jsonl file loading example to a more appropriate location. " ]
2,123,134,128	6,646	Better multi-gpu example	closed	2024-02-07T14:15:01	2024-02-09T17:43:32	2024-02-07T14:59:11	https://github.com/huggingface/datasets/pull/6646	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6646", "html_url": "https://github.com/huggingface/datasets/pull/6646", "diff_url": "https://github.com/huggingface/datasets/pull/6646.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6646.patch", "merged_at": "2024-02-07T14:59:11" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6646). 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) \| 0.005598 / 0.011353 (-0.005755) \| 0.003640 / 0.011008 (-0.007369) \| 0.064557 / 0.038508 (0.026049) \| 0.029645 / 0.023109 (0.006536) \| 0.243695 / 0.275898 (-0.032203) \| 0.261252 / 0.323480 (-0.062228) \| 0.004067 / 0.007986 (-0.003919) \| 0.002883 / 0.004328 (-0.001446) \| 0.049192 / 0.004250 (0.044942) \| 0.045299 / 0.037052 (0.008246) \| 0.273207 / 0.258489 (0.014718) \| 0.288668 / 0.293841 (-0.005173) \| 0.028114 / 0.128546 (-0.100432) \| 0.010597 / 0.075646 (-0.065049) \| 0.215345 / 0.419271 (-0.203927) \| 0.036119 / 0.043533 (-0.007414) \| 0.243718 / 0.255139 (-0.011421) \| 0.266657 / 0.283200 (-0.016543) \| 0.018176 / 0.141683 (-0.123507) \| 1.127926 / 1.452155 (-0.324229) \| 1.168066 / 1.492716 (-0.324650) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096001 / 0.018006 (0.077994) \| 0.304317 / 0.000490 (0.303828) \| 0.000209 / 0.000200 (0.000009) \| 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) \| 0.018241 / 0.037411 (-0.019170) \| 0.061505 / 0.014526 (0.046979) \| 0.072456 / 0.176557 (-0.104101) \| 0.118315 / 0.737135 (-0.618821) \| 0.075154 / 0.296338 (-0.221184) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278748 / 0.215209 (0.063538) \| 2.729923 / 2.077655 (0.652268) \| 1.416835 / 1.504120 (-0.087285) \| 1.294016 / 1.541195 (-0.247179) \| 1.323249 / 1.468490 (-0.145241) \| 0.575389 / 4.584777 (-4.009388) \| 2.404923 / 3.745712 (-1.340789) \| 2.769233 / 5.269862 (-2.500629) \| 1.742340 / 4.565676 (-2.823336) \| 0.062664 / 0.424275 (-0.361611) \| 0.004951 / 0.007607 (-0.002656) \| 0.335024 / 0.226044 (0.108979) \| 3.291446 / 2.268929 (1.022518) \| 1.797095 / 55.444624 (-53.647530) \| 1.532963 / 6.876477 (-5.343513) \| 1.529315 / 2.142072 (-0.612758) \| 0.654922 / 4.805227 (-4.150305) \| 0.118772 / 6.500664 (-6.381892) \| 0.042034 / 0.075469 (-0.033435) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983646 / 1.841788 (-0.858141) \| 11.518625 / 8.074308 (3.444317) \| 9.538781 / 10.191392 (-0.652611) \| 0.140300 / 0.680424 (-0.540124) \| 0.013966 / 0.534201 (-0.520235) \| 0.287071 / 0.579283 (-0.292212) \| 0.270201 / 0.434364 (-0.164163) \| 0.323294 / 0.540337 (-0.217044) \| 0.418130 / 1.386936 (-0.968806) \|\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) \| 0.005508 / 0.011353 (-0.005844) \| 0.003714 / 0.011008 (-0.007294) \| 0.050031 / 0.038508 (0.011523) \| 0.031866 / 0.023109 (0.008756) \| 0.272248 / 0.275898 (-0.003650) \| 0.295105 / 0.323480 (-0.028375) \| 0.005179 / 0.007986 (-0.002807) \| 0.002820 / 0.004328 (-0.001508) \| 0.048896 / 0.004250 (0.044646) \| 0.045975 / 0.037052 (0.008922) \| 0.287662 / 0.258489 (0.029173) \| 0.321139 / 0.293841 (0.027298) \| 0.049242 / 0.128546 (-0.079304) \| 0.010732 / 0.075646 (-0.064914) \| 0.057943 / 0.419271 (-0.361328) \| 0.033527 / 0.043533 (-0.010006) \| 0.271746 / 0.255139 (0.016607) \| 0.291404 / 0.283200 (0.008204) \| 0.019351 / 0.141683 (-0.122332) \| 1.157221 / 1.452155 (-0.294934) \| 1.215757 / 1.492716 (-0.276959) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096950 / 0.018006 (0.078944) \| 0.312002 / 0.000490 (0.311512) \| 0.000223 / 0.000200 (0.000023) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022288 / 0.037411 (-0.015123) \| 0.075282 / 0.014526 (0.060756) \| 0.087445 / 0.176557 (-0.089112) \| 0.125617 / 0.737135 (-0.611519) \| 0.088878 / 0.296338 (-0.207460) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291961 / 0.215209 (0.076752) \| 2.881445 / 2.077655 (0.803790) \| 1.586128 / 1.504120 (0.082008) \| 1.458636 / 1.541195 (-0.082558) \| 1.487001 / 1.468490 (0.018511) \| 0.575466 / 4.584777 (-4.009311) \| 2.454941 / 3.745712 (-1.290771) \| 2.878077 / 5.269862 (-2.391785) \| 1.787215 / 4.565676 (-2.778462) \| 0.064010 / 0.424275 (-0.360265) \| 0.005092 / 0.007607 (-0.002516) \| 0.360500 / 0.226044 (0.134455) \| 3.465574 / 2.268929 (1.196646) \| 1.957516 / 55.444624 (-53.487108) \| 1.666282 / 6.876477 (-5.210195) \| 1.690070 / 2.142072 (-0.452002) \| 0.661323 / 4.805227 (-4.143905) \| 0.117824 / 6.500664 (-6.382840) \| 0.042286 / 0.075469 (-0.033183) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.026517 / 1.841788 (-0.815270) \| 12.083347 / 8.074308 (4.009039) \| 10.269319 / 10.191392 (0.077927) \| 0.139253 / 0.680424 (-0.541171) \| 0.016258 / 0.534201 (-0.517943) \| 0.290583 / 0.579283 (-0.288700) \| 0.284338 / 0.434364 (-0.150026) \| 0.335865 / 0.540337 (-0.204473) \| 0.416600 / 1.386936 (-0.970336) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ba3cfad91e9366cda0ba203700fc745d8bcd1f17 \"CML watermark\")\n", "Thanks, I was needing this example today <3 " ]
2,122,956,818	6,645	Support fsspec 2024.2	closed	2024-02-07T12:45:29	2024-02-29T15:12:19	2024-02-29T15:12:19	https://github.com/huggingface/datasets/issues/6645	null	albertvillanova	false	[ "I'd be very grateful. This upper bound banished me straight into dependency hell today. :(" ]
2,122,955,282	6,644	Support fsspec 2023.12	closed	2024-02-07T12:44:39	2024-02-29T15:12:18	2024-02-29T15:12:18	https://github.com/huggingface/datasets/issues/6644	null	albertvillanova	false	[ "The pinned fsspec version range dependency conflict has been affecting several of our users in https://github.com/iterative/dvc. I've opened an initial PR that I think should resolve the glob behavior changes with using datasets + the latest fsspec release.\r\n\r\nPlease let us know if there's any other fsspec related behavior in datasets that needs to be updated to get 2024.2 supported, we'd like to get this conflict resolved as quickly as possible and we're willing to contribute any additional work that's required here.\r\n\r\ncc @dberenbaum" ]
2,121,239,039	6,643	Faiss GPU index cannot be serialised when passed to trainer	open	2024-02-06T16:41:00	2024-02-15T10:29:32	null	https://github.com/huggingface/datasets/issues/6643	null	rubenweitzman	false	[ "Hi ! make sure your query embeddings are numpy arrays, not torch tensors ;)", "Hi Quentin, not sure how that solves the problem number 1. I am trying to pass on a dataset with a faiss gpu for training to the standard trainer but getting this serialisation error. What is a workaround this? I do not want to remove the faiss index, as I would want to use it to create batches of retrieved samples from the dataset. \r\nThanks in advance for your help!", "Issue number one seems to be an issue with FAISS indexes not being compatible with copy.deepcopy.\r\n\r\nMaybe you try to not remove the columns, e.g. by passing `remove_unused_columns=False`" ]
2,119,085,766	6,642	Differently dataset object saved than it is loaded.	closed	2024-02-05T17:28:57	2024-02-06T09:50:19	2024-02-06T09:50:19	https://github.com/huggingface/datasets/issues/6642	null	MFajcik	false	[ "I see now, that I have to use `load_from_disk`, in order to load dataset properly, not `load_dataset`. Why is this behavior split? Why do we need both, `load_dataset` and `load_from_disk`?\r\n\r\nUnless answered, I believe this might be helpful for other hf datasets newbies.\r\n\r\nAnyway, made a `load_dataset` compatible dataset in a following way. I created a directory, and just copied jsonl there as `train.jsonl/test.jsonl`.\r\n```python\r\noutput_folder = os.path.join(args.output_folder, f\"{task_meta_type}_{task_type}\")\r\nos.makedirs(output_folder, exist_ok=True)\r\nfile = f\"{task_meta_type}_{task_type}_train.jsonl\"\r\nshutil.copy(os.path.join(input_folder, file),\r\n os.path.join(output_folder, \"train.jsonl\"))\r\n# now test\r\nfile = f\"{task_meta_type}_{task_type}_test.jsonl\"\r\nshutil.copy(os.path.join(input_folder, file),\r\n os.path.join(output_folder, \"test.jsonl\"))\r\n```\r\n", "Hi @MFajcik, \r\n\r\nYou can find information about save_to_disk/load_from_disk in our docs:\r\n- https://huggingface.co/docs/datasets/v2.16.1/en/process#save\r\n- https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/main_classes#datasets.Dataset.save_to_disk\r\n- https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/main_classes#datasets.Dataset.load_from_disk" ]
2,116,963,132	6,641	unicodedecodeerror: 'utf-8' codec can't decode byte 0xac in position 25: invalid start byte	closed	2024-02-04T08:49:31	2024-02-06T09:26:07	2024-02-06T09:11:45	https://github.com/huggingface/datasets/issues/6641	null	Hughhuh	false	[ "Hi @Hughhuh. \r\n\r\nI have formatted the issue because it was not easily readable. Additionally, the environment info is incomplete: it seems you did not run the proposed CLI command `datasets-cli env` and essential information is missing: version of `datasets`, version of `pyarrow`,...\r\n\r\nWith the information you provided, it seems an issue with the specific \"samsum\" dataset. I'm transferring the issue to the corresponding dataset page: https://huggingface.co/datasets/samsum/discussions/5" ]
2,115,864,531	6,640	Sign Language Support	open	2024-02-02T21:54:51	2024-02-02T21:54:51	null	https://github.com/huggingface/datasets/issues/6640	null	Merterm	false	[]
2,114,620,200	6,639	Run download_and_prepare if missing splits	open	2024-02-02T10:36:49	2024-02-06T16:54:22	null	https://github.com/huggingface/datasets/pull/6639	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6639", "html_url": "https://github.com/huggingface/datasets/pull/6639", "diff_url": "https://github.com/huggingface/datasets/pull/6639.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6639.patch", "merged_at": null }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6639). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]
2,113,329,257	6,638	Cannot download wmt16 dataset	closed	2024-02-01T19:41:42	2024-02-01T20:07:29	2024-02-01T20:07:29	https://github.com/huggingface/datasets/issues/6638	null	vidyasiv	false	[ "Looks like it works with latest datasets repository\r\n```\r\n- `datasets` version: 2.16.2.dev0\r\n- Platform: Linux-5.15.0-92-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- `huggingface_hub` version: 0.20.3\r\n- PyArrow version: 15.0.0\r\n- Pandas version: 2.0.1\r\n- `fsspec` version: 2023.10.0\r\n```\r\n\r\nCould you explain which is the minimum version that fixes this?\r\nEdit: Looks like that's 2.16.0, will close out issue" ]
2,113,025,975	6,637	'with_format' is extremely slow when used together with 'interleave_datasets' or 'shuffle' on IterableDatasets	open	2024-02-01T17:16:54	2024-02-05T10:43:47	null	https://github.com/huggingface/datasets/issues/6637	null	tobycrisford	false	[ "The \"torch\" formatting is usually fast because we do zero-copy conversion from the Arrow data on your disk to Torch tensors. However IterableDataset shuffling seems to do data copies that slow down the pipeline, and it shuffles python objects instead of Arrow data.\r\n\r\nTo fix this we need to implement `BufferShuffledExamplesIterable.iter_arrow()` (same as regular `BufferShuffledExamplesIterable.__iter__()` but yields Arrow tables)\r\n\r\nhttps://github.com/huggingface/datasets/blob/b7d854b7fd3e9a330e21b76ee8421d4a7ebb4a7a/src/datasets/iterable_dataset.py#L968-L974\r\n" ]
2,110,781,097	6,636	Faster column validation and reordering	closed	2024-01-31T19:08:28	2024-02-07T19:39:00	2024-02-06T23:03:38	https://github.com/huggingface/datasets/pull/6636	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6636", "html_url": "https://github.com/huggingface/datasets/pull/6636", "diff_url": "https://github.com/huggingface/datasets/pull/6636.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6636.patch", "merged_at": "2024-02-06T23:03:38" }	psmyth94	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6636). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Thanks @mariosasko, I made the changes. However, I did some tests with `map` and I still saw that it took ~3.5 minutes per batch on 6000 features when using `dataset.map(lambda x: x, batched=True)`. From the profile, the culprits were mainly with `ArrowWriter.write_batch` and `ArrowWriter._build_writer`. The slow down from `_build_writer` is due to updating existing features with the inferred ones. I don't think this can be optimized any further, but fortunately, I can avoid this by setting the `features` in `map`. On the other hand, `write_batch` selects cols based on intersection and difference between schema names and example keys using two for loops. The same exists in `ArrowWriter.write_examples_on_file`. Optimizing the column selection using set operations effectively brings it from 3.5 minutes per batch down to 6 seconds per batch. Can we add these changes along with this PR?\r\n\r\nEdit: Ah just realized you can avoid the issue with inferring features altogether when you set the format to arrow (or pandas).", "<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) \| 0.004990 / 0.011353 (-0.006363) \| 0.003138 / 0.011008 (-0.007870) \| 0.062368 / 0.038508 (0.023860) \| 0.028634 / 0.023109 (0.005524) \| 0.241297 / 0.275898 (-0.034601) \| 0.264433 / 0.323480 (-0.059047) \| 0.003133 / 0.007986 (-0.004852) \| 0.003444 / 0.004328 (-0.000885) \| 0.048522 / 0.004250 (0.044271) \| 0.043700 / 0.037052 (0.006648) \| 0.257054 / 0.258489 (-0.001435) \| 0.277551 / 0.293841 (-0.016290) \| 0.027132 / 0.128546 (-0.101414) \| 0.010395 / 0.075646 (-0.065251) \| 0.208003 / 0.419271 (-0.211269) \| 0.035814 / 0.043533 (-0.007719) \| 0.250098 / 0.255139 (-0.005041) \| 0.266726 / 0.283200 (-0.016474) \| 0.018424 / 0.141683 (-0.123259) \| 1.129242 / 1.452155 (-0.322912) \| 1.167674 / 1.492716 (-0.325042) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091808 / 0.018006 (0.073802) \| 0.298726 / 0.000490 (0.298236) \| 0.000219 / 0.000200 (0.000019) \| 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) \| 0.019119 / 0.037411 (-0.018292) \| 0.061969 / 0.014526 (0.047443) \| 0.073392 / 0.176557 (-0.103165) \| 0.119460 / 0.737135 (-0.617675) \| 0.074072 / 0.296338 (-0.222266) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281435 / 0.215209 (0.066226) \| 2.702094 / 2.077655 (0.624439) \| 1.411541 / 1.504120 (-0.092579) \| 1.284084 / 1.541195 (-0.257111) \| 1.302638 / 1.468490 (-0.165852) \| 0.562420 / 4.584777 (-4.022357) \| 2.364890 / 3.745712 (-1.380822) \| 2.744033 / 5.269862 (-2.525828) \| 1.699000 / 4.565676 (-2.866677) \| 0.062315 / 0.424275 (-0.361961) \| 0.004982 / 0.007607 (-0.002625) \| 0.334385 / 0.226044 (0.108341) \| 3.203268 / 2.268929 (0.934339) \| 1.766998 / 55.444624 (-53.677627) \| 1.497164 / 6.876477 (-5.379313) \| 1.509996 / 2.142072 (-0.632077) \| 0.633014 / 4.805227 (-4.172213) \| 0.115317 / 6.500664 (-6.385347) \| 0.041120 / 0.075469 (-0.034349) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965877 / 1.841788 (-0.875911) \| 11.219909 / 8.074308 (3.145601) \| 9.333822 / 10.191392 (-0.857570) \| 0.136482 / 0.680424 (-0.543941) \| 0.013632 / 0.534201 (-0.520569) \| 0.287251 / 0.579283 (-0.292032) \| 0.262786 / 0.434364 (-0.171578) \| 0.322893 / 0.540337 (-0.217444) \| 0.418180 / 1.386936 (-0.968756) \|\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) \| 0.005444 / 0.011353 (-0.005909) \| 0.003147 / 0.011008 (-0.007862) \| 0.049242 / 0.038508 (0.010734) \| 0.030944 / 0.023109 (0.007834) \| 0.281901 / 0.275898 (0.006003) \| 0.303820 / 0.323480 (-0.019660) \| 0.004326 / 0.007986 (-0.003659) \| 0.002696 / 0.004328 (-0.001632) \| 0.048306 / 0.004250 (0.044055) \| 0.044145 / 0.037052 (0.007093) \| 0.297253 / 0.258489 (0.038764) \| 0.324062 / 0.293841 (0.030221) \| 0.046724 / 0.128546 (-0.081823) \| 0.010079 / 0.075646 (-0.065567) \| 0.057635 / 0.419271 (-0.361636) \| 0.033621 / 0.043533 (-0.009912) \| 0.282303 / 0.255139 (0.027164) \| 0.300761 / 0.283200 (0.017561) \| 0.017116 / 0.141683 (-0.124567) \| 1.156519 / 1.452155 (-0.295636) \| 1.216087 / 1.492716 (-0.276630) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093011 / 0.018006 (0.075005) \| 0.301310 / 0.000490 (0.300820) \| 0.000223 / 0.000200 (0.000023) \| 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) \| 0.023112 / 0.037411 (-0.014299) \| 0.075192 / 0.014526 (0.060666) \| 0.086213 / 0.176557 (-0.090343) \| 0.125853 / 0.737135 (-0.611282) \| 0.087754 / 0.296338 (-0.208585) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.301095 / 0.215209 (0.085886) \| 2.911769 / 2.077655 (0.834114) \| 1.614708 / 1.504120 (0.110588) \| 1.494497 / 1.541195 (-0.046698) \| 1.506978 / 1.468490 (0.038488) \| 0.572743 / 4.584777 (-4.012034) \| 2.417142 / 3.745712 (-1.328570) \| 2.755338 / 5.269862 (-2.514523) \| 1.711026 / 4.565676 (-2.854650) \| 0.062732 / 0.424275 (-0.361543) \| 0.005031 / 0.007607 (-0.002576) \| 0.352343 / 0.226044 (0.126298) \| 3.465183 / 2.268929 (1.196255) \| 1.958795 / 55.444624 (-53.485829) \| 1.682239 / 6.876477 (-5.194238) \| 1.688897 / 2.142072 (-0.453176) \| 0.643311 / 4.805227 (-4.161916) \| 0.115426 / 6.500664 (-6.385238) \| 0.040338 / 0.075469 (-0.035131) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005322 / 1.841788 (-0.836466) \| 11.779380 / 8.074308 (3.705072) \| 10.041574 / 10.191392 (-0.149818) \| 0.127617 / 0.680424 (-0.552807) \| 0.015840 / 0.534201 (-0.518361) \| 0.286905 / 0.579283 (-0.292378) \| 0.275180 / 0.434364 (-0.159183) \| 0.332498 / 0.540337 (-0.207840) \| 0.410719 / 1.386936 (-0.976217) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#32b206d47f582380f9c64578dcfa6c48252db3b8 \"CML watermark\")\n" ]
2,110,659,519	6,635	Fix missing info when loading some datasets from Parquet export	closed	2024-01-31T17:55:21	2024-02-07T16:48:55	2024-02-07T16:41:04	https://github.com/huggingface/datasets/pull/6635	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6635", "html_url": "https://github.com/huggingface/datasets/pull/6635", "diff_url": "https://github.com/huggingface/datasets/pull/6635.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6635.patch", "merged_at": "2024-02-07T16:41:04" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6635). 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) \| 0.005577 / 0.011353 (-0.005776) \| 0.004452 / 0.011008 (-0.006556) \| 0.067849 / 0.038508 (0.029341) \| 0.032328 / 0.023109 (0.009219) \| 0.256924 / 0.275898 (-0.018974) \| 0.273410 / 0.323480 (-0.050070) \| 0.004359 / 0.007986 (-0.003626) \| 0.003484 / 0.004328 (-0.000845) \| 0.053880 / 0.004250 (0.049630) \| 0.058142 / 0.037052 (0.021089) \| 0.268863 / 0.258489 (0.010374) \| 0.307977 / 0.293841 (0.014136) \| 0.028840 / 0.128546 (-0.099707) \| 0.011808 / 0.075646 (-0.063839) \| 0.216277 / 0.419271 (-0.202995) \| 0.039245 / 0.043533 (-0.004288) \| 0.250420 / 0.255139 (-0.004719) \| 0.273642 / 0.283200 (-0.009557) \| 0.019340 / 0.141683 (-0.122342) \| 1.176734 / 1.452155 (-0.275421) \| 1.250643 / 1.492716 (-0.242074) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.181210 / 0.018006 (0.163204) \| 1.070750 / 0.000490 (1.070261) \| 0.000315 / 0.000200 (0.000115) \| 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) \| 0.022905 / 0.037411 (-0.014507) \| 0.064549 / 0.014526 (0.050023) \| 0.077113 / 0.176557 (-0.099443) \| 0.131976 / 0.737135 (-0.605159) \| 0.081266 / 0.296338 (-0.215072) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291363 / 0.215209 (0.076154) \| 2.851691 / 2.077655 (0.774036) \| 1.592815 / 1.504120 (0.088695) \| 1.494550 / 1.541195 (-0.046645) \| 1.516464 / 1.468490 (0.047974) \| 0.583244 / 4.584777 (-4.001532) \| 2.504907 / 3.745712 (-1.240805) \| 3.183490 / 5.269862 (-2.086371) \| 1.932854 / 4.565676 (-2.632823) \| 0.067564 / 0.424275 (-0.356711) \| 0.006587 / 0.007607 (-0.001020) \| 0.346368 / 0.226044 (0.120324) \| 3.428256 / 2.268929 (1.159327) \| 1.994176 / 55.444624 (-53.450448) \| 1.688116 / 6.876477 (-5.188360) \| 1.767653 / 2.142072 (-0.374420) \| 0.673867 / 4.805227 (-4.131360) \| 0.125582 / 6.500664 (-6.375082) \| 0.047198 / 0.075469 (-0.028271) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.002895 / 1.841788 (-0.838893) \| 16.332893 / 8.074308 (8.258585) \| 10.781993 / 10.191392 (0.590601) \| 0.153919 / 0.680424 (-0.526505) \| 0.015528 / 0.534201 (-0.518673) \| 0.306182 / 0.579283 (-0.273101) \| 0.296380 / 0.434364 (-0.137984) \| 0.341432 / 0.540337 (-0.198905) \| 0.455900 / 1.386936 (-0.931036) \|\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) \| 0.006442 / 0.011353 (-0.004911) \| 0.004433 / 0.011008 (-0.006576) \| 0.053327 / 0.038508 (0.014819) \| 0.035966 / 0.023109 (0.012856) \| 0.280913 / 0.275898 (0.005015) \| 0.308419 / 0.323480 (-0.015061) \| 0.005842 / 0.007986 (-0.002144) \| 0.003789 / 0.004328 (-0.000539) \| 0.053983 / 0.004250 (0.049732) \| 0.069052 / 0.037052 (0.032000) \| 0.299225 / 0.258489 (0.040736) \| 0.336470 / 0.293841 (0.042629) \| 0.068170 / 0.128546 (-0.060377) \| 0.012259 / 0.075646 (-0.063388) \| 0.064166 / 0.419271 (-0.355106) \| 0.037291 / 0.043533 (-0.006241) \| 0.281318 / 0.255139 (0.026179) \| 0.297093 / 0.283200 (0.013893) \| 0.021358 / 0.141683 (-0.120324) \| 1.189584 / 1.452155 (-0.262571) \| 1.256985 / 1.492716 (-0.235731) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216726 / 0.018006 (0.198720) \| 2.496957 / 0.000490 (2.496467) \| 0.000336 / 0.000200 (0.000136) \| 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) \| 0.026604 / 0.037411 (-0.010807) \| 0.080398 / 0.014526 (0.065873) \| 0.094475 / 0.176557 (-0.082082) \| 0.136263 / 0.737135 (-0.600873) \| 0.097898 / 0.296338 (-0.198440) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295171 / 0.215209 (0.079962) \| 2.947530 / 2.077655 (0.869875) \| 1.607531 / 1.504120 (0.103411) \| 1.485045 / 1.541195 (-0.056150) \| 1.524899 / 1.468490 (0.056409) \| 0.572934 / 4.584777 (-4.011843) \| 2.544320 / 3.745712 (-1.201393) \| 3.292630 / 5.269862 (-1.977232) \| 1.927138 / 4.565676 (-2.638539) \| 0.068560 / 0.424275 (-0.355715) \| 0.005982 / 0.007607 (-0.001625) \| 0.345833 / 0.226044 (0.119789) \| 3.424253 / 2.268929 (1.155324) \| 2.195017 / 55.444624 (-53.249608) \| 1.712037 / 6.876477 (-5.164440) \| 1.763899 / 2.142072 (-0.378174) \| 0.653776 / 4.805227 (-4.151451) \| 0.123056 / 6.500664 (-6.377609) \| 0.044572 / 0.075469 (-0.030897) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.033400 / 1.841788 (-0.808388) \| 15.409887 / 8.074308 (7.335579) \| 11.220990 / 10.191392 (1.029597) \| 0.153603 / 0.680424 (-0.526821) \| 0.016866 / 0.534201 (-0.517335) \| 0.311945 / 0.579283 (-0.267338) \| 0.307048 / 0.434364 (-0.127316) \| 0.350422 / 0.540337 (-0.189915) \| 0.447308 / 1.386936 (-0.939628) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#14d9afbb7ae1b787c450261ca0ff374551993031 \"CML watermark\")\n" ]
2,110,242,376	6,634	Support data_dir parameter in push_to_hub	closed	2024-01-31T14:37:36	2024-02-05T10:32:49	2024-02-05T10:26:40	https://github.com/huggingface/datasets/pull/6634	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6634", "html_url": "https://github.com/huggingface/datasets/pull/6634", "diff_url": "https://github.com/huggingface/datasets/pull/6634.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6634.patch", "merged_at": "2024-02-05T10:26:40" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6634). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@huggingface/datasets, feel free to review this PR so that it can be included in the next release.", "<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) \| 0.005125 / 0.011353 (-0.006228) \| 0.003772 / 0.011008 (-0.007236) \| 0.063258 / 0.038508 (0.024750) \| 0.029479 / 0.023109 (0.006370) \| 0.245554 / 0.275898 (-0.030344) \| 0.266395 / 0.323480 (-0.057085) \| 0.003063 / 0.007986 (-0.004922) \| 0.003298 / 0.004328 (-0.001031) \| 0.049242 / 0.004250 (0.044991) \| 0.042390 / 0.037052 (0.005338) \| 0.258176 / 0.258489 (-0.000313) \| 0.279935 / 0.293841 (-0.013906) \| 0.027910 / 0.128546 (-0.100637) \| 0.011033 / 0.075646 (-0.064613) \| 0.207763 / 0.419271 (-0.211509) \| 0.036127 / 0.043533 (-0.007405) \| 0.247363 / 0.255139 (-0.007776) \| 0.261309 / 0.283200 (-0.021890) \| 0.020259 / 0.141683 (-0.121424) \| 1.152760 / 1.452155 (-0.299395) \| 1.194853 / 1.492716 (-0.297863) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088936 / 0.018006 (0.070930) \| 0.298396 / 0.000490 (0.297906) \| 0.000211 / 0.000200 (0.000011) \| 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) \| 0.018434 / 0.037411 (-0.018977) \| 0.061991 / 0.014526 (0.047466) \| 0.072786 / 0.176557 (-0.103771) \| 0.120437 / 0.737135 (-0.616698) \| 0.078375 / 0.296338 (-0.217964) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275821 / 0.215209 (0.060612) \| 2.703358 / 2.077655 (0.625703) \| 1.446783 / 1.504120 (-0.057337) \| 1.333556 / 1.541195 (-0.207639) \| 1.325753 / 1.468490 (-0.142737) \| 0.565196 / 4.584777 (-4.019581) \| 2.411193 / 3.745712 (-1.334520) \| 2.702764 / 5.269862 (-2.567098) \| 1.727425 / 4.565676 (-2.838252) \| 0.062966 / 0.424275 (-0.361309) \| 0.004985 / 0.007607 (-0.002622) \| 0.333473 / 0.226044 (0.107428) \| 3.270615 / 2.268929 (1.001687) \| 1.822213 / 55.444624 (-53.622411) \| 1.546572 / 6.876477 (-5.329905) \| 1.568767 / 2.142072 (-0.573305) \| 0.655907 / 4.805227 (-4.149321) \| 0.117173 / 6.500664 (-6.383491) \| 0.042415 / 0.075469 (-0.033054) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.987966 / 1.841788 (-0.853822) \| 11.851206 / 8.074308 (3.776898) \| 10.327751 / 10.191392 (0.136359) \| 0.127929 / 0.680424 (-0.552494) \| 0.013781 / 0.534201 (-0.520420) \| 0.286910 / 0.579283 (-0.292373) \| 0.273615 / 0.434364 (-0.160749) \| 0.323373 / 0.540337 (-0.216965) \| 0.426407 / 1.386936 (-0.960529) \|\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) \| 0.005412 / 0.011353 (-0.005941) \| 0.003619 / 0.011008 (-0.007389) \| 0.049603 / 0.038508 (0.011095) \| 0.031246 / 0.023109 (0.008136) \| 0.279723 / 0.275898 (0.003825) \| 0.298557 / 0.323480 (-0.024923) \| 0.004253 / 0.007986 (-0.003733) \| 0.002758 / 0.004328 (-0.001570) \| 0.048931 / 0.004250 (0.044680) \| 0.044245 / 0.037052 (0.007193) \| 0.295876 / 0.258489 (0.037387) \| 0.322720 / 0.293841 (0.028879) \| 0.046746 / 0.128546 (-0.081800) \| 0.010841 / 0.075646 (-0.064805) \| 0.058528 / 0.419271 (-0.360744) \| 0.034224 / 0.043533 (-0.009308) \| 0.279192 / 0.255139 (0.024053) \| 0.299775 / 0.283200 (0.016576) \| 0.017862 / 0.141683 (-0.123820) \| 1.154478 / 1.452155 (-0.297677) \| 1.190483 / 1.492716 (-0.302234) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088717 / 0.018006 (0.070710) \| 0.297905 / 0.000490 (0.297415) \| 0.000209 / 0.000200 (0.000009) \| 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) \| 0.021458 / 0.037411 (-0.015953) \| 0.075616 / 0.014526 (0.061090) \| 0.087080 / 0.176557 (-0.089476) \| 0.125315 / 0.737135 (-0.611821) \| 0.088958 / 0.296338 (-0.207381) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287085 / 0.215209 (0.071876) \| 2.807798 / 2.077655 (0.730143) \| 1.552201 / 1.504120 (0.048081) \| 1.422374 / 1.541195 (-0.118820) \| 1.437908 / 1.468490 (-0.030582) \| 0.569738 / 4.584777 (-4.015039) \| 2.493921 / 3.745712 (-1.251791) \| 2.648376 / 5.269862 (-2.621486) \| 1.741721 / 4.565676 (-2.823955) \| 0.063023 / 0.424275 (-0.361253) \| 0.005166 / 0.007607 (-0.002441) \| 0.336927 / 0.226044 (0.110882) \| 3.384517 / 2.268929 (1.115588) \| 1.909888 / 55.444624 (-53.534736) \| 1.641879 / 6.876477 (-5.234597) \| 1.727734 / 2.142072 (-0.414338) \| 0.647127 / 4.805227 (-4.158100) \| 0.115831 / 6.500664 (-6.384833) \| 0.041161 / 0.075469 (-0.034309) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.016310 / 1.841788 (-0.825477) \| 12.088500 / 8.074308 (4.014192) \| 10.799730 / 10.191392 (0.608338) \| 0.129049 / 0.680424 (-0.551375) \| 0.015379 / 0.534201 (-0.518822) \| 0.291352 / 0.579283 (-0.287931) \| 0.284579 / 0.434364 (-0.149785) \| 0.331214 / 0.540337 (-0.209124) \| 0.422902 / 1.386936 (-0.964034) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#991169ed4901d129d0e0ab8d7fccd6a0728da4b8 \"CML watermark\")\n" ]
2,110,124,475	6,633	dataset viewer requires no-script	closed	2024-01-31T13:41:54	2024-01-31T14:05:04	2024-01-31T13:59:01	https://github.com/huggingface/datasets/pull/6633	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6633", "html_url": "https://github.com/huggingface/datasets/pull/6633", "diff_url": "https://github.com/huggingface/datasets/pull/6633.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6633.patch", "merged_at": "2024-01-31T13:59:01" }	severo	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6633). 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) \| 0.005172 / 0.011353 (-0.006181) \| 0.003694 / 0.011008 (-0.007314) \| 0.063098 / 0.038508 (0.024590) \| 0.028161 / 0.023109 (0.005052) \| 0.262288 / 0.275898 (-0.013610) \| 0.281867 / 0.323480 (-0.041613) \| 0.004088 / 0.007986 (-0.003898) \| 0.002745 / 0.004328 (-0.001583) \| 0.049071 / 0.004250 (0.044820) \| 0.040629 / 0.037052 (0.003577) \| 0.282766 / 0.258489 (0.024277) \| 0.297998 / 0.293841 (0.004157) \| 0.028057 / 0.128546 (-0.100489) \| 0.010878 / 0.075646 (-0.064768) \| 0.207410 / 0.419271 (-0.211861) \| 0.035600 / 0.043533 (-0.007933) \| 0.260157 / 0.255139 (0.005018) \| 0.273252 / 0.283200 (-0.009948) \| 0.017403 / 0.141683 (-0.124280) \| 1.150798 / 1.452155 (-0.301356) \| 1.200485 / 1.492716 (-0.292231) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093783 / 0.018006 (0.075777) \| 0.302112 / 0.000490 (0.301622) \| 0.000225 / 0.000200 (0.000025) \| 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) \| 0.018254 / 0.037411 (-0.019158) \| 0.061083 / 0.014526 (0.046557) \| 0.074899 / 0.176557 (-0.101657) \| 0.119616 / 0.737135 (-0.617520) \| 0.075269 / 0.296338 (-0.221069) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275878 / 0.215209 (0.060669) \| 2.694778 / 2.077655 (0.617123) \| 1.423810 / 1.504120 (-0.080310) \| 1.309444 / 1.541195 (-0.231750) \| 1.327898 / 1.468490 (-0.140592) \| 0.568621 / 4.584777 (-4.016155) \| 2.345849 / 3.745712 (-1.399863) \| 2.901281 / 5.269862 (-2.368580) \| 1.777959 / 4.565676 (-2.787717) \| 0.063539 / 0.424275 (-0.360736) \| 0.005011 / 0.007607 (-0.002596) \| 0.331212 / 0.226044 (0.105168) \| 3.200379 / 2.268929 (0.931451) \| 1.780766 / 55.444624 (-53.663859) \| 1.517178 / 6.876477 (-5.359299) \| 1.587307 / 2.142072 (-0.554765) \| 0.651939 / 4.805227 (-4.153288) \| 0.116646 / 6.500664 (-6.384018) \| 0.043325 / 0.075469 (-0.032144) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.996894 / 1.841788 (-0.844894) \| 11.495397 / 8.074308 (3.421089) \| 10.255784 / 10.191392 (0.064392) \| 0.129006 / 0.680424 (-0.551418) \| 0.013967 / 0.534201 (-0.520234) \| 0.284847 / 0.579283 (-0.294436) \| 0.265610 / 0.434364 (-0.168754) \| 0.320176 / 0.540337 (-0.220162) \| 0.429526 / 1.386936 (-0.957410) \|\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) \| 0.005582 / 0.011353 (-0.005771) \| 0.003867 / 0.011008 (-0.007142) \| 0.050416 / 0.038508 (0.011908) \| 0.030996 / 0.023109 (0.007887) \| 0.275987 / 0.275898 (0.000089) \| 0.289487 / 0.323480 (-0.033993) \| 0.005149 / 0.007986 (-0.002837) \| 0.002806 / 0.004328 (-0.001522) \| 0.049617 / 0.004250 (0.045366) \| 0.046949 / 0.037052 (0.009897) \| 0.281596 / 0.258489 (0.023107) \| 0.330948 / 0.293841 (0.037108) \| 0.049645 / 0.128546 (-0.078901) \| 0.010953 / 0.075646 (-0.064693) \| 0.058546 / 0.419271 (-0.360725) \| 0.034010 / 0.043533 (-0.009523) \| 0.270525 / 0.255139 (0.015386) \| 0.289749 / 0.283200 (0.006550) \| 0.018755 / 0.141683 (-0.122927) \| 1.163072 / 1.452155 (-0.289082) \| 1.213400 / 1.492716 (-0.279316) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092397 / 0.018006 (0.074390) \| 0.299376 / 0.000490 (0.298886) \| 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) \| 0.022496 / 0.037411 (-0.014916) \| 0.076886 / 0.014526 (0.062361) \| 0.087186 / 0.176557 (-0.089371) \| 0.126092 / 0.737135 (-0.611044) \| 0.088832 / 0.296338 (-0.207507) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288885 / 0.215209 (0.073676) \| 2.839851 / 2.077655 (0.762196) \| 1.587556 / 1.504120 (0.083436) \| 1.470249 / 1.541195 (-0.070945) \| 1.518080 / 1.468490 (0.049590) \| 0.569646 / 4.584777 (-4.015131) \| 2.417574 / 3.745712 (-1.328138) \| 2.737368 / 5.269862 (-2.532494) \| 1.784419 / 4.565676 (-2.781257) \| 0.064104 / 0.424275 (-0.360171) \| 0.005138 / 0.007607 (-0.002469) \| 0.346214 / 0.226044 (0.120169) \| 3.439541 / 2.268929 (1.170612) \| 1.944792 / 55.444624 (-53.499832) \| 1.675762 / 6.876477 (-5.200714) \| 1.851871 / 2.142072 (-0.290201) \| 0.652932 / 4.805227 (-4.152295) \| 0.118953 / 6.500664 (-6.381711) \| 0.041011 / 0.075469 (-0.034459) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.017690 / 1.841788 (-0.824098) \| 12.610531 / 8.074308 (4.536223) \| 11.223165 / 10.191392 (1.031773) \| 0.131637 / 0.680424 (-0.548786) \| 0.016733 / 0.534201 (-0.517468) \| 0.288491 / 0.579283 (-0.290792) \| 0.275899 / 0.434364 (-0.158465) \| 0.331837 / 0.540337 (-0.208500) \| 0.421695 / 1.386936 (-0.965241) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d9dfa9a8c077c783729a279623926faa9e2f3f1 \"CML watermark\")\n" ]
2,108,541,678	6,632	Fix reload cache with data dir	closed	2024-01-30T18:52:23	2024-02-06T17:27:35	2024-02-06T17:21:24	https://github.com/huggingface/datasets/pull/6632	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6632", "html_url": "https://github.com/huggingface/datasets/pull/6632", "diff_url": "https://github.com/huggingface/datasets/pull/6632.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6632.patch", "merged_at": "2024-02-06T17:21:24" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6632). 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) \| 0.004913 / 0.011353 (-0.006440) \| 0.003595 / 0.011008 (-0.007413) \| 0.068385 / 0.038508 (0.029876) \| 0.028612 / 0.023109 (0.005503) \| 0.236590 / 0.275898 (-0.039308) \| 0.261890 / 0.323480 (-0.061590) \| 0.003027 / 0.007986 (-0.004958) \| 0.002674 / 0.004328 (-0.001654) \| 0.049255 / 0.004250 (0.045004) \| 0.040500 / 0.037052 (0.003447) \| 0.248759 / 0.258489 (-0.009730) \| 0.280299 / 0.293841 (-0.013542) \| 0.027300 / 0.128546 (-0.101247) \| 0.010475 / 0.075646 (-0.065171) \| 0.208744 / 0.419271 (-0.210527) \| 0.035214 / 0.043533 (-0.008319) \| 0.251922 / 0.255139 (-0.003217) \| 0.263582 / 0.283200 (-0.019618) \| 0.018738 / 0.141683 (-0.122945) \| 1.150940 / 1.452155 (-0.301215) \| 1.187240 / 1.492716 (-0.305476) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093505 / 0.018006 (0.075499) \| 0.301101 / 0.000490 (0.300611) \| 0.000232 / 0.000200 (0.000032) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.017779 / 0.037411 (-0.019632) \| 0.061412 / 0.014526 (0.046886) \| 0.074353 / 0.176557 (-0.102203) \| 0.118717 / 0.737135 (-0.618418) \| 0.074214 / 0.296338 (-0.222125) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281722 / 0.215209 (0.066513) \| 2.716867 / 2.077655 (0.639212) \| 1.423379 / 1.504120 (-0.080741) \| 1.315379 / 1.541195 (-0.225816) \| 1.294638 / 1.468490 (-0.173852) \| 0.549658 / 4.584777 (-4.035119) \| 2.349889 / 3.745712 (-1.395823) \| 2.722354 / 5.269862 (-2.547507) \| 1.700271 / 4.565676 (-2.865406) \| 0.061099 / 0.424275 (-0.363176) \| 0.004931 / 0.007607 (-0.002677) \| 0.339181 / 0.226044 (0.113136) \| 3.242467 / 2.268929 (0.973538) \| 1.777929 / 55.444624 (-53.666696) \| 1.498380 / 6.876477 (-5.378097) \| 1.511482 / 2.142072 (-0.630590) \| 0.627076 / 4.805227 (-4.178151) \| 0.115936 / 6.500664 (-6.384729) \| 0.041791 / 0.075469 (-0.033678) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983132 / 1.841788 (-0.858656) \| 11.431810 / 8.074308 (3.357502) \| 10.298918 / 10.191392 (0.107526) \| 0.139754 / 0.680424 (-0.540670) \| 0.013984 / 0.534201 (-0.520217) \| 0.283627 / 0.579283 (-0.295656) \| 0.264970 / 0.434364 (-0.169393) \| 0.323896 / 0.540337 (-0.216441) \| 0.420132 / 1.386936 (-0.966804) \|\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) \| 0.005323 / 0.011353 (-0.006030) \| 0.003725 / 0.011008 (-0.007283) \| 0.050191 / 0.038508 (0.011683) \| 0.032196 / 0.023109 (0.009087) \| 0.265037 / 0.275898 (-0.010861) \| 0.289573 / 0.323480 (-0.033907) \| 0.004345 / 0.007986 (-0.003640) \| 0.002794 / 0.004328 (-0.001534) \| 0.048955 / 0.004250 (0.044705) \| 0.045421 / 0.037052 (0.008369) \| 0.279792 / 0.258489 (0.021303) \| 0.307374 / 0.293841 (0.013533) \| 0.046997 / 0.128546 (-0.081549) \| 0.010531 / 0.075646 (-0.065115) \| 0.058921 / 0.419271 (-0.360351) \| 0.033620 / 0.043533 (-0.009912) \| 0.268138 / 0.255139 (0.012999) \| 0.285941 / 0.283200 (0.002742) \| 0.018396 / 0.141683 (-0.123287) \| 1.151089 / 1.452155 (-0.301066) \| 1.209351 / 1.492716 (-0.283365) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092258 / 0.018006 (0.074252) \| 0.300893 / 0.000490 (0.300403) \| 0.000212 / 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) \| 0.022233 / 0.037411 (-0.015178) \| 0.075220 / 0.014526 (0.060694) \| 0.085901 / 0.176557 (-0.090656) \| 0.125080 / 0.737135 (-0.612056) \| 0.086978 / 0.296338 (-0.209361) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292877 / 0.215209 (0.077667) \| 2.841005 / 2.077655 (0.763350) \| 1.555168 / 1.504120 (0.051048) \| 1.420801 / 1.541195 (-0.120394) \| 1.431475 / 1.468490 (-0.037015) \| 0.569803 / 4.584777 (-4.014974) \| 2.451731 / 3.745712 (-1.293981) \| 2.662825 / 5.269862 (-2.607036) \| 1.732260 / 4.565676 (-2.833416) \| 0.063030 / 0.424275 (-0.361245) \| 0.004971 / 0.007607 (-0.002637) \| 0.345250 / 0.226044 (0.119206) \| 3.390909 / 2.268929 (1.121980) \| 1.908666 / 55.444624 (-53.535959) \| 1.628976 / 6.876477 (-5.247501) \| 1.719270 / 2.142072 (-0.422803) \| 0.653712 / 4.805227 (-4.151515) \| 0.116423 / 6.500664 (-6.384241) \| 0.040835 / 0.075469 (-0.034634) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005538 / 1.841788 (-0.836250) \| 12.105381 / 8.074308 (4.031073) \| 10.656295 / 10.191392 (0.464903) \| 0.131850 / 0.680424 (-0.548574) \| 0.016297 / 0.534201 (-0.517904) \| 0.285566 / 0.579283 (-0.293717) \| 0.276086 / 0.434364 (-0.158278) \| 0.326663 / 0.540337 (-0.213675) \| 0.410639 / 1.386936 (-0.976297) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1dc3f04586ee65c890b74649afc42316121af689 \"CML watermark\")\n" ]
2,107,802,473	6,631	Fix filelock: use current umask for filelock >= 3.10	closed	2024-01-30T12:56:01	2024-01-30T15:34:49	2024-01-30T15:28:37	https://github.com/huggingface/datasets/pull/6631	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6631", "html_url": "https://github.com/huggingface/datasets/pull/6631", "diff_url": "https://github.com/huggingface/datasets/pull/6631.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6631.patch", "merged_at": "2024-01-30T15:28:37" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6631). 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) \| 0.005076 / 0.011353 (-0.006277) \| 0.003665 / 0.011008 (-0.007343) \| 0.063602 / 0.038508 (0.025094) \| 0.029103 / 0.023109 (0.005993) \| 0.233133 / 0.275898 (-0.042765) \| 0.257000 / 0.323480 (-0.066480) \| 0.003059 / 0.007986 (-0.004926) \| 0.004007 / 0.004328 (-0.000321) \| 0.049804 / 0.004250 (0.045553) \| 0.039946 / 0.037052 (0.002893) \| 0.248003 / 0.258489 (-0.010486) \| 0.272729 / 0.293841 (-0.021112) \| 0.027542 / 0.128546 (-0.101004) \| 0.010745 / 0.075646 (-0.064901) \| 0.207686 / 0.419271 (-0.211586) \| 0.035438 / 0.043533 (-0.008095) \| 0.236864 / 0.255139 (-0.018275) \| 0.258610 / 0.283200 (-0.024590) \| 0.017225 / 0.141683 (-0.124458) \| 1.130894 / 1.452155 (-0.321261) \| 1.171266 / 1.492716 (-0.321450) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092532 / 0.018006 (0.074525) \| 0.301650 / 0.000490 (0.301161) \| 0.000216 / 0.000200 (0.000016) \| 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) \| 0.018175 / 0.037411 (-0.019237) \| 0.061538 / 0.014526 (0.047012) \| 0.073673 / 0.176557 (-0.102884) \| 0.120676 / 0.737135 (-0.616460) \| 0.074753 / 0.296338 (-0.221586) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283625 / 0.215209 (0.068416) \| 2.794903 / 2.077655 (0.717248) \| 1.485149 / 1.504120 (-0.018970) \| 1.361154 / 1.541195 (-0.180041) \| 1.371436 / 1.468490 (-0.097054) \| 0.580401 / 4.584777 (-4.004376) \| 2.457068 / 3.745712 (-1.288644) \| 2.760878 / 5.269862 (-2.508984) \| 1.725507 / 4.565676 (-2.840169) \| 0.063632 / 0.424275 (-0.360644) \| 0.005036 / 0.007607 (-0.002572) \| 0.337167 / 0.226044 (0.111122) \| 3.314508 / 2.268929 (1.045579) \| 1.863412 / 55.444624 (-53.581213) \| 1.621966 / 6.876477 (-5.254511) \| 1.600422 / 2.142072 (-0.541651) \| 0.647753 / 4.805227 (-4.157475) \| 0.117169 / 6.500664 (-6.383495) \| 0.042338 / 0.075469 (-0.033131) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981818 / 1.841788 (-0.859969) \| 12.044657 / 8.074308 (3.970349) \| 10.654091 / 10.191392 (0.462699) \| 0.130693 / 0.680424 (-0.549731) \| 0.014733 / 0.534201 (-0.519468) \| 0.317432 / 0.579283 (-0.261851) \| 0.267196 / 0.434364 (-0.167168) \| 0.329310 / 0.540337 (-0.211028) \| 0.433379 / 1.386936 (-0.953557) \|\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) \| 0.005502 / 0.011353 (-0.005851) \| 0.003951 / 0.011008 (-0.007057) \| 0.050651 / 0.038508 (0.012143) \| 0.031802 / 0.023109 (0.008693) \| 0.281384 / 0.275898 (0.005485) \| 0.303900 / 0.323480 (-0.019580) \| 0.004451 / 0.007986 (-0.003534) \| 0.002801 / 0.004328 (-0.001527) \| 0.048688 / 0.004250 (0.044438) \| 0.044717 / 0.037052 (0.007664) \| 0.295017 / 0.258489 (0.036528) \| 0.328003 / 0.293841 (0.034162) \| 0.048421 / 0.128546 (-0.080125) \| 0.011254 / 0.075646 (-0.064392) \| 0.058223 / 0.419271 (-0.361048) \| 0.033915 / 0.043533 (-0.009618) \| 0.279893 / 0.255139 (0.024754) \| 0.297605 / 0.283200 (0.014405) \| 0.017115 / 0.141683 (-0.124568) \| 1.146966 / 1.452155 (-0.305189) \| 1.191650 / 1.492716 (-0.301066) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092524 / 0.018006 (0.074518) \| 0.309332 / 0.000490 (0.308842) \| 0.000212 / 0.000200 (0.000012) \| 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) \| 0.022265 / 0.037411 (-0.015146) \| 0.075732 / 0.014526 (0.061206) \| 0.087340 / 0.176557 (-0.089217) \| 0.126079 / 0.737135 (-0.611056) \| 0.090349 / 0.296338 (-0.205990) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288882 / 0.215209 (0.073673) \| 2.833046 / 2.077655 (0.755392) \| 1.602905 / 1.504120 (0.098785) \| 1.473110 / 1.541195 (-0.068085) \| 1.491300 / 1.468490 (0.022810) \| 0.557799 / 4.584777 (-4.026978) \| 2.439526 / 3.745712 (-1.306186) \| 2.669336 / 5.269862 (-2.600526) \| 1.719472 / 4.565676 (-2.846204) \| 0.062456 / 0.424275 (-0.361819) \| 0.005058 / 0.007607 (-0.002549) \| 0.343706 / 0.226044 (0.117662) \| 3.422397 / 2.268929 (1.153469) \| 1.983679 / 55.444624 (-53.460946) \| 1.673784 / 6.876477 (-5.202693) \| 1.785144 / 2.142072 (-0.356928) \| 0.643127 / 4.805227 (-4.162100) \| 0.115254 / 6.500664 (-6.385410) \| 0.041235 / 0.075469 (-0.034235) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005448 / 1.841788 (-0.836340) \| 12.240100 / 8.074308 (4.165792) \| 11.051965 / 10.191392 (0.860573) \| 0.130438 / 0.680424 (-0.549986) \| 0.015918 / 0.534201 (-0.518283) \| 0.287468 / 0.579283 (-0.291815) \| 0.287699 / 0.434364 (-0.146665) \| 0.324561 / 0.540337 (-0.215777) \| 0.418820 / 1.386936 (-0.968116) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#237a2a688155e23cfbcdfadd2d491ce1667fa494 \"CML watermark\")\n" ]
2,106,478,275	6,630	Bump max range of dill to 0.3.8	closed	2024-01-29T21:35:55	2024-01-30T16:19:45	2024-01-30T15:12:25	https://github.com/huggingface/datasets/pull/6630	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6630", "html_url": "https://github.com/huggingface/datasets/pull/6630", "diff_url": "https://github.com/huggingface/datasets/pull/6630.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6630.patch", "merged_at": "2024-01-30T15:12:25" }	ringohoffman	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6630). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Hmm these errors look pretty weird... can they be retried?", "Hi, thanks for working on this! To fix the errors, you also need to update [this file](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/_dill.py) (by adding `version.parse(\"0.3.8\").release` to the lists)", "<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) \| 0.005068 / 0.011353 (-0.006285) \| 0.003657 / 0.011008 (-0.007351) \| 0.062914 / 0.038508 (0.024406) \| 0.027965 / 0.023109 (0.004855) \| 0.241804 / 0.275898 (-0.034094) \| 0.268069 / 0.323480 (-0.055411) \| 0.004066 / 0.007986 (-0.003920) \| 0.002704 / 0.004328 (-0.001624) \| 0.048745 / 0.004250 (0.044495) \| 0.042158 / 0.037052 (0.005106) \| 0.257670 / 0.258489 (-0.000819) \| 0.279419 / 0.293841 (-0.014422) \| 0.027193 / 0.128546 (-0.101353) \| 0.010379 / 0.075646 (-0.065267) \| 0.207009 / 0.419271 (-0.212262) \| 0.035494 / 0.043533 (-0.008039) \| 0.246025 / 0.255139 (-0.009114) \| 0.265906 / 0.283200 (-0.017294) \| 0.017335 / 0.141683 (-0.124348) \| 1.134052 / 1.452155 (-0.318103) \| 1.184668 / 1.492716 (-0.308049) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093137 / 0.018006 (0.075130) \| 0.302279 / 0.000490 (0.301789) \| 0.000210 / 0.000200 (0.000010) \| 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) \| 0.018190 / 0.037411 (-0.019221) \| 0.061436 / 0.014526 (0.046910) \| 0.073102 / 0.176557 (-0.103454) \| 0.119782 / 0.737135 (-0.617354) \| 0.074292 / 0.296338 (-0.222046) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285905 / 0.215209 (0.070696) \| 2.809051 / 2.077655 (0.731397) \| 1.470305 / 1.504120 (-0.033814) \| 1.350457 / 1.541195 (-0.190738) \| 1.349111 / 1.468490 (-0.119379) \| 0.568277 / 4.584777 (-4.016500) \| 2.353046 / 3.745712 (-1.392666) \| 2.805862 / 5.269862 (-2.463999) \| 1.750275 / 4.565676 (-2.815401) \| 0.062370 / 0.424275 (-0.361905) \| 0.004954 / 0.007607 (-0.002653) \| 0.335609 / 0.226044 (0.109564) \| 3.367200 / 2.268929 (1.098271) \| 1.829431 / 55.444624 (-53.615193) \| 1.545093 / 6.876477 (-5.331384) \| 1.571107 / 2.142072 (-0.570966) \| 0.640279 / 4.805227 (-4.164949) \| 0.116209 / 6.500664 (-6.384455) \| 0.042308 / 0.075469 (-0.033161) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.982972 / 1.841788 (-0.858816) \| 11.424370 / 8.074308 (3.350062) \| 10.427111 / 10.191392 (0.235719) \| 0.129477 / 0.680424 (-0.550946) \| 0.014166 / 0.534201 (-0.520035) \| 0.287597 / 0.579283 (-0.291686) \| 0.265588 / 0.434364 (-0.168776) \| 0.324007 / 0.540337 (-0.216330) \| 0.430766 / 1.386936 (-0.956170) \|\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) \| 0.005347 / 0.011353 (-0.006005) \| 0.003733 / 0.011008 (-0.007275) \| 0.049520 / 0.038508 (0.011011) \| 0.031177 / 0.023109 (0.008068) \| 0.281854 / 0.275898 (0.005956) \| 0.300937 / 0.323480 (-0.022543) \| 0.004385 / 0.007986 (-0.003601) \| 0.002841 / 0.004328 (-0.001488) \| 0.048661 / 0.004250 (0.044411) \| 0.044258 / 0.037052 (0.007205) \| 0.295651 / 0.258489 (0.037162) \| 0.322872 / 0.293841 (0.029031) \| 0.048924 / 0.128546 (-0.079622) \| 0.010742 / 0.075646 (-0.064905) \| 0.059327 / 0.419271 (-0.359944) \| 0.033938 / 0.043533 (-0.009595) \| 0.282235 / 0.255139 (0.027096) \| 0.297432 / 0.283200 (0.014233) \| 0.018295 / 0.141683 (-0.123388) \| 1.164459 / 1.452155 (-0.287696) \| 1.214511 / 1.492716 (-0.278205) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091441 / 0.018006 (0.073435) \| 0.303023 / 0.000490 (0.302533) \| 0.000211 / 0.000200 (0.000011) \| 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) \| 0.022024 / 0.037411 (-0.015388) \| 0.075570 / 0.014526 (0.061044) \| 0.086761 / 0.176557 (-0.089796) \| 0.126437 / 0.737135 (-0.610698) \| 0.088354 / 0.296338 (-0.207984) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289360 / 0.215209 (0.074151) \| 2.816433 / 2.077655 (0.738779) \| 1.561442 / 1.504120 (0.057322) \| 1.438168 / 1.541195 (-0.103027) \| 1.453398 / 1.468490 (-0.015092) \| 0.579474 / 4.584777 (-4.005303) \| 2.458640 / 3.745712 (-1.287072) \| 2.638572 / 5.269862 (-2.631290) \| 1.725218 / 4.565676 (-2.840458) \| 0.063550 / 0.424275 (-0.360725) \| 0.005220 / 0.007607 (-0.002387) \| 0.338883 / 0.226044 (0.112838) \| 3.353585 / 2.268929 (1.084656) \| 1.913186 / 55.444624 (-53.531438) \| 1.667445 / 6.876477 (-5.209032) \| 1.740085 / 2.142072 (-0.401987) \| 0.646369 / 4.805227 (-4.158859) \| 0.116737 / 6.500664 (-6.383927) \| 0.041052 / 0.075469 (-0.034417) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.023180 / 1.841788 (-0.818608) \| 12.078398 / 8.074308 (4.004090) \| 10.952012 / 10.191392 (0.760620) \| 0.131335 / 0.680424 (-0.549089) \| 0.015701 / 0.534201 (-0.518499) \| 0.289709 / 0.579283 (-0.289574) \| 0.270495 / 0.434364 (-0.163869) \| 0.331773 / 0.540337 (-0.208565) \| 0.417660 / 1.386936 (-0.969276) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3b21d74f5c0ab8a85838af04de8ad85e71b0ac4f \"CML watermark\")\n" ]
2,105,774,482	6,629	Support push_to_hub without org/user to default to logged-in user	closed	2024-01-29T15:36:52	2024-02-05T12:35:43	2024-02-05T12:29:36	https://github.com/huggingface/datasets/pull/6629	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6629", "html_url": "https://github.com/huggingface/datasets/pull/6629", "diff_url": "https://github.com/huggingface/datasets/pull/6629.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6629.patch", "merged_at": "2024-02-05T12:29:36" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6629). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@huggingface/datasets, feel free to review this PR so that it can be included in the next release.", "<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) \| 0.005222 / 0.011353 (-0.006131) \| 0.003621 / 0.011008 (-0.007387) \| 0.063091 / 0.038508 (0.024583) \| 0.029395 / 0.023109 (0.006285) \| 0.231445 / 0.275898 (-0.044453) \| 0.256716 / 0.323480 (-0.066764) \| 0.004905 / 0.007986 (-0.003081) \| 0.002703 / 0.004328 (-0.001625) \| 0.048526 / 0.004250 (0.044276) \| 0.041382 / 0.037052 (0.004330) \| 0.247468 / 0.258489 (-0.011021) \| 0.270670 / 0.293841 (-0.023171) \| 0.028088 / 0.128546 (-0.100458) \| 0.010661 / 0.075646 (-0.064985) \| 0.205812 / 0.419271 (-0.213459) \| 0.035880 / 0.043533 (-0.007653) \| 0.237310 / 0.255139 (-0.017829) \| 0.255440 / 0.283200 (-0.027760) \| 0.018334 / 0.141683 (-0.123349) \| 1.128815 / 1.452155 (-0.323340) \| 1.204771 / 1.492716 (-0.287945) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089175 / 0.018006 (0.071169) \| 0.298584 / 0.000490 (0.298095) \| 0.000206 / 0.000200 (0.000006) \| 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) \| 0.018532 / 0.037411 (-0.018880) \| 0.061158 / 0.014526 (0.046632) \| 0.074177 / 0.176557 (-0.102380) \| 0.119408 / 0.737135 (-0.617728) \| 0.073821 / 0.296338 (-0.222518) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277630 / 0.215209 (0.062420) \| 2.735038 / 2.077655 (0.657383) \| 1.437251 / 1.504120 (-0.066868) \| 1.304596 / 1.541195 (-0.236598) \| 1.316830 / 1.468490 (-0.151661) \| 0.551057 / 4.584777 (-4.033720) \| 2.337247 / 3.745712 (-1.408465) \| 2.761501 / 5.269862 (-2.508361) \| 1.729000 / 4.565676 (-2.836677) \| 0.069398 / 0.424275 (-0.354877) \| 0.005059 / 0.007607 (-0.002548) \| 0.359594 / 0.226044 (0.133550) \| 3.283325 / 2.268929 (1.014397) \| 1.777410 / 55.444624 (-53.667214) \| 1.518522 / 6.876477 (-5.357954) \| 1.546712 / 2.142072 (-0.595361) \| 0.627047 / 4.805227 (-4.178180) \| 0.117058 / 6.500664 (-6.383606) \| 0.043437 / 0.075469 (-0.032032) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.056303 / 1.841788 (-0.785484) \| 11.552295 / 8.074308 (3.477987) \| 10.184582 / 10.191392 (-0.006810) \| 0.129061 / 0.680424 (-0.551363) \| 0.014093 / 0.534201 (-0.520108) \| 0.292268 / 0.579283 (-0.287015) \| 0.264750 / 0.434364 (-0.169614) \| 0.334770 / 0.540337 (-0.205567) \| 0.436749 / 1.386936 (-0.950187) \|\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) \| 0.005408 / 0.011353 (-0.005945) \| 0.003650 / 0.011008 (-0.007358) \| 0.054263 / 0.038508 (0.015755) \| 0.031112 / 0.023109 (0.008003) \| 0.270582 / 0.275898 (-0.005316) \| 0.303506 / 0.323480 (-0.019974) \| 0.004351 / 0.007986 (-0.003635) \| 0.002654 / 0.004328 (-0.001674) \| 0.049631 / 0.004250 (0.045381) \| 0.045209 / 0.037052 (0.008156) \| 0.284992 / 0.258489 (0.026503) \| 0.316653 / 0.293841 (0.022812) \| 0.049526 / 0.128546 (-0.079020) \| 0.010696 / 0.075646 (-0.064951) \| 0.057859 / 0.419271 (-0.361413) \| 0.034227 / 0.043533 (-0.009306) \| 0.269656 / 0.255139 (0.014517) \| 0.288766 / 0.283200 (0.005567) \| 0.017892 / 0.141683 (-0.123791) \| 1.167492 / 1.452155 (-0.284662) \| 1.217263 / 1.492716 (-0.275454) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089306 / 0.018006 (0.071299) \| 0.300774 / 0.000490 (0.300284) \| 0.000198 / 0.000200 (-0.000002) \| 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) \| 0.022050 / 0.037411 (-0.015361) \| 0.076781 / 0.014526 (0.062255) \| 0.086597 / 0.176557 (-0.089959) \| 0.125094 / 0.737135 (-0.612042) \| 0.089412 / 0.296338 (-0.206927) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287444 / 0.215209 (0.072235) \| 2.830047 / 2.077655 (0.752392) \| 1.567492 / 1.504120 (0.063372) \| 1.439875 / 1.541195 (-0.101320) \| 1.461699 / 1.468490 (-0.006791) \| 0.569595 / 4.584777 (-4.015182) \| 2.454391 / 3.745712 (-1.291322) \| 2.655829 / 5.269862 (-2.614032) \| 1.756122 / 4.565676 (-2.809554) \| 0.063333 / 0.424275 (-0.360942) \| 0.005086 / 0.007607 (-0.002521) \| 0.351210 / 0.226044 (0.125166) \| 3.375545 / 2.268929 (1.106617) \| 1.945367 / 55.444624 (-53.499258) \| 1.662635 / 6.876477 (-5.213841) \| 1.762859 / 2.142072 (-0.379213) \| 0.651889 / 4.805227 (-4.153339) \| 0.118341 / 6.500664 (-6.382323) \| 0.040897 / 0.075469 (-0.034572) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005270 / 1.841788 (-0.836518) \| 12.247847 / 8.074308 (4.173539) \| 10.828131 / 10.191392 (0.636739) \| 0.129741 / 0.680424 (-0.550683) \| 0.015184 / 0.534201 (-0.519017) \| 0.295440 / 0.579283 (-0.283843) \| 0.276759 / 0.434364 (-0.157605) \| 0.329046 / 0.540337 (-0.211291) \| 0.421750 / 1.386936 (-0.965186) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ea261ddc295527d0c1cd9f90fb61668f14135608 \"CML watermark\")\n" ]
2,105,760,502	6,628	Make CLI test support multi-processing	closed	2024-01-29T15:30:09	2024-02-05T10:29:20	2024-02-05T10:23:13	https://github.com/huggingface/datasets/pull/6628	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6628", "html_url": "https://github.com/huggingface/datasets/pull/6628", "diff_url": "https://github.com/huggingface/datasets/pull/6628.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6628.patch", "merged_at": "2024-02-05T10:23:13" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6628). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@huggingface/datasets, feel free to review this PR so that it can be included in the next release.", "<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) \| 0.004907 / 0.011353 (-0.006446) \| 0.003200 / 0.011008 (-0.007808) \| 0.062601 / 0.038508 (0.024093) \| 0.028607 / 0.023109 (0.005498) \| 0.242688 / 0.275898 (-0.033210) \| 0.263754 / 0.323480 (-0.059726) \| 0.003084 / 0.007986 (-0.004901) \| 0.002744 / 0.004328 (-0.001585) \| 0.048686 / 0.004250 (0.044436) \| 0.040734 / 0.037052 (0.003682) \| 0.262585 / 0.258489 (0.004096) \| 0.282822 / 0.293841 (-0.011019) \| 0.027470 / 0.128546 (-0.101076) \| 0.010356 / 0.075646 (-0.065290) \| 0.206397 / 0.419271 (-0.212874) \| 0.035440 / 0.043533 (-0.008093) \| 0.248599 / 0.255139 (-0.006540) \| 0.268869 / 0.283200 (-0.014331) \| 0.018542 / 0.141683 (-0.123141) \| 1.128139 / 1.452155 (-0.324016) \| 1.172115 / 1.492716 (-0.320602) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.107939 / 0.018006 (0.089933) \| 0.301801 / 0.000490 (0.301311) \| 0.000207 / 0.000200 (0.000007) \| 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) \| 0.018505 / 0.037411 (-0.018906) \| 0.061350 / 0.014526 (0.046824) \| 0.072645 / 0.176557 (-0.103912) \| 0.119459 / 0.737135 (-0.617676) \| 0.074711 / 0.296338 (-0.221628) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275132 / 0.215209 (0.059922) \| 2.714936 / 2.077655 (0.637281) \| 1.434204 / 1.504120 (-0.069916) \| 1.328358 / 1.541195 (-0.212837) \| 1.320706 / 1.468490 (-0.147784) \| 0.555723 / 4.584777 (-4.029054) \| 2.401335 / 3.745712 (-1.344378) \| 2.765609 / 5.269862 (-2.504253) \| 1.715207 / 4.565676 (-2.850470) \| 0.074990 / 0.424275 (-0.349285) \| 0.004999 / 0.007607 (-0.002608) \| 0.328435 / 0.226044 (0.102390) \| 3.254945 / 2.268929 (0.986017) \| 1.781105 / 55.444624 (-53.663519) \| 1.509491 / 6.876477 (-5.366985) \| 1.520670 / 2.142072 (-0.621402) \| 0.636411 / 4.805227 (-4.168817) \| 0.115616 / 6.500664 (-6.385048) \| 0.041633 / 0.075469 (-0.033836) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975462 / 1.841788 (-0.866326) \| 11.480359 / 8.074308 (3.406051) \| 10.528665 / 10.191392 (0.337273) \| 0.141323 / 0.680424 (-0.539100) \| 0.013510 / 0.534201 (-0.520691) \| 0.293570 / 0.579283 (-0.285713) \| 0.259956 / 0.434364 (-0.174408) \| 0.331440 / 0.540337 (-0.208898) \| 0.453487 / 1.386936 (-0.933449) \|\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) \| 0.005278 / 0.011353 (-0.006075) \| 0.003400 / 0.011008 (-0.007608) \| 0.049442 / 0.038508 (0.010934) \| 0.031738 / 0.023109 (0.008628) \| 0.292334 / 0.275898 (0.016436) \| 0.308931 / 0.323480 (-0.014549) \| 0.004290 / 0.007986 (-0.003696) \| 0.002738 / 0.004328 (-0.001591) \| 0.048944 / 0.004250 (0.044694) \| 0.044273 / 0.037052 (0.007221) \| 0.301434 / 0.258489 (0.042945) \| 0.333067 / 0.293841 (0.039226) \| 0.048741 / 0.128546 (-0.079805) \| 0.010357 / 0.075646 (-0.065289) \| 0.057777 / 0.419271 (-0.361495) \| 0.033892 / 0.043533 (-0.009641) \| 0.286921 / 0.255139 (0.031782) \| 0.306204 / 0.283200 (0.023005) \| 0.018764 / 0.141683 (-0.122919) \| 1.142000 / 1.452155 (-0.310155) \| 1.206728 / 1.492716 (-0.285988) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094233 / 0.018006 (0.076227) \| 0.302553 / 0.000490 (0.302063) \| 0.000213 / 0.000200 (0.000013) \| 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) \| 0.021814 / 0.037411 (-0.015598) \| 0.075143 / 0.014526 (0.060617) \| 0.087717 / 0.176557 (-0.088840) \| 0.126079 / 0.737135 (-0.611056) \| 0.089083 / 0.296338 (-0.207255) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293844 / 0.215209 (0.078635) \| 2.859481 / 2.077655 (0.781827) \| 1.580366 / 1.504120 (0.076246) \| 1.462633 / 1.541195 (-0.078562) \| 1.471052 / 1.468490 (0.002562) \| 0.574755 / 4.584777 (-4.010022) \| 2.408925 / 3.745712 (-1.336787) \| 2.673618 / 5.269862 (-2.596243) \| 1.746218 / 4.565676 (-2.819459) \| 0.063435 / 0.424275 (-0.360840) \| 0.005023 / 0.007607 (-0.002584) \| 0.341990 / 0.226044 (0.115946) \| 3.430862 / 2.268929 (1.161933) \| 1.953869 / 55.444624 (-53.490755) \| 1.661276 / 6.876477 (-5.215201) \| 1.761575 / 2.142072 (-0.380498) \| 0.656388 / 4.805227 (-4.148839) \| 0.117774 / 6.500664 (-6.382890) \| 0.040290 / 0.075469 (-0.035179) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.004315 / 1.841788 (-0.837473) \| 12.249719 / 8.074308 (4.175411) \| 10.942703 / 10.191392 (0.751311) \| 0.128552 / 0.680424 (-0.551872) \| 0.015958 / 0.534201 (-0.518242) \| 0.287330 / 0.579283 (-0.291953) \| 0.274336 / 0.434364 (-0.160028) \| 0.326233 / 0.540337 (-0.214104) \| 0.414548 / 1.386936 (-0.972388) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#db47d6d95c5346368710d3c852f20ffc1b0f1c1c \"CML watermark\")\n" ]
2,105,735,816	6,627	Disable `tqdm` bars in non-interactive environments	closed	2024-01-29T15:18:21	2024-01-29T15:47:34	2024-01-29T15:41:32	https://github.com/huggingface/datasets/pull/6627	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6627", "html_url": "https://github.com/huggingface/datasets/pull/6627", "diff_url": "https://github.com/huggingface/datasets/pull/6627.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6627.patch", "merged_at": "2024-01-29T15:41:32" }	mariosasko	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6627). 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) \| 0.004944 / 0.011353 (-0.006409) \| 0.003279 / 0.011008 (-0.007729) \| 0.063041 / 0.038508 (0.024533) \| 0.029888 / 0.023109 (0.006779) \| 0.259138 / 0.275898 (-0.016760) \| 0.276907 / 0.323480 (-0.046573) \| 0.004015 / 0.007986 (-0.003970) \| 0.002647 / 0.004328 (-0.001682) \| 0.048944 / 0.004250 (0.044693) \| 0.039412 / 0.037052 (0.002360) \| 0.278069 / 0.258489 (0.019580) \| 0.299139 / 0.293841 (0.005298) \| 0.027272 / 0.128546 (-0.101274) \| 0.010445 / 0.075646 (-0.065202) \| 0.206925 / 0.419271 (-0.212347) \| 0.035589 / 0.043533 (-0.007944) \| 0.256805 / 0.255139 (0.001666) \| 0.275128 / 0.283200 (-0.008072) \| 0.017888 / 0.141683 (-0.123795) \| 1.136983 / 1.452155 (-0.315172) \| 1.167495 / 1.492716 (-0.325222) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088167 / 0.018006 (0.070161) \| 0.297360 / 0.000490 (0.296871) \| 0.000231 / 0.000200 (0.000031) \| 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) \| 0.018114 / 0.037411 (-0.019297) \| 0.061217 / 0.014526 (0.046691) \| 0.072269 / 0.176557 (-0.104288) \| 0.120607 / 0.737135 (-0.616528) \| 0.073517 / 0.296338 (-0.222822) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282580 / 0.215209 (0.067371) \| 2.758650 / 2.077655 (0.680995) \| 1.425125 / 1.504120 (-0.078995) \| 1.303182 / 1.541195 (-0.238013) \| 1.341035 / 1.468490 (-0.127455) \| 0.549485 / 4.584777 (-4.035292) \| 2.346297 / 3.745712 (-1.399415) \| 2.686457 / 5.269862 (-2.583405) \| 1.684789 / 4.565676 (-2.880888) \| 0.061279 / 0.424275 (-0.362996) \| 0.004902 / 0.007607 (-0.002705) \| 0.333089 / 0.226044 (0.107044) \| 3.297016 / 2.268929 (1.028087) \| 1.765614 / 55.444624 (-53.679010) \| 1.499314 / 6.876477 (-5.377162) \| 1.501275 / 2.142072 (-0.640797) \| 0.619039 / 4.805227 (-4.186189) \| 0.114284 / 6.500664 (-6.386380) \| 0.041481 / 0.075469 (-0.033988) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973924 / 1.841788 (-0.867863) \| 11.268266 / 8.074308 (3.193958) \| 10.304738 / 10.191392 (0.113346) \| 0.129297 / 0.680424 (-0.551127) \| 0.014894 / 0.534201 (-0.519307) \| 0.287658 / 0.579283 (-0.291626) \| 0.266476 / 0.434364 (-0.167888) \| 0.322199 / 0.540337 (-0.218138) \| 0.419568 / 1.386936 (-0.967368) \|\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) \| 0.005220 / 0.011353 (-0.006133) \| 0.003310 / 0.011008 (-0.007698) \| 0.049707 / 0.038508 (0.011199) \| 0.031148 / 0.023109 (0.008039) \| 0.284644 / 0.275898 (0.008746) \| 0.302767 / 0.323480 (-0.020712) \| 0.004245 / 0.007986 (-0.003740) \| 0.002677 / 0.004328 (-0.001651) \| 0.049870 / 0.004250 (0.045620) \| 0.043922 / 0.037052 (0.006870) \| 0.294955 / 0.258489 (0.036466) \| 0.322144 / 0.293841 (0.028303) \| 0.047211 / 0.128546 (-0.081336) \| 0.010492 / 0.075646 (-0.065155) \| 0.058152 / 0.419271 (-0.361120) \| 0.033508 / 0.043533 (-0.010025) \| 0.281266 / 0.255139 (0.026127) \| 0.300010 / 0.283200 (0.016810) \| 0.017616 / 0.141683 (-0.124067) \| 1.124658 / 1.452155 (-0.327496) \| 1.167222 / 1.492716 (-0.325495) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089085 / 0.018006 (0.071079) \| 0.297912 / 0.000490 (0.297423) \| 0.000211 / 0.000200 (0.000011) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021669 / 0.037411 (-0.015742) \| 0.075648 / 0.014526 (0.061123) \| 0.086054 / 0.176557 (-0.090503) \| 0.125236 / 0.737135 (-0.611899) \| 0.088146 / 0.296338 (-0.208192) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295238 / 0.215209 (0.080029) \| 2.870002 / 2.077655 (0.792347) \| 1.582534 / 1.504120 (0.078414) \| 1.466710 / 1.541195 (-0.074485) \| 1.475352 / 1.468490 (0.006861) \| 0.554745 / 4.584777 (-4.030032) \| 2.412533 / 3.745712 (-1.333179) \| 2.583863 / 5.269862 (-2.685999) \| 1.689124 / 4.565676 (-2.876552) \| 0.061353 / 0.424275 (-0.362922) \| 0.005015 / 0.007607 (-0.002592) \| 0.338733 / 0.226044 (0.112688) \| 3.356710 / 2.268929 (1.087781) \| 1.932143 / 55.444624 (-53.512481) \| 1.660081 / 6.876477 (-5.216396) \| 1.764961 / 2.142072 (-0.377111) \| 0.640002 / 4.805227 (-4.165225) \| 0.115251 / 6.500664 (-6.385413) \| 0.040627 / 0.075469 (-0.034842) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.992296 / 1.841788 (-0.849492) \| 11.821259 / 8.074308 (3.746951) \| 10.715570 / 10.191392 (0.524178) \| 0.142934 / 0.680424 (-0.537489) \| 0.015680 / 0.534201 (-0.518521) \| 0.287435 / 0.579283 (-0.291848) \| 0.276817 / 0.434364 (-0.157547) \| 0.327823 / 0.540337 (-0.212515) \| 0.413404 / 1.386936 (-0.973532) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#82c78b614d34ee42180d35a882875a28d6281db0 \"CML watermark\")\n" ]
2,105,482,522	6,626	Raise error on bad split name	closed	2024-01-29T13:17:41	2024-01-29T15:18:25	2024-01-29T15:12:18	https://github.com/huggingface/datasets/pull/6626	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6626", "html_url": "https://github.com/huggingface/datasets/pull/6626", "diff_url": "https://github.com/huggingface/datasets/pull/6626.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6626.patch", "merged_at": "2024-01-29T15:12:18" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6626). 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) \| 0.005085 / 0.011353 (-0.006268) \| 0.003592 / 0.011008 (-0.007417) \| 0.062591 / 0.038508 (0.024083) \| 0.031063 / 0.023109 (0.007954) \| 0.247029 / 0.275898 (-0.028869) \| 0.273706 / 0.323480 (-0.049774) \| 0.004034 / 0.007986 (-0.003951) \| 0.002672 / 0.004328 (-0.001657) \| 0.048407 / 0.004250 (0.044156) \| 0.049229 / 0.037052 (0.012177) \| 0.264316 / 0.258489 (0.005827) \| 0.284953 / 0.293841 (-0.008888) \| 0.027712 / 0.128546 (-0.100834) \| 0.010619 / 0.075646 (-0.065027) \| 0.210017 / 0.419271 (-0.209254) \| 0.035636 / 0.043533 (-0.007897) \| 0.252830 / 0.255139 (-0.002309) \| 0.278772 / 0.283200 (-0.004428) \| 0.017356 / 0.141683 (-0.124326) \| 1.140202 / 1.452155 (-0.311953) \| 1.204807 / 1.492716 (-0.287909) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089130 / 0.018006 (0.071123) \| 0.300115 / 0.000490 (0.299626) \| 0.000213 / 0.000200 (0.000013) \| 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) \| 0.018352 / 0.037411 (-0.019059) \| 0.061431 / 0.014526 (0.046905) \| 0.073911 / 0.176557 (-0.102646) \| 0.121230 / 0.737135 (-0.615906) \| 0.074867 / 0.296338 (-0.221471) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282272 / 0.215209 (0.067063) \| 2.737413 / 2.077655 (0.659759) \| 1.446651 / 1.504120 (-0.057469) \| 1.319686 / 1.541195 (-0.221508) \| 1.327479 / 1.468490 (-0.141011) \| 0.558003 / 4.584777 (-4.026774) \| 2.361623 / 3.745712 (-1.384089) \| 2.770436 / 5.269862 (-2.499425) \| 1.703450 / 4.565676 (-2.862227) \| 0.062034 / 0.424275 (-0.362241) \| 0.005070 / 0.007607 (-0.002537) \| 0.337265 / 0.226044 (0.111221) \| 3.299438 / 2.268929 (1.030509) \| 1.781273 / 55.444624 (-53.663351) \| 1.512743 / 6.876477 (-5.363734) \| 1.530995 / 2.142072 (-0.611077) \| 0.630210 / 4.805227 (-4.175017) \| 0.116219 / 6.500664 (-6.384445) \| 0.042220 / 0.075469 (-0.033249) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.946341 / 1.841788 (-0.895446) \| 11.462179 / 8.074308 (3.387871) \| 10.603314 / 10.191392 (0.411922) \| 0.128826 / 0.680424 (-0.551598) \| 0.013994 / 0.534201 (-0.520207) \| 0.288142 / 0.579283 (-0.291141) \| 0.266941 / 0.434364 (-0.167422) \| 0.329392 / 0.540337 (-0.210946) \| 0.431720 / 1.386936 (-0.955216) \|\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) \| 0.005303 / 0.011353 (-0.006050) \| 0.003587 / 0.011008 (-0.007422) \| 0.049437 / 0.038508 (0.010929) \| 0.031940 / 0.023109 (0.008831) \| 0.276651 / 0.275898 (0.000752) \| 0.297240 / 0.323480 (-0.026240) \| 0.004202 / 0.007986 (-0.003784) \| 0.002709 / 0.004328 (-0.001619) \| 0.048647 / 0.004250 (0.044397) \| 0.044147 / 0.037052 (0.007095) \| 0.291171 / 0.258489 (0.032682) \| 0.319297 / 0.293841 (0.025456) \| 0.048167 / 0.128546 (-0.080379) \| 0.010630 / 0.075646 (-0.065016) \| 0.058402 / 0.419271 (-0.360869) \| 0.033817 / 0.043533 (-0.009716) \| 0.300546 / 0.255139 (0.045407) \| 0.319396 / 0.283200 (0.036197) \| 0.017736 / 0.141683 (-0.123946) \| 1.159590 / 1.452155 (-0.292565) \| 1.191778 / 1.492716 (-0.300939) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088971 / 0.018006 (0.070965) \| 0.299721 / 0.000490 (0.299231) \| 0.000219 / 0.000200 (0.000019) \| 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) \| 0.021895 / 0.037411 (-0.015516) \| 0.075388 / 0.014526 (0.060862) \| 0.087446 / 0.176557 (-0.089111) \| 0.126339 / 0.737135 (-0.610796) \| 0.089329 / 0.296338 (-0.207010) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296642 / 0.215209 (0.081433) \| 2.916023 / 2.077655 (0.838368) \| 1.593180 / 1.504120 (0.089060) \| 1.470491 / 1.541195 (-0.070704) \| 1.485713 / 1.468490 (0.017223) \| 0.577204 / 4.584777 (-4.007573) \| 2.436463 / 3.745712 (-1.309249) \| 2.651004 / 5.269862 (-2.618858) \| 1.754026 / 4.565676 (-2.811651) \| 0.064943 / 0.424275 (-0.359332) \| 0.005115 / 0.007607 (-0.002492) \| 0.362082 / 0.226044 (0.136038) \| 3.498198 / 2.268929 (1.229270) \| 1.951936 / 55.444624 (-53.492688) \| 1.682027 / 6.876477 (-5.194450) \| 1.751768 / 2.142072 (-0.390304) \| 0.668479 / 4.805227 (-4.136748) \| 0.119934 / 6.500664 (-6.380730) \| 0.041419 / 0.075469 (-0.034050) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.978145 / 1.841788 (-0.863643) \| 11.984984 / 8.074308 (3.910676) \| 10.732377 / 10.191392 (0.540985) \| 0.141868 / 0.680424 (-0.538555) \| 0.015256 / 0.534201 (-0.518945) \| 0.288488 / 0.579283 (-0.290795) \| 0.276091 / 0.434364 (-0.158273) \| 0.330429 / 0.540337 (-0.209908) \| 0.423964 / 1.386936 (-0.962972) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bb8497b9dec2a3807c887b8184f902d1d8d7c25a \"CML watermark\")\n" ]
2,103,950,718	6,624	How to download the laion-coco dataset	closed	2024-01-28T03:56:05	2024-02-06T09:43:31	2024-02-06T09:43:31	https://github.com/huggingface/datasets/issues/6624	null	vanpersie32	false	[ "Hi, this dataset has been disabled by the authors, so unfortunately it's no longer possible to download it." ]
2,103,870,123	6,623	streaming datasets doesn't work properly with multi-node	open	2024-01-27T23:46:13	2024-10-16T00:55:19	null	https://github.com/huggingface/datasets/issues/6623	null	rohitgr7	false	[ "@mariosasko, @lhoestq, @albertvillanova\r\nhey guys! can anyone help? or can you guys suggest who can help with this?", "Hi ! \r\n\r\n1. When the dataset is running of of examples, the last batches received by the GPU can be incomplete or empty/missing. We haven't implemented yet a way to ignore the last batch. It might require the datasets to provide the number of examples per shard though, so that we can know when to stop.\r\n2. Samplers are not compatible with IterableDatasets in pytorch\r\n3. if `dataset.n_shards % world_size != 0` then all the nodes will read/stream the full dataset in order (possibly reading/streaming the same data multiple times), BUT will only yield one example out of `world_size` so that each example goes to one exactly one GPU.\r\n4. no, sharding should be down up-front and can take some time depending on the dataset size and format", "> if dataset.n_shards % world_size != 0 then all the nodes will read/stream the full dataset in order (possibly reading/streaming the same data multiple times), BUT will only yield one example out of world_size so that each example goes to one exactly one GPU.\r\n\r\nconsidering there's just 1 shard and 2 worker nodes, do you mean each worker node will load the whole dataset but still receive half of that shard while streaming?", "Yes both nodes will stream from the 1 shard, but each node will skip half of the examples. This way in total each example is seen once and exactly once during you distributed training.\r\n\r\nThough it terms of I/O, the dataset is effectively read/streamed twice.", "what if the number of samples in that shard % num_nodes != 0? it will break/get stuck? or is the data repeated in that case for gradient sync?", "In the case one at least one of the nodes will get an empty/incomplete batch. The data is not repeated in that case. If the training loop doesn't take this into account it can lead to unexpected behaviors indeed.\r\n\r\nIn the future we'd like to add a feature that would allow the nodes to ignore the last batch, this way all the nodes would only have full batches.", "> In the case one at least one of the noes will get an empty/incomplete batch. The data is not repeated in that case. If the training loop doesn't take this into account it can lead to unexpected behaviors indeed.\r\n> \r\n> In the future we'd like to add a feature that would allow the nodes to ignore the last batch, this way all the nodes would only have full batches.\r\n\r\nIs there any method to modify one dataset's n_shard? modify the number of files is ok? one file == one shard?", "> modify the number of files is ok? one file == one shard?\r\n\r\nYep, one file == one shard :)", "Hi @lhoestq, do you have any advice on how to implement a fix for the case dataset.n_shards % world_size != 0 while such a fix is not supported in the library?\r\n\r\nIt seems essential for performing validation in a ddp setting\r\n\r\nSimply limiting the number of files is a bit brittle as it relies on world size being consistent to ensure different runs see the same data\r\n\r\nHow should a user either ignore the last batch or handle the empty batch?\r\n\r\nIs the issue of overhanging batches also relevant for map-style datasets?", "> How should a user either ignore the last batch or handle the empty batch?\r\n\r\nCheck the batch size in the training loop and use all_reduce (or any communication method) to make sure all the nodes got their data before passing them to the model. If some data are missing you can decide to stop the training loop or repeat examples until all the nodes have exhausted their data.\r\n\r\nCc @andrewkho in case you know a way to make the DataLoader stop or add extra samples automatically in case of distributed + unevenly divisible iterable dataset\r\n\r\n> Is the issue of overhanging batches also relevant for map-style datasets?\r\n\r\nThe DistributedSampler drops the last data by default to make the dataset evenly divisible.", "@lhoestq Unfortunately for IterableDataset there isn't a way to do this in general without introducing communciation between ranks, or having all the ranks read all the data before starting to figure out when to stop (which is pretty impractical). My recommendation for these situations where you don't know the total number of samples apriori is to, configure the iterable dataset to yield a fixed number of samples before raising StopIteration, and if necessary, repeat/reshuffle samples to hit that number ", "A heads up that we're planning to land something new in torchdata by end-of-year to help with these scenarios, we'll update this thread when we hvae some code landed ", "I made a quick example with communication between ranks to stop once all the data from all the ranks are exhausted (and repeating data if necessary to end up with a number of samples evenly divisible)\r\n\r\n```python\r\nimport torch\r\nimport torch.distributed as dist\r\nfrom datasets import Dataset\r\nfrom datasets.distributed import split_dataset_by_node\r\nfrom torch.utils.data import DataLoader\r\n\r\n\r\n# simulate a streaming dataset\r\nnum_shards = 1 # change here if you want to simulate a dataset made of many files/shards\r\nds = Dataset.from_dict({\"x\": [1, 2, 3, 4, 5]}).to_iterable_dataset(num_shards=num_shards)\r\n\r\n# split the dataset for distributed training\r\ndist.init_process_group()\r\nrank, world_size = dist.get_rank(), dist.get_world_size()\r\nds = split_dataset_by_node(ds, rank=rank,world_size=world_size)\r\ndl = DataLoader(ds)\r\n\r\nexhausted = torch.zeros(world_size, dtype=torch.bool)\r\n\r\n# IMPORTANT: Loop over the local dataset until the data from each rank has been exhausted\r\n\r\ndef loop():\r\n while True:\r\n yield from dl\r\n yield \"end\"\r\n\r\nfor x in loop():\r\n if x == \"end\":\r\n exhausted[rank] = True\r\n continue\r\n # stop once the data from all the ranks are exhausted\r\n dist.all_reduce(exhausted)\r\n if torch.all(exhausted):\r\n break\r\n # do your forward pass + loss here\r\n # model.forward(...)\r\n print(x)\r\n```\r\n\r\non my laptop I run `torchrun --nnodes=1 --nproc-per-node=2 main.py` and I get\r\n\r\n```\r\n{'x': tensor([2])}\r\n{'x': tensor([1])}\r\n{'x': tensor([3])}\r\n{'x': tensor([4])}\r\n{'x': tensor([5])}\r\n{'x': tensor([2])}\r\n```\r\n\r\nwe indeed end up with 6 samples, `{'x': tensor([2])}` was repeated to get 6 examples in total which is divisible by the world size 2.\r\n\r\nI also tried with more ranks and with `num_workers` in DataLoader and it works as expected (don't forget to add `if __name__ == '__main__':` if necessary for DataLoader multiprocessing)\r\n\r\nEDIT: replaced `cycle(chain(dl, [\"end\"]))` by `loop()` after comment https://github.com/huggingface/datasets/issues/6623#issuecomment-2401063649 by @ragavsachdeva", "great thanks for the example, will give it a try!", "@lhoestq in the case where dataset.n_shards is divisible by world_size, is it important that each shard contains exactly the same number of samples? what happens if this isn't the case (in what circumstances will this cause a timeout)?", "If your data are not evenly divisible (dataset.n_shards divisibility by world_size just changes the logic to distribute the data) you'll need some logic to make the GPUs happy at the end of training. E.g. with my [example above](https://github.com/huggingface/datasets/issues/6623#issuecomment-2379458138) to stop once all the data from all the ranks are exhausted (and repeating data if necessary to end up with a number of samples evenly divisible)\r\n\r\nThough if dataset.n_shards is divisible by world_size and each shard contains the same amount of data then your data IS evenly divisible so you are all good", "Ok makes sense, thanks for the explanation. I guess even if the shards all contain the same amount of data you still have an issue if you do any filtering (https://github.com/huggingface/datasets/issues/6719)\r\n\r\nWhat do you think of dataset.repeat(n).take(samples_per_epoch) as a simple way of handling this kind of situation? (c.f. issue I just opened #7192 ).\r\n\r\n", "yes it makes sense indeed", "> I made a quick example with communication between ranks to stop once all the data from all the ranks are exhausted (and repeating data if necessary to end up with a number of samples evenly divisible)\r\n> \r\n> ```python\r\n> from itertools import cycle, chain\r\n> ...\r\n> # IMPORTANT: Loop over the local dataset until the data from each rank has been exhausted\r\n> for x in cycle(chain(dl, [\"end\"])):\r\n> if x == \"end\":\r\n> exhausted[rank] = True\r\n> continue\r\n> # stop once the data from all the ranks are exhausted\r\n> dist.all_reduce(exhausted)\r\n> if torch.all(exhausted):\r\n> break\r\n> # do your forward pass + loss here\r\n> # model.forward(...)\r\n> print(x)\r\n> ```\r\n\r\nJust incase someone copy pastes this in their code (like I did), please be aware of https://github.com/pytorch/pytorch/issues/23900 and use https://github.com/pytorch/pytorch/issues/23900#issuecomment-518858050.", "Thanks for noticing @ragavsachdeva ! I edited my code to fix the issue", "I have a node with 8 cards and training files splited into 56 sub files, so my n_shards= 56 / 8 = 7; my initial num_workers = 32, and it report that n_shards = 7 < num_workers, so 25 wokers are stoped, as a result, my training can use only 7 cpu cores at all. should I set my num_wokers less then 7 to get more cpu cores worked?", "In your case each rank has a DataLoader with 7 running workers (and 25 stopped workers) so actually in total there are 87=56 DataLoader workers running (one per shard).\r\n\r\nIf you want to use more CPU for the DataLoader you can shard your dataset in more files than 56. E.g. if you want each rank to run 32 DataLoader workers you need 832=256 files.", "Thank you for the help, I will have a try" ]
2,103,780,697	6,622	multi-GPU map does not work	closed	2024-01-27T20:06:08	2024-02-08T11:18:21	2024-02-08T11:18:21	https://github.com/huggingface/datasets/issues/6622	null	kopyl	false	[ "This should now be fixed by https://github.com/huggingface/datasets/pull/6550 and updated with https://github.com/huggingface/datasets/pull/6646\r\n\r\nFeel free to re-open if you're still having issues :)" ]
2,103,675,294	6,621	deleted	closed	2024-01-27T16:59:58	2024-01-27T17:14:43	2024-01-27T17:14:43	https://github.com/huggingface/datasets/issues/6621	null	kopyl	false	[]
2,103,110,536	6,620	wiki_dpr.py error (ID mismatch between lines {id} and vector {vec_id}	closed	2024-01-27T01:00:09	2024-02-06T09:40:19	2024-02-06T09:40:19	https://github.com/huggingface/datasets/issues/6620	null	kiehls90	false	[ "Thanks for reporting, @kiehls90.\r\n\r\nAs this seems an issue with the specific \"wiki_dpr\" dataset, I am transferring the issue to the corresponding dataset page: https://huggingface.co/datasets/wiki_dpr/discussions/13" ]
2,102,407,478	6,619	Migrate from `setup.cfg` to `pyproject.toml`	closed	2024-01-26T15:27:10	2024-01-26T15:53:40	2024-01-26T15:47:32	https://github.com/huggingface/datasets/pull/6619	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6619", "html_url": "https://github.com/huggingface/datasets/pull/6619", "diff_url": "https://github.com/huggingface/datasets/pull/6619.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6619.patch", "merged_at": "2024-01-26T15:47:32" }	mariosasko	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6619). 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) \| 0.005066 / 0.011353 (-0.006287) \| 0.003678 / 0.011008 (-0.007330) \| 0.063057 / 0.038508 (0.024549) \| 0.031250 / 0.023109 (0.008140) \| 0.248856 / 0.275898 (-0.027042) \| 0.266932 / 0.323480 (-0.056548) \| 0.003814 / 0.007986 (-0.004172) \| 0.002843 / 0.004328 (-0.001485) \| 0.049210 / 0.004250 (0.044959) \| 0.041514 / 0.037052 (0.004462) \| 0.264874 / 0.258489 (0.006385) \| 0.288834 / 0.293841 (-0.005007) \| 0.027457 / 0.128546 (-0.101089) \| 0.011071 / 0.075646 (-0.064575) \| 0.206433 / 0.419271 (-0.212839) \| 0.035381 / 0.043533 (-0.008152) \| 0.246829 / 0.255139 (-0.008310) \| 0.271094 / 0.283200 (-0.012106) \| 0.017790 / 0.141683 (-0.123893) \| 1.134618 / 1.452155 (-0.317536) \| 1.182600 / 1.492716 (-0.310116) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094970 / 0.018006 (0.076964) \| 0.306438 / 0.000490 (0.305949) \| 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) \| 0.017786 / 0.037411 (-0.019625) \| 0.060652 / 0.014526 (0.046127) \| 0.072619 / 0.176557 (-0.103937) \| 0.119460 / 0.737135 (-0.617676) \| 0.073580 / 0.296338 (-0.222759) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279304 / 0.215209 (0.064095) \| 2.747179 / 2.077655 (0.669524) \| 1.438291 / 1.504120 (-0.065829) \| 1.313405 / 1.541195 (-0.227789) \| 1.354569 / 1.468490 (-0.113921) \| 0.578375 / 4.584777 (-4.006402) \| 2.424576 / 3.745712 (-1.321136) \| 2.831513 / 5.269862 (-2.438348) \| 1.756062 / 4.565676 (-2.809614) \| 0.064460 / 0.424275 (-0.359815) \| 0.005065 / 0.007607 (-0.002542) \| 0.335003 / 0.226044 (0.108958) \| 3.310500 / 2.268929 (1.041571) \| 1.778017 / 55.444624 (-53.666607) \| 1.504743 / 6.876477 (-5.371734) \| 1.532843 / 2.142072 (-0.609229) \| 0.662110 / 4.805227 (-4.143118) \| 0.118239 / 6.500664 (-6.382425) \| 0.042135 / 0.075469 (-0.033335) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945650 / 1.841788 (-0.896137) \| 11.623179 / 8.074308 (3.548871) \| 10.927315 / 10.191392 (0.735923) \| 0.131050 / 0.680424 (-0.549374) \| 0.014725 / 0.534201 (-0.519476) \| 0.290716 / 0.579283 (-0.288567) \| 0.272357 / 0.434364 (-0.162007) \| 0.323274 / 0.540337 (-0.217064) \| 0.426692 / 1.386936 (-0.960244) \|\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) \| 0.005478 / 0.011353 (-0.005875) \| 0.003618 / 0.011008 (-0.007390) \| 0.049599 / 0.038508 (0.011091) \| 0.030814 / 0.023109 (0.007705) \| 0.273663 / 0.275898 (-0.002235) \| 0.292099 / 0.323480 (-0.031381) \| 0.004196 / 0.007986 (-0.003790) \| 0.002779 / 0.004328 (-0.001550) \| 0.047812 / 0.004250 (0.043562) \| 0.045095 / 0.037052 (0.008043) \| 0.286288 / 0.258489 (0.027799) \| 0.314125 / 0.293841 (0.020284) \| 0.047940 / 0.128546 (-0.080606) \| 0.010714 / 0.075646 (-0.064932) \| 0.057453 / 0.419271 (-0.361819) \| 0.033482 / 0.043533 (-0.010051) \| 0.273391 / 0.255139 (0.018252) \| 0.284936 / 0.283200 (0.001736) \| 0.017805 / 0.141683 (-0.123878) \| 1.148303 / 1.452155 (-0.303852) \| 1.185268 / 1.492716 (-0.307448) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092442 / 0.018006 (0.074436) \| 0.309908 / 0.000490 (0.309418) \| 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) \| 0.022874 / 0.037411 (-0.014537) \| 0.078238 / 0.014526 (0.063712) \| 0.088844 / 0.176557 (-0.087713) \| 0.127054 / 0.737135 (-0.610081) \| 0.089809 / 0.296338 (-0.206530) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292360 / 0.215209 (0.077151) \| 2.842700 / 2.077655 (0.765045) \| 1.571071 / 1.504120 (0.066951) \| 1.450773 / 1.541195 (-0.090422) \| 1.467090 / 1.468490 (-0.001400) \| 0.583529 / 4.584777 (-4.001248) \| 2.469284 / 3.745712 (-1.276428) \| 2.844426 / 5.269862 (-2.425435) \| 1.773336 / 4.565676 (-2.792341) \| 0.064585 / 0.424275 (-0.359690) \| 0.005098 / 0.007607 (-0.002509) \| 0.342816 / 0.226044 (0.116771) \| 3.363309 / 2.268929 (1.094381) \| 1.922834 / 55.444624 (-53.521790) \| 1.649702 / 6.876477 (-5.226774) \| 1.672727 / 2.142072 (-0.469345) \| 0.665015 / 4.805227 (-4.140212) \| 0.124764 / 6.500664 (-6.375900) \| 0.041564 / 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) \| 0.988970 / 1.841788 (-0.852818) \| 12.148983 / 8.074308 (4.074675) \| 11.132697 / 10.191392 (0.941305) \| 0.131596 / 0.680424 (-0.548828) \| 0.015700 / 0.534201 (-0.518501) \| 0.288819 / 0.579283 (-0.290464) \| 0.276692 / 0.434364 (-0.157672) \| 0.330260 / 0.540337 (-0.210078) \| 0.421612 / 1.386936 (-0.965324) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d627fb8357f39d78d79e704712609c7b34bdeba4 \"CML watermark\")\n" ]
2,101,868,198	6,618	While importing load_dataset from datasets	closed	2024-01-26T09:21:57	2024-07-23T09:31:07	2024-02-06T09:25:54	https://github.com/huggingface/datasets/issues/6618	null	suprith-hub	false	[ "Hi! Can you please share the error's stack trace so we can see where it comes from?", "We cannot reproduce the issue and we do not have enough information: environment info (need to run `datasets-cli env`), stack trace,...\r\n\r\nI am closing the issue. Feel free to reopen it (with additional information) if the problem persists.", "Yeah 👍\r\n\r\nOn Tue, 6 Feb 2024 at 2:56 PM, Albert Villanova del Moral <\r\n*@.> wrote:\r\n\r\n> We cannot reproduce the issue and we do not have enough information:\r\n> environment info (need to run datasets-cli env), stack trace,...\r\n>\r\n> I am closing the issue. Feel free to reopen it (with additional\r\n> information) if the problem persists.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6618#issuecomment-1929102334>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/ASS4PJ3XOIIWISPY3VX3QRTYSHZK5AVCNFSM6AAAAABCL3BT4SVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSMRZGEYDEMZTGQ>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> @.*>\r\n>\r\n", "Please downgrade the version of urllib3 if you have the same issue:\r\n\r\n!pip install urllib3==1.25.11", "> Please downgrade the version of urllib3 if you have the same issue:\r\n> \r\n> !pip install urllib3==1.25.11\r\n\r\nThis worked for me. Thanks.\r\n\r\nI use python 3.11 and datasets==2.20.0. Downgrading urllib3 to 1.25.11 worked in my case." ]
2,100,459,449	6,617	Fix CI: pyarrow 15, pandas 2.2 and sqlachemy	closed	2024-01-25T13:57:41	2024-01-26T14:56:46	2024-01-26T14:50:44	https://github.com/huggingface/datasets/pull/6617	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6617", "html_url": "https://github.com/huggingface/datasets/pull/6617", "diff_url": "https://github.com/huggingface/datasets/pull/6617.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6617.patch", "merged_at": "2024-01-26T14:50:44" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6617). 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) \| 0.004774 / 0.011353 (-0.006579) \| 0.003397 / 0.011008 (-0.007611) \| 0.063862 / 0.038508 (0.025354) \| 0.029353 / 0.023109 (0.006244) \| 0.245921 / 0.275898 (-0.029977) \| 0.268414 / 0.323480 (-0.055066) \| 0.002834 / 0.007986 (-0.005152) \| 0.002606 / 0.004328 (-0.001723) \| 0.049690 / 0.004250 (0.045439) \| 0.041637 / 0.037052 (0.004585) \| 0.262526 / 0.258489 (0.004037) \| 0.288200 / 0.293841 (-0.005641) \| 0.027233 / 0.128546 (-0.101313) \| 0.010322 / 0.075646 (-0.065324) \| 0.213860 / 0.419271 (-0.205411) \| 0.034930 / 0.043533 (-0.008602) \| 0.249256 / 0.255139 (-0.005883) \| 0.270016 / 0.283200 (-0.013184) \| 0.019413 / 0.141683 (-0.122270) \| 1.124801 / 1.452155 (-0.327354) \| 1.166224 / 1.492716 (-0.326492) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091641 / 0.018006 (0.073635) \| 0.299679 / 0.000490 (0.299189) \| 0.000209 / 0.000200 (0.000009) \| 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) \| 0.018084 / 0.037411 (-0.019327) \| 0.060143 / 0.014526 (0.045617) \| 0.072556 / 0.176557 (-0.104001) \| 0.118555 / 0.737135 (-0.618580) \| 0.073786 / 0.296338 (-0.222553) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278193 / 0.215209 (0.062984) \| 2.707954 / 2.077655 (0.630300) \| 1.483575 / 1.504120 (-0.020545) \| 1.371939 / 1.541195 (-0.169256) \| 1.395009 / 1.468490 (-0.073481) \| 0.559949 / 4.584777 (-4.024828) \| 2.372529 / 3.745712 (-1.373183) \| 2.823641 / 5.269862 (-2.446221) \| 1.722999 / 4.565676 (-2.842678) \| 0.062535 / 0.424275 (-0.361741) \| 0.004970 / 0.007607 (-0.002637) \| 0.338625 / 0.226044 (0.112580) \| 3.317576 / 2.268929 (1.048648) \| 1.854552 / 55.444624 (-53.590073) \| 1.589323 / 6.876477 (-5.287154) \| 1.624630 / 2.142072 (-0.517442) \| 0.638388 / 4.805227 (-4.166839) \| 0.116675 / 6.500664 (-6.383989) \| 0.041850 / 0.075469 (-0.033619) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.938025 / 1.841788 (-0.903763) \| 11.450072 / 8.074308 (3.375764) \| 10.414943 / 10.191392 (0.223551) \| 0.128416 / 0.680424 (-0.552007) \| 0.013798 / 0.534201 (-0.520403) \| 0.287997 / 0.579283 (-0.291286) \| 0.259976 / 0.434364 (-0.174387) \| 0.320737 / 0.540337 (-0.219601) \| 0.424292 / 1.386936 (-0.962644) \|\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) \| 0.005107 / 0.011353 (-0.006246) \| 0.003374 / 0.011008 (-0.007634) \| 0.050067 / 0.038508 (0.011559) \| 0.031419 / 0.023109 (0.008310) \| 0.275303 / 0.275898 (-0.000595) \| 0.286736 / 0.323480 (-0.036744) \| 0.004177 / 0.007986 (-0.003808) \| 0.002742 / 0.004328 (-0.001586) \| 0.049011 / 0.004250 (0.044761) \| 0.044373 / 0.037052 (0.007321) \| 0.289189 / 0.258489 (0.030700) \| 0.320117 / 0.293841 (0.026276) \| 0.050154 / 0.128546 (-0.078392) \| 0.010541 / 0.075646 (-0.065106) \| 0.058318 / 0.419271 (-0.360954) \| 0.033090 / 0.043533 (-0.010443) \| 0.276820 / 0.255139 (0.021681) \| 0.290854 / 0.283200 (0.007654) \| 0.017268 / 0.141683 (-0.124415) \| 1.159345 / 1.452155 (-0.292809) \| 1.224829 / 1.492716 (-0.267887) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092468 / 0.018006 (0.074462) \| 0.301176 / 0.000490 (0.300686) \| 0.000216 / 0.000200 (0.000017) \| 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) \| 0.021858 / 0.037411 (-0.015553) \| 0.074873 / 0.014526 (0.060347) \| 0.086238 / 0.176557 (-0.090318) \| 0.125555 / 0.737135 (-0.611580) \| 0.087791 / 0.296338 (-0.208547) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292283 / 0.215209 (0.077073) \| 2.847306 / 2.077655 (0.769651) \| 1.600833 / 1.504120 (0.096713) \| 1.474253 / 1.541195 (-0.066942) \| 1.474871 / 1.468490 (0.006381) \| 0.576427 / 4.584777 (-4.008350) \| 2.380116 / 3.745712 (-1.365596) \| 2.782059 / 5.269862 (-2.487803) \| 1.730642 / 4.565676 (-2.835035) \| 0.063860 / 0.424275 (-0.360415) \| 0.005019 / 0.007607 (-0.002588) \| 0.343247 / 0.226044 (0.117202) \| 3.393427 / 2.268929 (1.124498) \| 1.935346 / 55.444624 (-53.509278) \| 1.680124 / 6.876477 (-5.196353) \| 1.665788 / 2.142072 (-0.476285) \| 0.648767 / 4.805227 (-4.156460) \| 0.121962 / 6.500664 (-6.378702) \| 0.040669 / 0.075469 (-0.034800) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.996535 / 1.841788 (-0.845252) \| 12.074553 / 8.074308 (4.000245) \| 10.812740 / 10.191392 (0.621348) \| 0.142690 / 0.680424 (-0.537734) \| 0.014977 / 0.534201 (-0.519224) \| 0.285619 / 0.579283 (-0.293664) \| 0.269401 / 0.434364 (-0.164963) \| 0.329882 / 0.540337 (-0.210456) \| 0.416169 / 1.386936 (-0.970767) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#129b9e0565e7a2ceaca64b99dcbf39504661cfa9 \"CML watermark\")\n" ]
2,100,125,709	6,616	Use schema metadata only if it matches features	closed	2024-01-25T11:01:14	2024-01-26T16:25:24	2024-01-26T16:19:12	https://github.com/huggingface/datasets/pull/6616	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6616", "html_url": "https://github.com/huggingface/datasets/pull/6616", "diff_url": "https://github.com/huggingface/datasets/pull/6616.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6616.patch", "merged_at": "2024-01-26T16:19:12" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6616). 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) \| 0.005382 / 0.011353 (-0.005970) \| 0.003853 / 0.011008 (-0.007155) \| 0.062629 / 0.038508 (0.024121) \| 0.030344 / 0.023109 (0.007234) \| 0.245394 / 0.275898 (-0.030505) \| 0.266004 / 0.323480 (-0.057476) \| 0.003183 / 0.007986 (-0.004802) \| 0.002795 / 0.004328 (-0.001533) \| 0.048357 / 0.004250 (0.044107) \| 0.043834 / 0.037052 (0.006782) \| 0.255979 / 0.258489 (-0.002510) \| 0.280803 / 0.293841 (-0.013038) \| 0.028200 / 0.128546 (-0.100347) \| 0.010856 / 0.075646 (-0.064791) \| 0.207076 / 0.419271 (-0.212195) \| 0.036286 / 0.043533 (-0.007247) \| 0.246492 / 0.255139 (-0.008647) \| 0.265861 / 0.283200 (-0.017338) \| 0.018309 / 0.141683 (-0.123374) \| 1.155136 / 1.452155 (-0.297018) \| 1.214342 / 1.492716 (-0.278375) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092530 / 0.018006 (0.074524) \| 0.344951 / 0.000490 (0.344461) \| 0.000207 / 0.000200 (0.000007) \| 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) \| 0.018324 / 0.037411 (-0.019087) \| 0.063137 / 0.014526 (0.048611) \| 0.074683 / 0.176557 (-0.101874) \| 0.120224 / 0.737135 (-0.616912) \| 0.083107 / 0.296338 (-0.213232) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288631 / 0.215209 (0.073422) \| 2.817992 / 2.077655 (0.740337) \| 1.473609 / 1.504120 (-0.030511) \| 1.336610 / 1.541195 (-0.204585) \| 1.354807 / 1.468490 (-0.113683) \| 0.568776 / 4.584777 (-4.016001) \| 2.412607 / 3.745712 (-1.333105) \| 2.832816 / 5.269862 (-2.437045) \| 1.789899 / 4.565676 (-2.775778) \| 0.063602 / 0.424275 (-0.360673) \| 0.004993 / 0.007607 (-0.002615) \| 0.338830 / 0.226044 (0.112786) \| 3.302550 / 2.268929 (1.033621) \| 1.827907 / 55.444624 (-53.616717) \| 1.589857 / 6.876477 (-5.286620) \| 1.647746 / 2.142072 (-0.494326) \| 0.658461 / 4.805227 (-4.146766) \| 0.120360 / 6.500664 (-6.380304) \| 0.042989 / 0.075469 (-0.032480) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945487 / 1.841788 (-0.896301) \| 11.846335 / 8.074308 (3.772027) \| 10.483199 / 10.191392 (0.291807) \| 0.131853 / 0.680424 (-0.548570) \| 0.014230 / 0.534201 (-0.519971) \| 0.288700 / 0.579283 (-0.290584) \| 0.276086 / 0.434364 (-0.158278) \| 0.326225 / 0.540337 (-0.214112) \| 0.422874 / 1.386936 (-0.964062) \|\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) \| 0.006234 / 0.011353 (-0.005118) \| 0.004104 / 0.011008 (-0.006904) \| 0.049967 / 0.038508 (0.011459) \| 0.037157 / 0.023109 (0.014048) \| 0.261892 / 0.275898 (-0.014006) \| 0.284304 / 0.323480 (-0.039176) \| 0.004482 / 0.007986 (-0.003504) \| 0.002920 / 0.004328 (-0.001409) \| 0.048827 / 0.004250 (0.044577) \| 0.052258 / 0.037052 (0.015206) \| 0.277121 / 0.258489 (0.018632) \| 0.304177 / 0.293841 (0.010336) \| 0.053537 / 0.128546 (-0.075009) \| 0.011137 / 0.075646 (-0.064509) \| 0.058188 / 0.419271 (-0.361083) \| 0.034283 / 0.043533 (-0.009250) \| 0.261912 / 0.255139 (0.006773) \| 0.273851 / 0.283200 (-0.009348) \| 0.017824 / 0.141683 (-0.123859) \| 1.130454 / 1.452155 (-0.321701) \| 1.176834 / 1.492716 (-0.315882) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.102104 / 0.018006 (0.084098) \| 0.302873 / 0.000490 (0.302383) \| 0.000208 / 0.000200 (0.000008) \| 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) \| 0.022470 / 0.037411 (-0.014941) \| 0.076776 / 0.014526 (0.062250) \| 0.088220 / 0.176557 (-0.088337) \| 0.130030 / 0.737135 (-0.607105) \| 0.089955 / 0.296338 (-0.206383) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284070 / 0.215209 (0.068861) \| 2.769130 / 2.077655 (0.691475) \| 1.546379 / 1.504120 (0.042259) \| 1.435849 / 1.541195 (-0.105346) \| 1.478616 / 1.468490 (0.010126) \| 0.569185 / 4.584777 (-4.015592) \| 2.504721 / 3.745712 (-1.240992) \| 2.778267 / 5.269862 (-2.491595) \| 1.860360 / 4.565676 (-2.705316) \| 0.073465 / 0.424275 (-0.350810) \| 0.005108 / 0.007607 (-0.002499) \| 0.335185 / 0.226044 (0.109140) \| 3.314799 / 2.268929 (1.045870) \| 1.934824 / 55.444624 (-53.509801) \| 1.656247 / 6.876477 (-5.220229) \| 1.785422 / 2.142072 (-0.356650) \| 0.673677 / 4.805227 (-4.131551) \| 0.117692 / 6.500664 (-6.382972) \| 0.041648 / 0.075469 (-0.033821) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972143 / 1.841788 (-0.869645) \| 12.980353 / 8.074308 (4.906045) \| 11.056189 / 10.191392 (0.864797) \| 0.134592 / 0.680424 (-0.545832) \| 0.015972 / 0.534201 (-0.518229) \| 0.301691 / 0.579283 (-0.277593) \| 0.286332 / 0.434364 (-0.148032) \| 0.329025 / 0.540337 (-0.211312) \| 0.422585 / 1.386936 (-0.964351) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6eb492c7072f21cb417801957c087888f252d2d1 \"CML watermark\")\n" ]
2,098,951,409	6,615	...	closed	2024-01-24T19:37:03	2024-01-24T19:42:30	2024-01-24T19:40:11	https://github.com/huggingface/datasets/issues/6615	null	ftkeys	false	[ "Sorry I posted in the wrong repo, please delete.. thanks!" ]
2,098,884,520	6,614	`datasets/downloads` cleanup tool	open	2024-01-24T18:52:10	2024-01-24T18:55:09	null	https://github.com/huggingface/datasets/issues/6614	null	stas00	false	[]
2,098,078,210	6,612	cnn_dailymail repeats itself	closed	2024-01-24T11:38:25	2024-02-01T08:14:50	2024-02-01T08:14:50	https://github.com/huggingface/datasets/issues/6612	null	KeremZaman	false	[ "Hi ! We recently updated `cnn_dailymail` and now `datasets>=2.14` is needed to load it.\r\n\r\nYou can update `datasets` with\r\n\r\n```\r\npip install -U datasets\r\n```" ]
2,096,004,858	6,611	`load_from_disk` with large dataset from S3 runs into `botocore.exceptions.ClientError`	open	2024-01-23T12:37:57	2024-01-23T12:37:57	null	https://github.com/huggingface/datasets/issues/6611	null	zotroneneis	false	[]
2,095,643,711	6,610	cast_column to Sequence(subfeatures_dict) has err	closed	2024-01-23T09:32:32	2024-01-25T02:15:23	2024-01-25T02:15:23	https://github.com/huggingface/datasets/issues/6610	null	neiblegy	false	[ "Hi! You are passing the wrong feature type to `cast_column`. This is the fixed call:\r\n```python\r\nais_dataset = ais_dataset.cast_column(\"my_labeled_bbox\", {\"bbox\": Sequence(Value(dtype=\"int64\")), \"label\": ClassLabel(names=[\"cat\", \"dog\"])})\r\n```", "> Hi! You are passing the wrong feature type to `cast_column`. This is the fixed call:\r\n> \r\n> ```python\r\n> ais_dataset = ais_dataset.cast_column(\"my_labeled_bbox\", {\"bbox\": Sequence(Value(dtype=\"int64\")), \"label\": ClassLabel(names=[\"cat\", \"dog\"])})\r\n> ```\r\n\r\nthanks" ]
2,095,085,650	6,609	Wrong path for cache directory in offline mode	closed	2024-01-23T01:47:19	2024-02-06T17:21:25	2024-02-06T17:21:25	https://github.com/huggingface/datasets/issues/6609	null	je-santos	false	[ "+1", "same error in 2.16.1", "@kongjiellx any luck with the issue?", "I opened https://github.com/huggingface/datasets/pull/6632 to fix this issue. Once it's merged we'll do a new release of `datasets`", "Thanks @lhoestq !" ]
2,094,153,292	6,608	Add `with_rank` param to `Dataset.filter`	closed	2024-01-22T15:19:16	2024-01-29T16:43:11	2024-01-29T16:36:53	https://github.com/huggingface/datasets/pull/6608	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6608", "html_url": "https://github.com/huggingface/datasets/pull/6608", "diff_url": "https://github.com/huggingface/datasets/pull/6608.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6608.patch", "merged_at": "2024-01-29T16:36:53" }	mariosasko	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6608). 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) \| 0.005376 / 0.011353 (-0.005977) \| 0.004691 / 0.011008 (-0.006317) \| 0.064061 / 0.038508 (0.025553) \| 0.030397 / 0.023109 (0.007288) \| 0.242656 / 0.275898 (-0.033242) \| 0.275586 / 0.323480 (-0.047894) \| 0.003460 / 0.007986 (-0.004526) \| 0.003125 / 0.004328 (-0.001203) \| 0.050496 / 0.004250 (0.046246) \| 0.045833 / 0.037052 (0.008781) \| 0.255222 / 0.258489 (-0.003267) \| 0.287303 / 0.293841 (-0.006538) \| 0.027755 / 0.128546 (-0.100791) \| 0.011251 / 0.075646 (-0.064396) \| 0.208456 / 0.419271 (-0.210816) \| 0.037219 / 0.043533 (-0.006314) \| 0.249592 / 0.255139 (-0.005547) \| 0.261243 / 0.283200 (-0.021957) \| 0.020735 / 0.141683 (-0.120948) \| 1.130017 / 1.452155 (-0.322137) \| 1.208558 / 1.492716 (-0.284158) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098891 / 0.018006 (0.080885) \| 0.439042 / 0.000490 (0.438552) \| 0.000333 / 0.000200 (0.000133) \| 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) \| 0.018356 / 0.037411 (-0.019055) \| 0.062416 / 0.014526 (0.047891) \| 0.075613 / 0.176557 (-0.100944) \| 0.122009 / 0.737135 (-0.615126) \| 0.078195 / 0.296338 (-0.218144) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.273804 / 0.215209 (0.058595) \| 2.706480 / 2.077655 (0.628826) \| 1.456196 / 1.504120 (-0.047924) \| 1.353301 / 1.541195 (-0.187893) \| 1.378913 / 1.468490 (-0.089577) \| 0.556885 / 4.584777 (-4.027892) \| 2.358961 / 3.745712 (-1.386752) \| 2.871830 / 5.269862 (-2.398031) \| 1.765212 / 4.565676 (-2.800464) \| 0.062172 / 0.424275 (-0.362103) \| 0.004974 / 0.007607 (-0.002633) \| 0.330375 / 0.226044 (0.104331) \| 3.264550 / 2.268929 (0.995621) \| 1.824444 / 55.444624 (-53.620181) \| 1.561189 / 6.876477 (-5.315287) \| 1.671020 / 2.142072 (-0.471052) \| 0.633408 / 4.805227 (-4.171819) \| 0.116080 / 6.500664 (-6.384584) \| 0.044606 / 0.075469 (-0.030863) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980757 / 1.841788 (-0.861031) \| 12.553534 / 8.074308 (4.479225) \| 10.517668 / 10.191392 (0.326276) \| 0.130528 / 0.680424 (-0.549896) \| 0.013960 / 0.534201 (-0.520241) \| 0.289615 / 0.579283 (-0.289668) \| 0.267277 / 0.434364 (-0.167087) \| 0.324139 / 0.540337 (-0.216198) \| 0.440325 / 1.386936 (-0.946611) \|\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) \| 0.005388 / 0.011353 (-0.005965) \| 0.004043 / 0.011008 (-0.006966) \| 0.050514 / 0.038508 (0.012005) \| 0.031413 / 0.023109 (0.008303) \| 0.275122 / 0.275898 (-0.000776) \| 0.307518 / 0.323480 (-0.015962) \| 0.004440 / 0.007986 (-0.003546) \| 0.003301 / 0.004328 (-0.001027) \| 0.049200 / 0.004250 (0.044949) \| 0.045704 / 0.037052 (0.008651) \| 0.285265 / 0.258489 (0.026776) \| 0.318942 / 0.293841 (0.025101) \| 0.053893 / 0.128546 (-0.074653) \| 0.011855 / 0.075646 (-0.063791) \| 0.060951 / 0.419271 (-0.358321) \| 0.034397 / 0.043533 (-0.009136) \| 0.276108 / 0.255139 (0.020969) \| 0.290981 / 0.283200 (0.007781) \| 0.019986 / 0.141683 (-0.121697) \| 1.205695 / 1.452155 (-0.246460) \| 1.255942 / 1.492716 (-0.236774) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.101910 / 0.018006 (0.083904) \| 0.320551 / 0.000490 (0.320061) \| 0.000299 / 0.000200 (0.000099) \| 0.000058 / 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) \| 0.022387 / 0.037411 (-0.015024) \| 0.076380 / 0.014526 (0.061854) \| 0.090404 / 0.176557 (-0.086153) \| 0.127106 / 0.737135 (-0.610030) \| 0.089873 / 0.296338 (-0.206465) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288433 / 0.215209 (0.073223) \| 2.827005 / 2.077655 (0.749350) \| 1.548760 / 1.504120 (0.044640) \| 1.419545 / 1.541195 (-0.121650) \| 1.456531 / 1.468490 (-0.011959) \| 0.570254 / 4.584777 (-4.014523) \| 2.441318 / 3.745712 (-1.304394) \| 2.778647 / 5.269862 (-2.491215) \| 1.755255 / 4.565676 (-2.810422) \| 0.062581 / 0.424275 (-0.361694) \| 0.005205 / 0.007607 (-0.002402) \| 0.342189 / 0.226044 (0.116145) \| 3.401208 / 2.268929 (1.132279) \| 1.941447 / 55.444624 (-53.503178) \| 1.652578 / 6.876477 (-5.223899) \| 1.768558 / 2.142072 (-0.373514) \| 0.656537 / 4.805227 (-4.148690) \| 0.116901 / 6.500664 (-6.383763) \| 0.041408 / 0.075469 (-0.034061) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.001715 / 1.841788 (-0.840073) \| 12.533073 / 8.074308 (4.458765) \| 11.086084 / 10.191392 (0.894692) \| 0.134368 / 0.680424 (-0.546055) \| 0.015255 / 0.534201 (-0.518946) \| 0.291769 / 0.579283 (-0.287514) \| 0.283311 / 0.434364 (-0.151053) \| 0.327857 / 0.540337 (-0.212481) \| 0.413854 / 1.386936 (-0.973083) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#46931085bd8a3fdbc63b68b5ee4b8f62029c7557 \"CML watermark\")\n" ]
2,091,766,063	6,607	Update features.py to avoid bfloat16 unsupported error	closed	2024-01-20T00:39:44	2024-05-17T09:46:29	2024-05-17T09:40:13	https://github.com/huggingface/datasets/pull/6607	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6607", "html_url": "https://github.com/huggingface/datasets/pull/6607", "diff_url": "https://github.com/huggingface/datasets/pull/6607.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6607.patch", "merged_at": "2024-05-17T09:40:13" }	skaulintel	true	[ "I think not all torch tensors should be converted to float, what if it's a tensor of integers for example ?\r\nMaybe you can check for the tensor dtype before converting", "@lhoestq Please could this be 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) \| 0.005552 / 0.011353 (-0.005801) \| 0.003707 / 0.011008 (-0.007301) \| 0.063794 / 0.038508 (0.025286) \| 0.031897 / 0.023109 (0.008788) \| 0.263086 / 0.275898 (-0.012812) \| 0.281184 / 0.323480 (-0.042296) \| 0.003183 / 0.007986 (-0.004802) \| 0.002681 / 0.004328 (-0.001648) \| 0.050259 / 0.004250 (0.046009) \| 0.048395 / 0.037052 (0.011342) \| 0.266925 / 0.258489 (0.008436) \| 0.298146 / 0.293841 (0.004305) \| 0.027995 / 0.128546 (-0.100551) \| 0.010689 / 0.075646 (-0.064957) \| 0.204956 / 0.419271 (-0.214316) \| 0.036453 / 0.043533 (-0.007080) \| 0.255406 / 0.255139 (0.000267) \| 0.271388 / 0.283200 (-0.011811) \| 0.019748 / 0.141683 (-0.121935) \| 1.103926 / 1.452155 (-0.348228) \| 1.167250 / 1.492716 (-0.325466) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.100483 / 0.018006 (0.082477) \| 0.307331 / 0.000490 (0.306841) \| 0.000216 / 0.000200 (0.000016) \| 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) \| 0.018918 / 0.037411 (-0.018493) \| 0.062569 / 0.014526 (0.048044) \| 0.074935 / 0.176557 (-0.101621) \| 0.122590 / 0.737135 (-0.614545) \| 0.076475 / 0.296338 (-0.219864) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279001 / 0.215209 (0.063792) \| 2.771630 / 2.077655 (0.693975) \| 1.439666 / 1.504120 (-0.064454) \| 1.303422 / 1.541195 (-0.237773) \| 1.355670 / 1.468490 (-0.112820) \| 0.576264 / 4.584777 (-4.008513) \| 2.394868 / 3.745712 (-1.350844) \| 2.941487 / 5.269862 (-2.328375) \| 1.808733 / 4.565676 (-2.756943) \| 0.063691 / 0.424275 (-0.360584) \| 0.005399 / 0.007607 (-0.002208) \| 0.335610 / 0.226044 (0.109566) \| 3.295903 / 2.268929 (1.026974) \| 1.771836 / 55.444624 (-53.672788) \| 1.511246 / 6.876477 (-5.365231) \| 1.535926 / 2.142072 (-0.606147) \| 0.649020 / 4.805227 (-4.156207) \| 0.119754 / 6.500664 (-6.380910) \| 0.043319 / 0.075469 (-0.032150) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.967275 / 1.841788 (-0.874513) \| 12.358482 / 8.074308 (4.284174) \| 9.933324 / 10.191392 (-0.258068) \| 0.133565 / 0.680424 (-0.546859) \| 0.015650 / 0.534201 (-0.518551) \| 0.286978 / 0.579283 (-0.292305) \| 0.262912 / 0.434364 (-0.171451) \| 0.330335 / 0.540337 (-0.210002) \| 0.427671 / 1.386936 (-0.959265) \|\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) \| 0.005660 / 0.011353 (-0.005693) \| 0.003908 / 0.011008 (-0.007101) \| 0.051874 / 0.038508 (0.013366) \| 0.033141 / 0.023109 (0.010032) \| 0.270512 / 0.275898 (-0.005386) \| 0.296790 / 0.323480 (-0.026690) \| 0.004335 / 0.007986 (-0.003651) \| 0.002842 / 0.004328 (-0.001487) \| 0.078264 / 0.004250 (0.074014) \| 0.044436 / 0.037052 (0.007384) \| 0.283230 / 0.258489 (0.024741) \| 0.318026 / 0.293841 (0.024185) \| 0.031459 / 0.128546 (-0.097087) \| 0.010710 / 0.075646 (-0.064937) \| 0.058152 / 0.419271 (-0.361119) \| 0.034021 / 0.043533 (-0.009512) \| 0.269956 / 0.255139 (0.014817) \| 0.288783 / 0.283200 (0.005583) \| 0.019246 / 0.141683 (-0.122436) \| 1.127264 / 1.452155 (-0.324891) \| 1.169777 / 1.492716 (-0.322939) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.101523 / 0.018006 (0.083516) \| 0.315120 / 0.000490 (0.314630) \| 0.000218 / 0.000200 (0.000018) \| 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) \| 0.023078 / 0.037411 (-0.014333) \| 0.080021 / 0.014526 (0.065495) \| 0.089574 / 0.176557 (-0.086982) \| 0.131258 / 0.737135 (-0.605877) \| 0.090604 / 0.296338 (-0.205734) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302197 / 0.215209 (0.086988) \| 2.980071 / 2.077655 (0.902416) \| 1.585480 / 1.504120 (0.081360) \| 1.462904 / 1.541195 (-0.078291) \| 1.501102 / 1.468490 (0.032612) \| 0.580342 / 4.584777 (-4.004435) \| 0.972118 / 3.745712 (-2.773594) \| 2.930530 / 5.269862 (-2.339331) \| 1.824132 / 4.565676 (-2.741545) \| 0.064711 / 0.424275 (-0.359564) \| 0.005084 / 0.007607 (-0.002523) \| 0.352693 / 0.226044 (0.126649) \| 3.522775 / 2.268929 (1.253847) \| 1.965063 / 55.444624 (-53.479561) \| 1.679250 / 6.876477 (-5.197226) \| 1.711691 / 2.142072 (-0.430382) \| 0.663719 / 4.805227 (-4.141509) \| 0.119858 / 6.500664 (-6.380806) \| 0.041744 / 0.075469 (-0.033725) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.017970 / 1.841788 (-0.823817) \| 12.898917 / 8.074308 (4.824609) \| 10.244728 / 10.191392 (0.053336) \| 0.133860 / 0.680424 (-0.546564) \| 0.016044 / 0.534201 (-0.518157) \| 0.287543 / 0.579283 (-0.291740) \| 0.126418 / 0.434364 (-0.307946) \| 0.394970 / 0.540337 (-0.145368) \| 0.420455 / 1.386936 (-0.966481) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b7d71ffeb10bc129f6f923cfadb5ccd9383b8033 \"CML watermark\")\n" ]
2,091,088,785	6,606	Dedicated RNG object for fingerprinting	closed	2024-01-19T18:34:47	2024-01-26T15:11:38	2024-01-26T15:05:34	https://github.com/huggingface/datasets/pull/6606	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6606", "html_url": "https://github.com/huggingface/datasets/pull/6606", "diff_url": "https://github.com/huggingface/datasets/pull/6606.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6606.patch", "merged_at": "2024-01-26T15:05:34" }	mariosasko	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6606). 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) \| 0.005625 / 0.011353 (-0.005728) \| 0.003313 / 0.011008 (-0.007695) \| 0.063997 / 0.038508 (0.025489) \| 0.028949 / 0.023109 (0.005839) \| 0.250069 / 0.275898 (-0.025829) \| 0.271412 / 0.323480 (-0.052068) \| 0.003837 / 0.007986 (-0.004148) \| 0.002632 / 0.004328 (-0.001697) \| 0.048351 / 0.004250 (0.044100) \| 0.040664 / 0.037052 (0.003612) \| 0.267540 / 0.258489 (0.009051) \| 0.285237 / 0.293841 (-0.008604) \| 0.026962 / 0.128546 (-0.101584) \| 0.010417 / 0.075646 (-0.065229) \| 0.211430 / 0.419271 (-0.207842) \| 0.035411 / 0.043533 (-0.008122) \| 0.258867 / 0.255139 (0.003728) \| 0.278562 / 0.283200 (-0.004638) \| 0.017690 / 0.141683 (-0.123993) \| 1.128813 / 1.452155 (-0.323342) \| 1.169384 / 1.492716 (-0.323333) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091322 / 0.018006 (0.073316) \| 0.303272 / 0.000490 (0.302782) \| 0.000202 / 0.000200 (0.000002) \| 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) \| 0.017551 / 0.037411 (-0.019861) \| 0.060027 / 0.014526 (0.045502) \| 0.073431 / 0.176557 (-0.103125) \| 0.120550 / 0.737135 (-0.616585) \| 0.073107 / 0.296338 (-0.223231) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283064 / 0.215209 (0.067855) \| 2.754593 / 2.077655 (0.676938) \| 1.477303 / 1.504120 (-0.026817) \| 1.341072 / 1.541195 (-0.200123) \| 1.366625 / 1.468490 (-0.101865) \| 0.573467 / 4.584777 (-4.011310) \| 2.395225 / 3.745712 (-1.350487) \| 2.777021 / 5.269862 (-2.492841) \| 1.720733 / 4.565676 (-2.844944) \| 0.063339 / 0.424275 (-0.360936) \| 0.004954 / 0.007607 (-0.002653) \| 0.350359 / 0.226044 (0.124315) \| 3.376221 / 2.268929 (1.107293) \| 1.835539 / 55.444624 (-53.609086) \| 1.558064 / 6.876477 (-5.318413) \| 1.582778 / 2.142072 (-0.559294) \| 0.649918 / 4.805227 (-4.155309) \| 0.117761 / 6.500664 (-6.382903) \| 0.041771 / 0.075469 (-0.033698) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.950202 / 1.841788 (-0.891586) \| 11.476160 / 8.074308 (3.401852) \| 10.290618 / 10.191392 (0.099226) \| 0.140659 / 0.680424 (-0.539765) \| 0.014525 / 0.534201 (-0.519676) \| 0.287253 / 0.579283 (-0.292030) \| 0.266204 / 0.434364 (-0.168160) \| 0.327818 / 0.540337 (-0.212519) \| 0.431680 / 1.386936 (-0.955256) \|\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) \| 0.005096 / 0.011353 (-0.006257) \| 0.003460 / 0.011008 (-0.007548) \| 0.049474 / 0.038508 (0.010966) \| 0.031063 / 0.023109 (0.007954) \| 0.272899 / 0.275898 (-0.002999) \| 0.291859 / 0.323480 (-0.031621) \| 0.004858 / 0.007986 (-0.003128) \| 0.002598 / 0.004328 (-0.001731) \| 0.049074 / 0.004250 (0.044824) \| 0.044722 / 0.037052 (0.007669) \| 0.285262 / 0.258489 (0.026772) \| 0.314168 / 0.293841 (0.020327) \| 0.046346 / 0.128546 (-0.082200) \| 0.010384 / 0.075646 (-0.065262) \| 0.058331 / 0.419271 (-0.360940) \| 0.033728 / 0.043533 (-0.009805) \| 0.276217 / 0.255139 (0.021078) \| 0.295465 / 0.283200 (0.012265) \| 0.018215 / 0.141683 (-0.123467) \| 1.163847 / 1.452155 (-0.288308) \| 1.213901 / 1.492716 (-0.278816) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091953 / 0.018006 (0.073947) \| 0.299977 / 0.000490 (0.299487) \| 0.000212 / 0.000200 (0.000012) \| 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) \| 0.022031 / 0.037411 (-0.015381) \| 0.075067 / 0.014526 (0.060541) \| 0.087305 / 0.176557 (-0.089251) \| 0.125530 / 0.737135 (-0.611605) \| 0.088761 / 0.296338 (-0.207578) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302682 / 0.215209 (0.087473) \| 2.941509 / 2.077655 (0.863854) \| 1.643399 / 1.504120 (0.139280) \| 1.530148 / 1.541195 (-0.011046) \| 1.542067 / 1.468490 (0.073577) \| 0.575883 / 4.584777 (-4.008894) \| 2.434320 / 3.745712 (-1.311392) \| 2.761683 / 5.269862 (-2.508179) \| 1.732068 / 4.565676 (-2.833609) \| 0.063543 / 0.424275 (-0.360732) \| 0.005089 / 0.007607 (-0.002518) \| 0.351314 / 0.226044 (0.125269) \| 3.494572 / 2.268929 (1.225643) \| 2.032503 / 55.444624 (-53.412121) \| 1.697949 / 6.876477 (-5.178528) \| 1.700392 / 2.142072 (-0.441680) \| 0.650757 / 4.805227 (-4.154471) \| 0.116719 / 6.500664 (-6.383945) \| 0.040559 / 0.075469 (-0.034910) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.978218 / 1.841788 (-0.863570) \| 11.972379 / 8.074308 (3.898071) \| 10.725735 / 10.191392 (0.534343) \| 0.130564 / 0.680424 (-0.549860) \| 0.015396 / 0.534201 (-0.518805) \| 0.286900 / 0.579283 (-0.292383) \| 0.279633 / 0.434364 (-0.154730) \| 0.327483 / 0.540337 (-0.212854) \| 0.417848 / 1.386936 (-0.969088) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#adfe8f8fa37b9f220c152f5b8b2473ba2cef0307 \"CML watermark\")\n" ]
2,090,188,376	6,605	ELI5 no longer available, but referenced in example code	closed	2024-01-19T10:21:52	2024-02-01T17:58:23	2024-02-01T17:58:22	https://github.com/huggingface/datasets/issues/6605	null	drdsgvo	false	[ "Addressed in https://github.com/huggingface/transformers/pull/28715." ]

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