Split (2)

train · 2.4k rows

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https://api.github.com/repos/huggingface/datasets/issues/5512	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5512/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5512/comments	https://api.github.com/repos/huggingface/datasets/issues/5512/events	https://github.com/huggingface/datasets/pull/5512	1,576,142,432	PR_kwDODunzps5JhtQy	5,512	Speed up batched PyTorch DataLoader	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) \| 0.008882 / 0.011353 (-0.002471) \| 0.004562 / 0.011008 (-0.006446) \| 0.100035 / 0.038508 (0.061527) \| 0.030654 / 0.023109 (0.007545) \| 0.298745 / 0.275898 (0.022847) \| 0.356869 / 0.323480 (0.033389) \| 0.007170 / 0.007986 (-0.000815) \| 0.003471 / 0.004328 (-0.000858) \| 0.077975 / 0.004250 (0.073725) \| 0.037861 / 0.037052 (0.000809) \| 0.311643 / 0.258489 (0.053154) \| 0.343504 / 0.293841 (0.049663) \| 0.033768 / 0.128546 (-0.094778) \| 0.011342 / 0.075646 (-0.064304) \| 0.323953 / 0.419271 (-0.095319) \| 0.040818 / 0.043533 (-0.002715) \| 0.298492 / 0.255139 (0.043353) \| 0.327292 / 0.283200 (0.044092) \| 0.088423 / 0.141683 (-0.053260) \| 1.489520 / 1.452155 (0.037366) \| 1.532962 / 1.492716 (0.040245) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223654 / 0.018006 (0.205647) \| 0.415134 / 0.000490 (0.414644) \| 0.007394 / 0.000200 (0.007194) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023616 / 0.037411 (-0.013795) \| 0.096652 / 0.014526 (0.082126) \| 0.105239 / 0.176557 (-0.071318) \| 0.148637 / 0.737135 (-0.588498) \| 0.107937 / 0.296338 (-0.188402) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426816 / 0.215209 (0.211607) \| 4.241533 / 2.077655 (2.163878) \| 1.946493 / 1.504120 (0.442373) \| 1.735765 / 1.541195 (0.194570) \| 1.781424 / 1.468490 (0.312934) \| 0.688082 / 4.584777 (-3.896694) \| 3.396444 / 3.745712 (-0.349268) \| 1.920333 / 5.269862 (-3.349528) \| 1.293833 / 4.565676 (-3.271843) \| 0.081967 / 0.424275 (-0.342308) \| 0.012911 / 0.007607 (0.005304) \| 0.536928 / 0.226044 (0.310884) \| 5.452327 / 2.268929 (3.183399) \| 2.505785 / 55.444624 (-52.938840) \| 2.173627 / 6.876477 (-4.702850) \| 2.119978 / 2.142072 (-0.022095) \| 0.809012 / 4.805227 (-3.996215) \| 0.149124 / 6.500664 (-6.351540) \| 0.066008 / 0.075469 (-0.009461) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.215702 / 1.841788 (-0.626085) \| 13.757525 / 8.074308 (5.683217) \| 13.999208 / 10.191392 (3.807816) \| 0.164875 / 0.680424 (-0.515549) \| 0.028517 / 0.534201 (-0.505684) \| 0.394829 / 0.579283 (-0.184454) \| 0.404962 / 0.434364 (-0.029401) \| 0.484455 / 0.540337 (-0.055882) \| 0.575008 / 1.386936 (-0.811928) \|\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) \| 0.006754 / 0.011353 (-0.004598) \| 0.004579 / 0.011008 (-0.006430) \| 0.076617 / 0.038508 (0.038109) \| 0.027902 / 0.023109 (0.004793) \| 0.346278 / 0.275898 (0.070380) \| 0.398060 / 0.323480 (0.074580) \| 0.004938 / 0.007986 (-0.003047) \| 0.004681 / 0.004328 (0.000353) \| 0.076336 / 0.004250 (0.072086) \| 0.038018 / 0.037052 (0.000966) \| 0.358701 / 0.258489 (0.100212) \| 0.408413 / 0.293841 (0.114572) \| 0.031772 / 0.128546 (-0.096774) \| 0.011604 / 0.075646 (-0.064042) \| 0.085964 / 0.419271 (-0.333308) \| 0.042030 / 0.043533 (-0.001502) \| 0.343568 / 0.255139 (0.088429) \| 0.381805 / 0.283200 (0.098605) \| 0.090759 / 0.141683 (-0.050924) \| 1.504553 / 1.452155 (0.052398) \| 1.594006 / 1.492716 (0.101289) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227395 / 0.018006 (0.209389) \| 0.403097 / 0.000490 (0.402608) \| 0.000413 / 0.000200 (0.000213) \| 0.000060 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024693 / 0.037411 (-0.012718) \| 0.100470 / 0.014526 (0.085944) \| 0.108481 / 0.176557 (-0.068076) \| 0.142791 / 0.737135 (-0.594345) \| 0.109949 / 0.296338 (-0.186389) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.443674 / 0.215209 (0.228465) \| 4.412207 / 2.077655 (2.334553) \| 2.073752 / 1.504120 (0.569632) \| 1.863153 / 1.541195 (0.321958) \| 1.940063 / 1.468490 (0.471573) \| 0.696456 / 4.584777 (-3.888321) \| 3.422120 / 3.745712 (-0.323592) \| 1.902579 / 5.269862 (-3.367282) \| 1.184948 / 4.565676 (-3.380729) \| 0.083079 / 0.424275 (-0.341196) \| 0.012649 / 0.007607 (0.005042) \| 0.542035 / 0.226044 (0.315991) \| 5.421826 / 2.268929 (3.152897) \| 2.525092 / 55.444624 (-52.919532) \| 2.177144 / 6.876477 (-4.699332) \| 2.225224 / 2.142072 (0.083151) \| 0.804739 / 4.805227 (-4.000488) \| 0.151000 / 6.500664 (-6.349664) \| 0.066987 / 0.075469 (-0.008482) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.277199 / 1.841788 (-0.564589) \| 14.184146 / 8.074308 (6.109838) \| 13.413348 / 10.191392 (3.221956) \| 0.128551 / 0.680424 (-0.551872) \| 0.016461 / 0.534201 (-0.517740) \| 0.379963 / 0.579283 (-0.199320) \| 0.381350 / 0.434364 (-0.053014) \| 0.439044 / 0.540337 (-0.101293) \| 0.521559 / 1.386936 (-0.865377) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4f3c152c1c35df250d2fbeb25d5823a65714f2d8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) \| 0.008876 / 0.011353 (-0.002477) \| 0.004629 / 0.011008 (-0.006379) \| 0.101697 / 0.038508 (0.063189) \| 0.030373 / 0.023109 (0.007264) \| 0.302206 / 0.275898 (0.026308) \| 0.365835 / 0.323480 (0.042355) \| 0.007877 / 0.007986 (-0.000109) \| 0.004473 / 0.004328 (0.000144) \| 0.077334 / 0.004250 (0.073084) \| 0.038066 / 0.037052 (0.001014) \| 0.308064 / 0.258489 (0.049575) \| 0.347329 / 0.293841 (0.053488) \| 0.034478 / 0.128546 (-0.094068) \| 0.011651 / 0.075646 (-0.063995) \| 0.323481 / 0.419271 (-0.095791) \| 0.043515 / 0.043533 (-0.000018) \| 0.299885 / 0.255139 (0.044746) \| 0.328959 / 0.283200 (0.045760) \| 0.095308 / 0.141683 (-0.046375) \| 1.474058 / 1.452155 (0.021903) \| 1.535335 / 1.492716 (0.042619) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.197416 / 0.018006 (0.179410) \| 0.421935 / 0.000490 (0.421446) \| 0.003490 / 0.000200 (0.003290) \| 0.000074 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024519 / 0.037411 (-0.012892) \| 0.100710 / 0.014526 (0.086185) \| 0.104520 / 0.176557 (-0.072036) \| 0.142048 / 0.737135 (-0.595087) \| 0.109274 / 0.296338 (-0.187064) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.408766 / 0.215209 (0.193557) \| 4.101720 / 2.077655 (2.024065) \| 1.812375 / 1.504120 (0.308256) \| 1.605819 / 1.541195 (0.064624) \| 1.688923 / 1.468490 (0.220433) \| 0.691198 / 4.584777 (-3.893579) \| 3.422137 / 3.745712 (-0.323575) \| 1.921318 / 5.269862 (-3.348544) \| 1.168770 / 4.565676 (-3.396906) \| 0.082840 / 0.424275 (-0.341435) \| 0.012740 / 0.007607 (0.005133) \| 0.524333 / 0.226044 (0.298289) \| 5.258077 / 2.268929 (2.989149) \| 2.273177 / 55.444624 (-53.171447) \| 1.931919 / 6.876477 (-4.944558) \| 1.988415 / 2.142072 (-0.153658) \| 0.812227 / 4.805227 (-3.993000) \| 0.150043 / 6.500664 (-6.350622) \| 0.066422 / 0.075469 (-0.009047) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.188069 / 1.841788 (-0.653718) \| 13.942681 / 8.074308 (5.868373) \| 14.104658 / 10.191392 (3.913266) \| 0.151966 / 0.680424 (-0.528458) \| 0.028833 / 0.534201 (-0.505368) \| 0.395125 / 0.579283 (-0.184158) \| 0.408512 / 0.434364 (-0.025852) \| 0.487587 / 0.540337 (-0.052751) \| 0.570023 / 1.386936 (-0.816913) \|\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) \| 0.006860 / 0.011353 (-0.004493) \| 0.004582 / 0.011008 (-0.006426) \| 0.079902 / 0.038508 (0.041394) \| 0.027565 / 0.023109 (0.004456) \| 0.341393 / 0.275898 (0.065495) \| 0.378911 / 0.323480 (0.055431) \| 0.005847 / 0.007986 (-0.002138) \| 0.004681 / 0.004328 (0.000353) \| 0.079422 / 0.004250 (0.075171) \| 0.039135 / 0.037052 (0.002083) \| 0.342026 / 0.258489 (0.083537) \| 0.387510 / 0.293841 (0.093669) \| 0.031999 / 0.128546 (-0.096547) \| 0.011782 / 0.075646 (-0.063865) \| 0.088563 / 0.419271 (-0.330709) \| 0.042435 / 0.043533 (-0.001098) \| 0.343055 / 0.255139 (0.087916) \| 0.367437 / 0.283200 (0.084237) \| 0.091578 / 0.141683 (-0.050104) \| 1.506828 / 1.452155 (0.054673) \| 1.599590 / 1.492716 (0.106874) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217939 / 0.018006 (0.199932) \| 0.408352 / 0.000490 (0.407863) \| 0.000394 / 0.000200 (0.000194) \| 0.000063 / 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) \| 0.026344 / 0.037411 (-0.011067) \| 0.102968 / 0.014526 (0.088442) \| 0.110340 / 0.176557 (-0.066217) \| 0.145696 / 0.737135 (-0.591439) \| 0.111632 / 0.296338 (-0.184707) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440764 / 0.215209 (0.225555) \| 4.423179 / 2.077655 (2.345524) \| 2.057016 / 1.504120 (0.552896) \| 1.848741 / 1.541195 (0.307546) \| 1.939827 / 1.468490 (0.471337) \| 0.699370 / 4.584777 (-3.885407) \| 3.472521 / 3.745712 (-0.273191) \| 3.232557 / 5.269862 (-2.037305) \| 1.755534 / 4.565676 (-2.810143) \| 0.083469 / 0.424275 (-0.340807) \| 0.012980 / 0.007607 (0.005373) \| 0.557662 / 0.226044 (0.331618) \| 5.435657 / 2.268929 (3.166729) \| 2.545106 / 55.444624 (-52.899519) \| 2.168047 / 6.876477 (-4.708430) \| 2.234070 / 2.142072 (0.091997) \| 0.804662 / 4.805227 (-4.000565) \| 0.152832 / 6.500664 (-6.347833) \| 0.069372 / 0.075469 (-0.006097) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.299189 / 1.841788 (-0.542598) \| 14.752880 / 8.074308 (6.678572) \| 13.607676 / 10.191392 (3.416284) \| 0.150773 / 0.680424 (-0.529650) \| 0.016701 / 0.534201 (-0.517500) \| 0.379507 / 0.579283 (-0.199776) \| 0.389401 / 0.434364 (-0.044963) \| 0.444199 / 0.540337 (-0.096139) \| 0.524264 / 1.386936 (-0.862672) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#12be850b36c0b9d4841af86c75e08c0a726ffb5c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) \| 0.008694 / 0.011353 (-0.002659) \| 0.004549 / 0.011008 (-0.006459) \| 0.101164 / 0.038508 (0.062656) \| 0.029644 / 0.023109 (0.006535) \| 0.294849 / 0.275898 (0.018950) \| 0.366755 / 0.323480 (0.043275) \| 0.007205 / 0.007986 (-0.000780) \| 0.004255 / 0.004328 (-0.000074) \| 0.077433 / 0.004250 (0.073183) \| 0.038024 / 0.037052 (0.000972) \| 0.310380 / 0.258489 (0.051891) \| 0.347093 / 0.293841 (0.053252) \| 0.033232 / 0.128546 (-0.095314) \| 0.011404 / 0.075646 (-0.064242) \| 0.323341 / 0.419271 (-0.095930) \| 0.040586 / 0.043533 (-0.002946) \| 0.296083 / 0.255139 (0.040944) \| 0.321870 / 0.283200 (0.038671) \| 0.087377 / 0.141683 (-0.054306) \| 1.466869 / 1.452155 (0.014715) \| 1.514763 / 1.492716 (0.022046) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010272 / 0.018006 (-0.007734) \| 0.414645 / 0.000490 (0.414155) \| 0.003730 / 0.000200 (0.003530) \| 0.000076 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024093 / 0.037411 (-0.013318) \| 0.098718 / 0.014526 (0.084192) \| 0.105526 / 0.176557 (-0.071030) \| 0.141578 / 0.737135 (-0.595557) \| 0.109679 / 0.296338 (-0.186660) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.412907 / 0.215209 (0.197698) \| 4.134934 / 2.077655 (2.057280) \| 1.881180 / 1.504120 (0.377060) \| 1.693207 / 1.541195 (0.152012) \| 1.753725 / 1.468490 (0.285235) \| 0.693077 / 4.584777 (-3.891700) \| 3.367409 / 3.745712 (-0.378303) \| 2.749035 / 5.269862 (-2.520827) \| 1.565015 / 4.565676 (-3.000662) \| 0.082609 / 0.424275 (-0.341666) \| 0.012500 / 0.007607 (0.004892) \| 0.523619 / 0.226044 (0.297575) \| 5.250188 / 2.268929 (2.981259) \| 2.314255 / 55.444624 (-53.130369) \| 1.962357 / 6.876477 (-4.914120) \| 2.020632 / 2.142072 (-0.121441) \| 0.812504 / 4.805227 (-3.992724) \| 0.149921 / 6.500664 (-6.350743) \| 0.065816 / 0.075469 (-0.009653) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.230811 / 1.841788 (-0.610977) \| 14.008566 / 8.074308 (5.934258) \| 14.371285 / 10.191392 (4.179893) \| 0.166323 / 0.680424 (-0.514101) \| 0.029702 / 0.534201 (-0.504499) \| 0.408629 / 0.579283 (-0.170654) \| 0.410529 / 0.434364 (-0.023835) \| 0.484482 / 0.540337 (-0.055855) \| 0.572360 / 1.386936 (-0.814576) \|\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) \| 0.006873 / 0.011353 (-0.004480) \| 0.004609 / 0.011008 (-0.006400) \| 0.075492 / 0.038508 (0.036984) \| 0.028560 / 0.023109 (0.005450) \| 0.340321 / 0.275898 (0.064423) \| 0.376758 / 0.323480 (0.053278) \| 0.005271 / 0.007986 (-0.002715) \| 0.004786 / 0.004328 (0.000457) \| 0.074843 / 0.004250 (0.070592) \| 0.041072 / 0.037052 (0.004019) \| 0.339952 / 0.258489 (0.081463) \| 0.384375 / 0.293841 (0.090534) \| 0.031771 / 0.128546 (-0.096775) \| 0.011607 / 0.075646 (-0.064039) \| 0.084338 / 0.419271 (-0.334933) \| 0.042251 / 0.043533 (-0.001282) \| 0.338904 / 0.255139 (0.083765) \| 0.365360 / 0.283200 (0.082160) \| 0.093151 / 0.141683 (-0.048532) \| 1.449833 / 1.452155 (-0.002322) \| 1.601946 / 1.492716 (0.109229) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225149 / 0.018006 (0.207142) \| 0.409855 / 0.000490 (0.409365) \| 0.000384 / 0.000200 (0.000184) \| 0.000060 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025914 / 0.037411 (-0.011497) \| 0.100443 / 0.014526 (0.085917) \| 0.108557 / 0.176557 (-0.067999) \| 0.150338 / 0.737135 (-0.586798) \| 0.111472 / 0.296338 (-0.184866) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440221 / 0.215209 (0.225012) \| 4.409268 / 2.077655 (2.331613) \| 2.096008 / 1.504120 (0.591888) \| 1.849443 / 1.541195 (0.308248) \| 1.934901 / 1.468490 (0.466410) \| 0.704072 / 4.584777 (-3.880705) \| 3.371370 / 3.745712 (-0.374343) \| 3.185478 / 5.269862 (-2.084384) \| 1.514541 / 4.565676 (-3.051135) \| 0.083724 / 0.424275 (-0.340551) \| 0.012674 / 0.007607 (0.005067) \| 0.542155 / 0.226044 (0.316111) \| 5.413456 / 2.268929 (3.144528) \| 2.508567 / 55.444624 (-52.936057) \| 2.163235 / 6.876477 (-4.713242) \| 2.193914 / 2.142072 (0.051842) \| 0.810955 / 4.805227 (-3.994272) \| 0.152769 / 6.500664 (-6.347895) \| 0.068009 / 0.075469 (-0.007460) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.272511 / 1.841788 (-0.569276) \| 14.334861 / 8.074308 (6.260553) \| 13.555445 / 10.191392 (3.364053) \| 0.160520 / 0.680424 (-0.519904) \| 0.018363 / 0.534201 (-0.515838) \| 0.384937 / 0.579283 (-0.194346) \| 0.409138 / 0.434364 (-0.025225) \| 0.484037 / 0.540337 (-0.056300) \| 0.565595 / 1.386936 (-0.821341) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#23f076ef0187a4009d3c62b14a02e146baf0e35f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) \| 0.010077 / 0.011353 (-0.001276) \| 0.005650 / 0.011008 (-0.005359) \| 0.101285 / 0.038508 (0.062777) \| 0.039571 / 0.023109 (0.016462) \| 0.291855 / 0.275898 (0.015957) \| 0.363582 / 0.323480 (0.040102) \| 0.008513 / 0.007986 (0.000527) \| 0.004472 / 0.004328 (0.000144) \| 0.077314 / 0.004250 (0.073064) \| 0.050707 / 0.037052 (0.013654) \| 0.317282 / 0.258489 (0.058792) \| 0.342348 / 0.293841 (0.048507) \| 0.042951 / 0.128546 (-0.085595) \| 0.012295 / 0.075646 (-0.063351) \| 0.337269 / 0.419271 (-0.082003) \| 0.048953 / 0.043533 (0.005420) \| 0.292547 / 0.255139 (0.037408) \| 0.325436 / 0.283200 (0.042236) \| 0.111859 / 0.141683 (-0.029824) \| 1.501958 / 1.452155 (0.049804) \| 1.522281 / 1.492716 (0.029565) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011775 / 0.018006 (-0.006231) \| 0.513283 / 0.000490 (0.512793) \| 0.002941 / 0.000200 (0.002741) \| 0.000099 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028702 / 0.037411 (-0.008710) \| 0.108465 / 0.014526 (0.093940) \| 0.121806 / 0.176557 (-0.054750) \| 0.158424 / 0.737135 (-0.578712) \| 0.128077 / 0.296338 (-0.168262) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.395392 / 0.215209 (0.180183) \| 3.944138 / 2.077655 (1.866483) \| 1.773698 / 1.504120 (0.269578) \| 1.588907 / 1.541195 (0.047712) \| 1.697794 / 1.468490 (0.229304) \| 0.690281 / 4.584777 (-3.894496) \| 3.819661 / 3.745712 (0.073948) \| 3.228006 / 5.269862 (-2.041856) \| 1.755625 / 4.565676 (-2.810052) \| 0.083169 / 0.424275 (-0.341106) \| 0.012337 / 0.007607 (0.004730) \| 0.504730 / 0.226044 (0.278686) \| 5.016916 / 2.268929 (2.747988) \| 2.245484 / 55.444624 (-53.199141) \| 1.911682 / 6.876477 (-4.964795) \| 1.957659 / 2.142072 (-0.184413) \| 0.818361 / 4.805227 (-3.986866) \| 0.162386 / 6.500664 (-6.338279) \| 0.062461 / 0.075469 (-0.013008) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.197654 / 1.841788 (-0.644134) \| 15.465611 / 8.074308 (7.391303) \| 14.409126 / 10.191392 (4.217734) \| 0.171776 / 0.680424 (-0.508647) \| 0.028749 / 0.534201 (-0.505452) \| 0.439666 / 0.579283 (-0.139618) \| 0.445159 / 0.434364 (0.010795) \| 0.543992 / 0.540337 (0.003655) \| 0.643911 / 1.386936 (-0.743025) \|\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) \| 0.007036 / 0.011353 (-0.004317) \| 0.005273 / 0.011008 (-0.005735) \| 0.075314 / 0.038508 (0.036806) \| 0.033075 / 0.023109 (0.009966) \| 0.350133 / 0.275898 (0.074235) \| 0.399366 / 0.323480 (0.075886) \| 0.005945 / 0.007986 (-0.002041) \| 0.004276 / 0.004328 (-0.000052) \| 0.074975 / 0.004250 (0.070725) \| 0.051758 / 0.037052 (0.014706) \| 0.355077 / 0.258489 (0.096588) \| 0.430296 / 0.293841 (0.136455) \| 0.036257 / 0.128546 (-0.092290) \| 0.012376 / 0.075646 (-0.063270) \| 0.087441 / 0.419271 (-0.331830) \| 0.049066 / 0.043533 (0.005534) \| 0.339867 / 0.255139 (0.084728) \| 0.384379 / 0.283200 (0.101179) \| 0.104843 / 0.141683 (-0.036840) \| 1.498897 / 1.452155 (0.046742) \| 1.551400 / 1.492716 (0.058684) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.334504 / 0.018006 (0.316498) \| 0.516551 / 0.000490 (0.516061) \| 0.000450 / 0.000200 (0.000250) \| 0.000057 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029313 / 0.037411 (-0.008099) \| 0.110667 / 0.014526 (0.096141) \| 0.124001 / 0.176557 (-0.052556) \| 0.159154 / 0.737135 (-0.577981) \| 0.129503 / 0.296338 (-0.166836) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.416749 / 0.215209 (0.201540) \| 4.171163 / 2.077655 (2.093508) \| 1.981071 / 1.504120 (0.476951) \| 1.788303 / 1.541195 (0.247108) \| 1.912118 / 1.468490 (0.443628) \| 0.708764 / 4.584777 (-3.876013) \| 3.815222 / 3.745712 (0.069510) \| 2.121633 / 5.269862 (-3.148229) \| 1.347866 / 4.565676 (-3.217811) \| 0.086340 / 0.424275 (-0.337935) \| 0.012646 / 0.007607 (0.005039) \| 0.525286 / 0.226044 (0.299241) \| 5.254922 / 2.268929 (2.985994) \| 2.488743 / 55.444624 (-52.955881) \| 2.128069 / 6.876477 (-4.748408) \| 2.180358 / 2.142072 (0.038286) \| 0.841011 / 4.805227 (-3.964216) \| 0.168732 / 6.500664 (-6.331932) \| 0.065559 / 0.075469 (-0.009910) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.270518 / 1.841788 (-0.571270) \| 15.557563 / 8.074308 (7.483255) \| 13.660757 / 10.191392 (3.469365) \| 0.185636 / 0.680424 (-0.494788) \| 0.018152 / 0.534201 (-0.516049) \| 0.423553 / 0.579283 (-0.155730) \| 0.412718 / 0.434364 (-0.021646) \| 0.528455 / 0.540337 (-0.011882) \| 0.635274 / 1.386936 (-0.751662) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d40f05ef827c52344a2c6e83f7c8d13bb6b660d3 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) \| 0.011194 / 0.011353 (-0.000159) \| 0.006344 / 0.011008 (-0.004664) \| 0.122013 / 0.038508 (0.083505) \| 0.044323 / 0.023109 (0.021214) \| 0.356665 / 0.275898 (0.080767) \| 0.439871 / 0.323480 (0.116391) \| 0.010694 / 0.007986 (0.002709) \| 0.004648 / 0.004328 (0.000320) \| 0.091140 / 0.004250 (0.086890) \| 0.052457 / 0.037052 (0.015404) \| 0.369282 / 0.258489 (0.110793) \| 0.403279 / 0.293841 (0.109438) \| 0.054075 / 0.128546 (-0.074472) \| 0.014484 / 0.075646 (-0.061162) \| 0.407932 / 0.419271 (-0.011340) \| 0.060681 / 0.043533 (0.017148) \| 0.350889 / 0.255139 (0.095750) \| 0.392041 / 0.283200 (0.108841) \| 0.121252 / 0.141683 (-0.020431) \| 1.809527 / 1.452155 (0.357373) \| 1.835141 / 1.492716 (0.342425) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227372 / 0.018006 (0.209366) \| 0.481908 / 0.000490 (0.481418) \| 0.007262 / 0.000200 (0.007062) \| 0.000148 / 0.000054 (0.000093) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031039 / 0.037411 (-0.006372) \| 0.133947 / 0.014526 (0.119421) \| 0.141935 / 0.176557 (-0.034622) \| 0.197854 / 0.737135 (-0.539281) \| 0.152393 / 0.296338 (-0.143945) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.517400 / 0.215209 (0.302191) \| 4.899972 / 2.077655 (2.822317) \| 2.171023 / 1.504120 (0.666903) \| 2.008706 / 1.541195 (0.467511) \| 1.988777 / 1.468490 (0.520287) \| 0.859872 / 4.584777 (-3.724905) \| 4.673923 / 3.745712 (0.928211) \| 2.703189 / 5.269862 (-2.566672) \| 1.891680 / 4.565676 (-2.673997) \| 0.109601 / 0.424275 (-0.314674) \| 0.014622 / 0.007607 (0.007015) \| 0.618990 / 0.226044 (0.392946) \| 6.255608 / 2.268929 (3.986679) \| 2.822199 / 55.444624 (-52.622425) \| 2.457684 / 6.876477 (-4.418793) \| 2.500041 / 2.142072 (0.357968) \| 1.054529 / 4.805227 (-3.750698) \| 0.209501 / 6.500664 (-6.291163) \| 0.074929 / 0.075469 (-0.000540) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.532780 / 1.841788 (-0.309008) \| 19.159455 / 8.074308 (11.085147) \| 17.817063 / 10.191392 (7.625671) \| 0.194078 / 0.680424 (-0.486346) \| 0.038211 / 0.534201 (-0.495990) \| 0.537366 / 0.579283 (-0.041917) \| 0.538995 / 0.434364 (0.104631) \| 0.679431 / 0.540337 (0.139094) \| 0.801960 / 1.386936 (-0.584976) \|\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) \| 0.008729 / 0.011353 (-0.002624) \| 0.005711 / 0.011008 (-0.005297) \| 0.091570 / 0.038508 (0.053062) \| 0.039805 / 0.023109 (0.016696) \| 0.413507 / 0.275898 (0.137609) \| 0.456342 / 0.323480 (0.132862) \| 0.006201 / 0.007986 (-0.001785) \| 0.009700 / 0.004328 (0.005372) \| 0.089146 / 0.004250 (0.084896) \| 0.057543 / 0.037052 (0.020490) \| 0.420806 / 0.258489 (0.162317) \| 0.471962 / 0.293841 (0.178121) \| 0.043940 / 0.128546 (-0.084606) \| 0.014457 / 0.075646 (-0.061190) \| 0.106674 / 0.419271 (-0.312598) \| 0.058930 / 0.043533 (0.015397) \| 0.419111 / 0.255139 (0.163972) \| 0.452974 / 0.283200 (0.169774) \| 0.124573 / 0.141683 (-0.017110) \| 1.864753 / 1.452155 (0.412599) \| 1.935387 / 1.492716 (0.442670) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275657 / 0.018006 (0.257651) \| 0.498096 / 0.000490 (0.497606) \| 0.000480 / 0.000200 (0.000280) \| 0.000066 / 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) \| 0.034377 / 0.037411 (-0.003035) \| 0.138050 / 0.014526 (0.123524) \| 0.153718 / 0.176557 (-0.022838) \| 0.201445 / 0.737135 (-0.535690) \| 0.160346 / 0.296338 (-0.135992) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.540670 / 0.215209 (0.325461) \| 5.376291 / 2.077655 (3.298636) \| 2.581799 / 1.504120 (1.077679) \| 2.328858 / 1.541195 (0.787663) \| 2.446458 / 1.468490 (0.977968) \| 0.923005 / 4.584777 (-3.661772) \| 4.815977 / 3.745712 (1.070265) \| 4.205725 / 5.269862 (-1.064137) \| 2.400466 / 4.565676 (-2.165211) \| 0.107207 / 0.424275 (-0.317068) \| 0.015427 / 0.007607 (0.007819) \| 0.657267 / 0.226044 (0.431222) \| 6.491256 / 2.268929 (4.222327) \| 3.179099 / 55.444624 (-52.265525) \| 2.722434 / 6.876477 (-4.154042) \| 2.788202 / 2.142072 (0.646129) \| 1.060016 / 4.805227 (-3.745211) \| 0.206899 / 6.500664 (-6.293766) \| 0.077868 / 0.075469 (0.002399) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.567894 / 1.841788 (-0.273893) \| 19.314330 / 8.074308 (11.240022) \| 17.597614 / 10.191392 (7.406222) \| 0.195777 / 0.680424 (-0.484647) \| 0.022160 / 0.534201 (-0.512041) \| 0.530592 / 0.579283 (-0.048691) \| 0.508591 / 0.434364 (0.074227) \| 0.619794 / 0.540337 (0.079457) \| 0.749773 / 1.386936 (-0.637163) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8637141a67639c510294620306c9bb25d31d34ef \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) \| 0.012431 / 0.011353 (0.001078) \| 0.006526 / 0.011008 (-0.004482) \| 0.132266 / 0.038508 (0.093757) \| 0.043199 / 0.023109 (0.020089) \| 0.405230 / 0.275898 (0.129332) \| 0.494643 / 0.323480 (0.171163) \| 0.009927 / 0.007986 (0.001941) \| 0.005227 / 0.004328 (0.000899) \| 0.110914 / 0.004250 (0.106664) \| 0.047815 / 0.037052 (0.010763) \| 0.419099 / 0.258489 (0.160610) \| 0.463405 / 0.293841 (0.169564) \| 0.057858 / 0.128546 (-0.070688) \| 0.018918 / 0.075646 (-0.056728) \| 0.450584 / 0.419271 (0.031313) \| 0.060457 / 0.043533 (0.016924) \| 0.408234 / 0.255139 (0.153095) \| 0.433722 / 0.283200 (0.150523) \| 0.119403 / 0.141683 (-0.022280) \| 1.966742 / 1.452155 (0.514587) \| 1.980685 / 1.492716 (0.487969) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292853 / 0.018006 (0.274847) \| 0.619697 / 0.000490 (0.619207) \| 0.002135 / 0.000200 (0.001935) \| 0.000117 / 0.000054 (0.000062) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031283 / 0.037411 (-0.006129) \| 0.128649 / 0.014526 (0.114123) \| 0.150116 / 0.176557 (-0.026441) \| 0.187605 / 0.737135 (-0.549530) \| 0.153334 / 0.296338 (-0.143005) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.659660 / 0.215209 (0.444451) \| 6.459749 / 2.077655 (4.382094) \| 2.764566 / 1.504120 (1.260446) \| 2.362630 / 1.541195 (0.821435) \| 2.426421 / 1.468490 (0.957931) \| 1.282407 / 4.584777 (-3.302370) \| 5.668865 / 3.745712 (1.923153) \| 3.236255 / 5.269862 (-2.033606) \| 2.248836 / 4.565676 (-2.316841) \| 0.145861 / 0.424275 (-0.278414) \| 0.015707 / 0.007607 (0.008100) \| 0.805218 / 0.226044 (0.579174) \| 8.146831 / 2.268929 (5.877903) \| 3.506283 / 55.444624 (-51.938341) \| 2.736682 / 6.876477 (-4.139795) \| 2.959039 / 2.142072 (0.816967) \| 1.528428 / 4.805227 (-3.276799) \| 0.270980 / 6.500664 (-6.229684) \| 0.086824 / 0.075469 (0.011355) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.682506 / 1.841788 (-0.159282) \| 18.844103 / 8.074308 (10.769795) \| 21.008471 / 10.191392 (10.817079) \| 0.258372 / 0.680424 (-0.422052) \| 0.046505 / 0.534201 (-0.487696) \| 0.574760 / 0.579283 (-0.004523) \| 0.663745 / 0.434364 (0.229381) \| 0.702411 / 0.540337 (0.162074) \| 0.824024 / 1.386936 (-0.562912) \|\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) \| 0.010016 / 0.011353 (-0.001337) \| 0.007459 / 0.011008 (-0.003549) \| 0.103954 / 0.038508 (0.065446) \| 0.036363 / 0.023109 (0.013254) \| 0.464079 / 0.275898 (0.188181) \| 0.504730 / 0.323480 (0.181250) \| 0.007865 / 0.007986 (-0.000121) \| 0.005210 / 0.004328 (0.000882) \| 0.105018 / 0.004250 (0.100767) \| 0.062191 / 0.037052 (0.025139) \| 0.483304 / 0.258489 (0.224815) \| 0.547030 / 0.293841 (0.253189) \| 0.055436 / 0.128546 (-0.073110) \| 0.021073 / 0.075646 (-0.054573) \| 0.120952 / 0.419271 (-0.298319) \| 0.075593 / 0.043533 (0.032060) \| 0.459930 / 0.255139 (0.204791) \| 0.486924 / 0.283200 (0.203724) \| 0.129465 / 0.141683 (-0.012218) \| 1.902322 / 1.452155 (0.450167) \| 1.980809 / 1.492716 (0.488092) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.259263 / 0.018006 (0.241257) \| 0.596703 / 0.000490 (0.596213) \| 0.004520 / 0.000200 (0.004320) \| 0.000124 / 0.000054 (0.000070) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032802 / 0.037411 (-0.004609) \| 0.138751 / 0.014526 (0.124225) \| 0.147106 / 0.176557 (-0.029451) \| 0.194791 / 0.737135 (-0.542345) \| 0.152643 / 0.296338 (-0.143696) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.678455 / 0.215209 (0.463246) \| 6.673643 / 2.077655 (4.595989) \| 2.943368 / 1.504120 (1.439248) \| 2.591223 / 1.541195 (1.050029) \| 2.741097 / 1.468490 (1.272607) \| 1.261178 / 4.584777 (-3.323599) \| 5.773853 / 3.745712 (2.028141) \| 3.171559 / 5.269862 (-2.098303) \| 2.124898 / 4.565676 (-2.440779) \| 0.161849 / 0.424275 (-0.262426) \| 0.015498 / 0.007607 (0.007891) \| 0.857984 / 0.226044 (0.631940) \| 8.456946 / 2.268929 (6.188018) \| 3.818787 / 55.444624 (-51.625837) \| 3.009953 / 6.876477 (-3.866523) \| 3.113006 / 2.142072 (0.970934) \| 1.477299 / 4.805227 (-3.327929) \| 0.267207 / 6.500664 (-6.233457) \| 0.087590 / 0.075469 (0.012121) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.757389 / 1.841788 (-0.084398) \| 19.287690 / 8.074308 (11.213381) \| 21.601991 / 10.191392 (11.410599) \| 0.260464 / 0.680424 (-0.419960) \| 0.028552 / 0.534201 (-0.505649) \| 0.558934 / 0.579283 (-0.020349) \| 0.673651 / 0.434364 (0.239287) \| 0.714448 / 0.540337 (0.174111) \| 0.857608 / 1.386936 (-0.529328) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2d3bd0134de444ffd10c4a39873dbf9aa3732c08 \"CML watermark\")\n", "Ready for review @mariosasko, LMKWYT :)\r\n\r\nSorry it tooks me a few tries to fix the CI - I ended up not trying to use the latest `torch` version in the CI.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) \| 0.009474 / 0.011353 (-0.001878) \| 0.005507 / 0.011008 (-0.005501) \| 0.101219 / 0.038508 (0.062711) \| 0.035591 / 0.023109 (0.012481) \| 0.305841 / 0.275898 (0.029943) \| 0.339135 / 0.323480 (0.015656) \| 0.007920 / 0.007986 (-0.000066) \| 0.004252 / 0.004328 (-0.000077) \| 0.076912 / 0.004250 (0.072662) \| 0.041923 / 0.037052 (0.004871) \| 0.301405 / 0.258489 (0.042916) \| 0.356488 / 0.293841 (0.062647) \| 0.039342 / 0.128546 (-0.089204) \| 0.012711 / 0.075646 (-0.062935) \| 0.334193 / 0.419271 (-0.085079) \| 0.049112 / 0.043533 (0.005579) \| 0.301484 / 0.255139 (0.046345) \| 0.315306 / 0.283200 (0.032106) \| 0.102959 / 0.141683 (-0.038724) \| 1.420677 / 1.452155 (-0.031478) \| 1.549493 / 1.492716 (0.056777) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284639 / 0.018006 (0.266633) \| 0.501226 / 0.000490 (0.500736) \| 0.004328 / 0.000200 (0.004128) \| 0.000091 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027034 / 0.037411 (-0.010377) \| 0.108066 / 0.014526 (0.093540) \| 0.122106 / 0.176557 (-0.054451) \| 0.162908 / 0.737135 (-0.574227) \| 0.127233 / 0.296338 (-0.169105) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.394023 / 0.215209 (0.178813) \| 3.932729 / 2.077655 (1.855075) \| 1.771195 / 1.504120 (0.267075) \| 1.582788 / 1.541195 (0.041594) \| 1.703219 / 1.468490 (0.234728) \| 0.702629 / 4.584777 (-3.882148) \| 3.780187 / 3.745712 (0.034475) \| 2.180433 / 5.269862 (-3.089428) \| 1.504806 / 4.565676 (-3.060871) \| 0.085289 / 0.424275 (-0.338986) \| 0.012580 / 0.007607 (0.004973) \| 0.515408 / 0.226044 (0.289363) \| 5.010613 / 2.268929 (2.741685) \| 2.256648 / 55.444624 (-53.187976) \| 1.914971 / 6.876477 (-4.961505) \| 2.038436 / 2.142072 (-0.103636) \| 0.846240 / 4.805227 (-3.958987) \| 0.164920 / 6.500664 (-6.335744) \| 0.063899 / 0.075469 (-0.011570) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.224160 / 1.841788 (-0.617627) \| 15.089995 / 8.074308 (7.015687) \| 14.777003 / 10.191392 (4.585611) \| 0.169873 / 0.680424 (-0.510551) \| 0.029233 / 0.534201 (-0.504968) \| 0.445424 / 0.579283 (-0.133859) \| 0.439194 / 0.434364 (0.004830) \| 0.536370 / 0.540337 (-0.003968) \| 0.636694 / 1.386936 (-0.750242) \|\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) \| 0.008230 / 0.011353 (-0.003122) \| 0.005499 / 0.011008 (-0.005509) \| 0.076108 / 0.038508 (0.037600) \| 0.037444 / 0.023109 (0.014335) \| 0.364420 / 0.275898 (0.088522) \| 0.412308 / 0.323480 (0.088828) \| 0.006704 / 0.007986 (-0.001282) \| 0.004359 / 0.004328 (0.000031) \| 0.075080 / 0.004250 (0.070830) \| 0.057698 / 0.037052 (0.020646) \| 0.366088 / 0.258489 (0.107599) \| 0.409583 / 0.293841 (0.115742) \| 0.037882 / 0.128546 (-0.090664) \| 0.012421 / 0.075646 (-0.063225) \| 0.087701 / 0.419271 (-0.331571) \| 0.050669 / 0.043533 (0.007136) \| 0.351139 / 0.255139 (0.096000) \| 0.384340 / 0.283200 (0.101140) \| 0.108097 / 0.141683 (-0.033586) \| 1.445010 / 1.452155 (-0.007145) \| 1.559570 / 1.492716 (0.066853) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.324114 / 0.018006 (0.306108) \| 0.549134 / 0.000490 (0.548644) \| 0.003544 / 0.000200 (0.003344) \| 0.000097 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030646 / 0.037411 (-0.006765) \| 0.108573 / 0.014526 (0.094047) \| 0.125291 / 0.176557 (-0.051266) \| 0.174798 / 0.737135 (-0.562338) \| 0.128000 / 0.296338 (-0.168338) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.428881 / 0.215209 (0.213672) \| 4.282320 / 2.077655 (2.204665) \| 2.061462 / 1.504120 (0.557342) \| 1.858477 / 1.541195 (0.317283) \| 1.971646 / 1.468490 (0.503156) \| 0.723631 / 4.584777 (-3.861146) \| 3.822376 / 3.745712 (0.076664) \| 2.174427 / 5.269862 (-3.095434) \| 1.386066 / 4.565676 (-3.179611) \| 0.088391 / 0.424275 (-0.335884) \| 0.012948 / 0.007607 (0.005341) \| 0.524423 / 0.226044 (0.298378) \| 5.249389 / 2.268929 (2.980460) \| 2.528662 / 55.444624 (-52.915962) \| 2.245329 / 6.876477 (-4.631147) \| 2.402733 / 2.142072 (0.260660) \| 0.868864 / 4.805227 (-3.936364) \| 0.174066 / 6.500664 (-6.326598) \| 0.066165 / 0.075469 (-0.009304) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.296922 / 1.841788 (-0.544865) \| 15.814109 / 8.074308 (7.739801) \| 14.086059 / 10.191392 (3.894667) \| 0.190952 / 0.680424 (-0.489472) \| 0.017679 / 0.534201 (-0.516522) \| 0.428872 / 0.579283 (-0.150411) \| 0.435399 / 0.434364 (0.001035) \| 0.540856 / 0.540337 (0.000519) \| 0.648904 / 1.386936 (-0.738032) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f401758c5019ede4404994d5d59220125984874d \"CML watermark\")\n" ]	2023-02-08T13:38:59Z	2023-02-19T18:35:09Z	2023-02-19T18:27:29Z	MEMBER	null	null	null	I implemented `__getitems__` to speed up batched data loading in PyTorch close https://github.com/huggingface/datasets/issues/5505	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": 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"https://api.github.com/repos/huggingface/datasets/issues/5512/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5512/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5512.diff", "html_url": "https://github.com/huggingface/datasets/pull/5512", "merged_at": "2023-02-19T18:27:29Z", "patch_url": "https://github.com/huggingface/datasets/pull/5512.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5512" }
https://api.github.com/repos/huggingface/datasets/issues/6711	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6711/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6711/comments	https://api.github.com/repos/huggingface/datasets/issues/6711/events	https://github.com/huggingface/datasets/pull/6711	2,165,507,817	PR_kwDODunzps5ohM1a	6,711	3x Faster Text Preprocessing	{ "avatar_url": "https://avatars.githubusercontent.com/u/1983160?v=4", "events_url": "https://api.github.com/users/ashvardanian/events{/privacy}", "followers_url": "https://api.github.com/users/ashvardanian/followers", "following_url": "https://api.github.com/users/ashvardanian/following{/other_user}", "gists_url": "https://api.github.com/users/ashvardanian/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ashvardanian", "id": 1983160, "login": "ashvardanian", "node_id": "MDQ6VXNlcjE5ODMxNjA=", "organizations_url": "https://api.github.com/users/ashvardanian/orgs", "received_events_url": "https://api.github.com/users/ashvardanian/received_events", "repos_url": "https://api.github.com/users/ashvardanian/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ashvardanian/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ashvardanian/subscriptions", "type": "User", "url": "https://api.github.com/users/ashvardanian", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Unfortunately, that won't improve the performance. StringZilla repository has extensive benchmarks comparing against different built-in functionality of several programming languages. Using `re.finditer` for tokenization is practically the slowest anti-pattern I've encountered in any language. The gap between that and a SIMD-accelerated kernel can be as big as 10 MB/s vs 10 GB/s.\n\nI understand the need to keep the dependencies minimal. It helps the package remain small and portable. At this point, StringZilla provides 105 binaries for different OS and hardware versions (more portable than NumPy) and the [binary size generally ranges from 50 KB to 250 KB](https://pypi.org/project/stringzilla/), smaller than a single JPEG. \n", "The `text` builder is not very popular, so I'm also not a fan of introducing a dependency for it.\r\n\r\nMoreover, I couldn't find any projects of this size/usage depending on StringZilla (with GitHub search), so we should at least wait for its greater adoption to merge this PR.\r\n", "> Moreover, I couldn't find any projects of this size/usage depending on StringZilla (with GitHub search), so we should at least wait for its greater adoption to merge this PR.\r\n\r\nMeanwhile I understand that you want to wait for a greater adoption - if things change in the future you would be stuck with an unsupported dependency (although I think, that really applies to everything) - the performance improvement is really significant!\r\nI wonder if it's worth, perhaps, to provide an additional 'datasets.extras' library by huggingface which support these 3rd party improvements.\r\nIt would reduce the risk on the core components and, at the same time, it would definitely help on the performance side!" ]	2024-03-03T19:03:04Z	2024-06-26T06:28:14Z	null	NONE	null	null	null	I was preparing some datasets for AI training and noticed that `datasets` by HuggingFace uses the conventional `open` mechanism to read the file and split it into chunks. I thought it can be significantly accelerated, and [started with a benchmark](https://gist.github.com/ashvardanian/55c2052e9f78b05b8d614aa90cb12347): ```sh $ pip install --upgrade --force-reinstall datasets $ python benchmark_huggingface_datasets.py xlsum.csv Generating train split: 1004598 examples [00:47, 21116.16 examples/s] Time taken to load the dataset: 48.66838526725769 seconds Time taken to chunk the dataset into parts of size 10000: 0.11466407775878906 seconds Total time taken: 48.78304934501648 seconds ``` For benchmarks I've used a [large CSV file with mixed UTF-8 content](https://github.com/ashvardanian/StringZilla/blob/main/CONTRIBUTING.md#benchmarking-datasets), most common in modern large-scale pre-training pipelines. I've later patched the `datasets` library to use `stringzilla`, which resulted in significantly lower memory consumption and in 2.9x throughput improvement on the AWS `r7iz` instances. That's using slow SSDs mounted over the network. Performance on local SSDs on something like a DGX-H100 should be even higher: ```sh $ pip install -e . $ python benchmark_huggingface_datasets.py xlsum.csv Generating train split: 1004598 examples [00:15, 64529.90 examples/s] Time taken to load the dataset: 16.45028805732727 seconds Time taken to chunk the dataset into parts of size 10000: 0.1291060447692871 seconds Total time taken: 16.579394102096558 seconds ``` I've already [pushed the patches to my fork](https://github.com/ashvardanian/datasets/tree/faster-text-parsers), and would love to contribute them to the upstream repository. --- All the tests pass, but they leave a couple of important questions open. The default Python `open(..., newline=None)` uses universal newlines, where `\n`, `\r`, and `\r\n` are all converted to `\n` on the fly. I am not sure if its a good idea for a general purpose dataset preparation pipeline? I can simulate the same behavior (which I don't yet do) for `"line"` splitter. Adjusting it for `"paragraph"`-splitter would be harder. Should we stick exactly to the old Pythonic behavior or stay closer to how C and other programming languages do that?	null	{ "+1": 8, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 8, "url": "https://api.github.com/repos/huggingface/datasets/issues/6711/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6711/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6711.diff", "html_url": "https://github.com/huggingface/datasets/pull/6711", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6711.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6711" }
https://api.github.com/repos/huggingface/datasets/issues/4682	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4682/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4682/comments	https://api.github.com/repos/huggingface/datasets/issues/4682/events	https://github.com/huggingface/datasets/issues/4682	1,304,788,215	I_kwDODunzps5NxXz3	4,682	weird issue/bug with columns (dataset iterable/stream mode)	{ "avatar_url": "https://avatars.githubusercontent.com/u/12104720?v=4", "events_url": "https://api.github.com/users/eunseojo/events{/privacy}", "followers_url": "https://api.github.com/users/eunseojo/followers", "following_url": "https://api.github.com/users/eunseojo/following{/other_user}", "gists_url": "https://api.github.com/users/eunseojo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/eunseojo", "id": 12104720, "login": "eunseojo", "node_id": "MDQ6VXNlcjEyMTA0NzIw", "organizations_url": "https://api.github.com/users/eunseojo/orgs", "received_events_url": "https://api.github.com/users/eunseojo/received_events", "repos_url": "https://api.github.com/users/eunseojo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/eunseojo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/eunseojo/subscriptions", "type": "User", "url": "https://api.github.com/users/eunseojo", "user_view_type": "public" }	[]	open	false	null	[]	null	[]	2022-07-14T13:26:47Z	2022-07-14T13:26:47Z	null	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	I have a dataset online (CloverSearch/cc-news-mutlilingual) that has a bunch of columns, two of which are "score_title_maintext" and "score_title_description". the original files are jsonl formatted. I was trying to iterate through via streaming mode and grab all "score_title_description" values, but I kept getting key not found after a certain point of iteration. I found that some json objects in the file don't have "score_title_description". And in SOME cases, this returns a NONE and in others it just gets a key error. Why is there an inconsistency here and how can I fix it?	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4682/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4682/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/4894	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4894/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4894/comments	https://api.github.com/repos/huggingface/datasets/issues/4894/events	https://github.com/huggingface/datasets/pull/4894	1,350,667,270	PR_kwDODunzps49yIvr	4,894	Add citation information to makhzan dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-08-25T10:16:40Z	2022-08-30T06:21:54Z	2022-08-25T13:19:41Z	MEMBER	null	null	null	This PR adds the citation information to `makhzan` dataset, once they have replied to our request for that information: - https://github.com/zeerakahmed/makhzan/issues/43	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4894/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4894/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4894.diff", "html_url": "https://github.com/huggingface/datasets/pull/4894", "merged_at": "2022-08-25T13:19:41Z", "patch_url": "https://github.com/huggingface/datasets/pull/4894.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4894" }
https://api.github.com/repos/huggingface/datasets/issues/4969	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4969/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4969/comments	https://api.github.com/repos/huggingface/datasets/issues/4969/events	https://github.com/huggingface/datasets/pull/4969	1,369,334,740	PR_kwDODunzps4-wPOk	4,969	Fix data URL and metadata of vivos dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-09-12T06:12:34Z	2022-09-12T07:16:15Z	2022-09-12T07:14:19Z	MEMBER	null	null	null	After contacting the authors of the VIVOS dataset to report that their data server is down, we have received a reply from Hieu-Thi Luong that their data is now hosted on Zenodo: https://doi.org/10.5281/zenodo.7068130 This PR updates their data URL and some metadata (homepage, citation and license). Fix #4936.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4969/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4969/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4969.diff", "html_url": "https://github.com/huggingface/datasets/pull/4969", "merged_at": "2022-09-12T07:14:19Z", "patch_url": "https://github.com/huggingface/datasets/pull/4969.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4969" }
https://api.github.com/repos/huggingface/datasets/issues/5020	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5020/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5020/comments	https://api.github.com/repos/huggingface/datasets/issues/5020/events	https://github.com/huggingface/datasets/pull/5020	1,384,684,078	PR_kwDODunzps4_istJ	5,020	Fix URLs of sbu_captions dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/1070872?v=4", "events_url": "https://api.github.com/users/donglixp/events{/privacy}", "followers_url": "https://api.github.com/users/donglixp/followers", "following_url": "https://api.github.com/users/donglixp/following{/other_user}", "gists_url": "https://api.github.com/users/donglixp/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/donglixp", "id": 1070872, "login": "donglixp", "node_id": "MDQ6VXNlcjEwNzA4NzI=", "organizations_url": "https://api.github.com/users/donglixp/orgs", "received_events_url": "https://api.github.com/users/donglixp/received_events", "repos_url": "https://api.github.com/users/donglixp/repos", "site_admin": false, "starred_url": "https://api.github.com/users/donglixp/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/donglixp/subscriptions", "type": "User", "url": "https://api.github.com/users/donglixp", "user_view_type": "public" }	[ { "color": "0e8a16", "default": false, "description": "Contribution to a dataset script", "id": 4564477500, "name": "dataset contribution", "node_id": "LA_kwDODunzps8AAAABEBBmPA", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20contribution" } ]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-09-24T14:00:33Z	2022-09-28T07:20:20Z	2022-09-28T07:18:23Z	CONTRIBUTOR	null	null	null	Forbidden You don't have permission to access /~vicente/sbucaptions/sbu-captions-all.tar.gz on this server. Additionally, a 403 Forbidden error was encountered while trying to use an ErrorDocument to handle the request. Apache/2.4.6 (Red Hat Enterprise Linux) OpenSSL/1.0.2k-fips PHP/5.4.16 mod_fcgid/2.3.9 mod_wsgi/3.4 Python/2.7.5 mod_perl/2.0.11 Perl/v5.16.3 Server at [www.cs.virginia.edu](mailto:csroot@virginia.edu) Port 443	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5020/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5020/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5020.diff", "html_url": "https://github.com/huggingface/datasets/pull/5020", "merged_at": "2022-09-28T07:18:23Z", "patch_url": "https://github.com/huggingface/datasets/pull/5020.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5020" }
https://api.github.com/repos/huggingface/datasets/issues/6982	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6982/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6982/comments	https://api.github.com/repos/huggingface/datasets/issues/6982/events	https://github.com/huggingface/datasets/issues/6982	2,361,661,469	I_kwDODunzps6MxBgd	6,982	cannot split dataset when using load_dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/17721894?v=4", "events_url": "https://api.github.com/users/cybest0608/events{/privacy}", "followers_url": "https://api.github.com/users/cybest0608/followers", "following_url": "https://api.github.com/users/cybest0608/following{/other_user}", "gists_url": "https://api.github.com/users/cybest0608/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/cybest0608", "id": 17721894, "login": "cybest0608", "node_id": "MDQ6VXNlcjE3NzIxODk0", "organizations_url": "https://api.github.com/users/cybest0608/orgs", "received_events_url": "https://api.github.com/users/cybest0608/received_events", "repos_url": "https://api.github.com/users/cybest0608/repos", "site_admin": false, "starred_url": "https://api.github.com/users/cybest0608/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/cybest0608/subscriptions", "type": "User", "url": "https://api.github.com/users/cybest0608", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "it seems the bug will happened in all windows system, I tried it in windows8.1, 10, 11 and all of them failed. But it won't happened in the Linux(Ubuntu and Centos7) and Mac (both my virtual and physical machine). I still don't know what the problem is. May be related to the path? I cannot run the split file in my windows server which created in Linux (even I replace the path in the arrow document)....work for it for a week but still cannot fix it .....upset", "Have you properly logged in? Are you using the a valid token?\r\n\r\nNote that this dataset is gated and you must follow the right procedure to be able to access it. You can find more info in the docs: https://huggingface.co/docs/hub/datasets-gated#access-gated-datasets-as-a-user", "> Have you properly logged in? Are you using the a valid token?\r\n> \r\n> Note that this dataset is gated and you must follow the right procedure to be able to access it. You can find more info in the docs: https://huggingface.co/docs/hub/datasets-gated#access-gated-datasets-as-a-user\r\n\r\nI finally found it what happened. It is not about the logging. When I copy the dataset from its original path (C:/Users/cybes/.cache/huggingface/datasets/downloads/extracted/XXX/cv-corpus-7.0-2021-07-21) to the desktop and load each tsv in it one by one , when I load the test spilt, the following warning occurs:\r\n\"ArrowInvalid: Failed to parse string: 'Benchmark' as a scalar of type double\"\r\n\r\nThen I manually deleted them in the \"segment\", the error won't happen anymore, even I replace the original path with these revised tsv and use the previous loading method (common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"train\", trust_remote_code=True)). It can work properly." ]	2024-06-19T08:07:16Z	2024-07-08T06:20:16Z	2024-07-08T06:20:16Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug when I use load_dataset methods to load mozilla-foundation/common_voice_7_0, it can successfully download and extracted the dataset but It cannot generating the arrow document, This bug happened in my server, my laptop, so as #6906 , but it won't happen in the google colab. I work for it for days, even I load the datasets from local path, it can Generating train split and validation split but bug happen again in test split. ### Steps to reproduce the bug from datasets import load_dataset, load_metric, Audio common_voice_train = load_dataset("mozilla-foundation/common_voice_7_0", "ja", split="train", token=selftoken, trust_remote_code=True) ### Expected behavior ``` { "name": "ValueError", "message": "Instruction \"train\" corresponds to no data!", "stack": "--------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[2], line 3 1 from datasets import load_dataset, load_metric, Audio ----> 3 common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"train\",token='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')#,trust_remote_code=True)#,streaming=True) 4 common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"test\",token='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')#,trust_remote_code=True)#,streaming=True) File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\load.py:2626, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, config_kwargs) 2622 # Build dataset for splits 2623 keep_in_memory = ( 2624 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2625 ) -> 2626 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) 2627 # Rename and cast features to match task schema 2628 if task is not None: 2629 # To avoid issuing the same warning twice File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1266, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory) 1263 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS) 1265 # Create a dataset for each of the given splits -> 1266 datasets = map_nested( 1267 partial( 1268 self._build_single_dataset, 1269 run_post_process=run_post_process, 1270 verification_mode=verification_mode, 1271 in_memory=in_memory, 1272 ), 1273 split, 1274 map_tuple=True, 1275 disable_tqdm=True, 1276 ) 1277 if isinstance(datasets, dict): 1278 datasets = DatasetDict(datasets) File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\utils\\py_utils.py:484, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, batched, batch_size, types, disable_tqdm, desc) 482 if batched: 483 data_struct = [data_struct] --> 484 mapped = function(data_struct) 485 if batched: 486 mapped = mapped[0] File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1296, in DatasetBuilder._build_single_dataset(self, split, run_post_process, verification_mode, in_memory) 1293 split = Split(split) 1295 # Build base dataset -> 1296 ds = self._as_dataset( 1297 split=split, 1298 in_memory=in_memory, 1299 ) 1300 if run_post_process: 1301 for resource_file_name in self._post_processing_resources(split).values(): File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1370, in DatasetBuilder._as_dataset(self, split, in_memory) 1368 if self._check_legacy_cache(): 1369 dataset_name = self.name -> 1370 dataset_kwargs = ArrowReader(cache_dir, self.info).read( 1371 name=dataset_name, 1372 instructions=split, 1373 split_infos=self.info.splits.values(), 1374 in_memory=in_memory, 1375 ) 1376 fingerprint = self._get_dataset_fingerprint(split) 1377 return Dataset(fingerprint=fingerprint, dataset_kwargs) File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\arrow_reader.py:256, in BaseReader.read(self, name, instructions, split_infos, in_memory) 254 msg = f'Instruction \"{instructions}\" corresponds to no data!' 255 #msg = f'Instruction \"{self._path}\",\"{name}\",\"{instructions}\",\"{split_infos}\" corresponds to no data!' --> 256 raise ValueError(msg) 257 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) ValueError: Instruction \"train\" corresponds to no data!" } ``` ### Environment info Environment: python 3.9 windows 11 pro VScode+jupyter	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6982/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6982/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/4583	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4583/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4583/comments	https://api.github.com/repos/huggingface/datasets/issues/4583/events	https://github.com/huggingface/datasets/pull/4583	1,286,790,871	PR_kwDODunzps46d7xo	4,583	<code> implementation of FLAC support using torchaudio	{ "avatar_url": "https://avatars.githubusercontent.com/u/45745870?v=4", "events_url": "https://api.github.com/users/rafael-ariascalles/events{/privacy}", "followers_url": "https://api.github.com/users/rafael-ariascalles/followers", "following_url": "https://api.github.com/users/rafael-ariascalles/following{/other_user}", "gists_url": "https://api.github.com/users/rafael-ariascalles/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/rafael-ariascalles", "id": 45745870, "login": "rafael-ariascalles", "node_id": "MDQ6VXNlcjQ1NzQ1ODcw", "organizations_url": "https://api.github.com/users/rafael-ariascalles/orgs", "received_events_url": "https://api.github.com/users/rafael-ariascalles/received_events", "repos_url": "https://api.github.com/users/rafael-ariascalles/repos", "site_admin": false, "starred_url": "https://api.github.com/users/rafael-ariascalles/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/rafael-ariascalles/subscriptions", "type": "User", "url": "https://api.github.com/users/rafael-ariascalles", "user_view_type": "public" }	[]	closed	false	null	[]	null	[]	2022-06-28T05:24:21Z	2022-06-28T05:47:02Z	2022-06-28T05:47:02Z	NONE	null	null	null	I had added Audio FLAC support with torchaudio given that Librosa and SoundFile can give problems. Also, FLAC is been used as audio from https://mlcommons.org/en/peoples-speech/	{ "avatar_url": "https://avatars.githubusercontent.com/u/45745870?v=4", "events_url": "https://api.github.com/users/rafael-ariascalles/events{/privacy}", "followers_url": "https://api.github.com/users/rafael-ariascalles/followers", "following_url": "https://api.github.com/users/rafael-ariascalles/following{/other_user}", "gists_url": "https://api.github.com/users/rafael-ariascalles/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/rafael-ariascalles", "id": 45745870, "login": "rafael-ariascalles", "node_id": "MDQ6VXNlcjQ1NzQ1ODcw", "organizations_url": "https://api.github.com/users/rafael-ariascalles/orgs", "received_events_url": "https://api.github.com/users/rafael-ariascalles/received_events", "repos_url": "https://api.github.com/users/rafael-ariascalles/repos", "site_admin": false, "starred_url": "https://api.github.com/users/rafael-ariascalles/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/rafael-ariascalles/subscriptions", "type": "User", "url": "https://api.github.com/users/rafael-ariascalles", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4583/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4583/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4583.diff", "html_url": "https://github.com/huggingface/datasets/pull/4583", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/4583.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4583" }
https://api.github.com/repos/huggingface/datasets/issues/7087	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7087/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7087/comments	https://api.github.com/repos/huggingface/datasets/issues/7087/events	https://github.com/huggingface/datasets/issues/7087	2,447,158,643	I_kwDODunzps6R3K1z	7,087	Unable to create dataset card for Lushootseed language	{ "avatar_url": "https://avatars.githubusercontent.com/u/134876525?v=4", "events_url": "https://api.github.com/users/vaishnavsudarshan/events{/privacy}", "followers_url": "https://api.github.com/users/vaishnavsudarshan/followers", "following_url": "https://api.github.com/users/vaishnavsudarshan/following{/other_user}", "gists_url": "https://api.github.com/users/vaishnavsudarshan/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/vaishnavsudarshan", "id": 134876525, "login": "vaishnavsudarshan", "node_id": "U_kgDOCAoNbQ", "organizations_url": "https://api.github.com/users/vaishnavsudarshan/orgs", "received_events_url": "https://api.github.com/users/vaishnavsudarshan/received_events", "repos_url": "https://api.github.com/users/vaishnavsudarshan/repos", "site_admin": false, "starred_url": "https://api.github.com/users/vaishnavsudarshan/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/vaishnavsudarshan/subscriptions", "type": "User", "url": "https://api.github.com/users/vaishnavsudarshan", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[ "Thanks for reporting.\r\n\r\nIt is weird, because the language entry is in the list. See: https://github.com/huggingface/huggingface.js/blob/98e32f0ed4ee057a596f66a1dec738e5db9643d5/packages/languages/src/languages_iso_639_3.ts#L15186-L15189\r\n\r\nI have reported the issue:\r\n- https://github.com/huggingface/huggingface.js/issues/834\r\n\r\n", "As explained in the reported issue above, the problem only appears in the autocomplete field: you can still enter the `lut` language directly in the markdown editor window." ]	2024-08-04T14:27:04Z	2024-08-06T06:59:23Z	2024-08-06T06:59:22Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request While I was creating the dataset which contained all documents from the Lushootseed Wikipedia, the dataset card asked me to enter which language the dataset was in. Since Lushootseed is a critically endangered language, it was not available as one of the options. Is it possible to allow entering languages that aren't available in the options? ### Motivation I'd like to add more information about my dataset in the dataset card, and the language is one of the most important pieces of information, since the entire dataset is primarily concerned collecting Lushootseed documents. ### Your contribution I can submit a pull request	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7087/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7087/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7425	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7425/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7425/comments	https://api.github.com/repos/huggingface/datasets/issues/7425/events	https://github.com/huggingface/datasets/issues/7425	2,883,684,686	I_kwDODunzps6r4YlO	7,425	load_dataset("livecodebench/code_generation_lite", version_tag="release_v2") TypeError: 'NoneType' object is not callable	{ "avatar_url": "https://avatars.githubusercontent.com/u/42167236?v=4", "events_url": "https://api.github.com/users/dshwei/events{/privacy}", "followers_url": "https://api.github.com/users/dshwei/followers", "following_url": "https://api.github.com/users/dshwei/following{/other_user}", "gists_url": "https://api.github.com/users/dshwei/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/dshwei", "id": 42167236, "login": "dshwei", "node_id": "MDQ6VXNlcjQyMTY3MjM2", "organizations_url": "https://api.github.com/users/dshwei/orgs", "received_events_url": "https://api.github.com/users/dshwei/received_events", "repos_url": "https://api.github.com/users/dshwei/repos", "site_admin": false, "starred_url": "https://api.github.com/users/dshwei/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/dshwei/subscriptions", "type": "User", "url": "https://api.github.com/users/dshwei", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "> datasets\n\nHi, have you solved this bug? Today I also met the same problem about `livecodebench/code_generation_lite` when evaluating the `Open-R1` repo. I am looking forward to your reply!\n\n![Image](https://github.com/user-attachments/assets/02e92fbf-da33-41b3-b8d4-f79b293a54f1)", "Hey guys,\nI tried to reproduce the issue and it works fine. I used google colab as enviroment.\n\n![Image](https://github.com/user-attachments/assets/024dd8e1-bd10-470b-9a6d-60759ffdb984)", "> Hey guys, I tried to reproduce the issue and it works fine. I used google colab as enviroment.\n> \n> ![Image](https://github.com/user-attachments/assets/024dd8e1-bd10-470b-9a6d-60759ffdb984)\n\nThanks for your kind reply! I wonder which Python version do you use? My Python version is 3.11.11 and datasets version is 3.3.2 but I still met this bug.\n\n<img width=\"1121\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/7c2c5007-ee55-4030-94b9-01fcdea0bf4a\" />", "@zwxandy It's Python 3.11.11", "@Serzhanov @zwxandy I have met the same problem, have this problem be solved?", "> [@Serzhanov](https://github.com/Serzhanov) [@zwxandy](https://github.com/zwxandy) I have met the same problem, have this problem be solved?\n\nI try to downgrade datasets version to 2.20.0，and it works for me @Serzhanov @dshwei , hope this work for you too :)", "> > datasets\n> \n> Hi, have you solved this bug? Today I also met the same problem about `livecodebench/code_generation_lite` when evaluating the `Open-R1` repo. I am looking forward to your reply!\n> \n> ![Image](https://github.com/user-attachments/assets/02e92fbf-da33-41b3-b8d4-f79b293a54f1)\n\nHi, have you resolved this problem? I meet the same bug when evaluating the ’Open-R1’, too. Looking forward to your reply!", "> > [@Serzhanov](https://github.com/Serzhanov) [@zwxandy](https://github.com/zwxandy) I have met the same problem, have this problem be solved?\n> \n> I try to downgrade datasets version to 2.20.0，and it works for me [@Serzhanov](https://github.com/Serzhanov) [@dshwei](https://github.com/dshwei) , hope this work for you too :)\n\nI still met the same bug after downgrading datasets version to 2.20.0. Moreover, it is not friendly to Open-R1 since there can be another bug: `open-r1 0.1.0.dev0 requires datasets>=3.2.0` with datasets==2.20.0", "> > > datasets\n> > \n> > \n> > Hi, have you solved this bug? Today I also met the same problem about `livecodebench/code_generation_lite` when evaluating the `Open-R1` repo. I am looking forward to your reply!\n> > ![Image](https://github.com/user-attachments/assets/02e92fbf-da33-41b3-b8d4-f79b293a54f1)\n> \n> Hi, have you resolved this problem? I meet the same bug when evaluating the ’Open-R1’, too. Looking forward to your reply!\n\nHi, I still cannot solve this bug introduced from datasets version. Downgrading datasets version to 2.20.0 cannot work for me and it introduces another problem `open-r1 0.1.0.dev0 requires datasets>=3.2.0` in Open-R1.\n\nLuckily, there is a tricky way to enable you to run Open-R1. You can remove or comment the code related to `lcb` in `~/anaconda3/envs/openr1/lib/python3.11/site-packages/lighteval/tasks/extended/__init__.py`. I have reproduce the results of DeepSeek-R1-Distill-Qwen-1.5B and 7B on MATH-500, GPQA, and AIME24.\n\nYou can have a try~", "The issue was resolved .\nbecause the file` livecodebench/code_generation_lite/code_generation_lite.py `was not downloaded. Manually downloading it fixed the problem." ]	2025-02-27T07:36:02Z	2025-03-27T05:05:33Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug from datasets import load_dataset lcb_codegen = load_dataset("livecodebench/code_generation_lite", version_tag="release_v2") or configs = get_dataset_config_names("livecodebench/code_generation_lite", trust_remote_code=True) both error: Traceback (most recent call last): File "", line 1, in File "/workspace/miniconda/envs/grpo/lib/python3.10/site-packages/datasets/load.py", line 2131, in load_dataset builder_instance = load_dataset_builder( File "/workspace/miniconda/envs/grpo/lib/python3.10/site-packages/datasets/load.py", line 1888, in load_dataset_builder builder_instance: DatasetBuilder = builder_cls( TypeError: 'NoneType' object is not callable ### Steps to reproduce the bug from datasets import get_dataset_config_names configs = get_dataset_config_names("livecodebench/code_generation_lite", trust_remote_code=True) OR lcb_codegen = load_dataset("livecodebench/code_generation_lite", version_tag="release_v2") ### Expected behavior load datasets livecodebench/code_generation_lite ### Environment info import datasets version '3.3.2'	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 1, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/7425/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7425/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6111	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6111/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6111/comments	https://api.github.com/repos/huggingface/datasets/issues/6111/events	https://github.com/huggingface/datasets/issues/6111	1,832,781,654	I_kwDODunzps5tPgdW	6,111	raise FileNotFoundError("Directory {dataset_path} is neither a `Dataset` directory nor a `DatasetDict` directory." )	{ "avatar_url": "https://avatars.githubusercontent.com/u/41530341?v=4", "events_url": "https://api.github.com/users/2catycm/events{/privacy}", "followers_url": "https://api.github.com/users/2catycm/followers", "following_url": "https://api.github.com/users/2catycm/following{/other_user}", "gists_url": "https://api.github.com/users/2catycm/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/2catycm", "id": 41530341, "login": "2catycm", "node_id": "MDQ6VXNlcjQxNTMwMzQx", "organizations_url": "https://api.github.com/users/2catycm/orgs", "received_events_url": "https://api.github.com/users/2catycm/received_events", "repos_url": "https://api.github.com/users/2catycm/repos", "site_admin": false, "starred_url": "https://api.github.com/users/2catycm/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/2catycm/subscriptions", "type": "User", "url": "https://api.github.com/users/2catycm", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "any idea?", "This should work: `load_dataset(\"path/to/downloaded_repo\")`\r\n\r\n`load_from_disk` is intended to be used on directories created with `Dataset.save_to_disk` or `DatasetDict.save_to_disk`", "> This should work: `load_dataset(\"path/to/downloaded_repo\")`\r\n> \r\n> `load_from_disk` is intended to be used on directories created with `Dataset.save_to_disk` or `DatasetDict.save_to_disk`\r\n\r\nThanks for your help. This works." ]	2023-08-02T09:17:29Z	2023-08-29T02:00:28Z	2023-08-29T02:00:28Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug For researchers in some countries or regions, it is usually the case that the download ability of `load_dataset` is disabled due to the complex network environment. People in these regions often prefer to use git clone or other programming tricks to manually download the files to the disk (for example, [How to elegantly download hf models, zhihu zhuanlan](https://zhuanlan.zhihu.com/p/475260268) proposed a crawlder based solution, and [Is there any mirror for hf_hub, zhihu answer](https://www.zhihu.com/question/371644077) provided some cloud based solutions, and [How to avoid pitfalls on Hugging face downloading, zhihu zhuanlan] gave some useful suggestions), and then use `load_from_disk` to get the dataset object. However, when one finally has the local files on the disk, it is still buggy when trying to load the files into objects. ### Steps to reproduce the bug Steps to reproduce the bug: 1. Found CIFAR dataset in hugging face: https://huggingface.co/datasets/cifar100/tree/main 2. Click ":" button to show "Clone repository" option, and then follow the prompts on the box: ```bash cd my_directory_absolute git lfs install git clone https://huggingface.co/datasets/cifar100 ls my_directory_absolute/cifar100 # confirm that the directory exists and it is OK. ``` 3. Write A python file to try to load the dataset ```python from datasets import load_dataset, load_from_disk dataset = load_from_disk("my_directory_absolute/cifar100") ``` Notice that according to issue #3700 , it is wrong to use load_dataset("my_directory_absolute/cifar100"), so we must use load_from_disk instead. 4. Then you will see the error reported: ```log --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Cell In[5], line 9 1 from datasets import load_dataset, load_from_disk ----> 9 dataset = load_from_disk("my_directory_absolute/cifar100") File [~/miniconda3/envs/ai/lib/python3.10/site-packages/datasets/load.py:2232), in load_from_disk(dataset_path, fs, keep_in_memory, storage_options) 2230 return DatasetDict.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options) 2231 else: -> 2232 raise FileNotFoundError( 2233 f"Directory {dataset_path} is neither a `Dataset` directory nor a `DatasetDict` directory." 2234 ) FileNotFoundError: Directory my_directory_absolute/cifar100 is neither a `Dataset` directory nor a `DatasetDict` directory. ``` ### Expected behavior The dataset should be load successfully. ### Environment info ```bash datasets-cli env ``` -> results: ```txt Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.14.2 - Platform: Linux-4.18.0-372.32.1.el8_6.x86_64-x86_64-with-glibc2.28 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/41530341?v=4", "events_url": "https://api.github.com/users/2catycm/events{/privacy}", "followers_url": "https://api.github.com/users/2catycm/followers", "following_url": "https://api.github.com/users/2catycm/following{/other_user}", "gists_url": "https://api.github.com/users/2catycm/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/2catycm", "id": 41530341, "login": "2catycm", "node_id": "MDQ6VXNlcjQxNTMwMzQx", "organizations_url": "https://api.github.com/users/2catycm/orgs", "received_events_url": "https://api.github.com/users/2catycm/received_events", "repos_url": "https://api.github.com/users/2catycm/repos", "site_admin": false, "starred_url": "https://api.github.com/users/2catycm/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/2catycm/subscriptions", "type": "User", "url": "https://api.github.com/users/2catycm", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6111/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6111/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6445	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6445/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6445/comments	https://api.github.com/repos/huggingface/datasets/issues/6445/events	https://github.com/huggingface/datasets/pull/6445	2,006,958,595	PR_kwDODunzps5gKg2d	6,445	Use `filelock` package for file locking	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "<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) \| 0.005431 / 0.011353 (-0.005922) \| 0.003255 / 0.011008 (-0.007753) \| 0.062867 / 0.038508 (0.024359) \| 0.051917 / 0.023109 (0.028808) \| 0.254229 / 0.275898 (-0.021669) \| 0.276949 / 0.323480 (-0.046531) \| 0.002868 / 0.007986 (-0.005117) \| 0.002539 / 0.004328 (-0.001789) \| 0.048366 / 0.004250 (0.044115) \| 0.038497 / 0.037052 (0.001445) \| 0.252158 / 0.258489 (-0.006332) \| 0.288868 / 0.293841 (-0.004973) \| 0.027956 / 0.128546 (-0.100591) \| 0.010500 / 0.075646 (-0.065147) \| 0.209263 / 0.419271 (-0.210008) \| 0.035415 / 0.043533 (-0.008118) \| 0.253104 / 0.255139 (-0.002035) \| 0.274646 / 0.283200 (-0.008554) \| 0.019923 / 0.141683 (-0.121760) \| 1.081870 / 1.452155 (-0.370285) \| 1.157159 / 1.492716 (-0.335557) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097420 / 0.018006 (0.079414) \| 0.315021 / 0.000490 (0.314531) \| 0.000218 / 0.000200 (0.000018) \| 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) \| 0.018826 / 0.037411 (-0.018585) \| 0.061921 / 0.014526 (0.047395) \| 0.086825 / 0.176557 (-0.089731) \| 0.120606 / 0.737135 (-0.616529) \| 0.074344 / 0.296338 (-0.221994) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283238 / 0.215209 (0.068028) \| 2.771817 / 2.077655 (0.694162) \| 1.500194 / 1.504120 (-0.003926) \| 1.379286 / 1.541195 (-0.161908) \| 1.447747 / 1.468490 (-0.020743) \| 0.587176 / 4.584777 (-3.997601) \| 2.411260 / 3.745712 (-1.334452) \| 2.897682 / 5.269862 (-2.372180) \| 1.821720 / 4.565676 (-2.743957) \| 0.063299 / 0.424275 (-0.360976) \| 0.004969 / 0.007607 (-0.002639) \| 0.346417 / 0.226044 (0.120373) \| 3.432936 / 2.268929 (1.164007) \| 1.898662 / 55.444624 (-53.545963) \| 1.624339 / 6.876477 (-5.252138) \| 1.641653 / 2.142072 (-0.500419) \| 0.655773 / 4.805227 (-4.149454) \| 0.118588 / 6.500664 (-6.382076) \| 0.043919 / 0.075469 (-0.031551) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.949466 / 1.841788 (-0.892322) \| 12.378025 / 8.074308 (4.303717) \| 10.750942 / 10.191392 (0.559550) \| 0.146575 / 0.680424 (-0.533849) \| 0.015453 / 0.534201 (-0.518748) \| 0.290608 / 0.579283 (-0.288676) \| 0.273000 / 0.434364 (-0.161364) \| 0.328019 / 0.540337 (-0.212318) \| 0.417396 / 1.386936 (-0.969540) \|\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) \| 0.005363 / 0.011353 (-0.005990) \| 0.003421 / 0.011008 (-0.007587) \| 0.049429 / 0.038508 (0.010920) \| 0.052774 / 0.023109 (0.029664) \| 0.274058 / 0.275898 (-0.001840) \| 0.297307 / 0.323480 (-0.026173) \| 0.004000 / 0.007986 (-0.003986) \| 0.002463 / 0.004328 (-0.001866) \| 0.048824 / 0.004250 (0.044574) \| 0.041064 / 0.037052 (0.004012) \| 0.279066 / 0.258489 (0.020577) \| 0.302420 / 0.293841 (0.008579) \| 0.029665 / 0.128546 (-0.098881) \| 0.010628 / 0.075646 (-0.065018) \| 0.057678 / 0.419271 (-0.361594) \| 0.032731 / 0.043533 (-0.010802) \| 0.274662 / 0.255139 (0.019523) \| 0.291878 / 0.283200 (0.008678) \| 0.018820 / 0.141683 (-0.122863) \| 1.124042 / 1.452155 (-0.328112) \| 1.175020 / 1.492716 (-0.317697) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099419 / 0.018006 (0.081413) \| 0.311511 / 0.000490 (0.311022) \| 0.000228 / 0.000200 (0.000028) \| 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) \| 0.022478 / 0.037411 (-0.014933) \| 0.071955 / 0.014526 (0.057429) \| 0.081423 / 0.176557 (-0.095134) \| 0.119574 / 0.737135 (-0.617561) \| 0.084724 / 0.296338 (-0.211615) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295537 / 0.215209 (0.080328) \| 2.893855 / 2.077655 (0.816201) \| 1.602065 / 1.504120 (0.097945) \| 1.478193 / 1.541195 (-0.063002) \| 1.508250 / 1.468490 (0.039760) \| 0.566140 / 4.584777 (-4.018637) \| 2.455474 / 3.745712 (-1.290238) \| 2.849525 / 5.269862 (-2.420337) \| 1.763830 / 4.565676 (-2.801846) \| 0.062375 / 0.424275 (-0.361900) \| 0.004992 / 0.007607 (-0.002615) \| 0.346068 / 0.226044 (0.120023) \| 3.452421 / 2.268929 (1.183492) \| 1.970346 / 55.444624 (-53.474278) \| 1.690865 / 6.876477 (-5.185612) \| 1.705358 / 2.142072 (-0.436714) \| 0.644261 / 4.805227 (-4.160967) \| 0.120596 / 6.500664 (-6.380068) \| 0.042699 / 0.075469 (-0.032770) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980506 / 1.841788 (-0.861281) \| 12.401901 / 8.074308 (4.327593) \| 11.169413 / 10.191392 (0.978021) \| 0.142540 / 0.680424 (-0.537884) \| 0.015730 / 0.534201 (-0.518471) \| 0.288871 / 0.579283 (-0.290412) \| 0.287487 / 0.434364 (-0.146877) \| 0.325133 / 0.540337 (-0.215204) \| 0.417979 / 1.386936 (-0.968957) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#965685891db0d06979490aaebab72d5dc628e42b \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005062 / 0.011353 (-0.006291) \| 0.003024 / 0.011008 (-0.007984) \| 0.061801 / 0.038508 (0.023293) \| 0.048934 / 0.023109 (0.025825) \| 0.248024 / 0.275898 (-0.027874) \| 0.265665 / 0.323480 (-0.057815) \| 0.003885 / 0.007986 (-0.004100) \| 0.002371 / 0.004328 (-0.001957) \| 0.047895 / 0.004250 (0.043644) \| 0.039015 / 0.037052 (0.001963) \| 0.252320 / 0.258489 (-0.006169) \| 0.286533 / 0.293841 (-0.007308) \| 0.027694 / 0.128546 (-0.100852) \| 0.010254 / 0.075646 (-0.065392) \| 0.206586 / 0.419271 (-0.212685) \| 0.035681 / 0.043533 (-0.007852) \| 0.251645 / 0.255139 (-0.003494) \| 0.285462 / 0.283200 (0.002262) \| 0.017326 / 0.141683 (-0.124357) \| 1.086927 / 1.452155 (-0.365228) \| 1.153172 / 1.492716 (-0.339545) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093020 / 0.018006 (0.075014) \| 0.300018 / 0.000490 (0.299528) \| 0.000208 / 0.000200 (0.000008) \| 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) \| 0.018828 / 0.037411 (-0.018584) \| 0.062569 / 0.014526 (0.048043) \| 0.074130 / 0.176557 (-0.102427) \| 0.119304 / 0.737135 (-0.617832) \| 0.076409 / 0.296338 (-0.219930) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285938 / 0.215209 (0.070729) \| 2.780662 / 2.077655 (0.703007) \| 1.522401 / 1.504120 (0.018281) \| 1.392475 / 1.541195 (-0.148720) \| 1.412517 / 1.468490 (-0.055973) \| 0.562768 / 4.584777 (-4.022009) \| 2.421406 / 3.745712 (-1.324306) \| 2.786271 / 5.269862 (-2.483591) \| 1.737193 / 4.565676 (-2.828484) \| 0.062775 / 0.424275 (-0.361500) \| 0.004908 / 0.007607 (-0.002699) \| 0.345070 / 0.226044 (0.119026) \| 3.383700 / 2.268929 (1.114771) \| 1.795974 / 55.444624 (-53.648651) \| 1.527656 / 6.876477 (-5.348820) \| 1.514035 / 2.142072 (-0.628037) \| 0.647652 / 4.805227 (-4.157575) \| 0.120121 / 6.500664 (-6.380543) \| 0.042259 / 0.075469 (-0.033210) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948951 / 1.841788 (-0.892837) \| 11.514971 / 8.074308 (3.440663) \| 10.722668 / 10.191392 (0.531276) \| 0.143034 / 0.680424 (-0.537390) \| 0.014800 / 0.534201 (-0.519401) \| 0.286189 / 0.579283 (-0.293094) \| 0.270735 / 0.434364 (-0.163629) \| 0.323907 / 0.540337 (-0.216430) \| 0.417569 / 1.386936 (-0.969367) \|\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) \| 0.005670 / 0.011353 (-0.005683) \| 0.003238 / 0.011008 (-0.007770) \| 0.048520 / 0.038508 (0.010012) \| 0.051341 / 0.023109 (0.028232) \| 0.273883 / 0.275898 (-0.002015) \| 0.295165 / 0.323480 (-0.028315) \| 0.004755 / 0.007986 (-0.003231) \| 0.002471 / 0.004328 (-0.001857) \| 0.047487 / 0.004250 (0.043237) \| 0.040225 / 0.037052 (0.003172) \| 0.276758 / 0.258489 (0.018269) \| 0.301182 / 0.293841 (0.007341) \| 0.029749 / 0.128546 (-0.098797) \| 0.010340 / 0.075646 (-0.065306) \| 0.057193 / 0.419271 (-0.362079) \| 0.033067 / 0.043533 (-0.010466) \| 0.272716 / 0.255139 (0.017577) \| 0.292301 / 0.283200 (0.009101) \| 0.019075 / 0.141683 (-0.122608) \| 1.101778 / 1.452155 (-0.350376) \| 1.173573 / 1.492716 (-0.319143) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091008 / 0.018006 (0.073002) \| 0.300749 / 0.000490 (0.300259) \| 0.000218 / 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) \| 0.021760 / 0.037411 (-0.015651) \| 0.071407 / 0.014526 (0.056881) \| 0.081151 / 0.176557 (-0.095406) \| 0.120140 / 0.737135 (-0.616995) \| 0.082408 / 0.296338 (-0.213931) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294828 / 0.215209 (0.079619) \| 2.880701 / 2.077655 (0.803047) \| 1.604187 / 1.504120 (0.100068) \| 1.479236 / 1.541195 (-0.061959) \| 1.498875 / 1.468490 (0.030385) \| 0.561950 / 4.584777 (-4.022827) \| 2.462531 / 3.745712 (-1.283181) \| 2.800905 / 5.269862 (-2.468957) \| 1.746535 / 4.565676 (-2.819141) \| 0.062732 / 0.424275 (-0.361544) \| 0.004932 / 0.007607 (-0.002675) \| 0.347125 / 0.226044 (0.121081) \| 3.431343 / 2.268929 (1.162415) \| 1.964999 / 55.444624 (-53.479625) \| 1.669709 / 6.876477 (-5.206768) \| 1.675148 / 2.142072 (-0.466924) \| 0.635436 / 4.805227 (-4.169792) \| 0.116598 / 6.500664 (-6.384066) \| 0.041447 / 0.075469 (-0.034022) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975751 / 1.841788 (-0.866037) \| 12.060246 / 8.074308 (3.985938) \| 10.871641 / 10.191392 (0.680249) \| 0.142936 / 0.680424 (-0.537488) \| 0.015779 / 0.534201 (-0.518422) \| 0.287120 / 0.579283 (-0.292163) \| 0.283963 / 0.434364 (-0.150401) \| 0.341231 / 0.540337 (-0.199107) \| 0.419518 / 1.386936 (-0.967418) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0943ff0072dcef473530d8a494f314048f3a3d51 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005105 / 0.011353 (-0.006248) \| 0.002855 / 0.011008 (-0.008153) \| 0.062044 / 0.038508 (0.023536) \| 0.052948 / 0.023109 (0.029839) \| 0.249841 / 0.275898 (-0.026057) \| 0.276687 / 0.323480 (-0.046792) \| 0.003792 / 0.007986 (-0.004194) \| 0.002385 / 0.004328 (-0.001943) \| 0.048648 / 0.004250 (0.044398) \| 0.038317 / 0.037052 (0.001264) \| 0.255235 / 0.258489 (-0.003254) \| 0.287870 / 0.293841 (-0.005971) \| 0.027429 / 0.128546 (-0.101117) \| 0.010182 / 0.075646 (-0.065464) \| 0.206980 / 0.419271 (-0.212291) \| 0.035444 / 0.043533 (-0.008089) \| 0.255073 / 0.255139 (-0.000066) \| 0.270636 / 0.283200 (-0.012563) \| 0.018003 / 0.141683 (-0.123680) \| 1.124691 / 1.452155 (-0.327463) \| 1.191872 / 1.492716 (-0.300844) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088824 / 0.018006 (0.070818) \| 0.302771 / 0.000490 (0.302281) \| 0.000210 / 0.000200 (0.000010) \| 0.000048 / 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) \| 0.018102 / 0.037411 (-0.019310) \| 0.062131 / 0.014526 (0.047605) \| 0.073230 / 0.176557 (-0.103327) \| 0.119789 / 0.737135 (-0.617346) \| 0.074804 / 0.296338 (-0.221534) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293244 / 0.215209 (0.078035) \| 2.891401 / 2.077655 (0.813746) \| 1.504481 / 1.504120 (0.000361) \| 1.381251 / 1.541195 (-0.159944) \| 1.387245 / 1.468490 (-0.081245) \| 0.552732 / 4.584777 (-4.032045) \| 2.386439 / 3.745712 (-1.359273) \| 2.718918 / 5.269862 (-2.550944) \| 1.725401 / 4.565676 (-2.840275) \| 0.061946 / 0.424275 (-0.362329) \| 0.004957 / 0.007607 (-0.002650) \| 0.342776 / 0.226044 (0.116731) \| 3.418911 / 2.268929 (1.149983) \| 1.838283 / 55.444624 (-53.606341) \| 1.538013 / 6.876477 (-5.338464) \| 1.545144 / 2.142072 (-0.596928) \| 0.637857 / 4.805227 (-4.167370) \| 0.116451 / 6.500664 (-6.384213) \| 0.042228 / 0.075469 (-0.033241) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.943575 / 1.841788 (-0.898212) \| 11.492939 / 8.074308 (3.418631) \| 10.601605 / 10.191392 (0.410212) \| 0.139084 / 0.680424 (-0.541340) \| 0.013691 / 0.534201 (-0.520510) \| 0.286696 / 0.579283 (-0.292587) \| 0.259979 / 0.434364 (-0.174385) \| 0.322578 / 0.540337 (-0.217759) \| 0.411950 / 1.386936 (-0.974986) \|\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) \| 0.005168 / 0.011353 (-0.006185) \| 0.003238 / 0.011008 (-0.007770) \| 0.049028 / 0.038508 (0.010520) \| 0.052930 / 0.023109 (0.029821) \| 0.274750 / 0.275898 (-0.001148) \| 0.294023 / 0.323480 (-0.029457) \| 0.003829 / 0.007986 (-0.004157) \| 0.002372 / 0.004328 (-0.001956) \| 0.048689 / 0.004250 (0.044439) \| 0.040056 / 0.037052 (0.003003) \| 0.280147 / 0.258489 (0.021658) \| 0.304871 / 0.293841 (0.011030) \| 0.028734 / 0.128546 (-0.099812) \| 0.010624 / 0.075646 (-0.065022) \| 0.058705 / 0.419271 (-0.360566) \| 0.032140 / 0.043533 (-0.011393) \| 0.276702 / 0.255139 (0.021563) \| 0.293186 / 0.283200 (0.009987) \| 0.018124 / 0.141683 (-0.123559) \| 1.139398 / 1.452155 (-0.312757) \| 1.174862 / 1.492716 (-0.317855) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.087627 / 0.018006 (0.069620) \| 0.298376 / 0.000490 (0.297886) \| 0.000238 / 0.000200 (0.000038) \| 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) \| 0.021344 / 0.037411 (-0.016067) \| 0.070208 / 0.014526 (0.055682) \| 0.081177 / 0.176557 (-0.095380) \| 0.120170 / 0.737135 (-0.616965) \| 0.082472 / 0.296338 (-0.213866) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293227 / 0.215209 (0.078018) \| 2.844619 / 2.077655 (0.766964) \| 1.586922 / 1.504120 (0.082803) \| 1.460256 / 1.541195 (-0.080938) \| 1.475955 / 1.468490 (0.007465) \| 0.553226 / 4.584777 (-4.031551) \| 2.418869 / 3.745712 (-1.326843) \| 2.709256 / 5.269862 (-2.560606) \| 1.705935 / 4.565676 (-2.859741) \| 0.062391 / 0.424275 (-0.361884) \| 0.004929 / 0.007607 (-0.002678) \| 0.350358 / 0.226044 (0.124313) \| 3.448824 / 2.268929 (1.179896) \| 1.929451 / 55.444624 (-53.515174) \| 1.669438 / 6.876477 (-5.207038) \| 1.660923 / 2.142072 (-0.481150) \| 0.633107 / 4.805227 (-4.172120) \| 0.114657 / 6.500664 (-6.386007) \| 0.041256 / 0.075469 (-0.034214) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.968408 / 1.841788 (-0.873380) \| 11.749754 / 8.074308 (3.675446) \| 10.796670 / 10.191392 (0.605278) \| 0.128881 / 0.680424 (-0.551543) \| 0.015326 / 0.534201 (-0.518875) \| 0.286407 / 0.579283 (-0.292876) \| 0.276324 / 0.434364 (-0.158040) \| 0.326201 / 0.540337 (-0.214136) \| 0.419854 / 1.386936 (-0.967082) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1731d5a8cd103533ef6b438b4429ab51d3a6a0ce \"CML watermark\")\n" ]	2023-11-22T19:04:45Z	2023-11-23T18:47:30Z	2023-11-23T18:41:23Z	COLLABORATOR	null	null	null	Use the `filelock` package instead of `datasets.utils.filelock` for file locking to be consistent with `huggingface_hub` and not to be responsible for improving the `filelock` capabilities 🙂. (Reverts https://github.com/huggingface/datasets/pull/859, but these `INFO` logs are not printed by default (anymore?), so this should be okay)	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6445/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6445/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6445.diff", "html_url": "https://github.com/huggingface/datasets/pull/6445", "merged_at": "2023-11-23T18:41:22Z", "patch_url": "https://github.com/huggingface/datasets/pull/6445.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6445" }
https://api.github.com/repos/huggingface/datasets/issues/4672	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4672/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4672/comments	https://api.github.com/repos/huggingface/datasets/issues/4672/events	https://github.com/huggingface/datasets/pull/4672	1,300,911,467	PR_kwDODunzps47NEfV	4,672	Support extract 7-zip compressed data files	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "Cool! Can you please remove `Fix #3541` from the description as this PR doesn't add support for streaming/`iter_archive`, so it only partially addresses the issue?\r\n\r\nSide note:\r\nI think we can use `libarchive` (`libarchive-c` is a Python package with the bindings) for streaming 7z archives. The only issue with this lib is that it's tricky to install on Windows/Mac." ]	2022-07-11T15:56:51Z	2022-07-15T13:14:27Z	2022-07-15T13:02:07Z	MEMBER	null	null	null	Fix partially #3541, fix #4670.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4672/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4672/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4672.diff", "html_url": "https://github.com/huggingface/datasets/pull/4672", "merged_at": "2022-07-15T13:02:07Z", "patch_url": "https://github.com/huggingface/datasets/pull/4672.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4672" }
https://api.github.com/repos/huggingface/datasets/issues/5878	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5878/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5878/comments	https://api.github.com/repos/huggingface/datasets/issues/5878/events	https://github.com/huggingface/datasets/issues/5878	1,718,203,843	I_kwDODunzps5mabXD	5,878	Prefetching for IterableDataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/30946190?v=4", "events_url": "https://api.github.com/users/vyeevani/events{/privacy}", "followers_url": "https://api.github.com/users/vyeevani/followers", "following_url": "https://api.github.com/users/vyeevani/following{/other_user}", "gists_url": "https://api.github.com/users/vyeevani/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/vyeevani", "id": 30946190, "login": "vyeevani", "node_id": "MDQ6VXNlcjMwOTQ2MTkw", "organizations_url": "https://api.github.com/users/vyeevani/orgs", "received_events_url": "https://api.github.com/users/vyeevani/received_events", "repos_url": "https://api.github.com/users/vyeevani/repos", "site_admin": false, "starred_url": "https://api.github.com/users/vyeevani/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/vyeevani/subscriptions", "type": "User", "url": "https://api.github.com/users/vyeevani", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	[ "Very cool! Do you have a link to the code that you're using to eagerly fetch the data? Would also be interested in hacking around something here for pre-fetching iterable datasets", "I ended up just switching back to the pytorch dataloader and using it's multiprocessing functionality to handle this :(. I'm just not that familiar with python multiprocessing to get something to work in jupyter (kept having weird behaviors happening with zombies living after the cell finished).", "Ultimately settled on using webdataset to circumvent huggingface datasets entirely. Would definitely switch back if: https://github.com/huggingface/datasets/issues/5337 was resolved.", "Hi! You can combine `datasets` with `torchdata` to prefetch `IterableDataset`'s samples:\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchdata.datapipes.iter import IterableWrapper, HuggingFaceHubReader\r\nfrom torch.utils.data import DataLoader\r\n\r\nds = load_dataset(\"sst\", split=\"train\", streaming=True)\r\n# processing...\r\ndp = IterableWrapper(ds)\r\ndp = dp.prefetch(100)\r\ndl = DataLoader(dp, batch_size=8)\r\n\r\ni = iter(dl)\r\nnext(i)\r\n```", "Hey @mariosasko! Thanks for the tip here - introducing prefetch with `torchdata` didn't really give me any performance difference vs not prefetching, but the concept is definitely one that could be really beneficial. Are there any benchmarks that show the speed-up you can get with `torchdata`'s prefetch just for comparison?", "I want to perform mapping() in advance by prefetching within the IterableDataset. Are there any recent updates?", "> Ultimately settled on using webdataset to circumvent huggingface datasets entirely. Would definitely switch back if: [#5337](https://github.com/huggingface/datasets/issues/5337) was resolved.\n\nThe HF dataset now supports the webdataset format. On a custom dataset with multiple .tar files that is on a single HDD. The read speed is ~180 MB/s when building the buffer.\n\nUsing the following setup, I observe ~170MB/s read speed when training with hf accelerate (multi-gpu)\n\n```\n train_dataset = load_dataset(\n \"path_to_py\",\n split=\"train\",\n streaming=True,\n trust_remote_code=True\n )\n train_dataset = train_dataset.shuffle(seed=general_config.seed, buffer_size=1000)\n train_dataset = train_dataset.with_format(\"torch\")\n train_dataloader = DataLoader(train_dataset, num_workers=1, batch_size=8, prefetch_factor=4)\n # Then accelerator.prepare (I used dispatch_batches=False as my custom dataset does not return tuple of tensors)\n```\n" ]	2023-05-20T15:25:40Z	2025-01-24T17:13:55Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request Add support for prefetching the next n batches through iterabledataset to reduce batch loading bottleneck in training loop. ### Motivation The primary motivation behind this is to use hardware accelerators alongside a streaming dataset. This is required when you are in a low ram or low disk space setting as well as quick iteration where you're iterating though different accelerator environments (e.x changing ec2 instances quickly to figure out batch/sec for a particular architecture). Currently, using the IterableDataset results in accelerators becoming basically useless due to the massive bottleneck induced by the dataset lazy loading/transform/mapping. I've considered two alternatives: PyTorch dataloader that handles this. However, I'm using jax, and I believe this is a piece of functionality that should live in the stream class. Replicating the "num_workers" part of the PyTorch DataLoader to eagerly load batches and apply the transform so Arrow caching will automatically cache results and make them accessible. ### Your contribution I may or may not have time to do this. Currently, I've written the basic multiprocessor approach to handle the eager DataLoader for my own use case with code that's not integrated to datasets. I'd definitely see this as being the default over the regular Dataset for most people given that they wouldn't have to wait on the datasets while also not worrying about performance.	null	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/5878/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5878/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7436	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7436/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7436/comments	https://api.github.com/repos/huggingface/datasets/issues/7436/events	https://github.com/huggingface/datasets/pull/7436	2,898,385,725	PR_kwDODunzps6NiArv	7,436	chore: fix typos	{ "avatar_url": "https://avatars.githubusercontent.com/u/35225576?v=4", "events_url": "https://api.github.com/users/afuetterer/events{/privacy}", "followers_url": "https://api.github.com/users/afuetterer/followers", "following_url": "https://api.github.com/users/afuetterer/following{/other_user}", "gists_url": "https://api.github.com/users/afuetterer/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/afuetterer", "id": 35225576, "login": "afuetterer", "node_id": "MDQ6VXNlcjM1MjI1NTc2", "organizations_url": "https://api.github.com/users/afuetterer/orgs", "received_events_url": "https://api.github.com/users/afuetterer/received_events", "repos_url": "https://api.github.com/users/afuetterer/repos", "site_admin": false, "starred_url": "https://api.github.com/users/afuetterer/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/afuetterer/subscriptions", "type": "User", "url": "https://api.github.com/users/afuetterer", "user_view_type": "public" }	[]	open	false	null	[]	null	[]	2025-03-05T20:17:54Z	2025-03-05T20:17:54Z	null	NONE	null	null	null	null	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7436/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7436/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7436.diff", "html_url": "https://github.com/huggingface/datasets/pull/7436", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/7436.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7436" }
https://api.github.com/repos/huggingface/datasets/issues/5746	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5746/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5746/comments	https://api.github.com/repos/huggingface/datasets/issues/5746/events	https://github.com/huggingface/datasets/pull/5746	1,667,102,459	PR_kwDODunzps5ORIUU	5,746	Fix link in docs	{ "avatar_url": "https://avatars.githubusercontent.com/u/7485661?v=4", "events_url": "https://api.github.com/users/bbbxyz/events{/privacy}", "followers_url": "https://api.github.com/users/bbbxyz/followers", "following_url": "https://api.github.com/users/bbbxyz/following{/other_user}", "gists_url": "https://api.github.com/users/bbbxyz/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/bbbxyz", "id": 7485661, "login": "bbbxyz", "node_id": "MDQ6VXNlcjc0ODU2NjE=", "organizations_url": "https://api.github.com/users/bbbxyz/orgs", "received_events_url": "https://api.github.com/users/bbbxyz/received_events", "repos_url": "https://api.github.com/users/bbbxyz/repos", "site_admin": false, "starred_url": "https://api.github.com/users/bbbxyz/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/bbbxyz/subscriptions", "type": "User", "url": "https://api.github.com/users/bbbxyz", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006461 / 0.011353 (-0.004892) \| 0.004671 / 0.011008 (-0.006337) \| 0.097329 / 0.038508 (0.058821) \| 0.028380 / 0.023109 (0.005270) \| 0.369892 / 0.275898 (0.093994) \| 0.398244 / 0.323480 (0.074764) \| 0.004795 / 0.007986 (-0.003190) \| 0.004866 / 0.004328 (0.000538) \| 0.075060 / 0.004250 (0.070809) \| 0.035678 / 0.037052 (-0.001374) \| 0.372197 / 0.258489 (0.113708) \| 0.407509 / 0.293841 (0.113668) \| 0.031557 / 0.128546 (-0.096989) \| 0.011608 / 0.075646 (-0.064038) \| 0.325467 / 0.419271 (-0.093805) \| 0.042590 / 0.043533 (-0.000943) \| 0.373738 / 0.255139 (0.118599) \| 0.395793 / 0.283200 (0.112593) \| 0.082335 / 0.141683 (-0.059348) \| 1.471582 / 1.452155 (0.019427) \| 1.535834 / 1.492716 (0.043117) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.192432 / 0.018006 (0.174426) \| 0.404423 / 0.000490 (0.403933) \| 0.003252 / 0.000200 (0.003052) \| 0.000073 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025312 / 0.037411 (-0.012099) \| 0.099964 / 0.014526 (0.085438) \| 0.108779 / 0.176557 (-0.067777) \| 0.170438 / 0.737135 (-0.566697) \| 0.110116 / 0.296338 (-0.186223) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420402 / 0.215209 (0.205193) \| 4.179142 / 2.077655 (2.101487) \| 1.858114 / 1.504120 (0.353994) \| 1.674452 / 1.541195 (0.133257) \| 1.697839 / 1.468490 (0.229349) \| 0.694707 / 4.584777 (-3.890070) \| 3.394321 / 3.745712 (-0.351391) \| 1.918437 / 5.269862 (-3.351425) \| 1.277954 / 4.565676 (-3.287723) \| 0.082357 / 0.424275 (-0.341918) \| 0.012206 / 0.007607 (0.004598) \| 0.522093 / 0.226044 (0.296049) \| 5.239604 / 2.268929 (2.970675) \| 2.347764 / 55.444624 (-53.096860) \| 1.996864 / 6.876477 (-4.879613) \| 2.050820 / 2.142072 (-0.091253) \| 0.806110 / 4.805227 (-3.999118) \| 0.151061 / 6.500664 (-6.349603) \| 0.066438 / 0.075469 (-0.009031) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.211233 / 1.841788 (-0.630554) \| 14.054422 / 8.074308 (5.980114) \| 14.110141 / 10.191392 (3.918749) \| 0.129962 / 0.680424 (-0.550462) \| 0.017271 / 0.534201 (-0.516930) \| 0.386410 / 0.579283 (-0.192873) \| 0.392648 / 0.434364 (-0.041716) \| 0.444940 / 0.540337 (-0.095398) \| 0.533535 / 1.386936 (-0.853401) \|\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) \| 0.006865 / 0.011353 (-0.004488) \| 0.004662 / 0.011008 (-0.006346) \| 0.077837 / 0.038508 (0.039329) \| 0.028258 / 0.023109 (0.005149) \| 0.346136 / 0.275898 (0.070238) \| 0.380414 / 0.323480 (0.056934) \| 0.005039 / 0.007986 (-0.002947) \| 0.004967 / 0.004328 (0.000638) \| 0.077774 / 0.004250 (0.073523) \| 0.037504 / 0.037052 (0.000452) \| 0.341550 / 0.258489 (0.083061) \| 0.382494 / 0.293841 (0.088653) \| 0.031881 / 0.128546 (-0.096665) \| 0.011746 / 0.075646 (-0.063901) \| 0.087087 / 0.419271 (-0.332185) \| 0.043108 / 0.043533 (-0.000425) \| 0.344103 / 0.255139 (0.088964) \| 0.366613 / 0.283200 (0.083413) \| 0.090399 / 0.141683 (-0.051284) \| 1.492675 / 1.452155 (0.040520) \| 1.588666 / 1.492716 (0.095950) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.191859 / 0.018006 (0.173853) \| 0.412514 / 0.000490 (0.412025) \| 0.001953 / 0.000200 (0.001753) \| 0.000084 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025159 / 0.037411 (-0.012252) \| 0.100125 / 0.014526 (0.085599) \| 0.106000 / 0.176557 (-0.070556) \| 0.160710 / 0.737135 (-0.576425) \| 0.110449 / 0.296338 (-0.185889) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436636 / 0.215209 (0.221427) \| 4.364597 / 2.077655 (2.286942) \| 2.077492 / 1.504120 (0.573372) \| 1.868248 / 1.541195 (0.327053) \| 1.911218 / 1.468490 (0.442728) \| 0.700306 / 4.584777 (-3.884471) \| 3.385428 / 3.745712 (-0.360284) \| 2.965384 / 5.269862 (-2.304478) \| 1.522093 / 4.565676 (-3.043583) \| 0.082805 / 0.424275 (-0.341470) \| 0.012432 / 0.007607 (0.004825) \| 0.538478 / 0.226044 (0.312433) \| 5.383207 / 2.268929 (3.114278) \| 2.525177 / 55.444624 (-52.919447) \| 2.179632 / 6.876477 (-4.696845) \| 2.280768 / 2.142072 (0.138695) \| 0.805869 / 4.805227 (-3.999358) \| 0.152716 / 6.500664 (-6.347948) \| 0.067848 / 0.075469 (-0.007621) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.318899 / 1.841788 (-0.522889) \| 14.416310 / 8.074308 (6.342002) \| 14.172804 / 10.191392 (3.981412) \| 0.141729 / 0.680424 (-0.538695) \| 0.016785 / 0.534201 (-0.517416) \| 0.378626 / 0.579283 (-0.200657) \| 0.387153 / 0.434364 (-0.047211) \| 0.439950 / 0.540337 (-0.100388) \| 0.523958 / 1.386936 (-0.862978) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7c3a9b057c476c40d157bd7a5d57f49066239df0 \"CML watermark\")\n" ]	2023-04-13T20:45:19Z	2023-04-14T13:15:38Z	2023-04-14T13:08:42Z	CONTRIBUTOR	null	null	null	Fixes a broken link in the use_with_pytorch docs	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5746/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5746/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5746.diff", "html_url": "https://github.com/huggingface/datasets/pull/5746", "merged_at": "2023-04-14T13:08:42Z", "patch_url": "https://github.com/huggingface/datasets/pull/5746.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5746" }
https://api.github.com/repos/huggingface/datasets/issues/4904	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4904/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4904/comments	https://api.github.com/repos/huggingface/datasets/issues/4904/events	https://github.com/huggingface/datasets/pull/4904	1,353,002,837	PR_kwDODunzps4959Ad	4,904	[LibriSpeech] Fix dev split local_extracted_archive for 'all' config	{ "avatar_url": "https://avatars.githubusercontent.com/u/93869735?v=4", "events_url": "https://api.github.com/users/sanchit-gandhi/events{/privacy}", "followers_url": "https://api.github.com/users/sanchit-gandhi/followers", "following_url": "https://api.github.com/users/sanchit-gandhi/following{/other_user}", "gists_url": "https://api.github.com/users/sanchit-gandhi/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sanchit-gandhi", "id": 93869735, "login": "sanchit-gandhi", "node_id": "U_kgDOBZhWpw", "organizations_url": "https://api.github.com/users/sanchit-gandhi/orgs", "received_events_url": "https://api.github.com/users/sanchit-gandhi/received_events", "repos_url": "https://api.github.com/users/sanchit-gandhi/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sanchit-gandhi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sanchit-gandhi/subscriptions", "type": "User", "url": "https://api.github.com/users/sanchit-gandhi", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "This PR fixes a bug introduced in:\r\n- #4184" ]	2022-08-27T10:04:57Z	2022-08-30T10:06:21Z	2022-08-30T10:03:25Z	CONTRIBUTOR	null	null	null	We define the keys for the `_DL_URLS` of the dev split as `dev.clean` and `dev.other`: https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L60-L61 These keys get forwarded to the `dl_manager` and thus the `local_extracted_archive`. However, when calling `SplitGenerator` for the dev sets, we query the `local_extracted_archive` keys `validation.clean` and `validation.other`: https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L212 https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L219 The consequence of this is that the `local_extracted_archive` arg passed to `_generate_examples` is always `None`, as the keys `validation.clean` and `validation.other` do not exists in the `local_extracted_archive`. When defining the `audio_file` in `_generate_examples`, since `local_extracted_archive` is always `None`, we always omit the `local_extracted_archive` path from the `audio_file` path, even if in non-streaming mode: https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L259-L263 Thus, `audio_file` will only ever be the streaming path (`audio_file`, not `os.path.join(local_extracted_archive, audio_file)`). This PR fixes the `.get()` keys for the `local_extracted_archive` for the dev splits.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4904/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4904/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4904.diff", "html_url": "https://github.com/huggingface/datasets/pull/4904", "merged_at": "2022-08-30T10:03:25Z", "patch_url": "https://github.com/huggingface/datasets/pull/4904.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4904" }
https://api.github.com/repos/huggingface/datasets/issues/5308	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5308/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5308/comments	https://api.github.com/repos/huggingface/datasets/issues/5308/events	https://github.com/huggingface/datasets/pull/5308	1,466,552,281	PR_kwDODunzps5Dz0Tv	5,308	Support `topdown` parameter in `xwalk`	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "I like the `kwargs` approach, thanks!" ]	2022-11-28T14:42:41Z	2022-12-09T12:58:55Z	2022-12-09T12:55:59Z	COLLABORATOR	null	null	null	Add support for the `topdown` parameter in `xwalk` when `fsspec>=2022.11.0` is installed.	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5308/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5308/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5308.diff", "html_url": "https://github.com/huggingface/datasets/pull/5308", "merged_at": "2022-12-09T12:55:59Z", "patch_url": "https://github.com/huggingface/datasets/pull/5308.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5308" }
https://api.github.com/repos/huggingface/datasets/issues/5484	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5484/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5484/comments	https://api.github.com/repos/huggingface/datasets/issues/5484/events	https://github.com/huggingface/datasets/pull/5484	1,562,877,070	PR_kwDODunzps5I1oaq	5,484	Update docs for `nyu_depth_v2` dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/36858976?v=4", "events_url": "https://api.github.com/users/awsaf49/events{/privacy}", "followers_url": "https://api.github.com/users/awsaf49/followers", "following_url": "https://api.github.com/users/awsaf49/following{/other_user}", "gists_url": "https://api.github.com/users/awsaf49/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/awsaf49", "id": 36858976, "login": "awsaf49", "node_id": "MDQ6VXNlcjM2ODU4OTc2", "organizations_url": "https://api.github.com/users/awsaf49/orgs", "received_events_url": "https://api.github.com/users/awsaf49/received_events", "repos_url": "https://api.github.com/users/awsaf49/repos", "site_admin": false, "starred_url": "https://api.github.com/users/awsaf49/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/awsaf49/subscriptions", "type": "User", "url": "https://api.github.com/users/awsaf49", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "I think I need to create another PR on https://huggingface.co/datasets/huggingface/documentation-images/tree/main/datasets for hosting the images there?", "_The documentation is not available anymore as the PR was closed or merged._", "Thanks for the update @awsaf49 !", "> Thanks a lot for the updates!\r\n> \r\n> Just some minor things remain and the we should be good to ship this 🚀\r\n\r\n@sayakpaul I have updated the minor things. Please approve the workflows", "I think this PR is good to go..\r\n@sayakpaul @lhoestq ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) \| 0.009064 / 0.011353 (-0.002289) \| 0.005262 / 0.011008 (-0.005746) \| 0.099608 / 0.038508 (0.061100) \| 0.035015 / 0.023109 (0.011906) \| 0.296501 / 0.275898 (0.020602) \| 0.353619 / 0.323480 (0.030139) \| 0.007903 / 0.007986 (-0.000083) \| 0.004093 / 0.004328 (-0.000235) \| 0.075260 / 0.004250 (0.071009) \| 0.043142 / 0.037052 (0.006089) \| 0.307755 / 0.258489 (0.049266) \| 0.336340 / 0.293841 (0.042499) \| 0.038596 / 0.128546 (-0.089950) \| 0.011861 / 0.075646 (-0.063786) \| 0.334226 / 0.419271 (-0.085045) \| 0.051472 / 0.043533 (0.007940) \| 0.298539 / 0.255139 (0.043400) \| 0.316856 / 0.283200 (0.033656) \| 0.108620 / 0.141683 (-0.033063) \| 1.434901 / 1.452155 (-0.017254) \| 1.468368 / 1.492716 (-0.024348) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208402 / 0.018006 (0.190395) \| 0.445799 / 0.000490 (0.445309) \| 0.003704 / 0.000200 (0.003504) \| 0.000084 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025435 / 0.037411 (-0.011976) \| 0.105874 / 0.014526 (0.091348) \| 0.115652 / 0.176557 (-0.060905) \| 0.150872 / 0.737135 (-0.586263) \| 0.121705 / 0.296338 (-0.174633) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.397816 / 0.215209 (0.182607) \| 3.977766 / 2.077655 (1.900111) \| 1.850848 / 1.504120 (0.346728) \| 1.686062 / 1.541195 (0.144867) \| 1.786277 / 1.468490 (0.317787) \| 0.696250 / 4.584777 (-3.888527) \| 3.785255 / 3.745712 (0.039543) \| 3.355013 / 5.269862 (-1.914849) \| 1.818232 / 4.565676 (-2.747444) \| 0.085408 / 0.424275 (-0.338867) \| 0.012567 / 0.007607 (0.004960) \| 0.524185 / 0.226044 (0.298140) \| 5.061975 / 2.268929 (2.793047) \| 2.299866 / 55.444624 (-53.144758) \| 1.966709 / 6.876477 (-4.909768) \| 2.018760 / 2.142072 (-0.123313) \| 0.841341 / 4.805227 (-3.963886) \| 0.166374 / 6.500664 (-6.334290) \| 0.061854 / 0.075469 (-0.013615) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.221666 / 1.841788 (-0.620122) \| 14.373194 / 8.074308 (6.298886) \| 14.253614 / 10.191392 (4.062222) \| 0.172979 / 0.680424 (-0.507445) \| 0.029176 / 0.534201 (-0.505025) \| 0.447399 / 0.579283 (-0.131884) \| 0.443663 / 0.434364 (0.009299) \| 0.537071 / 0.540337 (-0.003267) \| 0.640539 / 1.386936 (-0.746397) \|\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) \| 0.007019 / 0.011353 (-0.004334) \| 0.005091 / 0.011008 (-0.005917) \| 0.074588 / 0.038508 (0.036080) \| 0.032391 / 0.023109 (0.009282) \| 0.340548 / 0.275898 (0.064650) \| 0.367159 / 0.323480 (0.043679) \| 0.005594 / 0.007986 (-0.002392) \| 0.004003 / 0.004328 (-0.000325) \| 0.073946 / 0.004250 (0.069695) \| 0.045921 / 0.037052 (0.008868) \| 0.340245 / 0.258489 (0.081756) \| 0.397958 / 0.293841 (0.104117) \| 0.036539 / 0.128546 (-0.092007) \| 0.012258 / 0.075646 (-0.063388) \| 0.087406 / 0.419271 (-0.331865) \| 0.049276 / 0.043533 (0.005743) \| 0.345235 / 0.255139 (0.090096) \| 0.361250 / 0.283200 (0.078050) \| 0.100757 / 0.141683 (-0.040926) \| 1.464644 / 1.452155 (0.012489) \| 1.545852 / 1.492716 (0.053136) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222952 / 0.018006 (0.204945) \| 0.434607 / 0.000490 (0.434117) \| 0.000438 / 0.000200 (0.000238) \| 0.000060 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028834 / 0.037411 (-0.008577) \| 0.107523 / 0.014526 (0.092997) \| 0.122077 / 0.176557 (-0.054479) \| 0.156574 / 0.737135 (-0.580561) \| 0.122917 / 0.296338 (-0.173421) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417292 / 0.215209 (0.202083) \| 4.165980 / 2.077655 (2.088325) \| 1.996731 / 1.504120 (0.492611) \| 1.802946 / 1.541195 (0.261751) \| 1.878456 / 1.468490 (0.409966) \| 0.711035 / 4.584777 (-3.873742) \| 3.847357 / 3.745712 (0.101644) \| 2.088354 / 5.269862 (-3.181508) \| 1.344763 / 4.565676 (-3.220913) \| 0.086356 / 0.424275 (-0.337919) \| 0.012530 / 0.007607 (0.004923) \| 0.511693 / 0.226044 (0.285648) \| 5.126093 / 2.268929 (2.857165) \| 2.490023 / 55.444624 (-52.954602) \| 2.180274 / 6.876477 (-4.696202) \| 2.221511 / 2.142072 (0.079438) \| 0.836348 / 4.805227 (-3.968879) \| 0.169554 / 6.500664 (-6.331110) \| 0.064555 / 0.075469 (-0.010914) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.293466 / 1.841788 (-0.548321) \| 14.785700 / 8.074308 (6.711392) \| 13.858493 / 10.191392 (3.667101) \| 0.161777 / 0.680424 (-0.518646) \| 0.017794 / 0.534201 (-0.516407) \| 0.426286 / 0.579283 (-0.152997) \| 0.422517 / 0.434364 (-0.011847) \| 0.530777 / 0.540337 (-0.009560) \| 0.634822 / 1.386936 (-0.752114) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c6e08fcfc3a04e53430c26fa7c07da4cb18d977d \"CML watermark\")\n" ]	2023-01-30T17:37:08Z	2023-09-29T06:43:11Z	2023-02-05T14:15:04Z	CONTRIBUTOR	null	null	null	This PR will fix the issue mentioned in #5461. Here is brief overview, ## Bug: Discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got discretized/clipped resulting in depth maps that are different from actual ones. Here is a side-by-side comparison, ![image](https://user-images.githubusercontent.com/36858976/214381162-1d9582c2-6750-4114-a01a-61ca1cd5f872.png) ## Fix: When I first loaded the datasets from HF I noticed it was 30GB but in DenseDepth data is only 4GB with dtype=uint8. This means data from fast-depth (before loading to HF) must have high precision. So when I tried to dig deeper by directly loading depth_map from `h5py`, I found depth_map from `h5py` came with `float32`. But when the data is processed in HF with `datasets.Image()` it was directly converted to `uint8` from `float32` hence the discretized depth map. https://github.com/huggingface/datasets/blob/c78559cacbb0ca6e0bc8bfc313cc0359f8c23ead/src/datasets/features/image.py#L91-L93 cc: @sayakpaul @lhoestq	{ "avatar_url": "https://avatars.githubusercontent.com/u/22957388?v=4", "events_url": "https://api.github.com/users/sayakpaul/events{/privacy}", "followers_url": "https://api.github.com/users/sayakpaul/followers", "following_url": "https://api.github.com/users/sayakpaul/following{/other_user}", "gists_url": "https://api.github.com/users/sayakpaul/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sayakpaul", "id": 22957388, "login": "sayakpaul", "node_id": "MDQ6VXNlcjIyOTU3Mzg4", "organizations_url": "https://api.github.com/users/sayakpaul/orgs", "received_events_url": "https://api.github.com/users/sayakpaul/received_events", "repos_url": "https://api.github.com/users/sayakpaul/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sayakpaul/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sayakpaul/subscriptions", "type": "User", "url": "https://api.github.com/users/sayakpaul", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5484/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5484/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5484.diff", "html_url": "https://github.com/huggingface/datasets/pull/5484", "merged_at": "2023-02-05T14:15:04Z", "patch_url": "https://github.com/huggingface/datasets/pull/5484.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5484" }
https://api.github.com/repos/huggingface/datasets/issues/6390	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6390/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6390/comments	https://api.github.com/repos/huggingface/datasets/issues/6390/events	https://github.com/huggingface/datasets/pull/6390	1,983,725,707	PR_kwDODunzps5e7xQ3	6,390	handle future deprecation argument	{ "avatar_url": "https://avatars.githubusercontent.com/u/381258?v=4", "events_url": "https://api.github.com/users/winglian/events{/privacy}", "followers_url": "https://api.github.com/users/winglian/followers", "following_url": "https://api.github.com/users/winglian/following{/other_user}", "gists_url": "https://api.github.com/users/winglian/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/winglian", "id": 381258, "login": "winglian", "node_id": "MDQ6VXNlcjM4MTI1OA==", "organizations_url": "https://api.github.com/users/winglian/orgs", "received_events_url": "https://api.github.com/users/winglian/received_events", "repos_url": "https://api.github.com/users/winglian/repos", "site_admin": false, "starred_url": "https://api.github.com/users/winglian/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/winglian/subscriptions", "type": "User", "url": "https://api.github.com/users/winglian", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "<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) \| 0.004368 / 0.011353 (-0.006985) \| 0.002613 / 0.011008 (-0.008396) \| 0.061365 / 0.038508 (0.022856) \| 0.029553 / 0.023109 (0.006444) \| 0.240535 / 0.275898 (-0.035363) \| 0.280634 / 0.323480 (-0.042845) \| 0.002923 / 0.007986 (-0.005063) \| 0.003696 / 0.004328 (-0.000632) \| 0.049824 / 0.004250 (0.045573) \| 0.044935 / 0.037052 (0.007882) \| 0.246870 / 0.258489 (-0.011619) \| 0.317248 / 0.293841 (0.023407) \| 0.022717 / 0.128546 (-0.105829) \| 0.006933 / 0.075646 (-0.068713) \| 0.201118 / 0.419271 (-0.218154) \| 0.053422 / 0.043533 (0.009890) \| 0.266262 / 0.255139 (0.011123) \| 0.269114 / 0.283200 (-0.014086) \| 0.016908 / 0.141683 (-0.124775) \| 1.154296 / 1.452155 (-0.297859) \| 1.218825 / 1.492716 (-0.273892) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089908 / 0.018006 (0.071902) \| 0.300029 / 0.000490 (0.299539) \| 0.000209 / 0.000200 (0.000009) \| 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) \| 0.018518 / 0.037411 (-0.018894) \| 0.062246 / 0.014526 (0.047720) \| 0.073542 / 0.176557 (-0.103014) \| 0.119386 / 0.737135 (-0.617749) \| 0.075256 / 0.296338 (-0.221082) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280812 / 0.215209 (0.065603) \| 2.701282 / 2.077655 (0.623628) \| 1.455146 / 1.504120 (-0.048974) \| 1.310198 / 1.541195 (-0.230996) \| 1.335287 / 1.468490 (-0.133203) \| 0.388245 / 4.584777 (-4.196532) \| 2.357770 / 3.745712 (-1.387942) \| 2.534640 / 5.269862 (-2.735222) \| 1.541382 / 4.565676 (-3.024295) \| 0.045597 / 0.424275 (-0.378678) \| 0.004842 / 0.007607 (-0.002765) \| 0.325416 / 0.226044 (0.099371) \| 3.221873 / 2.268929 (0.952944) \| 1.791061 / 55.444624 (-53.653563) \| 1.485094 / 6.876477 (-5.391382) \| 1.512354 / 2.142072 (-0.629718) \| 0.471241 / 4.805227 (-4.333986) \| 0.098672 / 6.500664 (-6.401992) \| 0.041668 / 0.075469 (-0.033801) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.953553 / 1.841788 (-0.888234) \| 11.378394 / 8.074308 (3.304086) \| 10.355970 / 10.191392 (0.164578) \| 0.126891 / 0.680424 (-0.553533) \| 0.013808 / 0.534201 (-0.520393) \| 0.267800 / 0.579283 (-0.311484) \| 0.266436 / 0.434364 (-0.167928) \| 0.306668 / 0.540337 (-0.233670) \| 0.427666 / 1.386936 (-0.959270) \|\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) \| 0.004908 / 0.011353 (-0.006445) \| 0.002698 / 0.011008 (-0.008310) \| 0.047492 / 0.038508 (0.008984) \| 0.049906 / 0.023109 (0.026797) \| 0.271466 / 0.275898 (-0.004432) \| 0.291030 / 0.323480 (-0.032449) \| 0.003938 / 0.007986 (-0.004047) \| 0.002457 / 0.004328 (-0.001871) \| 0.047347 / 0.004250 (0.043096) \| 0.038599 / 0.037052 (0.001547) \| 0.269950 / 0.258489 (0.011461) \| 0.303026 / 0.293841 (0.009185) \| 0.024196 / 0.128546 (-0.104351) \| 0.006889 / 0.075646 (-0.068757) \| 0.053357 / 0.419271 (-0.365914) \| 0.032249 / 0.043533 (-0.011284) \| 0.271660 / 0.255139 (0.016521) \| 0.286395 / 0.283200 (0.003196) \| 0.017914 / 0.141683 (-0.123769) \| 1.128762 / 1.452155 (-0.323393) \| 1.206495 / 1.492716 (-0.286221) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093384 / 0.018006 (0.075378) \| 0.305504 / 0.000490 (0.305014) \| 0.000227 / 0.000200 (0.000027) \| 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) \| 0.021183 / 0.037411 (-0.016229) \| 0.070113 / 0.014526 (0.055587) \| 0.080288 / 0.176557 (-0.096269) \| 0.120798 / 0.737135 (-0.616337) \| 0.082896 / 0.296338 (-0.213442) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292758 / 0.215209 (0.077549) \| 2.893975 / 2.077655 (0.816320) \| 1.584909 / 1.504120 (0.080789) \| 1.455509 / 1.541195 (-0.085686) \| 1.501625 / 1.468490 (0.033135) \| 0.400772 / 4.584777 (-4.184005) \| 2.446319 / 3.745712 (-1.299393) \| 2.530690 / 5.269862 (-2.739172) \| 1.525957 / 4.565676 (-3.039719) \| 0.046070 / 0.424275 (-0.378205) \| 0.004756 / 0.007607 (-0.002851) \| 0.343039 / 0.226044 (0.116995) \| 3.366772 / 2.268929 (1.097844) \| 1.948895 / 55.444624 (-53.495729) \| 1.666419 / 6.876477 (-5.210058) \| 1.658258 / 2.142072 (-0.483814) \| 0.470835 / 4.805227 (-4.334392) \| 0.098008 / 6.500664 (-6.402656) \| 0.040743 / 0.075469 (-0.034726) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.978025 / 1.841788 (-0.863763) \| 11.945229 / 8.074308 (3.870920) \| 11.025810 / 10.191392 (0.834418) \| 0.129706 / 0.680424 (-0.550717) \| 0.015148 / 0.534201 (-0.519053) \| 0.269160 / 0.579283 (-0.310123) \| 0.284306 / 0.434364 (-0.150058) \| 0.307154 / 0.540337 (-0.233183) \| 0.409153 / 1.386936 (-0.977783) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9c75c104fd79cbf53be25f0fbbeb001e535f7e9b \"CML watermark\")\n" ]	2023-11-08T14:21:25Z	2023-11-21T02:10:24Z	2023-11-14T15:15:59Z	CONTRIBUTOR	null	null	null	getting this error: ``` /root/miniconda3/envs/py3.10/lib/python3.10/site-packages/datasets/table.py:1387: FutureWarning: promote has been superseded by mode='default'. return cls._concat_blocks(pa_tables_to_concat_vertically, axis=0) ``` Since datasets supports arrow greater than 8.0.0, we need to handle both cases. [Arrow v14 docs](https://arrow.apache.org/docs/python/generated/pyarrow.concat_tables.html) [Arrow v13 docs](https://arrow.apache.org/docs/13.0/python/generated/pyarrow.concat_tables.html)	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6390/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6390/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6390.diff", "html_url": "https://github.com/huggingface/datasets/pull/6390", "merged_at": "2023-11-14T15:15:59Z", "patch_url": "https://github.com/huggingface/datasets/pull/6390.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6390" }
https://api.github.com/repos/huggingface/datasets/issues/4863	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4863/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4863/comments	https://api.github.com/repos/huggingface/datasets/issues/4863/events	https://github.com/huggingface/datasets/issues/4863	1,343,737,668	I_kwDODunzps5QF89E	4,863	TFDS wiki_dialog dataset to Huggingface dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/12378820?v=4", "events_url": "https://api.github.com/users/djaym7/events{/privacy}", "followers_url": "https://api.github.com/users/djaym7/followers", "following_url": "https://api.github.com/users/djaym7/following{/other_user}", "gists_url": "https://api.github.com/users/djaym7/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/djaym7", "id": 12378820, "login": "djaym7", "node_id": "MDQ6VXNlcjEyMzc4ODIw", "organizations_url": "https://api.github.com/users/djaym7/orgs", "received_events_url": "https://api.github.com/users/djaym7/received_events", "repos_url": "https://api.github.com/users/djaym7/repos", "site_admin": false, "starred_url": "https://api.github.com/users/djaym7/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/djaym7/subscriptions", "type": "User", "url": "https://api.github.com/users/djaym7", "user_view_type": "public" }	[ { "color": "e99695", "default": false, "description": "Requesting to add a new dataset", "id": 2067376369, "name": "dataset request", "node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request" } ]	closed	false	null	[]	null	[ "@albertvillanova any help ? The linked dataset is in beam format which is similar to wikipedia dataset in huggingface that you scripted..", "Nvm, I was able to port it to huggingface datasets, will upload to the hub soon", "https://huggingface.co/datasets/djaym7/wiki_dialog", "Thanks for the addition, @djaym7." ]	2022-08-18T23:06:30Z	2022-08-22T09:41:45Z	2022-08-22T05:18:53Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	## Adding a Dataset - Name: Wiki_dialog - Description: https://github.com/google-research/dialog-inpainting#:~:text=JSON%20object%2C%20for-,example,-%3A - Paper: https://arxiv.org/abs/2205.09073 - Data: https://github.com/google-research/dialog-inpainting - Motivation:** Research and Development on biggest corpus of dialog data Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).	{ "avatar_url": "https://avatars.githubusercontent.com/u/12378820?v=4", "events_url": "https://api.github.com/users/djaym7/events{/privacy}", "followers_url": "https://api.github.com/users/djaym7/followers", "following_url": "https://api.github.com/users/djaym7/following{/other_user}", "gists_url": "https://api.github.com/users/djaym7/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/djaym7", "id": 12378820, "login": "djaym7", "node_id": "MDQ6VXNlcjEyMzc4ODIw", "organizations_url": "https://api.github.com/users/djaym7/orgs", "received_events_url": "https://api.github.com/users/djaym7/received_events", "repos_url": "https://api.github.com/users/djaym7/repos", "site_admin": false, "starred_url": "https://api.github.com/users/djaym7/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/djaym7/subscriptions", "type": "User", "url": "https://api.github.com/users/djaym7", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4863/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4863/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7053	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7053/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7053/comments	https://api.github.com/repos/huggingface/datasets/issues/7053/events	https://github.com/huggingface/datasets/issues/7053	2,416,423,791	I_kwDODunzps6QB7Nv	7,053	Datasets.datafiles resolve_pattern `TypeError: can only concatenate tuple (not "str") to tuple`	{ "avatar_url": "https://avatars.githubusercontent.com/u/48289218?v=4", "events_url": "https://api.github.com/users/MatthewYZhang/events{/privacy}", "followers_url": "https://api.github.com/users/MatthewYZhang/followers", "following_url": "https://api.github.com/users/MatthewYZhang/following{/other_user}", "gists_url": "https://api.github.com/users/MatthewYZhang/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/MatthewYZhang", "id": 48289218, "login": "MatthewYZhang", "node_id": "MDQ6VXNlcjQ4Mjg5MjE4", "organizations_url": "https://api.github.com/users/MatthewYZhang/orgs", "received_events_url": "https://api.github.com/users/MatthewYZhang/received_events", "repos_url": "https://api.github.com/users/MatthewYZhang/repos", "site_admin": false, "starred_url": "https://api.github.com/users/MatthewYZhang/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/MatthewYZhang/subscriptions", "type": "User", "url": "https://api.github.com/users/MatthewYZhang", "user_view_type": "public" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[ "Hi,\r\n\r\nThis issue was fixed in `datasets` 2.15.0:\r\n- #6105\r\n\r\nYou will need to update your `datasets`:\r\n```\r\npip install -U datasets\r\n```", "Duplicate of:\r\n- #6100" ]	2024-07-18T13:42:35Z	2024-07-18T15:17:42Z	2024-07-18T15:16:18Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug in data_files.py, line 332, `fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options)` If we run the code on AWS, as fs.protocol will be a tuple like: `('file', 'local')` So, `isinstance(fs.protocol, str) == False` and `protocol_prefix = fs.protocol + "://" if fs.protocol != "file" else ""` will raise `TypeError: can only concatenate tuple (not "str") to tuple`. ### Steps to reproduce the bug Steps to reproduce: 1. Run on a cloud server like AWS, 2. `import datasets.data_files as datafile` 3. datafile.resolve_pattern('path/to/dataset', '.') 4. `TypeError: can only concatenate tuple (not "str") to tuple` ### Expected behavior Should return path of the dataset, with fs.protocol at the beginning ### Environment info - `datasets` version: 2.14.0 - Platform: Linux-3.10.0-1160.119.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.8.19 - Huggingface_hub version: 0.23.5 - PyArrow version: 16.1.0 - Pandas version: 1.1.5	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7053/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7053/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6493	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6493/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6493/comments	https://api.github.com/repos/huggingface/datasets/issues/6493/events	https://github.com/huggingface/datasets/pull/6493	2,038,221,490	PR_kwDODunzps5h0XJK	6,493	Lazy data files resolution and offline cache reload	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6493). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> Naive question: is there any breaking change when loading?\r\n\r\nNo breaking changes except that the cache folders are different\r\n\r\ne.g. for glue sst2 (has parquet export)\r\n\r\n```\r\nThis branch (new format is config/version/commit_sha)\r\n~/.cache/huggingface/datasets/glue/sst2/1.0.0/fd8e86499fa5c264fcaad392a8f49ddf58bf4037\r\nOn main\r\n~/.cache/huggingface/datasets/glue/sst2/0.0.0/74a75637ac4acd3f\r\nOn 2.15.0\r\n~/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\r\n```\r\n\r\ne.g. for wikimedia/wikipedia 20231101.ab (has metadata configs)\r\n\r\n\r\n```\r\nThis branch (new format is config/version/commit_sha)\r\n~/.cache/huggingface/datasets/wikimedia___wikipedia/20231101.ab/0.0.0/4cb9b0d719291f1a10f96f67d609c5d442980dc9\r\nOn main (takes ages to load)\r\n~/.cache/huggingface/datasets/wikimedia___wikipedia/20231101.ab/0.0.0/cfa627e27933df13\r\nOn 2.15.0 (takes ages to load)\r\n~/.cache/huggingface/datasets/wikimedia___wikipedia/20231101.ab/0.0.0/e92ee7a91c466564\r\n```\r\n\r\n\r\ne.g. for lhoestq/demo1 (no metadata configs)\r\n\r\n\r\n```\r\nThis branch (new format is config/version/commit_sha)\r\n~/.cache/huggingface/datasets/lhoestq___demo1/default/0.0.0/87ecf163bedca9d80598b528940a9c4f99e14c11\r\nOn main\r\n~/.cache/huggingface/datasets/lhoestq___demo1/default-8a4a0b7a240d3c5e/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d\r\nOn 2.15.0\r\n~/.cache/huggingface/datasets/lhoestq___demo1/default-59d4029e0bb36ae0/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d\r\n```", "There was a last bug I just fixed: if you modify a dataset and reload it from the hub it won't download the new version - I think I need to use another hash to name the cache directory\r\nedit: fixed", "I switched to using the git commit sha for the cache directory, which is now `config/version/commit_sha` :) much cleaner than before.\r\n\r\nAnd for local file it's a hash that takes into account the resolved files (and their last modified dates)", "I also ran the `transformers` CI on this branch and it's green", "FYI `huggingface_hub` will have a release on tuesday/wednesday (will speed up load_dataset data files resolution which is now needed for datasets loaded from parquet export) so we can aim on merging this around the same time and do a release on thursday", "Merging this one, and hopefully the cache backward compatibility PR soon too :)\r\n\r\nThen it will be release time", "<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) \| 0.005444 / 0.011353 (-0.005909) \| 0.003562 / 0.011008 (-0.007446) \| 0.063183 / 0.038508 (0.024675) \| 0.048885 / 0.023109 (0.025776) \| 0.248422 / 0.275898 (-0.027476) \| 0.277844 / 0.323480 (-0.045636) \| 0.003019 / 0.007986 (-0.004966) \| 0.002660 / 0.004328 (-0.001669) \| 0.048928 / 0.004250 (0.044677) \| 0.044850 / 0.037052 (0.007798) \| 0.248505 / 0.258489 (-0.009984) \| 0.282231 / 0.293841 (-0.011610) \| 0.028302 / 0.128546 (-0.100244) \| 0.010829 / 0.075646 (-0.064818) \| 0.206738 / 0.419271 (-0.212533) \| 0.035485 / 0.043533 (-0.008048) \| 0.244575 / 0.255139 (-0.010564) \| 0.281411 / 0.283200 (-0.001789) \| 0.019563 / 0.141683 (-0.122120) \| 1.113769 / 1.452155 (-0.338386) \| 1.176831 / 1.492716 (-0.315885) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004718 / 0.018006 (-0.013288) \| 0.304103 / 0.000490 (0.303614) \| 0.000214 / 0.000200 (0.000014) \| 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) \| 0.019642 / 0.037411 (-0.017769) \| 0.060275 / 0.014526 (0.045749) \| 0.073072 / 0.176557 (-0.103484) \| 0.119789 / 0.737135 (-0.617346) \| 0.074535 / 0.296338 (-0.221804) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278799 / 0.215209 (0.063590) \| 2.725320 / 2.077655 (0.647665) \| 1.419048 / 1.504120 (-0.085071) \| 1.335041 / 1.541195 (-0.206154) \| 1.373029 / 1.468490 (-0.095461) \| 0.566774 / 4.584777 (-4.018003) \| 2.383796 / 3.745712 (-1.361916) \| 2.734804 / 5.269862 (-2.535057) \| 1.712277 / 4.565676 (-2.853399) \| 0.062119 / 0.424275 (-0.362156) \| 0.004949 / 0.007607 (-0.002658) \| 0.336126 / 0.226044 (0.110082) \| 3.298602 / 2.268929 (1.029674) \| 1.842815 / 55.444624 (-53.601809) \| 1.544028 / 6.876477 (-5.332449) \| 1.566717 / 2.142072 (-0.575355) \| 0.643006 / 4.805227 (-4.162221) \| 0.118241 / 6.500664 (-6.382423) \| 0.042453 / 0.075469 (-0.033016) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.949015 / 1.841788 (-0.892773) \| 11.717958 / 8.074308 (3.643649) \| 10.482448 / 10.191392 (0.291056) \| 0.128564 / 0.680424 (-0.551860) \| 0.014792 / 0.534201 (-0.519408) \| 0.288636 / 0.579283 (-0.290647) \| 0.263345 / 0.434364 (-0.171019) \| 0.325753 / 0.540337 (-0.214584) \| 0.421294 / 1.386936 (-0.965642) \|\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) \| 0.005367 / 0.011353 (-0.005985) \| 0.003802 / 0.011008 (-0.007206) \| 0.049322 / 0.038508 (0.010814) \| 0.055201 / 0.023109 (0.032092) \| 0.287811 / 0.275898 (0.011913) \| 0.305141 / 0.323480 (-0.018339) \| 0.004095 / 0.007986 (-0.003890) \| 0.002733 / 0.004328 (-0.001595) \| 0.049508 / 0.004250 (0.045258) \| 0.039199 / 0.037052 (0.002147) \| 0.282719 / 0.258489 (0.024230) \| 0.311156 / 0.293841 (0.017315) \| 0.029469 / 0.128546 (-0.099077) \| 0.010709 / 0.075646 (-0.064937) \| 0.057646 / 0.419271 (-0.361626) \| 0.032696 / 0.043533 (-0.010837) \| 0.285087 / 0.255139 (0.029948) \| 0.294142 / 0.283200 (0.010942) \| 0.019779 / 0.141683 (-0.121904) \| 1.176844 / 1.452155 (-0.275310) \| 1.190925 / 1.492716 (-0.301792) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092885 / 0.018006 (0.074879) \| 0.301129 / 0.000490 (0.300640) \| 0.000232 / 0.000200 (0.000032) \| 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) \| 0.023202 / 0.037411 (-0.014210) \| 0.076850 / 0.014526 (0.062325) \| 0.090058 / 0.176557 (-0.086499) \| 0.128091 / 0.737135 (-0.609045) \| 0.091098 / 0.296338 (-0.205240) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292973 / 0.215209 (0.077764) \| 2.876022 / 2.077655 (0.798367) \| 1.672115 / 1.504120 (0.167995) \| 1.555103 / 1.541195 (0.013909) \| 1.559832 / 1.468490 (0.091342) \| 0.558017 / 4.584777 (-4.026760) \| 2.428448 / 3.745712 (-1.317264) \| 2.812024 / 5.269862 (-2.457837) \| 1.738470 / 4.565676 (-2.827207) \| 0.062669 / 0.424275 (-0.361607) \| 0.005071 / 0.007607 (-0.002536) \| 0.351804 / 0.226044 (0.125759) \| 3.412207 / 2.268929 (1.143279) \| 2.023478 / 55.444624 (-53.421147) \| 1.761281 / 6.876477 (-5.115195) \| 1.770789 / 2.142072 (-0.371283) \| 0.643062 / 4.805227 (-4.162165) \| 0.116616 / 6.500664 (-6.384048) \| 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) \| 0.988430 / 1.841788 (-0.853357) \| 12.278636 / 8.074308 (4.204328) \| 11.066185 / 10.191392 (0.874793) \| 0.141191 / 0.680424 (-0.539233) \| 0.015547 / 0.534201 (-0.518654) \| 0.288045 / 0.579283 (-0.291238) \| 0.279651 / 0.434364 (-0.154713) \| 0.329869 / 0.540337 (-0.210469) \| 0.420391 / 1.386936 (-0.966545) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef3b5dd3633995c95d77f35fb17f89ff44990bc4 \"CML watermark\")\n" ]	2023-12-12T17:15:17Z	2023-12-21T15:19:20Z	2023-12-21T15:13:11Z	MEMBER	null	null	null	Includes both https://github.com/huggingface/datasets/pull/6458 and https://github.com/huggingface/datasets/pull/6459 This PR should be merged instead of the two individually, since they are conflicting ## Offline cache reload it can reload datasets that were pushed to hub if they exist in the cache. example: ```python >>> Dataset.from_dict({"a": [1, 2]}).push_to_hub("lhoestq/tmp") >>> load_dataset("lhoestq/tmp") DatasetDict({ train: Dataset({ features: ['a'], num_rows: 2 }) }) ``` and later, without connection: ```python >>> load_dataset("lhoestq/tmp") Using the latest cached version of the dataset since lhoestq/tmp couldn't be found on the Hugging Face Hub Found the latest cached dataset configuration 'default' at /Users/quentinlhoest/.cache/huggingface/datasets/lhoestq___tmp/default/0.0.0/da0e902a945afeb9 (last modified on Wed Dec 13 14:55:52 2023). DatasetDict({ train: Dataset({ features: ['a'], num_rows: 2 }) }) ``` - Updated `CachedDatasetModuleFactory` to look for datasets in the cache at `<namespace>___<dataset_name>/<config_id>` - Since the metadata configs parameters are not available in offline mode, we don't know which folder to load (config_id and hash change), so I simply load the latest one - I instantiate a BuilderConfig even if there is no metadata config with the right config_name - Its config_id is equal to the config_name to be able to retrieve it in the cache (no more suffix for configs from metadata configs) - We can reload this config if offline mode by specifying the right config_name (same as online !) - Consequences of this change: - Only when there are user's parameters it creates a custom builder config with config_id = config_name + user parameters hash - the hash used to name the cache folder takes into account the metadata config and the dataset info, so that the right cache can be reloaded when there is internet connection without redownloading the data or resolving the data files. For local directories I hash the builder configs and dataset info, and for datasets on the hub I use the commit sha as hash. - cache directories now look like `config/version/commit_sha` for hub datasets which is clean :) Fix https://github.com/huggingface/datasets/issues/3547 ## Lazy data files resolution this makes this code run in 2sec instead of >10sec ```python from datasets import load_dataset ds = load_dataset("glue", "sst2", streaming=True, trust_remote_code=False) ``` For some datasets with many configs and files it can be up to 100x faster. This is particularly important now that some datasets will be loaded from the Parquet export instead of the scripts. The data files are only resolved in the builder `__init__`. To do so I added DataFilesPatternsList and DataFilesPatternsDict that have `.resolve()` to return resolved DataFilesList and DataFilesDict	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6493/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6493/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6493.diff", "html_url": "https://github.com/huggingface/datasets/pull/6493", "merged_at": "2023-12-21T15:13:11Z", "patch_url": "https://github.com/huggingface/datasets/pull/6493.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6493" }
https://api.github.com/repos/huggingface/datasets/issues/5315	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5315/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5315/comments	https://api.github.com/repos/huggingface/datasets/issues/5315/events	https://github.com/huggingface/datasets/issues/5315	1,470,026,797	I_kwDODunzps5XntQt	5,315	Adding new splits to a dataset script with existing old splits info in metadata's `dataset_info` fails	{ "avatar_url": "https://avatars.githubusercontent.com/u/16348744?v=4", "events_url": "https://api.github.com/users/polinaeterna/events{/privacy}", "followers_url": "https://api.github.com/users/polinaeterna/followers", "following_url": "https://api.github.com/users/polinaeterna/following{/other_user}", "gists_url": "https://api.github.com/users/polinaeterna/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/polinaeterna", "id": 16348744, "login": "polinaeterna", "node_id": "MDQ6VXNlcjE2MzQ4NzQ0", "organizations_url": "https://api.github.com/users/polinaeterna/orgs", "received_events_url": "https://api.github.com/users/polinaeterna/received_events", "repos_url": "https://api.github.com/users/polinaeterna/repos", "site_admin": false, "starred_url": "https://api.github.com/users/polinaeterna/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/polinaeterna/subscriptions", "type": "User", "url": "https://api.github.com/users/polinaeterna", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	open	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/16348744?v=4", "events_url": "https://api.github.com/users/polinaeterna/events{/privacy}", "followers_url": "https://api.github.com/users/polinaeterna/followers", "following_url": "https://api.github.com/users/polinaeterna/following{/other_user}", "gists_url": "https://api.github.com/users/polinaeterna/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/polinaeterna", "id": 16348744, "login": "polinaeterna", "node_id": "MDQ6VXNlcjE2MzQ4NzQ0", "organizations_url": "https://api.github.com/users/polinaeterna/orgs", "received_events_url": "https://api.github.com/users/polinaeterna/received_events", "repos_url": "https://api.github.com/users/polinaeterna/repos", "site_admin": false, "starred_url": "https://api.github.com/users/polinaeterna/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/polinaeterna/subscriptions", "type": "User", "url": "https://api.github.com/users/polinaeterna", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/16348744?v=4", "events_url": "https://api.github.com/users/polinaeterna/events{/privacy}", "followers_url": "https://api.github.com/users/polinaeterna/followers", "following_url": "https://api.github.com/users/polinaeterna/following{/other_user}", "gists_url": "https://api.github.com/users/polinaeterna/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/polinaeterna", "id": 16348744, "login": "polinaeterna", "node_id": "MDQ6VXNlcjE2MzQ4NzQ0", "organizations_url": "https://api.github.com/users/polinaeterna/orgs", "received_events_url": "https://api.github.com/users/polinaeterna/received_events", "repos_url": "https://api.github.com/users/polinaeterna/repos", "site_admin": false, "starred_url": "https://api.github.com/users/polinaeterna/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/polinaeterna/subscriptions", "type": "User", "url": "https://api.github.com/users/polinaeterna", "user_view_type": "public" } ]	null	[ "EDIT:\r\nI think in this case, the metadata files (either README or JSON) should not be read (i.e. `self.info.splits` should be None).\r\n\r\nOne idea: \r\n- I think ideally we should set this behavior when we pass `--save_info` to the CLI `test`\r\n- However, currently, the builder is unaware of this: `save_info` arg is not passed to it", "> I think in this case\r\n\r\n@albertvillanova You mean in cases when the script was changed? \r\n\r\nI suggest that we:\r\n* add a check on the slice (like 'split_name[n%]) kind of format here: https://github.com/huggingface/datasets/blob/main/src/datasets/splits.py#L523 to catch things like this. \r\n* Error here happens before splits verification, but in `_prepare_split`, and `_prepare_split` doesn't perform any verification and don't know about it. so we can pass this parameter and take splits from `split_generator`, not from `split.info` in case when `verify_infos` is False\r\n* we can check if split names from split_generators and self.info.splits are the same before preparing splits (if `verify_info=True`) so that we don't spend time on generating unwanted data. \r\n* provide some user-friendly warnings about `ignore_verifications` parameter so that users know that if something is not matching they can ignore it\r\n\r\nI started it here: https://github.com/huggingface/datasets/pull/5327/files\r\n\r\nWhat do you think @albertvillanova ?", "I edited my previous comment:\r\n- First I proposed setting `self.info.splits` to None when `ignore_verifications=True`\r\n - I thought it was the easiest implementation because `ignore_verifications` is passed to `DatasetBuilder.download_and_prepare`\r\n - However, afterwards, I realized this might not be a good idea for this use case:\r\n - A user wants to optimize the loading of the dataset, and passes `ignore_verifications=False` to avoid all the verifications\r\n - In this case, we want `self.info.splits` to be read from metadata file\r\n- Then, I thought that it might be better to set `self.info.splits` to None when we pass `--save_info` to the CLI test: if we are going to save the info to the metadata file, it makes no sense to read the info from the metadata file\r\n - This implementation is not so easy because the Builder knows nothing about `--save_info`\r\n\r\nI agree with you there are 2 things to be addressed here:\r\n- One is what I have just commented: `self.info.splits` should be None in this case\r\n- The other, a validation should be implemented when calling `make_file_instructions` and/or `SplitDict.__getitem__`, so that when passing \"training\" to it, we get a more descriptive error other than `TypeError: expected str, bytes or os.PathLike object, not NoneType` " ]	2022-11-30T18:02:15Z	2022-12-02T07:02:53Z	null	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug If you first create a custom dataset with a specific set of splits, generate metadata with `datasets-cli test ... --save_info`, then change your script to include more splits, it fails. That's what happened in https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/discussions/2#6385fd1269634850f8ddff48. ### Steps to reproduce the bug 1. create a dataset with a custom split that returns, for example, only `"train"` split in `_splits_generators'`. specifically, if really want to reproduce, copy `https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/blob/main/food_vision_199_classes.py 2. run `datasets-cli test dataset_script.py --save_info --all_configs` - this would generate metadata yaml in `README.md` that would contain info about splits, for example, like this: ``` splits: - name: train num_bytes: 2973286 num_examples: 19747 ``` 3. make changes to your script so that it returns another set of splits, for example, `"train"` and `"test"` (uncomment [these lines](https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/blob/main/food_vision_199_classes.py#L271)) 4. run `load_dataset` and get the following error: ```python Traceback (most recent call last): File "/home/daniel/code/pytorch/env/bin/datasets-cli", line 8, in <module> sys.exit(main()) File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/commands/datasets_cli.py", line 39, in main service.run() File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/commands/test.py", line 141, in run builder.download_and_prepare( File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/builder.py", line 822, in download_and_prepare self._download_and_prepare( File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/builder.py", line 1555, in _download_and_prepare super()._download_and_prepare( File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/builder.py", line 913, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/builder.py", line 1356, in _prepare_split split_info = self.info.splits[split_generator.name] File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/splits.py", line 525, in __getitem__ instructions = make_file_instructions( File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/arrow_reader.py", line 111, in make_file_instructions name2filenames = { File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/arrow_reader.py", line 112, in <dictcomp> info.name: filenames_for_dataset_split( File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/naming.py", line 78, in filenames_for_dataset_split prefix = filename_prefix_for_split(dataset_name, split) File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/naming.py", line 57, in filename_prefix_for_split if os.path.basename(name) != name: File "/home/daniel/code/pytorch/env/lib/python3.8/posixpath.py", line 143, in basename p = os.fspath(p) TypeError: expected str, bytes or os.PathLike object, not NoneType ``` 5. bonus: try to regenerate metadata in `README.md` with `datasets-cli` as in step 2 and get the same error. This is because `dataset.info.splits` contains only `"train"` split so when we are doing `self.info.splits[split_generator.name]` it tries to infer smth like `info.splits['train[50%]']` and that's not the case and it fails. ### Expected behavior to be discussed? This can be solved by removing splits information from metadata file first. But I wonder if there is a better way. ### Environment info - Datasets version: 2.7.1 - Python version: 3.8.13	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5315/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5315/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7260	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7260/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7260/comments	https://api.github.com/repos/huggingface/datasets/issues/7260/events	https://github.com/huggingface/datasets/issues/7260	2,620,014,285	I_kwDODunzps6cKj7N	7,260	cache can't cleaned or disabled	{ "avatar_url": "https://avatars.githubusercontent.com/u/15007828?v=4", "events_url": "https://api.github.com/users/charliedream1/events{/privacy}", "followers_url": "https://api.github.com/users/charliedream1/followers", "following_url": "https://api.github.com/users/charliedream1/following{/other_user}", "gists_url": "https://api.github.com/users/charliedream1/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/charliedream1", "id": 15007828, "login": "charliedream1", "node_id": "MDQ6VXNlcjE1MDA3ODI4", "organizations_url": "https://api.github.com/users/charliedream1/orgs", "received_events_url": "https://api.github.com/users/charliedream1/received_events", "repos_url": "https://api.github.com/users/charliedream1/repos", "site_admin": false, "starred_url": "https://api.github.com/users/charliedream1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/charliedream1/subscriptions", "type": "User", "url": "https://api.github.com/users/charliedream1", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Hey I have a similar problem and found a workaround using [temporary directories](https://docs.python.org/3/library/tempfile.html):\r\n\r\n```python\r\nfrom tempfile import TemporaryDirectory\r\n\r\nwith TemporaryDirectory() as cache_dir:\r\n data = load_dataset('json', data_files=save_local_path, split='train', cache_dir=cache_dir)\r\n```\r\n\r\nBut I do agree that it would be more intuitive if `datasets` supported this directly. Especially `disable_caching` is confusing, since it basically doesn't disable caching." ]	2024-10-29T03:15:28Z	2024-12-11T09:04:52Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I tried following ways, the cache can't be disabled. I got 2T data, but I also got more than 2T cache file. I got pressure on storage. I need to diable cache or cleaned immediately after processed. Following ways are all not working, please give some help! ```python from datasets import disable_caching from transformers import AutoTokenizer disable_caching() tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path) def tokenization_fn(examples): column_name = 'text' if 'text' in examples else 'data' tokenized_inputs = tokenizer( examples[column_name], return_special_tokens_mask=True, truncation=False, max_length=tokenizer.model_max_length ) return tokenized_inputs data = load_dataset('json', data_files=save_local_path, split='train', cache_dir=None) data.cleanup_cache_files() updated_dataset = data.map(tokenization_fn, load_from_cache_file=False) updated_dataset .cleanup_cache_files() ``` ### Expected behavior no cache file generated ### Environment info Ubuntu 20.04.6 LTS datasets 3.0.2	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7260/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7260/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7110	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7110/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7110/comments	https://api.github.com/repos/huggingface/datasets/issues/7110/events	https://github.com/huggingface/datasets/pull/7110	2,474,747,695	PR_kwDODunzps54zz3r	7,110	Fix ConnectionError for gated datasets and unauthenticated users	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7110). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Note that the CI error is unrelated to this PR and should be addressed in another PR. See:\r\n- #7111", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005354 / 0.011353 (-0.005999) \| 0.004031 / 0.011008 (-0.006977) \| 0.062470 / 0.038508 (0.023962) \| 0.030882 / 0.023109 (0.007773) \| 0.244816 / 0.275898 (-0.031082) \| 0.264324 / 0.323480 (-0.059156) \| 0.004164 / 0.007986 (-0.003822) \| 0.002858 / 0.004328 (-0.001471) \| 0.049008 / 0.004250 (0.044758) \| 0.042139 / 0.037052 (0.005086) \| 0.279496 / 0.258489 (0.021007) \| 0.279408 / 0.293841 (-0.014433) \| 0.029701 / 0.128546 (-0.098845) \| 0.012501 / 0.075646 (-0.063145) \| 0.203267 / 0.419271 (-0.216004) \| 0.035964 / 0.043533 (-0.007569) \| 0.239361 / 0.255139 (-0.015778) \| 0.258942 / 0.283200 (-0.024257) \| 0.017956 / 0.141683 (-0.123727) \| 1.160468 / 1.452155 (-0.291687) \| 1.203475 / 1.492716 (-0.289242) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004639 / 0.018006 (-0.013367) \| 0.298020 / 0.000490 (0.297530) \| 0.000212 / 0.000200 (0.000012) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019371 / 0.037411 (-0.018040) \| 0.063311 / 0.014526 (0.048785) \| 0.076412 / 0.176557 (-0.100145) \| 0.122574 / 0.737135 (-0.614561) \| 0.078076 / 0.296338 (-0.218263) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275381 / 0.215209 (0.060172) \| 2.713220 / 2.077655 (0.635565) \| 1.441940 / 1.504120 (-0.062179) \| 1.325545 / 1.541195 (-0.215650) \| 1.363859 / 1.468490 (-0.104631) \| 0.715147 / 4.584777 (-3.869630) \| 2.356482 / 3.745712 (-1.389230) \| 2.882792 / 5.269862 (-2.387069) \| 1.833399 / 4.565676 (-2.732278) \| 0.077872 / 0.424275 (-0.346403) \| 0.005172 / 0.007607 (-0.002435) \| 0.326361 / 0.226044 (0.100316) \| 3.239202 / 2.268929 (0.970273) \| 1.837745 / 55.444624 (-53.606879) \| 1.517299 / 6.876477 (-5.359178) \| 1.552938 / 2.142072 (-0.589134) \| 0.801496 / 4.805227 (-4.003731) \| 0.133351 / 6.500664 (-6.367314) \| 0.042052 / 0.075469 (-0.033418) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.957887 / 1.841788 (-0.883901) \| 11.625291 / 8.074308 (3.550983) \| 9.679413 / 10.191392 (-0.511979) \| 0.140271 / 0.680424 (-0.540153) \| 0.013991 / 0.534201 (-0.520210) \| 0.299874 / 0.579283 (-0.279409) \| 0.267164 / 0.434364 (-0.167200) \| 0.338143 / 0.540337 (-0.202194) \| 0.434105 / 1.386936 (-0.952831) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005833 / 0.011353 (-0.005520) \| 0.003761 / 0.011008 (-0.007247) \| 0.049699 / 0.038508 (0.011191) \| 0.032786 / 0.023109 (0.009677) \| 0.265100 / 0.275898 (-0.010798) \| 0.291045 / 0.323480 (-0.032435) \| 0.004281 / 0.007986 (-0.003705) \| 0.002737 / 0.004328 (-0.001591) \| 0.048524 / 0.004250 (0.044274) \| 0.040783 / 0.037052 (0.003731) \| 0.281122 / 0.258489 (0.022633) \| 0.311349 / 0.293841 (0.017508) \| 0.032143 / 0.128546 (-0.096403) \| 0.011747 / 0.075646 (-0.063899) \| 0.059432 / 0.419271 (-0.359840) \| 0.034362 / 0.043533 (-0.009171) \| 0.261061 / 0.255139 (0.005922) \| 0.279536 / 0.283200 (-0.003663) \| 0.019172 / 0.141683 (-0.122510) \| 1.160069 / 1.452155 (-0.292086) \| 1.224160 / 1.492716 (-0.268556) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093596 / 0.018006 (0.075590) \| 0.302862 / 0.000490 (0.302372) \| 0.000208 / 0.000200 (0.000008) \| 0.000047 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022785 / 0.037411 (-0.014626) \| 0.079263 / 0.014526 (0.064737) \| 0.091340 / 0.176557 (-0.085216) \| 0.129453 / 0.737135 (-0.607682) \| 0.091349 / 0.296338 (-0.204989) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298166 / 0.215209 (0.082957) \| 3.003146 / 2.077655 (0.925491) \| 1.575903 / 1.504120 (0.071783) \| 1.445231 / 1.541195 (-0.095963) \| 1.477116 / 1.468490 (0.008625) \| 0.726496 / 4.584777 (-3.858281) \| 0.959827 / 3.745712 (-2.785885) \| 2.941142 / 5.269862 (-2.328720) \| 1.878581 / 4.565676 (-2.687096) \| 0.078475 / 0.424275 (-0.345800) \| 0.005137 / 0.007607 (-0.002470) \| 0.352078 / 0.226044 (0.126034) \| 3.486113 / 2.268929 (1.217184) \| 1.965024 / 55.444624 (-53.479600) \| 1.667223 / 6.876477 (-5.209254) \| 1.665254 / 2.142072 (-0.476819) \| 0.803543 / 4.805227 (-4.001684) \| 0.133003 / 6.500664 (-6.367661) \| 0.041462 / 0.075469 (-0.034008) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.045534 / 1.841788 (-0.796254) \| 12.124988 / 8.074308 (4.050680) \| 10.418723 / 10.191392 (0.227331) \| 0.142453 / 0.680424 (-0.537971) \| 0.015686 / 0.534201 (-0.518515) \| 0.300557 / 0.579283 (-0.278726) \| 0.119851 / 0.434364 (-0.314512) \| 0.342297 / 0.540337 (-0.198040) \| 0.441263 / 1.386936 (-0.945673) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#90b1d94ef419cb26f0bb24d982897dca39aa8a46 \"CML watermark\")\n", "lgtm!" ]	2024-08-20T05:26:54Z	2024-08-20T15:11:35Z	2024-08-20T09:14:35Z	MEMBER	null	null	null	Fix `ConnectionError` for gated datasets and unauthenticated users. See: - https://github.com/huggingface/dataset-viewer/issues/3025 Note that a recent change in the Hub returns dataset info for gated datasets and unauthenticated users, instead of raising a `GatedRepoError` as before. See: - https://github.com/huggingface/huggingface_hub/issues/2457 This PR adds an additional check (/auth-check) for gated datasets and raises `DatasetNotFoundError` for unauthenticated users, as it was the case before the change in the Hub. - Fix suggested by @Pierrci (thanks @Wauplin for pointing it out). Fix #7109.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7110/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7110/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7110.diff", "html_url": "https://github.com/huggingface/datasets/pull/7110", "merged_at": "2024-08-20T09:14:34Z", "patch_url": "https://github.com/huggingface/datasets/pull/7110.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7110" }
https://api.github.com/repos/huggingface/datasets/issues/6899	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6899/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6899/comments	https://api.github.com/repos/huggingface/datasets/issues/6899/events	https://github.com/huggingface/datasets/issues/6899	2,298,059,597	I_kwDODunzps6I-ZtN	6,899	List of dictionary features get standardized	{ "avatar_url": "https://avatars.githubusercontent.com/u/11831521?v=4", "events_url": "https://api.github.com/users/sohamparikh/events{/privacy}", "followers_url": "https://api.github.com/users/sohamparikh/followers", "following_url": "https://api.github.com/users/sohamparikh/following{/other_user}", "gists_url": "https://api.github.com/users/sohamparikh/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sohamparikh", "id": 11831521, "login": "sohamparikh", "node_id": "MDQ6VXNlcjExODMxNTIx", "organizations_url": "https://api.github.com/users/sohamparikh/orgs", "received_events_url": "https://api.github.com/users/sohamparikh/received_events", "repos_url": "https://api.github.com/users/sohamparikh/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sohamparikh/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sohamparikh/subscriptions", "type": "User", "url": "https://api.github.com/users/sohamparikh", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "I think this may be a limitation of the arrow format", "Dupe of #5950\n" ]	2024-05-15T14:11:35Z	2025-04-01T20:48:03Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Hi, i’m trying to create a HF dataset from a list using Dataset.from_list. Each sample in the list is a dict with the same keys (which will be my features). The values for each feature are a list of dictionaries, and each such dictionary has a different set of keys. However, the datasets library standardizes all dictionaries under a feature and adds all possible keys (with None value) from all the dictionaries under that feature. How can I keep the same set of keys as in the original list for each dictionary under a feature? ### Steps to reproduce the bug ``` from datasets import Dataset # Define a function to generate a sample with "tools" feature def generate_sample(): # Generate random sample data sample_data = { "text": "Sample text", "feature_1": [] } # Add feature_1 with random keys for this sample feature_1 = [{"key1": "value1"}, {"key2": "value2"}] # Example feature_1 with random keys sample_data["feature_1"].extend(feature_1) return sample_data # Generate multiple samples num_samples = 10 samples = [generate_sample() for _ in range(num_samples)] # Create a Hugging Face Dataset dataset = Dataset.from_list(samples) dataset[0] ``` ```{'text': 'Sample text', 'feature_1': [{'key1': 'value1', 'key2': None}, {'key1': None, 'key2': 'value2'}]}``` ### Expected behavior ```{'text': 'Sample text', 'feature_1': [{'key1': 'value1'}, {'key2': 'value2'}]}``` ### Environment info - `datasets` version: 2.19.1 - Platform: Linux-5.15.0-1040-nvidia-x86_64-with-glibc2.35 - Python version: 3.10.13 - `huggingface_hub` version: 0.23.0 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6899/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6899/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5786	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5786/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5786/comments	https://api.github.com/repos/huggingface/datasets/issues/5786/events	https://github.com/huggingface/datasets/issues/5786	1,680,957,070	I_kwDODunzps5kMV6O	5,786	Multiprocessing in a `filter` or `map` function with a Pytorch model	{ "avatar_url": "https://avatars.githubusercontent.com/u/44556846?v=4", "events_url": "https://api.github.com/users/HugoLaurencon/events{/privacy}", "followers_url": "https://api.github.com/users/HugoLaurencon/followers", "following_url": "https://api.github.com/users/HugoLaurencon/following{/other_user}", "gists_url": "https://api.github.com/users/HugoLaurencon/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/HugoLaurencon", "id": 44556846, "login": "HugoLaurencon", "node_id": "MDQ6VXNlcjQ0NTU2ODQ2", "organizations_url": "https://api.github.com/users/HugoLaurencon/orgs", "received_events_url": "https://api.github.com/users/HugoLaurencon/received_events", "repos_url": "https://api.github.com/users/HugoLaurencon/repos", "site_admin": false, "starred_url": "https://api.github.com/users/HugoLaurencon/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/HugoLaurencon/subscriptions", "type": "User", "url": "https://api.github.com/users/HugoLaurencon", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi ! PyTorch may hang when calling `load_state_dict()` in a subprocess. To fix that, set the multiprocessing start method to \"spawn\". Since `datasets` uses `multiprocess`, you should do:\r\n\r\n```python\r\n# Required to avoid issues with pytorch (otherwise hangs during load_state_dict in multiprocessing)\r\nimport multiprocess.context as ctx\r\nctx._force_start_method('spawn')\r\n```\r\n\r\nAlso make sure to run your main code in `if __name__ == \"__main__\":` to avoid issues with python multiprocesing", "Thanks!", "@lhoestq Hello, I also encountered this problem but maybe with another reason. Here is my code:\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir, model_max_length=training_args.model_max_length)\r\ndata = load_dataset(\"json\", data_files=data_args.train_file, cache_dir=data_args.data_cache_dir)\r\ndef func(samples):\r\n # main operation\r\n for sentence_value in samples:\r\n sentence_ids = tokenizer.encode(sentence_value, add_special_tokens=False, max_length=tokenizer.model_max_length, truncation=True)\r\n ... ...\r\ntrain_data = data[\"train\"].shuffle().map(func, num_proc=os.cpu_count())\r\n```\r\nIt hangs after the progress reaches 100%. Could you help me point out the reason?", "@SkyAndCloud your issue doesn't seem related to the original post - could you open a new issue and provide more details ? (size of the dataset, number of cpus, how much time it took to run, `datasets` version)", "@lhoestq Hi, I just solved this problem. Because the input is extremely long and the tokenizer requests a large amount of memory, which leads to a OOM error and may eventually causes the hang. I didn't filter those too-long sentences because I thought `tokenizer` would stop once the length exceeds the `max_length`. However, it actually firstly complete the tokenization of entire sentence and then truncate it." ]	2023-04-24T10:38:07Z	2023-05-30T09:56:30Z	2023-04-24T10:43:58Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I am trying to use a Pytorch model loaded on CPUs with multiple processes with a `.map` or a `.filter` method. Usually, when dealing with models that are non-pickable, creating a class such that the `map` function is the method `__call__`, and adding `reduce` helps to solve the problem. However, here, the command hangs without throwing an error. ### Steps to reproduce the bug ``` from datasets import Dataset import torch from torch import nn from torchvision import models class FilterFunction: #__slots__ = ("path_model", "model") # Doesn't change anything uncommented def __init__(self, path_model): self.path_model = path_model model = models.resnet50() model.fc = nn.Sequential( nn.Linear(2048, 512), nn.ReLU(), nn.Dropout(0.2), nn.Linear(512, 10), nn.LogSoftmax(dim=1) ) model.load_state_dict(torch.load(path_model, map_location=torch.device("cpu"))) model.eval() self.model = model def __call__(self, batch): return [True] * len(batch["id"]) # Comment this to have an error def __reduce__(self): return (self.__class__, (self.path_model,)) dataset = Dataset.from_dict({"id": [0, 1, 2, 4]}) # Download (100 MB) at https://github.com/emiliantolo/pytorch_nsfw_model/raw/master/ResNet50_nsfw_model.pth path_model = "/fsx/hugo/nsfw_image/ResNet50_nsfw_model.pth" filter_function = FilterFunction(path_model=path_model) # Works filtered_dataset = dataset.filter(filter_function, num_proc=1, batched=True, batch_size=2) # Doesn't work filtered_dataset = dataset.filter(filter_function, num_proc=2, batched=True, batch_size=2) ``` ### Expected behavior The command `filtered_dataset = dataset.filter(filter_function, num_proc=2, batched=True, batch_size=2)` should work and not hang. ### Environment info Datasets: 2.11.0 Pyarrow: 11.0.0 Ubuntu	{ "avatar_url": "https://avatars.githubusercontent.com/u/44556846?v=4", "events_url": "https://api.github.com/users/HugoLaurencon/events{/privacy}", "followers_url": "https://api.github.com/users/HugoLaurencon/followers", "following_url": "https://api.github.com/users/HugoLaurencon/following{/other_user}", "gists_url": "https://api.github.com/users/HugoLaurencon/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/HugoLaurencon", "id": 44556846, "login": "HugoLaurencon", "node_id": "MDQ6VXNlcjQ0NTU2ODQ2", "organizations_url": "https://api.github.com/users/HugoLaurencon/orgs", "received_events_url": "https://api.github.com/users/HugoLaurencon/received_events", "repos_url": "https://api.github.com/users/HugoLaurencon/repos", "site_admin": false, "starred_url": "https://api.github.com/users/HugoLaurencon/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/HugoLaurencon/subscriptions", "type": "User", "url": "https://api.github.com/users/HugoLaurencon", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5786/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5786/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5670	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5670/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5670/comments	https://api.github.com/repos/huggingface/datasets/issues/5670/events	https://github.com/huggingface/datasets/issues/5670	1,640,607,045	I_kwDODunzps5hya1F	5,670	Unable to load multi class classification datasets	{ "avatar_url": "https://avatars.githubusercontent.com/u/19690506?v=4", "events_url": "https://api.github.com/users/ysahil97/events{/privacy}", "followers_url": "https://api.github.com/users/ysahil97/followers", "following_url": "https://api.github.com/users/ysahil97/following{/other_user}", "gists_url": "https://api.github.com/users/ysahil97/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ysahil97", "id": 19690506, "login": "ysahil97", "node_id": "MDQ6VXNlcjE5NjkwNTA2", "organizations_url": "https://api.github.com/users/ysahil97/orgs", "received_events_url": "https://api.github.com/users/ysahil97/received_events", "repos_url": "https://api.github.com/users/ysahil97/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ysahil97/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ysahil97/subscriptions", "type": "User", "url": "https://api.github.com/users/ysahil97", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi ! This sounds related to https://github.com/huggingface/datasets/issues/5406\r\n\r\nUpdating `datasets` fixes the issue ;)", "Thanks @lhoestq!\r\n\r\nI'll close this issue now." ]	2023-03-25T18:06:15Z	2023-03-27T22:54:56Z	2023-03-27T22:54:56Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I've been playing around with huggingface library, mostly with `datasets` and wanted to download the multi class classification datasets to fine tune BERT on this task. ([link](https://huggingface.co/docs/transformers/training#train-with-pytorch-trainer)). While loading the dataset, I'm getting the following error snippet. ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[44], line 3 1 from datasets import load_dataset ----> 3 imdb_dataset = load_dataset("yelp_review_full") 4 imdb_dataset File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/load.py:1719, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, config_kwargs) 1716 ignore_verifications = ignore_verifications or save_infos 1718 # Create a dataset builder -> 1719 builder_instance = load_dataset_builder( 1720 path=path, 1721 name=name, 1722 data_dir=data_dir, 1723 data_files=data_files, 1724 cache_dir=cache_dir, 1725 features=features, 1726 download_config=download_config, 1727 download_mode=download_mode, 1728 revision=revision, 1729 use_auth_token=use_auth_token, 1730 config_kwargs, 1731 ) 1733 # Return iterable dataset in case of streaming 1734 if streaming: File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/load.py:1523, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, config_kwargs) 1520 raise ValueError(error_msg) 1522 # Instantiate the dataset builder -> 1523 builder_instance: DatasetBuilder = builder_cls( 1524 cache_dir=cache_dir, 1525 config_name=config_name, 1526 data_dir=data_dir, 1527 data_files=data_files, 1528 hash=hash, 1529 features=features, 1530 use_auth_token=use_auth_token, 1531 builder_kwargs, 1532 *config_kwargs, 1533 ) 1535 return builder_instance File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/builder.py:1292, in GeneratorBasedBuilder.__init__(self, writer_batch_size, args, *kwargs) 1291 def __init__(self, args, writer_batch_size=None, *kwargs): -> 1292 super().__init__(args, kwargs) 1293 # Batch size used by the ArrowWriter 1294 # It defines the number of samples that are kept in memory before writing them 1295 # and also the length of the arrow chunks 1296 # None means that the ArrowWriter will use its default value 1297 self._writer_batch_size = writer_batch_size or self.DEFAULT_WRITER_BATCH_SIZE File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/builder.py:312, in DatasetBuilder.__init__(self, cache_dir, config_name, hash, base_path, info, features, use_auth_token, repo_id, data_files, data_dir, name, config_kwargs) 309 # prepare info: DatasetInfo are a standardized dataclass across all datasets 310 # Prefill datasetinfo 311 if info is None: --> 312 info = self.get_exported_dataset_info() 313 info.update(self._info()) 314 info.builder_name = self.name File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/builder.py:412, in DatasetBuilder.get_exported_dataset_info(self) 400 def get_exported_dataset_info(self) -> DatasetInfo: 401 """Empty DatasetInfo if doesn't exist 402 403 Example: (...) 410 ``` 411 """ --> 412 return self.get_all_exported_dataset_infos().get(self.config.name, DatasetInfo()) File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/builder.py:398, in DatasetBuilder.get_all_exported_dataset_infos(cls) 385 @classmethod 386 def get_all_exported_dataset_infos(cls) -> DatasetInfosDict: 387 """Empty dict if doesn't exist 388 389 Example: (...) 396 ``` 397 """ --> 398 return DatasetInfosDict.from_directory(cls.get_imported_module_dir()) File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/info.py:370, in DatasetInfosDict.from_directory(cls, dataset_infos_dir) 368 dataset_metadata = DatasetMetadata.from_readme(Path(dataset_infos_dir) / "README.md") 369 if "dataset_info" in dataset_metadata: --> 370 return cls.from_metadata(dataset_metadata) 371 if os.path.exists(os.path.join(dataset_infos_dir, config.DATASETDICT_INFOS_FILENAME)): 372 # this is just to have backward compatibility with dataset_infos.json files 373 with open(os.path.join(dataset_infos_dir, config.DATASETDICT_INFOS_FILENAME), encoding="utf-8") as f: File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/info.py:396, in DatasetInfosDict.from_metadata(cls, dataset_metadata) 387 return cls( 388 { 389 dataset_info_yaml_dict.get("config_name", "default"): DatasetInfo._from_yaml_dict( (...) 393 } 394 ) 395 else: --> 396 dataset_info = DatasetInfo._from_yaml_dict(dataset_metadata["dataset_info"]) 397 dataset_info.config_name = dataset_metadata["dataset_info"].get("config_name", "default") 398 return cls({dataset_info.config_name: dataset_info}) File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/info.py:332, in DatasetInfo._from_yaml_dict(cls, yaml_data) 330 yaml_data = copy.deepcopy(yaml_data) 331 if yaml_data.get("features") is not None: --> 332 yaml_data["features"] = Features._from_yaml_list(yaml_data["features"]) 333 if yaml_data.get("splits") is not None: 334 yaml_data["splits"] = SplitDict._from_yaml_list(yaml_data["splits"]) File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/features/features.py:1745, in Features._from_yaml_list(cls, yaml_data) 1742 else: 1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}") -> 1745 return cls.from_dict(from_yaml_inner(yaml_data)) File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/features/features.py:1741, in Features._from_yaml_list.<locals>.from_yaml_inner(obj) 1739 elif isinstance(obj, list): 1740 names = [_feature.pop("name") for _feature in obj] -> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)} 1742 else: 1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}") File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/features/features.py:1741, in <dictcomp>(.0) 1739 elif isinstance(obj, list): 1740 names = [_feature.pop("name") for _feature in obj] -> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)} 1742 else: 1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}") File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/features/features.py:1736, in Features._from_yaml_list.<locals>.from_yaml_inner(obj) 1734 return {"_type": snakecase_to_camelcase(obj["dtype"])} 1735 else: -> 1736 return from_yaml_inner(obj["dtype"]) 1737 else: 1738 return {"_type": snakecase_to_camelcase(_type), unsimplify(obj)[_type]} File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/features/features.py:1738, in Features._from_yaml_list.<locals>.from_yaml_inner(obj) 1736 return from_yaml_inner(obj["dtype"]) 1737 else: -> 1738 return {"_type": snakecase_to_camelcase(_type), unsimplify(obj)[_type]} 1739 elif isinstance(obj, list): 1740 names = [_feature.pop("name") for _feature in obj] File /work/pi_adrozdov_umass_edu/syerawar_umass_edu/envs/vadops/lib/python3.10/site-packages/datasets/features/features.py:1706, in Features._from_yaml_list.<locals>.unsimplify(feature) 1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict): 1705 label_ids = sorted(feature["class_label"]["names"]) -> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)): 1707 raise ValueError( 1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing." 1709 ) 1710 feature["class_label"]["names"] = [feature["class_label"]["names"][label_id] for label_id in label_ids] TypeError: can only concatenate str (not "int") to str ``` The same issue happens when I try to load `go-emotions` multi class classification dataset. Could somebody guide me on how to fix this issue? ### Steps to reproduce the bug Run the following code snippet in a python script/ notebook cell: ``` from datasets import load_dataset yelp_dataset = load_dataset("yelp_review_full") yelp_dataset ``` ### Expected behavior The dataset should be loaded perfectly, which showing the train, test and unsupervised splits with the basic data statistics ### Environment info - `datasets` version: 2.6.1 - Platform: Linux-5.4.0-124-generic-x86_64-with-glibc2.31 - Python version: 3.10.9 - PyArrow version: 8.0.0 - Pandas version: 1.5.3	{ "avatar_url": "https://avatars.githubusercontent.com/u/19690506?v=4", "events_url": "https://api.github.com/users/ysahil97/events{/privacy}", "followers_url": "https://api.github.com/users/ysahil97/followers", "following_url": "https://api.github.com/users/ysahil97/following{/other_user}", "gists_url": "https://api.github.com/users/ysahil97/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ysahil97", "id": 19690506, "login": "ysahil97", "node_id": "MDQ6VXNlcjE5NjkwNTA2", "organizations_url": "https://api.github.com/users/ysahil97/orgs", "received_events_url": "https://api.github.com/users/ysahil97/received_events", "repos_url": "https://api.github.com/users/ysahil97/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ysahil97/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ysahil97/subscriptions", "type": "User", "url": "https://api.github.com/users/ysahil97", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5670/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5670/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5309	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5309/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5309/comments	https://api.github.com/repos/huggingface/datasets/issues/5309/events	https://github.com/huggingface/datasets/pull/5309	1,466,758,987	PR_kwDODunzps5D0g1y	5,309	Close stream in `ArrowWriter.finalize` before inference error	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-11-28T16:59:39Z	2022-12-07T12:55:20Z	2022-12-07T12:52:15Z	COLLABORATOR	null	null	null	Ensure the file stream is closed in `ArrowWriter.finalize` before raising the `SchemaInferenceError` to avoid the `PermissionError` on Windows in `incomplete_dir`'s `shutil.rmtree`.	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5309/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5309/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5309.diff", "html_url": "https://github.com/huggingface/datasets/pull/5309", "merged_at": "2022-12-07T12:52:15Z", "patch_url": "https://github.com/huggingface/datasets/pull/5309.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5309" }
https://api.github.com/repos/huggingface/datasets/issues/6373	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6373/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6373/comments	https://api.github.com/repos/huggingface/datasets/issues/6373/events	https://github.com/huggingface/datasets/pull/6373	1,973,349,695	PR_kwDODunzps5eYsZc	6,373	Fix typo in `Dataset.map` docstring	{ "avatar_url": "https://avatars.githubusercontent.com/u/3905501?v=4", "events_url": "https://api.github.com/users/bryant1410/events{/privacy}", "followers_url": "https://api.github.com/users/bryant1410/followers", "following_url": "https://api.github.com/users/bryant1410/following{/other_user}", "gists_url": "https://api.github.com/users/bryant1410/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/bryant1410", "id": 3905501, "login": "bryant1410", "node_id": "MDQ6VXNlcjM5MDU1MDE=", "organizations_url": "https://api.github.com/users/bryant1410/orgs", "received_events_url": "https://api.github.com/users/bryant1410/received_events", "repos_url": "https://api.github.com/users/bryant1410/repos", "site_admin": false, "starred_url": "https://api.github.com/users/bryant1410/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/bryant1410/subscriptions", "type": "User", "url": "https://api.github.com/users/bryant1410", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006709 / 0.011353 (-0.004643) \| 0.004102 / 0.011008 (-0.006906) \| 0.084449 / 0.038508 (0.045941) \| 0.076078 / 0.023109 (0.052969) \| 0.319831 / 0.275898 (0.043933) \| 0.359918 / 0.323480 (0.036438) \| 0.006092 / 0.007986 (-0.001894) \| 0.003402 / 0.004328 (-0.000926) \| 0.064715 / 0.004250 (0.060465) \| 0.054541 / 0.037052 (0.017488) \| 0.330394 / 0.258489 (0.071905) \| 0.366048 / 0.293841 (0.072207) \| 0.031594 / 0.128546 (-0.096952) \| 0.008591 / 0.075646 (-0.067056) \| 0.292983 / 0.419271 (-0.126288) \| 0.052986 / 0.043533 (0.009453) \| 0.322253 / 0.255139 (0.067114) \| 0.340082 / 0.283200 (0.056882) \| 0.023390 / 0.141683 (-0.118293) \| 1.459038 / 1.452155 (0.006883) \| 1.536256 / 1.492716 (0.043540) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.233527 / 0.018006 (0.215521) \| 0.459145 / 0.000490 (0.458655) \| 0.007471 / 0.000200 (0.007271) \| 0.000281 / 0.000054 (0.000227) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028158 / 0.037411 (-0.009253) \| 0.083079 / 0.014526 (0.068553) \| 0.097159 / 0.176557 (-0.079397) \| 0.151927 / 0.737135 (-0.585208) \| 0.098024 / 0.296338 (-0.198314) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.386882 / 0.215209 (0.171673) \| 3.849635 / 2.077655 (1.771981) \| 1.832885 / 1.504120 (0.328765) \| 1.668356 / 1.541195 (0.127162) \| 1.745066 / 1.468490 (0.276576) \| 0.484476 / 4.584777 (-4.100301) \| 3.547604 / 3.745712 (-0.198108) \| 3.480338 / 5.269862 (-1.789523) \| 2.066837 / 4.565676 (-2.498840) \| 0.056755 / 0.424275 (-0.367520) \| 0.007747 / 0.007607 (0.000140) \| 0.467999 / 0.226044 (0.241955) \| 4.678875 / 2.268929 (2.409946) \| 2.341930 / 55.444624 (-53.102695) \| 1.985632 / 6.876477 (-4.890844) \| 2.046998 / 2.142072 (-0.095074) \| 0.579860 / 4.805227 (-4.225367) \| 0.131488 / 6.500664 (-6.369176) \| 0.060193 / 0.075469 (-0.015276) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.249656 / 1.841788 (-0.592132) \| 19.079517 / 8.074308 (11.005209) \| 14.328827 / 10.191392 (4.137435) \| 0.173707 / 0.680424 (-0.506717) \| 0.018250 / 0.534201 (-0.515951) \| 0.392225 / 0.579283 (-0.187058) \| 0.413920 / 0.434364 (-0.020444) \| 0.464124 / 0.540337 (-0.076214) \| 0.640283 / 1.386936 (-0.746653) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006859 / 0.011353 (-0.004494) \| 0.004068 / 0.011008 (-0.006940) \| 0.063936 / 0.038508 (0.025428) \| 0.077187 / 0.023109 (0.054078) \| 0.365098 / 0.275898 (0.089200) \| 0.391003 / 0.323480 (0.067523) \| 0.005571 / 0.007986 (-0.002415) \| 0.003425 / 0.004328 (-0.000904) \| 0.063220 / 0.004250 (0.058970) \| 0.056964 / 0.037052 (0.019912) \| 0.367793 / 0.258489 (0.109304) \| 0.398776 / 0.293841 (0.104935) \| 0.033182 / 0.128546 (-0.095364) \| 0.008601 / 0.075646 (-0.067045) \| 0.070276 / 0.419271 (-0.348996) \| 0.048383 / 0.043533 (0.004850) \| 0.360414 / 0.255139 (0.105275) \| 0.368171 / 0.283200 (0.084971) \| 0.023114 / 0.141683 (-0.118569) \| 1.503503 / 1.452155 (0.051349) \| 1.567279 / 1.492716 (0.074562) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224296 / 0.018006 (0.206290) \| 0.455138 / 0.000490 (0.454648) \| 0.004014 / 0.000200 (0.003814) \| 0.000104 / 0.000054 (0.000050) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032337 / 0.037411 (-0.005074) \| 0.094385 / 0.014526 (0.079859) \| 0.109870 / 0.176557 (-0.066687) \| 0.156978 / 0.737135 (-0.580157) \| 0.107559 / 0.296338 (-0.188780) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.427409 / 0.215209 (0.212200) \| 4.261772 / 2.077655 (2.184117) \| 2.276106 / 1.504120 (0.771986) \| 2.115232 / 1.541195 (0.574038) \| 2.192048 / 1.468490 (0.723558) \| 0.488459 / 4.584777 (-4.096318) \| 3.675463 / 3.745712 (-0.070249) \| 3.322475 / 5.269862 (-1.947387) \| 2.072253 / 4.565676 (-2.493424) \| 0.058259 / 0.424275 (-0.366017) \| 0.007319 / 0.007607 (-0.000288) \| 0.499513 / 0.226044 (0.273469) \| 4.994774 / 2.268929 (2.725845) \| 2.760927 / 55.444624 (-52.683697) \| 2.391947 / 6.876477 (-4.484530) \| 2.600557 / 2.142072 (0.458484) \| 0.587597 / 4.805227 (-4.217630) \| 0.131444 / 6.500664 (-6.369220) \| 0.057334 / 0.075469 (-0.018135) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.354636 / 1.841788 (-0.487152) \| 19.685735 / 8.074308 (11.611427) \| 14.295920 / 10.191392 (4.104528) \| 0.171921 / 0.680424 (-0.508503) \| 0.019926 / 0.534201 (-0.514274) \| 0.395216 / 0.579283 (-0.184068) \| 0.432791 / 0.434364 (-0.001573) \| 0.473055 / 0.540337 (-0.067282) \| 0.638633 / 1.386936 (-0.748303) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fad7c899ec9218a717311223aa6ef5c09a6c7885 \"CML watermark\")\n" ]	2023-11-02T01:36:49Z	2023-11-02T15:18:22Z	2023-11-02T10:11:38Z	CONTRIBUTOR	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6373/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6373/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6373.diff", "html_url": "https://github.com/huggingface/datasets/pull/6373", "merged_at": "2023-11-02T10:11:38Z", "patch_url": "https://github.com/huggingface/datasets/pull/6373.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6373" }
https://api.github.com/repos/huggingface/datasets/issues/4795	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4795/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4795/comments	https://api.github.com/repos/huggingface/datasets/issues/4795/events	https://github.com/huggingface/datasets/issues/4795	1,329,525,732	I_kwDODunzps5PPvPk	4,795	Missing MBPP splits	{ "avatar_url": "https://avatars.githubusercontent.com/u/2452384?v=4", "events_url": "https://api.github.com/users/stadlerb/events{/privacy}", "followers_url": "https://api.github.com/users/stadlerb/followers", "following_url": "https://api.github.com/users/stadlerb/following{/other_user}", "gists_url": "https://api.github.com/users/stadlerb/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/stadlerb", "id": 2452384, "login": "stadlerb", "node_id": "MDQ6VXNlcjI0NTIzODQ=", "organizations_url": "https://api.github.com/users/stadlerb/orgs", "received_events_url": "https://api.github.com/users/stadlerb/received_events", "repos_url": "https://api.github.com/users/stadlerb/repos", "site_admin": false, "starred_url": "https://api.github.com/users/stadlerb/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/stadlerb/subscriptions", "type": "User", "url": "https://api.github.com/users/stadlerb", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	null	[]	null	[ "Thanks for reporting this as well, @stadlerb.\r\n\r\nI suggest waiting for the answer of the data owners... ", "@albertvillanova The first author of the paper responded to the upstream issue:\r\n> Task IDs 11-510 are the 500 test problems. We use 90 problems (511-600) for validation and then remaining 374 for fine-tuning (601-974). The other problems can be used as desired, either for training or few-shot prompting (although this should be specified).", "Thanks for the follow-up, @stadlerb.\r\n\r\nWould you be willing to open a Pull Request to address this issue? :wink: ", "Opened a [PR](https://github.com/huggingface/datasets/pull/4943) to implement this--lmk if you have any feedback" ]	2022-08-05T06:51:01Z	2022-09-13T12:27:24Z	2022-09-13T12:27:24Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	(@albertvillanova) The [MBPP dataset on the Hub](https://huggingface.co/datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split: > In the experiments described later in the paper, we hold out 10 problems for few-shot prompting, another 500 as our test dataset (which is used to evaluate both few-shot inference and fine-tuned models), 374 problems for fine-tuning, and the rest for validation. If the dataset on the Hub should reproduce most closely what the original authors use, I guess this four-way split should be reflected. The paper doesn't explicitly state the task_id ranges of the splits, but the [GitHub readme](https://github.com/google-research/google-research/tree/master/mbpp) referenced in the paper specifies exact task_id ranges, although it misstates the total number of samples: > We specify a train and test split to use for evaluation. Specifically: > > * Task IDs 11-510 are used for evaluation. > * Task IDs 1-10 and 511-1000 are used for training and/or prompting. We typically used 1-10 for few-shot prompting, although you can feel free to use any of the training examples. I.e. the few-shot, train and validation splits are combined into one split, with a soft suggestion of using the first ten for few-shot prompting. It is not explicitly stated whether the 374 fine-tuning samples mentioned in the paper have task_id 511 to 784 or 601 to 974 or are randomly sampled from task_id 511 to 974. Regarding the "sanitized" split the paper states the following: > For evaluations involving the edited dataset, we perform comparisons with 100 problems that appear in both the original and edited dataset, using the same held out 10 problems for few-shot prompting and 374 problems for fine-tuning. The statement doesn't appear to be very precise, as among the 10 few-shot problems, those with task_id 1, 5 and 10 are not even part of the sanitized variant, and many from the task_id range from 511 to 974 are missing (e.g. task_id 511 to 553). I suppose the idea the task_id ranges for each split remain the same, even if some of the task_ids are not present. That would result in 7 few-shot, 257 test, 141 train and 22 validation examples in the sanitized split.	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4795/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4795/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5268	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5268/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5268/comments	https://api.github.com/repos/huggingface/datasets/issues/5268/events	https://github.com/huggingface/datasets/pull/5268	1,455,633,978	PR_kwDODunzps5DPIsp	5,268	Sharded save_to_disk + multiprocessing	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "Added both num_shards and max_shard_size in push_to_hub/save_to_disk. Will take care of updating the tests later", "It's ready for a final review @mariosasko and @albertvillanova, let me know what you think :)", "Took your comments into account, and also changed `iflatmap_unordered` to take an iterable of kwargs to make the code more redable :)" ]	2022-11-18T18:50:01Z	2022-12-14T18:25:52Z	2022-12-14T18:22:58Z	MEMBER	null	null	null	Added `num_shards=` and `num_proc=` to `save_to_disk()` EDIT: also added `max_shard_size=` to `save_to_disk()`, and also `num_shards=` to `push_to_hub` I also: - deprecated the fs parameter in favor of storage_options (for consistency with the rest of the lib) in save_to_disk and load_from_disk - always embed the image/audio data in arrow when doing `save_to_disk` - added a tqdm bar in `save_to_disk` - Use the MockFileSystem in tests for `save_to_disk` and `load_from_disk` - removed the unused integration tests with S3, since we can now test with `mockfs` instead of `s3fs` TODO: - [x] implem save_to_disk for dataset dict - [x] save_to_disk for dataset dict tests - [x] deprecate fs in dataset dict load_from_disk as well - [x] update docs Close #5263 Close https://github.com/huggingface/datasets/issues/4196 Close https://github.com/huggingface/datasets/issues/4351	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5268/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5268/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5268.diff", "html_url": "https://github.com/huggingface/datasets/pull/5268", "merged_at": "2022-12-14T18:22:58Z", "patch_url": "https://github.com/huggingface/datasets/pull/5268.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5268" }
https://api.github.com/repos/huggingface/datasets/issues/7534	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7534/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7534/comments	https://api.github.com/repos/huggingface/datasets/issues/7534/events	https://github.com/huggingface/datasets/issues/7534	3,017,259,407	I_kwDODunzps6z17mP	7,534	TensorFlow RaggedTensor Support (batch-level)	{ "avatar_url": "https://avatars.githubusercontent.com/u/7490199?v=4", "events_url": "https://api.github.com/users/Lundez/events{/privacy}", "followers_url": "https://api.github.com/users/Lundez/followers", "following_url": "https://api.github.com/users/Lundez/following{/other_user}", "gists_url": "https://api.github.com/users/Lundez/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Lundez", "id": 7490199, "login": "Lundez", "node_id": "MDQ6VXNlcjc0OTAxOTk=", "organizations_url": "https://api.github.com/users/Lundez/orgs", "received_events_url": "https://api.github.com/users/Lundez/received_events", "repos_url": "https://api.github.com/users/Lundez/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Lundez/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Lundez/subscriptions", "type": "User", "url": "https://api.github.com/users/Lundez", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	[]	2025-04-24T13:14:52Z	2025-04-24T13:17:20Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request Hi, Currently datasets does not support RaggedTensor output on batch-level. When building a Object Detection Dataset (with TensorFlow) I need to enable RaggedTensors as that's how BBoxes & classes are expected from the Keras Model POV. Currently there's a error thrown saying that "Nested Data is not supported". It'd be very helpful if this was fixed! :) ### Motivation Enabling Object Detection pipelines for TensorFlow. ### Your contribution With guidance I'd happily help making the PR. The current implementation with DataCollator and later enforcing `np.array` is the problematic part (at the end of `np_get_batch` in `tf_utils.py`). As `numpy` don't support "Raggednes"	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7534/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7534/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5730	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5730/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5730/comments	https://api.github.com/repos/huggingface/datasets/issues/5730/events	https://github.com/huggingface/datasets/issues/5730	1,662,007,926	I_kwDODunzps5jEDp2	5,730	CI is broken: ValueError: Name (mock) already in the registry and clobber is False	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[]	2023-04-11T08:29:46Z	2023-04-11T08:47:56Z	2023-04-11T08:47:56Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	CI is broken for `test_py310`. See: https://github.com/huggingface/datasets/actions/runs/4665326892/jobs/8258580948 ``` =========================== short test summary info ============================ ERROR tests/test_builder.py::test_builder_with_filesystem_download_and_prepare - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_builder.py::test_builder_with_filesystem_download_and_prepare_reload - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_dataset_dict.py::test_dummy_datasetdict_serialize_fs - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_file_utils.py::test_get_from_cache_fsspec - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_filesystem.py::test_is_remote_filesystem - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xexists[tmp_path/file.txt-True] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xexists[tmp_path/file_that_doesnt_exist.txt-False] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xexists[mock://top_level/second_level/date=2019-10-01/a.parquet-True] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xexists[mock://top_level/second_level/date=2019-10-01/file_that_doesnt_exist.parquet-False] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xlistdir[tmp_path-expected_paths0] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xlistdir[mock://-expected_paths1] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xlistdir[mock://top_level-expected_paths2] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xlistdir[mock://top_level/second_level/date=2019-10-01-expected_paths3] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xisdir[tmp_path-True] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xisdir[tmp_path/file.txt-False] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xisdir[mock://-True] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xisdir[mock://top_level-True] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xisdir[mock://dir_that_doesnt_exist-False] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xisfile[tmp_path/file.txt-True] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xisfile[tmp_path/file_that_doesnt_exist.txt-False] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xisfile[mock://-False] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xisfile[mock://top_level/second_level/date=2019-10-01/a.parquet-True] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xgetsize[tmp_path/file.txt-100] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xgetsize[mock://-0] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xgetsize[mock://top_level/second_level/date=2019-10-01/a.parquet-100] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xglob[tmp_path/.txt-expected_paths0] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xglob[mock://-expected_paths1] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xglob[mock://top_-expected_paths2] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xglob[mock://top_level/second_level/date=2019-10-0[1-4]-expected_paths3] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xglob[mock://top_level/second_level/date=2019-10-0[1-4]/-expected_paths4] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xwalk[tmp_path-expected_outputs0] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::test_xwalk[mock://top_level/second_level-expected_outputs1] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_exists[tmp_path/file.txt-True] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_exists[tmp_path/file_that_doesnt_exist.txt-False] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_exists[mock://top_level/second_level/date=2019-10-01/a.parquet-True] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_exists[mock://top_level/second_level/date=2019-10-01/file_that_doesnt_exist.parquet-False] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_glob[tmp_path-.txt-expected_paths0] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_glob[mock://--expected_paths1] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_glob[mock://-top_-expected_paths2] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_glob[mock://top_level/second_level-date=2019-10-0[1-4]-expected_paths3] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_glob[mock://top_level/second_level-date=2019-10-0[1-4]/-expected_paths4] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[tmp_path-.txt-expected_paths0] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://-date=2019-10-0[1-4]-expected_paths1] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://top_level-date=2019-10-0[1-4]-expected_paths2] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://-date=2019-10-0[1-4]/-expected_paths3] - ValueError: Name (mock) already in the registry and clobber is False ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://top_level-date=2019-10-0[1-4]/*-expected_paths4] - ValueError: Name (mock) already in the registry and clobber is False ===== 2105 passed, 18 skipped, 38 warnings, 46 errors in 236.22s (0:03:56) ===== ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5730/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5730/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7139	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7139/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7139/comments	https://api.github.com/repos/huggingface/datasets/issues/7139/events	https://github.com/huggingface/datasets/issues/7139	2,508,078,858	I_kwDODunzps6Vfj8K	7,139	Use load_dataset to load imagenet-1K But find a empty dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/105094708?v=4", "events_url": "https://api.github.com/users/fscdc/events{/privacy}", "followers_url": "https://api.github.com/users/fscdc/followers", "following_url": "https://api.github.com/users/fscdc/following{/other_user}", "gists_url": "https://api.github.com/users/fscdc/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/fscdc", "id": 105094708, "login": "fscdc", "node_id": "U_kgDOBkOeNA", "organizations_url": "https://api.github.com/users/fscdc/orgs", "received_events_url": "https://api.github.com/users/fscdc/received_events", "repos_url": "https://api.github.com/users/fscdc/repos", "site_admin": false, "starred_url": "https://api.github.com/users/fscdc/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/fscdc/subscriptions", "type": "User", "url": "https://api.github.com/users/fscdc", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Imagenet-1k is a gated dataset which means you’ll have to agree to share your contact info to access it. Have you tried this yet? Once you have, you can sign in with your user token (you can find this in your Hugging Face account settings) when prompted by running.\r\n\r\n```\r\nhuggingface-cli login\r\ntrain_set = load_dataset('imagenet-1k', split='train', use_auth_token=True)\r\n``` ", "Thanks a lot! It helps me" ]	2024-09-05T15:12:22Z	2024-10-09T04:02:41Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug ```python def get_dataset(data_path, train_folder="train", val_folder="val"): traindir = os.path.join(data_path, train_folder) valdir = os.path.join(data_path, val_folder) def transform_val_examples(examples): transform = Compose([ Resize(256), CenterCrop(224), ToTensor(), ]) examples["image"] = [transform(image.convert("RGB")) for image in examples["image"]] return examples def transform_train_examples(examples): transform = Compose([ RandomResizedCrop(224), RandomHorizontalFlip(), ToTensor(), ]) examples["image"] = [transform(image.convert("RGB")) for image in examples["image"]] return examples # @fengsicheng: This way is very slow for big dataset like ImageNet-1K (but can pass the network problem using local dataset) # train_set = load_dataset("imagefolder", data_dir=traindir, num_proc=4) # test_set = load_dataset("imagefolder", data_dir=valdir, num_proc=4) train_set = load_dataset("imagenet-1K", split="train", trust_remote_code=True) test_set = load_dataset("imagenet-1K", split="test", trust_remote_code=True) print(train_set["label"]) train_set.set_transform(transform_train_examples) test_set.set_transform(transform_val_examples) return train_set, test_set ``` above the code, but output of the print is a list of None: <img width="952" alt="image" src="https://github.com/user-attachments/assets/c4e2fdd8-3b8f-481e-8f86-9bbeb49d79fb"> ### Steps to reproduce the bug 1. just ran the code 2. see the print ### Expected behavior I do not know how to fix this, can anyone provide help or something? It is hurry for me ### Environment info - `datasets` version: 2.21.0 - Platform: Linux-5.4.0-190-generic-x86_64-with-glibc2.31 - Python version: 3.10.14 - `huggingface_hub` version: 0.24.6 - PyArrow version: 17.0.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.6.1	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7139/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7139/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5720	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5720/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5720/comments	https://api.github.com/repos/huggingface/datasets/issues/5720/events	https://github.com/huggingface/datasets/issues/5720	1,659,610,705	I_kwDODunzps5i66ZR	5,720	Streaming IterableDatasets do not work with torch DataLoaders	{ "avatar_url": "https://avatars.githubusercontent.com/u/29244648?v=4", "events_url": "https://api.github.com/users/jlehrer1/events{/privacy}", "followers_url": "https://api.github.com/users/jlehrer1/followers", "following_url": "https://api.github.com/users/jlehrer1/following{/other_user}", "gists_url": "https://api.github.com/users/jlehrer1/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jlehrer1", "id": 29244648, "login": "jlehrer1", "node_id": "MDQ6VXNlcjI5MjQ0NjQ4", "organizations_url": "https://api.github.com/users/jlehrer1/orgs", "received_events_url": "https://api.github.com/users/jlehrer1/received_events", "repos_url": "https://api.github.com/users/jlehrer1/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jlehrer1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jlehrer1/subscriptions", "type": "User", "url": "https://api.github.com/users/jlehrer1", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Edit: This behavior is true even without `.take/.set`", "I'm experiencing the same problem that @jlehrer1. I was able to reproduce it with a very small example:\r\n\r\n```py\r\nfrom datasets import Dataset, load_dataset, load_dataset_builder\r\nfrom torch.utils.data import DataLoader\r\n\r\n\r\ndef my_gen():\r\n for i in range(1, 4):\r\n yield {\"a\": i}\r\n\r\n# Saving the dataset as a parquet file\r\ndataset = Dataset.from_generator(my_gen)\r\ntrain_path = \"/tmp/test.parquet\"\r\ndataset.to_parquet(train_path)\r\n\r\n# Creating a local dataset from the parquet file\r\ndata_files = {\"train\": [str(train_path)]}\r\nbuilder = load_dataset_builder(\"parquet\", data_files=data_files)\r\nbuilder.download_and_prepare(\"/tmp/test_ds\", file_format=\"parquet\")\r\n\r\n# Loading the dataset from the local directory as streaming\r\ndataset = load_dataset(\"parquet\", data_dir=\"/tmp/test_ds\", split=\"train\", streaming=True)\r\ndataset.with_format(\"torch\")\r\n\r\ndl = DataLoader(dataset, batch_size=2, num_workers=1)\r\nfor row in dl:\r\n print(row)\r\n```\r\n\r\nMy env info:\r\n```\r\ndatasets 2.11.0\r\ntorch 2.0.0\r\ntorchvision 0.15.1\r\nPython 3.9.16\r\n```\r\n\r\nNote that the example above doesn't fail if the number of workers used is `0`", "I cannot reproduce this error, not even with your MRE @ivanprado (your env appears to be the same as Colab's, and your code runs there without issues). ", "@mariosasko you are right, it works on Colab. I digged deeper and found that the problem arises when the multiprocessing method is set to be `spawn`. This code reproduces the problem in Colab:\r\n\r\n```py\r\nfrom datasets import Dataset, load_dataset, load_dataset_builder\r\nfrom torch.utils.data import DataLoader\r\nimport multiprocessing as mp\r\n\r\nmp.set_start_method('spawn')\r\n\r\ndef my_gen():\r\n for i in range(1, 4):\r\n yield {\"a\": i}\r\n\r\n\r\ndef main():\r\n # Saving the dataset as a parquet file\r\n dataset = Dataset.from_generator(my_gen)\r\n train_path = \"/tmp/test.parquet\"\r\n dataset.to_parquet(train_path)\r\n\r\n # Creating a local dataset from the parquet file\r\n data_files = {\"train\": [str(train_path)]}\r\n builder = load_dataset_builder(\"parquet\", data_files=data_files)\r\n builder.download_and_prepare(\"/tmp/test_ds\", file_format=\"parquet\")\r\n\r\n # Loading the dataset from the local directory as streaming\r\n dataset = load_dataset(\"parquet\", data_dir=\"/tmp/test_ds\", split=\"train\", streaming=True)\r\n dataset.with_format(\"torch\")\r\n\r\n dl = DataLoader(dataset, batch_size=2, num_workers=1)\r\n for row in dl:\r\n print(row)\r\n\r\nmain()\r\n```", "So is there a way to fix this by changing the `mp` method? This is blocking any usage of the `datasets` library for me", "@jlehrer1 can you try adding `mp.set_start_method('fork')` at the beginning of your code? Maybe this helps you. Keep us posted. ", "I have a similar issue: \r\n> mp.set_start_method('fork')\r\n\r\n\r\nDidnt work", "What if I want to use GPU? spawn is a must have @ivanprado ", "@ivanprado you're right, this problem gets solved in case number of workers is set to 0, but this essentially destroys any level parallelism we can get.", "Exactly guys, agree with you. I'm just one like yours here. I'm not a datasets contributor. This issue prevented me to use this library." ]	2023-04-08T18:45:48Z	2025-03-19T14:06:47Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug When using streaming datasets set up with train/val split using `.skip()` and `.take()`, the following error occurs when iterating over a torch dataloader: ``` File "/Users/julian/miniconda3/envs/sims/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 363, in __iter__ self._iterator = self._get_iterator() File "/Users/julian/miniconda3/envs/sims/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 314, in _get_iterator return _MultiProcessingDataLoaderIter(self) File "/Users/julian/miniconda3/envs/sims/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 927, in __init__ w.start() File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/process.py", line 121, in start self._popen = self._Popen(self) File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/context.py", line 224, in _Popen return _default_context.get_context().Process._Popen(process_obj) File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/context.py", line 284, in _Popen return Popen(process_obj) File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 32, in __init__ super().__init__(process_obj) File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/popen_fork.py", line 19, in __init__ self._launch(process_obj) File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 47, in _launch reduction.dump(process_obj, fp) File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/reduction.py", line 60, in dump ForkingPickler(file, protocol).dump(obj) AttributeError: Can't pickle local object '_generate_examples_from_tables_wrapper.<locals>.wrapper' ``` To reproduce, run the code ``` from datasets import load_dataset data = load_dataset(args.dataset_name, split="train", streaming=True) train_len = 5000 val_len = 100 train, val = data.take(train_len), data.skip(train_len).take(val_len) traindata = IterableClipDataset(data, context_length=args.max_len, tokenizer=tokenizer, image_key="url", text_key="text") traindata = DataLoader(traindata, batch_size=args.batch_size, num_workers=args.num_workers, persistent_workers=True) ``` Where the class IterableClipDataset is a simple wrapper to cast the dataset to a torch iterabledataset, defined via ``` from torch.utils.data import Dataset, IterableDataset from torchvision.transforms import Compose, Resize, ToTensor from transformers import AutoTokenizer import requests from PIL import Image class IterableClipDataset(IterableDataset): def __init__(self, dataset, context_length: int, image_transform=None, tokenizer=None, image_key="image", text_key="text"): self.dataset = dataset self.context_length = context_length self.image_transform = Compose([Resize((224, 224)), ToTensor()]) if image_transform is None else image_transform self.tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") if tokenizer is None else tokenizer self.image_key = image_key self.text_key = text_key def read_image(self, url: str): try: # Try to read the image image = Image.open(requests.get(url, stream=True).raw) except: image = Image.new("RGB", (224, 224), (0, 0, 0)) return image def process_sample(self, image, text): if isinstance(image, str): image = self.read_image(image) if self.image_transform is not None: image = self.image_transform(image) text = self.tokenizer.encode( text, add_special_tokens=True, max_length=self.context_length, truncation=True, padding="max_length" ) text = torch.tensor(text, dtype=torch.long) return image, text def __iter__(self): for sample in self.dataset: image, text = sample[self.image_key], sample[self.text_key] yield self.process_sample(image, text) ``` ### Steps to reproduce the bug Steps to reproduce 1. Install `datasets`, `torch`, and `PIL` (if you want to reproduce exactly) 2. Run the code above ### Expected behavior Batched data is produced from the dataloader ### Environment info ``` datasets == 2.9.0 python == 3.9.12 torch == 1.11.0 ```	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5720/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5720/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6985	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6985/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6985/comments	https://api.github.com/repos/huggingface/datasets/issues/6985/events	https://github.com/huggingface/datasets/issues/6985	2,362,378,276	I_kwDODunzps6Mzwgk	6,985	AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'	{ "avatar_url": "https://avatars.githubusercontent.com/u/26666267?v=4", "events_url": "https://api.github.com/users/firmai/events{/privacy}", "followers_url": "https://api.github.com/users/firmai/followers", "following_url": "https://api.github.com/users/firmai/following{/other_user}", "gists_url": "https://api.github.com/users/firmai/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/firmai", "id": 26666267, "login": "firmai", "node_id": "MDQ6VXNlcjI2NjY2MjY3", "organizations_url": "https://api.github.com/users/firmai/orgs", "received_events_url": "https://api.github.com/users/firmai/received_events", "repos_url": "https://api.github.com/users/firmai/repos", "site_admin": false, "starred_url": "https://api.github.com/users/firmai/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/firmai/subscriptions", "type": "User", "url": "https://api.github.com/users/firmai", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Please note that the error is raised just at import:\r\n```python\r\nimport pyarrow.parquet as pq\r\n```\r\n\r\nTherefore it must be caused by some problem with your pyarrow installation. I would recommend you uninstall and install pyarrow again.\r\n\r\nI also see that it seems you use conda to install pyarrow. Please note that pyarrow offers 3 different packages in conda-forge: https://arrow.apache.org/docs/python/install.html#using-conda\r\n```\r\nconda install -c conda-forge pyarrow\r\n```\r\n> While the pyarrow [conda-forge](https://conda-forge.org/) package is the right choice for most users, both a minimal and maximal variant of the package exist, either of which may be better for your use case. See [Differences between conda-forge packages](https://arrow.apache.org/docs/python/install.html#python-conda-differences).\r\n\r\nPlease, make sure you install the right one: I guess it is either `pyarrow` (or `pyarrow-all`).", "I have same issue, please downgrade pyarrow==15.0.2, it seem datasets library need to be fix", "It is not a problem with the `datasets` library: we support latest version of `pyarrow` and our Continuous Integration tests are using pyarrow 16.1.0 without any problem.\r\n\r\nThe error reported here is raised when importing pyarrow.parquet:\r\n```\r\n---> 29 import pyarrow.parquet as pq\r\n```\r\n```\r\nFile /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/__init__.py:20\r\n 1 # Licensed to the Apache Software Foundation (ASF) under one\r\n 2 # or more contributor license agreements. See the NOTICE file\r\n 3 # distributed with this work for additional information\r\n (...)\r\n 17 \r\n 18 # flake8: noqa\r\n---> 20 from .core import *\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/core.py:33\r\n 30 import pyarrow as pa\r\n 32 try:\r\n---> 33 import pyarrow._parquet as _parquet\r\n 34 except ImportError as exc:\r\n 35 raise ImportError(\r\n 36 \"The pyarrow installation is not built with support \"\r\n 37 f\"for the Parquet file format ({str(exc)})\"\r\n 38 ) from None\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/pyarrow/_parquet.pyx:1, in init pyarrow._parquet()\r\n\r\nAttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'\r\n```\r\n\r\nThis can only be explained if pyarrow was not properly installed. \r\n\r\nIf the user just installed `pyarrow-core` from conda-forge, then its parquet subpackage is not installed and cannot be imported. You can check pyarrow docs:\r\n- Differences between conda-forge packages: https://arrow.apache.org/docs/python/install.html#python-conda-differences\r\n> The `pyarrow-core` package includes the following functionality:\r\n> ...\r\n> The `pyarrow` package adds the following:\r\n> ...\r\n> Parquet (i.e., `pyarrow.parquet`)", "I'm still seeing the same issue on datasets version 2.20.0. I installed pyarrow version 17.0.0 with `pip install`. Downgrading to pyarrow==15.0.2 also did not resolve the issue.", "@RenaLu As of UTC time 07/27/2024 23:20:00, I hit the same issue and reinstalling `pyarrow==15.0.2` resolved the issue for me. You may want to check if your `pyarrow` is successfully downgraded.", "I can confirm @albertvillanova's [analysis & suggestion](https://github.com/huggingface/datasets/issues/6985#issuecomment-2188022888) - `pip uninstall pyarrow` followed by `pip install pyarrow` solved it for me. \r\n\r\nI suspect this is because pyarrow was initially installed as a pandas extra `pandas[...,parquet,...]`, then pip-upgrading pyarrow resulted in the issue.\r\n\r\n@RenaLu did you uninstall pyarrow between changing versions?", "After trying all the above combinations and failing, running the following in the notebook fixed the error!!\r\n`!conda install -c conda-forge -y datasets pyarrow libparquet`\r\nNote : Uninstall any existing dataset and pyarrow installations in the env before executing the above.", "If on colab, remember to restart the runtime so the new pyarrow is imported. I also upgraded pip which is recommended in pyarrow's installation instructions.", "fixed doing this: !pip install --upgrade datasets\r\n\r\n!pip show pyarrow\r\n!pip show datasets\r\n!pip uninstall -y pyarrow\r\n!pip install pyarrow --no-cache-dir\r\n!pip install pyarrow\r\n!pip install transformers\r\n!pip install --upgrade datasets\r\n!pip install datasets\r\n! pip install pyarrow\r\n! pip install pyarrow.parquet\r\n!pip install transformers\r\n\r\n# Import necessary libraries\r\nfrom datasets import load_dataset\r\nimport pyarrow.parquet as pq\r\nimport pyarrow.lib as lib\r\nimport pandas as pd\r\nfrom transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments\r\n", "but now i cant run test, so i remove it, ERROR: Could not find a version that satisfies the requirement pyarrow.parquet (from versions: none)\r\nERROR: No matching distribution found for pyarrow.parquet will still running but will tell you this", "I have the same question right now, python3.12 and transformers4.44.2, I have not fixed it", "I did most of the suggestions above and I still got the error, but after restarting my computer the error was fixed", "how to fix this, still have this error. ", "have we figured out what causes it?\n" ]	2024-06-19T13:22:28Z	2025-03-14T18:47:53Z	2024-06-25T05:40:51Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I have been struggling with this for two days, any help would be appreciated. Python 3.10 ``` from setfit import SetFitModel from huggingface_hub import login access_token_read = "cccxxxccc" # Authenticate with the Hugging Face Hub login(token=access_token_read) # Load the models from the Hugging Face Hub trainer_relv = SetFitModel.from_pretrained("snowdere/trainer_relevance") trainer_trust = SetFitModel.from_pretrained("snowdere/trainer_trust") trainer_sent = SetFitModel.from_pretrained("snowdere/trainer_sent") trainer_topic = SetFitModel.from_pretrained("snowdere/trainer_topic") ``` ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[6], line 1 ----> 1 from setfit import SetFitModel 2 from huggingface_hub import login 4 access_token_read = "ccsddsds" File /opt/conda/lib/python3.10/site-packages/setfit/__init__.py:7 4 import os 5 import warnings ----> 7 from .data import get_templated_dataset, sample_dataset 8 from .model_card import SetFitModelCardData 9 from .modeling import SetFitHead, SetFitModel File /opt/conda/lib/python3.10/site-packages/setfit/data.py:5 3 import pandas as pd 4 import torch ----> 5 from datasets import Dataset, DatasetDict, load_dataset 6 from torch.utils.data import Dataset as TorchDataset 8 from . import logging File /opt/conda/lib/python3.10/site-packages/datasets/__init__.py:18 1 # ruff: noqa 2 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors. 3 # (...) 13 # See the License for the specific language governing permissions and 14 # limitations under the License. 16 __version__ = "2.19.0" ---> 18 from .arrow_dataset import Dataset 19 from .arrow_reader import ReadInstruction 20 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:76 73 from tqdm.contrib.concurrent import thread_map 75 from . import config ---> 76 from .arrow_reader import ArrowReader 77 from .arrow_writer import ArrowWriter, OptimizedTypedSequence 78 from .data_files import sanitize_patterns File /opt/conda/lib/python3.10/site-packages/datasets/arrow_reader.py:29 26 from typing import TYPE_CHECKING, List, Optional, Union 28 import pyarrow as pa ---> 29 import pyarrow.parquet as pq 30 from tqdm.contrib.concurrent import thread_map 32 from .download.download_config import DownloadConfig File /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/__init__.py:20 1 # Licensed to the Apache Software Foundation (ASF) under one 2 # or more contributor license agreements. See the NOTICE file 3 # distributed with this work for additional information (...) 17 18 # flake8: noqa ---> 20 from .core import * File /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/core.py:33 30 import pyarrow as pa 32 try: ---> 33 import pyarrow._parquet as _parquet 34 except ImportError as exc: 35 raise ImportError( 36 "The pyarrow installation is not built with support " 37 f"for the Parquet file format ({str(exc)})" 38 ) from None File /opt/conda/lib/python3.10/site-packages/pyarrow/_parquet.pyx:1, in init pyarrow._parquet() AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType' ``` setfit: 1.0.3 transformers: 4.41.2 lingua-language-detector: 2.0.2 polars: 0.20.31 lightning: None google-cloud-bigquery: 3.24.0 shapely: 2.0.4 pyarrow: 16.0.0 ### Steps to reproduce the bug I have tried all version combinations for Dataset and Pyarrow, the all have the same error since a few days ago. This is accross multiple scripts I have. ### Expected behavior Just ron normally. ### Environment info 3.10	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6985/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6985/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6112	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6112/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6112/comments	https://api.github.com/repos/huggingface/datasets/issues/6112/events	https://github.com/huggingface/datasets/issues/6112	1,833,693,299	I_kwDODunzps5tS_Bz	6,112	yaml error using push_to_hub with generated README.md	{ "avatar_url": "https://avatars.githubusercontent.com/u/1643887?v=4", "events_url": "https://api.github.com/users/kevintee/events{/privacy}", "followers_url": "https://api.github.com/users/kevintee/followers", "following_url": "https://api.github.com/users/kevintee/following{/other_user}", "gists_url": "https://api.github.com/users/kevintee/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/kevintee", "id": 1643887, "login": "kevintee", "node_id": "MDQ6VXNlcjE2NDM4ODc=", "organizations_url": "https://api.github.com/users/kevintee/orgs", "received_events_url": "https://api.github.com/users/kevintee/received_events", "repos_url": "https://api.github.com/users/kevintee/repos", "site_admin": false, "starred_url": "https://api.github.com/users/kevintee/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kevintee/subscriptions", "type": "User", "url": "https://api.github.com/users/kevintee", "user_view_type": "public" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" } ]	null	[ "Thanks for reporting! This is a bug in converting the `ArrayXD` types to YAML. It will be fixed soon." ]	2023-08-02T18:21:21Z	2023-12-12T15:00:44Z	2023-12-12T15:00:44Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug When I construct a dataset with the following features: ``` features = Features( { "pixel_values": Array3D(dtype="float64", shape=(3, 224, 224)), "input_ids": Sequence(feature=Value(dtype="int64")), "attention_mask": Sequence(Value(dtype="int64")), "tokens": Sequence(Value(dtype="string")), "bbox": Array2D(dtype="int64", shape=(512, 4)), } ) ``` and run `push_to_hub`, the individual `.parquet` files are pushed, but when trying to upload the auto-generated README, I run into the following error: ``` Traceback (most recent call last): File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 261, in hf_raise_for_status response.raise_for_status() File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/requests/models.py", line 1021, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/looppayments/multitask_document_classification_dataset/commit/main The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 297, in <module> build_dataset() File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 290, in build_dataset push_to_hub(dataset, "multitask_document_classification_dataset") File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 135, in push_to_hub dataset.push_to_hub(f"looppayments/{dataset_name}", private=True) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 5577, in push_to_hub HfApi(endpoint=config.HF_ENDPOINT).upload_file( File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(args, *kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 828, in _inner return fn(self, args, *kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 3221, in upload_file commit_info = self.create_commit( File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(args, *kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 828, in _inner return fn(self, args, **kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2728, in create_commit hf_raise_for_status(commit_resp, endpoint_name="commit") File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 299, in hf_raise_for_status raise BadRequestError(message, response=response) from e huggingface_hub.utils._errors.BadRequestError: (Request ID: Root=1-64ca9c3d-2d2bbef354e102482a9a168e;bc00371c-8549-4859-9f41-43ff140ad36e) Bad request for commit endpoint: Invalid YAML in README.md: unknown tag !<tag:yaml.org,2002:python/tuple> (10:9) 7 \| - 3 8 \| - 224 9 \| - 224 10 \| dtype: float64 --------------^ 11 \| - name: input_ids 12 \| sequence: int64 ``` My guess is that the auto-generated yaml is unable to be parsed for some reason. ### Steps to reproduce the bug The description contains most of what's needed to reproduce the issue, but I've added a shortened code snippet: ``` from datasets import Array2D, Array3D, ClassLabel, Dataset, Features, Sequence, Value from PIL import Image from transformers import AutoProcessor features = Features( { "pixel_values": Array3D(dtype="float64", shape=(3, 224, 224)), "input_ids": Sequence(feature=Value(dtype="int64")), "attention_mask": Sequence(Value(dtype="int64")), "tokens": Sequence(Value(dtype="string")), "bbox": Array2D(dtype="int64", shape=(512, 4)), } ) processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) def preprocess_dataset(rows): # Get images images = [ Image.open(png_filename).convert("RGB") for png_filename in rows["png_filename"] ] encoding = processor( images, rows["tokens"], boxes=rows["bbox"], truncation=True, padding="max_length", ) encoding["tokens"] = rows["tokens"] return encoding dataset = dataset.map( preprocess_dataset, batched=True, batch_size=5, features=features, ) ``` ### Expected behavior Using datasets==2.11.0, I'm able to succesfully push_to_hub, no issues, but with datasets==2.14.2, I run into the above error. ### Environment info - `datasets` version: 2.14.2 - Platform: macOS-12.5-arm64-arm-64bit - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6112/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6112/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6871	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6871/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6871/comments	https://api.github.com/repos/huggingface/datasets/issues/6871/events	https://github.com/huggingface/datasets/pull/6871	2,280,102,869	PR_kwDODunzps5umJS6	6,871	Fix download for dict of dicts of URLs	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6871). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Once merged, I think a patch release is needed.", "Once the CI is green, I am merging this PR and making a patch release, @huggingface/datasets. ", "<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) \| 0.005352 / 0.011353 (-0.006001) \| 0.004140 / 0.011008 (-0.006868) \| 0.063844 / 0.038508 (0.025336) \| 0.030712 / 0.023109 (0.007603) \| 0.232790 / 0.275898 (-0.043108) \| 0.262334 / 0.323480 (-0.061145) \| 0.003264 / 0.007986 (-0.004721) \| 0.002654 / 0.004328 (-0.001674) \| 0.049775 / 0.004250 (0.045524) \| 0.046803 / 0.037052 (0.009751) \| 0.250667 / 0.258489 (-0.007822) \| 0.283581 / 0.293841 (-0.010260) \| 0.027660 / 0.128546 (-0.100886) \| 0.010560 / 0.075646 (-0.065087) \| 0.208676 / 0.419271 (-0.210596) \| 0.035415 / 0.043533 (-0.008118) \| 0.235380 / 0.255139 (-0.019759) \| 0.261220 / 0.283200 (-0.021980) \| 0.019551 / 0.141683 (-0.122132) \| 1.140196 / 1.452155 (-0.311959) \| 1.173021 / 1.492716 (-0.319696) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092665 / 0.018006 (0.074659) \| 0.301524 / 0.000490 (0.301034) \| 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) \| 0.018485 / 0.037411 (-0.018927) \| 0.061722 / 0.014526 (0.047196) \| 0.074701 / 0.176557 (-0.101855) \| 0.121443 / 0.737135 (-0.615692) \| 0.076268 / 0.296338 (-0.220070) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284143 / 0.215209 (0.068934) \| 2.789979 / 2.077655 (0.712324) \| 1.501156 / 1.504120 (-0.002964) \| 1.379414 / 1.541195 (-0.161781) \| 1.419092 / 1.468490 (-0.049398) \| 0.554107 / 4.584777 (-4.030670) \| 2.365659 / 3.745712 (-1.380053) \| 2.763963 / 5.269862 (-2.505898) \| 1.712587 / 4.565676 (-2.853090) \| 0.060961 / 0.424275 (-0.363314) \| 0.005301 / 0.007607 (-0.002306) \| 0.346253 / 0.226044 (0.120209) \| 3.351833 / 2.268929 (1.082905) \| 1.831946 / 55.444624 (-53.612679) \| 1.556530 / 6.876477 (-5.319947) \| 1.574185 / 2.142072 (-0.567887) \| 0.630396 / 4.805227 (-4.174831) \| 0.116126 / 6.500664 (-6.384538) \| 0.042391 / 0.075469 (-0.033078) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981430 / 1.841788 (-0.860358) \| 11.619671 / 8.074308 (3.545363) \| 9.718227 / 10.191392 (-0.473165) \| 0.130918 / 0.680424 (-0.549506) \| 0.014116 / 0.534201 (-0.520085) \| 0.288729 / 0.579283 (-0.290554) \| 0.259183 / 0.434364 (-0.175181) \| 0.323764 / 0.540337 (-0.216574) \| 0.420336 / 1.386936 (-0.966600) \|\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) \| 0.005255 / 0.011353 (-0.006098) \| 0.003664 / 0.011008 (-0.007344) \| 0.051376 / 0.038508 (0.012868) \| 0.030429 / 0.023109 (0.007320) \| 0.263090 / 0.275898 (-0.012808) \| 0.289959 / 0.323480 (-0.033521) \| 0.004214 / 0.007986 (-0.003772) \| 0.002782 / 0.004328 (-0.001546) \| 0.049043 / 0.004250 (0.044793) \| 0.041016 / 0.037052 (0.003964) \| 0.275616 / 0.258489 (0.017127) \| 0.303350 / 0.293841 (0.009509) \| 0.029484 / 0.128546 (-0.099062) \| 0.010329 / 0.075646 (-0.065317) \| 0.058680 / 0.419271 (-0.360591) \| 0.032818 / 0.043533 (-0.010715) \| 0.263368 / 0.255139 (0.008229) \| 0.286839 / 0.283200 (0.003640) \| 0.018029 / 0.141683 (-0.123654) \| 1.169207 / 1.452155 (-0.282948) \| 1.206568 / 1.492716 (-0.286148) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.101394 / 0.018006 (0.083387) \| 0.310414 / 0.000490 (0.309924) \| 0.000213 / 0.000200 (0.000013) \| 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) \| 0.021662 / 0.037411 (-0.015749) \| 0.075320 / 0.014526 (0.060794) \| 0.086607 / 0.176557 (-0.089949) \| 0.127268 / 0.737135 (-0.609867) \| 0.088244 / 0.296338 (-0.208095) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293591 / 0.215209 (0.078382) \| 2.871845 / 2.077655 (0.794190) \| 1.543624 / 1.504120 (0.039504) \| 1.426698 / 1.541195 (-0.114497) \| 1.445348 / 1.468490 (-0.023142) \| 0.565156 / 4.584777 (-4.019621) \| 0.961782 / 3.745712 (-2.783930) \| 2.827904 / 5.269862 (-2.441958) \| 1.747728 / 4.565676 (-2.817949) \| 0.063275 / 0.424275 (-0.361000) \| 0.004987 / 0.007607 (-0.002620) \| 0.349652 / 0.226044 (0.123607) \| 3.448635 / 2.268929 (1.179707) \| 1.891734 / 55.444624 (-53.552890) \| 1.624274 / 6.876477 (-5.252202) \| 1.641531 / 2.142072 (-0.500541) \| 0.642081 / 4.805227 (-4.163146) \| 0.116136 / 6.500664 (-6.384528) \| 0.040807 / 0.075469 (-0.034662) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.002090 / 1.841788 (-0.839697) \| 12.401097 / 8.074308 (4.326788) \| 9.799316 / 10.191392 (-0.392076) \| 0.131770 / 0.680424 (-0.548654) \| 0.016817 / 0.534201 (-0.517384) \| 0.301136 / 0.579283 (-0.278147) \| 0.136810 / 0.434364 (-0.297554) \| 0.384740 / 0.540337 (-0.155598) \| 0.423779 / 1.386936 (-0.963157) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2ebd8233ad8142da73bc8b4d380e9a32046d7829 \"CML watermark\")\n" ]	2024-05-06T06:06:52Z	2024-05-06T09:32:03Z	2024-05-06T09:25:52Z	MEMBER	null	null	null	Fix download for a dict of dicts of URLs when batched (default), introduced by: - #6794 This PR also implements regression tests. Fix #6869, fix #6850.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6871/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6871/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6871.diff", "html_url": "https://github.com/huggingface/datasets/pull/6871", "merged_at": "2024-05-06T09:25:52Z", "patch_url": "https://github.com/huggingface/datasets/pull/6871.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6871" }
https://api.github.com/repos/huggingface/datasets/issues/5460	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5460/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5460/comments	https://api.github.com/repos/huggingface/datasets/issues/5460/events	https://github.com/huggingface/datasets/pull/5460	1,555,387,532	PR_kwDODunzps5Icn9C	5,460	Document that removing all the columns returns an empty document and the num_row is lost	{ "avatar_url": "https://avatars.githubusercontent.com/u/24695242?v=4", "events_url": "https://api.github.com/users/thomasw21/events{/privacy}", "followers_url": "https://api.github.com/users/thomasw21/followers", "following_url": "https://api.github.com/users/thomasw21/following{/other_user}", "gists_url": "https://api.github.com/users/thomasw21/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/thomasw21", "id": 24695242, "login": "thomasw21", "node_id": "MDQ6VXNlcjI0Njk1MjQy", "organizations_url": "https://api.github.com/users/thomasw21/orgs", "received_events_url": "https://api.github.com/users/thomasw21/received_events", "repos_url": "https://api.github.com/users/thomasw21/repos", "site_admin": false, "starred_url": "https://api.github.com/users/thomasw21/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/thomasw21/subscriptions", "type": "User", "url": "https://api.github.com/users/thomasw21", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) \| 0.011812 / 0.011353 (0.000459) \| 0.006878 / 0.011008 (-0.004130) \| 0.128720 / 0.038508 (0.090212) \| 0.038506 / 0.023109 (0.015397) \| 0.359670 / 0.275898 (0.083772) \| 0.422908 / 0.323480 (0.099428) \| 0.010115 / 0.007986 (0.002129) \| 0.004332 / 0.004328 (0.000004) \| 0.096281 / 0.004250 (0.092031) \| 0.048850 / 0.037052 (0.011798) \| 0.373795 / 0.258489 (0.115306) \| 0.414643 / 0.293841 (0.120802) \| 0.057568 / 0.128546 (-0.070978) \| 0.024135 / 0.075646 (-0.051512) \| 0.411764 / 0.419271 (-0.007507) \| 0.060167 / 0.043533 (0.016634) \| 0.367119 / 0.255139 (0.111980) \| 0.391813 / 0.283200 (0.108613) \| 0.112125 / 0.141683 (-0.029558) \| 1.869560 / 1.452155 (0.417406) \| 1.845649 / 1.492716 (0.352932) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211449 / 0.018006 (0.193443) \| 0.522453 / 0.000490 (0.521963) \| 0.003984 / 0.000200 (0.003784) \| 0.000096 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026015 / 0.037411 (-0.011397) \| 0.117747 / 0.014526 (0.103221) \| 0.125037 / 0.176557 (-0.051520) \| 0.168351 / 0.737135 (-0.568785) \| 0.132390 / 0.296338 (-0.163949) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.605653 / 0.215209 (0.390444) \| 5.883452 / 2.077655 (3.805798) \| 2.367052 / 1.504120 (0.862932) \| 2.137671 / 1.541195 (0.596476) \| 2.042370 / 1.468490 (0.573880) \| 1.168442 / 4.584777 (-3.416335) \| 5.205236 / 3.745712 (1.459524) \| 2.992514 / 5.269862 (-2.277348) \| 2.191829 / 4.565676 (-2.373847) \| 0.137702 / 0.424275 (-0.286574) \| 0.015898 / 0.007607 (0.008291) \| 0.783987 / 0.226044 (0.557942) \| 7.768965 / 2.268929 (5.500036) \| 3.249149 / 55.444624 (-52.195476) \| 2.530687 / 6.876477 (-4.345790) \| 2.675212 / 2.142072 (0.533140) \| 1.482804 / 4.805227 (-3.322423) \| 0.276845 / 6.500664 (-6.223819) \| 0.080597 / 0.075469 (0.005128) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.519086 / 1.841788 (-0.322701) \| 17.394093 / 8.074308 (9.319785) \| 19.613554 / 10.191392 (9.422162) \| 0.253291 / 0.680424 (-0.427133) \| 0.047746 / 0.534201 (-0.486455) \| 0.547114 / 0.579283 (-0.032170) \| 0.623873 / 0.434364 (0.189509) \| 0.631924 / 0.540337 (0.091586) \| 0.744390 / 1.386936 (-0.642546) \|\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) \| 0.009229 / 0.011353 (-0.002124) \| 0.006206 / 0.011008 (-0.004802) \| 0.121866 / 0.038508 (0.083357) \| 0.033629 / 0.023109 (0.010519) \| 0.435172 / 0.275898 (0.159274) \| 0.472093 / 0.323480 (0.148613) \| 0.006946 / 0.007986 (-0.001039) \| 0.004848 / 0.004328 (0.000519) \| 0.097289 / 0.004250 (0.093038) \| 0.046982 / 0.037052 (0.009930) \| 0.447365 / 0.258489 (0.188876) \| 0.491213 / 0.293841 (0.197372) \| 0.055486 / 0.128546 (-0.073060) \| 0.019788 / 0.075646 (-0.055858) \| 0.399830 / 0.419271 (-0.019441) \| 0.058943 / 0.043533 (0.015411) \| 0.447658 / 0.255139 (0.192519) \| 0.465752 / 0.283200 (0.182552) \| 0.110441 / 0.141683 (-0.031242) \| 1.773155 / 1.452155 (0.321001) \| 1.899370 / 1.492716 (0.406653) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.191188 / 0.018006 (0.173181) \| 0.523721 / 0.000490 (0.523232) \| 0.004008 / 0.000200 (0.003808) \| 0.000126 / 0.000054 (0.000072) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032579 / 0.037411 (-0.004833) \| 0.120870 / 0.014526 (0.106344) \| 0.154991 / 0.176557 (-0.021565) \| 0.175450 / 0.737135 (-0.561685) \| 0.136526 / 0.296338 (-0.159813) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.627262 / 0.215209 (0.412052) \| 6.457989 / 2.077655 (4.380334) \| 2.935188 / 1.504120 (1.431068) \| 2.558705 / 1.541195 (1.017510) \| 2.669455 / 1.468490 (1.200965) \| 1.228791 / 4.584777 (-3.355985) \| 5.621262 / 3.745712 (1.875549) \| 3.181775 / 5.269862 (-2.088086) \| 2.115116 / 4.565676 (-2.450560) \| 0.159348 / 0.424275 (-0.264927) \| 0.013598 / 0.007607 (0.005991) \| 0.834732 / 0.226044 (0.608687) \| 8.051097 / 2.268929 (5.782168) \| 3.761681 / 55.444624 (-51.682943) \| 2.898158 / 6.876477 (-3.978319) \| 2.936289 / 2.142072 (0.794217) \| 1.476307 / 4.805227 (-3.328920) \| 0.269845 / 6.500664 (-6.230819) \| 0.087225 / 0.075469 (0.011756) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.632522 / 1.841788 (-0.209266) \| 17.615297 / 8.074308 (9.540989) \| 20.501172 / 10.191392 (10.309780) \| 0.248845 / 0.680424 (-0.431579) \| 0.024852 / 0.534201 (-0.509349) \| 0.498957 / 0.579283 (-0.080326) \| 0.588566 / 0.434364 (0.154202) \| 0.611051 / 0.540337 (0.070714) \| 0.726321 / 1.386936 (-0.660615) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#adaaf0b5ad596538c744d41bb56ce472834b6573 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) \| 0.008920 / 0.011353 (-0.002433) \| 0.004666 / 0.011008 (-0.006342) \| 0.098584 / 0.038508 (0.060076) \| 0.030213 / 0.023109 (0.007103) \| 0.298180 / 0.275898 (0.022282) \| 0.358932 / 0.323480 (0.035452) \| 0.007182 / 0.007986 (-0.000804) \| 0.005430 / 0.004328 (0.001102) \| 0.077962 / 0.004250 (0.073712) \| 0.038516 / 0.037052 (0.001463) \| 0.308840 / 0.258489 (0.050351) \| 0.343678 / 0.293841 (0.049837) \| 0.033701 / 0.128546 (-0.094845) \| 0.011460 / 0.075646 (-0.064186) \| 0.319809 / 0.419271 (-0.099462) \| 0.040731 / 0.043533 (-0.002802) \| 0.299772 / 0.255139 (0.044633) \| 0.324292 / 0.283200 (0.041092) \| 0.087755 / 0.141683 (-0.053928) \| 1.493077 / 1.452155 (0.040922) \| 1.527462 / 1.492716 (0.034746) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.187927 / 0.018006 (0.169921) \| 0.412785 / 0.000490 (0.412296) \| 0.003235 / 0.000200 (0.003035) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023313 / 0.037411 (-0.014098) \| 0.095663 / 0.014526 (0.081137) \| 0.105094 / 0.176557 (-0.071463) \| 0.140389 / 0.737135 (-0.596746) \| 0.108477 / 0.296338 (-0.187861) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.410680 / 0.215209 (0.195471) \| 4.109287 / 2.077655 (2.031632) \| 1.833214 / 1.504120 (0.329094) \| 1.622837 / 1.541195 (0.081642) \| 1.679899 / 1.468490 (0.211409) \| 0.686920 / 4.584777 (-3.897857) \| 3.463267 / 3.745712 (-0.282445) \| 1.867035 / 5.269862 (-3.402826) \| 1.150631 / 4.565676 (-3.415046) \| 0.081209 / 0.424275 (-0.343066) \| 0.012384 / 0.007607 (0.004777) \| 0.521070 / 0.226044 (0.295026) \| 5.208829 / 2.268929 (2.939900) \| 2.289032 / 55.444624 (-53.155592) \| 1.942976 / 6.876477 (-4.933501) \| 1.990660 / 2.142072 (-0.151413) \| 0.802976 / 4.805227 (-4.002252) \| 0.148199 / 6.500664 (-6.352465) \| 0.064644 / 0.075469 (-0.010825) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.277029 / 1.841788 (-0.564759) \| 13.915489 / 8.074308 (5.841181) \| 14.035486 / 10.191392 (3.844094) \| 0.138205 / 0.680424 (-0.542219) \| 0.028968 / 0.534201 (-0.505232) \| 0.394275 / 0.579283 (-0.185008) \| 0.399967 / 0.434364 (-0.034397) \| 0.460595 / 0.540337 (-0.079742) \| 0.537625 / 1.386936 (-0.849311) \|\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) \| 0.006485 / 0.011353 (-0.004868) \| 0.004534 / 0.011008 (-0.006474) \| 0.097742 / 0.038508 (0.059234) \| 0.027231 / 0.023109 (0.004122) \| 0.431321 / 0.275898 (0.155423) \| 0.469212 / 0.323480 (0.145732) \| 0.004894 / 0.007986 (-0.003092) \| 0.004147 / 0.004328 (-0.000181) \| 0.073650 / 0.004250 (0.069400) \| 0.037052 / 0.037052 (-0.000000) \| 0.434196 / 0.258489 (0.175707) \| 0.480539 / 0.293841 (0.186698) \| 0.031923 / 0.128546 (-0.096623) \| 0.011522 / 0.075646 (-0.064124) \| 0.317062 / 0.419271 (-0.102209) \| 0.041124 / 0.043533 (-0.002409) \| 0.432013 / 0.255139 (0.176874) \| 0.456760 / 0.283200 (0.173560) \| 0.089757 / 0.141683 (-0.051925) \| 1.497752 / 1.452155 (0.045597) \| 1.585342 / 1.492716 (0.092626) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227784 / 0.018006 (0.209778) \| 0.404570 / 0.000490 (0.404080) \| 0.000556 / 0.000200 (0.000356) \| 0.000065 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025201 / 0.037411 (-0.012210) \| 0.099348 / 0.014526 (0.084822) \| 0.114984 / 0.176557 (-0.061573) \| 0.147039 / 0.737135 (-0.590097) \| 0.109727 / 0.296338 (-0.186611) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.468415 / 0.215209 (0.253206) \| 4.692228 / 2.077655 (2.614573) \| 2.403382 / 1.504120 (0.899262) \| 2.196026 / 1.541195 (0.654832) \| 2.234736 / 1.468490 (0.766246) \| 0.703011 / 4.584777 (-3.881766) \| 3.451513 / 3.745712 (-0.294199) \| 2.596811 / 5.269862 (-2.673051) \| 1.544079 / 4.565676 (-3.021598) \| 0.083153 / 0.424275 (-0.341123) \| 0.012605 / 0.007607 (0.004998) \| 0.570265 / 0.226044 (0.344220) \| 5.735996 / 2.268929 (3.467067) \| 2.865336 / 55.444624 (-52.579288) \| 2.508340 / 6.876477 (-4.368137) \| 2.547144 / 2.142072 (0.405072) \| 0.813018 / 4.805227 (-3.992210) \| 0.150327 / 6.500664 (-6.350337) \| 0.065837 / 0.075469 (-0.009632) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.268941 / 1.841788 (-0.572847) \| 13.835698 / 8.074308 (5.761390) \| 13.992726 / 10.191392 (3.801334) \| 0.127751 / 0.680424 (-0.552673) \| 0.016673 / 0.534201 (-0.517528) \| 0.381921 / 0.579283 (-0.197362) \| 0.390688 / 0.434364 (-0.043676) \| 0.446234 / 0.540337 (-0.094103) \| 0.532631 / 1.386936 (-0.854305) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1492df3311bfeac55aaedf34c93c014630c4403e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) \| 0.008486 / 0.011353 (-0.002867) \| 0.004573 / 0.011008 (-0.006435) \| 0.100096 / 0.038508 (0.061588) \| 0.029449 / 0.023109 (0.006340) \| 0.298384 / 0.275898 (0.022486) \| 0.361886 / 0.323480 (0.038406) \| 0.006813 / 0.007986 (-0.001173) \| 0.003394 / 0.004328 (-0.000935) \| 0.077563 / 0.004250 (0.073312) \| 0.035605 / 0.037052 (-0.001447) \| 0.306864 / 0.258489 (0.048375) \| 0.346438 / 0.293841 (0.052597) \| 0.033156 / 0.128546 (-0.095390) \| 0.011567 / 0.075646 (-0.064079) \| 0.322189 / 0.419271 (-0.097083) \| 0.040161 / 0.043533 (-0.003372) \| 0.299329 / 0.255139 (0.044190) \| 0.326375 / 0.283200 (0.043175) \| 0.086572 / 0.141683 (-0.055111) \| 1.502473 / 1.452155 (0.050319) \| 1.528539 / 1.492716 (0.035823) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008502 / 0.018006 (-0.009505) \| 0.411045 / 0.000490 (0.410555) \| 0.003179 / 0.000200 (0.002980) \| 0.000073 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023177 / 0.037411 (-0.014234) \| 0.096948 / 0.014526 (0.082422) \| 0.104068 / 0.176557 (-0.072489) \| 0.138739 / 0.737135 (-0.598396) \| 0.108241 / 0.296338 (-0.188097) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.411156 / 0.215209 (0.195947) \| 4.092992 / 2.077655 (2.015337) \| 1.841903 / 1.504120 (0.337783) \| 1.637449 / 1.541195 (0.096254) \| 1.670968 / 1.468490 (0.202478) \| 0.697301 / 4.584777 (-3.887476) \| 3.354717 / 3.745712 (-0.390995) \| 1.851518 / 5.269862 (-3.418344) \| 1.160367 / 4.565676 (-3.405309) \| 0.082613 / 0.424275 (-0.341662) \| 0.012477 / 0.007607 (0.004870) \| 0.524839 / 0.226044 (0.298795) \| 5.264173 / 2.268929 (2.995245) \| 2.294530 / 55.444624 (-53.150094) \| 1.933233 / 6.876477 (-4.943244) \| 1.968959 / 2.142072 (-0.173113) \| 0.817104 / 4.805227 (-3.988123) \| 0.149072 / 6.500664 (-6.351592) \| 0.064911 / 0.075469 (-0.010558) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.222215 / 1.841788 (-0.619573) \| 13.607545 / 8.074308 (5.533237) \| 13.990230 / 10.191392 (3.798838) \| 0.150855 / 0.680424 (-0.529568) \| 0.028844 / 0.534201 (-0.505357) \| 0.396169 / 0.579283 (-0.183114) \| 0.406957 / 0.434364 (-0.027407) \| 0.464069 / 0.540337 (-0.076268) \| 0.554027 / 1.386936 (-0.832909) \|\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) \| 0.006296 / 0.011353 (-0.005057) \| 0.004563 / 0.011008 (-0.006445) \| 0.097719 / 0.038508 (0.059211) \| 0.027106 / 0.023109 (0.003996) \| 0.409333 / 0.275898 (0.133435) \| 0.445397 / 0.323480 (0.121917) \| 0.004906 / 0.007986 (-0.003080) \| 0.003316 / 0.004328 (-0.001012) \| 0.075363 / 0.004250 (0.071112) \| 0.039366 / 0.037052 (0.002314) \| 0.412710 / 0.258489 (0.154221) \| 0.451789 / 0.293841 (0.157948) \| 0.031810 / 0.128546 (-0.096736) \| 0.011681 / 0.075646 (-0.063965) \| 0.318484 / 0.419271 (-0.100788) \| 0.046741 / 0.043533 (0.003208) \| 0.411631 / 0.255139 (0.156492) \| 0.435274 / 0.283200 (0.152074) \| 0.092366 / 0.141683 (-0.049317) \| 1.492243 / 1.452155 (0.040089) \| 1.617603 / 1.492716 (0.124887) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217376 / 0.018006 (0.199369) \| 0.400940 / 0.000490 (0.400450) \| 0.003700 / 0.000200 (0.003500) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023733 / 0.037411 (-0.013678) \| 0.098553 / 0.014526 (0.084027) \| 0.105790 / 0.176557 (-0.070767) \| 0.139537 / 0.737135 (-0.597598) \| 0.109862 / 0.296338 (-0.186477) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.476562 / 0.215209 (0.261353) \| 4.773469 / 2.077655 (2.695814) \| 2.447302 / 1.504120 (0.943182) \| 2.240596 / 1.541195 (0.699401) \| 2.271370 / 1.468490 (0.802880) \| 0.698913 / 4.584777 (-3.885864) \| 3.345648 / 3.745712 (-0.400064) \| 1.845008 / 5.269862 (-3.424854) \| 1.163213 / 4.565676 (-3.402464) \| 0.082456 / 0.424275 (-0.341819) \| 0.012315 / 0.007607 (0.004708) \| 0.575881 / 0.226044 (0.349836) \| 5.769575 / 2.268929 (3.500647) \| 2.909759 / 55.444624 (-52.534865) \| 2.580259 / 6.876477 (-4.296218) \| 2.590473 / 2.142072 (0.448401) \| 0.802765 / 4.805227 (-4.002462) \| 0.151514 / 6.500664 (-6.349150) \| 0.067718 / 0.075469 (-0.007751) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.293014 / 1.841788 (-0.548773) \| 13.934072 / 8.074308 (5.859763) \| 13.538760 / 10.191392 (3.347368) \| 0.126490 / 0.680424 (-0.553934) \| 0.016653 / 0.534201 (-0.517548) \| 0.381220 / 0.579283 (-0.198064) \| 0.387571 / 0.434364 (-0.046793) \| 0.444674 / 0.540337 (-0.095663) \| 0.550802 / 1.386936 (-0.836134) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bed576f2205c96f6cb26b5c6522345cb8b06ecfc \"CML watermark\")\n" ]	2023-01-24T17:33:38Z	2023-01-25T16:11:10Z	2023-01-25T16:04:03Z	CONTRIBUTOR	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/24695242?v=4", "events_url": "https://api.github.com/users/thomasw21/events{/privacy}", "followers_url": "https://api.github.com/users/thomasw21/followers", "following_url": "https://api.github.com/users/thomasw21/following{/other_user}", "gists_url": "https://api.github.com/users/thomasw21/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/thomasw21", "id": 24695242, "login": "thomasw21", "node_id": "MDQ6VXNlcjI0Njk1MjQy", "organizations_url": "https://api.github.com/users/thomasw21/orgs", "received_events_url": "https://api.github.com/users/thomasw21/received_events", "repos_url": "https://api.github.com/users/thomasw21/repos", "site_admin": false, "starred_url": "https://api.github.com/users/thomasw21/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/thomasw21/subscriptions", "type": "User", "url": "https://api.github.com/users/thomasw21", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5460/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5460/timeline	null	null	0	{ "diff_url": 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https://api.github.com/repos/huggingface/datasets/issues/5959	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5959/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5959/comments	https://api.github.com/repos/huggingface/datasets/issues/5959/events	https://github.com/huggingface/datasets/issues/5959	1,757,397,507	I_kwDODunzps5ov8ID	5,959	read metric glue.py from local file	{ "avatar_url": "https://avatars.githubusercontent.com/u/31148397?v=4", "events_url": "https://api.github.com/users/JiazhaoLi/events{/privacy}", "followers_url": "https://api.github.com/users/JiazhaoLi/followers", "following_url": "https://api.github.com/users/JiazhaoLi/following{/other_user}", "gists_url": "https://api.github.com/users/JiazhaoLi/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/JiazhaoLi", "id": 31148397, "login": "JiazhaoLi", "node_id": "MDQ6VXNlcjMxMTQ4Mzk3", "organizations_url": "https://api.github.com/users/JiazhaoLi/orgs", "received_events_url": "https://api.github.com/users/JiazhaoLi/received_events", "repos_url": "https://api.github.com/users/JiazhaoLi/repos", "site_admin": false, "starred_url": "https://api.github.com/users/JiazhaoLi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/JiazhaoLi/subscriptions", "type": "User", "url": "https://api.github.com/users/JiazhaoLi", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Sorry, I solve this by call `evaluate.load('glue_metric.py','sst-2')`\r\n" ]	2023-06-14T17:59:35Z	2023-06-14T18:04:16Z	2023-06-14T18:04:16Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Currently, The server is off-line. I am using the glue metric from the local file downloaded from the hub. I download / cached datasets using `load_dataset('glue','sst2', cache_dir='/xxx')` to cache them and then in the off-line mode, I use `load_dataset('xxx/glue.py','sst2', cache_dir='/xxx')`. I can successfully reuse cached datasets. My problem is about the load_metric. When I run `load_dataset('xxx/glue_metric.py','sst2',cache_dir='/xxx')` , it returns ` File "xx/lib64/python3.9/site-packages/datasets/utils/deprecation_utils.py", line 46, in wrapper return deprecated_function(args, *kwargs) File "xx//lib64/python3.9/site-packages/datasets/load.py", line 1392, in load_metric metric = metric_cls( TypeError: 'NoneType' object is not callable` Thanks in advance for help! ### Steps to reproduce the bug N/A ### Expected behavior N/A ### Environment info `datasets == 2.12.0`	{ "avatar_url": "https://avatars.githubusercontent.com/u/31148397?v=4", "events_url": "https://api.github.com/users/JiazhaoLi/events{/privacy}", "followers_url": "https://api.github.com/users/JiazhaoLi/followers", "following_url": "https://api.github.com/users/JiazhaoLi/following{/other_user}", "gists_url": "https://api.github.com/users/JiazhaoLi/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/JiazhaoLi", "id": 31148397, "login": "JiazhaoLi", "node_id": "MDQ6VXNlcjMxMTQ4Mzk3", "organizations_url": "https://api.github.com/users/JiazhaoLi/orgs", "received_events_url": "https://api.github.com/users/JiazhaoLi/received_events", "repos_url": "https://api.github.com/users/JiazhaoLi/repos", "site_admin": false, "starred_url": "https://api.github.com/users/JiazhaoLi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/JiazhaoLi/subscriptions", "type": "User", "url": "https://api.github.com/users/JiazhaoLi", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5959/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5959/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6834	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6834/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6834/comments	https://api.github.com/repos/huggingface/datasets/issues/6834/events	https://github.com/huggingface/datasets/issues/6834	2,261,078,104	I_kwDODunzps6GxVBY	6,834	largelisttype not supported (.from_polars())	{ "avatar_url": "https://avatars.githubusercontent.com/u/37351874?v=4", "events_url": "https://api.github.com/users/Modexus/events{/privacy}", "followers_url": "https://api.github.com/users/Modexus/followers", "following_url": "https://api.github.com/users/Modexus/following{/other_user}", "gists_url": "https://api.github.com/users/Modexus/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Modexus", "id": 37351874, "login": "Modexus", "node_id": "MDQ6VXNlcjM3MzUxODc0", "organizations_url": "https://api.github.com/users/Modexus/orgs", "received_events_url": "https://api.github.com/users/Modexus/received_events", "repos_url": "https://api.github.com/users/Modexus/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Modexus/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Modexus/subscriptions", "type": "User", "url": "https://api.github.com/users/Modexus", "user_view_type": "public" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[]	2024-04-24T11:33:43Z	2024-08-12T14:43:46Z	2024-08-12T14:43:46Z	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug The following code fails because LargeListType is not supported. This is especially a problem for .from_polars since polars uses LargeListType. ### Steps to reproduce the bug ```python import datasets import polars as pl df = pl.DataFrame({"list": [[]]}) datasets.Dataset.from_polars(df) ``` ### Expected behavior Convert LargeListType to list. ### Environment info - `datasets` version: 2.19.1.dev0 - Platform: Linux-6.8.7-200.fc39.x86_64-x86_64-with-glibc2.38 - Python version: 3.12.2 - `huggingface_hub` version: 0.22.2 - PyArrow version: 16.0.0 - Pandas version: 2.1.4 - `fsspec` version: 2024.3.1	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6834/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6834/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6496	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6496/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6496/comments	https://api.github.com/repos/huggingface/datasets/issues/6496/events	https://github.com/huggingface/datasets/issues/6496	2,041,589,386	I_kwDODunzps55sC6K	6,496	Error when writing a dataset to HF Hub: A commit has happened since. Please refresh and try again.	{ "avatar_url": "https://avatars.githubusercontent.com/u/35808396?v=4", "events_url": "https://api.github.com/users/GeorgesLorre/events{/privacy}", "followers_url": "https://api.github.com/users/GeorgesLorre/followers", "following_url": "https://api.github.com/users/GeorgesLorre/following{/other_user}", "gists_url": "https://api.github.com/users/GeorgesLorre/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/GeorgesLorre", "id": 35808396, "login": "GeorgesLorre", "node_id": "MDQ6VXNlcjM1ODA4Mzk2", "organizations_url": "https://api.github.com/users/GeorgesLorre/orgs", "received_events_url": "https://api.github.com/users/GeorgesLorre/received_events", "repos_url": "https://api.github.com/users/GeorgesLorre/repos", "site_admin": false, "starred_url": "https://api.github.com/users/GeorgesLorre/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/GeorgesLorre/subscriptions", "type": "User", "url": "https://api.github.com/users/GeorgesLorre", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "I transferred from datasets-server, since the issue is more about `datasets` and the integration with `huggingface_hub`." ]	2023-12-14T11:24:54Z	2023-12-14T12:22:21Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	Describe the bug Getting a `412 Client Error: Precondition Failed` when trying to write a dataset to the HF hub. ``` huggingface_hub.utils._errors.HfHubHTTPError: 412 Client Error: Precondition Failed for url: https://huggingface.co/api/datasets/GLorr/test-dask/commit/main (Request ID: Root=1-657ae26f-3bd92bf861bb254b2cc0826c;50a09ab7-9347-406a-ba49-69f98abee9cc) A commit has happened since. Please refresh and try again. ``` Steps to reproduce the bug This is a minimal reproducer: ``` import dask.dataframe as dd import pandas as pd import random import os import huggingface_hub import datasets huggingface_hub.login(token=os.getenv("HF_TOKEN")) data = {"number": [random.randint(0,10) for _ in range(1000)]} df = pd.DataFrame.from_dict(data) dataframe = dd.from_pandas(df, npartitions=1) dataframe = dataframe.repartition(npartitions=3) schema = datasets.Features({"number": datasets.Value("int64")}).arrow_schema repo_id = "GLorr/test-dask" repo_path = f"hf://datasets/{repo_id}" huggingface_hub.create_repo(repo_id=repo_id, repo_type="dataset", exist_ok=True) dd.to_parquet(dataframe, path=f"{repo_path}/data", schema=schema) ``` Expected behavior Would expect to write to the hub without any problem. Environment info ``` datasets==2.15.0 huggingface-hub==0.19.4 ```	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6496/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6496/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5570	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5570/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5570/comments	https://api.github.com/repos/huggingface/datasets/issues/5570/events	https://github.com/huggingface/datasets/issues/5570	1,597,190,926	I_kwDODunzps5fMzMO	5,570	load_dataset gives FileNotFoundError on imagenet-1k if license is not accepted on the hub	{ "avatar_url": "https://avatars.githubusercontent.com/u/38630200?v=4", "events_url": "https://api.github.com/users/buoi/events{/privacy}", "followers_url": "https://api.github.com/users/buoi/followers", "following_url": "https://api.github.com/users/buoi/following{/other_user}", "gists_url": "https://api.github.com/users/buoi/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/buoi", "id": 38630200, "login": "buoi", "node_id": "MDQ6VXNlcjM4NjMwMjAw", "organizations_url": "https://api.github.com/users/buoi/orgs", "received_events_url": "https://api.github.com/users/buoi/received_events", "repos_url": "https://api.github.com/users/buoi/repos", "site_admin": false, "starred_url": "https://api.github.com/users/buoi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/buoi/subscriptions", "type": "User", "url": "https://api.github.com/users/buoi", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi, thanks for the feedback! Would it help to add a tip or note saying the dataset is gated and you need to accept the license before downloading it?", "The error is now more informative:\r\n```\r\nFileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the same directory. Couldn't find 'imagenet-1k' on the Hugging Face Hub either: FileNotFoundError: Dataset 'imagenet-1k' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`.\r\n```\r\n\r\n" ]	2023-02-23T16:44:32Z	2023-07-24T15:18:50Z	2023-07-24T15:18:50Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug When calling ```load_dataset('imagenet-1k')``` FileNotFoundError is raised, if not logged in and if logged in with huggingface-cli but not having accepted the licence on the hub. There is no error once accepting. ### Steps to reproduce the bug ``` from datasets import load_dataset imagenet = load_dataset("imagenet-1k", split="train", streaming=True) FileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the same directory. Couldn't find 'imagenet-1k' on the Hugging Face Hub either: FileNotFoundError: Dataset 'imagenet-1k' doesn't exist on the Hub ``` tested on a colab notebook. ### Expected behavior I would expect a specific error indicating that I have to login then accept the dataset licence. I find this bug very relevant as this code is on a guide on the [Huggingface documentation for Datasets](https://huggingface.co/docs/datasets/about_mapstyle_vs_iterable) ### Environment info google colab cpu-only instance	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5570/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5570/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7181	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7181/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7181/comments	https://api.github.com/repos/huggingface/datasets/issues/7181/events	https://github.com/huggingface/datasets/pull/7181	2,554,917,019	PR_kwDODunzps59Br4E	7,181	Fix datasets export to JSON	{ "avatar_url": "https://avatars.githubusercontent.com/u/20443618?v=4", "events_url": "https://api.github.com/users/varadhbhatnagar/events{/privacy}", "followers_url": "https://api.github.com/users/varadhbhatnagar/followers", "following_url": "https://api.github.com/users/varadhbhatnagar/following{/other_user}", "gists_url": "https://api.github.com/users/varadhbhatnagar/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/varadhbhatnagar", "id": 20443618, "login": "varadhbhatnagar", "node_id": "MDQ6VXNlcjIwNDQzNjE4", "organizations_url": "https://api.github.com/users/varadhbhatnagar/orgs", "received_events_url": "https://api.github.com/users/varadhbhatnagar/received_events", "repos_url": "https://api.github.com/users/varadhbhatnagar/repos", "site_admin": false, "starred_url": "https://api.github.com/users/varadhbhatnagar/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/varadhbhatnagar/subscriptions", "type": "User", "url": "https://api.github.com/users/varadhbhatnagar", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Linked Issue: #7037\r\nIdeas: #7039 ", "@albertvillanova / @lhoestq any early feedback?\r\n\r\nAFAIK there is no param `orient` in `load_dataset()`. So for orientations other than \"records\", the loading isn't very accurate. Any thoughts?", "`orient = \"split\"` can also be handled. I will add the changes soon", "Thanks for diving into this ! I don't think we want the JSON export to be that complex though, especially if people can do `ds.to_pandas().to_json(orient=...)`. Maybe we can just raise an error and suggest users to use pandas ? And also note that it loads the full dataset in memory so it's mainly for small scale datasets. The only acceptable option for large scale datasets is probably just JSON Lines anyway since it enables streaming.", "@lhoestq Simply doing `ds.to_pandas().to_json(orient=...)` is not going to give any batching or multiprocessing benefits right? Also, which function are you referring to - when you say that its meant for small scale datasets only?", "Yes indeed. Though I think it's fine since using something else than orient=\"lines\" is only suitable/useful for small datasets. Or you know a case where a big dataset need to be in a format that is not orient=\"lines\" ?", "@lhoestq Let me close this PR and open another one where I will add an error message, as suggested here.\r\n\r\n> Thanks for diving into this ! I don't think we want the JSON export to be that complex though, especially if people can do `ds.to_pandas().to_json(orient=...)`. Maybe we can just raise an error and suggest users to use pandas ? And also note that it loads the full dataset in memory so it's mainly for small scale datasets. The only acceptable option for large scale datasets is probably just JSON Lines anyway since it enables streaming.\r\n\r\n", "Addressed here: #7273 \r\n@lhoestq " ]	2024-09-29T12:45:20Z	2024-11-01T11:55:36Z	2024-11-01T11:55:36Z	CONTRIBUTOR	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/20443618?v=4", "events_url": "https://api.github.com/users/varadhbhatnagar/events{/privacy}", "followers_url": "https://api.github.com/users/varadhbhatnagar/followers", "following_url": "https://api.github.com/users/varadhbhatnagar/following{/other_user}", "gists_url": "https://api.github.com/users/varadhbhatnagar/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/varadhbhatnagar", "id": 20443618, "login": "varadhbhatnagar", "node_id": "MDQ6VXNlcjIwNDQzNjE4", "organizations_url": "https://api.github.com/users/varadhbhatnagar/orgs", "received_events_url": "https://api.github.com/users/varadhbhatnagar/received_events", "repos_url": "https://api.github.com/users/varadhbhatnagar/repos", "site_admin": false, "starred_url": "https://api.github.com/users/varadhbhatnagar/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/varadhbhatnagar/subscriptions", "type": "User", "url": "https://api.github.com/users/varadhbhatnagar", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7181/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7181/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7181.diff", "html_url": "https://github.com/huggingface/datasets/pull/7181", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/7181.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7181" }
https://api.github.com/repos/huggingface/datasets/issues/6109	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6109/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6109/comments	https://api.github.com/repos/huggingface/datasets/issues/6109/events	https://github.com/huggingface/datasets/issues/6109	1,830,753,793	I_kwDODunzps5tHxYB	6,109	Problems in downloading Amazon reviews from HF	{ "avatar_url": "https://avatars.githubusercontent.com/u/52964960?v=4", "events_url": "https://api.github.com/users/610v4nn1/events{/privacy}", "followers_url": "https://api.github.com/users/610v4nn1/followers", "following_url": "https://api.github.com/users/610v4nn1/following{/other_user}", "gists_url": "https://api.github.com/users/610v4nn1/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/610v4nn1", "id": 52964960, "login": "610v4nn1", "node_id": "MDQ6VXNlcjUyOTY0OTYw", "organizations_url": "https://api.github.com/users/610v4nn1/orgs", "received_events_url": "https://api.github.com/users/610v4nn1/received_events", "repos_url": "https://api.github.com/users/610v4nn1/repos", "site_admin": false, "starred_url": "https://api.github.com/users/610v4nn1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/610v4nn1/subscriptions", "type": "User", "url": "https://api.github.com/users/610v4nn1", "user_view_type": "public" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[ "Thanks for reporting, @610v4nn1.\r\n\r\nIndeed, the source data files are no longer available. We have contacted the authors of the dataset and they report that Amazon has decided to stop distributing the multilingual reviews dataset.\r\n\r\nWe are adding a notification about this issue to the dataset card.\r\n\r\nSee: https://huggingface.co/datasets/amazon_reviews_multi/discussions/4#64c3898db63057f1fd3ce1a0 ", "The dataset can be accessed from https://www.kaggle.com/datasets/mexwell/amazon-reviews-multi.", "For those willing to transform the csv files from Kaggle into Huggingface datasets for their NLP course (exercise on summarisation), you can use this code on Google Collab:\r\n\r\n`from datasets import load_dataset\r\n\r\nimport pandas as pd\r\nfrom datasets import Dataset, DatasetDict\r\n\r\n# Load your CSV previously downloaded files from Kaggle on Google Collab\r\ntrain_csv_path = \"/content/train.csv\"\r\nvalidation_csv_path = \"/content/validation.csv\"\r\ntest_csv_path = '/content/test.csv'\r\n\r\n# Read CSV files into pandas DataFrames\r\ntrain_df = pd.read_csv(train_csv_path, engine='python')\r\nvalidation_df = pd.read_csv(validation_csv_path, engine='python')\r\ntest_df = pd.read_csv(test_csv_path, engine='python')\r\n\r\n# Filter by language ('es' for Spanish and 'en' for English)\r\nspanish_train_df = train_df[train_df['language'] == 'es']\r\nspanish_validation_df = validation_df[validation_df['language'] == 'es']\r\nspanish_test_df = test_df[test_df['language'] == 'es']\r\n\r\nenglish_train_df = train_df[train_df['language'] == 'en']\r\nenglish_validation_df = validation_df[validation_df['language'] == 'en']\r\nenglish_test_df = test_df[test_df['language'] == 'en']\r\n\r\n# Create Hugging Face datasets\r\nspanish_dataset = DatasetDict({\r\n 'train': Dataset.from_pandas(spanish_train_df),\r\n 'validation': Dataset.from_pandas(spanish_validation_df),\r\n 'test': Dataset.from_pandas(spanish_test_df)\r\n})\r\n\r\nenglish_dataset = DatasetDict({\r\n 'train': Dataset.from_pandas(english_train_df),\r\n 'validation': Dataset.from_pandas(english_validation_df),\r\n 'test': Dataset.from_pandas(english_test_df)\r\n})\r\nenglish_dataset = english_dataset.remove_columns(['Unnamed: 0', '__index_level_0__'])\r\nspanish_dataset = spanish_dataset.remove_columns(['Unnamed: 0', '__index_level_0__'])`" ]	2023-08-01T08:38:29Z	2024-06-25T13:48:38Z	2023-08-02T07:12:07Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I have a script downloading `amazon_reviews_multi`. When the download starts, I get ``` Downloading data files: 0%\| \| 0/1 [00:00<?, ?it/s] Downloading data: 243B [00:00, 1.43MB/s] Downloading data files: 100%\|██████████\| 1/1 [00:01<00:00, 1.54s/it] Extracting data files: 100%\|██████████\| 1/1 [00:00<00:00, 842.40it/s] Downloading data files: 0%\| \| 0/1 [00:00<?, ?it/s] Downloading data: 243B [00:00, 928kB/s] Downloading data files: 100%\|██████████\| 1/1 [00:01<00:00, 1.42s/it] Extracting data files: 100%\|██████████\| 1/1 [00:00<00:00, 832.70it/s] Downloading data files: 0%\| \| 0/1 [00:00<?, ?it/s] Downloading data: 243B [00:00, 1.81MB/s] Downloading data files: 100%\|██████████\| 1/1 [00:01<00:00, 1.40s/it] Extracting data files: 100%\|██████████\| 1/1 [00:00<00:00, 1294.14it/s] Generating train split: 0%\| \| 0/200000 [00:00<?, ? examples/s] ``` the file is clearly too small to contain the requested dataset, in fact it contains en error message: ``` <?xml version="1.0" encoding="UTF-8"?> <Error><Code>AccessDenied</Code><Message>Access Denied</Message><RequestId>AGJWSY3ZADT2QVWE</RequestId><HostId>Gx1O2KXnxtQFqvzDLxyVSTq3+TTJuTnuVFnJL3SP89Yp8UzvYLPTVwd1PpniE4EvQzT3tCaqEJw=</HostId></Error> ``` obviously the script fails: ``` > raise DatasetGenerationError("An error occurred while generating the dataset") from e E datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug 1. load_dataset("amazon_reviews_multi", name="en", split="train", cache_dir="ADDYOURPATHHERE") ### Expected behavior I would expect the dataset to be downloaded and processed ### Environment info * The problem is present with both datasets 2.12.0 and 2.14.2 * python version 3.10.12	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6109/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6109/timeline	null	not_planned	null	null
https://api.github.com/repos/huggingface/datasets/issues/6015	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6015/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6015/comments	https://api.github.com/repos/huggingface/datasets/issues/6015/events	https://github.com/huggingface/datasets/pull/6015	1,798,807,893	PR_kwDODunzps5VMhgB	6,015	Add metadata ui screenshot in docs	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007633 / 0.011353 (-0.003720) \| 0.004666 / 0.011008 (-0.006343) \| 0.097768 / 0.038508 (0.059260) \| 0.085153 / 0.023109 (0.062044) \| 0.400315 / 0.275898 (0.124417) \| 0.452903 / 0.323480 (0.129423) \| 0.006227 / 0.007986 (-0.001759) \| 0.003814 / 0.004328 (-0.000515) \| 0.074586 / 0.004250 (0.070336) \| 0.064295 / 0.037052 (0.027242) \| 0.408082 / 0.258489 (0.149593) \| 0.446921 / 0.293841 (0.153080) \| 0.034593 / 0.128546 (-0.093953) \| 0.009191 / 0.075646 (-0.066456) \| 0.337099 / 0.419271 (-0.082173) \| 0.075320 / 0.043533 (0.031787) \| 0.403488 / 0.255139 (0.148349) \| 0.435309 / 0.283200 (0.152109) \| 0.035675 / 0.141683 (-0.106008) \| 1.732642 / 1.452155 (0.280487) \| 1.770238 / 1.492716 (0.277522) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235879 / 0.018006 (0.217873) \| 0.500330 / 0.000490 (0.499841) \| 0.005221 / 0.000200 (0.005021) \| 0.000150 / 0.000054 (0.000096) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032479 / 0.037411 (-0.004933) \| 0.095873 / 0.014526 (0.081348) \| 0.107118 / 0.176557 (-0.069438) \| 0.173809 / 0.737135 (-0.563326) \| 0.109832 / 0.296338 (-0.186507) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.444342 / 0.215209 (0.229133) \| 4.459010 / 2.077655 (2.381355) \| 2.209687 / 1.504120 (0.705567) \| 2.007556 / 1.541195 (0.466362) \| 2.113683 / 1.468490 (0.645193) \| 0.544281 / 4.584777 (-4.040496) \| 4.037151 / 3.745712 (0.291439) \| 4.852644 / 5.269862 (-0.417217) \| 3.134126 / 4.565676 (-1.431550) \| 0.066815 / 0.424275 (-0.357460) \| 0.008836 / 0.007607 (0.001229) \| 0.560904 / 0.226044 (0.334859) \| 5.302760 / 2.268929 (3.033832) \| 2.750182 / 55.444624 (-52.694442) \| 2.322595 / 6.876477 (-4.553882) \| 2.547486 / 2.142072 (0.405414) \| 0.665766 / 4.805227 (-4.139461) \| 0.151613 / 6.500664 (-6.349051) \| 0.071155 / 0.075469 (-0.004314) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.473717 / 1.841788 (-0.368071) \| 22.584179 / 8.074308 (14.509871) \| 15.888001 / 10.191392 (5.696609) \| 0.181073 / 0.680424 (-0.499351) \| 0.021395 / 0.534201 (-0.512806) \| 0.452693 / 0.579283 (-0.126590) \| 0.447709 / 0.434364 (0.013345) \| 0.529599 / 0.540337 (-0.010738) \| 0.699241 / 1.386936 (-0.687695) \|\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) \| 0.007917 / 0.011353 (-0.003436) \| 0.004544 / 0.011008 (-0.006464) \| 0.074566 / 0.038508 (0.036058) \| 0.087530 / 0.023109 (0.064421) \| 0.419753 / 0.275898 (0.143854) \| 0.452352 / 0.323480 (0.128872) \| 0.005882 / 0.007986 (-0.002104) \| 0.003904 / 0.004328 (-0.000425) \| 0.073539 / 0.004250 (0.069289) \| 0.071320 / 0.037052 (0.034267) \| 0.432899 / 0.258489 (0.174409) \| 0.470365 / 0.293841 (0.176524) \| 0.036198 / 0.128546 (-0.092348) \| 0.009342 / 0.075646 (-0.066304) \| 0.080970 / 0.419271 (-0.338301) \| 0.058769 / 0.043533 (0.015236) \| 0.413397 / 0.255139 (0.158258) \| 0.448362 / 0.283200 (0.165162) \| 0.034177 / 0.141683 (-0.107506) \| 1.706217 / 1.452155 (0.254063) \| 1.776743 / 1.492716 (0.284026) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.198779 / 0.018006 (0.180773) \| 0.499862 / 0.000490 (0.499372) \| 0.003891 / 0.000200 (0.003692) \| 0.000108 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034671 / 0.037411 (-0.002740) \| 0.103165 / 0.014526 (0.088639) \| 0.115813 / 0.176557 (-0.060744) \| 0.177407 / 0.737135 (-0.559728) \| 0.117733 / 0.296338 (-0.178606) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.476859 / 0.215209 (0.261650) \| 4.823063 / 2.077655 (2.745409) \| 2.524133 / 1.504120 (1.020013) \| 2.374482 / 1.541195 (0.833288) \| 2.518047 / 1.468490 (1.049557) \| 0.559131 / 4.584777 (-4.025646) \| 4.126213 / 3.745712 (0.380501) \| 6.488570 / 5.269862 (1.218708) \| 3.816540 / 4.565676 (-0.749137) \| 0.064742 / 0.424275 (-0.359533) \| 0.008476 / 0.007607 (0.000869) \| 0.576432 / 0.226044 (0.350387) \| 5.835133 / 2.268929 (3.566205) \| 3.237833 / 55.444624 (-52.206791) \| 2.726596 / 6.876477 (-4.149880) \| 2.799212 / 2.142072 (0.657139) \| 0.661628 / 4.805227 (-4.143599) \| 0.153997 / 6.500664 (-6.346667) \| 0.070621 / 0.075469 (-0.004848) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.648505 / 1.841788 (-0.193282) \| 22.454019 / 8.074308 (14.379711) \| 16.077098 / 10.191392 (5.885706) \| 0.217875 / 0.680424 (-0.462549) \| 0.021285 / 0.534201 (-0.512916) \| 0.459837 / 0.579283 (-0.119446) \| 0.476211 / 0.434364 (0.041847) \| 0.525903 / 0.540337 (-0.014435) \| 0.717224 / 1.386936 (-0.669712) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b767e9c3ef30f9da30d47cfcaccf9a7ac2500c43 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008929 / 0.011353 (-0.002424) \| 0.004188 / 0.011008 (-0.006820) \| 0.097030 / 0.038508 (0.058522) \| 0.071363 / 0.023109 (0.048254) \| 0.333116 / 0.275898 (0.057218) \| 0.371272 / 0.323480 (0.047792) \| 0.006430 / 0.007986 (-0.001555) \| 0.003689 / 0.004328 (-0.000639) \| 0.068666 / 0.004250 (0.064416) \| 0.057562 / 0.037052 (0.020510) \| 0.347208 / 0.258489 (0.088719) \| 0.390514 / 0.293841 (0.096673) \| 0.050560 / 0.128546 (-0.077987) \| 0.013372 / 0.075646 (-0.062275) \| 0.311345 / 0.419271 (-0.107927) \| 0.068990 / 0.043533 (0.025457) \| 0.363026 / 0.255139 (0.107887) \| 0.379793 / 0.283200 (0.096593) \| 0.036891 / 0.141683 (-0.104792) \| 1.583481 / 1.452155 (0.131327) \| 1.688727 / 1.492716 (0.196011) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.209777 / 0.018006 (0.191771) \| 0.507267 / 0.000490 (0.506777) \| 0.003637 / 0.000200 (0.003438) \| 0.000105 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029309 / 0.037411 (-0.008102) \| 0.088386 / 0.014526 (0.073861) \| 0.104974 / 0.176557 (-0.071582) \| 0.171999 / 0.737135 (-0.565137) \| 0.110797 / 0.296338 (-0.185542) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.543465 / 0.215209 (0.328256) \| 5.361491 / 2.077655 (3.283836) \| 2.348712 / 1.504120 (0.844592) \| 2.012527 / 1.541195 (0.471332) \| 2.069776 / 1.468490 (0.601286) \| 0.874262 / 4.584777 (-3.710515) \| 4.877317 / 3.745712 (1.131605) \| 5.327459 / 5.269862 (0.057597) \| 3.336823 / 4.565676 (-1.228854) \| 0.100456 / 0.424275 (-0.323819) \| 0.008503 / 0.007607 (0.000895) \| 0.692009 / 0.226044 (0.465965) \| 6.912731 / 2.268929 (4.643802) \| 3.110548 / 55.444624 (-52.334076) \| 2.443665 / 6.876477 (-4.432811) \| 2.528713 / 2.142072 (0.386641) \| 1.076358 / 4.805227 (-3.728869) \| 0.220352 / 6.500664 (-6.280312) \| 0.080293 / 0.075469 (0.004824) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.538444 / 1.841788 (-0.303344) \| 21.121221 / 8.074308 (13.046913) \| 19.810609 / 10.191392 (9.619216) \| 0.225406 / 0.680424 (-0.455018) \| 0.026652 / 0.534201 (-0.507549) \| 0.430372 / 0.579283 (-0.148911) \| 0.510722 / 0.434364 (0.076358) \| 0.514347 / 0.540337 (-0.025991) \| 0.686050 / 1.386936 (-0.700886) \|\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) \| 0.007675 / 0.011353 (-0.003678) \| 0.004542 / 0.011008 (-0.006466) \| 0.069655 / 0.038508 (0.031147) \| 0.069338 / 0.023109 (0.046229) \| 0.436505 / 0.275898 (0.160607) \| 0.481806 / 0.323480 (0.158326) \| 0.005315 / 0.007986 (-0.002670) \| 0.004455 / 0.004328 (0.000127) \| 0.072674 / 0.004250 (0.068424) \| 0.058088 / 0.037052 (0.021035) \| 0.445825 / 0.258489 (0.187336) \| 0.501706 / 0.293841 (0.207865) \| 0.047123 / 0.128546 (-0.081424) \| 0.012943 / 0.075646 (-0.062703) \| 0.093491 / 0.419271 (-0.325780) \| 0.060169 / 0.043533 (0.016637) \| 0.436530 / 0.255139 (0.181391) \| 0.466873 / 0.283200 (0.183674) \| 0.040453 / 0.141683 (-0.101230) \| 1.586438 / 1.452155 (0.134283) \| 1.671081 / 1.492716 (0.178365) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.180607 / 0.018006 (0.162601) \| 0.520145 / 0.000490 (0.519655) \| 0.004824 / 0.000200 (0.004624) \| 0.000116 / 0.000054 (0.000061) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029308 / 0.037411 (-0.008103) \| 0.093652 / 0.014526 (0.079126) \| 0.102332 / 0.176557 (-0.074224) \| 0.162414 / 0.737135 (-0.574721) \| 0.098017 / 0.296338 (-0.198321) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.583949 / 0.215209 (0.368740) \| 6.035191 / 2.077655 (3.957536) \| 2.801274 / 1.504120 (1.297155) \| 2.566150 / 1.541195 (1.024955) \| 2.437122 / 1.468490 (0.968632) \| 0.865038 / 4.584777 (-3.719739) \| 4.841727 / 3.745712 (1.096015) \| 4.683919 / 5.269862 (-0.585943) \| 2.941240 / 4.565676 (-1.624437) \| 0.104888 / 0.424275 (-0.319387) \| 0.007747 / 0.007607 (0.000140) \| 0.780041 / 0.226044 (0.553997) \| 7.771314 / 2.268929 (5.502385) \| 3.680814 / 55.444624 (-51.763811) \| 2.938472 / 6.876477 (-3.938004) \| 2.981740 / 2.142072 (0.839668) \| 1.065411 / 4.805227 (-3.739816) \| 0.222265 / 6.500664 (-6.278399) \| 0.082428 / 0.075469 (0.006959) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.626774 / 1.841788 (-0.215014) \| 21.618284 / 8.074308 (13.543976) \| 20.596743 / 10.191392 (10.405351) \| 0.240969 / 0.680424 (-0.439454) \| 0.025630 / 0.534201 (-0.508570) \| 0.481981 / 0.579283 (-0.097302) \| 0.547914 / 0.434364 (0.113550) \| 0.522296 / 0.540337 (-0.018041) \| 0.729174 / 1.386936 (-0.657762) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b8067c0262073891180869f700ebef5ac3dc5cce \"CML watermark\")\n" ]	2023-07-11T12:16:29Z	2023-07-11T16:07:28Z	2023-07-11T15:56:46Z	MEMBER	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6015/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6015/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6015.diff", "html_url": "https://github.com/huggingface/datasets/pull/6015", 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https://api.github.com/repos/huggingface/datasets/issues/6214	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6214/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6214/comments	https://api.github.com/repos/huggingface/datasets/issues/6214/events	https://github.com/huggingface/datasets/issues/6214	1,881,736,469	I_kwDODunzps5wKQUV	6,214	Unpin fsspec < 2023.9.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" } ]	null	[]	2023-09-05T11:02:58Z	2023-09-26T15:32:52Z	2023-09-26T15:32:52Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	Once root issue is fixed, remove temporary pin of fsspec < 2023.9.0 introduced by: - #6210 Related to issue: - #6209 After investigation, I think the root issue is related to the new glob behavior with double asterisk `**` they have introduced in: - https://github.com/fsspec/filesystem_spec/pull/1329	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6214/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6214/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6962	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6962/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6962/comments	https://api.github.com/repos/huggingface/datasets/issues/6962/events	https://github.com/huggingface/datasets/pull/6962	2,343,394,378	PR_kwDODunzps5x8yHt	6,962	fix(ci): remove unnecessary permissions	{ "avatar_url": "https://avatars.githubusercontent.com/u/9112841?v=4", "events_url": "https://api.github.com/users/McPatate/events{/privacy}", "followers_url": "https://api.github.com/users/McPatate/followers", "following_url": "https://api.github.com/users/McPatate/following{/other_user}", "gists_url": "https://api.github.com/users/McPatate/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/McPatate", "id": 9112841, "login": "McPatate", "node_id": "MDQ6VXNlcjkxMTI4NDE=", "organizations_url": "https://api.github.com/users/McPatate/orgs", "received_events_url": "https://api.github.com/users/McPatate/received_events", "repos_url": "https://api.github.com/users/McPatate/repos", "site_admin": false, "starred_url": "https://api.github.com/users/McPatate/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/McPatate/subscriptions", "type": "User", "url": "https://api.github.com/users/McPatate", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6962). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005520 / 0.011353 (-0.005833) \| 0.003989 / 0.011008 (-0.007019) \| 0.064786 / 0.038508 (0.026278) \| 0.031075 / 0.023109 (0.007966) \| 0.241619 / 0.275898 (-0.034279) \| 0.275341 / 0.323480 (-0.048139) \| 0.003139 / 0.007986 (-0.004847) \| 0.002820 / 0.004328 (-0.001508) \| 0.049766 / 0.004250 (0.045515) \| 0.045047 / 0.037052 (0.007995) \| 0.251906 / 0.258489 (-0.006583) \| 0.285889 / 0.293841 (-0.007952) \| 0.028297 / 0.128546 (-0.100249) \| 0.010683 / 0.075646 (-0.064963) \| 0.206467 / 0.419271 (-0.212805) \| 0.036267 / 0.043533 (-0.007266) \| 0.250720 / 0.255139 (-0.004419) \| 0.268565 / 0.283200 (-0.014635) \| 0.020394 / 0.141683 (-0.121289) \| 1.114283 / 1.452155 (-0.337872) \| 1.163884 / 1.492716 (-0.328833) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.112698 / 0.018006 (0.094692) \| 0.302740 / 0.000490 (0.302251) \| 0.000209 / 0.000200 (0.000009) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019337 / 0.037411 (-0.018075) \| 0.062854 / 0.014526 (0.048328) \| 0.077088 / 0.176557 (-0.099468) \| 0.120926 / 0.737135 (-0.616209) \| 0.075594 / 0.296338 (-0.220744) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290787 / 0.215209 (0.075578) \| 2.867894 / 2.077655 (0.790239) \| 1.490043 / 1.504120 (-0.014076) \| 1.356383 / 1.541195 (-0.184812) \| 1.400229 / 1.468490 (-0.068261) \| 0.582076 / 4.584777 (-4.002701) \| 2.398270 / 3.745712 (-1.347442) \| 2.856459 / 5.269862 (-2.413403) \| 1.815545 / 4.565676 (-2.750131) \| 0.063259 / 0.424275 (-0.361016) \| 0.005056 / 0.007607 (-0.002551) \| 0.347699 / 0.226044 (0.121655) \| 3.466511 / 2.268929 (1.197582) \| 1.862096 / 55.444624 (-53.582528) \| 1.532324 / 6.876477 (-5.344152) \| 1.599411 / 2.142072 (-0.542661) \| 0.657350 / 4.805227 (-4.147878) \| 0.118981 / 6.500664 (-6.381683) \| 0.042224 / 0.075469 (-0.033245) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965649 / 1.841788 (-0.876139) \| 11.896501 / 8.074308 (3.822193) \| 9.873923 / 10.191392 (-0.317469) \| 0.141165 / 0.680424 (-0.539258) \| 0.013885 / 0.534201 (-0.520316) \| 0.291464 / 0.579283 (-0.287819) \| 0.273153 / 0.434364 (-0.161211) \| 0.324395 / 0.540337 (-0.215942) \| 0.422040 / 1.386936 (-0.964897) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005640 / 0.011353 (-0.005713) \| 0.004035 / 0.011008 (-0.006973) \| 0.050831 / 0.038508 (0.012323) \| 0.032841 / 0.023109 (0.009732) \| 0.272226 / 0.275898 (-0.003672) \| 0.297880 / 0.323480 (-0.025599) \| 0.004397 / 0.007986 (-0.003588) \| 0.002762 / 0.004328 (-0.001566) \| 0.049887 / 0.004250 (0.045637) \| 0.040372 / 0.037052 (0.003320) \| 0.286337 / 0.258489 (0.027848) \| 0.320015 / 0.293841 (0.026174) \| 0.029992 / 0.128546 (-0.098554) \| 0.010781 / 0.075646 (-0.064865) \| 0.059391 / 0.419271 (-0.359880) \| 0.034410 / 0.043533 (-0.009123) \| 0.273024 / 0.255139 (0.017885) \| 0.288953 / 0.283200 (0.005754) \| 0.018072 / 0.141683 (-0.123611) \| 1.125742 / 1.452155 (-0.326413) \| 1.175233 / 1.492716 (-0.317483) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093470 / 0.018006 (0.075463) \| 0.313248 / 0.000490 (0.312758) \| 0.000324 / 0.000200 (0.000124) \| 0.000081 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023529 / 0.037411 (-0.013882) \| 0.077305 / 0.014526 (0.062779) \| 0.088916 / 0.176557 (-0.087640) \| 0.128792 / 0.737135 (-0.608344) \| 0.090141 / 0.296338 (-0.206197) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291110 / 0.215209 (0.075901) \| 2.848118 / 2.077655 (0.770464) \| 1.581664 / 1.504120 (0.077544) \| 1.446390 / 1.541195 (-0.094804) \| 1.452594 / 1.468490 (-0.015896) \| 0.571213 / 4.584777 (-4.013564) \| 0.976382 / 3.745712 (-2.769330) \| 2.756192 / 5.269862 (-2.513670) \| 1.770274 / 4.565676 (-2.795403) \| 0.064513 / 0.424275 (-0.359763) \| 0.005334 / 0.007607 (-0.002273) \| 0.347380 / 0.226044 (0.121335) \| 3.424800 / 2.268929 (1.155871) \| 1.942374 / 55.444624 (-53.502250) \| 1.636069 / 6.876477 (-5.240407) \| 1.795327 / 2.142072 (-0.346745) \| 0.658942 / 4.805227 (-4.146285) \| 0.119542 / 6.500664 (-6.381123) \| 0.041826 / 0.075469 (-0.033643) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.007230 / 1.841788 (-0.834558) \| 12.293084 / 8.074308 (4.218776) \| 10.618104 / 10.191392 (0.426712) \| 0.133691 / 0.680424 (-0.546733) \| 0.015725 / 0.534201 (-0.518476) \| 0.288860 / 0.579283 (-0.290423) \| 0.130546 / 0.434364 (-0.303818) \| 0.327279 / 0.540337 (-0.213059) \| 0.428768 / 1.386936 (-0.958168) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#af3acfdfcf76bb980dbac871540e30c2cade0cf9 \"CML watermark\")\n" ]	2024-06-10T09:28:02Z	2024-06-11T08:31:52Z	2024-06-11T08:25:47Z	MEMBER	null	null	null	### What does this PR do? Remove unnecessary permissions granted to the actions workflow. Sorry for the mishap.	{ "avatar_url": "https://avatars.githubusercontent.com/u/9112841?v=4", "events_url": "https://api.github.com/users/McPatate/events{/privacy}", "followers_url": "https://api.github.com/users/McPatate/followers", "following_url": "https://api.github.com/users/McPatate/following{/other_user}", "gists_url": "https://api.github.com/users/McPatate/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/McPatate", "id": 9112841, "login": "McPatate", "node_id": "MDQ6VXNlcjkxMTI4NDE=", "organizations_url": "https://api.github.com/users/McPatate/orgs", "received_events_url": "https://api.github.com/users/McPatate/received_events", "repos_url": "https://api.github.com/users/McPatate/repos", "site_admin": false, "starred_url": "https://api.github.com/users/McPatate/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/McPatate/subscriptions", "type": "User", "url": "https://api.github.com/users/McPatate", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6962/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6962/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6962.diff", "html_url": "https://github.com/huggingface/datasets/pull/6962", "merged_at": "2024-06-11T08:25:47Z", "patch_url": "https://github.com/huggingface/datasets/pull/6962.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6962" }
https://api.github.com/repos/huggingface/datasets/issues/5413	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5413/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5413/comments	https://api.github.com/repos/huggingface/datasets/issues/5413/events	https://github.com/huggingface/datasets/issues/5413	1,524,591,837	I_kwDODunzps5a32zd	5,413	concatenate_datasets fails when two dataset with shards > 1 and unequal shard numbers	{ "avatar_url": "https://avatars.githubusercontent.com/u/38279341?v=4", "events_url": "https://api.github.com/users/ZeguanXiao/events{/privacy}", "followers_url": "https://api.github.com/users/ZeguanXiao/followers", "following_url": "https://api.github.com/users/ZeguanXiao/following{/other_user}", "gists_url": "https://api.github.com/users/ZeguanXiao/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ZeguanXiao", "id": 38279341, "login": "ZeguanXiao", "node_id": "MDQ6VXNlcjM4Mjc5MzQx", "organizations_url": "https://api.github.com/users/ZeguanXiao/orgs", "received_events_url": "https://api.github.com/users/ZeguanXiao/received_events", "repos_url": "https://api.github.com/users/ZeguanXiao/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ZeguanXiao/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ZeguanXiao/subscriptions", "type": "User", "url": "https://api.github.com/users/ZeguanXiao", "user_view_type": "public" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" } ]	null	[ "Hi ! Thanks for reporting :)\r\n\r\nI managed to reproduce the hub using\r\n```python\r\n\r\nfrom datasets import concatenate_datasets, Dataset, load_from_disk\r\n\r\nDataset.from_dict({\"a\": range(9)}).save_to_disk(\"tmp/ds1\")\r\nds1 = load_from_disk(\"tmp/ds1\")\r\nds1 = concatenate_datasets([ds1, ds1])\r\n\r\nDataset.from_dict({\"b\": range(6)}).save_to_disk(\"tmp/ds2\")\r\nds2 = load_from_disk(\"tmp/ds2\")\r\nds2 = concatenate_datasets([ds2, ds2, ds2])\r\n\r\nconcatenate_datasets([ds1, ds2], axis=1)\r\n```\r\nand I get\r\n```python\r\nTraceback (most recent call last): \r\n File \"test.py\", line 98, in <module>\r\n dds = concatenate_datasets([ds1, ds2], axis=1)\r\n File \"/Users/.../datasets/combine.py\", line 182, in concatenate_datasets\r\n return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis)\r\n File \"/Users/.../datasets/arrow_dataset.py\", line 5499, in _concatenate_map_style_datasets\r\n table = concat_tables([dset._data for dset in dsets], axis=axis)\r\n File \"/Users/.../datasets/table.py\", line 1778, in concat_tables\r\n return ConcatenationTable.from_tables(tables, axis=axis)\r\n File \"/Users/.../datasets/table.py\", line 1483, in from_tables\r\n blocks = _extend_blocks(blocks, table_blocks, axis=axis)\r\n File \"/Users/.../datasets/table.py\", line 1477, in _extend_blocks\r\n result[i].extend(row_blocks)\r\nIndexError: list index out of range\r\n```\r\n\r\nIt appears to happen when the two datasets have a number of shards that is not the same" ]	2023-01-08T17:01:52Z	2023-01-26T09:27:21Z	2023-01-26T09:27:21Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug When using `concatenate_datasets([dataset1, dataset2], axis = 1)` to concatenate two datasets with shards > 1, it fails: ``` File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/combine.py", line 182, in concatenate_datasets return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis) File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 5499, in _concatenate_map_style_datasets table = concat_tables([dset._data for dset in dsets], axis=axis) File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1778, in concat_tables return ConcatenationTable.from_tables(tables, axis=axis) File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1483, in from_tables blocks = _extend_blocks(blocks, table_blocks, axis=axis) File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1477, in _extend_blocks result[i].extend(row_blocks) IndexError: list index out of range ``` ### Steps to reproduce the bug dataset = concatenate_datasets([dataset1, dataset2], axis = 1) ### Expected behavior The datasets are correctly concatenated. ### Environment info datasets==2.8.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5413/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5413/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5149	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5149/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5149/comments	https://api.github.com/repos/huggingface/datasets/issues/5149/events	https://github.com/huggingface/datasets/pull/5149	1,420,415,639	PR_kwDODunzps5BZJab	5,149	Make iter_files deterministic	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-10-24T08:16:27Z	2022-10-27T09:53:23Z	2022-10-27T09:51:09Z	MEMBER	null	null	null	Fix #5145.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/5149/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5149/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5149.diff", "html_url": "https://github.com/huggingface/datasets/pull/5149", "merged_at": "2022-10-27T09:51:09Z", "patch_url": "https://github.com/huggingface/datasets/pull/5149.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5149" }
https://api.github.com/repos/huggingface/datasets/issues/4932	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4932/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4932/comments	https://api.github.com/repos/huggingface/datasets/issues/4932/events	https://github.com/huggingface/datasets/issues/4932	1,362,522,423	I_kwDODunzps5RNnE3	4,932	Dataset Viewer issue for bigscience-biomedical/biosses	{ "avatar_url": "https://avatars.githubusercontent.com/u/663051?v=4", "events_url": "https://api.github.com/users/galtay/events{/privacy}", "followers_url": "https://api.github.com/users/galtay/followers", "following_url": "https://api.github.com/users/galtay/following{/other_user}", "gists_url": "https://api.github.com/users/galtay/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/galtay", "id": 663051, "login": "galtay", "node_id": "MDQ6VXNlcjY2MzA1MQ==", "organizations_url": "https://api.github.com/users/galtay/orgs", "received_events_url": "https://api.github.com/users/galtay/received_events", "repos_url": "https://api.github.com/users/galtay/repos", "site_admin": false, "starred_url": "https://api.github.com/users/galtay/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/galtay/subscriptions", "type": "User", "url": "https://api.github.com/users/galtay", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Possibly not related to the dataset viewer in itself. cc @huggingface/datasets.\r\n\r\nIn particular, I think that the import of bigbiohub is not working here: https://huggingface.co/datasets/bigscience-biomedical/biosses/blob/main/biosses.py#L29 (requires a relative path?)\r\n\r\n```python\r\n>>> from datasets import get_dataset_config_names\r\n>>> get_dataset_config_names('bigscience-biomedical/biosses')\r\nDownloading builder script: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 8.00k/8.00k [00:00<00:00, 7.47MB/s]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 289, in get_dataset_config_names\r\n dataset_module = dataset_module_factory(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 1247, in dataset_module_factory\r\n raise e1 from None\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 1220, in dataset_module_factory\r\n return HubDatasetModuleFactoryWithScript(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 931, in get_module\r\n local_imports = _download_additional_modules(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 215, in _download_additional_modules\r\n raise ImportError(\r\nImportError: To be able to use bigscience-biomedical/biosses, you need to install the following dependency: bigbiohub.\r\nPlease install it using 'pip install bigbiohub' for instance'\r\n```", "Opened a PR here to (hopefully) fix the dataset script: https://huggingface.co/datasets/bigscience-biomedical/biosses/discussions/1/files", "thanks for taking a look @severo . agree this isn't related to dataset viewer (sorry just clicked on the auto issue creator). also thanks @lhoestq , I see the format to use for relative imports. was a bit confused b/c it seems to be working here \r\n\r\nhttps://huggingface.co/datasets/bigscience-biomedical/scitail/blob/main/scitail.py#L31\r\n\r\nI'll try this PR a see what happens. ", "closing as I think the issue is relative imports and attempting to read json files directly in the repo (thanks again @lhoestq ) " ]	2022-09-05T22:40:32Z	2022-09-06T14:24:56Z	2022-09-06T14:24:56Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Link https://huggingface.co/datasets/bigscience-biomedical/biosses ### Description I've just been working on adding the dataset loader script to this dataset and working with the relative imports. I'm not sure how to interpret the error below (show where the dataset preview used to be) . ``` Status code: 400 Exception: ModuleNotFoundError Message: No module named 'datasets_modules.datasets.bigscience-biomedical--biosses.ddbd5893bf6c2f4db06f407665eaeac619520ba41f69d94ead28f7cc5b674056.bigbiohub' ``` ### Owner Yes	{ "avatar_url": "https://avatars.githubusercontent.com/u/663051?v=4", "events_url": "https://api.github.com/users/galtay/events{/privacy}", "followers_url": "https://api.github.com/users/galtay/followers", "following_url": "https://api.github.com/users/galtay/following{/other_user}", "gists_url": "https://api.github.com/users/galtay/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/galtay", "id": 663051, "login": "galtay", "node_id": "MDQ6VXNlcjY2MzA1MQ==", "organizations_url": "https://api.github.com/users/galtay/orgs", "received_events_url": "https://api.github.com/users/galtay/received_events", "repos_url": "https://api.github.com/users/galtay/repos", "site_admin": false, "starred_url": "https://api.github.com/users/galtay/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/galtay/subscriptions", "type": "User", "url": "https://api.github.com/users/galtay", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4932/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4932/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7509	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7509/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7509/comments	https://api.github.com/repos/huggingface/datasets/issues/7509/events	https://github.com/huggingface/datasets/issues/7509	2,991,484,542	I_kwDODunzps6yTm5-	7,509	Dataset uses excessive memory when loading files	{ "avatar_url": "https://avatars.githubusercontent.com/u/36810152?v=4", "events_url": "https://api.github.com/users/avishaiElmakies/events{/privacy}", "followers_url": "https://api.github.com/users/avishaiElmakies/followers", "following_url": "https://api.github.com/users/avishaiElmakies/following{/other_user}", "gists_url": "https://api.github.com/users/avishaiElmakies/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/avishaiElmakies", "id": 36810152, "login": "avishaiElmakies", "node_id": "MDQ6VXNlcjM2ODEwMTUy", "organizations_url": "https://api.github.com/users/avishaiElmakies/orgs", "received_events_url": "https://api.github.com/users/avishaiElmakies/received_events", "repos_url": "https://api.github.com/users/avishaiElmakies/repos", "site_admin": false, "starred_url": "https://api.github.com/users/avishaiElmakies/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/avishaiElmakies/subscriptions", "type": "User", "url": "https://api.github.com/users/avishaiElmakies", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "small update: I converted the jsons to parquet and it now works well with 32 proc and the same node. \nI still think this needs to be understood, since json is a very popular and easy-to-use format. ", "Hi ! The JSON loader loads full files in memory, unless they are JSON Lines. In this case it iterates on the JSON Lines in a memory efficient manner.\n\nI know there is an `ijson` package that works similarly but for general JSON files, maybe it can help and remove the need to load full JSON files in memory", "Hi, i understand that json files are probably loaded into memory to read them but aren't they released when we write all the file content into arrow or something? ", "Yes correct, the JSON data is only in memory during the conversion to Arrow. Then, the data is memory mapped from you disk", "so the json files are all loaded into memory before converting to arrow? or do they convert 1 json at a time and then they are realeased?\nI don't understand how 200GB worth of jsons fill a 378GB node's memory.", "Each process converts one JSON file at at time, So the total memory usage is num_proc * json_file_size * overhead, where overhead can be around 2 or 3 for the conversion.\n\nSo it's indeed surprising that you run out of memory. Is the dataset available somewhere ? or a subset maybe ?", "This is a tokenized dataset I created for training a speech-language model with a few features (so it is not private but not easily available). I can send/upload a shard or two and you can copy them however many times you want so you can debug. this should give you something comparable to what I have, but will be easier than creating it yourself. so if you want that, let me know :)", "Maybe you can measure the memory usage when loading 1 file with num_proc=1 ? This should already be helpful.\n\nMemory usage for tokenized data can be bigger than just text, for example the tokens type can be inferred as int64 and the lists offsets are int32", "OK, I will try to do this in the near future. I am a little swamped at the moment. do you have a preferred tool?\n\nalso My data is just list of ints, there is no offsets", "> so the json files are all loaded into memory before converting to arrow? or do they convert 1 json at a time and then they are realeased? I don't understand how 200GB worth of jsons fill a 378GB node's memory.\n\nHello! Is your query solved? I have the same confusion and would like to ask you for advice", "no, the issue is still present. I converted the json files to parquet, but json seems to have a problem.\n\nUnfortunately i didn't have the time to try and profile the memory usage for 1 file. So if you want to do that, it will be great! " ]	2025-04-13T21:09:49Z	2025-04-26T15:33:13Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Hi I am having an issue when loading a dataset. I have about 200 json files each about 1GB (total about 215GB). each row has a few features which are a list of ints. I am trying to load the dataset using `load_dataset`. The dataset is about 1.5M samples I use `num_proc=32` and a node with 378GB of memory. About a third of the way there I get an OOM. I also saw an old bug with a similar issue, which says to set `writer_batch_size`. I tried to lower it to 10, but it still crashed. I also tried to lower the `num_proc` to 16 and even 8, but still the same issue. ### Steps to reproduce the bug `dataset = load_dataset("json", data_dir=data_config.train_path, num_proc=data_config.num_proc, writer_batch_size=50)["train"]` ### Expected behavior Loading a dataset with more than 100GB to spare should not cause an OOM error. maybe i am missing something but I would love some help. ### Environment info - `datasets` version: 3.5.0 - Platform: Linux-6.6.20-aufs-1-x86_64-with-glibc2.36 - Python version: 3.11.2 - `huggingface_hub` version: 0.29.1 - PyArrow version: 19.0.1 - Pandas version: 2.2.3 - `fsspec` version: 2024.9.0	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7509/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7509/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5303	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5303/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5303/comments	https://api.github.com/repos/huggingface/datasets/issues/5303/events	https://github.com/huggingface/datasets/pull/5303	1,464,837,251	PR_kwDODunzps5DuVTa	5,303	Skip dataset verifications by default	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "100% agree that the checksum verification is overkill and not super useful. But I think this PR would also disable the check on num_examples no ?\r\n \r\nAs a user I would like to know if the dataset I'm loading changed significantly.\r\nAnd I also think it can be useful to make sure the metadata are up to date.\r\n\r\nWhat do you think ?\r\n\r\nWe could have a default `ignore_verifications=\"ignore_checksums\"`", "> We could have a default `ignore_verifications=\"ignore_checksums\"`\r\n\r\nAccepting multiple types (booleans and strings) at the same time is not the best design. Maybe we could define an enum for this parameter?", "Yes an enum sounds good !", "so we can have three verification levels, - smth like \"ignore_all\" (to skip both checksums and all other info like num_examples verification), \"ignore_checksums\" (to skip only checksums verification), and \"verify_all\" (to perform all verification)?\r\nand deprecate `ignore_verifications` param.\r\n\r\n@mariosasko if you're not going to work on this PR in the coming days, I can take over it if you want (this PR will help me with [this issue](https://github.com/huggingface/datasets/issues/5315), not super urgent though).", "Okay, I propose deprecating `ignore_verifications` in favor of `verification_mode` (`load_dataset` already has `download_mode`; some other projects use this name for verification control). `verification_mode` would accept the following enum (or strings in the same manner as `download_mode` does):\r\n\r\n```python\r\nclass VerificationMode(enum.Enum):\r\n FULL = \"full\" # runs all verification checks \r\n BASIC = \"basic\" # default, runs only the cheap ones (skips the checksum check)\r\n NONE = \"none\" # skips all the checks\r\n```\r\n\r\nWDTY?", "(copy paste from my message on slack)\r\n\r\nWhat do you think of a config variable in config.py to switch from one verification mode to another ? This way we don’t deprecate anything\r\n\r\nMany users are familiar with ignore_verifications=True, it might be overkill to deprecate it", "@lhoestq So we have \"basic\" verification mode in `config.py` and continue to have `False` as a default \r\nvalue for `ignore_verifications`? That way running all verifications including checksums would not be possible without switching the config var, right? \r\n\r\nI like having a `VerificationMode` enum because it's aligned with `DownloadMode` and sounds more natural to me (`ignore_verifications` feels a bit semantically reverted but this is probably just my feeling) and it's flexible (no need to worry about `config.py`, I'm not sure that users even know it exists, wdyt?).\r\n\r\nThe usage point seems also valid to me, but cases when users are stuck with NonMatchingX errors also happen from time to time and to figure out what's wrong is non-trivial here. \r\n\r\nAs a note aside - I suggest to add instructions to the NonMatchingX error message (how to use `ignore_verifications` / `verification_mode`), this would save users who don't know about this param a lot of time.", "Ok I see. I'm fine with the new parameter then (even though I had a small pref for the config variable) :)", "I like the idea of an enum and the `verification_mode` parameter. \r\n\r\nIn relation with the config parameter, we could additionally add a `DEFAULT_VERIFICATION_MODE`, maybe only if users require it. Note that until now there wasn't any config parameter for a default `ignore_verifications` value: I guess people are explicitly passing `ignore_verifications=True`...\r\n\r\nAs a note aside, I like the suggestion by @polinaeterna: we could give actionable messages when verifying checksums. This could be done in other PR.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) \| 0.012891 / 0.011353 (0.001538) \| 0.006474 / 0.011008 (-0.004535) \| 0.144038 / 0.038508 (0.105530) \| 0.036151 / 0.023109 (0.013042) \| 0.404366 / 0.275898 (0.128468) \| 0.479988 / 0.323480 (0.156508) \| 0.010219 / 0.007986 (0.002233) \| 0.005319 / 0.004328 (0.000990) \| 0.099705 / 0.004250 (0.095455) \| 0.046639 / 0.037052 (0.009586) \| 0.398997 / 0.258489 (0.140508) \| 0.478431 / 0.293841 (0.184590) \| 0.069125 / 0.128546 (-0.059421) \| 0.019603 / 0.075646 (-0.056043) \| 0.400829 / 0.419271 (-0.018443) \| 0.066549 / 0.043533 (0.023016) \| 0.398343 / 0.255139 (0.143204) \| 0.417928 / 0.283200 (0.134728) \| 0.121124 / 0.141683 (-0.020559) \| 1.751513 / 1.452155 (0.299358) \| 1.821239 / 1.492716 (0.328523) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.251603 / 0.018006 (0.233597) \| 0.579916 / 0.000490 (0.579427) \| 0.003257 / 0.000200 (0.003058) \| 0.000109 / 0.000054 (0.000054) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031502 / 0.037411 (-0.005909) \| 0.134688 / 0.014526 (0.120162) \| 0.152306 / 0.176557 (-0.024251) \| 0.198943 / 0.737135 (-0.538192) \| 0.142551 / 0.296338 (-0.153788) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.634672 / 0.215209 (0.419463) \| 6.370215 / 2.077655 (4.292561) \| 2.548123 / 1.504120 (1.044003) \| 2.184263 / 1.541195 (0.643069) \| 2.239026 / 1.468490 (0.770536) \| 1.233340 / 4.584777 (-3.351437) \| 5.791824 / 3.745712 (2.046112) \| 5.093032 / 5.269862 (-0.176830) \| 2.849833 / 4.565676 (-1.715844) \| 0.143787 / 0.424275 (-0.280488) \| 0.015279 / 0.007607 (0.007672) \| 0.757984 / 0.226044 (0.531939) \| 7.883604 / 2.268929 (5.614675) \| 3.321591 / 55.444624 (-52.123033) \| 2.671777 / 6.876477 (-4.204700) \| 2.685215 / 2.142072 (0.543142) \| 1.546709 / 4.805227 (-3.258519) \| 0.247186 / 6.500664 (-6.253478) \| 0.085117 / 0.075469 (0.009648) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.679809 / 1.841788 (-0.161979) \| 18.528893 / 8.074308 (10.454585) \| 23.168590 / 10.191392 (12.977198) \| 0.277618 / 0.680424 (-0.402806) \| 0.045109 / 0.534201 (-0.489092) \| 0.568873 / 0.579283 (-0.010410) \| 0.695017 / 0.434364 (0.260653) \| 0.671024 / 0.540337 (0.130687) \| 0.823817 / 1.386936 (-0.563119) \|\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) \| 0.009809 / 0.011353 (-0.001544) \| 0.006890 / 0.011008 (-0.004118) \| 0.099211 / 0.038508 (0.060703) \| 0.035387 / 0.023109 (0.012278) \| 0.507603 / 0.275898 (0.231705) \| 0.535553 / 0.323480 (0.212073) \| 0.007346 / 0.007986 (-0.000640) \| 0.007559 / 0.004328 (0.003231) \| 0.099132 / 0.004250 (0.094882) \| 0.048048 / 0.037052 (0.010996) \| 0.518096 / 0.258489 (0.259607) \| 0.561134 / 0.293841 (0.267294) \| 0.057580 / 0.128546 (-0.070966) \| 0.023665 / 0.075646 (-0.051982) \| 0.138409 / 0.419271 (-0.280862) \| 0.061989 / 0.043533 (0.018456) \| 0.510568 / 0.255139 (0.255429) \| 0.552722 / 0.283200 (0.269522) \| 0.115990 / 0.141683 (-0.025693) \| 1.884900 / 1.452155 (0.432745) \| 1.990604 / 1.492716 (0.497888) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280638 / 0.018006 (0.262632) \| 0.592837 / 0.000490 (0.592347) \| 0.000465 / 0.000200 (0.000265) \| 0.000078 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030253 / 0.037411 (-0.007158) \| 0.141580 / 0.014526 (0.127054) \| 0.135114 / 0.176557 (-0.041443) \| 0.190003 / 0.737135 (-0.547133) \| 0.160230 / 0.296338 (-0.136109) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.699762 / 0.215209 (0.484553) \| 6.632344 / 2.077655 (4.554689) \| 2.718803 / 1.504120 (1.214683) \| 2.485294 / 1.541195 (0.944099) \| 2.579889 / 1.468490 (1.111399) \| 1.268795 / 4.584777 (-3.315982) \| 5.777745 / 3.745712 (2.032033) \| 3.232551 / 5.269862 (-2.037311) \| 2.127699 / 4.565676 (-2.437977) \| 0.146570 / 0.424275 (-0.277705) \| 0.015971 / 0.007607 (0.008364) \| 0.803181 / 0.226044 (0.577137) \| 8.377192 / 2.268929 (6.108264) \| 3.551242 / 55.444624 (-51.893382) \| 2.865228 / 6.876477 (-4.011249) \| 2.774869 / 2.142072 (0.632797) \| 1.553856 / 4.805227 (-3.251371) \| 0.264510 / 6.500664 (-6.236154) \| 0.087918 / 0.075469 (0.012449) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.653396 / 1.841788 (-0.188391) \| 18.703863 / 8.074308 (10.629555) \| 22.067331 / 10.191392 (11.875939) \| 0.257424 / 0.680424 (-0.422999) \| 0.026448 / 0.534201 (-0.507753) \| 0.550100 / 0.579283 (-0.029183) \| 0.647296 / 0.434364 (0.212932) \| 0.657476 / 0.540337 (0.117138) \| 0.781119 / 1.386936 (-0.605817) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8c4a9cb95f8742a2850f11d59abbef71d6c1f60c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) \| 0.008889 / 0.011353 (-0.002464) \| 0.004563 / 0.011008 (-0.006445) \| 0.101627 / 0.038508 (0.063118) \| 0.030526 / 0.023109 (0.007417) \| 0.297175 / 0.275898 (0.021277) \| 0.368454 / 0.323480 (0.044974) \| 0.007246 / 0.007986 (-0.000740) \| 0.003565 / 0.004328 (-0.000763) \| 0.078644 / 0.004250 (0.074394) \| 0.038616 / 0.037052 (0.001564) \| 0.310521 / 0.258489 (0.052032) \| 0.348014 / 0.293841 (0.054173) \| 0.033463 / 0.128546 (-0.095083) \| 0.011544 / 0.075646 (-0.064102) \| 0.323281 / 0.419271 (-0.095990) \| 0.040187 / 0.043533 (-0.003346) \| 0.298015 / 0.255139 (0.042876) \| 0.326392 / 0.283200 (0.043193) \| 0.088730 / 0.141683 (-0.052952) \| 1.503387 / 1.452155 (0.051233) \| 1.548704 / 1.492716 (0.055988) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.185983 / 0.018006 (0.167977) \| 0.451889 / 0.000490 (0.451400) \| 0.001433 / 0.000200 (0.001233) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023396 / 0.037411 (-0.014015) \| 0.118236 / 0.014526 (0.103710) \| 0.124594 / 0.176557 (-0.051962) \| 0.159089 / 0.737135 (-0.578047) \| 0.129369 / 0.296338 (-0.166969) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.423161 / 0.215209 (0.207952) \| 4.228211 / 2.077655 (2.150556) \| 1.853862 / 1.504120 (0.349742) \| 1.649471 / 1.541195 (0.108276) \| 1.708631 / 1.468490 (0.240141) \| 0.697456 / 4.584777 (-3.887321) \| 3.473244 / 3.745712 (-0.272468) \| 1.942586 / 5.269862 (-3.327275) \| 1.291592 / 4.565676 (-3.274084) \| 0.082758 / 0.424275 (-0.341517) \| 0.012256 / 0.007607 (0.004649) \| 0.528355 / 0.226044 (0.302311) \| 5.277620 / 2.268929 (3.008691) \| 2.299604 / 55.444624 (-53.145020) \| 1.954940 / 6.876477 (-4.921537) \| 2.055543 / 2.142072 (-0.086529) \| 0.814723 / 4.805227 (-3.990505) \| 0.149937 / 6.500664 (-6.350727) \| 0.064529 / 0.075469 (-0.010941) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.266240 / 1.841788 (-0.575547) \| 14.144016 / 8.074308 (6.069708) \| 14.331733 / 10.191392 (4.140340) \| 0.138963 / 0.680424 (-0.541461) \| 0.029034 / 0.534201 (-0.505167) \| 0.397325 / 0.579283 (-0.181958) \| 0.405293 / 0.434364 (-0.029071) \| 0.480745 / 0.540337 (-0.059592) \| 0.573386 / 1.386936 (-0.813550) \|\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) \| 0.007214 / 0.011353 (-0.004139) \| 0.004569 / 0.011008 (-0.006439) \| 0.078718 / 0.038508 (0.040209) \| 0.031104 / 0.023109 (0.007995) \| 0.342562 / 0.275898 (0.066664) \| 0.387802 / 0.323480 (0.064322) \| 0.005378 / 0.007986 (-0.002608) \| 0.003414 / 0.004328 (-0.000915) \| 0.077249 / 0.004250 (0.072999) \| 0.044337 / 0.037052 (0.007285) \| 0.341397 / 0.258489 (0.082907) \| 0.385536 / 0.293841 (0.091695) \| 0.033257 / 0.128546 (-0.095289) \| 0.011825 / 0.075646 (-0.063821) \| 0.086723 / 0.419271 (-0.332549) \| 0.045951 / 0.043533 (0.002418) \| 0.340914 / 0.255139 (0.085775) \| 0.367126 / 0.283200 (0.083926) \| 0.096326 / 0.141683 (-0.045357) \| 1.608612 / 1.452155 (0.156458) \| 1.687251 / 1.492716 (0.194534) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227595 / 0.018006 (0.209589) \| 0.418502 / 0.000490 (0.418013) \| 0.000392 / 0.000200 (0.000192) \| 0.000059 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026232 / 0.037411 (-0.011179) \| 0.101020 / 0.014526 (0.086494) \| 0.110017 / 0.176557 (-0.066539) \| 0.153497 / 0.737135 (-0.583639) \| 0.110602 / 0.296338 (-0.185737) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.433789 / 0.215209 (0.218579) \| 4.329350 / 2.077655 (2.251696) \| 2.052136 / 1.504120 (0.548016) \| 1.848457 / 1.541195 (0.307262) \| 1.936791 / 1.468490 (0.468301) \| 0.700609 / 4.584777 (-3.884168) \| 3.391983 / 3.745712 (-0.353729) \| 1.903220 / 5.269862 (-3.366642) \| 1.179463 / 4.565676 (-3.386213) \| 0.084025 / 0.424275 (-0.340250) \| 0.012743 / 0.007607 (0.005136) \| 0.536816 / 0.226044 (0.310772) \| 5.420230 / 2.268929 (3.151302) \| 2.507438 / 55.444624 (-52.937187) \| 2.178907 / 6.876477 (-4.697570) \| 2.228586 / 2.142072 (0.086514) \| 0.812527 / 4.805227 (-3.992701) \| 0.153382 / 6.500664 (-6.347282) \| 0.069932 / 0.075469 (-0.005537) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.256861 / 1.841788 (-0.584927) \| 14.309236 / 8.074308 (6.234928) \| 13.740323 / 10.191392 (3.548931) \| 0.142698 / 0.680424 (-0.537726) \| 0.016998 / 0.534201 (-0.517203) \| 0.385489 / 0.579283 (-0.193794) \| 0.391515 / 0.434364 (-0.042849) \| 0.472704 / 0.540337 (-0.067633) \| 0.565042 / 1.386936 (-0.821894) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4b0713ddf2e2e7129d9ccda791d265684c96675c \"CML watermark\")\n", "This is ready for review. \r\n\r\nIf `verification_mode` is None, it defaults to `VerificationMode.BASIC` instead of `VerificationMode.NONE`, so maybe we should find a better name for the latter to avoid confusion.\r\n\r\nPS: `ignore_verifications` is still present in the `test`/`run_beam` commands for simplicity. Let me know if you think these commands should support all three modes.", "> I would also prefer to change the name for the NONE verification mode, but don't have really good ideas in mind. maybe smth like SKIP_ALL ?\r\n\r\nI decided to go with the following names:\r\n* `no_checks` (previously `none`)\r\n* `basic_checks` (previously `basic`)\r\n* `all_checks` (previously `full`)\r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008900 / 0.011353 (-0.002453) \| 0.004492 / 0.011008 (-0.006516) \| 0.100957 / 0.038508 (0.062449) \| 0.030145 / 0.023109 (0.007036) \| 0.302531 / 0.275898 (0.026633) \| 0.344072 / 0.323480 (0.020592) \| 0.007032 / 0.007986 (-0.000953) \| 0.004150 / 0.004328 (-0.000178) \| 0.078272 / 0.004250 (0.074021) \| 0.034142 / 0.037052 (-0.002910) \| 0.310798 / 0.258489 (0.052308) \| 0.350077 / 0.293841 (0.056236) \| 0.034497 / 0.128546 (-0.094050) \| 0.011417 / 0.075646 (-0.064230) \| 0.323427 / 0.419271 (-0.095844) \| 0.045664 / 0.043533 (0.002132) \| 0.304688 / 0.255139 (0.049549) \| 0.336591 / 0.283200 (0.053391) \| 0.086116 / 0.141683 (-0.055567) \| 1.519278 / 1.452155 (0.067123) \| 1.576728 / 1.492716 (0.084011) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242482 / 0.018006 (0.224476) \| 0.403548 / 0.000490 (0.403058) \| 0.001217 / 0.000200 (0.001017) \| 0.000073 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023466 / 0.037411 (-0.013945) \| 0.095220 / 0.014526 (0.080694) \| 0.104119 / 0.176557 (-0.072438) \| 0.141107 / 0.737135 (-0.596029) \| 0.107236 / 0.296338 (-0.189102) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.416290 / 0.215209 (0.201081) \| 4.159068 / 2.077655 (2.081413) \| 1.846014 / 1.504120 (0.341894) \| 1.634789 / 1.541195 (0.093594) \| 1.724687 / 1.468490 (0.256196) \| 0.696887 / 4.584777 (-3.887890) \| 3.313861 / 3.745712 (-0.431851) \| 1.907239 / 5.269862 (-3.362622) \| 1.266815 / 4.565676 (-3.298861) \| 0.081660 / 0.424275 (-0.342615) \| 0.012290 / 0.007607 (0.004683) \| 0.522866 / 0.226044 (0.296822) \| 5.237356 / 2.268929 (2.968428) \| 2.294645 / 55.444624 (-53.149979) \| 1.946407 / 6.876477 (-4.930069) \| 1.995441 / 2.142072 (-0.146632) \| 0.808340 / 4.805227 (-3.996887) \| 0.149670 / 6.500664 (-6.350994) \| 0.065162 / 0.075469 (-0.010307) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.219476 / 1.841788 (-0.622312) \| 13.868709 / 8.074308 (5.794401) \| 14.115783 / 10.191392 (3.924391) \| 0.149403 / 0.680424 (-0.531021) \| 0.028514 / 0.534201 (-0.505686) \| 0.398194 / 0.579283 (-0.181089) \| 0.410898 / 0.434364 (-0.023466) \| 0.485763 / 0.540337 (-0.054574) \| 0.574924 / 1.386936 (-0.812012) \|\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) \| 0.006906 / 0.011353 (-0.004447) \| 0.004446 / 0.011008 (-0.006562) \| 0.075936 / 0.038508 (0.037428) \| 0.027693 / 0.023109 (0.004584) \| 0.339505 / 0.275898 (0.063607) \| 0.383315 / 0.323480 (0.059835) \| 0.005138 / 0.007986 (-0.002847) \| 0.004636 / 0.004328 (0.000308) \| 0.074829 / 0.004250 (0.070578) \| 0.040327 / 0.037052 (0.003274) \| 0.340516 / 0.258489 (0.082027) \| 0.388569 / 0.293841 (0.094729) \| 0.031562 / 0.128546 (-0.096984) \| 0.011585 / 0.075646 (-0.064061) \| 0.084753 / 0.419271 (-0.334518) \| 0.041310 / 0.043533 (-0.002223) \| 0.338272 / 0.255139 (0.083133) \| 0.367243 / 0.283200 (0.084043) \| 0.092653 / 0.141683 (-0.049029) \| 1.515973 / 1.452155 (0.063818) \| 1.582869 / 1.492716 (0.090152) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229366 / 0.018006 (0.211360) \| 0.414404 / 0.000490 (0.413914) \| 0.002922 / 0.000200 (0.002723) \| 0.000075 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026391 / 0.037411 (-0.011020) \| 0.106754 / 0.014526 (0.092228) \| 0.110718 / 0.176557 (-0.065839) \| 0.145786 / 0.737135 (-0.591350) \| 0.113180 / 0.296338 (-0.183159) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.446340 / 0.215209 (0.231131) \| 4.499756 / 2.077655 (2.422101) \| 2.071485 / 1.504120 (0.567365) \| 1.873223 / 1.541195 (0.332029) \| 1.931562 / 1.468490 (0.463071) \| 0.699270 / 4.584777 (-3.885507) \| 3.452383 / 3.745712 (-0.293329) \| 2.970630 / 5.269862 (-2.299232) \| 1.300859 / 4.565676 (-3.264817) \| 0.083971 / 0.424275 (-0.340304) \| 0.012489 / 0.007607 (0.004882) \| 0.544190 / 0.226044 (0.318146) \| 5.460097 / 2.268929 (3.191169) \| 2.700244 / 55.444624 (-52.744380) \| 2.396694 / 6.876477 (-4.479783) \| 2.376334 / 2.142072 (0.234262) \| 0.812845 / 4.805227 (-3.992382) \| 0.154441 / 6.500664 (-6.346223) \| 0.069510 / 0.075469 (-0.005959) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.278836 / 1.841788 (-0.562952) \| 14.153158 / 8.074308 (6.078850) \| 13.821290 / 10.191392 (3.629898) \| 0.160464 / 0.680424 (-0.519960) \| 0.016742 / 0.534201 (-0.517459) \| 0.379840 / 0.579283 (-0.199443) \| 0.391903 / 0.434364 (-0.042461) \| 0.461646 / 0.540337 (-0.078691) \| 0.550691 / 1.386936 (-0.836245) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aeb637daab938d51b8b15ad4d175d06817e99512 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) \| 0.009858 / 0.011353 (-0.001495) \| 0.005383 / 0.011008 (-0.005625) \| 0.100527 / 0.038508 (0.062019) \| 0.037176 / 0.023109 (0.014067) \| 0.295204 / 0.275898 (0.019306) \| 0.364511 / 0.323480 (0.041031) \| 0.008486 / 0.007986 (0.000500) \| 0.004273 / 0.004328 (-0.000055) \| 0.076538 / 0.004250 (0.072288) \| 0.046250 / 0.037052 (0.009197) \| 0.307102 / 0.258489 (0.048613) \| 0.339313 / 0.293841 (0.045472) \| 0.040783 / 0.128546 (-0.087763) \| 0.012323 / 0.075646 (-0.063323) \| 0.336216 / 0.419271 (-0.083055) \| 0.050480 / 0.043533 (0.006947) \| 0.293689 / 0.255139 (0.038550) \| 0.315034 / 0.283200 (0.031834) \| 0.113775 / 0.141683 (-0.027908) \| 1.438738 / 1.452155 (-0.013416) \| 1.499874 / 1.492716 (0.007157) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.202392 / 0.018006 (0.184386) \| 0.442784 / 0.000490 (0.442295) \| 0.003004 / 0.000200 (0.002804) \| 0.000087 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027792 / 0.037411 (-0.009620) \| 0.110886 / 0.014526 (0.096360) \| 0.121041 / 0.176557 (-0.055515) \| 0.166803 / 0.737135 (-0.570333) \| 0.127617 / 0.296338 (-0.168722) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.409762 / 0.215209 (0.194553) \| 4.073297 / 2.077655 (1.995643) \| 1.836375 / 1.504120 (0.332255) \| 1.651507 / 1.541195 (0.110312) \| 1.734134 / 1.468490 (0.265644) \| 0.690900 / 4.584777 (-3.893877) \| 3.812045 / 3.745712 (0.066333) \| 2.101378 / 5.269862 (-3.168483) \| 1.438242 / 4.565676 (-3.127434) \| 0.083256 / 0.424275 (-0.341020) \| 0.012436 / 0.007607 (0.004829) \| 0.501702 / 0.226044 (0.275658) \| 5.007679 / 2.268929 (2.738751) \| 2.315158 / 55.444624 (-53.129466) \| 2.003934 / 6.876477 (-4.872543) \| 2.154658 / 2.142072 (0.012586) \| 0.831749 / 4.805227 (-3.973478) \| 0.165058 / 6.500664 (-6.335606) \| 0.062166 / 0.075469 (-0.013303) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.212435 / 1.841788 (-0.629353) \| 15.022673 / 8.074308 (6.948365) \| 14.649631 / 10.191392 (4.458239) \| 0.172121 / 0.680424 (-0.508303) \| 0.028791 / 0.534201 (-0.505410) \| 0.440290 / 0.579283 (-0.138993) \| 0.437359 / 0.434364 (0.002995) \| 0.543603 / 0.540337 (0.003265) \| 0.643241 / 1.386936 (-0.743695) \|\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) \| 0.007572 / 0.011353 (-0.003781) \| 0.005207 / 0.011008 (-0.005801) \| 0.074427 / 0.038508 (0.035919) \| 0.033384 / 0.023109 (0.010275) \| 0.334538 / 0.275898 (0.058640) \| 0.371556 / 0.323480 (0.048076) \| 0.006453 / 0.007986 (-0.001532) \| 0.004010 / 0.004328 (-0.000319) \| 0.073488 / 0.004250 (0.069238) \| 0.048082 / 0.037052 (0.011030) \| 0.337325 / 0.258489 (0.078836) \| 0.395143 / 0.293841 (0.101302) \| 0.036714 / 0.128546 (-0.091832) \| 0.012089 / 0.075646 (-0.063557) \| 0.086008 / 0.419271 (-0.333263) \| 0.049277 / 0.043533 (0.005744) \| 0.333848 / 0.255139 (0.078709) \| 0.354003 / 0.283200 (0.070803) \| 0.105012 / 0.141683 (-0.036671) \| 1.450769 / 1.452155 (-0.001386) \| 1.554538 / 1.492716 (0.061821) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208407 / 0.018006 (0.190400) \| 0.438778 / 0.000490 (0.438288) \| 0.000399 / 0.000200 (0.000199) \| 0.000059 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030180 / 0.037411 (-0.007232) \| 0.115432 / 0.014526 (0.100906) \| 0.126106 / 0.176557 (-0.050451) \| 0.167508 / 0.737135 (-0.569627) \| 0.130566 / 0.296338 (-0.165772) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.421408 / 0.215209 (0.206198) \| 4.208492 / 2.077655 (2.130838) \| 2.024177 / 1.504120 (0.520057) \| 1.834356 / 1.541195 (0.293161) \| 1.923234 / 1.468490 (0.454744) \| 0.699548 / 4.584777 (-3.885229) \| 3.933775 / 3.745712 (0.188063) \| 2.124526 / 5.269862 (-3.145336) \| 1.360934 / 4.565676 (-3.204742) \| 0.086568 / 0.424275 (-0.337707) \| 0.012351 / 0.007607 (0.004744) \| 0.517431 / 0.226044 (0.291387) \| 5.175428 / 2.268929 (2.906499) \| 2.471031 / 55.444624 (-52.973593) \| 2.131529 / 6.876477 (-4.744948) \| 2.202512 / 2.142072 (0.060440) \| 0.849364 / 4.805227 (-3.955863) \| 0.171505 / 6.500664 (-6.329159) \| 0.065864 / 0.075469 (-0.009605) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.270054 / 1.841788 (-0.571734) \| 15.254502 / 8.074308 (7.180194) \| 13.874969 / 10.191392 (3.683577) \| 0.144131 / 0.680424 (-0.536293) \| 0.017743 / 0.534201 (-0.516458) \| 0.421990 / 0.579283 (-0.157293) \| 0.423924 / 0.434364 (-0.010439) \| 0.522560 / 0.540337 (-0.017778) \| 0.626159 / 1.386936 (-0.760777) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05bd726a575a3c1c337022424fa7d226f1a2ebee \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) \| 0.008643 / 0.011353 (-0.002710) \| 0.004479 / 0.011008 (-0.006529) \| 0.102372 / 0.038508 (0.063864) \| 0.029703 / 0.023109 (0.006594) \| 0.301479 / 0.275898 (0.025581) \| 0.370970 / 0.323480 (0.047490) \| 0.007044 / 0.007986 (-0.000942) \| 0.004868 / 0.004328 (0.000540) \| 0.079568 / 0.004250 (0.075318) \| 0.035344 / 0.037052 (-0.001708) \| 0.308091 / 0.258489 (0.049602) \| 0.353812 / 0.293841 (0.059971) \| 0.033406 / 0.128546 (-0.095140) \| 0.011476 / 0.075646 (-0.064170) \| 0.324343 / 0.419271 (-0.094929) \| 0.040293 / 0.043533 (-0.003240) \| 0.300007 / 0.255139 (0.044868) \| 0.334410 / 0.283200 (0.051210) \| 0.086553 / 0.141683 (-0.055130) \| 1.463814 / 1.452155 (0.011659) \| 1.501580 / 1.492716 (0.008864) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.198032 / 0.018006 (0.180025) \| 0.409970 / 0.000490 (0.409480) \| 0.001075 / 0.000200 (0.000875) \| 0.000076 / 0.000054 (0.000022) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022941 / 0.037411 (-0.014471) \| 0.097320 / 0.014526 (0.082794) \| 0.106445 / 0.176557 (-0.070111) \| 0.139073 / 0.737135 (-0.598063) \| 0.108408 / 0.296338 (-0.187930) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419315 / 0.215209 (0.204106) \| 4.199273 / 2.077655 (2.121618) \| 1.877689 / 1.504120 (0.373569) \| 1.670442 / 1.541195 (0.129247) \| 1.735034 / 1.468490 (0.266544) \| 0.694691 / 4.584777 (-3.890086) \| 3.323644 / 3.745712 (-0.422069) \| 2.884349 / 5.269862 (-2.385513) \| 1.518882 / 4.565676 (-3.046794) \| 0.082390 / 0.424275 (-0.341886) \| 0.012884 / 0.007607 (0.005277) \| 0.525103 / 0.226044 (0.299058) \| 5.277297 / 2.268929 (3.008369) \| 2.328639 / 55.444624 (-53.115985) \| 1.983210 / 6.876477 (-4.893267) \| 2.037985 / 2.142072 (-0.104088) \| 0.809520 / 4.805227 (-3.995707) \| 0.150150 / 6.500664 (-6.350514) \| 0.065578 / 0.075469 (-0.009891) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.221971 / 1.841788 (-0.619817) \| 13.692361 / 8.074308 (5.618052) \| 13.874582 / 10.191392 (3.683190) \| 0.138182 / 0.680424 (-0.542242) \| 0.028618 / 0.534201 (-0.505583) \| 0.395104 / 0.579283 (-0.184179) \| 0.397169 / 0.434364 (-0.037195) \| 0.457509 / 0.540337 (-0.082829) \| 0.537275 / 1.386936 (-0.849661) \|\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) \| 0.006835 / 0.011353 (-0.004518) \| 0.004585 / 0.011008 (-0.006423) \| 0.076877 / 0.038508 (0.038369) \| 0.027305 / 0.023109 (0.004196) \| 0.349085 / 0.275898 (0.073187) \| 0.401416 / 0.323480 (0.077936) \| 0.004912 / 0.007986 (-0.003074) \| 0.003315 / 0.004328 (-0.001014) \| 0.075676 / 0.004250 (0.071425) \| 0.038960 / 0.037052 (0.001907) \| 0.346196 / 0.258489 (0.087707) \| 0.403185 / 0.293841 (0.109344) \| 0.032054 / 0.128546 (-0.096493) \| 0.011742 / 0.075646 (-0.063905) \| 0.086631 / 0.419271 (-0.332640) \| 0.041633 / 0.043533 (-0.001900) \| 0.343519 / 0.255139 (0.088380) \| 0.385413 / 0.283200 (0.102213) \| 0.091430 / 0.141683 (-0.050253) \| 1.478886 / 1.452155 (0.026731) \| 1.546873 / 1.492716 (0.054156) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.167882 / 0.018006 (0.149876) \| 0.396464 / 0.000490 (0.395974) \| 0.003629 / 0.000200 (0.003429) \| 0.000085 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024829 / 0.037411 (-0.012583) \| 0.099607 / 0.014526 (0.085081) \| 0.106187 / 0.176557 (-0.070370) \| 0.142379 / 0.737135 (-0.594756) \| 0.109307 / 0.296338 (-0.187032) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.442276 / 0.215209 (0.227067) \| 4.427099 / 2.077655 (2.349444) \| 2.093407 / 1.504120 (0.589287) \| 1.880973 / 1.541195 (0.339778) \| 1.915592 / 1.468490 (0.447102) \| 0.708196 / 4.584777 (-3.876581) \| 3.417649 / 3.745712 (-0.328063) \| 2.859953 / 5.269862 (-2.409909) \| 1.528380 / 4.565676 (-3.037297) \| 0.084054 / 0.424275 (-0.340221) \| 0.012585 / 0.007607 (0.004978) \| 0.537614 / 0.226044 (0.311569) \| 5.409915 / 2.268929 (3.140987) \| 2.555853 / 55.444624 (-52.888771) \| 2.195075 / 6.876477 (-4.681402) \| 2.232775 / 2.142072 (0.090703) \| 0.814994 / 4.805227 (-3.990233) \| 0.152882 / 6.500664 (-6.347782) \| 0.067467 / 0.075469 (-0.008002) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.306007 / 1.841788 (-0.535780) \| 13.923981 / 8.074308 (5.849673) \| 13.385881 / 10.191392 (3.194489) \| 0.150712 / 0.680424 (-0.529712) \| 0.016731 / 0.534201 (-0.517470) \| 0.376557 / 0.579283 (-0.202726) \| 0.379396 / 0.434364 (-0.054968) \| 0.456251 / 0.540337 (-0.084087) \| 0.545731 / 1.386936 (-0.841205) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cc637d107ef3e3b9948691379312a8099b6476aa \"CML watermark\")\n" ]	2022-11-25T18:39:09Z	2023-02-13T16:50:42Z	2023-02-13T16:43:47Z	COLLABORATOR	null	null	null	Skip the dataset verifications (split and checksum verifications, duplicate keys check) by default unless a dataset is being tested (`datasets-cli test/run_beam`). The main goal is to avoid running the checksum check in the default case due to how expensive it can be for large datasets. PS: Maybe we should deprecate `ignore_verifications`, which is `True` now by default, and give it a different name?	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 3, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 3, "url": "https://api.github.com/repos/huggingface/datasets/issues/5303/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5303/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5303.diff", "html_url": "https://github.com/huggingface/datasets/pull/5303", "merged_at": "2023-02-13T16:43:47Z", "patch_url": "https://github.com/huggingface/datasets/pull/5303.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5303" }
https://api.github.com/repos/huggingface/datasets/issues/5018	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5018/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5018/comments	https://api.github.com/repos/huggingface/datasets/issues/5018/events	https://github.com/huggingface/datasets/pull/5018	1,384,146,585	PR_kwDODunzps4_hA0V	5,018	Create all YAML dataset_info	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[ { "color": "0e8a16", "default": false, "description": "Contribution to a dataset script", "id": 4564477500, "name": "dataset contribution", "node_id": "LA_kwDODunzps8AAAABEBBmPA", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20contribution" } ]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5018). All of your documentation changes will be reflected on that endpoint.", "Closing since https://github.com/huggingface/datasets/pull/4974 removed all the datasets scripts.\r\n\r\nIndividual PRs must be opened on the Hugging face Hub to add the YAML metadata" ]	2022-09-23T18:08:15Z	2023-09-24T09:33:21Z	2022-10-03T17:08:05Z	MEMBER	null	null	null	Following https://github.com/huggingface/datasets/pull/4926 Creates all the `dataset_info` YAML fields in the dataset cards The JSON are also updated using the simplified backward compatible format added in https://github.com/huggingface/datasets/pull/4926 Needs https://github.com/huggingface/datasets/pull/4926 to be merged first	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5018/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5018/timeline	null	null	1	{ "diff_url": "https://github.com/huggingface/datasets/pull/5018.diff", "html_url": "https://github.com/huggingface/datasets/pull/5018", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5018.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5018" }
https://api.github.com/repos/huggingface/datasets/issues/6092	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6092/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6092/comments	https://api.github.com/repos/huggingface/datasets/issues/6092/events	https://github.com/huggingface/datasets/pull/6092	1,826,111,806	PR_kwDODunzps5Wo1mh	6,092	Minor fix in `iter_files` for hidden files	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007873 / 0.011353 (-0.003480) \| 0.004585 / 0.011008 (-0.006423) \| 0.101622 / 0.038508 (0.063114) \| 0.092459 / 0.023109 (0.069350) \| 0.365157 / 0.275898 (0.089259) \| 0.405943 / 0.323480 (0.082463) \| 0.006229 / 0.007986 (-0.001756) \| 0.003811 / 0.004328 (-0.000518) \| 0.073831 / 0.004250 (0.069580) \| 0.065097 / 0.037052 (0.028045) \| 0.378912 / 0.258489 (0.120423) \| 0.422174 / 0.293841 (0.128333) \| 0.036244 / 0.128546 (-0.092302) \| 0.009677 / 0.075646 (-0.065970) \| 0.345164 / 0.419271 (-0.074107) \| 0.061632 / 0.043533 (0.018099) \| 0.370350 / 0.255139 (0.115211) \| 0.418245 / 0.283200 (0.135046) \| 0.027272 / 0.141683 (-0.114411) \| 1.774047 / 1.452155 (0.321892) \| 1.880278 / 1.492716 (0.387562) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217238 / 0.018006 (0.199231) \| 0.489560 / 0.000490 (0.489071) \| 0.004013 / 0.000200 (0.003813) \| 0.000092 / 0.000054 (0.000038) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034139 / 0.037411 (-0.003272) \| 0.103831 / 0.014526 (0.089305) \| 0.114353 / 0.176557 (-0.062204) \| 0.182034 / 0.737135 (-0.555102) \| 0.116171 / 0.296338 (-0.180168) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.448658 / 0.215209 (0.233449) \| 4.520849 / 2.077655 (2.443195) \| 2.216121 / 1.504120 (0.712001) \| 2.034596 / 1.541195 (0.493402) \| 2.193216 / 1.468490 (0.724725) \| 0.568166 / 4.584777 (-4.016611) \| 4.133587 / 3.745712 (0.387875) \| 4.641117 / 5.269862 (-0.628744) \| 2.772913 / 4.565676 (-1.792764) \| 0.067664 / 0.424275 (-0.356611) \| 0.008719 / 0.007607 (0.001112) \| 0.547723 / 0.226044 (0.321678) \| 5.438325 / 2.268929 (3.169397) \| 2.877667 / 55.444624 (-52.566958) \| 2.477503 / 6.876477 (-4.398974) \| 2.688209 / 2.142072 (0.546136) \| 0.692593 / 4.805227 (-4.112634) \| 0.154549 / 6.500664 (-6.346115) \| 0.073286 / 0.075469 (-0.002183) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.610927 / 1.841788 (-0.230861) \| 23.413345 / 8.074308 (15.339037) \| 16.851819 / 10.191392 (6.660427) \| 0.170076 / 0.680424 (-0.510348) \| 0.021428 / 0.534201 (-0.512773) \| 0.468184 / 0.579283 (-0.111099) \| 0.491820 / 0.434364 (0.057456) \| 0.553453 / 0.540337 (0.013115) \| 0.762303 / 1.386936 (-0.624633) \|\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) \| 0.008033 / 0.011353 (-0.003320) \| 0.004638 / 0.011008 (-0.006370) \| 0.077044 / 0.038508 (0.038536) \| 0.096529 / 0.023109 (0.073420) \| 0.428735 / 0.275898 (0.152837) \| 0.477303 / 0.323480 (0.153823) \| 0.006040 / 0.007986 (-0.001946) \| 0.003808 / 0.004328 (-0.000521) \| 0.076042 / 0.004250 (0.071791) \| 0.066123 / 0.037052 (0.029071) \| 0.445482 / 0.258489 (0.186993) \| 0.481350 / 0.293841 (0.187509) \| 0.036951 / 0.128546 (-0.091595) \| 0.009944 / 0.075646 (-0.065703) \| 0.082731 / 0.419271 (-0.336541) \| 0.057490 / 0.043533 (0.013958) \| 0.432668 / 0.255139 (0.177529) \| 0.461146 / 0.283200 (0.177947) \| 0.027330 / 0.141683 (-0.114353) \| 1.784195 / 1.452155 (0.332040) \| 1.834776 / 1.492716 (0.342059) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254104 / 0.018006 (0.236097) \| 0.475810 / 0.000490 (0.475321) \| 0.000459 / 0.000200 (0.000259) \| 0.000069 / 0.000054 (0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037058 / 0.037411 (-0.000353) \| 0.114962 / 0.014526 (0.100436) \| 0.123725 / 0.176557 (-0.052832) \| 0.188885 / 0.737135 (-0.548251) \| 0.125668 / 0.296338 (-0.170670) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.492627 / 0.215209 (0.277418) \| 4.900625 / 2.077655 (2.822970) \| 2.546349 / 1.504120 (1.042229) \| 2.360350 / 1.541195 (0.819155) \| 2.477975 / 1.468490 (1.009485) \| 0.574042 / 4.584777 (-4.010735) \| 4.408414 / 3.745712 (0.662702) \| 3.836640 / 5.269862 (-1.433222) \| 2.438450 / 4.565676 (-2.127227) \| 0.067706 / 0.424275 (-0.356569) \| 0.009165 / 0.007607 (0.001558) \| 0.580313 / 0.226044 (0.354269) \| 5.798211 / 2.268929 (3.529283) \| 3.098480 / 55.444624 (-52.346145) \| 2.740180 / 6.876477 (-4.136296) \| 2.984548 / 2.142072 (0.842476) \| 0.702550 / 4.805227 (-4.102677) \| 0.158248 / 6.500664 (-6.342416) \| 0.073999 / 0.075469 (-0.001470) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.636034 / 1.841788 (-0.205754) \| 24.068000 / 8.074308 (15.993692) \| 17.123987 / 10.191392 (6.932595) \| 0.210101 / 0.680424 (-0.470323) \| 0.022555 / 0.534201 (-0.511646) \| 0.509354 / 0.579283 (-0.069929) \| 0.540739 / 0.434364 (0.106375) \| 0.546048 / 0.540337 (0.005711) \| 0.719155 / 1.386936 (-0.667781) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#40530382ba98f54445de8820943b1236d4a4704f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007342 / 0.011353 (-0.004010) \| 0.004579 / 0.011008 (-0.006429) \| 0.087050 / 0.038508 (0.048542) \| 0.089001 / 0.023109 (0.065892) \| 0.307319 / 0.275898 (0.031421) \| 0.377573 / 0.323480 (0.054093) \| 0.006472 / 0.007986 (-0.001514) \| 0.004287 / 0.004328 (-0.000041) \| 0.067226 / 0.004250 (0.062976) \| 0.063147 / 0.037052 (0.026094) \| 0.314541 / 0.258489 (0.056052) \| 0.369919 / 0.293841 (0.076078) \| 0.031283 / 0.128546 (-0.097263) \| 0.009175 / 0.075646 (-0.066471) \| 0.289211 / 0.419271 (-0.130061) \| 0.053444 / 0.043533 (0.009911) \| 0.307308 / 0.255139 (0.052169) \| 0.346221 / 0.283200 (0.063021) \| 0.027948 / 0.141683 (-0.113735) \| 1.475177 / 1.452155 (0.023022) \| 1.575971 / 1.492716 (0.083255) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291092 / 0.018006 (0.273086) \| 0.696951 / 0.000490 (0.696461) \| 0.005211 / 0.000200 (0.005011) \| 0.000094 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031787 / 0.037411 (-0.005625) \| 0.084382 / 0.014526 (0.069857) \| 0.106474 / 0.176557 (-0.070083) \| 0.161472 / 0.737135 (-0.575663) \| 0.108650 / 0.296338 (-0.187688) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.379656 / 0.215209 (0.164447) \| 3.784072 / 2.077655 (1.706417) \| 1.826580 / 1.504120 (0.322460) \| 1.654916 / 1.541195 (0.113721) \| 1.730698 / 1.468490 (0.262208) \| 0.478003 / 4.584777 (-4.106774) \| 3.564920 / 3.745712 (-0.180792) \| 5.824873 / 5.269862 (0.555012) \| 3.454563 / 4.565676 (-1.111113) \| 0.056646 / 0.424275 (-0.367629) \| 0.007410 / 0.007607 (-0.000197) \| 0.461781 / 0.226044 (0.235737) \| 4.600928 / 2.268929 (2.331999) \| 2.351887 / 55.444624 (-53.092738) \| 1.986470 / 6.876477 (-4.890007) \| 2.311623 / 2.142072 (0.169551) \| 0.571247 / 4.805227 (-4.233980) \| 0.132191 / 6.500664 (-6.368473) \| 0.059943 / 0.075469 (-0.015526) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.253142 / 1.841788 (-0.588646) \| 21.294983 / 8.074308 (13.220675) \| 14.522429 / 10.191392 (4.331037) \| 0.166663 / 0.680424 (-0.513761) \| 0.019694 / 0.534201 (-0.514507) \| 0.395908 / 0.579283 (-0.183375) \| 0.413283 / 0.434364 (-0.021081) \| 0.457739 / 0.540337 (-0.082599) \| 0.664361 / 1.386936 (-0.722575) \|\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) \| 0.007228 / 0.011353 (-0.004124) \| 0.004941 / 0.011008 (-0.006067) \| 0.065381 / 0.038508 (0.026873) \| 0.090790 / 0.023109 (0.067681) \| 0.391315 / 0.275898 (0.115417) \| 0.416518 / 0.323480 (0.093038) \| 0.007015 / 0.007986 (-0.000970) \| 0.004417 / 0.004328 (0.000089) \| 0.067235 / 0.004250 (0.062985) \| 0.068092 / 0.037052 (0.031039) \| 0.403031 / 0.258489 (0.144542) \| 0.434013 / 0.293841 (0.140172) \| 0.032004 / 0.128546 (-0.096542) \| 0.009242 / 0.075646 (-0.066404) \| 0.071222 / 0.419271 (-0.348050) \| 0.054207 / 0.043533 (0.010674) \| 0.386198 / 0.255139 (0.131059) \| 0.404350 / 0.283200 (0.121150) \| 0.036284 / 0.141683 (-0.105399) \| 1.488814 / 1.452155 (0.036660) \| 1.587785 / 1.492716 (0.095069) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.313760 / 0.018006 (0.295754) \| 0.747778 / 0.000490 (0.747289) \| 0.003307 / 0.000200 (0.003107) \| 0.000113 / 0.000054 (0.000058) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034321 / 0.037411 (-0.003090) \| 0.088266 / 0.014526 (0.073740) \| 0.112874 / 0.176557 (-0.063682) \| 0.171554 / 0.737135 (-0.565581) \| 0.111356 / 0.296338 (-0.184982) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422624 / 0.215209 (0.207415) \| 4.212079 / 2.077655 (2.134425) \| 2.242742 / 1.504120 (0.738622) \| 2.072555 / 1.541195 (0.531360) \| 2.192648 / 1.468490 (0.724158) \| 0.488214 / 4.584777 (-4.096563) \| 3.597013 / 3.745712 (-0.148699) \| 3.477556 / 5.269862 (-1.792305) \| 2.184340 / 4.565676 (-2.381337) \| 0.057170 / 0.424275 (-0.367105) \| 0.007772 / 0.007607 (0.000165) \| 0.499455 / 0.226044 (0.273411) \| 4.988953 / 2.268929 (2.720024) \| 2.797894 / 55.444624 (-52.646731) \| 2.402215 / 6.876477 (-4.474262) \| 2.725069 / 2.142072 (0.582997) \| 0.596213 / 4.805227 (-4.209014) \| 0.136564 / 6.500664 (-6.364100) \| 0.061799 / 0.075469 (-0.013670) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.360739 / 1.841788 (-0.481049) \| 21.846457 / 8.074308 (13.772149) \| 14.568842 / 10.191392 (4.377450) \| 0.168980 / 0.680424 (-0.511444) \| 0.018795 / 0.534201 (-0.515406) \| 0.396173 / 0.579283 (-0.183110) \| 0.418651 / 0.434364 (-0.015713) \| 0.480042 / 0.540337 (-0.060295) \| 0.650803 / 1.386936 (-0.736133) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b7d460304487d4daab0a64ca0ca707e896367ca1 \"CML watermark\")\n" ]	2023-07-28T09:50:12Z	2023-07-28T10:59:28Z	2023-07-28T10:50:10Z	COLLABORATOR	null	null	null	Fix #6090	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6092/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6092/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6092.diff", "html_url": "https://github.com/huggingface/datasets/pull/6092", "merged_at": "2023-07-28T10:50:09Z", "patch_url": "https://github.com/huggingface/datasets/pull/6092.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6092" }
https://api.github.com/repos/huggingface/datasets/issues/7155	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7155/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7155/comments	https://api.github.com/repos/huggingface/datasets/issues/7155/events	https://github.com/huggingface/datasets/issues/7155	2,533,641,870	I_kwDODunzps6XBE6O	7,155	Dataset viewer not working! Failure due to more than 32 splits.	{ "avatar_url": "https://avatars.githubusercontent.com/u/81933585?v=4", "events_url": "https://api.github.com/users/sleepingcat4/events{/privacy}", "followers_url": "https://api.github.com/users/sleepingcat4/followers", "following_url": "https://api.github.com/users/sleepingcat4/following{/other_user}", "gists_url": "https://api.github.com/users/sleepingcat4/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sleepingcat4", "id": 81933585, "login": "sleepingcat4", "node_id": "MDQ6VXNlcjgxOTMzNTg1", "organizations_url": "https://api.github.com/users/sleepingcat4/orgs", "received_events_url": "https://api.github.com/users/sleepingcat4/received_events", "repos_url": "https://api.github.com/users/sleepingcat4/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sleepingcat4/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sleepingcat4/subscriptions", "type": "User", "url": "https://api.github.com/users/sleepingcat4", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "I have fixed it! But I would appreciate a new feature wheere I could iterate over and see what each file looks like. " ]	2024-09-18T12:43:21Z	2024-09-18T13:20:03Z	2024-09-18T13:20:03Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	Hello guys, I have a dataset and I didn't know I couldn't upload more than 32 splits. Now, my dataset viewer is not working. I don't have the dataset locally on my node anymore and recreating would take a week. And I have to publish the dataset coming Monday. I read about the practice, how I can resolve it and avoid this issue in the future. But, at the moment I need a hard fix for two of my datasets. And I don't want to mess or change anything and allow everyone in public to see the dataset and interact with it. Can you please help me? https://huggingface.co/datasets/laion/Wikipedia-X https://huggingface.co/datasets/laion/Wikipedia-X-Full	{ "avatar_url": "https://avatars.githubusercontent.com/u/81933585?v=4", "events_url": "https://api.github.com/users/sleepingcat4/events{/privacy}", "followers_url": "https://api.github.com/users/sleepingcat4/followers", "following_url": "https://api.github.com/users/sleepingcat4/following{/other_user}", "gists_url": "https://api.github.com/users/sleepingcat4/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sleepingcat4", "id": 81933585, "login": "sleepingcat4", "node_id": "MDQ6VXNlcjgxOTMzNTg1", "organizations_url": "https://api.github.com/users/sleepingcat4/orgs", "received_events_url": "https://api.github.com/users/sleepingcat4/received_events", "repos_url": "https://api.github.com/users/sleepingcat4/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sleepingcat4/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sleepingcat4/subscriptions", "type": "User", "url": "https://api.github.com/users/sleepingcat4", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7155/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7155/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6266	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6266/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6266/comments	https://api.github.com/repos/huggingface/datasets/issues/6266/events	https://github.com/huggingface/datasets/pull/6266	1,916,334,394	PR_kwDODunzps5bYYb8	6,266	Use LibYAML with PyYAML if available	{ "avatar_url": "https://avatars.githubusercontent.com/u/3905501?v=4", "events_url": "https://api.github.com/users/bryant1410/events{/privacy}", "followers_url": "https://api.github.com/users/bryant1410/followers", "following_url": "https://api.github.com/users/bryant1410/following{/other_user}", "gists_url": "https://api.github.com/users/bryant1410/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/bryant1410", "id": 3905501, "login": "bryant1410", "node_id": "MDQ6VXNlcjM5MDU1MDE=", "organizations_url": "https://api.github.com/users/bryant1410/orgs", "received_events_url": "https://api.github.com/users/bryant1410/received_events", "repos_url": "https://api.github.com/users/bryant1410/repos", "site_admin": false, "starred_url": "https://api.github.com/users/bryant1410/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/bryant1410/subscriptions", "type": "User", "url": "https://api.github.com/users/bryant1410", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6266). All of your documentation changes will be reflected on that endpoint.", "On Ubuntu, if `libyaml-dev` is installed, you can install PyYAML 6.0.1 with LibYAML with the following command (as it's automatically detected):\r\n\r\n```bash\r\npip install git+https://github.com/yaml/pyyaml.git@6.0.1\r\n```", "Are the failing tests flaky?", "We use `huggingface_hub`'s RepoCard API instead of these modules to parse the YAML block (notice the deprecations), so the `huggingface_hub` repo is the right place to suggest these changes.\r\n\r\nPersonally, I'm not a fan of these changes, as a single non-standard usage of the `ClassLabel` type is not a sufficient reason to merge them. Also, the dataset in question stores data in a single Parquet file, with the features info embedded in its (schema) metadata, which means the YAML parsing can be skipped while preserving the features by directly loading the Parquet file:\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"parquet\", data_files=\"https://huggingface.co/datasets/HuggingFaceM4/SugarCrepe_swap_obj/resolve/main/data/test-00000-of-00001-ca2ae6017a2336d7.parquet\")\r\n```\r\n\r\nPS: Yes, these tests are flaky. We are working on fixing them.", "Oh, I didn't realize they were deprecated. Thanks for the tip on how to work around this issue!\r\n\r\nFor future reference, the places to change the code in `huggingface_hub` would be:\r\n\r\nhttps://github.com/huggingface/huggingface_hub/blob/89cc69105074f1d071e0471144605f3cdfe1dab3/src/huggingface_hub/repocard.py#L506\r\n\r\nhttps://github.com/huggingface/huggingface_hub/blob/89cc69105074f1d071e0471144605f3cdfe1dab3/src/huggingface_hub/utils/_fixes.py#L34" ]	2023-09-27T21:13:36Z	2023-09-28T14:29:24Z	null	CONTRIBUTOR	null	null	null	PyYAML, the YAML framework used in this library, allows the use of LibYAML to accelerate the methods `load` and `dump`. To use it, a user would need to first install a PyYAML version that uses LibYAML (not available in PyPI; needs to be manually installed). Then, to actually use them, PyYAML suggests importing the LibYAML version of the `Loader` and `Dumper` and falling back to the default ones. This PR implements this change. See [PyYAML docs](https://pyyaml.org/wiki/PyYAMLDocumentation) for more info. This change was motivated after trying to use any of [the SugarCREPE datasets in the Hub](https://huggingface.co/datasets?search=sugarcrepe) provided by [the org HuggingFaceM4](https://huggingface.co/datasets/HuggingFaceM4). Such datasets save a lot of information (~1MB) in the YAML metadata from the `README.md` file and I noticed this slowed down the data loading process. BTW, I also noticed cache files for it is also slow because it tries to hash an instance of `DatasetInfo`, which in turn has all this metadata. Also, I changed two list comprehensions into generator expressions to avoid allocating extra memory unnecessarily. And BTW, there's [an issue in PyYAML suggesting to make this automatic](https://github.com/yaml/pyyaml/issues/437).	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6266/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6266/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6266.diff", "html_url": "https://github.com/huggingface/datasets/pull/6266", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6266.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6266" }
https://api.github.com/repos/huggingface/datasets/issues/6410	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6410/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6410/comments	https://api.github.com/repos/huggingface/datasets/issues/6410/events	https://github.com/huggingface/datasets/issues/6410	1,992,100,209	I_kwDODunzps52vQlx	6,410	Datasets does not load HuggingFace Repository properly	{ "avatar_url": "https://avatars.githubusercontent.com/u/40600201?v=4", "events_url": "https://api.github.com/users/MikeDoes/events{/privacy}", "followers_url": "https://api.github.com/users/MikeDoes/followers", "following_url": "https://api.github.com/users/MikeDoes/following{/other_user}", "gists_url": "https://api.github.com/users/MikeDoes/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/MikeDoes", "id": 40600201, "login": "MikeDoes", "node_id": "MDQ6VXNlcjQwNjAwMjAx", "organizations_url": "https://api.github.com/users/MikeDoes/orgs", "received_events_url": "https://api.github.com/users/MikeDoes/received_events", "repos_url": "https://api.github.com/users/MikeDoes/repos", "site_admin": false, "starred_url": "https://api.github.com/users/MikeDoes/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/MikeDoes/subscriptions", "type": "User", "url": "https://api.github.com/users/MikeDoes", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Hi! You can avoid the error by requesting only the `jsonl` files. `dataset = load_dataset(\"ai4privacy/pii-masking-200k\", data_files=[\"*.jsonl\"])`.\r\n\r\nOur data file inference does not filter out (incompatible) `json` files because `json` and `jsonl` use the same builder. Still, I think the inference should differentiate these extensions because it's safe to assume that loading them together will lead to an error. WDYT @lhoestq? ", "Raising an error if there is a mix of json and jsonl in the builder makes sense yea" ]	2023-11-14T06:50:49Z	2023-11-16T06:54:36Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Dear Datasets team, We just have published a dataset on Huggingface: https://huggingface.co/ai4privacy However, when trying to read it using the Dataset library we get an error. As I understand jsonl files are compatible, could you please clarify how we can solve the issue? Please let me know and we would be more than happy to adapt the structure of the repository or meta data so it works easier: ```python from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k") ``` ``` Downloading readme: 100% 11.8k/11.8k [00:00<00:00, 512kB/s] Downloading data files: 100% 1/1 [00:11<00:00, 11.16s/it] Downloading data: 100% 64.3M/64.3M [00:02<00:00, 32.9MB/s] Downloading data: 100% 113M/113M [00:03<00:00, 35.0MB/s] Downloading data: 100% 97.7M/97.7M [00:02<00:00, 46.1MB/s] Downloading data: 100% 90.8M/90.8M [00:02<00:00, 44.9MB/s] Downloading data: 100% 7.63k/7.63k [00:00<00:00, 41.0kB/s] Downloading data: 100% 1.03k/1.03k [00:00<00:00, 9.44kB/s] Extracting data files: 100% 1/1 [00:00<00:00, 29.26it/s] Generating train split: 209261/0 [00:05<00:00, 41201.25 examples/s] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1939 ) -> 1940 writer.write_table(table) 1941 num_examples_progress_update += len(table) 8 frames [/usr/local/lib/python3.10/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in write_table(self, pa_table, writer_batch_size) 571 pa_table = pa_table.combine_chunks() --> 572 pa_table = table_cast(pa_table, self._schema) 573 if self.embed_local_files: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in table_cast(table, schema) 2327 if table.schema != schema: -> 2328 return cast_table_to_schema(table, schema) 2329 elif table.schema.metadata != schema.metadata: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in cast_table_to_schema(table, schema) 2285 if sorted(table.column_names) != sorted(features): -> 2286 raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match") 2287 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] ValueError: Couldn't cast JOBTYPE: int64 PHONEIMEI: int64 ACCOUNTNAME: int64 VEHICLEVIN: int64 GENDER: int64 CURRENCYCODE: int64 CREDITCARDISSUER: int64 JOBTITLE: int64 SEX: int64 CURRENCYSYMBOL: int64 IP: int64 EYECOLOR: int64 MASKEDNUMBER: int64 SECONDARYADDRESS: int64 JOBAREA: int64 ACCOUNTNUMBER: int64 language: string BITCOINADDRESS: int64 MAC: int64 SSN: int64 EMAIL: int64 ETHEREUMADDRESS: int64 DOB: int64 VEHICLEVRM: int64 IPV6: int64 AMOUNT: int64 URL: int64 PHONENUMBER: int64 PIN: int64 TIME: int64 CREDITCARDNUMBER: int64 FIRSTNAME: int64 IBAN: int64 BIC: int64 COUNTY: int64 STATE: int64 LASTNAME: int64 ZIPCODE: int64 HEIGHT: int64 ORDINALDIRECTION: int64 MIDDLENAME: int64 STREET: int64 USERNAME: int64 CURRENCY: int64 PREFIX: int64 USERAGENT: int64 CURRENCYNAME: int64 LITECOINADDRESS: int64 CREDITCARDCVV: int64 AGE: int64 CITY: int64 PASSWORD: int64 BUILDINGNUMBER: int64 IPV4: int64 NEARBYGPSCOORDINATE: int64 DATE: int64 COMPANYNAME: int64 to {'masked_text': Value(dtype='string', id=None), 'unmasked_text': Value(dtype='string', id=None), 'privacy_mask': Value(dtype='string', id=None), 'span_labels': Value(dtype='string', id=None), 'bio_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'tokenised_text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)} because column names don't match The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) [<ipython-input-2-f1c6811e9c83>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("ai4privacy/pii-masking-200k") [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs) 2151 2152 # Download and prepare data -> 2153 builder_instance.download_and_prepare( 2154 download_config=download_config, 2155 download_mode=download_mode, [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verification_mode, prepare_split_kwargs) 1047 try: 1048 # Prepare split will record examples associated to the split -> 1049 self._prepare_split(split_generator, prepare_split_kwargs) 1050 except OSError as e: 1051 raise OSError( [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1811 job_id = 0 1812 with pbar: -> 1813 for job_id, done, content in self._prepare_split_single( 1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1815 ): [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1957 e = e.__context__ -> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1959 1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` Thank you and have a great day ahead ### Steps to reproduce the bug Open Google Colab Notebook: Run command: !pip3 install datasets Run code: from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k") ### Expected behavior Download the dataset successfully from HuggingFace to the notebook so that we can start working with it ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.19.1 - PyArrow version: 9.0.0 - Pandas version: 1.5.3	null	{ "+1": 2, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/6410/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6410/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/4665	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4665/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4665/comments	https://api.github.com/repos/huggingface/datasets/issues/4665/events	https://github.com/huggingface/datasets/issues/4665	1,299,652,638	I_kwDODunzps5NdyAe	4,665	Unable to create dataset having Python dataset script only	{ "avatar_url": "https://avatars.githubusercontent.com/u/1479733?v=4", "events_url": "https://api.github.com/users/aleSuglia/events{/privacy}", "followers_url": "https://api.github.com/users/aleSuglia/followers", "following_url": "https://api.github.com/users/aleSuglia/following{/other_user}", "gists_url": "https://api.github.com/users/aleSuglia/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/aleSuglia", "id": 1479733, "login": "aleSuglia", "node_id": "MDQ6VXNlcjE0Nzk3MzM=", "organizations_url": "https://api.github.com/users/aleSuglia/orgs", "received_events_url": "https://api.github.com/users/aleSuglia/received_events", "repos_url": "https://api.github.com/users/aleSuglia/repos", "site_admin": false, "starred_url": "https://api.github.com/users/aleSuglia/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/aleSuglia/subscriptions", "type": "User", "url": "https://api.github.com/users/aleSuglia", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[ "Hi @aleSuglia, thanks for reporting.\r\n\r\nWe are having a look at it. \r\n\r\nWe transfer this issue to the Community tab of the corresponding Hub dataset: https://huggingface.co/datasets/Heriot-WattUniversity/dialog-babi/discussions" ]	2022-07-09T11:45:46Z	2022-07-11T07:10:09Z	2022-07-11T07:10:01Z	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	## Describe the bug Hi there, I'm trying to add the following dataset to Huggingface datasets: https://huggingface.co/datasets/Heriot-WattUniversity/dialog-babi/blob/ I'm trying to do so using the CLI commands but seems that this command generates the wrong `dataset_info.json` file (you can find it in the repo already): ``` datasets-cli test Heriot-WattUniversity/dialog-babi/dialog_babi.py --save_infos --all-configs ``` while it errors when I remove the python script: ``` datasets-cli test Heriot-WattUniversity/dialog-babi/ --save_infos --all-configs ``` The error message is the following: ``` FileNotFoundError: Unable to resolve any data file that matches '['**']' at /Users/as2180/workspace/Heriot-WattUniversity/dialog-babi with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip'] ``` ## Environment info - `datasets` version: 2.3.2 - Platform: macOS-12.4-arm64-arm-64bit - Python version: 3.9.9 - PyArrow version: 8.0.0 - Pandas version: 1.4.3	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": 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https://api.github.com/repos/huggingface/datasets/issues/5874	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5874/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5874/comments	https://api.github.com/repos/huggingface/datasets/issues/5874/events	https://github.com/huggingface/datasets/issues/5874	1,715,708,930	I_kwDODunzps5mQ6QC	5,874	Using as_dataset on a "parquet" builder	{ "avatar_url": "https://avatars.githubusercontent.com/u/9039058?v=4", "events_url": "https://api.github.com/users/rems75/events{/privacy}", "followers_url": "https://api.github.com/users/rems75/followers", "following_url": "https://api.github.com/users/rems75/following{/other_user}", "gists_url": "https://api.github.com/users/rems75/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/rems75", "id": 9039058, "login": "rems75", "node_id": "MDQ6VXNlcjkwMzkwNTg=", "organizations_url": "https://api.github.com/users/rems75/orgs", "received_events_url": "https://api.github.com/users/rems75/received_events", "repos_url": "https://api.github.com/users/rems75/repos", "site_admin": false, "starred_url": "https://api.github.com/users/rems75/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/rems75/subscriptions", "type": "User", "url": "https://api.github.com/users/rems75", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi! You can refer to [this doc](https://huggingface.co/docs/datasets/filesystems#load-and-save-your-datasets-using-your-cloud-storage-filesystem) to see the intended usage (basically, it skips the Arrow -> Parquet conversion step in `ds = load_dataset(...); ds.to_parquet(\"path/to/parquet\")`) and allows writing Parquet to remote storage unlike `to_parquet`).\r\n\r\n> I guess I'd expect as_dataset to generate the dataset in arrow format if it has to, or to suggest an alternative way to load the dataset (I've also tried other methods with load_dataset to no avail, probably due to misunderstandings on my part).\r\n\r\n`as_dataset` does not work with `file_format=\"parquet\"` files as Parquet files cannot be memory-mapped, so I think we should just raise an error in that case.\r\n" ]	2023-05-18T14:09:03Z	2023-05-31T13:23:55Z	2023-05-31T13:23:55Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I used a custom builder to ``download_and_prepare`` a dataset. The first (very minor) issue is that the doc seems to suggest ``download_and_prepare`` will return the dataset, while it does not ([builder.py](https://github.com/huggingface/datasets/blob/main/src/datasets/builder.py#L718-L738)). ``` >>> from datasets import load_dataset_builder >>> builder = load_dataset_builder("rotten_tomatoes") >>> ds = builder.download_and_prepare("./output_dir", file_format="parquet") ``` The main issue I am facing is loading the dataset from those parquet files. I used the `as_dataset` method suggested by the doc, however it returns: ` FileNotFoundError: [Errno 2] Failed to open local file 'output_dir/__main__-train-00000-of-00245.arrow'. Detail: [errno 2] No such file or directory. ` ### Steps to reproduce the bug 1. Create a custom builder of some sort: `builder = CustomBuilder()`. 2. Run `download_and_prepare` with the parquet format: `builder.download_and_prepare("./output_dir", file_format="parquet")`. 3. Run `dataset = builder.as_dataset()`. ### Expected behavior I guess I'd expect `as_dataset` to generate the dataset in arrow format if it has to, or to suggest an alternative way to load the dataset (I've also tried other methods with `load_dataset` to no avail, probably due to misunderstandings on my part). ### Environment info ``` - `datasets` version: 2.12.0 - Platform: Linux-5.15.0-1027-gcp-x86_64-with-glibc2.31 - Python version: 3.10.0 - Huggingface_hub version: 0.14.1 - PyArrow version: 8.0.0 - Pandas version: 1.5.3 ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5874/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5874/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6856	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6856/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6856/comments	https://api.github.com/repos/huggingface/datasets/issues/6856/events	https://github.com/huggingface/datasets/issues/6856	2,274,828,933	I_kwDODunzps6HlyKF	6,856	CI fails on Windows for test_delete_from_hub and test_xgetsize_private due to new-line character	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[ "After investigation, I have found that when a local file is uploaded to the Hub, the new line character is no longer transformed to \"\\n\": on Windows machine now it is kept as \"\\r\\n\".\r\n\r\nAny idea why this changed?\r\nCC: @lhoestq " ]	2024-05-02T07:37:03Z	2024-05-02T11:43:01Z	2024-05-02T11:43:01Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	CI fails on Windows for test_delete_from_hub after the merge of: - #6820 This is weird because the CI was green in the PR branch before merging to main. ``` FAILED tests/test_hub.py::test_delete_from_hub - AssertionError: assert [CommitOperat...\r\n---\r\n')] == [CommitOperat...in/\n---\n')] At index 1 diff: CommitOperationAdd(path_in_repo='README.md', path_or_fileobj=b'---\r\nconfigs:\r\n- config_name: cats\r\n data_files:\r\n - split: train\r\n path: cats/train/\r\n---\r\n') != CommitOperationAdd(path_in_repo='README.md', path_or_fileobj=b'---\nconfigs:\n- config_name: cats\n data_files:\n - split: train\n path: cats/train/\n---\n') Full diff: [ CommitOperationDelete( path_in_repo='dogs/train/0000.csv', is_folder=False, ), CommitOperationAdd( path_in_repo='README.md', - path_or_fileobj=b'---\nconfigs:\n- config_name: cats\n data_files:\n ' ? -------- + path_or_fileobj=b'---\r\nconfigs:\r\n- config_name: cats\r\n data_f' ? ++ ++ ++ - b' - split: train\n path: cats/train/\n---\n', ? ^^^^^^ - + b'iles:\r\n - split: train\r\n path: cats/train/*\r' ? ++++++++++ ++ ^ + b'\n---\r\n', ), ] ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6856/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6856/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5103	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5103/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5103/comments	https://api.github.com/repos/huggingface/datasets/issues/5103/events	https://github.com/huggingface/datasets/pull/5103	1,405,956,311	PR_kwDODunzps5Ao5gI	5,103	url encode hub url (#5099)	{ "avatar_url": "https://avatars.githubusercontent.com/u/9295277?v=4", "events_url": "https://api.github.com/users/riccardobucco/events{/privacy}", "followers_url": "https://api.github.com/users/riccardobucco/followers", "following_url": "https://api.github.com/users/riccardobucco/following{/other_user}", "gists_url": "https://api.github.com/users/riccardobucco/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/riccardobucco", "id": 9295277, "login": "riccardobucco", "node_id": "MDQ6VXNlcjkyOTUyNzc=", "organizations_url": "https://api.github.com/users/riccardobucco/orgs", "received_events_url": "https://api.github.com/users/riccardobucco/received_events", "repos_url": "https://api.github.com/users/riccardobucco/repos", "site_admin": false, "starred_url": "https://api.github.com/users/riccardobucco/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/riccardobucco/subscriptions", "type": "User", "url": "https://api.github.com/users/riccardobucco", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-10-12T10:22:12Z	2022-10-12T15:27:24Z	2022-10-12T15:24:47Z	CONTRIBUTOR	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5103/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5103/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5103.diff", "html_url": "https://github.com/huggingface/datasets/pull/5103", "merged_at": "2022-10-12T15:24:47Z", "patch_url": "https://github.com/huggingface/datasets/pull/5103.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5103" }
https://api.github.com/repos/huggingface/datasets/issues/5520	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5520/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5520/comments	https://api.github.com/repos/huggingface/datasets/issues/5520/events	https://github.com/huggingface/datasets/issues/5520	1,578,417,074	I_kwDODunzps5eFLuy	5,520	ClassLabel.cast_storage raises TypeError when called on an empty IntegerArray	{ "avatar_url": "https://avatars.githubusercontent.com/u/6591505?v=4", "events_url": "https://api.github.com/users/marioga/events{/privacy}", "followers_url": "https://api.github.com/users/marioga/followers", "following_url": "https://api.github.com/users/marioga/following{/other_user}", "gists_url": "https://api.github.com/users/marioga/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/marioga", "id": 6591505, "login": "marioga", "node_id": "MDQ6VXNlcjY1OTE1MDU=", "organizations_url": "https://api.github.com/users/marioga/orgs", "received_events_url": "https://api.github.com/users/marioga/received_events", "repos_url": "https://api.github.com/users/marioga/repos", "site_admin": false, "starred_url": "https://api.github.com/users/marioga/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/marioga/subscriptions", "type": "User", "url": "https://api.github.com/users/marioga", "user_view_type": "public" }	[]	closed	false	null	[]	null	[]	2023-02-09T18:46:52Z	2023-02-12T11:17:18Z	2023-02-12T11:17:18Z	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug `ClassLabel.cast_storage` raises `TypeError` when called on an empty `IntegerArray`. ### Steps to reproduce the bug Minimal steps: ```python import pyarrow as pa from datasets import ClassLabel ClassLabel(names=['foo', 'bar']).cast_storage(pa.array([], pa.int64())) ``` In practice, this bug arises in situations like the one below: ```python from datasets import ClassLabel, Dataset, Features, Sequence dataset = Dataset.from_dict({'labels': [[], []]}, features=Features({'labels': Sequence(ClassLabel(names=['foo', 'bar']))})) # this raises TypeError dataset.map(batched=True, batch_size=1) ``` ### Expected behavior `ClassLabel.cast_storage` should return an empty Int64Array. ### Environment info - `datasets` version: 2.9.1.dev0 - Platform: Linux-4.15.0-1032-aws-x86_64-with-glibc2.27 - Python version: 3.10.6 - PyArrow version: 11.0.0 - Pandas version: 1.5.3	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5520/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5520/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7270	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7270/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7270/comments	https://api.github.com/repos/huggingface/datasets/issues/7270/events	https://github.com/huggingface/datasets/pull/7270	2,627,107,016	PR_kwDODunzps6AiTJm	7,270	Release: 3.1.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7270). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]	2024-10-31T15:10:01Z	2024-10-31T15:14:23Z	2024-10-31T15:14:20Z	MEMBER	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7270/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7270/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7270.diff", "html_url": "https://github.com/huggingface/datasets/pull/7270", "merged_at": "2024-10-31T15:14:20Z", "patch_url": "https://github.com/huggingface/datasets/pull/7270.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7270" }
https://api.github.com/repos/huggingface/datasets/issues/5918	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5918/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5918/comments	https://api.github.com/repos/huggingface/datasets/issues/5918/events	https://github.com/huggingface/datasets/issues/5918	1,735,313,549	I_kwDODunzps5nbsiN	5,918	File not found for audio dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/1783950?v=4", "events_url": "https://api.github.com/users/RobertBaruch/events{/privacy}", "followers_url": "https://api.github.com/users/RobertBaruch/followers", "following_url": "https://api.github.com/users/RobertBaruch/following{/other_user}", "gists_url": "https://api.github.com/users/RobertBaruch/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/RobertBaruch", "id": 1783950, "login": "RobertBaruch", "node_id": "MDQ6VXNlcjE3ODM5NTA=", "organizations_url": "https://api.github.com/users/RobertBaruch/orgs", "received_events_url": "https://api.github.com/users/RobertBaruch/received_events", "repos_url": "https://api.github.com/users/RobertBaruch/repos", "site_admin": false, "starred_url": "https://api.github.com/users/RobertBaruch/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/RobertBaruch/subscriptions", "type": "User", "url": "https://api.github.com/users/RobertBaruch", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "load_dataset () did not work for loading local files either " ]	2023-06-01T02:15:29Z	2023-06-11T06:02:25Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug After loading an audio dataset, and looking at a sample entry, the `path` element, which is supposed to be the path to the audio file, doesn't actually exist. ### Steps to reproduce the bug Run bug.py: ```py import os.path from datasets import load_dataset def run() -> None: cv13 = load_dataset( "mozilla-foundation/common_voice_13_0", "hi", split="train", ) print(cv13[0]) audio_file = cv13[0]["path"] if not os.path.exists(audio_file): raise ValueError(f'File {audio_file} does not exist.') if __name__ == "__main__": run() ``` The result (on my machine): ```json {'client_id': '0f018a99663f33afbb7d38aee281fb1afcfd07f9e7acd00383f604e1e17c38d6ed8adf1bd2ccbf927a52c5adefb8ac4b158ce27a7c2ed9581e71202eb302dfb3', 'path': 'C:\\Users\\rober\\.cache\\huggingface\\datasets\\downloads\\extracted\\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\\common_voice_hi_26008353.mp3', 'audio': {'path': 'C:\\Users\\rober\\.cache\\huggingface\\datasets\\downloads\\extracted\\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\\common_voice_hi_26008353.mp3', 'array': array([ 6.46234854e-26, -1.35709319e-25, -8.07793567e-26, ..., 1.06425944e-07, 4.46417090e-08, 2.61451660e-09]), 'sampling_rate': 48000}, 'sentence': 'हमने उसका जन्मदिन मनाया।', 'up_votes': 2, 'down_votes': 0, 'age': '', 'gender': '', 'accent': '', 'locale': 'hi', 'segment': '' ', 'variant': ''} ``` ```txt Traceback (most recent call last): File "F:\eo-reco\bug.py", line 18, in <module> run() File "F:\eo-reco\bug.py", line 15, in run raise ValueError(f'File {audio_file} does not exist.') ValueError: File C:\Users\rober\.cache\huggingface\datasets\downloads\extracted\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\common_voice_hi_26008353.mp3 does not exist. ``` ### Expected behavior The `path` element points to the correct file, which happens to be: ``` C:\Users\rober\.cache\huggingface\datasets\downloads\extracted\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\hi_train_0\common_voice_hi_26008353.mp3 ``` That is, there's an extra directory `hi_train_0` that is not in the `path` element. ### Environment info - `datasets` version: 2.12.0 - Platform: Windows-10-10.0.22621-SP0 - Python version: 3.11.3 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1 -	null	{ "+1": 3, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 3, "url": "https://api.github.com/repos/huggingface/datasets/issues/5918/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5918/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7020	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7020/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7020/comments	https://api.github.com/repos/huggingface/datasets/issues/7020/events	https://github.com/huggingface/datasets/issues/7020	2,387,940,990	I_kwDODunzps6OVRZ-	7,020	Casting list array to fixed size list raises error	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[]	2024-07-03T07:54:49Z	2024-07-03T08:41:56Z	2024-07-03T08:41:56Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	When trying to cast a list array to fixed size list, an AttributeError is raised: > AttributeError: 'pyarrow.lib.FixedSizeListType' object has no attribute 'length' Steps to reproduce the bug: ```python import pyarrow as pa from datasets.table import array_cast arr = pa.array([[0, 1]]) array_cast(arr, pa.list_(pa.int64(), 2)) ``` Stack trace: ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-12-6cb90a1d8216> in <module> 3 4 arr = pa.array([[0, 1]]) ----> 5 array_cast(arr, pa.list_(pa.int64(), 2)) ~/huggingface/datasets/src/datasets/table.py in wrapper(array, args, kwargs) 1802 return pa.chunked_array([func(chunk, args, *kwargs) for chunk in array.chunks]) 1803 else: -> 1804 return func(array, args, *kwargs) 1805 1806 return wrapper ~/huggingface/datasets/src/datasets/table.py in array_cast(array, pa_type, allow_primitive_to_str, allow_decimal_to_str) 1920 else: 1921 array_values = array.values[ -> 1922 array.offset pa_type.length : (array.offset + len(array)) * pa_type.length 1923 ] 1924 return pa.FixedSizeListArray.from_arrays(_c(array_values, pa_type.value_type), pa_type.list_size) AttributeError: 'pyarrow.lib.FixedSizeListType' object has no attribute 'length' ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", 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https://api.github.com/repos/huggingface/datasets/issues/4991	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4991/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4991/comments	https://api.github.com/repos/huggingface/datasets/issues/4991/events	https://github.com/huggingface/datasets/pull/4991	1,378,898,752	PR_kwDODunzps4_P5hI	4,991	Fix missing tags in dataset cards	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-09-20T06:42:07Z	2022-09-22T12:25:32Z	2022-09-20T07:37:30Z	MEMBER	null	null	null	Fix missing tags in dataset cards: - aeslc - empathetic_dialogues - event2Mind - gap - iwslt2017 - newsgroup - qa4mre - scicite This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task. Related to: - #4833 - #4891 - #4896 - #4908 - #4921 - #4931 - #4979	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4991/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4991/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4991.diff", "html_url": "https://github.com/huggingface/datasets/pull/4991", "merged_at": "2022-09-20T07:37:30Z", "patch_url": "https://github.com/huggingface/datasets/pull/4991.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4991" }
https://api.github.com/repos/huggingface/datasets/issues/6414	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6414/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6414/comments	https://api.github.com/repos/huggingface/datasets/issues/6414/events	https://github.com/huggingface/datasets/pull/6414	1,992,482,491	PR_kwDODunzps5fZZ2l	6,414	Set `usedforsecurity=False` in hashlib methods (FIPS compliance)	{ "avatar_url": "https://avatars.githubusercontent.com/u/11801849?v=4", "events_url": "https://api.github.com/users/Wauplin/events{/privacy}", "followers_url": "https://api.github.com/users/Wauplin/followers", "following_url": "https://api.github.com/users/Wauplin/following{/other_user}", "gists_url": "https://api.github.com/users/Wauplin/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Wauplin", "id": 11801849, "login": "Wauplin", "node_id": "MDQ6VXNlcjExODAxODQ5", "organizations_url": "https://api.github.com/users/Wauplin/orgs", "received_events_url": "https://api.github.com/users/Wauplin/received_events", "repos_url": "https://api.github.com/users/Wauplin/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Wauplin/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Wauplin/subscriptions", "type": "User", "url": "https://api.github.com/users/Wauplin", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "<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) \| 0.008434 / 0.011353 (-0.002919) \| 0.006755 / 0.011008 (-0.004253) \| 0.106169 / 0.038508 (0.067661) \| 0.049329 / 0.023109 (0.026220) \| 0.433610 / 0.275898 (0.157712) \| 0.441993 / 0.323480 (0.118513) \| 0.004703 / 0.007986 (-0.003282) \| 0.006996 / 0.004328 (0.002667) \| 0.080330 / 0.004250 (0.076080) \| 0.066098 / 0.037052 (0.029045) \| 0.435444 / 0.258489 (0.176955) \| 0.490442 / 0.293841 (0.196601) \| 0.047050 / 0.128546 (-0.081496) \| 0.014520 / 0.075646 (-0.061127) \| 0.339805 / 0.419271 (-0.079467) \| 0.101161 / 0.043533 (0.057629) \| 0.423236 / 0.255139 (0.168097) \| 0.455627 / 0.283200 (0.172427) \| 0.036218 / 0.141683 (-0.105465) \| 1.766128 / 1.452155 (0.313973) \| 1.923919 / 1.492716 (0.431203) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242939 / 0.018006 (0.224933) \| 0.515582 / 0.000490 (0.515093) \| 0.020271 / 0.000200 (0.020071) \| 0.000383 / 0.000054 (0.000328) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030927 / 0.037411 (-0.006484) \| 0.093951 / 0.014526 (0.079425) \| 0.109028 / 0.176557 (-0.067529) \| 0.174947 / 0.737135 (-0.562188) \| 0.120538 / 0.296338 (-0.175800) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.553884 / 0.215209 (0.338675) \| 5.424566 / 2.077655 (3.346911) \| 2.439420 / 1.504120 (0.935301) \| 2.019324 / 1.541195 (0.478129) \| 2.170781 / 1.468490 (0.702290) \| 0.924424 / 4.584777 (-3.660353) \| 5.706029 / 3.745712 (1.960317) \| 5.096911 / 5.269862 (-0.172951) \| 3.168261 / 4.565676 (-1.397416) \| 0.094336 / 0.424275 (-0.329940) \| 0.015899 / 0.007607 (0.008292) \| 0.709684 / 0.226044 (0.483639) \| 7.476865 / 2.268929 (5.207936) \| 3.350983 / 55.444624 (-52.093641) \| 2.653419 / 6.876477 (-4.223058) \| 2.802201 / 2.142072 (0.660129) \| 1.081442 / 4.805227 (-3.723785) \| 0.217025 / 6.500664 (-6.283639) \| 0.077248 / 0.075469 (0.001779) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.598621 / 1.841788 (-0.243167) \| 23.490338 / 8.074308 (15.416030) \| 21.853488 / 10.191392 (11.662096) \| 0.209625 / 0.680424 (-0.470799) \| 0.028166 / 0.534201 (-0.506035) \| 0.473883 / 0.579283 (-0.105400) \| 0.584226 / 0.434364 (0.149862) \| 0.538605 / 0.540337 (-0.001732) \| 0.837060 / 1.386936 (-0.549876) \|\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) \| 0.009029 / 0.011353 (-0.002324) \| 0.004945 / 0.011008 (-0.006063) \| 0.084539 / 0.038508 (0.046031) \| 0.081014 / 0.023109 (0.057905) \| 0.431291 / 0.275898 (0.155393) \| 0.478913 / 0.323480 (0.155433) \| 0.006107 / 0.007986 (-0.001879) \| 0.003939 / 0.004328 (-0.000390) \| 0.079932 / 0.004250 (0.075682) \| 0.057936 / 0.037052 (0.020884) \| 0.437295 / 0.258489 (0.178806) \| 0.489790 / 0.293841 (0.195949) \| 0.049544 / 0.128546 (-0.079003) \| 0.013675 / 0.075646 (-0.061972) \| 0.093143 / 0.419271 (-0.326128) \| 0.064104 / 0.043533 (0.020571) \| 0.444699 / 0.255139 (0.189560) \| 0.443688 / 0.283200 (0.160489) \| 0.034331 / 0.141683 (-0.107352) \| 1.753014 / 1.452155 (0.300859) \| 1.877274 / 1.492716 (0.384558) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.250460 / 0.018006 (0.232454) \| 0.527241 / 0.000490 (0.526752) \| 0.007679 / 0.000200 (0.007479) \| 0.000115 / 0.000054 (0.000061) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033269 / 0.037411 (-0.004142) \| 0.111262 / 0.014526 (0.096736) \| 0.133503 / 0.176557 (-0.043053) \| 0.177998 / 0.737135 (-0.559137) \| 0.117899 / 0.296338 (-0.178440) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.633588 / 0.215209 (0.418379) \| 6.105283 / 2.077655 (4.027628) \| 2.779309 / 1.504120 (1.275189) \| 2.445788 / 1.541195 (0.904594) \| 2.396443 / 1.468490 (0.927953) \| 0.925928 / 4.584777 (-3.658849) \| 5.266142 / 3.745712 (1.520430) \| 4.868830 / 5.269862 (-0.401031) \| 2.998768 / 4.565676 (-1.566909) \| 0.103135 / 0.424275 (-0.321140) \| 0.008059 / 0.007607 (0.000452) \| 0.753159 / 0.226044 (0.527115) \| 7.532170 / 2.268929 (5.263242) \| 3.563941 / 55.444624 (-51.880683) \| 2.829208 / 6.876477 (-4.047269) \| 2.913954 / 2.142072 (0.771881) \| 1.085843 / 4.805227 (-3.719384) \| 0.214195 / 6.500664 (-6.286469) \| 0.071509 / 0.075469 (-0.003960) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.544819 / 1.841788 (-0.296968) \| 23.790149 / 8.074308 (15.715841) \| 23.086019 / 10.191392 (12.894627) \| 0.242695 / 0.680424 (-0.437729) \| 0.041706 / 0.534201 (-0.492495) \| 0.552402 / 0.579283 (-0.026881) \| 0.652518 / 0.434364 (0.218154) \| 0.581876 / 0.540337 (0.041539) \| 0.795425 / 1.386936 (-0.591511) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#117fdfccc8523fe150521ad74e478459fe2f297c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004573 / 0.011353 (-0.006780) \| 0.002965 / 0.011008 (-0.008043) \| 0.061913 / 0.038508 (0.023405) \| 0.029474 / 0.023109 (0.006365) \| 0.258117 / 0.275898 (-0.017781) \| 0.279854 / 0.323480 (-0.043626) \| 0.003954 / 0.007986 (-0.004031) \| 0.002479 / 0.004328 (-0.001850) \| 0.048685 / 0.004250 (0.044434) \| 0.044733 / 0.037052 (0.007681) \| 0.256659 / 0.258489 (-0.001830) \| 0.285235 / 0.293841 (-0.008606) \| 0.023566 / 0.128546 (-0.104981) \| 0.007291 / 0.075646 (-0.068355) \| 0.202701 / 0.419271 (-0.216570) \| 0.055706 / 0.043533 (0.012173) \| 0.258790 / 0.255139 (0.003651) \| 0.278675 / 0.283200 (-0.004525) \| 0.018574 / 0.141683 (-0.123109) \| 1.109359 / 1.452155 (-0.342796) \| 1.184434 / 1.492716 (-0.308282) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095048 / 0.018006 (0.077042) \| 0.305027 / 0.000490 (0.304537) \| 0.000310 / 0.000200 (0.000110) \| 0.000066 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018183 / 0.037411 (-0.019228) \| 0.066130 / 0.014526 (0.051604) \| 0.073948 / 0.176557 (-0.102608) \| 0.120458 / 0.737135 (-0.616678) \| 0.075995 / 0.296338 (-0.220343) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279419 / 0.215209 (0.064210) \| 2.728591 / 2.077655 (0.650936) \| 1.439016 / 1.504120 (-0.065104) \| 1.325798 / 1.541195 (-0.215397) \| 1.352050 / 1.468490 (-0.116440) \| 0.395041 / 4.584777 (-4.189736) \| 2.377651 / 3.745712 (-1.368061) \| 2.618473 / 5.269862 (-2.651389) \| 1.587580 / 4.565676 (-2.978096) \| 0.045910 / 0.424275 (-0.378365) \| 0.004843 / 0.007607 (-0.002764) \| 0.335491 / 0.226044 (0.109447) \| 3.378441 / 2.268929 (1.109512) \| 1.827757 / 55.444624 (-53.616868) \| 1.502360 / 6.876477 (-5.374117) \| 1.508460 / 2.142072 (-0.633612) \| 0.471309 / 4.805227 (-4.333918) \| 0.098934 / 6.500664 (-6.401730) \| 0.041705 / 0.075469 (-0.033764) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945067 / 1.841788 (-0.896720) \| 11.548209 / 8.074308 (3.473900) \| 10.422628 / 10.191392 (0.231236) \| 0.141494 / 0.680424 (-0.538929) \| 0.014345 / 0.534201 (-0.519856) \| 0.267750 / 0.579283 (-0.311533) \| 0.261488 / 0.434364 (-0.172876) \| 0.307192 / 0.540337 (-0.233145) \| 0.427926 / 1.386936 (-0.959010) \|\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) \| 0.004831 / 0.011353 (-0.006522) \| 0.002876 / 0.011008 (-0.008132) \| 0.048629 / 0.038508 (0.010121) \| 0.055090 / 0.023109 (0.031981) \| 0.271381 / 0.275898 (-0.004517) \| 0.292350 / 0.323480 (-0.031130) \| 0.004001 / 0.007986 (-0.003985) \| 0.002389 / 0.004328 (-0.001939) \| 0.047527 / 0.004250 (0.043277) \| 0.038065 / 0.037052 (0.001012) \| 0.277387 / 0.258489 (0.018898) \| 0.307209 / 0.293841 (0.013368) \| 0.025136 / 0.128546 (-0.103411) \| 0.007309 / 0.075646 (-0.068338) \| 0.054483 / 0.419271 (-0.364789) \| 0.032807 / 0.043533 (-0.010726) \| 0.274364 / 0.255139 (0.019225) \| 0.290280 / 0.283200 (0.007080) \| 0.017855 / 0.141683 (-0.123828) \| 1.185912 / 1.452155 (-0.266243) \| 1.228141 / 1.492716 (-0.264576) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094787 / 0.018006 (0.076781) \| 0.314191 / 0.000490 (0.313701) \| 0.000217 / 0.000200 (0.000017) \| 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) \| 0.020920 / 0.037411 (-0.016491) \| 0.070446 / 0.014526 (0.055920) \| 0.081371 / 0.176557 (-0.095186) \| 0.119127 / 0.737135 (-0.618009) \| 0.085658 / 0.296338 (-0.210680) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290601 / 0.215209 (0.075392) \| 2.874091 / 2.077655 (0.796436) \| 1.598934 / 1.504120 (0.094814) \| 1.464329 / 1.541195 (-0.076866) \| 1.504943 / 1.468490 (0.036453) \| 0.410457 / 4.584777 (-4.174320) \| 2.428706 / 3.745712 (-1.317006) \| 2.596510 / 5.269862 (-2.673352) \| 1.547084 / 4.565676 (-3.018592) \| 0.047546 / 0.424275 (-0.376729) \| 0.004740 / 0.007607 (-0.002867) \| 0.351168 / 0.226044 (0.125123) \| 3.424554 / 2.268929 (1.155626) \| 1.969792 / 55.444624 (-53.474832) \| 1.676731 / 6.876477 (-5.199745) \| 1.668769 / 2.142072 (-0.473304) \| 0.482486 / 4.805227 (-4.322741) \| 0.100018 / 6.500664 (-6.400646) \| 0.040956 / 0.075469 (-0.034513) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.966306 / 1.841788 (-0.875482) \| 12.158909 / 8.074308 (4.084601) \| 10.926447 / 10.191392 (0.735055) \| 0.130359 / 0.680424 (-0.550065) \| 0.016162 / 0.534201 (-0.518039) \| 0.269977 / 0.579283 (-0.309306) \| 0.283366 / 0.434364 (-0.150997) \| 0.304517 / 0.540337 (-0.235821) \| 0.410398 / 1.386936 (-0.976539) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d5d6e57913465c22bb8074b0c0f968252cb12b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004686 / 0.011353 (-0.006667) \| 0.002764 / 0.011008 (-0.008244) \| 0.061411 / 0.038508 (0.022902) \| 0.030450 / 0.023109 (0.007341) \| 0.247648 / 0.275898 (-0.028250) \| 0.278033 / 0.323480 (-0.045447) \| 0.002903 / 0.007986 (-0.005082) \| 0.002350 / 0.004328 (-0.001979) \| 0.047514 / 0.004250 (0.043264) \| 0.044446 / 0.037052 (0.007393) \| 0.256170 / 0.258489 (-0.002319) \| 0.285977 / 0.293841 (-0.007864) \| 0.023407 / 0.128546 (-0.105139) \| 0.007223 / 0.075646 (-0.068423) \| 0.201274 / 0.419271 (-0.217997) \| 0.054022 / 0.043533 (0.010489) \| 0.253841 / 0.255139 (-0.001298) \| 0.278219 / 0.283200 (-0.004980) \| 0.017796 / 0.141683 (-0.123886) \| 1.105950 / 1.452155 (-0.346205) \| 1.182021 / 1.492716 (-0.310695) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089584 / 0.018006 (0.071578) \| 0.299338 / 0.000490 (0.298849) \| 0.000202 / 0.000200 (0.000003) \| 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) \| 0.018974 / 0.037411 (-0.018437) \| 0.062352 / 0.014526 (0.047826) \| 0.073667 / 0.176557 (-0.102889) \| 0.119225 / 0.737135 (-0.617911) \| 0.075393 / 0.296338 (-0.220945) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282749 / 0.215209 (0.067540) \| 2.795822 / 2.077655 (0.718167) \| 1.492946 / 1.504120 (-0.011174) \| 1.382340 / 1.541195 (-0.158855) \| 1.377281 / 1.468490 (-0.091209) \| 0.397361 / 4.584777 (-4.187415) \| 2.379416 / 3.745712 (-1.366296) \| 2.552967 / 5.269862 (-2.716895) \| 1.546347 / 4.565676 (-3.019330) \| 0.045851 / 0.424275 (-0.378424) \| 0.004830 / 0.007607 (-0.002777) \| 0.351194 / 0.226044 (0.125150) \| 3.407406 / 2.268929 (1.138478) \| 1.852983 / 55.444624 (-53.591641) \| 1.536381 / 6.876477 (-5.340095) \| 1.542786 / 2.142072 (-0.599287) \| 0.471960 / 4.805227 (-4.333267) \| 0.098336 / 6.500664 (-6.402328) \| 0.041569 / 0.075469 (-0.033900) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.912718 / 1.841788 (-0.929070) \| 11.339404 / 8.074308 (3.265095) \| 10.480593 / 10.191392 (0.289201) \| 0.139508 / 0.680424 (-0.540916) \| 0.014210 / 0.534201 (-0.519991) \| 0.268152 / 0.579283 (-0.311131) \| 0.260503 / 0.434364 (-0.173860) \| 0.304735 / 0.540337 (-0.235602) \| 0.422155 / 1.386936 (-0.964781) \|\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) \| 0.004714 / 0.011353 (-0.006638) \| 0.002638 / 0.011008 (-0.008370) \| 0.047967 / 0.038508 (0.009459) \| 0.050758 / 0.023109 (0.027649) \| 0.265619 / 0.275898 (-0.010279) \| 0.286920 / 0.323480 (-0.036560) \| 0.003936 / 0.007986 (-0.004050) \| 0.002351 / 0.004328 (-0.001977) \| 0.047642 / 0.004250 (0.043392) \| 0.038412 / 0.037052 (0.001360) \| 0.269561 / 0.258489 (0.011072) \| 0.302057 / 0.293841 (0.008216) \| 0.023893 / 0.128546 (-0.104653) \| 0.006793 / 0.075646 (-0.068854) \| 0.053091 / 0.419271 (-0.366180) \| 0.032228 / 0.043533 (-0.011305) \| 0.267110 / 0.255139 (0.011971) \| 0.287211 / 0.283200 (0.004011) \| 0.017945 / 0.141683 (-0.123738) \| 1.191770 / 1.452155 (-0.260384) \| 1.269644 / 1.492716 (-0.223072) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088067 / 0.018006 (0.070061) \| 0.298383 / 0.000490 (0.297893) \| 0.000202 / 0.000200 (0.000002) \| 0.000048 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020685 / 0.037411 (-0.016726) \| 0.069883 / 0.014526 (0.055357) \| 0.080107 / 0.176557 (-0.096450) \| 0.119311 / 0.737135 (-0.617825) \| 0.080791 / 0.296338 (-0.215548) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295781 / 0.215209 (0.080572) \| 2.905536 / 2.077655 (0.827881) \| 1.579184 / 1.504120 (0.075064) \| 1.475937 / 1.541195 (-0.065258) \| 1.533708 / 1.468490 (0.065218) \| 0.409851 / 4.584777 (-4.174926) \| 2.443217 / 3.745712 (-1.302496) \| 2.543980 / 5.269862 (-2.725882) \| 1.512187 / 4.565676 (-3.053489) \| 0.046390 / 0.424275 (-0.377885) \| 0.004762 / 0.007607 (-0.002845) \| 0.345066 / 0.226044 (0.119021) \| 3.485133 / 2.268929 (1.216204) \| 1.954690 / 55.444624 (-53.489934) \| 1.671104 / 6.876477 (-5.205372) \| 1.655330 / 2.142072 (-0.486743) \| 0.487910 / 4.805227 (-4.317317) \| 0.097707 / 6.500664 (-6.402957) \| 0.040379 / 0.075469 (-0.035090) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981620 / 1.841788 (-0.860168) \| 11.806530 / 8.074308 (3.732222) \| 10.868275 / 10.191392 (0.676883) \| 0.141230 / 0.680424 (-0.539194) \| 0.015785 / 0.534201 (-0.518416) \| 0.271416 / 0.579283 (-0.307867) \| 0.276048 / 0.434364 (-0.158316) \| 0.310988 / 0.540337 (-0.229349) \| 0.410078 / 1.386936 (-0.976858) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ec565740dee10c466ade16f81dee2783e442ba55 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004803 / 0.011353 (-0.006550) \| 0.002961 / 0.011008 (-0.008047) \| 0.061431 / 0.038508 (0.022923) \| 0.030189 / 0.023109 (0.007080) \| 0.255755 / 0.275898 (-0.020143) \| 0.277841 / 0.323480 (-0.045639) \| 0.003083 / 0.007986 (-0.004902) \| 0.002432 / 0.004328 (-0.001896) \| 0.047674 / 0.004250 (0.043424) \| 0.045066 / 0.037052 (0.008014) \| 0.268701 / 0.258489 (0.010211) \| 0.286673 / 0.293841 (-0.007168) \| 0.023663 / 0.128546 (-0.104883) \| 0.007148 / 0.075646 (-0.068499) \| 0.201962 / 0.419271 (-0.217310) \| 0.054953 / 0.043533 (0.011420) \| 0.257155 / 0.255139 (0.002016) \| 0.277769 / 0.283200 (-0.005431) \| 0.017803 / 0.141683 (-0.123880) \| 1.100270 / 1.452155 (-0.351884) \| 1.146975 / 1.492716 (-0.345741) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092776 / 0.018006 (0.074770) \| 0.303786 / 0.000490 (0.303296) \| 0.000237 / 0.000200 (0.000037) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019647 / 0.037411 (-0.017765) \| 0.063211 / 0.014526 (0.048686) \| 0.076684 / 0.176557 (-0.099873) \| 0.121952 / 0.737135 (-0.615184) \| 0.077202 / 0.296338 (-0.219137) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282087 / 0.215209 (0.066878) \| 2.789204 / 2.077655 (0.711550) \| 1.510376 / 1.504120 (0.006256) \| 1.384241 / 1.541195 (-0.156954) \| 1.414949 / 1.468490 (-0.053541) \| 0.402206 / 4.584777 (-4.182570) \| 2.377601 / 3.745712 (-1.368111) \| 2.585354 / 5.269862 (-2.684508) \| 1.592937 / 4.565676 (-2.972740) \| 0.045217 / 0.424275 (-0.379058) \| 0.004772 / 0.007607 (-0.002835) \| 0.339584 / 0.226044 (0.113539) \| 3.373184 / 2.268929 (1.104256) \| 1.855196 / 55.444624 (-53.589428) \| 1.599559 / 6.876477 (-5.276918) \| 1.604421 / 2.142072 (-0.537651) \| 0.467754 / 4.805227 (-4.337474) \| 0.098244 / 6.500664 (-6.402420) \| 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) \| 0.947680 / 1.841788 (-0.894108) \| 11.539875 / 8.074308 (3.465567) \| 10.340830 / 10.191392 (0.149438) \| 0.145591 / 0.680424 (-0.534833) \| 0.014367 / 0.534201 (-0.519834) \| 0.270506 / 0.579283 (-0.308777) \| 0.268825 / 0.434364 (-0.165539) \| 0.308372 / 0.540337 (-0.231966) \| 0.425039 / 1.386936 (-0.961897) \|\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) \| 0.004813 / 0.011353 (-0.006540) \| 0.002931 / 0.011008 (-0.008078) \| 0.047997 / 0.038508 (0.009489) \| 0.050753 / 0.023109 (0.027644) \| 0.272704 / 0.275898 (-0.003194) \| 0.294045 / 0.323480 (-0.029435) \| 0.004059 / 0.007986 (-0.003927) \| 0.002491 / 0.004328 (-0.001838) \| 0.047621 / 0.004250 (0.043371) \| 0.038824 / 0.037052 (0.001772) \| 0.275322 / 0.258489 (0.016833) \| 0.306447 / 0.293841 (0.012606) \| 0.024402 / 0.128546 (-0.104145) \| 0.007252 / 0.075646 (-0.068394) \| 0.053346 / 0.419271 (-0.365925) \| 0.032224 / 0.043533 (-0.011309) \| 0.271468 / 0.255139 (0.016329) \| 0.289429 / 0.283200 (0.006229) \| 0.018285 / 0.141683 (-0.123398) \| 1.116743 / 1.452155 (-0.335412) \| 1.182724 / 1.492716 (-0.309993) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091899 / 0.018006 (0.073893) \| 0.299161 / 0.000490 (0.298671) \| 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) \| 0.021823 / 0.037411 (-0.015588) \| 0.071227 / 0.014526 (0.056701) \| 0.080503 / 0.176557 (-0.096053) \| 0.120243 / 0.737135 (-0.616892) \| 0.082328 / 0.296338 (-0.214010) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.324951 / 0.215209 (0.109742) \| 2.842358 / 2.077655 (0.764703) \| 1.602317 / 1.504120 (0.098197) \| 1.481103 / 1.541195 (-0.060091) \| 1.497557 / 1.468490 (0.029067) \| 0.406523 / 4.584777 (-4.178254) \| 2.402743 / 3.745712 (-1.342970) \| 2.545435 / 5.269862 (-2.724427) \| 1.534071 / 4.565676 (-3.031605) \| 0.046914 / 0.424275 (-0.377361) \| 0.004728 / 0.007607 (-0.002879) \| 0.341544 / 0.226044 (0.115499) \| 3.412017 / 2.268929 (1.143089) \| 1.937442 / 55.444624 (-53.507182) \| 1.668774 / 6.876477 (-5.207703) \| 1.668908 / 2.142072 (-0.473165) \| 0.477398 / 4.805227 (-4.327829) \| 0.098531 / 6.500664 (-6.402133) \| 0.041077 / 0.075469 (-0.034392) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983888 / 1.841788 (-0.857900) \| 12.072703 / 8.074308 (3.998395) \| 11.028622 / 10.191392 (0.837230) \| 0.148097 / 0.680424 (-0.532327) \| 0.015869 / 0.534201 (-0.518332) \| 0.267609 / 0.579283 (-0.311674) \| 0.272345 / 0.434364 (-0.162019) \| 0.303840 / 0.540337 (-0.236497) \| 0.409199 / 1.386936 (-0.977737) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1487df064580bd23458234fab2e85876d9364e03 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005016 / 0.011353 (-0.006337) \| 0.002931 / 0.011008 (-0.008077) \| 0.062142 / 0.038508 (0.023634) \| 0.030758 / 0.023109 (0.007648) \| 0.251689 / 0.275898 (-0.024209) \| 0.272114 / 0.323480 (-0.051366) \| 0.004102 / 0.007986 (-0.003884) \| 0.002500 / 0.004328 (-0.001828) \| 0.049187 / 0.004250 (0.044937) \| 0.047150 / 0.037052 (0.010098) \| 0.256497 / 0.258489 (-0.001992) \| 0.288069 / 0.293841 (-0.005772) \| 0.023915 / 0.128546 (-0.104632) \| 0.007204 / 0.075646 (-0.068442) \| 0.204257 / 0.419271 (-0.215015) \| 0.063879 / 0.043533 (0.020346) \| 0.253008 / 0.255139 (-0.002131) \| 0.266554 / 0.283200 (-0.016645) \| 0.018929 / 0.141683 (-0.122754) \| 1.140547 / 1.452155 (-0.311608) \| 1.197049 / 1.492716 (-0.295668) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094111 / 0.018006 (0.076105) \| 0.301618 / 0.000490 (0.301128) \| 0.000219 / 0.000200 (0.000019) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018614 / 0.037411 (-0.018797) \| 0.062426 / 0.014526 (0.047900) \| 0.073079 / 0.176557 (-0.103477) \| 0.120313 / 0.737135 (-0.616823) \| 0.076445 / 0.296338 (-0.219894) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285151 / 0.215209 (0.069942) \| 2.754272 / 2.077655 (0.676617) \| 1.485254 / 1.504120 (-0.018866) \| 1.368412 / 1.541195 (-0.172783) \| 1.402819 / 1.468490 (-0.065671) \| 0.396561 / 4.584777 (-4.188216) \| 2.375708 / 3.745712 (-1.370004) \| 2.656088 / 5.269862 (-2.613773) \| 1.588676 / 4.565676 (-2.977001) \| 0.048662 / 0.424275 (-0.375613) \| 0.004963 / 0.007607 (-0.002644) \| 0.339747 / 0.226044 (0.113702) \| 3.315841 / 2.268929 (1.046912) \| 1.841439 / 55.444624 (-53.603186) \| 1.547803 / 6.876477 (-5.328674) \| 1.601872 / 2.142072 (-0.540200) \| 0.468637 / 4.805227 (-4.336591) \| 0.099423 / 6.500664 (-6.401241) \| 0.041926 / 0.075469 (-0.033543) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.933058 / 1.841788 (-0.908730) \| 11.680870 / 8.074308 (3.606561) \| 10.239009 / 10.191392 (0.047617) \| 0.129974 / 0.680424 (-0.550450) \| 0.014081 / 0.534201 (-0.520120) \| 0.273076 / 0.579283 (-0.306207) \| 0.261914 / 0.434364 (-0.172450) \| 0.305982 / 0.540337 (-0.234356) \| 0.430623 / 1.386936 (-0.956313) \|\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) \| 0.004969 / 0.011353 (-0.006384) \| 0.003084 / 0.011008 (-0.007924) \| 0.048686 / 0.038508 (0.010178) \| 0.057234 / 0.023109 (0.034125) \| 0.295408 / 0.275898 (0.019510) \| 0.323774 / 0.323480 (0.000294) \| 0.004014 / 0.007986 (-0.003972) \| 0.002423 / 0.004328 (-0.001905) \| 0.048000 / 0.004250 (0.043749) \| 0.039872 / 0.037052 (0.002820) \| 0.294717 / 0.258489 (0.036228) \| 0.331149 / 0.293841 (0.037309) \| 0.027884 / 0.128546 (-0.100662) \| 0.007155 / 0.075646 (-0.068491) \| 0.053812 / 0.419271 (-0.365460) \| 0.032483 / 0.043533 (-0.011050) \| 0.293402 / 0.255139 (0.038263) \| 0.312553 / 0.283200 (0.029354) \| 0.017848 / 0.141683 (-0.123835) \| 1.125600 / 1.452155 (-0.326554) \| 1.189469 / 1.492716 (-0.303248) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096198 / 0.018006 (0.078191) \| 0.305096 / 0.000490 (0.304607) \| 0.000229 / 0.000200 (0.000029) \| 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) \| 0.021992 / 0.037411 (-0.015419) \| 0.072082 / 0.014526 (0.057556) \| 0.082704 / 0.176557 (-0.093853) \| 0.124512 / 0.737135 (-0.612624) \| 0.084541 / 0.296338 (-0.211797) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296440 / 0.215209 (0.081231) \| 2.923392 / 2.077655 (0.845738) \| 1.599057 / 1.504120 (0.094937) \| 1.480473 / 1.541195 (-0.060722) \| 1.551837 / 1.468490 (0.083347) \| 0.418618 / 4.584777 (-4.166159) \| 2.472727 / 3.745712 (-1.272985) \| 2.796141 / 5.269862 (-2.473721) \| 1.629139 / 4.565676 (-2.936538) \| 0.047703 / 0.424275 (-0.376572) \| 0.004971 / 0.007607 (-0.002636) \| 0.354453 / 0.226044 (0.128408) \| 3.514861 / 2.268929 (1.245932) \| 1.993597 / 55.444624 (-53.451028) \| 1.694386 / 6.876477 (-5.182090) \| 1.748562 / 2.142072 (-0.393510) \| 0.487158 / 4.805227 (-4.318070) \| 0.102021 / 6.500664 (-6.398643) \| 0.042648 / 0.075469 (-0.032821) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974950 / 1.841788 (-0.866837) \| 13.391204 / 8.074308 (5.316896) \| 11.474696 / 10.191392 (1.283304) \| 0.142618 / 0.680424 (-0.537806) \| 0.016163 / 0.534201 (-0.518038) \| 0.271453 / 0.579283 (-0.307830) \| 0.287049 / 0.434364 (-0.147315) \| 0.309069 / 0.540337 (-0.231268) \| 0.417117 / 1.386936 (-0.969819) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#35a3422cfcebfef5b09ae70c22843ffadaf44c46 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004974 / 0.011353 (-0.006379) \| 0.002950 / 0.011008 (-0.008058) \| 0.061856 / 0.038508 (0.023348) \| 0.030539 / 0.023109 (0.007429) \| 0.250105 / 0.275898 (-0.025793) \| 0.276687 / 0.323480 (-0.046793) \| 0.003077 / 0.007986 (-0.004908) \| 0.002412 / 0.004328 (-0.001916) \| 0.048336 / 0.004250 (0.044086) \| 0.045849 / 0.037052 (0.008797) \| 0.251757 / 0.258489 (-0.006732) \| 0.284914 / 0.293841 (-0.008927) \| 0.024033 / 0.128546 (-0.104513) \| 0.007343 / 0.075646 (-0.068303) \| 0.202867 / 0.419271 (-0.216405) \| 0.061294 / 0.043533 (0.017762) \| 0.263590 / 0.255139 (0.008451) \| 0.272744 / 0.283200 (-0.010455) \| 0.019613 / 0.141683 (-0.122070) \| 1.104263 / 1.452155 (-0.347892) \| 1.164128 / 1.492716 (-0.328588) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094261 / 0.018006 (0.076255) \| 0.303340 / 0.000490 (0.302850) \| 0.000215 / 0.000200 (0.000015) \| 0.000057 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018381 / 0.037411 (-0.019030) \| 0.062727 / 0.014526 (0.048201) \| 0.074955 / 0.176557 (-0.101602) \| 0.124810 / 0.737135 (-0.612326) \| 0.074335 / 0.296338 (-0.222004) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279368 / 0.215209 (0.064159) \| 2.721641 / 2.077655 (0.643986) \| 1.510773 / 1.504120 (0.006653) \| 1.364349 / 1.541195 (-0.176845) \| 1.386044 / 1.468490 (-0.082446) \| 0.403051 / 4.584777 (-4.181726) \| 2.416525 / 3.745712 (-1.329187) \| 2.623198 / 5.269862 (-2.646663) \| 1.560869 / 4.565676 (-3.004808) \| 0.046613 / 0.424275 (-0.377662) \| 0.004861 / 0.007607 (-0.002746) \| 0.337875 / 0.226044 (0.111830) \| 3.289956 / 2.268929 (1.021028) \| 1.851707 / 55.444624 (-53.592917) \| 1.571092 / 6.876477 (-5.305385) \| 1.600328 / 2.142072 (-0.541745) \| 0.480766 / 4.805227 (-4.324461) \| 0.099138 / 6.500664 (-6.401526) \| 0.041691 / 0.075469 (-0.033779) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941162 / 1.841788 (-0.900626) \| 11.745335 / 8.074308 (3.671027) \| 10.645509 / 10.191392 (0.454117) \| 0.132506 / 0.680424 (-0.547918) \| 0.015192 / 0.534201 (-0.519009) \| 0.272483 / 0.579283 (-0.306800) \| 0.270269 / 0.434364 (-0.164094) \| 0.309580 / 0.540337 (-0.230758) \| 0.431513 / 1.386936 (-0.955423) \|\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) \| 0.005068 / 0.011353 (-0.006285) \| 0.003069 / 0.011008 (-0.007939) \| 0.048605 / 0.038508 (0.010097) \| 0.059557 / 0.023109 (0.036448) \| 0.275092 / 0.275898 (-0.000806) \| 0.298910 / 0.323480 (-0.024570) \| 0.004198 / 0.007986 (-0.003788) \| 0.002499 / 0.004328 (-0.001830) \| 0.048248 / 0.004250 (0.043997) \| 0.040302 / 0.037052 (0.003249) \| 0.279539 / 0.258489 (0.021050) \| 0.312500 / 0.293841 (0.018659) \| 0.025407 / 0.128546 (-0.103140) \| 0.007364 / 0.075646 (-0.068282) \| 0.053086 / 0.419271 (-0.366186) \| 0.033291 / 0.043533 (-0.010242) \| 0.276521 / 0.255139 (0.021382) \| 0.292943 / 0.283200 (0.009743) \| 0.019416 / 0.141683 (-0.122267) \| 1.151734 / 1.452155 (-0.300421) \| 1.205021 / 1.492716 (-0.287695) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094112 / 0.018006 (0.076106) \| 0.309534 / 0.000490 (0.309044) \| 0.000219 / 0.000200 (0.000019) \| 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) \| 0.021539 / 0.037411 (-0.015872) \| 0.070325 / 0.014526 (0.055799) \| 0.080468 / 0.176557 (-0.096089) \| 0.121095 / 0.737135 (-0.616040) \| 0.082008 / 0.296338 (-0.214331) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302591 / 0.215209 (0.087382) \| 2.943475 / 2.077655 (0.865820) \| 1.597970 / 1.504120 (0.093850) \| 1.468774 / 1.541195 (-0.072421) \| 1.504812 / 1.468490 (0.036322) \| 0.413715 / 4.584777 (-4.171062) \| 2.418319 / 3.745712 (-1.327393) \| 2.616656 / 5.269862 (-2.653206) \| 1.558165 / 4.565676 (-3.007512) \| 0.047169 / 0.424275 (-0.377106) \| 0.004761 / 0.007607 (-0.002846) \| 0.347225 / 0.226044 (0.121180) \| 3.479624 / 2.268929 (1.210696) \| 1.961253 / 55.444624 (-53.483371) \| 1.673532 / 6.876477 (-5.202944) \| 1.698900 / 2.142072 (-0.443172) \| 0.488373 / 4.805227 (-4.316855) \| 0.098322 / 6.500664 (-6.402342) \| 0.040832 / 0.075469 (-0.034637) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.009133 / 1.841788 (-0.832655) \| 13.373258 / 8.074308 (5.298949) \| 11.327360 / 10.191392 (1.135968) \| 0.135778 / 0.680424 (-0.544646) \| 0.015813 / 0.534201 (-0.518388) \| 0.275404 / 0.579283 (-0.303879) \| 0.282564 / 0.434364 (-0.151799) \| 0.311830 / 0.540337 (-0.228507) \| 0.419008 / 1.386936 (-0.967928) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4592709e5399f91b5b392f4fd73687985365c909 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004899 / 0.011353 (-0.006454) \| 0.002780 / 0.011008 (-0.008229) \| 0.061997 / 0.038508 (0.023489) \| 0.029909 / 0.023109 (0.006800) \| 0.233445 / 0.275898 (-0.042453) \| 0.254128 / 0.323480 (-0.069351) \| 0.002927 / 0.007986 (-0.005058) \| 0.002396 / 0.004328 (-0.001932) \| 0.048118 / 0.004250 (0.043868) \| 0.044520 / 0.037052 (0.007468) \| 0.237594 / 0.258489 (-0.020895) \| 0.268407 / 0.293841 (-0.025434) \| 0.023517 / 0.128546 (-0.105029) \| 0.007035 / 0.075646 (-0.068612) \| 0.202803 / 0.419271 (-0.216469) \| 0.057692 / 0.043533 (0.014159) \| 0.237058 / 0.255139 (-0.018081) \| 0.252966 / 0.283200 (-0.030233) \| 0.017934 / 0.141683 (-0.123748) \| 1.096406 / 1.452155 (-0.355749) \| 1.153509 / 1.492716 (-0.339207) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091812 / 0.018006 (0.073806) \| 0.298410 / 0.000490 (0.297920) \| 0.000228 / 0.000200 (0.000028) \| 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) \| 0.018333 / 0.037411 (-0.019078) \| 0.062685 / 0.014526 (0.048159) \| 0.073295 / 0.176557 (-0.103261) \| 0.119234 / 0.737135 (-0.617901) \| 0.074603 / 0.296338 (-0.221736) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279078 / 0.215209 (0.063869) \| 2.768535 / 2.077655 (0.690880) \| 1.457049 / 1.504120 (-0.047071) \| 1.326870 / 1.541195 (-0.214325) \| 1.349657 / 1.468490 (-0.118833) \| 0.405003 / 4.584777 (-4.179774) \| 2.428726 / 3.745712 (-1.316986) \| 2.595776 / 5.269862 (-2.674086) \| 1.557879 / 4.565676 (-3.007797) \| 0.045985 / 0.424275 (-0.378291) \| 0.004854 / 0.007607 (-0.002753) \| 0.336437 / 0.226044 (0.110392) \| 3.317330 / 2.268929 (1.048401) \| 1.784525 / 55.444624 (-53.660100) \| 1.500295 / 6.876477 (-5.376182) \| 1.529869 / 2.142072 (-0.612203) \| 0.473426 / 4.805227 (-4.331801) \| 0.099609 / 6.500664 (-6.401055) \| 0.042054 / 0.075469 (-0.033415) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.937154 / 1.841788 (-0.904633) \| 11.482383 / 8.074308 (3.408075) \| 10.468769 / 10.191392 (0.277377) \| 0.132724 / 0.680424 (-0.547700) \| 0.015242 / 0.534201 (-0.518959) \| 0.281124 / 0.579283 (-0.298159) \| 0.268603 / 0.434364 (-0.165761) \| 0.311410 / 0.540337 (-0.228928) \| 0.431817 / 1.386936 (-0.955119) \|\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) \| 0.004695 / 0.011353 (-0.006658) \| 0.002873 / 0.011008 (-0.008135) \| 0.048133 / 0.038508 (0.009625) \| 0.052505 / 0.023109 (0.029396) \| 0.271679 / 0.275898 (-0.004219) \| 0.292530 / 0.323480 (-0.030950) \| 0.003844 / 0.007986 (-0.004142) \| 0.002417 / 0.004328 (-0.001912) \| 0.048619 / 0.004250 (0.044369) \| 0.039152 / 0.037052 (0.002100) \| 0.276575 / 0.258489 (0.018086) \| 0.307836 / 0.293841 (0.013995) \| 0.023877 / 0.128546 (-0.104669) \| 0.006897 / 0.075646 (-0.068749) \| 0.053241 / 0.419271 (-0.366031) \| 0.032487 / 0.043533 (-0.011046) \| 0.274205 / 0.255139 (0.019066) \| 0.289701 / 0.283200 (0.006502) \| 0.018250 / 0.141683 (-0.123432) \| 1.137902 / 1.452155 (-0.314253) \| 1.202043 / 1.492716 (-0.290673) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091453 / 0.018006 (0.073446) \| 0.297032 / 0.000490 (0.296543) \| 0.000224 / 0.000200 (0.000024) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021062 / 0.037411 (-0.016349) \| 0.069848 / 0.014526 (0.055322) \| 0.084337 / 0.176557 (-0.092219) \| 0.119951 / 0.737135 (-0.617184) \| 0.082805 / 0.296338 (-0.213533) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297056 / 0.215209 (0.081846) \| 2.890110 / 2.077655 (0.812456) \| 1.609918 / 1.504120 (0.105798) \| 1.491184 / 1.541195 (-0.050011) \| 1.529433 / 1.468490 (0.060943) \| 0.396081 / 4.584777 (-4.188696) \| 2.408310 / 3.745712 (-1.337402) \| 2.567905 / 5.269862 (-2.701957) \| 1.514465 / 4.565676 (-3.051212) \| 0.045329 / 0.424275 (-0.378946) \| 0.004738 / 0.007607 (-0.002869) \| 0.344373 / 0.226044 (0.118328) \| 3.428333 / 2.268929 (1.159404) \| 1.981401 / 55.444624 (-53.463223) \| 1.688007 / 6.876477 (-5.188470) \| 1.685542 / 2.142072 (-0.456531) \| 0.478045 / 4.805227 (-4.327182) \| 0.096664 / 6.500664 (-6.404001) \| 0.040335 / 0.075469 (-0.035135) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972912 / 1.841788 (-0.868876) \| 12.055045 / 8.074308 (3.980737) \| 10.821073 / 10.191392 (0.629681) \| 0.139177 / 0.680424 (-0.541247) \| 0.015046 / 0.534201 (-0.519155) \| 0.275670 / 0.579283 (-0.303613) \| 0.280366 / 0.434364 (-0.153998) \| 0.315781 / 0.540337 (-0.224556) \| 0.424536 / 1.386936 (-0.962400) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0684b471d6ca8a235162f5575f624b6eda7956c5 \"CML watermark\")\n", "I'm finally merging as `transformers`/`tokenizers` dependency pins have been removed + `huggingface_hub 0.19.4` has fixed the deps incompatibility issue. All good now :)", "<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) \| 0.004435 / 0.011353 (-0.006918) \| 0.002924 / 0.011008 (-0.008084) \| 0.062159 / 0.038508 (0.023651) \| 0.029639 / 0.023109 (0.006529) \| 0.237470 / 0.275898 (-0.038428) \| 0.269641 / 0.323480 (-0.053839) \| 0.004124 / 0.007986 (-0.003862) \| 0.002528 / 0.004328 (-0.001800) \| 0.048114 / 0.004250 (0.043864) \| 0.046055 / 0.037052 (0.009002) \| 0.245844 / 0.258489 (-0.012645) \| 0.278085 / 0.293841 (-0.015756) \| 0.023152 / 0.128546 (-0.105394) \| 0.007194 / 0.075646 (-0.068452) \| 0.206493 / 0.419271 (-0.212778) \| 0.055687 / 0.043533 (0.012155) \| 0.243301 / 0.255139 (-0.011838) \| 0.267645 / 0.283200 (-0.015555) \| 0.017413 / 0.141683 (-0.124270) \| 1.113071 / 1.452155 (-0.339083) \| 1.201436 / 1.492716 (-0.291280) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092576 / 0.018006 (0.074570) \| 0.303516 / 0.000490 (0.303027) \| 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) \| 0.019108 / 0.037411 (-0.018303) \| 0.062326 / 0.014526 (0.047800) \| 0.073711 / 0.176557 (-0.102846) \| 0.120414 / 0.737135 (-0.616721) \| 0.075837 / 0.296338 (-0.220501) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278267 / 0.215209 (0.063058) \| 2.766231 / 2.077655 (0.688576) \| 1.455613 / 1.504120 (-0.048507) \| 1.337128 / 1.541195 (-0.204066) \| 1.357659 / 1.468490 (-0.110831) \| 0.404549 / 4.584777 (-4.180228) \| 2.409084 / 3.745712 (-1.336628) \| 2.645000 / 5.269862 (-2.624861) \| 1.600475 / 4.565676 (-2.965201) \| 0.046680 / 0.424275 (-0.377595) \| 0.004887 / 0.007607 (-0.002720) \| 0.340338 / 0.226044 (0.114294) \| 3.332647 / 2.268929 (1.063719) \| 1.852529 / 55.444624 (-53.592096) \| 1.532442 / 6.876477 (-5.344035) \| 1.550383 / 2.142072 (-0.591689) \| 0.482702 / 4.805227 (-4.322525) \| 0.101067 / 6.500664 (-6.399597) \| 0.042132 / 0.075469 (-0.033337) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945481 / 1.841788 (-0.896307) \| 11.886240 / 8.074308 (3.811932) \| 10.484620 / 10.191392 (0.293228) \| 0.130906 / 0.680424 (-0.549518) \| 0.014880 / 0.534201 (-0.519321) \| 0.268836 / 0.579283 (-0.310447) \| 0.268112 / 0.434364 (-0.166251) \| 0.304300 / 0.540337 (-0.236038) \| 0.440262 / 1.386936 (-0.946674) \|\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) \| 0.005028 / 0.011353 (-0.006325) \| 0.002937 / 0.011008 (-0.008071) \| 0.049038 / 0.038508 (0.010530) \| 0.057763 / 0.023109 (0.034653) \| 0.273196 / 0.275898 (-0.002702) \| 0.295519 / 0.323480 (-0.027961) \| 0.004102 / 0.007986 (-0.003883) \| 0.002487 / 0.004328 (-0.001841) \| 0.049148 / 0.004250 (0.044898) \| 0.040303 / 0.037052 (0.003251) \| 0.279187 / 0.258489 (0.020698) \| 0.311086 / 0.293841 (0.017245) \| 0.024961 / 0.128546 (-0.103585) \| 0.007264 / 0.075646 (-0.068382) \| 0.055711 / 0.419271 (-0.363561) \| 0.032355 / 0.043533 (-0.011178) \| 0.274304 / 0.255139 (0.019165) \| 0.290953 / 0.283200 (0.007753) \| 0.018358 / 0.141683 (-0.123325) \| 1.115984 / 1.452155 (-0.336170) \| 1.190409 / 1.492716 (-0.302308) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095765 / 0.018006 (0.077759) \| 0.287947 / 0.000490 (0.287457) \| 0.000242 / 0.000200 (0.000042) \| 0.000047 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022165 / 0.037411 (-0.015246) \| 0.070465 / 0.014526 (0.055940) \| 0.082078 / 0.176557 (-0.094479) \| 0.120209 / 0.737135 (-0.616926) \| 0.084573 / 0.296338 (-0.211765) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298492 / 0.215209 (0.083283) \| 2.924981 / 2.077655 (0.847327) \| 1.597326 / 1.504120 (0.093206) \| 1.459132 / 1.541195 (-0.082062) \| 1.511471 / 1.468490 (0.042981) \| 0.406671 / 4.584777 (-4.178106) \| 2.443154 / 3.745712 (-1.302558) \| 2.591131 / 5.269862 (-2.678731) \| 1.549931 / 4.565676 (-3.015745) \| 0.047042 / 0.424275 (-0.377234) \| 0.004891 / 0.007607 (-0.002716) \| 0.346274 / 0.226044 (0.120230) \| 3.456050 / 2.268929 (1.187121) \| 1.959328 / 55.444624 (-53.485296) \| 1.647631 / 6.876477 (-5.228845) \| 1.692024 / 2.142072 (-0.450049) \| 0.478307 / 4.805227 (-4.326920) \| 0.098738 / 6.500664 (-6.401926) \| 0.041743 / 0.075469 (-0.033726) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.984619 / 1.841788 (-0.857168) \| 12.403984 / 8.074308 (4.329676) \| 10.974347 / 10.191392 (0.782955) \| 0.132893 / 0.680424 (-0.547530) \| 0.015504 / 0.534201 (-0.518697) \| 0.275354 / 0.579283 (-0.303929) \| 0.283312 / 0.434364 (-0.151052) \| 0.313661 / 0.540337 (-0.226677) \| 0.419065 / 1.386936 (-0.967871) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c65315e4a8308f04fcb025039afe2a2e43b5684e \"CML watermark\")\n" ]	2023-11-14T10:47:09Z	2023-11-17T14:23:20Z	2023-11-17T14:17:00Z	CONTRIBUTOR	null	null	null	Related to https://github.com/huggingface/transformers/issues/27034 and https://github.com/huggingface/huggingface_hub/pull/1782. TL;DR: `hashlib` is not a secure library for cryptography-related stuff. We are only using `hashlib` for non-security-related purposes in `datasets` so it's fine. From Python 3.9 we set can `usedforsecurity=False` in any `hashlib` method which is mandatory for companies that forbid the use of `hashlib` for security purposes. This PR fixes that. Note: before merging this we need to release a new tokenizers version that would allow the newest `huggingface_hub` version (see https://github.com/huggingface/tokenizers/pull/1385). Otherwise it might create friction to users that want to install `datasets` + `tokenizers` at the same time.	{ "avatar_url": "https://avatars.githubusercontent.com/u/11801849?v=4", "events_url": "https://api.github.com/users/Wauplin/events{/privacy}", "followers_url": "https://api.github.com/users/Wauplin/followers", "following_url": "https://api.github.com/users/Wauplin/following{/other_user}", "gists_url": "https://api.github.com/users/Wauplin/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Wauplin", "id": 11801849, "login": "Wauplin", "node_id": "MDQ6VXNlcjExODAxODQ5", "organizations_url": "https://api.github.com/users/Wauplin/orgs", "received_events_url": "https://api.github.com/users/Wauplin/received_events", "repos_url": "https://api.github.com/users/Wauplin/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Wauplin/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Wauplin/subscriptions", "type": "User", "url": "https://api.github.com/users/Wauplin", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6414/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6414/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6414.diff", "html_url": "https://github.com/huggingface/datasets/pull/6414", "merged_at": "2023-11-17T14:17:00Z", "patch_url": "https://github.com/huggingface/datasets/pull/6414.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6414" }
https://api.github.com/repos/huggingface/datasets/issues/6841	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6841/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6841/comments	https://api.github.com/repos/huggingface/datasets/issues/6841/events	https://github.com/huggingface/datasets/issues/6841	2,264,687,683	I_kwDODunzps6G_GRD	6,841	Unable to load wiki_auto_asset_turk from GEM	{ "avatar_url": "https://avatars.githubusercontent.com/u/23074600?v=4", "events_url": "https://api.github.com/users/abhinavsethy/events{/privacy}", "followers_url": "https://api.github.com/users/abhinavsethy/followers", "following_url": "https://api.github.com/users/abhinavsethy/following{/other_user}", "gists_url": "https://api.github.com/users/abhinavsethy/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/abhinavsethy", "id": 23074600, "login": "abhinavsethy", "node_id": "MDQ6VXNlcjIzMDc0NjAw", "organizations_url": "https://api.github.com/users/abhinavsethy/orgs", "received_events_url": "https://api.github.com/users/abhinavsethy/received_events", "repos_url": "https://api.github.com/users/abhinavsethy/repos", "site_admin": false, "starred_url": "https://api.github.com/users/abhinavsethy/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/abhinavsethy/subscriptions", "type": "User", "url": "https://api.github.com/users/abhinavsethy", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi! I've opened a [PR](https://huggingface.co/datasets/GEM/wiki_auto_asset_turk/discussions/5) with a fix. While waiting for it to be merged, you can load the dataset from the PR branch with `datasets.load_dataset(\"GEM/wiki_auto_asset_turk\", revision=\"refs/pr/5\")`", "Thanks Mario. Still getting the same issue though with the suggested fix\r\n\r\n#cat gem_sari.py\r\nimport datasets\r\nprint (datasets.__version__)\r\ndataset =datasets.load_dataset(\"GEM/wiki_auto_asset_turk\", revision=\"refs/pr/5\")\r\n\r\nEnd up with \r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/load.py\", line 2582, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py\", line 1005, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py\", line 1767, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py\", line 1100, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py\", line 1565, in _prepare_split\r\n split_info = self.info.splits[split_generator.name]\r\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/splits.py\", line 532, in __getitem__\r\n instructions = make_file_instructions(\r\n ^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/arrow_reader.py\", line 121, in make_file_instructions\r\n info.name: filenames_for_dataset_split(\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/naming.py\", line 72, in filenames_for_dataset_split\r\n prefix = os.path.join(path, prefix)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"<frozen posixpath>\", line 76, in join\r\nTypeError: expected str, bytes or os.PathLike object, not NoneType", "Hmm, that's weird. Maybe try deleting the cache with `!rm -rf ~/.cache/huggingface/datasets` and then re-download.", "Tried that a couple of time. It does download the data fresh but end up with same error. Is there a way to see if its using the right version ?", "You can check the version with `python -c \"import datasets; print(datasets.__version__)\"`", "the datasets version is 2.18. \r\n\r\nI wanted to see if the command datasets.load_dataset(\"GEM/wiki_auto_asset_turk\", revision=\"refs/pr/5\") is using the right revision (refs/pr/5). \r\n\r\n\r\n\r\n\r\n\r\n ", "Still have this problem", "The issue is fixed once the fixing PR has been merged and the dataset has been converted to Parquet.\r\n\r\nIf the problem persists on your side, you should update your `datasets` library:\r\n```shell\r\npip install -U datasets\r\n```\r\nAnd if you have already the latest version of `datasets`, then you need to delete the old version of this dataset in your cache:\r\n```shell\r\nrm -fr ~/.cache/huggingface/datasets/GEM___wiki_auto_asset_turk\r\nrm -fr ~/.cache/huggingface/modules/datasets_modules/datasets/GEM--wiki_auto_asset_turk\r\n```" ]	2024-04-26T00:08:47Z	2024-05-29T13:54:03Z	2024-04-26T16:12:29Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I am unable to load the wiki_auto_asset_turk dataset. I get a fatal error while trying to access wiki_auto_asset_turk and load it with datasets.load_dataset. The error (TypeError: expected str, bytes or os.PathLike object, not NoneType) is from filenames_for_dataset_split in a os.path.join call >>import datasets >>print (datasets.__version__) >>dataset = datasets.load_dataset("GEM/wiki_auto_asset_turk") System output: Generating train split: 100%\|█\| 483801/483801 [00:03<00:00, 127164.26 examples/s Generating validation split: 100%\|█\| 20000/20000 [00:00<00:00, 116052.94 example Generating test_asset split: 100%\|██\| 359/359 [00:00<00:00, 76155.93 examples/s] Generating test_turk split: 100%\|███\| 359/359 [00:00<00:00, 87691.76 examples/s] Traceback (most recent call last): File "/Users/abhinav.sethy/Code/openai_evals/evals/evals/grammarly_tasks/gem_sari.py", line 3, in <module> dataset = datasets.load_dataset("GEM/wiki_auto_asset_turk") ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/load.py", line 2582, in load_dataset builder_instance.download_and_prepare( File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py", line 1005, in download_and_prepare self._download_and_prepare( File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py", line 1767, in _download_and_prepare super()._download_and_prepare( File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py", line 1100, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/builder.py", line 1565, in _prepare_split split_info = self.info.splits[split_generator.name] ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/splits.py", line 532, in __getitem__ instructions = make_file_instructions( ^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/arrow_reader.py", line 121, in make_file_instructions info.name: filenames_for_dataset_split( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/naming.py", line 72, in filenames_for_dataset_split prefix = os.path.join(path, prefix) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "<frozen posixpath>", line 76, in join TypeError: expected str, bytes or os.PathLike object, not NoneType ### Steps to reproduce the bug import datasets print (datasets.__version__) dataset = datasets.load_dataset("GEM/wiki_auto_asset_turk") ### Expected behavior Should be able to load the dataset without any issues ### Environment info datasets version 2.18.0 (was able to reproduce bug with older versions 2.16 and 2.14 also) Python 3.12.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6841/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6841/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7427	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7427/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7427/comments	https://api.github.com/repos/huggingface/datasets/issues/7427/events	https://github.com/huggingface/datasets/issues/7427	2,886,032,571	I_kwDODunzps6sBVy7	7,427	Error splitting the input into NAL units.	{ "avatar_url": "https://avatars.githubusercontent.com/u/47114466?v=4", "events_url": "https://api.github.com/users/MengHao666/events{/privacy}", "followers_url": "https://api.github.com/users/MengHao666/followers", "following_url": "https://api.github.com/users/MengHao666/following{/other_user}", "gists_url": "https://api.github.com/users/MengHao666/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/MengHao666", "id": 47114466, "login": "MengHao666", "node_id": "MDQ6VXNlcjQ3MTE0NDY2", "organizations_url": "https://api.github.com/users/MengHao666/orgs", "received_events_url": "https://api.github.com/users/MengHao666/received_events", "repos_url": "https://api.github.com/users/MengHao666/repos", "site_admin": false, "starred_url": "https://api.github.com/users/MengHao666/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/MengHao666/subscriptions", "type": "User", "url": "https://api.github.com/users/MengHao666", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "First time I see this error :/ maybe it's an issue with your version of `multiprocess` and `dill` ? Make sure they are compatible with `datasets`", "> First time I see this error :/ maybe it's an issue with your version of `multiprocess` and `dill` ? Make sure they are compatible with `datasets`\n\nany recommendation for `multiprocess` and `dill`" ]	2025-02-28T02:30:15Z	2025-03-04T01:40:28Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I am trying to finetune qwen2.5-vl on 16 * 80G GPUS, and I use `LLaMA-Factory` and set `preprocessing_num_workers=16`. However, I met the following error and the program seem to got crush. It seems that the error come from `datasets` library The error logging is like following: ```text Converting format of dataset (num_proc=16): 100%\|█████████▉\| 19265/19267 [11:44<00:00, 5.88 examples/s] Converting format of dataset (num_proc=16): 100%\|█████████▉\| 19266/19267 [11:44<00:00, 5.02 examples/s] Converting format of dataset (num_proc=16): 100%\|██████████\| 19267/19267 [11:44<00:00, 5.44 examples/s] Converting format of dataset (num_proc=16): 100%\|██████████\| 19267/19267 [11:44<00:00, 27.34 examples/s] Running tokenizer on dataset (num_proc=16): 0%\| \| 0/19267 [00:00<?, ? examples/s] Invalid NAL unit size (45405 > 35540). Invalid NAL unit size (86720 > 54856). Invalid NAL unit size (7131 > 3225). missing picture in access unit with size 54860 Invalid NAL unit size (48042 > 33645). missing picture in access unit with size 3229 missing picture in access unit with size 33649 Invalid NAL unit size (86720 > 54856). Invalid NAL unit size (48042 > 33645). Error splitting the input into NAL units. missing picture in access unit with size 35544 Invalid NAL unit size (45405 > 35540). Error splitting the input into NAL units. Error splitting the input into NAL units. Invalid NAL unit size (8187 > 7069). missing picture in access unit with size 7073 Invalid NAL unit size (8187 > 7069). Error splitting the input into NAL units. Invalid NAL unit size (7131 > 3225). Error splitting the input into NAL units. Invalid NAL unit size (14013 > 5998). missing picture in access unit with size 6002 Invalid NAL unit size (14013 > 5998). Error splitting the input into NAL units. Invalid NAL unit size (17173 > 7231). missing picture in access unit with size 7235 Invalid NAL unit size (17173 > 7231). Error splitting the input into NAL units. Invalid NAL unit size (16964 > 6055). missing picture in access unit with size 6059 Invalid NAL unit size (16964 > 6055). Exception in thread Thread-9 (accepter)Error splitting the input into NAL units. : Traceback (most recent call last): File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 1016, in _bootstrap_inner Running tokenizer on dataset (num_proc=16): 0%\| \| 0/19267 [13:22<?, ? examples/s] self.run() File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 953, in run Invalid NAL unit size (7032 > 2927). missing picture in access unit with size 2931 self._target(self._args, self._kwargs) File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/multiprocess/managers.py", line 194, in accepter Invalid NAL unit size (7032 > 2927). Error splitting the input into NAL units. t.start() File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 935, in start Invalid NAL unit size (28973 > 6121). missing picture in access unit with size 6125 _start_new_thread(self._bootstrap, ())Invalid NAL unit size (28973 > 6121). RuntimeError: can't start new threadError splitting the input into NAL units. Invalid NAL unit size (4411 > 296). missing picture in access unit with size 300 Invalid NAL unit size (4411 > 296). Error splitting the input into NAL units. Invalid NAL unit size (14414 > 1471). missing picture in access unit with size 1475 Invalid NAL unit size (14414 > 1471). Error splitting the input into NAL units. Invalid NAL unit size (5283 > 1792). missing picture in access unit with size 1796 Invalid NAL unit size (5283 > 1792). Error splitting the input into NAL units. Invalid NAL unit size (79147 > 10042). missing picture in access unit with size 10046 Invalid NAL unit size (79147 > 10042). Error splitting the input into NAL units. Invalid NAL unit size (45405 > 35540). Invalid NAL unit size (86720 > 54856). Invalid NAL unit size (7131 > 3225). missing picture in access unit with size 54860 Invalid NAL unit size (48042 > 33645). missing picture in access unit with size 3229 missing picture in access unit with size 33649 Invalid NAL unit size (86720 > 54856). Invalid NAL unit size (48042 > 33645). Error splitting the input into NAL units. missing picture in access unit with size 35544 Invalid NAL unit size (45405 > 35540). Error splitting the input into NAL units. Error splitting the input into NAL units. Invalid NAL unit size (8187 > 7069). missing picture in access unit with size 7073 Invalid NAL unit size (8187 > 7069). Error splitting the input into NAL units. Invalid NAL unit size (7131 > 3225). Error splitting the input into NAL units. Invalid NAL unit size (14013 > 5998). missing picture in access unit with size 6002 Invalid NAL unit size (14013 > 5998). Error splitting the input into NAL units. Invalid NAL unit size (17173 > 7231). missing picture in access unit with size 7235 Invalid NAL unit size (17173 > 7231). Error splitting the input into NAL units. Invalid NAL unit size (16964 > 6055). missing picture in access unit with size 6059 Invalid NAL unit size (16964 > 6055). Exception in thread Thread-9 (accepter)Error splitting the input into NAL units. : Traceback (most recent call last): File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 1016, in _bootstrap_inner Running tokenizer on dataset (num_proc=16): 0%\| \| 0/19267 [13:22<?, ? examples/s] self.run() File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 953, in run Invalid NAL unit size (7032 > 2927). missing picture in access unit with size 2931 self._target(self._args, *self._kwargs) File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/multiprocess/managers.py", line 194, in accepter Invalid NAL unit size (7032 > 2927). Error splitting the input into NAL units. t.start() File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 935, in start Invalid NAL unit size (28973 > 6121). missing picture in access unit with size 6125 _start_new_thread(self._bootstrap, ())Invalid NAL unit size (28973 > 6121). RuntimeError: can't start new threadError splitting the input into NAL units. Invalid NAL unit size (4411 > 296). missing picture in access unit with size 300 Invalid NAL unit size (4411 > 296). Error splitting the input into NAL units. Invalid NAL unit size (14414 > 1471). missing picture in access unit with size 1475 Invalid NAL unit size (14414 > 1471). Error splitting the input into NAL units. Invalid NAL unit size (5283 > 1792). missing picture in access unit with size 1796 Invalid NAL unit size (5283 > 1792). Error splitting the input into NAL units. Invalid NAL unit size (79147 > 10042). missing picture in access unit with size 10046 Invalid NAL unit size (79147 > 10042). Error splitting the input into NAL units. Invalid NAL unit size (45405 > 35540). Invalid NAL unit size (86720 > 54856). Invalid NAL unit size (7131 > 3225). missing picture in access unit with size 54860 Invalid NAL unit size (48042 > 33645). missing picture in access unit with size 3229 missing picture in access unit with size 33649 Invalid NAL unit size (86720 > 54856). Invalid NAL unit size (48042 > 33645). Error splitting the input into NAL units. missing picture in access unit with size 35544 Invalid NAL unit size (45405 > 35540). Error splitting the input into NAL units. Error splitting the input into NAL units. Invalid NAL unit size (8187 > 7069). missing picture in access unit with size 7073 Invalid NAL unit size (8187 > 7069). Error splitting the input into NAL units. Invalid NAL unit size (7131 > 3225). Error splitting the input into NAL units. Invalid NAL unit size (14013 > 5998). missing picture in access unit with size 6002 Invalid NAL unit size (14013 > 5998). Error splitting the input into NAL units. Invalid NAL unit size (17173 > 7231). missing picture in access unit with size 7235 Invalid NAL unit size (17173 > 7231). Error splitting the input into NAL units. Invalid NAL unit size (16964 > 6055). missing picture in access unit with size 6059 Invalid NAL unit size (16964 > 6055). Exception in thread Thread-9 (accepter)Error splitting the input into NAL units. : Traceback (most recent call last): File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 1016, in _bootstrap_inner Running tokenizer on dataset (num_proc=16): 0%\| \| 0/19267 [13:22<?, ? examples/s] self.run() File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 953, in run Invalid NAL unit size (7032 > 2927). missing picture in access unit with size 2931 self._target(self._args, *self._kwargs) File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/multiprocess/managers.py", line 194, in accepter Invalid NAL unit size (7032 > 2927). Error splitting the input into NAL units. t.start() File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 935, in start Invalid NAL unit size (28973 > 6121). missing picture in access unit with size 6125 _start_new_thread(self._bootstrap, ())Invalid NAL unit size (28973 > 6121). RuntimeError: can't start new threadError splitting the input into NAL units. Invalid NAL unit size (4411 > 296). missing picture in access unit with size 300 Invalid NAL unit size (4411 > 296). Error splitting the input into NAL units. Invalid NAL unit size (14414 > 1471). missing picture in access unit with size 1475 Invalid NAL unit size (14414 > 1471). Error splitting the input into NAL units. Invalid NAL unit size (5283 > 1792). missing picture in access unit with size 1796 Invalid NAL unit size (5283 > 1792). Error splitting the input into NAL units. Invalid NAL unit size (79147 > 10042). missing picture in access unit with size 10046 Invalid NAL unit size (79147 > 10042). Error splitting the input into NAL units. Invalid NAL unit size (45405 > 35540). Invalid NAL unit size (86720 > 54856). Invalid NAL unit size (7131 > 3225). missing picture in access unit with size 54860 Invalid NAL unit size (48042 > 33645). missing picture in access unit with size 3229 missing picture in access unit with size 33649 Invalid NAL unit size (86720 > 54856). Invalid NAL unit size (48042 > 33645). Error splitting the input into NAL units. missing picture in access unit with size 35544 Invalid NAL unit size (45405 > 35540). Error splitting the input into NAL units. Error splitting the input into NAL units. Invalid NAL unit size (8187 > 7069). missing picture in access unit with size 7073 Invalid NAL unit size (8187 > 7069). Error splitting the input into NAL units. Invalid NAL unit size (7131 > 3225). Error splitting the input into NAL units. Invalid NAL unit size (14013 > 5998). missing picture in access unit with size 6002 Invalid NAL unit size (14013 > 5998). Error splitting the input into NAL units. Invalid NAL unit size (17173 > 7231). missing picture in access unit with size 7235 Invalid NAL unit size (17173 > 7231). Error splitting the input into NAL units. Invalid NAL unit size (16964 > 6055). missing picture in access unit with size 6059 Invalid NAL unit size (16964 > 6055). Exception in thread Thread-9 (accepter)Error splitting the input into NAL units. : Traceback (most recent call last): File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 1016, in _bootstrap_inner Running tokenizer on dataset (num_proc=16): 0%\| \| 0/19267 [13:22<?, ? examples/s] self.run() File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 953, in run Invalid NAL unit size (7032 > 2927). missing picture in access unit with size 2931 self._target(self._args, **self._kwargs) File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/multiprocess/managers.py", line 194, in accepter Invalid NAL unit size (7032 > 2927). Error splitting the input into NAL units. t.start() File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 935, in start Invalid NAL unit size (28973 > 6121). missing picture in access unit with size 6125 _start_new_thread(self._bootstrap, ())Invalid NAL unit size (28973 > 6121). RuntimeError: can't start new threadError splitting the input into NAL units. Invalid NAL unit size (4411 > 296). missing picture in access unit with size 300 Invalid NAL unit size (4411 > 296). Error splitting the input into NAL units. Invalid NAL unit size (14414 > 1471). missing picture in access unit with size 1475 Invalid NAL unit size (14414 > 1471). Error splitting the input into NAL units. Invalid NAL unit size (5283 > 1792). missing picture in access unit with size 1796 Invalid NAL unit size (5283 > 1792). Error splitting the input into NAL units. Invalid NAL unit size (79147 > 10042). missing picture in access unit with size 10046 Invalid NAL unit size (79147 > 10042). Error splitting the input into NAL units. ``` ### Others _No response_ ### Steps to reproduce the bug None ### Expected behavior excpect to run successfully ### Environment info ``` transformers==4.49.0 datasets==3.2.0 accelerate==1.2.1 peft==0.12.0 trl==0.9.6 tokenizers==0.21.0 gradio>=4.38.0,<=5.18.0 pandas>=2.0.0 scipy einops sentencepiece tiktoken protobuf uvicorn pydantic fastapi sse-starlette matplotlib>=3.7.0 fire packaging pyyaml numpy<2.0.0 av librosa tyro<0.9.0 openlm-hub qwen-vl-utils ```	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7427/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7427/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6618	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6618/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6618/comments	https://api.github.com/repos/huggingface/datasets/issues/6618/events	https://github.com/huggingface/datasets/issues/6618	2,101,868,198	I_kwDODunzps59R_am	6,618	While importing load_dataset from datasets	{ "avatar_url": "https://avatars.githubusercontent.com/u/77973415?v=4", "events_url": "https://api.github.com/users/suprith-hub/events{/privacy}", "followers_url": "https://api.github.com/users/suprith-hub/followers", "following_url": "https://api.github.com/users/suprith-hub/following{/other_user}", "gists_url": "https://api.github.com/users/suprith-hub/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/suprith-hub", "id": 77973415, "login": "suprith-hub", "node_id": "MDQ6VXNlcjc3OTczNDE1", "organizations_url": "https://api.github.com/users/suprith-hub/orgs", "received_events_url": "https://api.github.com/users/suprith-hub/received_events", "repos_url": "https://api.github.com/users/suprith-hub/repos", "site_admin": false, "starred_url": "https://api.github.com/users/suprith-hub/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/suprith-hub/subscriptions", "type": "User", "url": "https://api.github.com/users/suprith-hub", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "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." ]	2024-01-26T09:21:57Z	2024-07-23T09:31:07Z	2024-02-06T09:25:54Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug cannot import name 'DEFAULT_CIPHERS' from 'urllib3.util.ssl_' this is the error i received ### Steps to reproduce the bug from datasets import load_dataset ### Expected behavior No errors ### Environment info python 3.11.5	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6618/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6618/timeline	null	not_planned	null	null
https://api.github.com/repos/huggingface/datasets/issues/6756	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6756/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6756/comments	https://api.github.com/repos/huggingface/datasets/issues/6756/events	https://github.com/huggingface/datasets/issues/6756	2,205,557,725	I_kwDODunzps6DdiPd	6,756	Support SQLite files?	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	closed	false	null	[]	null	[ "You can use `Dataset.from_sql(path_to_sql_file)` already. Though we haven't added the Sql dataset builder to the `_PACKAGED_DATASETS_MODULES` list or in `_EXTENSION_TO_MODULE` to map `.sqlite` to the Sql dataset builder\r\n\r\nThis would allow to load a dataset repository with a `.sqlite` file using `load_dataset` and enable the Dataset Viewer", "Considering `Dataset.from_sql`'s (extremely) low usage, I don't think many users are interested in using this format for their datasets. Also, SQLite files are hard/impossible to stream efficiently and require custom logic to define splits/subsets, so IMO we shouldn't encourage people to use SQLite on the Hub.\r\n\r\n@severo Do you have some real-world examples of datasets published in this format?", "No. Indeed, it seems better to explicitly not support sqlite" ]	2024-03-25T11:48:05Z	2024-03-26T16:09:32Z	2024-03-26T16:09:32Z	COLLABORATOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request Support loading a dataset from a SQLite file https://huggingface.co/datasets/severo/test_iris_sqlite/tree/main ### Motivation SQLite is a popular file format. ### Your contribution See discussion on slack: https://huggingface.slack.com/archives/C04L6P8KNQ5/p1702481859117909 (internal) In particular: a SQLite file can contain multiple tables, which might be matched to multiple configs. Maybe the detail of splits and configs should be defined in the README YAML, or use the same format as for ZIP files: `Iris.sqlite::Iris`. See dataset here: https://huggingface.co/datasets/severo/test_iris_sqlite Note: should we also support DuckDB files?	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6756/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6756/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5600	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5600/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5600/comments	https://api.github.com/repos/huggingface/datasets/issues/5600/events	https://github.com/huggingface/datasets/issues/5600	1,606,585,596	I_kwDODunzps5fwoz8	5,600	Dataloader getitem not working for DreamboothDatasets	{ "avatar_url": "https://avatars.githubusercontent.com/u/76955987?v=4", "events_url": "https://api.github.com/users/salahiguiliz/events{/privacy}", "followers_url": "https://api.github.com/users/salahiguiliz/followers", "following_url": "https://api.github.com/users/salahiguiliz/following{/other_user}", "gists_url": "https://api.github.com/users/salahiguiliz/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/salahiguiliz", "id": 76955987, "login": "salahiguiliz", "node_id": "MDQ6VXNlcjc2OTU1OTg3", "organizations_url": "https://api.github.com/users/salahiguiliz/orgs", "received_events_url": "https://api.github.com/users/salahiguiliz/received_events", "repos_url": "https://api.github.com/users/salahiguiliz/repos", "site_admin": false, "starred_url": "https://api.github.com/users/salahiguiliz/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/salahiguiliz/subscriptions", "type": "User", "url": "https://api.github.com/users/salahiguiliz", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi! \r\n\r\n> (see example of DreamboothDatasets)\r\n\r\n\r\nCould you please provide a link to it? If you are referring to the example in the `diffusers` repo, your issue is unrelated to `datasets` as that example uses `Dataset` from PyTorch to load data." ]	2023-03-02T11:00:27Z	2023-03-13T17:59:35Z	2023-03-13T17:59:35Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Dataloader getitem is not working as before (see example of [DreamboothDatasets](https://github.com/huggingface/peft/blob/main/examples/lora_dreambooth/train_dreambooth.py#L451C14-L529)) moving Datasets to 2.8.0 solved the issue. ### Steps to reproduce the bug 1- using DreamBoothDataset to load some images 2- error after loading when trying to visualise the images ### Expected behavior I was expecting a numpy array of the image ### Environment info - Platform: Linux-5.10.147+-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 9.0.0 - Pandas version: 1.3.5	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5600/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5600/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5957	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5957/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5957/comments	https://api.github.com/repos/huggingface/datasets/issues/5957/events	https://github.com/huggingface/datasets/pull/5957	1,757,252,466	PR_kwDODunzps5TA1EB	5,957	Release: 2.13.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006498 / 0.011353 (-0.004855) \| 0.003970 / 0.011008 (-0.007038) \| 0.099242 / 0.038508 (0.060734) \| 0.044363 / 0.023109 (0.021254) \| 0.313900 / 0.275898 (0.038002) \| 0.386562 / 0.323480 (0.063082) \| 0.003837 / 0.007986 (-0.004149) \| 0.004203 / 0.004328 (-0.000125) \| 0.076191 / 0.004250 (0.071940) \| 0.058823 / 0.037052 (0.021771) \| 0.333838 / 0.258489 (0.075349) \| 0.368235 / 0.293841 (0.074394) \| 0.030774 / 0.128546 (-0.097772) \| 0.008787 / 0.075646 (-0.066860) \| 0.326474 / 0.419271 (-0.092798) \| 0.050903 / 0.043533 (0.007370) \| 0.303928 / 0.255139 (0.048789) \| 0.321532 / 0.283200 (0.038333) \| 0.024162 / 0.141683 (-0.117520) \| 1.479662 / 1.452155 (0.027507) \| 1.520300 / 1.492716 (0.027584) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212403 / 0.018006 (0.194397) \| 0.448019 / 0.000490 (0.447529) \| 0.005465 / 0.000200 (0.005265) \| 0.000388 / 0.000054 (0.000334) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027533 / 0.037411 (-0.009878) \| 0.117477 / 0.014526 (0.102952) \| 0.121182 / 0.176557 (-0.055374) \| 0.181150 / 0.737135 (-0.555985) \| 0.128557 / 0.296338 (-0.167782) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.397763 / 0.215209 (0.182554) \| 3.959460 / 2.077655 (1.881805) \| 1.822057 / 1.504120 (0.317937) \| 1.627020 / 1.541195 (0.085826) \| 1.695394 / 1.468490 (0.226904) \| 0.536848 / 4.584777 (-4.047929) \| 3.765205 / 3.745712 (0.019493) \| 3.196300 / 5.269862 (-2.073561) \| 1.623583 / 4.565676 (-2.942094) \| 0.065823 / 0.424275 (-0.358452) \| 0.011062 / 0.007607 (0.003455) \| 0.500428 / 0.226044 (0.274384) \| 5.008816 / 2.268929 (2.739888) \| 2.314660 / 55.444624 (-53.129965) \| 2.007429 / 6.876477 (-4.869047) \| 2.141438 / 2.142072 (-0.000635) \| 0.656697 / 4.805227 (-4.148530) \| 0.143555 / 6.500664 (-6.357109) \| 0.063928 / 0.075469 (-0.011541) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.169038 / 1.841788 (-0.672750) \| 15.027186 / 8.074308 (6.952878) \| 13.571484 / 10.191392 (3.380092) \| 0.166437 / 0.680424 (-0.513986) \| 0.017656 / 0.534201 (-0.516545) \| 0.397725 / 0.579283 (-0.181558) \| 0.451019 / 0.434364 (0.016655) \| 0.469134 / 0.540337 (-0.071203) \| 0.575885 / 1.386936 (-0.811051) \|\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) \| 0.006887 / 0.011353 (-0.004465) \| 0.004166 / 0.011008 (-0.006842) \| 0.077137 / 0.038508 (0.038629) \| 0.055631 / 0.023109 (0.032522) \| 0.397658 / 0.275898 (0.121760) \| 0.473981 / 0.323480 (0.150502) \| 0.005365 / 0.007986 (-0.002621) \| 0.003401 / 0.004328 (-0.000928) \| 0.076481 / 0.004250 (0.072231) \| 0.056014 / 0.037052 (0.018961) \| 0.415253 / 0.258489 (0.156764) \| 0.457620 / 0.293841 (0.163779) \| 0.031850 / 0.128546 (-0.096696) \| 0.008869 / 0.075646 (-0.066777) \| 0.083475 / 0.419271 (-0.335796) \| 0.049232 / 0.043533 (0.005699) \| 0.392947 / 0.255139 (0.137808) \| 0.417243 / 0.283200 (0.134043) \| 0.024554 / 0.141683 (-0.117129) \| 1.508081 / 1.452155 (0.055926) \| 1.541845 / 1.492716 (0.049129) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228470 / 0.018006 (0.210464) \| 0.450933 / 0.000490 (0.450443) \| 0.001508 / 0.000200 (0.001308) \| 0.000083 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030189 / 0.037411 (-0.007222) \| 0.118853 / 0.014526 (0.104327) \| 0.124809 / 0.176557 (-0.051747) \| 0.175066 / 0.737135 (-0.562069) \| 0.129819 / 0.296338 (-0.166519) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.451830 / 0.215209 (0.236621) \| 4.505352 / 2.077655 (2.427698) \| 2.309303 / 1.504120 (0.805183) \| 2.120983 / 1.541195 (0.579789) \| 2.198808 / 1.468490 (0.730317) \| 0.543836 / 4.584777 (-4.040940) \| 3.836650 / 3.745712 (0.090938) \| 1.872293 / 5.269862 (-3.397568) \| 1.122335 / 4.565676 (-3.443342) \| 0.067463 / 0.424275 (-0.356812) \| 0.012143 / 0.007607 (0.004536) \| 0.553674 / 0.226044 (0.327630) \| 5.572101 / 2.268929 (3.303173) \| 2.772151 / 55.444624 (-52.672473) \| 2.451557 / 6.876477 (-4.424920) \| 2.521241 / 2.142072 (0.379169) \| 0.665799 / 4.805227 (-4.139428) \| 0.143842 / 6.500664 (-6.356822) \| 0.065373 / 0.075469 (-0.010096) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.271013 / 1.841788 (-0.570775) \| 15.290054 / 8.074308 (7.215746) \| 14.807044 / 10.191392 (4.615652) \| 0.163767 / 0.680424 (-0.516657) \| 0.017383 / 0.534201 (-0.516818) \| 0.393046 / 0.579283 (-0.186237) \| 0.423056 / 0.434364 (-0.011308) \| 0.459193 / 0.540337 (-0.081145) \| 0.559964 / 1.386936 (-0.826972) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#011b75f044ef7fa6b8981ef3496615296aeb315b \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006112 / 0.011353 (-0.005241) \| 0.003712 / 0.011008 (-0.007297) \| 0.099996 / 0.038508 (0.061488) \| 0.037526 / 0.023109 (0.014417) \| 0.305834 / 0.275898 (0.029936) \| 0.361368 / 0.323480 (0.037888) \| 0.004849 / 0.007986 (-0.003136) \| 0.002912 / 0.004328 (-0.001417) \| 0.077729 / 0.004250 (0.073479) \| 0.053203 / 0.037052 (0.016151) \| 0.318088 / 0.258489 (0.059599) \| 0.371745 / 0.293841 (0.077904) \| 0.029384 / 0.128546 (-0.099162) \| 0.008504 / 0.075646 (-0.067142) \| 0.318472 / 0.419271 (-0.100799) \| 0.046043 / 0.043533 (0.002510) \| 0.310418 / 0.255139 (0.055279) \| 0.335044 / 0.283200 (0.051844) \| 0.020364 / 0.141683 (-0.121319) \| 1.503201 / 1.452155 (0.051047) \| 1.556408 / 1.492716 (0.063692) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.210245 / 0.018006 (0.192239) \| 0.418918 / 0.000490 (0.418428) \| 0.002552 / 0.000200 (0.002352) \| 0.000084 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022295 / 0.037411 (-0.015116) \| 0.099534 / 0.014526 (0.085008) \| 0.106432 / 0.176557 (-0.070124) \| 0.165110 / 0.737135 (-0.572026) \| 0.109851 / 0.296338 (-0.186488) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.423947 / 0.215209 (0.208738) \| 4.232978 / 2.077655 (2.155323) \| 2.004849 / 1.504120 (0.500729) \| 1.814345 / 1.541195 (0.273151) \| 1.809192 / 1.468490 (0.340702) \| 0.561146 / 4.584777 (-4.023631) \| 3.385043 / 3.745712 (-0.360669) \| 1.708265 / 5.269862 (-3.561597) \| 1.030290 / 4.565676 (-3.535387) \| 0.067095 / 0.424275 (-0.357180) \| 0.011052 / 0.007607 (0.003445) \| 0.522416 / 0.226044 (0.296371) \| 5.207003 / 2.268929 (2.938075) \| 2.367067 / 55.444624 (-53.077558) \| 1.998705 / 6.876477 (-4.877772) \| 2.068633 / 2.142072 (-0.073439) \| 0.672396 / 4.805227 (-4.132831) \| 0.135818 / 6.500664 (-6.364846) \| 0.065229 / 0.075469 (-0.010240) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.187079 / 1.841788 (-0.654709) \| 13.893153 / 8.074308 (5.818845) \| 13.951328 / 10.191392 (3.759936) \| 0.142519 / 0.680424 (-0.537905) \| 0.016546 / 0.534201 (-0.517655) \| 0.364008 / 0.579283 (-0.215275) \| 0.385957 / 0.434364 (-0.048407) \| 0.425218 / 0.540337 (-0.115120) \| 0.519586 / 1.386936 (-0.867350) \|\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) \| 0.005914 / 0.011353 (-0.005439) \| 0.003619 / 0.011008 (-0.007389) \| 0.077806 / 0.038508 (0.039298) \| 0.037254 / 0.023109 (0.014144) \| 0.378976 / 0.275898 (0.103078) \| 0.433620 / 0.323480 (0.110140) \| 0.003291 / 0.007986 (-0.004694) \| 0.004523 / 0.004328 (0.000194) \| 0.077604 / 0.004250 (0.073353) \| 0.047493 / 0.037052 (0.010441) \| 0.396027 / 0.258489 (0.137538) \| 0.453345 / 0.293841 (0.159504) \| 0.028170 / 0.128546 (-0.100376) \| 0.008431 / 0.075646 (-0.067215) \| 0.083985 / 0.419271 (-0.335286) \| 0.045149 / 0.043533 (0.001617) \| 0.369364 / 0.255139 (0.114225) \| 0.407191 / 0.283200 (0.123991) \| 0.024033 / 0.141683 (-0.117649) \| 1.516838 / 1.452155 (0.064683) \| 1.564260 / 1.492716 (0.071544) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.200848 / 0.018006 (0.182842) \| 0.407818 / 0.000490 (0.407328) \| 0.003971 / 0.000200 (0.003771) \| 0.000077 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025033 / 0.037411 (-0.012378) \| 0.103585 / 0.014526 (0.089059) \| 0.108741 / 0.176557 (-0.067816) \| 0.161061 / 0.737135 (-0.576075) \| 0.112763 / 0.296338 (-0.183576) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.479913 / 0.215209 (0.264704) \| 4.801904 / 2.077655 (2.724249) \| 2.511433 / 1.504120 (1.007313) \| 2.307523 / 1.541195 (0.766328) \| 2.338343 / 1.468490 (0.869853) \| 0.557731 / 4.584777 (-4.027046) \| 3.386261 / 3.745712 (-0.359451) \| 2.999978 / 5.269862 (-2.269883) \| 1.463058 / 4.565676 (-3.102619) \| 0.067645 / 0.424275 (-0.356630) \| 0.011224 / 0.007607 (0.003617) \| 0.596854 / 0.226044 (0.370810) \| 5.940946 / 2.268929 (3.672017) \| 2.980194 / 55.444624 (-52.464430) \| 2.634961 / 6.876477 (-4.241516) \| 2.648160 / 2.142072 (0.506088) \| 0.669728 / 4.805227 (-4.135499) \| 0.135536 / 6.500664 (-6.365128) \| 0.066865 / 0.075469 (-0.008604) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.287151 / 1.841788 (-0.554637) \| 14.491681 / 8.074308 (6.417373) \| 14.185752 / 10.191392 (3.994360) \| 0.129391 / 0.680424 (-0.551032) \| 0.016650 / 0.534201 (-0.517551) \| 0.380111 / 0.579283 (-0.199172) \| 0.392877 / 0.434364 (-0.041487) \| 0.439402 / 0.540337 (-0.100935) \| 0.530865 / 1.386936 (-0.856071) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9aaee6fd0b2bcbe18e4829602084bcd83d669c5e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011446 / 0.011353 (0.000093) \| 0.006623 / 0.011008 (-0.004386) \| 0.131915 / 0.038508 (0.093407) \| 0.047364 / 0.023109 (0.024255) \| 0.369203 / 0.275898 (0.093305) \| 0.451509 / 0.323480 (0.128029) \| 0.006265 / 0.007986 (-0.001720) \| 0.004072 / 0.004328 (-0.000257) \| 0.098626 / 0.004250 (0.094375) \| 0.079523 / 0.037052 (0.042470) \| 0.406038 / 0.258489 (0.147549) \| 0.450564 / 0.293841 (0.156723) \| 0.050793 / 0.128546 (-0.077753) \| 0.014667 / 0.075646 (-0.060979) \| 0.401359 / 0.419271 (-0.017913) \| 0.072299 / 0.043533 (0.028767) \| 0.404456 / 0.255139 (0.149317) \| 0.396223 / 0.283200 (0.113023) \| 0.037048 / 0.141683 (-0.104635) \| 1.869123 / 1.452155 (0.416968) \| 1.953621 / 1.492716 (0.460905) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237246 / 0.018006 (0.219240) \| 0.533207 / 0.000490 (0.532717) \| 0.007392 / 0.000200 (0.007192) \| 0.000117 / 0.000054 (0.000062) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029458 / 0.037411 (-0.007954) \| 0.112438 / 0.014526 (0.097912) \| 0.139115 / 0.176557 (-0.037441) \| 0.215225 / 0.737135 (-0.521911) \| 0.134440 / 0.296338 (-0.161898) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.616783 / 0.215209 (0.401574) \| 6.113925 / 2.077655 (4.036270) \| 2.403465 / 1.504120 (0.899345) \| 1.967523 / 1.541195 (0.426329) \| 2.042144 / 1.468490 (0.573654) \| 0.927447 / 4.584777 (-3.657330) \| 5.280413 / 3.745712 (1.534701) \| 2.715335 / 5.269862 (-2.554527) \| 1.755640 / 4.565676 (-2.810036) \| 0.114370 / 0.424275 (-0.309905) \| 0.013583 / 0.007607 (0.005976) \| 0.761701 / 0.226044 (0.535657) \| 7.466049 / 2.268929 (5.197120) \| 3.041943 / 55.444624 (-52.402682) \| 2.314477 / 6.876477 (-4.562000) \| 2.469285 / 2.142072 (0.327213) \| 1.216055 / 4.805227 (-3.589172) \| 0.214205 / 6.500664 (-6.286459) \| 0.080901 / 0.075469 (0.005432) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.565185 / 1.841788 (-0.276603) \| 18.387986 / 8.074308 (10.313678) \| 19.665109 / 10.191392 (9.473717) \| 0.226670 / 0.680424 (-0.453754) \| 0.028430 / 0.534201 (-0.505771) \| 0.510526 / 0.579283 (-0.068757) \| 0.623178 / 0.434364 (0.188814) \| 0.592039 / 0.540337 (0.051702) \| 0.728462 / 1.386936 (-0.658474) \|\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) \| 0.009161 / 0.011353 (-0.002192) \| 0.004891 / 0.011008 (-0.006117) \| 0.106502 / 0.038508 (0.067994) \| 0.048234 / 0.023109 (0.025125) \| 0.451173 / 0.275898 (0.175275) \| 0.557948 / 0.323480 (0.234468) \| 0.005350 / 0.007986 (-0.002635) \| 0.004559 / 0.004328 (0.000230) \| 0.110393 / 0.004250 (0.106142) \| 0.060624 / 0.037052 (0.023572) \| 0.459265 / 0.258489 (0.200776) \| 0.575302 / 0.293841 (0.281461) \| 0.051379 / 0.128546 (-0.077167) \| 0.015576 / 0.075646 (-0.060070) \| 0.116650 / 0.419271 (-0.302621) \| 0.065534 / 0.043533 (0.022001) \| 0.461431 / 0.255139 (0.206292) \| 0.487677 / 0.283200 (0.204477) \| 0.037773 / 0.141683 (-0.103910) \| 1.992416 / 1.452155 (0.540261) \| 1.991280 / 1.492716 (0.498564) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.233607 / 0.018006 (0.215601) \| 0.507539 / 0.000490 (0.507049) \| 0.001307 / 0.000200 (0.001107) \| 0.000096 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032897 / 0.037411 (-0.004514) \| 0.126549 / 0.014526 (0.112023) \| 0.137893 / 0.176557 (-0.038663) \| 0.192124 / 0.737135 (-0.545012) \| 0.147300 / 0.296338 (-0.149038) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.679371 / 0.215209 (0.464162) \| 6.673249 / 2.077655 (4.595595) \| 2.979141 / 1.504120 (1.475022) \| 2.568789 / 1.541195 (1.027594) \| 2.537540 / 1.468490 (1.069050) \| 0.973555 / 4.584777 (-3.611222) \| 5.313536 / 3.745712 (1.567824) \| 2.693283 / 5.269862 (-2.576579) \| 1.819483 / 4.565676 (-2.746194) \| 0.111644 / 0.424275 (-0.312631) \| 0.013218 / 0.007607 (0.005611) \| 0.776114 / 0.226044 (0.550070) \| 7.758907 / 2.268929 (5.489978) \| 3.417611 / 55.444624 (-52.027013) \| 2.859502 / 6.876477 (-4.016975) \| 2.927726 / 2.142072 (0.785653) \| 1.163671 / 4.805227 (-3.641556) \| 0.228636 / 6.500664 (-6.272028) \| 0.082077 / 0.075469 (0.006607) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.746150 / 1.841788 (-0.095637) \| 17.961955 / 8.074308 (9.887647) \| 21.590545 / 10.191392 (11.399153) \| 0.210017 / 0.680424 (-0.470406) \| 0.028435 / 0.534201 (-0.505766) \| 0.509253 / 0.579283 (-0.070030) \| 0.606993 / 0.434364 (0.172629) \| 0.587189 / 0.540337 (0.046851) \| 0.684023 / 1.386936 (-0.702913) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9aaee6fd0b2bcbe18e4829602084bcd83d669c5e \"CML watermark\")\n" ]	2023-06-14T16:17:26Z	2023-06-14T16:33:39Z	2023-06-14T16:24:39Z	MEMBER	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", 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https://api.github.com/repos/huggingface/datasets/issues/7098	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7098/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7098/comments	https://api.github.com/repos/huggingface/datasets/issues/7098/events	https://github.com/huggingface/datasets/pull/7098	2,465,016,562	PR_kwDODunzps54UPMS	7,098	Release: 2.21.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7098). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]	2024-08-14T06:35:13Z	2024-08-14T06:41:07Z	2024-08-14T06:41:06Z	MEMBER	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7098/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7098/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7098.diff", "html_url": "https://github.com/huggingface/datasets/pull/7098", "merged_at": "2024-08-14T06:41:06Z", "patch_url": "https://github.com/huggingface/datasets/pull/7098.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7098" }
https://api.github.com/repos/huggingface/datasets/issues/6002	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6002/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6002/comments	https://api.github.com/repos/huggingface/datasets/issues/6002/events	https://github.com/huggingface/datasets/pull/6002	1,786,053,060	PR_kwDODunzps5UhP-Z	6,002	Add KLUE-MRC metrics	{ "avatar_url": "https://avatars.githubusercontent.com/u/37537248?v=4", "events_url": "https://api.github.com/users/ingyuseong/events{/privacy}", "followers_url": "https://api.github.com/users/ingyuseong/followers", "following_url": "https://api.github.com/users/ingyuseong/following{/other_user}", "gists_url": "https://api.github.com/users/ingyuseong/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ingyuseong", "id": 37537248, "login": "ingyuseong", "node_id": "MDQ6VXNlcjM3NTM3MjQ4", "organizations_url": "https://api.github.com/users/ingyuseong/orgs", "received_events_url": "https://api.github.com/users/ingyuseong/received_events", "repos_url": "https://api.github.com/users/ingyuseong/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ingyuseong/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ingyuseong/subscriptions", "type": "User", "url": "https://api.github.com/users/ingyuseong", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The metrics API in `datasets` is deprecated as of version 2.0, and `evaulate` is our new library for metrics. You can add a new metric to it by following [these steps](https://huggingface.co/docs/evaluate/creating_and_sharing)." ]	2023-07-03T12:11:10Z	2023-07-09T11:57:20Z	2023-07-09T11:57:20Z	NONE	null	null	null	## Metrics for KLUE-MRC (Korean Language Understanding Evaluation — Machine Reading Comprehension) Adding metrics for [KLUE-MRC](https://huggingface.co/datasets/klue). KLUE-MRC is very similar to SQuAD 2.0 but has a slightly different format which is why I added metrics for KLUE-MRC. Specifically, in the case of [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness), it leverages the scoring script of SQuAD to evaluate SQuAD 2.0 and KorQuAD. But the script isn't suitable for KLUE-MRC because KLUE-MRC is a bit different from SQuAD 2.0. And this is why I added the scoring script for KLUE-MRC. - [x] All tests passed - [x] Added a metric card (referred the metric card of SQuAD 2.0) - [x] Compatibility test with [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) passed ### References - [KLUE: Korean Language Understanding Evaluation](https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/98dce83da57b0395e163467c9dae521b-Paper-round2.pdf) - [KLUE on Hugging Face Datasets](https://huggingface.co/datasets/klue) - #2416	{ "avatar_url": "https://avatars.githubusercontent.com/u/37537248?v=4", "events_url": "https://api.github.com/users/ingyuseong/events{/privacy}", "followers_url": "https://api.github.com/users/ingyuseong/followers", "following_url": "https://api.github.com/users/ingyuseong/following{/other_user}", "gists_url": "https://api.github.com/users/ingyuseong/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ingyuseong", "id": 37537248, "login": "ingyuseong", "node_id": "MDQ6VXNlcjM3NTM3MjQ4", "organizations_url": "https://api.github.com/users/ingyuseong/orgs", "received_events_url": "https://api.github.com/users/ingyuseong/received_events", "repos_url": "https://api.github.com/users/ingyuseong/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ingyuseong/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ingyuseong/subscriptions", "type": "User", "url": "https://api.github.com/users/ingyuseong", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6002/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6002/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6002.diff", "html_url": "https://github.com/huggingface/datasets/pull/6002", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6002.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6002" }
https://api.github.com/repos/huggingface/datasets/issues/5486	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5486/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5486/comments	https://api.github.com/repos/huggingface/datasets/issues/5486/events	https://github.com/huggingface/datasets/issues/5486	1,564,059,749	I_kwDODunzps5dOahl	5,486	Adding `sep` to TextConfig	{ "avatar_url": "https://avatars.githubusercontent.com/u/29576434?v=4", "events_url": "https://api.github.com/users/omar-araboghli/events{/privacy}", "followers_url": "https://api.github.com/users/omar-araboghli/followers", "following_url": "https://api.github.com/users/omar-araboghli/following{/other_user}", "gists_url": "https://api.github.com/users/omar-araboghli/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/omar-araboghli", "id": 29576434, "login": "omar-araboghli", "node_id": "MDQ6VXNlcjI5NTc2NDM0", "organizations_url": "https://api.github.com/users/omar-araboghli/orgs", "received_events_url": "https://api.github.com/users/omar-araboghli/received_events", "repos_url": "https://api.github.com/users/omar-araboghli/repos", "site_admin": false, "starred_url": "https://api.github.com/users/omar-araboghli/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/omar-araboghli/subscriptions", "type": "User", "url": "https://api.github.com/users/omar-araboghli", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Hi @omar-araboghli, thanks for your proposal.\r\n\r\nHave you tried to use \"csv\" loader instead of \"text\"? That already has a `sep` argument.", "Hi @albertvillanova, thanks for the quick response!\r\n\r\nIndeed, I have been trying to use `csv` instead of `text`. However I am still not able to define range of rows as one sequence, that is achievable with passing `sample_by='paragraph'` to the `TextConfig`\r\n\r\nFor instance, the below code\r\n\r\n```python\r\nimport datasets\r\n\r\ndataset = datasets.load_dataset(\r\n path='csv',\r\n data_files={'train': TRAINING_SET_PATH},\r\n sep='\\t',\r\n header=None,\r\n column_names=['tokens', 'pos_tags', 'chunk_tags', 'ner_tags']\r\n)\r\n```\r\n\r\nleads to \r\n\r\n```python\r\ndataset\r\n>>> DatasetDict({\r\n train: Dataset({\r\n features: ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 62543\r\n })\r\n})\r\n\r\ndataset['train'][0]\r\n>>> {'tokens': 'Distribution',\r\n 'pos_tags': 'NN',\r\n 'chunk_tags': 'O',\r\n 'ner_tags': 'O'\r\n}\r\n```\r\nIs there a way to deal with multiple csv rows as one dataset instance, where each column is a sequence of those rows ?" ]	2023-01-31T10:39:53Z	2023-01-31T14:50:18Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	I have a local a `.txt` file that follows the `CONLL2003` format which I need to load using `load_script`. However, by using `sample_by='line'`, one can only split the dataset into lines without splitting each line into columns. Would it be reasonable to add a `sep` argument in combination with `sample_by='paragraph'` to parse a paragraph into an array for each column ? If so, I am happy to contribute! ## Environment * `python 3.8.10` * `datasets 2.9.0` ## Snippet of `train.txt` ```txt Distribution NN O O and NN O O dynamics NN O O of NN O O electron NN O B-RP complexes NN O I-RP in NN O O cyanobacterial NN O B-R membranes NN O I-R The NN O O occurrence NN O O of NN O O prostaglandin NN O B-R F2α NN O I-R in NN O O Pharbitis NN O B-R seedlings NN O I-R grown NN O O under NN O O short NN O B-P days NN O I-P or NN O I-P days NN O I-P ``` ## Current Behaviour ```python # defining 4 features ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags'] here would fail with `ValueError: Length of names (4) does not match length of arrays (1)` dataset = datasets.load_dataset(path='text', features=features, data_files={'train': 'train.txt'}, sample_by='line') dataset['train']['tokens'][0] >>> 'Distribution\tNN\tO\tO' ``` ## Expected Behaviour / Suggestion ```python # suppose we defined 4 features ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags'] dataset = datasets.load_dataset(path='text', features=features, data_files={'train': 'train.txt'}, sample_by='paragraph', sep='\t') dataset['train']['tokens'][0] >>> ['Distribution', 'and', 'dynamics', ... ] dataset['train']['ner_tags'][0] >>> ['O', 'O', 'O', ... ] ```	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5486/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5486/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6149	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6149/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6149/comments	https://api.github.com/repos/huggingface/datasets/issues/6149/events	https://github.com/huggingface/datasets/issues/6149	1,850,700,624	I_kwDODunzps5uT3NQ	6,149	Dataset.from_parquet cannot load subset of columns	{ "avatar_url": "https://avatars.githubusercontent.com/u/2512762?v=4", "events_url": "https://api.github.com/users/dwyatte/events{/privacy}", "followers_url": "https://api.github.com/users/dwyatte/followers", "following_url": "https://api.github.com/users/dwyatte/following{/other_user}", "gists_url": "https://api.github.com/users/dwyatte/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/dwyatte", "id": 2512762, "login": "dwyatte", "node_id": "MDQ6VXNlcjI1MTI3NjI=", "organizations_url": "https://api.github.com/users/dwyatte/orgs", "received_events_url": "https://api.github.com/users/dwyatte/received_events", "repos_url": "https://api.github.com/users/dwyatte/repos", "site_admin": false, "starred_url": "https://api.github.com/users/dwyatte/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/dwyatte/subscriptions", "type": "User", "url": "https://api.github.com/users/dwyatte", "user_view_type": "public" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" } ]	null	[ "Looks like this regression was introduced in `datasets==2.13.0` (`2.12.0` could load a subset of columns)\r\n\r\nThis does not appear to be fixed by https://github.com/huggingface/datasets/pull/6045 (bug still exists on `main`)" ]	2023-08-14T23:28:22Z	2023-08-17T22:36:05Z	2023-08-17T22:36:05Z	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug When using `Dataset.from_parquet(path_or_paths, columns=[...])` and a subset of columns, loading fails with a variant of the following ``` ValueError: Couldn't cast a: int64 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 273 to {'a': Value(dtype='int64', id=None), 'b': Value(dtype='int64', id=None)} because column names don't match The above exception was the direct cause of the following exception: ``` Looks to be triggered by https://github.com/huggingface/datasets/blob/c02a44715c036b5261686669727394b1308a3a4b/src/datasets/table.py#L2285-L2286 ### Steps to reproduce the bug ``` import pandas as pd from datasets import Dataset pd.DataFrame([{"a": 1, "b": 2}]).to_parquet("test.pq") Dataset.from_parquet("test.pq", columns=["a"]) ``` ### Expected behavior A subset of columns should be loaded without error ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.10.0-23-cloud-amd64-x86_64-with-glibc2.2.5 - Python version: 3.8.16 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6149/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6149/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5286	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5286/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5286/comments	https://api.github.com/repos/huggingface/datasets/issues/5286/events	https://github.com/huggingface/datasets/issues/5286	1,461,908,087	I_kwDODunzps5XIvJ3	5,286	FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json	{ "avatar_url": "https://avatars.githubusercontent.com/u/32490135?v=4", "events_url": "https://api.github.com/users/roritol/events{/privacy}", "followers_url": "https://api.github.com/users/roritol/followers", "following_url": "https://api.github.com/users/roritol/following{/other_user}", "gists_url": "https://api.github.com/users/roritol/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/roritol", "id": 32490135, "login": "roritol", "node_id": "MDQ6VXNlcjMyNDkwMTM1", "organizations_url": "https://api.github.com/users/roritol/orgs", "received_events_url": "https://api.github.com/users/roritol/received_events", "repos_url": "https://api.github.com/users/roritol/repos", "site_admin": false, "starred_url": "https://api.github.com/users/roritol/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/roritol/subscriptions", "type": "User", "url": "https://api.github.com/users/roritol", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "I found a solution \r\n\r\nIf you specifically install datasets==1.18 and then run\r\n\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.en')\r\nthen this should work (it worked for me.)", "I have the same problem here but installing datasets==1.18 wont work for me\r\n", "This works with datasets==2.14.5\r\n\r\n`>>> datasets.__version__`\r\n`'2.14.5'`\r\n`>>> ds = load_dataset(\"wikimedia/wikipedia\", \"20231101.en\")`\r\n\r\nsource:\r\nhttps://huggingface.co/datasets/wikimedia/wikipedia" ]	2022-11-23T14:54:15Z	2024-11-23T01:16:41Z	2022-11-25T11:33:14Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I follow the steps provided on the website [https://huggingface.co/datasets/wikipedia](https://huggingface.co/datasets/wikipedia) $ pip install apache_beam mwparserfromhell >>> from datasets import load_dataset >>> load_dataset("wikipedia", "20220301.en") however this results in the following error: raise MissingBeamOptions( datasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/ If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). Example of usage: `load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')` If I then prompt the system with: >>> load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner') the following error occurs: raise FileNotFoundError(f"Couldn't find file at {url}") FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json Here is the exact code: Python 3.10.6 (main, Nov 2 2022, 18:53:38) [GCC 11.3.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> load_dataset('wikipedia', '20220301.en') Downloading and preparing dataset wikipedia/20220301.en to /home/[EDITED]/.cache/huggingface/datasets/wikipedia/20220301.en/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559... Downloading: 100%\|████████████████████████████████████████████████████████████████████████████\| 15.3k/15.3k [00:00<00:00, 22.2MB/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 1741, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 822, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1879, in _download_and_prepare raise MissingBeamOptions( datasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/ If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). Example of usage: `load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')` >>> load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner') Downloading and preparing dataset wikipedia/20220301.en to /home/[EDITED]/.cache/huggingface/datasets/wikipedia/20220301.en/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559... Downloading: 100%\|████████████████████████████████████████████████████████████████████████████\| 15.3k/15.3k [00:00<00:00, 18.8MB/s] Downloading data files: 0%\| \| 0/1 [00:00<?, ?it/s]Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 1741, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 822, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1909, in _download_and_prepare super()._download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 891, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/rorytol/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559/wikipedia.py", line 945, in _split_generators downloaded_files = dl_manager.download_and_extract({"info": info_url}) File "/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py", line 447, in download_and_extract return self.extract(self.download(url_or_urls)) File "/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py", line 311, in download downloaded_path_or_paths = map_nested( File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 444, in map_nested mapped = [ File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 445, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 346, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py", line 338, in _download return cached_path(url_or_filename, download_config=download_config) File "/usr/local/lib/python3.10/dist-packages/datasets/utils/file_utils.py", line 183, in cached_path output_path = get_from_cache( File "/usr/local/lib/python3.10/dist-packages/datasets/utils/file_utils.py", line 530, in get_from_cache raise FileNotFoundError(f"Couldn't find file at {url}") FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json ### Steps to reproduce the bug $ pip install apache_beam mwparserfromhell >>> from datasets import load_dataset >>> load_dataset("wikipedia", "20220301.en") >>> load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner') ### Expected behavior Download the dataset ### Environment info Running linux on a remote workstation operated through a macbook terminal Python 3.10.6	{ "avatar_url": "https://avatars.githubusercontent.com/u/32490135?v=4", "events_url": "https://api.github.com/users/roritol/events{/privacy}", "followers_url": "https://api.github.com/users/roritol/followers", "following_url": "https://api.github.com/users/roritol/following{/other_user}", "gists_url": "https://api.github.com/users/roritol/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/roritol", "id": 32490135, "login": "roritol", "node_id": "MDQ6VXNlcjMyNDkwMTM1", "organizations_url": "https://api.github.com/users/roritol/orgs", "received_events_url": "https://api.github.com/users/roritol/received_events", "repos_url": "https://api.github.com/users/roritol/repos", "site_admin": false, "starred_url": "https://api.github.com/users/roritol/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/roritol/subscriptions", "type": "User", "url": "https://api.github.com/users/roritol", "user_view_type": "public" }	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/5286/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5286/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5454	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5454/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5454/comments	https://api.github.com/repos/huggingface/datasets/issues/5454/events	https://github.com/huggingface/datasets/issues/5454	1,552,890,419	I_kwDODunzps5cjzoz	5,454	Save and resume the state of a DataLoader	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" }, { "color": "c5def5", "default": false, "description": "Generic discussion on the library", "id": 2067400324, "name": "generic discussion", "node_id": "MDU6TGFiZWwyMDY3NDAwMzI0", "url": "https://api.github.com/repos/huggingface/datasets/labels/generic%20discussion" } ]	open	false	null	[]	null	[ "Something that'd be nice to have is \"manual update of state\". One of the learning from training LLMs is the ability to skip some batches whenever we notice huge spike might be handy.", "Your outline spec is very sound and clear, @lhoestq - thank you!\r\n\r\n@thomasw21, indeed that would be a wonderful extra feature. In Megatron-Deepspeed we manually drained the dataloader for the range we wanted. I wasn't very satisfied with the way we did it, since its behavior would change if you were to do multiple range skips. I think it should remember all the ranges it skipped and not just skip the last range - since otherwise the data is inconsistent (but we probably should discuss this in a separate issue not to derail this much bigger one).", "Hi there! I think this is a critical issue and have an urgent need for it, in my attempt to train on a super large-scale dataset using `datasets`. It is impossible to resume a time-consuming (like one month) experiment by iterating all seen data again, which could possibly cost several days.\r\n\r\n@stas00 @thomasw21 @lhoestq Any updates on this problem after 1 year passed?", "any update？", "No update so far, I wonder if someone implemented a resumable pytorch Sampler somwhere.\r\n\r\nThen regarding resuming a streaming dataset, we'd first like to have an efficient way to skip shards automatically but this is not implemented yet", "I opened a draft here for IterableDataset: https://github.com/huggingface/datasets/pull/6658\r\n\r\n\r\n\r\n```python\r\n\"\"\"Requires https://github.com/huggingface/datasets/pull/6658 (WIP)\"\"\"\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\n\r\nds = load_dataset(..., streaming=True)\r\n# ds = ds.map(tokenize)\r\n# ds = ds.shuffle(seed=42, buffer_size=1000)\r\n\r\n# Init the dataset state_dict, or load it from a checkpoint\r\ndataset_state_dict = ds.state_dict()\r\n\r\n# Resumable training loop\r\nds.load_state_dict(dataset_state_dict)\r\ndataloader = DataLoader(ds, batch_size=batch_size)\r\nfor step, batch in enumerate(dataloader):\r\n ...\r\n if step % save_steps == 0:\r\n dataset_state_dict = ds.state_dict()\r\n```", "Hi @lhoestq - can you provide more information and how to implement on saving and restoring vanilla DataLoader states with map-style datasets?\r\n\r\n", "For now the easiest is probably to use the vanilla DataLoader only for batching and multiprocessing, and implement the resuming logic using a `Dataset` (it has `.select()` to skip examples) and a `dataset_state_dict`:\r\n\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\n\r\nds = load_dataset(...)\r\n# ds = ds.map(tokenize)\r\n# ds = ds.shuffle(seed=42)\r\n\r\n# Init the dataset state_dict, or load it from a checkpoint\r\ndataset_state_dict = {\"step\": 0} \r\n\r\n# Resumable training loop\r\nstart_step = dataset_state_dict[\"step\"]\r\ndataloader = DataLoader(ds.select(range(start_step * batch_size, len(ds))), batch_size=batch_size)\r\nfor step, batch in enumerate(dataloader, start=start_step):\r\n ...\r\n if step % save_steps == 0:\r\n dataset_state_dict = {\"step\": step}\r\n```", "Hello, I found a similar implementation online that seems to solve your problem. https://github.com/facebookresearch/vissl/blob/main/vissl/data/data_helper.py#L93\r\nit looks like we can set_start_iter in StatefulDistributedSampler to implement the stateful resume requirement we want.\r\n\r\n", "Hi y'all, @lhoestq I wanted to flag that we currently have a StatefulDataLoader in `pytorch/data/torchdata` that has state_dict/load_state_dict methods, which will call a dataset's state_dict/load_state_dict methods but also handle multiprocessing under the hood. Any chance we can collaborate on this and try to get them to work well together? Please have a look here for some basic examples: https://github.com/pytorch/data/tree/main/torchdata/stateful_dataloader#saving-and-loading-state ", "Fantastic ! This will help pushing our IterableDataset state_dict implementation at https://github.com/huggingface/datasets/pull/6658 :) I'll check if there is anything missing to maker them work together, and add tests and some docs referring to the StatefulDataLoader :)", "Ah I just saw this disclaimer in the torchdata README and it feels like people should not rely on it. Should the StatefulDataLoader live elsewhere @andrewkho ?\r\n\r\n> ⚠️ As of July 2023, we have paused active development on TorchData and have paused new releases. We have learnt a lot from building it and hearing from users, but also believe we need to re-evaluate the technical design and approach given how much the industry has changed since we began the project. During the rest of 2023 we will be re-evaluating our plans in this space. Please reach out if you suggestions or comments (please use https://github.com/pytorch/data/issues/1196 for feedback).", "@lhoestq Good find, we are in the midst of updating this disclaimer as we're re-starting development and regular releases, though our approach will be to iterate on DL V1 (ie StatefulDataLoader) instead of continuing development on datapipes+DLV2. Let's discuss on a call at some point to figure out the best path forward! ", "As a heads up, `IterableDataset` state_dict has been added in https://github.com/huggingface/datasets/pull/6658\r\n\r\n...and it works out of the box with the `torchdata` `StatefulDataLoader` :)\r\n\r\nSee the docs at https://huggingface.co/docs/datasets/main/en/use_with_pytorch#checkpoint-and-resume", "amazing! Thank you, @lhoestq \r\n\r\ndoes it work with non-iterable dataset as well? the docs only mention iterable dataset", "It's for iterable dataset only. For regular dataset I believe the sampler should implement state_dict, but maybe @andrewkho might know best how to resume a regular dataset with torchdata", "@stas00 stateful dataloader will save and resume samplers for map style datasets. If no state_dict/load_state_dict is provided by the sampler, it will naively skip samples to fast forward. See here for more details https://github.com/pytorch/data/blob/main/torchdata/stateful_dataloader/README.md \n\nHope this helps! ", "Thank you very much for clarifying that, Andrew.\r\n\r\n", "👋 I am trying to use `HF Streaming Dataset + TorchDDP + Stateful Dataloader`, to train using multiple nodes and large datasets. \r\n\r\nSo far, I have been able to use HF Streaming Dataset + TorchDDP with Vanilla Datasets. To do so, I implemented a custom iterable to make sure that shards are distributed across the multiple nodes, while letting the `dataset` take care of the multiple workers. The implementation uses `split_dataset_by_node`:\r\n\r\n```\r\nimport torch\r\nfrom torch.distributed import get_rank, get_world_size\r\nfrom torch.utils.data import DataLoader, IterableDataset\r\n\r\nclass MyIterableDataset(IterableDataset):\r\n def __init__(self, dataset):\r\n super().__init__()\r\n self.dataset = dataset\r\n self._iterable_by_node = None\r\n\r\n def __iter__(self):\r\n if torch.distributed.is_available() and torch.distributed.is_initialized():\r\n world_size = get_world_size()\r\n process_rank = get_rank()\r\n else:\r\n world_size = 1\r\n process_rank = 0\r\n\r\n if world_size > 1:\r\n self._iterable_by_node = split_dataset_by_node(\r\n self.dataset, rank=process_rank, world_size=world_size\r\n )\r\n else:\r\n self._iterable_by_node = self.dataset\r\n\r\n for example in self._iterable_by_node:\r\n # Trying with _state_dict, since `.state_dict()` creates a copy\r\n self._state_dict.update(self._iterable_by_node._state_dict)\r\n yield example\r\n\r\n def state_dict(self):\r\n return self._state_dict\r\n\r\n def load_state_dict(self, state):\r\n pass # Not implemented yet\r\n\r\n```\r\n \r\nThis doesn't seem to work with `StatefulDataLoader` though. I can see the state of the worker's dataset being updated in its corresponding workers' processes, but somehow the updates are not propagated back to the main process. I have tried with different variants of the above code without success. \r\n\r\nI confirmed that if I skip the custom class and pass `dataset` directly to the loader as in the [docs](https://huggingface.co/docs/datasets/main/en/use_with_pytorch#checkpoint-and-resume), the StatefulDataLoader sees the updates for each worker. However, if I do this, multiple nodes will see the same examples, which I definitely don't want.\r\n\r\nIs there something I am missing? It would be nice if streaming `dataset`s would support by default the multinode training (unless it already does it and I am missing something).\r\n\r\n\r\n", "Hi ! Have you tried using `split_dataset_by_node()` and pass the result to the StatefulDataLoader ?\r\n\r\n```python\r\ndataloader = StatefulDataLoader(split_dataset_by_node(dataset, rank=process_rank, world_size=world_size))\r\n```", "> Hi ! Have you tried using split_dataset_by_node() and pass the result to the StatefulDataLoader ?\r\n\r\n@lhoestq it took me some time to test, but it works like a charm. Thanks for the pointer. Totally missed this 🤦. " ]	2023-01-23T10:58:54Z	2024-11-27T01:19:21Z	null	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	It would be nice when using `datasets` with a PyTorch DataLoader to be able to resume a training from a DataLoader state (e.g. to resume a training that crashed) What I have in mind (but lmk if you have other ideas or comments): For map-style datasets, this requires to have a PyTorch Sampler state that can be saved and reloaded per node and worker. For iterable datasets, this requires to save the state of the dataset iterator, which includes: - the current shard idx and row position in the current shard - the epoch number - the rng state - the shuffle buffer Right now you can already resume the data loading of an iterable dataset by using `IterableDataset.skip` but it takes a lot of time because it re-iterates on all the past data until it reaches the resuming point. cc @stas00 @sgugger	null	{ "+1": 3, "-1": 0, "confused": 0, "eyes": 0, "heart": 7, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 10, "url": "https://api.github.com/repos/huggingface/datasets/issues/5454/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5454/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6852	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6852/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6852/comments	https://api.github.com/repos/huggingface/datasets/issues/6852/events	https://github.com/huggingface/datasets/issues/6852	2,272,465,011	I_kwDODunzps6HcxBz	6,852	Write token isn't working while pushing to datasets	{ "avatar_url": "https://avatars.githubusercontent.com/u/130903099?v=4", "events_url": "https://api.github.com/users/realzai/events{/privacy}", "followers_url": "https://api.github.com/users/realzai/followers", "following_url": "https://api.github.com/users/realzai/following{/other_user}", "gists_url": "https://api.github.com/users/realzai/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/realzai", "id": 130903099, "login": "realzai", "node_id": "U_kgDOB81sOw", "organizations_url": "https://api.github.com/users/realzai/orgs", "received_events_url": "https://api.github.com/users/realzai/received_events", "repos_url": "https://api.github.com/users/realzai/repos", "site_admin": false, "starred_url": "https://api.github.com/users/realzai/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/realzai/subscriptions", "type": "User", "url": "https://api.github.com/users/realzai", "user_view_type": "public" }	[]	closed	false	null	[]	null	[]	2024-04-30T21:18:20Z	2024-05-02T00:55:46Z	2024-05-02T00:55:46Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug <img width="1001" alt="Screenshot 2024-05-01 at 3 37 06 AM" src="https://github.com/huggingface/datasets/assets/130903099/00fcf12c-fcc1-4749-8592-d263d4efcbcc"> As you can see I logged in to my account and the write token is valid. But I can't upload on my main account and I am getting that error. It was okay on my test account at first try. (I refreshed the token, tried a new token but still doesn't work) ### Steps to reproduce the bug 1. I loaded a dataset. 2. I logged in using both cli and huggingface_hub 3. I pushed to my down dataset (It went well without any issues on my test account) ### Expected behavior It should have gone smoothly and this is not even my first time uploading to huggingface datasets ### Environment info colab, dataset (tried multiple versions)	{ "avatar_url": "https://avatars.githubusercontent.com/u/130903099?v=4", "events_url": "https://api.github.com/users/realzai/events{/privacy}", "followers_url": "https://api.github.com/users/realzai/followers", "following_url": "https://api.github.com/users/realzai/following{/other_user}", "gists_url": "https://api.github.com/users/realzai/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/realzai", "id": 130903099, "login": "realzai", "node_id": "U_kgDOB81sOw", "organizations_url": "https://api.github.com/users/realzai/orgs", "received_events_url": "https://api.github.com/users/realzai/received_events", "repos_url": "https://api.github.com/users/realzai/repos", "site_admin": false, "starred_url": "https://api.github.com/users/realzai/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/realzai/subscriptions", "type": "User", "url": "https://api.github.com/users/realzai", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6852/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6852/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/4865	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4865/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4865/comments	https://api.github.com/repos/huggingface/datasets/issues/4865/events	https://github.com/huggingface/datasets/issues/4865	1,344,552,626	I_kwDODunzps5QJD6y	4,865	Dataset Viewer issue for MoritzLaurer/multilingual_nli	{ "avatar_url": "https://avatars.githubusercontent.com/u/41862082?v=4", "events_url": "https://api.github.com/users/MoritzLaurer/events{/privacy}", "followers_url": "https://api.github.com/users/MoritzLaurer/followers", "following_url": "https://api.github.com/users/MoritzLaurer/following{/other_user}", "gists_url": "https://api.github.com/users/MoritzLaurer/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/MoritzLaurer", "id": 41862082, "login": "MoritzLaurer", "node_id": "MDQ6VXNlcjQxODYyMDgy", "organizations_url": "https://api.github.com/users/MoritzLaurer/orgs", "received_events_url": "https://api.github.com/users/MoritzLaurer/received_events", "repos_url": "https://api.github.com/users/MoritzLaurer/repos", "site_admin": false, "starred_url": "https://api.github.com/users/MoritzLaurer/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/MoritzLaurer/subscriptions", "type": "User", "url": "https://api.github.com/users/MoritzLaurer", "user_view_type": "public" }	[ { "color": "E5583E", "default": false, "description": "Related to the dataset viewer on huggingface.co", "id": 3470211881, "name": "dataset-viewer", "node_id": "LA_kwDODunzps7O1zsp", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset-viewer" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[ "Thanks for reporting @MoritzLaurer.\r\n\r\nCurrently, the dataset preview is working properly: https://huggingface.co/datasets/MoritzLaurer/multilingual_nli\r\n\r\nPlease note that when a dataset is modified, it might take some time until the preview is completely updated.\r\n\r\n@severo might it be worth adding a clearer error message, something like \"The preview is updating, please retry later\"?", "Thanks for your response. You are right, its now working well. I had waited for 30 min or so and refreshed several times and thought there was some other error. Yeah, a different error message sounds like a good idea to avoid confusion. ", "I'm closing this issue then.", "> @severo might it be worth adding a clearer error message, something like \"The preview is updating, please retry later\"?\r\n\r\nYes, it's a known issue, and we're about to ship a better version" ]	2022-08-19T14:55:20Z	2022-08-22T14:47:14Z	2022-08-22T06:13:20Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Link _No response_ ### Description I've just uploaded a new dataset to the hub and the viewer does not work for some reason, see here: https://huggingface.co/datasets/MoritzLaurer/multilingual_nli It displays the error: ``` Status code: 400 Exception: Status400Error Message: The dataset does not exist. ``` Weirdly enough the dataviewer works for an earlier version of the same dataset. The only difference is that it is smaller, but I'm not aware of other changes I have made: https://huggingface.co/datasets/MoritzLaurer/multilingual_nli_test Do you know why the dataviewer is not working? ### Owner _No response_	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4865/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4865/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5006	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5006/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5006/comments	https://api.github.com/repos/huggingface/datasets/issues/5006/events	https://github.com/huggingface/datasets/pull/5006	1,380,968,395	PR_kwDODunzps4_Wm8z	5,006	Revert input_columns change	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "Merging this one and I'll check if it fixes the `transformers` CI before doing a patch release" ]	2022-09-21T13:49:20Z	2022-09-21T14:14:33Z	2022-09-21T14:11:57Z	MEMBER	null	null	null	Revert https://github.com/huggingface/datasets/pull/4971 Fix https://github.com/huggingface/datasets/issues/5005	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5006/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5006/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5006.diff", "html_url": "https://github.com/huggingface/datasets/pull/5006", "merged_at": "2022-09-21T14:11:57Z", "patch_url": "https://github.com/huggingface/datasets/pull/5006.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5006" }
https://api.github.com/repos/huggingface/datasets/issues/5360	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5360/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5360/comments	https://api.github.com/repos/huggingface/datasets/issues/5360/events	https://github.com/huggingface/datasets/issues/5360	1,496,947,177	I_kwDODunzps5ZOZnp	5,360	IterableDataset returns duplicated data using PyTorch DDP	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" } ]	null	[ "If you use huggingface trainer, you will find the trainer has wrapped a `IterableDatasetShard` to avoid duplication.\r\nSee:\r\nhttps://github.com/huggingface/transformers/blob/dfd818420dcbad68e05a502495cf666d338b2bfb/src/transformers/trainer.py#L835\r\n", "If you want to support it by datasets natively, maybe we also need to change the code in `transformers` ?", "Opened https://github.com/huggingface/transformers/issues/20770 to discuss this :)", "Maybe something like this then ?\r\n```python\r\nfrom datasets.distributed import split_dataset_by_node\r\nds = split_dataset_by_node(ds, rank=rank, world_size=world_size)\r\n```\r\n\r\nFor map-style datasets the implementation is trivial (it can simply use `.shard()`).\r\n\r\nFor iterable datasets we would need to implement a new ExamplesIterable that would only iterate on a subset of the (possibly shuffled and re-shuffled after each epoch) list of shards, based on the rank and world size.", "My plan is to skip examples by default to not end up with duplicates.\r\n\r\nAnd if a dataset has a number of shards that is a factor of the world size, then I'd make it more optimized by distributing the shards evenly across nodes instead.", "Opened a PR here: https://github.com/huggingface/datasets/pull/5369\r\n\r\nfeel free to play with it and share your feedbacks :)", "@lhoestq I add shuffle after split_dataset_by_node, duplicated data still exist. \r\nFor example, we have a directory named `mock_pretraining_data`, which has three files, `part-00000`, `part-00002`,`part-00002`. \r\nText in `part-00000` is like this: \r\n{\"id\": 0}\r\n{\"id\": 1}\r\n{\"id\": 2}\r\n{\"id\": 3}\r\n{\"id\": 4}\r\n{\"id\": 5}\r\n{\"id\": 6}\r\n{\"id\": 7}\r\n{\"id\": 8}\r\n{\"id\": 9}\r\n\r\nand `part-00001`\r\n{\"id\": 10}\r\n{\"id\": 11}\r\n{\"id\": 12}\r\n{\"id\": 13}\r\n{\"id\": 14}\r\n{\"id\": 15}\r\n{\"id\": 16}\r\n{\"id\": 17}\r\n{\"id\": 18}\r\n{\"id\": 19}\r\n\r\nand `part-00002`\r\n{\"id\": 20}\r\n{\"id\": 21}\r\n{\"id\": 22}\r\n{\"id\": 23}\r\n{\"id\": 24}\r\n{\"id\": 25}\r\n{\"id\": 26}\r\n{\"id\": 27}\r\n{\"id\": 28}\r\n{\"id\": 29}\r\n\r\nAnd code in `test_dist.py` like this,\r\n```python\r\nimport torch\r\nfrom torch.utils.data import Dataset, DataLoader\r\nfrom datasets import load_dataset\r\nimport os\r\nfrom transformers import AutoTokenizer, NezhaForPreTraining\r\nfrom transformers import AdamW, get_linear_schedule_with_warmup\r\nimport torch.nn.functional as F\r\nimport torch.nn as nn\r\nimport torch.distributed as dist\r\nfrom datasets.distributed import split_dataset_by_node\r\nfrom torch.nn.parallel import DistributedDataParallel as DDP\r\n\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = '5,6,7'\r\n\r\ndist.init_process_group(\"nccl\")\r\nlocal_rank = int(os.environ['LOCAL_RANK'])\r\nworld_size = torch.distributed.get_world_size()\r\ndevice = torch.device('cuda', local_rank)\r\ndata_dir = './'\r\n\r\ndef load_trainset(train_path):\r\n dataset = load_dataset('json', data_dir=os.path.join(data_dir, train_path), split='train', streaming=True)\r\n return dataset\r\n\r\ndef collate_fn(examples):\r\n input_ids = []\r\n for example in examples:\r\n input_ids.append(example['id'])\r\n return torch.LongTensor(input_ids).to(device)\r\n\r\n\r\ndataset = load_trainset('mock_pretraining_data')\r\ndataset = split_dataset_by_node(dataset, rank=local_rank, world_size=world_size).shuffle(buffer_size=512)\r\n# train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)\r\nbatch_size = 3\r\nprint('batch_size: {}'.format(batch_size))\r\ntrain_dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=collate_fn)\r\n\r\nfor x in train_dataloader:\r\n print({'rank': local_rank, 'id': x})\r\n```\r\nrun `python -m torch.distributed.launch --nproc_per_node=3 test_dist.py`\r\nThe output is\r\n```\r\n{'rank': 1, 'id': tensor([12, 15, 14], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([16, 10, 18], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([17, 13, 19], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([11], device='cuda:1')}\r\n{'rank': 0, 'id': tensor([0, 2, 9], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([4, 8, 1], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([5, 3, 6], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([7], device='cuda:0')}\r\n{'rank': 2, 'id': tensor([13, 15, 14], device='cuda:2')}\r\n{'rank': 2, 'id': tensor([19, 17, 18], device='cuda:2')}\r\n{'rank': 2, 'id': tensor([12, 16, 11], device='cuda:2')}\r\n{'rank': 2, 'id': tensor([10], device='cuda:2')}\r\n```\r\n`part-00001` is loaded twice, `part-00002` isn't loaded.\r\n\r\nIf I run `python -m torch.distributed.launch --nproc_per_node=2 test_dist.py`\r\nThe output is weirder，many numbers appear twice\r\n```\r\n{'rank': 1, 'id': tensor([26, 8, 13], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([22, 19, 20], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([12, 28, 11], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([24, 2, 14], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([ 6, 27, 3], device='cuda:1')}\r\n{'rank': 0, 'id': tensor([ 8, 25, 1], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([20, 4, 12], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([14, 29, 5], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([ 7, 18, 23], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([19, 17, 11], device='cuda:0')}\r\n``` ", "Hi ! Thanks for reporting, you need to pass `seed=` to `shuffle()` or the processes won't use the same seed to shuffle the shards order before assigning each shard to a node.\r\n\r\nThe issue is that the workers are not using the same seed to shuffle the shards before splitting the shards list by node.", "Opened https://github.com/huggingface/datasets/issues/5696", "I have the same issue\r\n```\r\nds['train'] = load_dataset(streaming=True)\r\nds['train'] = split_dataset_by_node(ds['train'], rank=int(os.environ[\"RANK\"]), world_size=int(os.environ[\"WORLD_SIZE\"]))\r\nvectorized_datasets = ds.map(\r\n prepare_dataset,\r\n remove_columns=raw_datasets_features,\r\n).with_format(\"torch\")\r\n\r\nvectorized_datasets[\"train\"] = vectorized_datasets[\"train\"].shuffle(\r\n buffer_size=500,\r\n seed=42,\r\n)\r\n\r\ndef prepare_dataset(batch):\r\n ....\r\n print(f\"sentence: {batch['sentence']}, target_text: {batch['target_text']}\")\r\n return batch\r\n```\r\nWhen using split_dataset_by_node(), the data being read is indeed different for each GPU ID.\r\n\r\n```\r\ntrainer = Trainer(\r\n model=model,\r\n data_collator=data_collator,\r\n args=training_args,\r\n compute_metrics=compute_metrics,\r\n train_dataset=vectorized_datasets[\"train\"] if training_args.do_train else None,\r\n eval_dataset=vectorized_datasets[\"eval\"] if training_args.do_eval else None,\r\n tokenizer=processor,\r\n callbacks=[ShuffleCallback()],\r\n )\r\n...\r\ntrain_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n```\r\nHowever, when I execute trainer.train(), the data being read is different from what I expected.\r\nBecause I print the batch value in prepare_dataset() , I observe that the data is the same for each GPU ID.\r\n\r\nHow should I handle this issue?\r\n\r\n\r\n", "There are two ways an iterable dataset can be split by node:\r\n1. if the number of shards is a factor of number of GPUs: in that case the shards are evenly distributed per GPU\r\n2. otherwise, each GPU iterate on the data and at the end keeps 1 sample out of n(GPUs) - skipping the others.\r\n\r\nIn case 2. it's therefore possible to have the same examples passed to `prepare_dataset` for each GPU.\r\n\r\nThis doesn't sound optimized though, because it runs the preprocessing on samples that won't be used in the end.\r\n\r\nCould you open a new issue so that we can discuss about this and find a solution ?" ]	2022-12-14T16:06:19Z	2023-06-15T09:51:13Z	2023-01-16T13:33:33Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	As mentioned in https://github.com/huggingface/datasets/issues/3423, when using PyTorch DDP the dataset ends up with duplicated data. We already check for the PyTorch `worker_info` for single node, but we should also check for `torch.distributed.get_world_size()` and `torch.distributed.get_rank()`	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5360/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5360/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5945	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5945/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5945/comments	https://api.github.com/repos/huggingface/datasets/issues/5945/events	https://github.com/huggingface/datasets/issues/5945	1,754,084,577	I_kwDODunzps5ojTTh	5,945	Failing to upload dataset to the hub	{ "avatar_url": "https://avatars.githubusercontent.com/u/77382661?v=4", "events_url": "https://api.github.com/users/Ar770/events{/privacy}", "followers_url": "https://api.github.com/users/Ar770/followers", "following_url": "https://api.github.com/users/Ar770/following{/other_user}", "gists_url": "https://api.github.com/users/Ar770/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Ar770", "id": 77382661, "login": "Ar770", "node_id": "MDQ6VXNlcjc3MzgyNjYx", "organizations_url": "https://api.github.com/users/Ar770/orgs", "received_events_url": "https://api.github.com/users/Ar770/received_events", "repos_url": "https://api.github.com/users/Ar770/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Ar770/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Ar770/subscriptions", "type": "User", "url": "https://api.github.com/users/Ar770", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi ! Feel free to re-run your code later, it will resume automatically where you left", "Tried many times in the last 2 weeks, problem remains.", "Alternatively you can save your dataset in parquet files locally and upload them to the hub manually\r\n\r\n```python\r\nfrom tqdm import tqdm\r\nnum_shards = 60\r\nfor index in tqdm(range(num_shards)):\r\n ds.shard(num_shards=num_shards, index=index, contiguous=True).to_parquet(f\"{index:05d}.parquet\")\r\n````" ]	2023-06-13T05:46:46Z	2023-07-24T11:56:40Z	2023-07-24T11:56:40Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Trying to upload a dataset of hundreds of thousands of audio samples (the total volume is not very large, 60 gb) to the hub with push_to_hub, it doesn't work. From time to time one piece of the data (parquet) gets pushed and then I get RemoteDisconnected even though my internet is stable. Please help. I'm trying to upload the dataset for almost a week. Thanks ### Steps to reproduce the bug not relevant ### Expected behavior Be able to upload thedataset ### Environment info python: 3.9	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5945/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5945/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7472	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7472/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7472/comments	https://api.github.com/repos/huggingface/datasets/issues/7472/events	https://github.com/huggingface/datasets/issues/7472	2,937,607,272	I_kwDODunzps6vGFRo	7,472	Label casting during `map` process is canceled after the `map` process	{ "avatar_url": "https://avatars.githubusercontent.com/u/11156001?v=4", "events_url": "https://api.github.com/users/yoshitomo-matsubara/events{/privacy}", "followers_url": "https://api.github.com/users/yoshitomo-matsubara/followers", "following_url": "https://api.github.com/users/yoshitomo-matsubara/following{/other_user}", "gists_url": "https://api.github.com/users/yoshitomo-matsubara/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/yoshitomo-matsubara", "id": 11156001, "login": "yoshitomo-matsubara", "node_id": "MDQ6VXNlcjExMTU2MDAx", "organizations_url": "https://api.github.com/users/yoshitomo-matsubara/orgs", "received_events_url": "https://api.github.com/users/yoshitomo-matsubara/received_events", "repos_url": "https://api.github.com/users/yoshitomo-matsubara/repos", "site_admin": false, "starred_url": "https://api.github.com/users/yoshitomo-matsubara/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/yoshitomo-matsubara/subscriptions", "type": "User", "url": "https://api.github.com/users/yoshitomo-matsubara", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi ! By default `map()` tries to keep the types of each column of the dataset, so here it reuses the int type since all your float values can be converted to integers. But I agree it would be nice to store float values as float values and don't try to reuse the same type in this case.\n\nIn the meantime, you can either store the float values in a new column, or pass the output `features=` manually to `map()`", "Hi @lhoestq \n\nThank you for the answer & suggestion!\n\nCan we add some flag to `map()` function like `reuses_original_type=True` and skip reusing the original type when it's False?\n\nLet me know if it sounds like a reasonable solution. I am happy to submit a PR for this.", "In general we try to avoid adding new parameters when it's already possible to achieve the same results with existing parameters (here `features=`). But since it's not always convenient to know in advance the `features=` I'm open to contributions to adding this parameter yes", "Thank you for sharing the context. Good to know that. \n\nI submitted a PR #7483. Could you review the PR?", "Hi @lhoestq \n\nLet me know if there is something that I should add to [the PR](https://github.com/huggingface/datasets/pull/7483)!", "Closing this issue as the PR #7483 was merged" ]	2025-03-21T07:56:22Z	2025-04-10T05:11:15Z	2025-04-10T05:11:14Z	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug When preprocessing a multi-label dataset, I introduced a step to convert int labels to float labels as [BCEWithLogitsLoss](https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html) expects float labels and forward function of models in transformers package internally use `BCEWithLogitsLoss` However, the casting was canceled after `.map` process and the label values still use int values, which leads to an error ``` File "/home/yoshitomo/anaconda3/envs/torchdistill/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py", line 1711, in forward loss = loss_fct(logits, labels) File "/home/yoshitomo/anaconda3/envs/torchdistill/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl return self._call_impl(args, kwargs) File "/home/yoshitomo/anaconda3/envs/torchdistill/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl return forward_call(args, *kwargs) File "/home/yoshitomo/anaconda3/envs/torchdistill/lib/python3.10/site-packages/torch/nn/modules/loss.py", line 819, in forward return F.binary_cross_entropy_with_logits( File "/home/yoshitomo/anaconda3/envs/torchdistill/lib/python3.10/site-packages/torch/nn/functional.py", line 3628, in binary_cross_entropy_with_logits return torch.binary_cross_entropy_with_logits( RuntimeError: result type Float can't be cast to the desired output type Long ``` This seems like happening only when the original labels are int values (see examples below) ### Steps to reproduce the bug If the original dataset uses a list of int labels, it will cancel the int->float casting ```python from datasets import Dataset data = { 'text': ['text1', 'text2', 'text3', 'text4'], 'labels': [[0, 1, 2], [3], [3, 4], [3]] } dataset = Dataset.from_dict(data) label_set = set([label for labels in data['labels'] for label in labels]) label2idx = {label: idx for idx, label in enumerate(sorted(label_set))} def multi_labels_to_ids(labels): ids = [0.0] len(label2idx) for label in labels: ids[label2idx[label]] = 1.0 return ids def preprocess(examples): result = {'sentence': [[0, 3, 4] for _ in range(len(examples['labels']))]} print('"labels" are int', examples['labels']) result['labels'] = [multi_labels_to_ids(l) for l in examples['labels']] print('"labels" were converted to multi-label format with float values', result['labels']) return result preprocessed_dataset = dataset.map(preprocess, batched=True, remove_columns=['labels', 'text']) print(preprocessed_dataset[0]['labels']) # Output: "[1, 1, 1, 0, 0]" # Expected: "[1.0, 1.0, 1.0, 0.0, 0.0]" ``` If the original dataset uses non-int labels, it works as expected. ```python from datasets import Dataset data = { 'text': ['text1', 'text2', 'text3', 'text4'], 'labels': [['label1', 'label2', 'label3'], ['label4'], ['label4', 'label5'], ['label4']] } dataset = Dataset.from_dict(data) label_set = set([label for labels in data['labels'] for label in labels]) label2idx = {label: idx for idx, label in enumerate(sorted(label_set))} def multi_labels_to_ids(labels): ids = [0.0] * len(label2idx) for label in labels: ids[label2idx[label]] = 1.0 return ids def preprocess(examples): result = {'sentence': [[0, 3, 4] for _ in range(len(examples['labels']))]} print('"labels" are int', examples['labels']) result['labels'] = [multi_labels_to_ids(l) for l in examples['labels']] print('"labels" were converted to multi-label format with float values', result['labels']) return result preprocessed_dataset = dataset.map(preprocess, batched=True, remove_columns=['labels', 'text']) print(preprocessed_dataset[0]['labels']) # Output: "[1.0, 1.0, 1.0, 0.0, 0.0]" # Expected: "[1.0, 1.0, 1.0, 0.0, 0.0]" ``` Note that the only difference between these two examples is > 'labels': [[0, 1, 2], [3], [3, 4], [3]] v.s > 'labels': [['label1', 'label2', 'label3'], ['label4'], ['label4', 'label5'], ['label4']] ### Expected behavior Even if the original dataset uses a list of int labels, the int->float casting during `.map` process should not be canceled as shown in the above example ### Environment info OS Ubuntu 22.04 LTS Python 3.10.11 datasets v3.4.1	{ "avatar_url": "https://avatars.githubusercontent.com/u/11156001?v=4", "events_url": "https://api.github.com/users/yoshitomo-matsubara/events{/privacy}", "followers_url": "https://api.github.com/users/yoshitomo-matsubara/followers", "following_url": "https://api.github.com/users/yoshitomo-matsubara/following{/other_user}", "gists_url": "https://api.github.com/users/yoshitomo-matsubara/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/yoshitomo-matsubara", "id": 11156001, "login": "yoshitomo-matsubara", "node_id": "MDQ6VXNlcjExMTU2MDAx", "organizations_url": "https://api.github.com/users/yoshitomo-matsubara/orgs", "received_events_url": "https://api.github.com/users/yoshitomo-matsubara/received_events", "repos_url": "https://api.github.com/users/yoshitomo-matsubara/repos", "site_admin": false, "starred_url": "https://api.github.com/users/yoshitomo-matsubara/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/yoshitomo-matsubara/subscriptions", "type": "User", "url": "https://api.github.com/users/yoshitomo-matsubara", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7472/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7472/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6114	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6114/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6114/comments	https://api.github.com/repos/huggingface/datasets/issues/6114/events	https://github.com/huggingface/datasets/issues/6114	1,834,015,584	I_kwDODunzps5tUNtg	6,114	Cache not being used when loading commonvoice 8.0.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/31082141?v=4", "events_url": "https://api.github.com/users/clabornd/events{/privacy}", "followers_url": "https://api.github.com/users/clabornd/followers", "following_url": "https://api.github.com/users/clabornd/following{/other_user}", "gists_url": "https://api.github.com/users/clabornd/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/clabornd", "id": 31082141, "login": "clabornd", "node_id": "MDQ6VXNlcjMxMDgyMTQx", "organizations_url": "https://api.github.com/users/clabornd/orgs", "received_events_url": "https://api.github.com/users/clabornd/received_events", "repos_url": "https://api.github.com/users/clabornd/repos", "site_admin": false, "starred_url": "https://api.github.com/users/clabornd/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/clabornd/subscriptions", "type": "User", "url": "https://api.github.com/users/clabornd", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "You can avoid this by using the `revision` parameter in `load_dataset` to always force downloading a specific commit (if not specified it defaults to HEAD, hence the redownload).", "Thanks @mariosasko this works well, looks like I should have read the documentation a bit more carefully. \r\n\r\nIt is still a bit confusing which hash I should provide: passing `revision = c8fd66e85f086e3abb11eeee55b1737a3d1e8487` from https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/commits/main caused the cached version at `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a` to be loaded, so I had to know that it was the previous commit unless I've missed something else." ]	2023-08-02T23:18:11Z	2023-08-18T23:59:00Z	2023-08-18T23:59:00Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I have commonvoice 8.0.0 downloaded in `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a`. The folder contains all the arrow files etc, and was used as the cached version last time I touched the ec2 instance I'm working on. Now, with the same command that downloaded it initially: ``` dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>") ``` it tries to redownload the dataset to `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/05bdc7940b0a336ceeaeef13470c89522c29a8e4494cbeece64fb472a87acb32` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. ```dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")``` 2. dataset is updated by maintainers 3. ```dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")``` ### Expected behavior I expect that it uses the already downloaded data in `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a`. Not sure what's happening in 2. but if, say it's an issue with the dataset referenced by "mozilla-foundation/common_voice_8_0" being modified by the maintainers, how would I force datasets to point to the original version I downloaded? EDIT: It was indeed that the maintainers had updated the dataset (v 8.0.0). However I still cant load the dataset from disk instead of redownloading, with for example: ``` load_dataset(".cache/huggingface/datasets/downloads/extracted/<hash>/cv-corpus-8.0-2022-01-19/en/", "en") > ... > File [~/miniconda3/envs/aa_torch2/lib/python3.10/site-packages/datasets/table.py:1938](.../ python3.10/site-packages/datasets/table.py:1938), in cast_array_to_feature(array, feature, allow_number_to_str) 1937 elif not isinstance(feature, (Sequence, dict, list, tuple)): -> 1938 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) ... 1794 e = e.__context__ -> 1795 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1797 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Environment info datasets==2.7.0 python==3.10.8 OS: AWS Linux	{ "avatar_url": "https://avatars.githubusercontent.com/u/31082141?v=4", "events_url": "https://api.github.com/users/clabornd/events{/privacy}", "followers_url": "https://api.github.com/users/clabornd/followers", "following_url": "https://api.github.com/users/clabornd/following{/other_user}", "gists_url": "https://api.github.com/users/clabornd/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/clabornd", "id": 31082141, "login": "clabornd", "node_id": "MDQ6VXNlcjMxMDgyMTQx", "organizations_url": "https://api.github.com/users/clabornd/orgs", "received_events_url": "https://api.github.com/users/clabornd/received_events", "repos_url": "https://api.github.com/users/clabornd/repos", "site_admin": false, "starred_url": "https://api.github.com/users/clabornd/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/clabornd/subscriptions", "type": "User", "url": "https://api.github.com/users/clabornd", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6114/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6114/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7051	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7051/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7051/comments	https://api.github.com/repos/huggingface/datasets/issues/7051/events	https://github.com/huggingface/datasets/issues/7051	2,409,353,929	I_kwDODunzps6Pm9LJ	7,051	How to set_epoch with interleave_datasets?	{ "avatar_url": "https://avatars.githubusercontent.com/u/511073?v=4", "events_url": "https://api.github.com/users/jonathanasdf/events{/privacy}", "followers_url": "https://api.github.com/users/jonathanasdf/followers", "following_url": "https://api.github.com/users/jonathanasdf/following{/other_user}", "gists_url": "https://api.github.com/users/jonathanasdf/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jonathanasdf", "id": 511073, "login": "jonathanasdf", "node_id": "MDQ6VXNlcjUxMTA3Mw==", "organizations_url": "https://api.github.com/users/jonathanasdf/orgs", "received_events_url": "https://api.github.com/users/jonathanasdf/received_events", "repos_url": "https://api.github.com/users/jonathanasdf/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jonathanasdf/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jonathanasdf/subscriptions", "type": "User", "url": "https://api.github.com/users/jonathanasdf", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "This is not possible right now afaik :/\r\n\r\nMaybe we could have something like this ? wdyt ?\r\n\r\n```python\r\nds = interleave_datasets(\r\n [shuffled_dataset_a, dataset_b],\r\n probabilities=probabilities,\r\n stopping_strategy='all_exhausted',\r\n reshuffle_each_iteration=True,\r\n)", "That would be helpful for this case! \r\n\r\nIf there was some way for from_generator to iterate over just a single shard of some dataset that would probably be more ideal. Maybe something like\r\n\r\n```\r\ndef from_dataset_generator(dataset, generator_fn, gen_kwargs):\r\n # calls generator_fn(dataset=dataset_shard, **gen_kwargs)\r\n```\r\n\r\nAnother transform I was trying to implement is an input bucketing transform. Essentially you need to iterate through a dataset and reorder the examples in them, which is not really possible with a `map()` call. But using `from_generator()` causes the final dataset to be a single shard and loses speed gains from multiple dataloader workers", "I see, there are some internal functions to get a single shard already but the public `.shard()` method hasn't been implemented yet for `IterableDataset` :/\r\n\r\n(see the use of `ex_iterable.shard_data_sources` in `IterableDataset._prepare_ex_iterable_for_iteration` for example)", "Would that be something planned on the roadmap for the near future, or do you suggest hacking through with internal APIs for now?", "Ok this turned out to be not too difficult. Are there any obvious issues with my implementation?\r\n\r\n```\r\nclass ShuffleEveryEpochIterable(iterable_dataset._BaseExamplesIterable):\r\n \"\"\"ExamplesIterable that reshuffles the dataset every epoch.\"\"\"\r\n\r\n def __init__(\r\n self,\r\n ex_iterable: iterable_dataset._BaseExamplesIterable,\r\n generator: np.random.Generator,\r\n ):\r\n \"\"\"Constructor.\"\"\"\r\n super().__init__()\r\n self.ex_iterable = ex_iterable\r\n self.generator = generator\r\n\r\n def _init_state_dict(self) -> dict:\r\n self._state_dict = {\r\n 'ex_iterable': self.ex_iterable._init_state_dict(),\r\n 'epoch': 0,\r\n }\r\n return self._state_dict\r\n\r\n @typing.override\r\n def __iter__(self):\r\n epoch = self._state_dict['epoch'] if self._state_dict else 0\r\n for i in itertools.count(epoch):\r\n # Create effective seed using i (subtract in order to avoir overflow in long_scalars)\r\n effective_seed = copy.deepcopy(self.generator).integers(0, 1 << 63) - i\r\n effective_seed = (1 << 63) + effective_seed if effective_seed < 0 else effective_seed\r\n generator = np.random.default_rng(effective_seed)\r\n self.ex_iterable = self.ex_iterable.shuffle_data_sources(generator)\r\n if self._state_dict:\r\n self._state_dict['epoch'] = i\r\n self._state_dict['ex_iterable'] = self.ex_iterable._init_state_dict()\r\n it = iter(self.ex_iterable)\r\n yield from it\r\n\r\n @typing.override\r\n def shuffle_data_sources(self, generator):\r\n ex_iterable = self.ex_iterable.shuffle_data_sources(generator)\r\n return ShuffleEveryEpochIterable(ex_iterable, generator=generator)\r\n\r\n @typing.override\r\n def shard_data_sources(self, worker_id: int, num_workers: int):\r\n ex_iterable = self.ex_iterable.shard_data_sources(worker_id, num_workers)\r\n return ShuffleEveryEpochIterable(ex_iterable, generator=self.generator)\r\n\r\n @typing.override\r\n @property\r\n def n_shards(self) -> int:\r\n return self.ex_iterable.n_shards\r\n \r\ngenerator = np.random.default_rng(seed)\r\nshuffling = iterable_dataset.ShufflingConfig(generator=generator, _original_seed=seed)\r\nex_iterable = iterable_dataset.BufferShuffledExamplesIterable(\r\n dataset._ex_iterable, buffer_size=buffer_size, generator=generator\r\n)\r\nex_iterable = ShuffleEveryEpochIterable(ex_iterable, generator=generator)\r\ndataset = datasets.IterableDataset(\r\n ex_iterable=ex_iterable,\r\n info=dataset._info.copy(),\r\n split=dataset._split,\r\n formatting=dataset._formatting,\r\n shuffling=shuffling,\r\n distributed=copy.deepcopy(dataset._distributed),\r\n token_per_repo_id=dataset._token_per_repo_id,\r\n)\r\n```\r\n", "Nice ! This iterable is infinite though no ? How would `interleave_dataset` know when to stop ?\r\n\r\nMaybe the re-shuffling can be implemented directly in `RandomlyCyclingMultiSourcesExamplesIterable` (which is the iterable used by `interleave_dataset`) ?", "Infinite is fine for my usecases fortunately." ]	2024-07-15T18:24:52Z	2024-08-05T20:58:04Z	2024-08-05T20:58:04Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	Let's say I have dataset A which has 100k examples, and dataset B which has 100m examples. I want to train on an interleaved dataset of A+B, with stopping_strategy='all_exhausted' so dataset B doesn't repeat any examples. But every time A is exhausted I want it to be reshuffled (eg. calling set_epoch) Of course I want to interleave as IterableDatasets / streaming mode so B doesn't have to get tokenized completely at the start. How could I achieve this? I was thinking something like, if I wrap dataset A in some new IterableDataset with from_generator() and manually call set_epoch before interleaving it? But I'm not sure how to keep the number of shards in that dataset... Something like ``` dataset_a = load_dataset(...) dataset_b = load_dataset(...) def epoch_shuffled_dataset(ds): # How to make this maintain the number of shards in ds?? for epoch in itertools.count(): ds.set_epoch(epoch) yield from iter(ds) shuffled_dataset_a = IterableDataset.from_generator(epoch_shuffled_dataset, gen_kwargs={'ds': dataset_a}) interleaved = interleave_datasets([shuffled_dataset_a, dataset_b], probs, stopping_strategy='all_exhausted') ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/511073?v=4", "events_url": "https://api.github.com/users/jonathanasdf/events{/privacy}", "followers_url": "https://api.github.com/users/jonathanasdf/followers", "following_url": "https://api.github.com/users/jonathanasdf/following{/other_user}", "gists_url": "https://api.github.com/users/jonathanasdf/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jonathanasdf", "id": 511073, "login": "jonathanasdf", "node_id": "MDQ6VXNlcjUxMTA3Mw==", "organizations_url": "https://api.github.com/users/jonathanasdf/orgs", "received_events_url": "https://api.github.com/users/jonathanasdf/received_events", "repos_url": "https://api.github.com/users/jonathanasdf/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jonathanasdf/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jonathanasdf/subscriptions", "type": "User", "url": "https://api.github.com/users/jonathanasdf", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 2, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/7051/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7051/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5141	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5141/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5141/comments	https://api.github.com/repos/huggingface/datasets/issues/5141/events	https://github.com/huggingface/datasets/pull/5141	1,415,479,438	PR_kwDODunzps5BIp1l	5,141	Raise ImportError instead of OSError	{ "avatar_url": "https://avatars.githubusercontent.com/u/114604338?v=4", "events_url": "https://api.github.com/users/ayushthe1/events{/privacy}", "followers_url": "https://api.github.com/users/ayushthe1/followers", "following_url": "https://api.github.com/users/ayushthe1/following{/other_user}", "gists_url": "https://api.github.com/users/ayushthe1/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ayushthe1", "id": 114604338, "login": "ayushthe1", "node_id": "U_kgDOBtS5Mg", "organizations_url": "https://api.github.com/users/ayushthe1/orgs", "received_events_url": "https://api.github.com/users/ayushthe1/received_events", "repos_url": "https://api.github.com/users/ayushthe1/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ayushthe1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ayushthe1/subscriptions", "type": "User", "url": "https://api.github.com/users/ayushthe1", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks @mariosasko ,i commited the changes as you said.\r\n\r\n" ]	2022-10-19T19:30:05Z	2022-10-25T15:59:25Z	2022-10-25T15:56:58Z	CONTRIBUTOR	null	null	null	fixes #5134 : Replaced OSError with ImportError if required extraction library is not installed.	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5141/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5141/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5141.diff", "html_url": "https://github.com/huggingface/datasets/pull/5141", "merged_at": "2022-10-25T15:56:58Z", "patch_url": "https://github.com/huggingface/datasets/pull/5141.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5141" }
https://api.github.com/repos/huggingface/datasets/issues/5207	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5207/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5207/comments	https://api.github.com/repos/huggingface/datasets/issues/5207/events	https://github.com/huggingface/datasets/issues/5207	1,437,858,506	I_kwDODunzps5Vs_rK	5,207	Connection error of the HuggingFace's dataset Hub due to SSLError with proxy	{ "avatar_url": "https://avatars.githubusercontent.com/u/82404?v=4", "events_url": "https://api.github.com/users/leemgs/events{/privacy}", "followers_url": "https://api.github.com/users/leemgs/followers", "following_url": "https://api.github.com/users/leemgs/following{/other_user}", "gists_url": "https://api.github.com/users/leemgs/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/leemgs", "id": 82404, "login": "leemgs", "node_id": "MDQ6VXNlcjgyNDA0", "organizations_url": "https://api.github.com/users/leemgs/orgs", "received_events_url": "https://api.github.com/users/leemgs/received_events", "repos_url": "https://api.github.com/users/leemgs/repos", "site_admin": false, "starred_url": "https://api.github.com/users/leemgs/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/leemgs/subscriptions", "type": "User", "url": "https://api.github.com/users/leemgs", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Hi ! It looks like an issue with your python environment, can you make sure you're able to run GET requests to https://huggingface.co using `requests` in python ?", "Thanks for your reply. Does this mean that I have to use the `do_dataset `function and the `requests `function to download the dataset from the company's proxy environment?\r\n\r\n\r\n* Reference: \r\n```bash\r\n### How to load this dataset directly with the [datasets](https://github.com/huggingface/datasets) library\r\n\r\n\r\n* https://huggingface.co/datasets/moyix/debian_csrc\r\n\r\n* from datasets import load_dataset\r\ndataset = load_dataset(\"moyix/debian_csrc\")\r\n\r\n\r\n\r\n### Or just clone the dataset repo\r\n\r\n\r\ngit lfs install\r\ngit clone https://huggingface.co/datasets/moyix/debian_csrc\r\n# if you want to clone without large files – just their pointers\r\n# prepend your git clone with the following env var:\r\nGIT_LFS_SKIP_SMUDGE=1\r\n```", "You can use `requests` to see if downloading a file from the Hugging Face Hub works. If so, then `datasets` should work as well. If not, then you have to find another way using an internet connection that works", "I resolved this issue by applying to \"unblock websites\" at https://huggingface.com in a corporate network environment with a firewall. \r\n", "> Hi ! It looks like an issue with your python environment, can you make sure you're able to run GET requests to https://huggingface.co using `requests` in python ?\r\n\r\nyes，but still not work\r\n\r\n![image](https://github.com/huggingface/datasets/assets/40717349/a502e051-beff-4a5e-aef6-90b525add877)\r\n![image](https://github.com/huggingface/datasets/assets/40717349/88b131fd-ee26-4a8e-9e72-6ecdc126404f)\r\n", "I read https://github.com/huggingface/datasets/blob/main/src/datasets/load.py, it fail when get the dataset metadata, so download_config has not worked.\r\n```python\r\n hf_api = HfApi(config.HF_ENDPOINT)\r\n try:\r\n dataset_info = hf_api.dataset_info(\r\n repo_id=path,\r\n revision=revision,\r\n token=download_config.token,\r\n timeout=100.0,\r\n )\r\n except Exception as e: # noqa catch any exception of hf_hub and consider that the dataset doesn't exist\r\n if isinstance(\r\n e,\r\n (\r\n OfflineModeIsEnabled,\r\n requests.exceptions.ConnectTimeout,\r\n requests.exceptions.ConnectionError,\r\n ),\r\n ):\r\n raise ConnectionError(f\"Couldn't reach '{path}' on the Hub ({type(e).__name__})\")\r\n```\r\nI configure the huggingface_hub api, use configure_http_backend\r\n```python\r\nfrom huggingface_hub import configure_http_backend\r\ndef backend_factory() -> requests.Session:\r\n session = requests.Session()\r\n session.proxies = proxy\r\n session.verify = False\r\n return session\r\n\r\nconfigure_http_backend(backend_factory=backend_factory)\r\n```\r\nIt works.", "Even tough it does not look like a certificate error in the error message, I had the same error and adding following lines to my code solved my problem.\r\n\r\nimport os\r\nos.environ['CURL_CA_BUNDLE'] = ''", "@kuikuikuizzZ Could you please explain where the configuration code is added?", "> Even tough it does not look like a certificate error in the error message, I had the same error and adding following lines to my code solved my problem.\r\n> \r\n> import os os.environ['CURL_CA_BUNDLE'] = ''\r\n\r\nWorked for as well!\r\nI faced the issue while submitting jobs through SLURM.", "> Even tough it does not look like a certificate error in the error message, I had the same error and adding following lines to my code solved my problem.\r\n> \r\n> import os os.environ['CURL_CA_BUNDLE'] = ''\r\n\r\ndoesn't work , what does this code mean?", "If you're working on a cluster, may be that they disabled remote connections for security purposes, you will have to download the files on your local machine and then transfer them to your cluster through scp or some other transfer protocol. I know you've probably resolved the issue, but that is for anyone in the future who might stumble across this thread and needs help because I struggled with that even after reading this thread.", "> Even tough it does not look like a certificate error in the error message, I had the same error and adding following lines to my code solved my problem.\r\n> \r\n> import os os.environ['CURL_CA_BUNDLE'] = ''\r\n\r\nIf this not work, try this:\r\n```bash\r\nexport http_proxy=\"http://127.0.0.1:10810\"\r\nexport https_proxy=\"http://127.0.0.1:10810\"\r\ngit config --global http.proxy http://127.0.0.1:10810\r\ngit config --global https.proxy http://127.0.0.1:10810\r\n\r\njupyter notebook\r\n```\r\n\r\nset your proxy env first, then start notebook in this session\r\n", "> If you're working on a cluster, may be that they disabled remote connections for security purposes, you will have to download the files on your local machine and then transfer them to your cluster through scp or some other transfer protocol. I know you've probably resolved the issue, but that is for anyone in the future who might stumble across this thread and needs help because I struggled with that even after reading this thread.\r\n\r\nThank you buddy!", "@shafferjohn \nexport http_proxy=\"http://127.0.0.1:10810\"\nexport https_proxy=\"http://127.0.0.1:10810\"\ngit config --global http.proxy http://127.0.0.1:10810\ngit config --global https.proxy http://127.0.0.1:10810\n\njupyter notebook\n\nthis way worked for me" ]	2022-11-07T06:56:23Z	2025-03-08T09:04:10Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug It's weird. I could not normally connect the dataset Hub of HuggingFace due to a SSLError in my office. Even when I try to connect using my company's proxy address (e.g., http_proxy and https_proxy), I'm getting the SSLError issue. What should I do to download the datanet stored in HuggingFace normally? I welcome any comments. I think those comments will be helpful to me. * Dataset address - https://huggingface.co/datasets/moyix/debian_csrc/viewer/moyix--debian_csrc * Log message ``` ............ OMISSION .............. Traceback (most recent call last): File "/data/home/geunsik-lim/qtlab/./transformers/examples/pytorch/language-modeling/run_clm.py", line 587, in <module> main() File "/data/home/geunsik-lim/qtlab/./transformers/examples/pytorch/language-modeling/run_clm.py", line 278, in main raw_datasets = load_dataset( File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1719, in load_dataset builder_instance = load_dataset_builder( File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1497, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1222, in dataset_module_factory raise e1 from None File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1179, in dataset_module_factory raise ConnectionError(f"Couldn't reach '{path}' on the Hub ({type(e).__name__})") ConnectionError: Couldn't reach 'moyix/debian_csrc' on the Hub (SSLError) [2022-11-07 15:23:38,476] [INFO] [launch.py:318:sigkill_handler] Killing subprocess 6760 [2022-11-07 15:23:38,476] [ERROR] [launch.py:324:sigkill_handler] ['/home/geunsik-lim/anaconda3/envs/deepspeed/bin/python', '-u', './transformers/examples/pytorch/language-modeling/run_clm.py', '--local_rank=0', '--model_name_or_path=Salesforce/codegen-350M-multi', '--per_device_train_batch_size=1', '--learning_rate', '2e-5', '--num_train_epochs', '1', '--output_dir=./codegen-350M-finetuned', '--overwrite_output_dir', '--dataset_name', 'moyix/debian_csrc', '--cache_dir', '/data/home/geunsik-lim/.cache', '--tokenizer_name', 'Salesforce/codegen-350M-multi', '--block_size', '2048', '--gradient_accumulation_steps', '32', '--do_train', '--fp16', '--deepspeed', 'ds_config_zero2.json'] exits with return code = 1 real 0m7.742s user 0m4.930s ``` ### Steps to reproduce the bug Steps to reproduce this behavior. ``` (deepspeed) geunsik-lim@ai02:~/qtlab$ ./test_debian_csrc_dataset.py Traceback (most recent call last): File "/data/home/geunsik-lim/qtlab/./test_debian_csrc_dataset.py", line 6, in <module> dataset = load_dataset("moyix/debian_csrc") File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1719, in load_dataset builder_instance = load_dataset_builder( File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1497, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1222, in dataset_module_factory raise e1 from None File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1179, in dataset_module_factory raise ConnectionError(f"Couldn't reach '{path}' on the Hub ({type(e).__name__})") ConnectionError: Couldn't reach 'moyix/debian_csrc' on the Hub (SSLError) (deepspeed) geunsik-lim@ai02:~/qtlab$ (deepspeed) geunsik-lim@ai02:~/qtlab$ (deepspeed) geunsik-lim@ai02:~/qtlab$ (deepspeed) geunsik-lim@ai02:~/qtlab$ cat ./test_debian_csrc_dataset.py #!/usr/bin/env python from datasets import load_dataset dataset = load_dataset("moyix/debian_csrc") ``` 1. Adde proxy address of a company in /etc/profile 2. Download dataset with load_dataset() function of datasets package that is provided by HuggingFace. 3. In this case, the address would be "moyix--debian_csrc". 4. I get the "`ConnectionError: Couldn't reach 'moyix/debian_csrc' on the Hub (SSLError`)" error message. ### Expected behavior * error message: ConnectionError: Couldn't reach 'moyix/debian_csrc' on the Hub (SSLError) ### Environment info * software version information: ``` (deepspeed) geunsik-lim@ai02:~$ (deepspeed) geunsik-lim@ai02:~$ conda list -f pytorch # packages in environment at /home/geunsik-lim/anaconda3/envs/deepspeed: # # Name Version Build Channel pytorch 1.13.0 py3.10_cuda11.7_cudnn8.5.0_0 pytorch (deepspeed) geunsik-lim@ai02:~$ conda list -f python # packages in environment at /home/geunsik-lim/anaconda3/envs/deepspeed: # # Name Version Build Channel python 3.10.6 haa1d7c7_1 (deepspeed) geunsik-lim@ai02:~$ conda list -f datasets # packages in environment at /home/geunsik-lim/anaconda3/envs/deepspeed: # # Name Version Build Channel datasets 2.6.1 py_0 huggingface (deepspeed) geunsik-lim@ai02:~$ uname -a Linux ai02 5.4.0-131-generic #147-Ubuntu SMP Fri Oct 14 17:07:22 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux (deepspeed) geunsik-lim@ai02:~$ cat /etc/lsb-release DISTRIB_ID=Ubuntu DISTRIB_RELEASE=20.04 DISTRIB_CODENAME=focal DISTRIB_DESCRIPTION="Ubuntu 20.04.5 LTS" ```	null	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/5207/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5207/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/4878	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4878/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4878/comments	https://api.github.com/repos/huggingface/datasets/issues/4878/events	https://github.com/huggingface/datasets/issues/4878	1,348,270,141	I_kwDODunzps5QXPg9	4,878	[not really a bug] `identical_ok` is deprecated in huggingface-hub's `upload_file`	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	[ { "color": "008672", "default": true, "description": "Extra attention is needed", "id": 1935892884, "name": "help wanted", "node_id": "MDU6TGFiZWwxOTM1ODkyODg0", "url": "https://api.github.com/repos/huggingface/datasets/labels/help%20wanted" }, { "color": "d876e3", "default": true, "description": "Further information is requested", "id": 1935892912, "name": "question", "node_id": "MDU6TGFiZWwxOTM1ODkyOTEy", "url": "https://api.github.com/repos/huggingface/datasets/labels/question" } ]	closed	false	null	[]	null	[ "Resolved via https://github.com/huggingface/datasets/pull/4937." ]	2022-08-23T17:09:55Z	2022-09-13T14:00:06Z	2022-09-13T14:00:05Z	COLLABORATOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	In the huggingface-hub dependency, the `identical_ok` argument has no effect in `upload_file` (and it will be removed soon) See https://github.com/huggingface/huggingface_hub/blob/43499582b19df1ed081a5b2bd7a364e9cacdc91d/src/huggingface_hub/hf_api.py#L2164-L2169 It's used here: https://github.com/huggingface/datasets/blob/fcfcc951a73efbc677f9def9a8707d0af93d5890/src/datasets/dataset_dict.py#L1373-L1381 https://github.com/huggingface/datasets/blob/fdcb8b144ce3ef241410281e125bd03e87b8caa1/src/datasets/arrow_dataset.py#L4354-L4362 https://github.com/huggingface/datasets/blob/fdcb8b144ce3ef241410281e125bd03e87b8caa1/src/datasets/arrow_dataset.py#L4197-L4213 We should remove it. Maybe the third code sample has an unexpected behavior since it uses the non-default value `identical_ok = False`, but the argument is ignored.	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4878/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4878/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6186	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6186/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6186/comments	https://api.github.com/repos/huggingface/datasets/issues/6186/events	https://github.com/huggingface/datasets/issues/6186	1,869,431,457	I_kwDODunzps5vbUKh	6,186	Feature request: add code example of multi-GPU processing	{ "avatar_url": "https://avatars.githubusercontent.com/u/48327001?v=4", "events_url": "https://api.github.com/users/NielsRogge/events{/privacy}", "followers_url": "https://api.github.com/users/NielsRogge/followers", "following_url": "https://api.github.com/users/NielsRogge/following{/other_user}", "gists_url": "https://api.github.com/users/NielsRogge/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/NielsRogge", "id": 48327001, "login": "NielsRogge", "node_id": "MDQ6VXNlcjQ4MzI3MDAx", "organizations_url": "https://api.github.com/users/NielsRogge/orgs", "received_events_url": "https://api.github.com/users/NielsRogge/received_events", "repos_url": "https://api.github.com/users/NielsRogge/repos", "site_admin": false, "starred_url": "https://api.github.com/users/NielsRogge/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/NielsRogge/subscriptions", "type": "User", "url": "https://api.github.com/users/NielsRogge", "user_view_type": "public" }	[ { "color": "0075ca", "default": true, "description": "Improvements or additions to documentation", "id": 1935892861, "name": "documentation", "node_id": "MDU6TGFiZWwxOTM1ODkyODYx", "url": "https://api.github.com/repos/huggingface/datasets/labels/documentation" }, { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	closed	false	null	[]	null	[ "That'd be a great idea! @mariosasko or @lhoestq, would it be possible to fix the code snippet or do you have another suggested way for doing this?", "Indeed `if __name__ == \"__main__\"` is important in this case.\r\n\r\nNot sure about the imbalanced GPU usage though, but maybe you can try using the `torch.cuda.device` context manager ?\r\n\r\n> also, should I do it like this or use nn.DataParallel?\r\n\r\nIn this case you wouldn't need a multiprocessed map no ? Since nn.DataParallel would take care of parallelism", "Adding this Tweet for reference: https://twitter.com/jxmnop/status/1716834517909119019.", "I think the issue is that we set `CUDA_VISIBLE_DEVICES` after pytorch is imported ?\r\n\r\nWe should use `torch.cuda.set_device(...)` instead", "@lhoestq \r\n> In this case you wouldn't need a multiprocessed map no ?\r\n\r\nYes. But how to load a model to 2 GPU simultaneously without something like accelerate?", "> @lhoestq\r\n> \r\n> > In this case you wouldn't need a multiprocessed map no ?\r\n> \r\n> Yes. But how to load a model to 2 GPU simultaneously without something like accelerate?\r\n\r\nTake a look at this fix #6550 . Basically, you move the model to each GPU inside of the function to be mapped. \r\n\r\n", "In case someone also runs into this issue, I wrote a [blog post](https://forrestbao.github.io/2024/01/30/datasets_map_with_rank_multiple_GPUs.html) with a complete working example by compiling information from several PRs and issues here. Hope it can help. This issue cost me a few hours. I hope my blog post can save you time before the official document gets fixed. ", "Thanks ! I updated the docs in https://github.com/huggingface/datasets/pull/6550", "hey @forrestbao , i was too struggling with the same issue for weeks hence i checked out your blog. great work on the blog. \r\nhowever i wanted to ask you could we scale up the process by reinitializing the same model on the same GPU multiple times for even more speedups ? \r\n\r\ni mean to say given that on a multi GPU setup where GPU vram is above 40GB each, after intializing the translation model which is barely 1-2GB in VRAM size, the rest of VRAM sits idle, how could i keep creating multiple instances of the same model on the same GPU for all GPUs to maxmize flops ? ", "You can use one single instance on your GPU and increase the batch size until you fill the VRAM", "@lhoestq i tried that, but i noticed that after a certain number of batch_size, using a larger batch_size makes the overall process really slow than using a lower batch_size.", "Hi @lhoestq , could you help with my two questions: \r\n1. You mentioned `if __name__ == \"__main__\"`, why is that? I tried with a toy dataset and didn't put this line, my two GPU usage looks balanced. \r\n2. Is there any difference between \r\n`from multiprocess import set_start_method` and `from multiprocessing import set_start_method`? The latter is Python's built-in library. In [the official doc](https://huggingface.co/docs/datasets/en/process), it uses `from multiprocess import set_start_method`, but it gives me error like \r\n```\r\n[jobuser@f6e2419a0a63d45638da-n0-0 ~]$ python test.py\r\nTraceback (most recent call last):\r\n File \"/home/jobuser/test.py\", line 33, in <module>\r\n updated_dataset = dataset.map(\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 593, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, kwargs)\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 558, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, **kwargs)\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3189, in map\r\n with Pool(len(kwargs_per_job)) as pool:\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/context.py\", line 119, in Pool\r\n return Pool(processes, initializer, initargs, maxtasksperchild,\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/pool.py\", line 191, in __init__\r\n self._setup_queues()\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/pool.py\", line 343, in _setup_queues\r\n self._inqueue = self._ctx.SimpleQueue()\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/context.py\", line 113, in SimpleQueue\r\n return SimpleQueue(ctx=self.get_context())\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/queues.py\", line 339, in __init__\r\n self._rlock = ctx.Lock()\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/context.py\", line 68, in Lock\r\n return Lock(ctx=self.get_context())\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/synchronize.py\", line 168, in __init__\r\n SemLock.__init__(self, SEMAPHORE, 1, 1, ctx=ctx)\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/synchronize.py\", line 86, in __init__\r\n register(self._semlock.name, \"semaphore\")\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/resource_tracker.py\", line 150, in register\r\n self._send('REGISTER', name, rtype)\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/resource_tracker.py\", line 157, in _send\r\n self.ensure_running()\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/resource_tracker.py\", line 124, in ensure_running\r\n pid = util.spawnv_passfds(exe, args, fds_to_pass)\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/util.py\", line 452, in spawnv_passfds\r\n return _posixsubprocess.fork_exec(\r\nTypeError: fork_exec() takes exactly 21 arguments (17 given)\r\n```\r\nwhich seems caused by python version. I am using Python 3.10.2. ", "Hi ! \r\n\r\n> You mentioned if __name__ == \"__main__\", why is that? I tried with a toy dataset and didn't put this line, my two GPU usage looks balanced.\r\n\r\nIt's a good practice when doing multiprocessing in python. Depending on the multiprocessing method and your python version, python could re-run the code in your main.py in subprocesses that you don't want to re-run (e.g. recursively spawning processes and failing). Though some multiprocessing methods don't re-run main.py and it appears to be your case ;)\r\n\r\n> Is there any difference between\r\nfrom multiprocess import set_start_method and from multiprocessing import set_start_method? The latter is Python's built-in library. In [the official doc](https://huggingface.co/docs/datasets/en/process), it uses from multiprocess import set_start_method, but it gives me error like\r\n\r\nYes, `datasets` uses `multiprocess` which is a separate library from the built-in `multiprocessing`.\r\n\r\n`multiprocess` is an extended version of `multiprocessing` which allows e.g. to pass `lambda` functions to subprocesses", "Thanks @lhoestq for explanation. Is it okay we use `multiprocessing` for set_start_method given the above-mentioned issue for multiprocess? From my run with toy example, it's fine. Just want to check if you foresee any problems. ", "Not sure whether `multiprocessing.set_start_method` has any effect actually since we use `dill` for multiprocessed `map()`", "I'm running the [code example of multi-GPU processing](https://huggingface.co/docs/datasets/en/process#multiprocessing) on a Linux 8x A100 instance. The entire python code run time is 30 seconds faster if I add one line to set torch number of threads immediately after the `import torch` statement. It loads faster to the eight GPUs (however the map() progress bars take similar amount of time without/with this additional line).\r\n```\r\nimport torch\r\ntorch.set_num_threads(1) # I added this line.\r\n\r\nfrom multiprocess import set_start_method\r\n```\r\nFWIW: my instance has these versions.\r\n```\r\nCUDA 12.2 driver 535.161.08\r\nPython 3.10.12\r\ntorch '2.2.2'\r\nmultiprocess '0.70.16'\r\ntransformers '4.39.2'\r\ndatasets '2.18.0'\r\n```", "@lhoestq Thanks for the updated GPU multiprocessing documentation! When I tried to add `updated_dataset.save_to_disk()` after the map function with multiple GPUs, I get an error during saving: \r\n```\r\nSaving the dataset (0/20 shards): 0%\| \| 78000/84761821 [01:07<20:22:25, 1154.59 examples/s]Exception ignored in: <generator object Dataset._save_to_disk_single at 0x7f2498f15070>\r\nTraceback (most recent call last):\r\n File \"/home/ubuntu/lib/python3.10/site-packages/datasets/utils/py_utils.py\", line 679, in _write_generator_to_queue\r\n queue.put(result)\r\nRuntimeError: generator ignored GeneratorExit\r\n```\r\nDo you have any thoughts?", "Hmm first time I see this, and it's even more surprising given there is no generator in `_write_generator_to_queue`. Could you open a new issue ?" ]	2023-08-28T10:00:59Z	2024-10-07T09:39:51Z	2023-11-22T15:42:20Z	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request Would be great to add a code example of how to do multi-GPU processing with 🤗 Datasets in the documentation. cc @stevhliu Currently the docs has a small [section](https://huggingface.co/docs/datasets/v2.3.2/en/process#map) on this saying "your big GPU call goes here", however it didn't work for me out-of-the-box. Let's say you have a PyTorch model that can do translation, and you have multiple GPUs. In that case, you'd like to duplicate the model on each GPU, each processing (translating) a chunk of the data in parallel. Here's how I tried to do that: ``` from datasets import load_dataset from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from multiprocess import set_start_method import torch import os dataset = load_dataset("mlfoundations/datacomp_small") tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M") model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M") # put model on each available GPU # also, should I do it like this or use nn.DataParallel? model.to("cuda:0") model.to("cuda:1") set_start_method("spawn") def translate_captions(batch, rank): os.environ["CUDA_VISIBLE_DEVICES"] = str(rank % torch.cuda.device_count()) texts = batch["text"] inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt").to(model.device) translated_tokens = model.generate( **inputs, forced_bos_token_id=tokenizer.lang_code_to_id["eng_Latn"], max_length=30 ) translated_texts = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True) batch["translated_text"] = translated_texts return batch updated_dataset = dataset.map(translate_captions, with_rank=True, num_proc=2, batched=True, batch_size=256) ``` I've personally tried running this script on a machine with 2 A100 GPUs. ## Error 1 Running the code snippet above from the terminal (python script.py) resulted in the following error: ``` Traceback (most recent call last): File "<string>", line 1, in <module> File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 116, in spawn_main exitcode = _main(fd, parent_sentinel) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 125, in _main prepare(preparation_data) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 236, in prepare _fixup_main_from_path(data['init_main_from_path']) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 287, in _fixup_main_from_path main_content = runpy.run_path(main_path, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 289, in run_path return _run_module_code(code, init_globals, run_name, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 96, in _run_module_code _run_code(code, mod_globals, init_globals, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/home/niels/python_projects/datacomp/datasets_multi_gpu.py", line 16, in <module> set_start_method("spawn") File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 247, in set_start_method raise RuntimeError('context has already been set') RuntimeError: context has already been set ``` ## Error 2 Then, based on [this Stackoverflow answer](https://stackoverflow.com/a/71616344/7762882), I put the `set_start_method("spawn")` section in a try: catch block. This resulted in the following error: ``` File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/datasets/dataset_dict.py", line 817, in <dictcomp> k: dataset.map( File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2926, in map with Pool(nb_of_missing_shards, initargs=initargs, initializer=initializer) as pool: File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 119, in Pool return Pool(processes, initializer, initargs, maxtasksperchild, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 215, in __init__ self._repopulate_pool() File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 306, in _repopulate_pool return self._repopulate_pool_static(self._ctx, self.Process, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 329, in _repopulate_pool_static w.start() File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/process.py", line 121, in start self._popen = self._Popen(self) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 288, in _Popen return Popen(process_obj) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_spawn_posix.py", line 32, in __init__ super().__init__(process_obj) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_fork.py", line 19, in __init__ self._launch(process_obj) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_spawn_posix.py", line 42, in _launch prep_data = spawn.get_preparation_data(process_obj._name) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 154, in get_preparation_data _check_not_importing_main() File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 134, in _check_not_importing_main raise RuntimeError(''' RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase. This probably means that you are not using fork to start your child processes and you have forgotten to use the proper idiom in the main module: if __name__ == '__main__': freeze_support() ... The "freeze_support()" line can be omitted if the program is not going to be frozen to produce an executable. ``` So then I put the last line under a `if __name__ == '__main__':` block. Then the code snippet seemed to work, but it seemed that it's only leveraging a single GPU (based on monitoring `nvidia-smi`): ``` Mon Aug 28 12:19:24 2023 +-----------------------------------------------------------------------------+ \| NVIDIA-SMI 515.65.01 Driver Version: 515.65.01 CUDA Version: 11.7 \| \|-------------------------------+----------------------+----------------------+ \| GPU Name Persistence-M\| Bus-Id Disp.A \| Volatile Uncorr. ECC \| \| Fan Temp Perf Pwr:Usage/Cap\| Memory-Usage \| GPU-Util Compute M. \| \| \| \| MIG M. \| \|===============================+======================+======================\| \| 0 NVIDIA A100-SXM... On \| 00000000:01:00.0 Off \| 0 \| \| N/A 55C P0 76W / 275W \| 8747MiB / 81920MiB \| 0% Default \| \| \| \| Disabled \| +-------------------------------+----------------------+----------------------+ \| 1 NVIDIA A100-SXM... On \| 00000000:47:00.0 Off \| 0 \| \| N/A 67C P0 274W / 275W \| 59835MiB / 81920MiB \| 100% Default \| \| \| \| Disabled \| ``` Both GPUs should have equal GPU usage, but I've always noticed that the last GPU has way more usage than the other ones. This made me think that `os.environ["CUDA_VISIBLE_DEVICES"] = str(rank % torch.cuda.device_count())` might not work inside a Python script, especially if done after importing PyTorch? ### Motivation Would be great to clarify how to do multi-GPU data processing. ### Your contribution If my code snippet can be fixed, I can contribute it to the docs :)	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6186/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6186/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6700	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6700/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6700/comments	https://api.github.com/repos/huggingface/datasets/issues/6700/events	https://github.com/huggingface/datasets/issues/6700	2,158,871,038	I_kwDODunzps6ArcH-	6,700	remove_columns is not in-place but the doc shows it is in-place	{ "avatar_url": "https://avatars.githubusercontent.com/u/32047804?v=4", "events_url": "https://api.github.com/users/shelfofclub/events{/privacy}", "followers_url": "https://api.github.com/users/shelfofclub/followers", "following_url": "https://api.github.com/users/shelfofclub/following{/other_user}", "gists_url": "https://api.github.com/users/shelfofclub/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/shelfofclub", "id": 32047804, "login": "shelfofclub", "node_id": "MDQ6VXNlcjMyMDQ3ODA0", "organizations_url": "https://api.github.com/users/shelfofclub/orgs", "received_events_url": "https://api.github.com/users/shelfofclub/received_events", "repos_url": "https://api.github.com/users/shelfofclub/repos", "site_admin": false, "starred_url": "https://api.github.com/users/shelfofclub/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/shelfofclub/subscriptions", "type": "User", "url": "https://api.github.com/users/shelfofclub", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "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." ]	2024-02-28T12:36:22Z	2024-04-02T17:15:28Z	2024-04-02T17:15:28Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug The doc of `datasets` v2.17.0/v2.17.1 shows that `remove_columns` is in-place. [link](https://huggingface.co/docs/datasets/v2.17.1/en/package_reference/main_classes#datasets.DatasetDict.remove_columns) In the text classification example of transformers v4.38.1, the columns are not removed. https://github.com/huggingface/transformers/blob/a0857740c0e6127485c11476650314df3accc2b6/examples/pytorch/text-classification/run_classification.py#L421 ### Steps to reproduce the bug https://github.com/huggingface/transformers/blob/a0857740c0e6127485c11476650314df3accc2b6/examples/pytorch/text-classification/run_classification.py#L421 ### Expected behavior Actually remove the columns. ### Environment info 1. datasets v2.17.0 2. transformers v4.38.1	{ "avatar_url": "https://avatars.githubusercontent.com/u/48595927?v=4", "events_url": "https://api.github.com/users/ArthurZucker/events{/privacy}", "followers_url": "https://api.github.com/users/ArthurZucker/followers", "following_url": "https://api.github.com/users/ArthurZucker/following{/other_user}", "gists_url": "https://api.github.com/users/ArthurZucker/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ArthurZucker", "id": 48595927, "login": "ArthurZucker", "node_id": "MDQ6VXNlcjQ4NTk1OTI3", "organizations_url": "https://api.github.com/users/ArthurZucker/orgs", "received_events_url": "https://api.github.com/users/ArthurZucker/received_events", "repos_url": "https://api.github.com/users/ArthurZucker/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ArthurZucker/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ArthurZucker/subscriptions", "type": "User", "url": "https://api.github.com/users/ArthurZucker", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6700/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6700/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/4810	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4810/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4810/comments	https://api.github.com/repos/huggingface/datasets/issues/4810/events	https://github.com/huggingface/datasets/pull/4810	1,333,038,702	PR_kwDODunzps484C9l	4,810	Add description to hellaswag dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/326577?v=4", "events_url": "https://api.github.com/users/julien-c/events{/privacy}", "followers_url": "https://api.github.com/users/julien-c/followers", "following_url": "https://api.github.com/users/julien-c/following{/other_user}", "gists_url": "https://api.github.com/users/julien-c/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/julien-c", "id": 326577, "login": "julien-c", "node_id": "MDQ6VXNlcjMyNjU3Nw==", "organizations_url": "https://api.github.com/users/julien-c/orgs", "received_events_url": "https://api.github.com/users/julien-c/received_events", "repos_url": "https://api.github.com/users/julien-c/repos", "site_admin": false, "starred_url": "https://api.github.com/users/julien-c/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/julien-c/subscriptions", "type": "User", "url": "https://api.github.com/users/julien-c", "user_view_type": "public" }	[ { "color": "0e8a16", "default": false, "description": "Contribution to a dataset script", "id": 4564477500, "name": "dataset contribution", "node_id": "LA_kwDODunzps8AAAABEBBmPA", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20contribution" } ]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "Are the `metadata JSON file` not on their way to deprecation? 😆😇\r\n\r\nIMO, more generally than this particular PR, the contribution process should be simplified now that many validation checks happen on the hub side.\r\n\r\nKeeping this open in the meantime to get more potential feedback!" ]	2022-08-09T10:21:14Z	2022-09-23T11:35:38Z	2022-09-23T11:33:44Z	MEMBER	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4810/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4810/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4810.diff", "html_url": "https://github.com/huggingface/datasets/pull/4810", "merged_at": "2022-09-23T11:33:44Z", "patch_url": "https://github.com/huggingface/datasets/pull/4810.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4810" }
https://api.github.com/repos/huggingface/datasets/issues/6607	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6607/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6607/comments	https://api.github.com/repos/huggingface/datasets/issues/6607/events	https://github.com/huggingface/datasets/pull/6607	2,091,766,063	PR_kwDODunzps5knGse	6,607	Update features.py to avoid bfloat16 unsupported error	{ "avatar_url": "https://avatars.githubusercontent.com/u/75697181?v=4", "events_url": "https://api.github.com/users/skaulintel/events{/privacy}", "followers_url": "https://api.github.com/users/skaulintel/followers", "following_url": "https://api.github.com/users/skaulintel/following{/other_user}", "gists_url": "https://api.github.com/users/skaulintel/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/skaulintel", "id": 75697181, "login": "skaulintel", "node_id": "MDQ6VXNlcjc1Njk3MTgx", "organizations_url": "https://api.github.com/users/skaulintel/orgs", "received_events_url": "https://api.github.com/users/skaulintel/received_events", "repos_url": "https://api.github.com/users/skaulintel/repos", "site_admin": false, "starred_url": "https://api.github.com/users/skaulintel/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/skaulintel/subscriptions", "type": "User", "url": "https://api.github.com/users/skaulintel", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "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" ]	2024-01-20T00:39:44Z	2024-05-17T09:46:29Z	2024-05-17T09:40:13Z	CONTRIBUTOR	null	null	null	Fixes https://github.com/huggingface/datasets/issues/6566 Let me know if there's any tests I need to clear.	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6607/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6607/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6607.diff", "html_url": "https://github.com/huggingface/datasets/pull/6607", "merged_at": "2024-05-17T09:40:13Z", "patch_url": "https://github.com/huggingface/datasets/pull/6607.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6607" }
https://api.github.com/repos/huggingface/datasets/issues/6713	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6713/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6713/comments	https://api.github.com/repos/huggingface/datasets/issues/6713/events	https://github.com/huggingface/datasets/pull/6713	2,166,797,560	PR_kwDODunzps5olmqh	6,713	Bump huggingface-hub lower version to 0.21.2	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6713). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@lhoestq if you agree, I could make a patch release tomorrow morning.", "sure :)", "<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) \| 0.005086 / 0.011353 (-0.006267) \| 0.003695 / 0.011008 (-0.007313) \| 0.063430 / 0.038508 (0.024922) \| 0.026798 / 0.023109 (0.003689) \| 0.253761 / 0.275898 (-0.022138) \| 0.301301 / 0.323480 (-0.022179) \| 0.004160 / 0.007986 (-0.003825) \| 0.002783 / 0.004328 (-0.001545) \| 0.050698 / 0.004250 (0.046448) \| 0.040899 / 0.037052 (0.003846) \| 0.269024 / 0.258489 (0.010535) \| 0.323467 / 0.293841 (0.029626) \| 0.027756 / 0.128546 (-0.100791) \| 0.010684 / 0.075646 (-0.064963) \| 0.207128 / 0.419271 (-0.212144) \| 0.035874 / 0.043533 (-0.007659) \| 0.251620 / 0.255139 (-0.003519) \| 0.268668 / 0.283200 (-0.014532) \| 0.017387 / 0.141683 (-0.124296) \| 1.139230 / 1.452155 (-0.312925) \| 1.183613 / 1.492716 (-0.309103) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096337 / 0.018006 (0.078331) \| 0.305014 / 0.000490 (0.304524) \| 0.000219 / 0.000200 (0.000019) \| 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) \| 0.018086 / 0.037411 (-0.019325) \| 0.061626 / 0.014526 (0.047100) \| 0.072598 / 0.176557 (-0.103959) \| 0.119944 / 0.737135 (-0.617192) \| 0.074549 / 0.296338 (-0.221789) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282661 / 0.215209 (0.067452) \| 2.804473 / 2.077655 (0.726818) \| 1.444602 / 1.504120 (-0.059517) \| 1.313977 / 1.541195 (-0.227217) \| 1.319426 / 1.468490 (-0.149064) \| 0.570176 / 4.584777 (-4.014601) \| 2.397895 / 3.745712 (-1.347818) \| 2.760208 / 5.269862 (-2.509654) \| 1.732457 / 4.565676 (-2.833220) \| 0.062743 / 0.424275 (-0.361533) \| 0.004950 / 0.007607 (-0.002657) \| 0.338500 / 0.226044 (0.112456) \| 3.287249 / 2.268929 (1.018320) \| 1.777495 / 55.444624 (-53.667130) \| 1.521255 / 6.876477 (-5.355222) \| 1.517317 / 2.142072 (-0.624756) \| 0.642202 / 4.805227 (-4.163025) \| 0.116501 / 6.500664 (-6.384163) \| 0.042418 / 0.075469 (-0.033052) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.968966 / 1.841788 (-0.872822) \| 11.490531 / 8.074308 (3.416223) \| 9.507803 / 10.191392 (-0.683589) \| 0.141570 / 0.680424 (-0.538854) \| 0.014000 / 0.534201 (-0.520201) \| 0.284237 / 0.579283 (-0.295046) \| 0.269341 / 0.434364 (-0.165022) \| 0.321654 / 0.540337 (-0.218683) \| 0.446914 / 1.386936 (-0.940022) \|\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) \| 0.005280 / 0.011353 (-0.006072) \| 0.003794 / 0.011008 (-0.007214) \| 0.050328 / 0.038508 (0.011820) \| 0.029756 / 0.023109 (0.006647) \| 0.273403 / 0.275898 (-0.002495) \| 0.297346 / 0.323480 (-0.026133) \| 0.004310 / 0.007986 (-0.003676) \| 0.002858 / 0.004328 (-0.001470) \| 0.048833 / 0.004250 (0.044583) \| 0.045696 / 0.037052 (0.008644) \| 0.291034 / 0.258489 (0.032545) \| 0.318899 / 0.293841 (0.025058) \| 0.029809 / 0.128546 (-0.098737) \| 0.010710 / 0.075646 (-0.064936) \| 0.058183 / 0.419271 (-0.361089) \| 0.051761 / 0.043533 (0.008228) \| 0.275022 / 0.255139 (0.019883) \| 0.291614 / 0.283200 (0.008414) \| 0.017975 / 0.141683 (-0.123708) \| 1.148489 / 1.452155 (-0.303666) \| 1.218111 / 1.492716 (-0.274605) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091806 / 0.018006 (0.073799) \| 0.299413 / 0.000490 (0.298923) \| 0.000219 / 0.000200 (0.000019) \| 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) \| 0.021506 / 0.037411 (-0.015905) \| 0.075537 / 0.014526 (0.061011) \| 0.087020 / 0.176557 (-0.089536) \| 0.125270 / 0.737135 (-0.611865) \| 0.088038 / 0.296338 (-0.208300) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300401 / 0.215209 (0.085192) \| 2.932571 / 2.077655 (0.854916) \| 1.609502 / 1.504120 (0.105383) \| 1.480078 / 1.541195 (-0.061117) \| 1.514902 / 1.468490 (0.046412) \| 0.575591 / 4.584777 (-4.009186) \| 2.461873 / 3.745712 (-1.283839) \| 2.728099 / 5.269862 (-2.541762) \| 1.760054 / 4.565676 (-2.805622) \| 0.064371 / 0.424275 (-0.359904) \| 0.004990 / 0.007607 (-0.002617) \| 0.350134 / 0.226044 (0.124090) \| 3.453249 / 2.268929 (1.184321) \| 1.979760 / 55.444624 (-53.464865) \| 1.741128 / 6.876477 (-5.135348) \| 1.825734 / 2.142072 (-0.316339) \| 0.654902 / 4.805227 (-4.150325) \| 0.116989 / 6.500664 (-6.383676) \| 0.040800 / 0.075469 (-0.034669) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.033352 / 1.841788 (-0.808436) \| 12.196711 / 8.074308 (4.122403) \| 10.315114 / 10.191392 (0.123722) \| 0.132541 / 0.680424 (-0.547882) \| 0.016455 / 0.534201 (-0.517746) \| 0.289025 / 0.579283 (-0.290258) \| 0.281464 / 0.434364 (-0.152900) \| 0.325302 / 0.540337 (-0.215036) \| 0.428469 / 1.386936 (-0.958467) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7093b4b1a69f413e452119c87669af9e8ceaf749 \"CML watermark\")\n" ]	2024-03-04T13:00:52Z	2024-03-04T18:14:03Z	2024-03-04T18:06:05Z	MEMBER	null	null	null	This should fix the version compatibility issue when using `huggingface_hub` < 0.21.2 and latest fsspec (>=2023.12.0). See my comment: https://github.com/huggingface/datasets/pull/6687#issuecomment-1976493336 >> EDIT: the fix has been released in `huggingface_hub` 0.21.2 - I removed my commits that were using `huggingface_hub@main` > >Please note that people using `huggingface_hub` < 0.21.2 and latest `fsspec` will have issues when using `datasets`: >- https://github.com/huggingface/lighteval/actions/runs/8139147047/job/22241658122?pr=86 >- https://github.com/huggingface/lighteval/pull/84 CC: @clefourrier	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 1, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6713/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6713/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6713.diff", "html_url": "https://github.com/huggingface/datasets/pull/6713", "merged_at": "2024-03-04T18:06:05Z", "patch_url": "https://github.com/huggingface/datasets/pull/6713.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6713" }
https://api.github.com/repos/huggingface/datasets/issues/4607	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4607/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4607/comments	https://api.github.com/repos/huggingface/datasets/issues/4607/events	https://github.com/huggingface/datasets/pull/4607	1,290,171,941	PR_kwDODunzps46pLnd	4,607	Align more metadata with other repo types (models,spaces)	{ "avatar_url": "https://avatars.githubusercontent.com/u/326577?v=4", "events_url": "https://api.github.com/users/julien-c/events{/privacy}", "followers_url": "https://api.github.com/users/julien-c/followers", "following_url": "https://api.github.com/users/julien-c/following{/other_user}", "gists_url": "https://api.github.com/users/julien-c/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/julien-c", "id": 326577, "login": "julien-c", "node_id": "MDQ6VXNlcjMyNjU3Nw==", "organizations_url": "https://api.github.com/users/julien-c/orgs", "received_events_url": "https://api.github.com/users/julien-c/received_events", "repos_url": "https://api.github.com/users/julien-c/repos", "site_admin": false, "starred_url": "https://api.github.com/users/julien-c/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/julien-c/subscriptions", "type": "User", "url": "https://api.github.com/users/julien-c", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "I just set a default value (None) for the deprecated licenses and languages fields, which should fix most of the CI failures.\r\n\r\nNote that the CI should still be red because you edited many dataset cards and they're still missing some content - but this is unrelated to this PR so we can ignore these failures", "thanks so much @lhoestq !!", "There's also a follow-up PR to this one, in #4613 – I would suggest to merge all of them at the same time and hope not too many things are broken 🙀 🙀 ", "Alright merging this one now, let's see how broken things get" ]	2022-06-30T13:52:12Z	2022-07-01T12:00:37Z	2022-07-01T11:49:14Z	MEMBER	null	null	null	see also associated PR on the `datasets-tagging` Space: https://huggingface.co/spaces/huggingface/datasets-tagging/discussions/2 (to merge after this one is merged)	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4607/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4607/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4607.diff", "html_url": "https://github.com/huggingface/datasets/pull/4607", "merged_at": "2022-07-01T11:49:14Z", "patch_url": "https://github.com/huggingface/datasets/pull/4607.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4607" }
https://api.github.com/repos/huggingface/datasets/issues/6048	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6048/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6048/comments	https://api.github.com/repos/huggingface/datasets/issues/6048/events	https://github.com/huggingface/datasets/issues/6048	1,809,629,346	I_kwDODunzps5r3MCi	6,048	when i use datasets.load_dataset, i encounter the http connect error!	{ "avatar_url": "https://avatars.githubusercontent.com/u/137855591?v=4", "events_url": "https://api.github.com/users/yangy1992/events{/privacy}", "followers_url": "https://api.github.com/users/yangy1992/followers", "following_url": "https://api.github.com/users/yangy1992/following{/other_user}", "gists_url": "https://api.github.com/users/yangy1992/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/yangy1992", "id": 137855591, "login": "yangy1992", "node_id": "U_kgDOCDeCZw", "organizations_url": "https://api.github.com/users/yangy1992/orgs", "received_events_url": "https://api.github.com/users/yangy1992/received_events", "repos_url": "https://api.github.com/users/yangy1992/repos", "site_admin": false, "starred_url": "https://api.github.com/users/yangy1992/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/yangy1992/subscriptions", "type": "User", "url": "https://api.github.com/users/yangy1992", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The `audiofolder` loader is not available in version `2.3.2`, hence the error. Please run the `pip install -U datasets` command to update the `datasets` installation to make `load_dataset(\"audiofolder\", ...)` work." ]	2023-07-18T10:16:34Z	2023-07-18T16:18:39Z	2023-07-18T16:18:39Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug `common_voice_test = load_dataset("audiofolder", data_dir="./dataset/",cache_dir="./cache",split=datasets.Split.TEST)` when i run the code above, i got the error as below: -------------------------------------------- ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.3.2/datasets/audiofolder/audiofolder.py (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.3.2/datasets/audiofolder/audiofolder.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f299ed082e0>: Failed to establish a new connection: [Errno 101] Network is unreachable'))"))) -------------------------------------------------- My all data is on local machine, why does it need to connect the internet? how can i fix it, because my machine cannot connect the internet. ### Steps to reproduce the bug 1 ### Expected behavior no error when i use the load_dataset func ### Environment info python=3.8.15	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6048/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6048/timeline	null	completed	null	null

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