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url stringlengths 58 61	repository_url stringclasses 1 value	labels_url stringlengths 72 75	comments_url stringlengths 67 70	events_url stringlengths 65 68	html_url stringlengths 46 51	id int64 599M 1.84B	node_id stringlengths 18 32	number int64 1 6.12k	title stringlengths 1 290	user dict	labels listlengths 0 4	state stringclasses 2 values	locked bool 1 class	assignee dict	assignees listlengths 0 4	milestone dict	comments sequencelengths 0 30	created_at unknown	updated_at unknown	closed_at unknown	author_association stringclasses 3 values	active_lock_reason float64	draft float64 0 1 ⌀	pull_request dict	body stringlengths 0 228k ⌀	reactions dict	timeline_url stringlengths 67 70	performed_via_github_app float64	state_reason stringclasses 3 values	is_pull_request bool 2 classes
https://api.github.com/repos/huggingface/datasets/issues/6117	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6117/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6117/comments	https://api.github.com/repos/huggingface/datasets/issues/6117/events	https://github.com/huggingface/datasets/pull/6117	1,835,213,848	PR_kwDODunzps5XHktw	6,117	Set dev version	{ "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" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6117). All of your documentation changes will be reflected on that endpoint.", "<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) \| 0.012516 / 0.011353 (0.001163) \| 0.004725 / 0.011008 (-0.006283) \| 0.112245 / 0.038508 (0.073736) \| 0.079146 / 0.023109 (0.056037) \| 0.386415 / 0.275898 (0.110517) \| 0.420441 / 0.323480 (0.096961) \| 0.005682 / 0.007986 (-0.002304) \| 0.004169 / 0.004328 (-0.000160) \| 0.077847 / 0.004250 (0.073597) \| 0.055763 / 0.037052 (0.018711) \| 0.385529 / 0.258489 (0.127040) \| 0.422711 / 0.293841 (0.128870) \| 0.047212 / 0.128546 (-0.081334) \| 0.013711 / 0.075646 (-0.061935) \| 0.342856 / 0.419271 (-0.076416) \| 0.066788 / 0.043533 (0.023255) \| 0.380728 / 0.255139 (0.125589) \| 0.416241 / 0.283200 (0.133041) \| 0.034676 / 0.141683 (-0.107007) \| 1.679661 / 1.452155 (0.227506) \| 1.838014 / 1.492716 (0.345297) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.219556 / 0.018006 (0.201550) \| 0.524728 / 0.000490 (0.524238) \| 0.005045 / 0.000200 (0.004845) \| 0.000124 / 0.000054 (0.000069) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025475 / 0.037411 (-0.011936) \| 0.085937 / 0.014526 (0.071412) \| 0.099245 / 0.176557 (-0.077311) \| 0.158995 / 0.737135 (-0.578141) \| 0.101504 / 0.296338 (-0.194835) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.582200 / 0.215209 (0.366991) \| 5.794340 / 2.077655 (3.716685) \| 2.473635 / 1.504120 (0.969515) \| 2.168135 / 1.541195 (0.626941) \| 2.215886 / 1.468490 (0.747396) \| 0.855599 / 4.584777 (-3.729178) \| 5.003067 / 3.745712 (1.257354) \| 4.503566 / 5.269862 (-0.766295) \| 2.912248 / 4.565676 (-1.653428) \| 0.103267 / 0.424275 (-0.321008) \| 0.012114 / 0.007607 (0.004507) \| 0.712240 / 0.226044 (0.486196) \| 7.131946 / 2.268929 (4.863017) \| 3.280052 / 55.444624 (-52.164573) \| 2.583472 / 6.876477 (-4.293004) \| 2.820758 / 2.142072 (0.678686) \| 1.132097 / 4.805227 (-3.673131) \| 0.232191 / 6.500664 (-6.268473) \| 0.082966 / 0.075469 (0.007497) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.581125 / 1.841788 (-0.260662) \| 22.723878 / 8.074308 (14.649570) \| 19.969347 / 10.191392 (9.777955) \| 0.234365 / 0.680424 (-0.446059) \| 0.030245 / 0.534201 (-0.503956) \| 0.470843 / 0.579283 (-0.108440) \| 0.558069 / 0.434364 (0.123705) \| 0.534878 / 0.540337 (-0.005460) \| 0.801025 / 1.386936 (-0.585911) \|\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) \| 0.008524 / 0.011353 (-0.002829) \| 0.005083 / 0.011008 (-0.005925) \| 0.078054 / 0.038508 (0.039546) \| 0.082025 / 0.023109 (0.058915) \| 0.458027 / 0.275898 (0.182129) \| 0.498232 / 0.323480 (0.174752) \| 0.005938 / 0.007986 (-0.002048) \| 0.003776 / 0.004328 (-0.000553) \| 0.080413 / 0.004250 (0.076163) \| 0.060485 / 0.037052 (0.023433) \| 0.462816 / 0.258489 (0.204327) \| 0.513970 / 0.293841 (0.220129) \| 0.047574 / 0.128546 (-0.080973) \| 0.013424 / 0.075646 (-0.062222) \| 0.087707 / 0.419271 (-0.331565) \| 0.065007 / 0.043533 (0.021474) \| 0.465844 / 0.255139 (0.210705) \| 0.498474 / 0.283200 (0.215274) \| 0.033518 / 0.141683 (-0.108164) \| 1.737507 / 1.452155 (0.285352) \| 1.848291 / 1.492716 (0.355574) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.316710 / 0.018006 (0.298703) \| 0.504415 / 0.000490 (0.503925) \| 0.042128 / 0.000200 (0.041928) \| 0.000171 / 0.000054 (0.000117) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032097 / 0.037411 (-0.005314) \| 0.099371 / 0.014526 (0.084845) \| 0.109311 / 0.176557 (-0.067246) \| 0.177373 / 0.737135 (-0.559762) \| 0.110753 / 0.296338 (-0.185585) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.688060 / 0.215209 (0.472851) \| 6.255219 / 2.077655 (4.177564) \| 2.696845 / 1.504120 (1.192725) \| 2.395424 / 1.541195 (0.854230) \| 2.414870 / 1.468490 (0.946380) \| 0.865704 / 4.584777 (-3.719073) \| 5.086828 / 3.745712 (1.341116) \| 4.648107 / 5.269862 (-0.621754) \| 3.091119 / 4.565676 (-1.474558) \| 0.101787 / 0.424275 (-0.322489) \| 0.008829 / 0.007607 (0.001222) \| 0.772398 / 0.226044 (0.546354) \| 7.700366 / 2.268929 (5.431438) \| 3.608632 / 55.444624 (-51.835992) \| 2.923309 / 6.876477 (-3.953168) \| 2.952141 / 2.142072 (0.810069) \| 1.093006 / 4.805227 (-3.712221) \| 0.224363 / 6.500664 (-6.276301) \| 0.074927 / 0.075469 (-0.000542) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.638414 / 1.841788 (-0.203374) \| 23.486781 / 8.074308 (15.412473) \| 21.129104 / 10.191392 (10.937712) \| 0.259955 / 0.680424 (-0.420469) \| 0.027305 / 0.534201 (-0.506895) \| 0.464448 / 0.579283 (-0.114835) \| 0.553737 / 0.434364 (0.119373) \| 0.571318 / 0.540337 (0.030981) \| 0.772917 / 1.386936 (-0.614019) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3ec5ee9e78b464364796651d995823c7ecb0f951 \"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) \| 0.009093 / 0.011353 (-0.002260) \| 0.005283 / 0.011008 (-0.005725) \| 0.112299 / 0.038508 (0.073791) \| 0.081341 / 0.023109 (0.058232) \| 0.363799 / 0.275898 (0.087901) \| 0.409261 / 0.323480 (0.085781) \| 0.006400 / 0.007986 (-0.001586) \| 0.003965 / 0.004328 (-0.000363) \| 0.074389 / 0.004250 (0.070139) \| 0.060654 / 0.037052 (0.023602) \| 0.391046 / 0.258489 (0.132557) \| 0.430514 / 0.293841 (0.136673) \| 0.054900 / 0.128546 (-0.073646) \| 0.017972 / 0.075646 (-0.057675) \| 0.410875 / 0.419271 (-0.008396) \| 0.067405 / 0.043533 (0.023873) \| 0.371468 / 0.255139 (0.116329) \| 0.435061 / 0.283200 (0.151861) \| 0.038063 / 0.141683 (-0.103620) \| 1.733509 / 1.452155 (0.281354) \| 1.833899 / 1.492716 (0.341182) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.243230 / 0.018006 (0.225224) \| 0.605636 / 0.000490 (0.605146) \| 0.004890 / 0.000200 (0.004690) \| 0.000098 / 0.000054 (0.000043) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027624 / 0.037411 (-0.009787) \| 0.084799 / 0.014526 (0.070273) \| 0.104405 / 0.176557 (-0.072152) \| 0.165383 / 0.737135 (-0.571752) \| 0.102083 / 0.296338 (-0.194255) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.578334 / 0.215209 (0.363125) \| 5.369520 / 2.077655 (3.291866) \| 2.294174 / 1.504120 (0.790055) \| 2.054195 / 1.541195 (0.513000) \| 2.007304 / 1.468490 (0.538814) \| 0.839283 / 4.584777 (-3.745494) \| 5.262288 / 3.745712 (1.516576) \| 4.363346 / 5.269862 (-0.906516) \| 2.854903 / 4.565676 (-1.710773) \| 0.096975 / 0.424275 (-0.327300) \| 0.008237 / 0.007607 (0.000630) \| 0.646746 / 0.226044 (0.420702) \| 6.250621 / 2.268929 (3.981693) \| 2.900377 / 55.444624 (-52.544247) \| 2.283238 / 6.876477 (-4.593239) \| 2.443785 / 2.142072 (0.301713) \| 0.991719 / 4.805227 (-3.813508) \| 0.189755 / 6.500664 (-6.310909) \| 0.067906 / 0.075469 (-0.007563) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.515563 / 1.841788 (-0.326225) \| 21.956499 / 8.074308 (13.882191) \| 19.161750 / 10.191392 (8.970358) \| 0.238199 / 0.680424 (-0.442225) \| 0.026771 / 0.534201 (-0.507430) \| 0.450195 / 0.579283 (-0.129088) \| 0.585168 / 0.434364 (0.150804) \| 0.522945 / 0.540337 (-0.017393) \| 0.776244 / 1.386936 (-0.610693) \|\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) \| 0.007997 / 0.011353 (-0.003356) \| 0.005021 / 0.011008 (-0.005988) \| 0.087308 / 0.038508 (0.048800) \| 0.077760 / 0.023109 (0.054650) \| 0.425313 / 0.275898 (0.149415) \| 0.451470 / 0.323480 (0.127990) \| 0.006848 / 0.007986 (-0.001137) \| 0.004812 / 0.004328 (0.000484) \| 0.071198 / 0.004250 (0.066947) \| 0.058325 / 0.037052 (0.021273) \| 0.427411 / 0.258489 (0.168922) \| 0.466069 / 0.293841 (0.172228) \| 0.048686 / 0.128546 (-0.079861) \| 0.011841 / 0.075646 (-0.063806) \| 0.086225 / 0.419271 (-0.333047) \| 0.060500 / 0.043533 (0.016967) \| 0.435580 / 0.255139 (0.180441) \| 0.456919 / 0.283200 (0.173719) \| 0.035094 / 0.141683 (-0.106588) \| 1.582805 / 1.452155 (0.130650) \| 1.717838 / 1.492716 (0.225122) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283967 / 0.018006 (0.265960) \| 0.517496 / 0.000490 (0.517006) \| 0.014747 / 0.000200 (0.014547) \| 0.000099 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027870 / 0.037411 (-0.009541) \| 0.083835 / 0.014526 (0.069309) \| 0.099157 / 0.176557 (-0.077400) \| 0.173210 / 0.737135 (-0.563925) \| 0.094212 / 0.296338 (-0.202127) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.535720 / 0.215209 (0.320511) \| 5.273730 / 2.077655 (3.196075) \| 2.422560 / 1.504120 (0.918440) \| 2.131416 / 1.541195 (0.590222) \| 2.192000 / 1.468490 (0.723510) \| 0.708469 / 4.584777 (-3.876308) \| 4.758092 / 3.745712 (1.012380) \| 3.940729 / 5.269862 (-1.329133) \| 2.553093 / 4.565676 (-2.012583) \| 0.084895 / 0.424275 (-0.339380) \| 0.008730 / 0.007607 (0.001123) \| 0.646975 / 0.226044 (0.420930) \| 6.294811 / 2.268929 (4.025883) \| 3.293964 / 55.444624 (-52.150660) \| 2.568985 / 6.876477 (-4.307492) \| 2.743786 / 2.142072 (0.601713) \| 0.899733 / 4.805227 (-3.905494) \| 0.193484 / 6.500664 (-6.307181) \| 0.070012 / 0.075469 (-0.005457) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.502255 / 1.841788 (-0.339532) \| 20.690234 / 8.074308 (12.615926) \| 18.375791 / 10.191392 (8.184399) \| 0.200135 / 0.680424 (-0.480289) \| 0.029434 / 0.534201 (-0.504767) \| 0.477267 / 0.579283 (-0.102016) \| 0.566869 / 0.434364 (0.132505) \| 0.543756 / 0.540337 (0.003418) \| 0.700476 / 1.386936 (-0.686460) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef17d9fd6c648bb41d43ba301c3de4d7b6f833d8 \"CML watermark\")\n" ]	"2023-08-03T14:46:04"	"2023-08-03T14:56:59"	"2023-08-03T14:46:18"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6117.diff", "html_url": "https://github.com/huggingface/datasets/pull/6117", "merged_at": "2023-08-03T14:46:18Z", "patch_url": "https://github.com/huggingface/datasets/pull/6117.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6117" }	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/6117/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6117/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6116	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6116/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6116/comments	https://api.github.com/repos/huggingface/datasets/issues/6116/events	https://github.com/huggingface/datasets/issues/6116	1,835,098,484	I_kwDODunzps5tYWF0	6,116	[Docs] The "Process" how-to guide lacks description of `select_columns` function	{ "avatar_url": "https://avatars.githubusercontent.com/u/18213435?v=4", "events_url": "https://api.github.com/users/unifyh/events{/privacy}", "followers_url": "https://api.github.com/users/unifyh/followers", "following_url": "https://api.github.com/users/unifyh/following{/other_user}", "gists_url": "https://api.github.com/users/unifyh/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/unifyh", "id": 18213435, "login": "unifyh", "node_id": "MDQ6VXNlcjE4MjEzNDM1", "organizations_url": "https://api.github.com/users/unifyh/orgs", "received_events_url": "https://api.github.com/users/unifyh/received_events", "repos_url": "https://api.github.com/users/unifyh/repos", "site_admin": false, "starred_url": "https://api.github.com/users/unifyh/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/unifyh/subscriptions", "type": "User", "url": "https://api.github.com/users/unifyh" }	[ { "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	[ "Great idea, feel free to open a PR! :)" ]	"2023-08-03T13:45:10"	"2023-08-03T17:40:58"	null	NONE	null	null	null	### Feature request The [how to process dataset guide](https://huggingface.co/docs/datasets/main/en/process) currently does not mention the [`select_columns`](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.select_columns) function. It would be nice to include it in the guide. ### Motivation This function is a commonly requested feature (see this [forum thread](https://discuss.huggingface.co/t/how-to-create-a-new-dataset-from-another-dataset-and-select-specific-columns-and-the-data-along-with-the-column/15120) and #5468 #5474). However, it has not been included in the guide since its implementation by PR #5480. Mentioning it in the guide would help future users discover this added feature. ### Your contribution I could submit a PR to add a brief description of the function to said guide.	{ "+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/6116/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6116/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6115	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6115/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6115/comments	https://api.github.com/repos/huggingface/datasets/issues/6115/events	https://github.com/huggingface/datasets/pull/6115	1,834,765,485	PR_kwDODunzps5XGChP	6,115	Release: 2.14.3	{ "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": "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" }	[]	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) \| 0.007578 / 0.011353 (-0.003775) \| 0.004271 / 0.011008 (-0.006738) \| 0.086607 / 0.038508 (0.048098) \| 0.063209 / 0.023109 (0.040099) \| 0.351724 / 0.275898 (0.075826) \| 0.399261 / 0.323480 (0.075781) \| 0.004767 / 0.007986 (-0.003219) \| 0.003487 / 0.004328 (-0.000842) \| 0.071483 / 0.004250 (0.067233) \| 0.051281 / 0.037052 (0.014229) \| 0.387726 / 0.258489 (0.129237) \| 0.408446 / 0.293841 (0.114605) \| 0.041189 / 0.128546 (-0.087357) \| 0.012446 / 0.075646 (-0.063200) \| 0.331147 / 0.419271 (-0.088124) \| 0.056721 / 0.043533 (0.013188) \| 0.361306 / 0.255139 (0.106167) \| 0.409651 / 0.283200 (0.126451) \| 0.035485 / 0.141683 (-0.106198) \| 1.461391 / 1.452155 (0.009236) \| 1.554820 / 1.492716 (0.062104) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237119 / 0.018006 (0.219113) \| 0.518731 / 0.000490 (0.518241) \| 0.004192 / 0.000200 (0.003992) \| 0.000114 / 0.000054 (0.000059) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024912 / 0.037411 (-0.012499) \| 0.089420 / 0.014526 (0.074894) \| 0.091209 / 0.176557 (-0.085347) \| 0.152580 / 0.737135 (-0.584555) \| 0.089660 / 0.296338 (-0.206678) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.515223 / 0.215209 (0.300014) \| 5.328359 / 2.077655 (3.250705) \| 1.974326 / 1.504120 (0.470206) \| 1.665216 / 1.541195 (0.124021) \| 1.736040 / 1.468490 (0.267550) \| 0.734746 / 4.584777 (-3.850031) \| 4.186613 / 3.745712 (0.440901) \| 3.535760 / 5.269862 (-1.734102) \| 2.333247 / 4.565676 (-2.232429) \| 0.071845 / 0.424275 (-0.352430) \| 0.006147 / 0.007607 (-0.001460) \| 0.546649 / 0.226044 (0.320605) \| 5.452281 / 2.268929 (3.183353) \| 2.512984 / 55.444624 (-52.931640) \| 2.104210 / 6.876477 (-4.772267) \| 2.409251 / 2.142072 (0.267178) \| 0.822797 / 4.805227 (-3.982430) \| 0.166648 / 6.500664 (-6.334016) \| 0.056350 / 0.075469 (-0.019119) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.397798 / 1.841788 (-0.443989) \| 20.549399 / 8.074308 (12.475091) \| 19.118168 / 10.191392 (8.926776) \| 0.216361 / 0.680424 (-0.464063) \| 0.027064 / 0.534201 (-0.507136) \| 0.410762 / 0.579283 (-0.168521) \| 0.559225 / 0.434364 (0.124861) \| 0.468028 / 0.540337 (-0.072309) \| 0.691520 / 1.386936 (-0.695416) \|\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) \| 0.006463 / 0.011353 (-0.004890) \| 0.003879 / 0.011008 (-0.007130) \| 0.058723 / 0.038508 (0.020215) \| 0.057202 / 0.023109 (0.034092) \| 0.344397 / 0.275898 (0.068499) \| 0.360388 / 0.323480 (0.036908) \| 0.005502 / 0.007986 (-0.002483) \| 0.004101 / 0.004328 (-0.000227) \| 0.058168 / 0.004250 (0.053917) \| 0.059112 / 0.037052 (0.022060) \| 0.362206 / 0.258489 (0.103717) \| 0.386444 / 0.293841 (0.092603) \| 0.036613 / 0.128546 (-0.091934) \| 0.010482 / 0.075646 (-0.065165) \| 0.065850 / 0.419271 (-0.353421) \| 0.046528 / 0.043533 (0.002995) \| 0.349568 / 0.255139 (0.094429) \| 0.360181 / 0.283200 (0.076981) \| 0.029030 / 0.141683 (-0.112653) \| 1.314569 / 1.452155 (-0.137586) \| 1.422393 / 1.492716 (-0.070324) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281554 / 0.018006 (0.263548) \| 0.608018 / 0.000490 (0.607528) \| 0.004568 / 0.000200 (0.004368) \| 0.000182 / 0.000054 (0.000127) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023515 / 0.037411 (-0.013896) \| 0.072994 / 0.014526 (0.058468) \| 0.080688 / 0.176557 (-0.095868) \| 0.125904 / 0.737135 (-0.611232) \| 0.085457 / 0.296338 (-0.210882) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.471530 / 0.215209 (0.256321) \| 4.796197 / 2.077655 (2.718542) \| 2.189181 / 1.504120 (0.685061) \| 1.886649 / 1.541195 (0.345454) \| 1.871067 / 1.468490 (0.402577) \| 0.661043 / 4.584777 (-3.923734) \| 4.344027 / 3.745712 (0.598315) \| 3.656967 / 5.269862 (-1.612895) \| 2.286033 / 4.565676 (-2.279644) \| 0.079146 / 0.424275 (-0.345129) \| 0.006840 / 0.007607 (-0.000767) \| 0.588750 / 0.226044 (0.362706) \| 6.301286 / 2.268929 (4.032357) \| 3.074702 / 55.444624 (-52.369923) \| 2.398739 / 6.876477 (-4.477738) \| 2.555057 / 2.142072 (0.412985) \| 0.874189 / 4.805227 (-3.931038) \| 0.191423 / 6.500664 (-6.309241) \| 0.061227 / 0.075469 (-0.014242) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.472763 / 1.841788 (-0.369024) \| 19.441304 / 8.074308 (11.366996) \| 15.974276 / 10.191392 (5.782884) \| 0.172503 / 0.680424 (-0.507921) \| 0.027016 / 0.534201 (-0.507185) \| 0.356085 / 0.579283 (-0.223198) \| 0.473251 / 0.434364 (0.038887) \| 0.427949 / 0.540337 (-0.112388) \| 0.588924 / 1.386936 (-0.798013) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0973da6e60ac7c1d24229ba6aa6881747b21858a \"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) \| 0.006166 / 0.011353 (-0.005187) \| 0.003558 / 0.011008 (-0.007450) \| 0.080576 / 0.038508 (0.042068) \| 0.066542 / 0.023109 (0.043432) \| 0.323997 / 0.275898 (0.048099) \| 0.369828 / 0.323480 (0.046348) \| 0.004896 / 0.007986 (-0.003090) \| 0.002909 / 0.004328 (-0.001419) \| 0.062553 / 0.004250 (0.058302) \| 0.049795 / 0.037052 (0.012742) \| 0.321369 / 0.258489 (0.062880) \| 0.422860 / 0.293841 (0.129019) \| 0.027394 / 0.128546 (-0.101152) \| 0.007954 / 0.075646 (-0.067693) \| 0.264122 / 0.419271 (-0.155149) \| 0.044881 / 0.043533 (0.001349) \| 0.316702 / 0.255139 (0.061563) \| 0.374718 / 0.283200 (0.091518) \| 0.021728 / 0.141683 (-0.119955) \| 1.394456 / 1.452155 (-0.057699) \| 1.474936 / 1.492716 (-0.017780) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.191902 / 0.018006 (0.173896) \| 0.430468 / 0.000490 (0.429979) \| 0.003790 / 0.000200 (0.003590) \| 0.000069 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024974 / 0.037411 (-0.012438) \| 0.073053 / 0.014526 (0.058527) \| 0.083801 / 0.176557 (-0.092756) \| 0.143457 / 0.737135 (-0.593678) \| 0.085099 / 0.296338 (-0.211240) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.428411 / 0.215209 (0.213202) \| 4.278077 / 2.077655 (2.200422) \| 2.230039 / 1.504120 (0.725919) \| 2.057191 / 1.541195 (0.515996) \| 2.120109 / 1.468490 (0.651619) \| 0.495242 / 4.584777 (-4.089535) \| 3.031299 / 3.745712 (-0.714413) \| 2.802685 / 5.269862 (-2.467176) \| 1.839828 / 4.565676 (-2.725849) \| 0.056875 / 0.424275 (-0.367401) \| 0.006446 / 0.007607 (-0.001161) \| 0.498958 / 0.226044 (0.272913) \| 4.980440 / 2.268929 (2.711511) \| 2.659659 / 55.444624 (-52.784965) \| 2.315174 / 6.876477 (-4.561303) \| 2.475920 / 2.142072 (0.333848) \| 0.586946 / 4.805227 (-4.218282) \| 0.124291 / 6.500664 (-6.376373) \| 0.060701 / 0.075469 (-0.014768) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.245062 / 1.841788 (-0.596725) \| 18.201444 / 8.074308 (10.127136) \| 13.723271 / 10.191392 (3.531879) \| 0.130203 / 0.680424 (-0.550221) \| 0.016773 / 0.534201 (-0.517428) \| 0.332909 / 0.579283 (-0.246374) \| 0.347469 / 0.434364 (-0.086895) \| 0.381364 / 0.540337 (-0.158973) \| 0.541723 / 1.386936 (-0.845213) \|\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) \| 0.005934 / 0.011353 (-0.005419) \| 0.003573 / 0.011008 (-0.007435) \| 0.062195 / 0.038508 (0.023687) \| 0.059026 / 0.023109 (0.035917) \| 0.413993 / 0.275898 (0.138095) \| 0.459552 / 0.323480 (0.136072) \| 0.004610 / 0.007986 (-0.003376) \| 0.002907 / 0.004328 (-0.001421) \| 0.062983 / 0.004250 (0.058733) \| 0.047797 / 0.037052 (0.010745) \| 0.415461 / 0.258489 (0.156972) \| 0.417424 / 0.293841 (0.123583) \| 0.027098 / 0.128546 (-0.101449) \| 0.008106 / 0.075646 (-0.067540) \| 0.067600 / 0.419271 (-0.351672) \| 0.041432 / 0.043533 (-0.002101) \| 0.407861 / 0.255139 (0.152722) \| 0.430774 / 0.283200 (0.147575) \| 0.020738 / 0.141683 (-0.120945) \| 1.435127 / 1.452155 (-0.017028) \| 1.486961 / 1.492716 (-0.005755) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.231174 / 0.018006 (0.213168) \| 0.421208 / 0.000490 (0.420718) \| 0.005411 / 0.000200 (0.005211) \| 0.000078 / 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) \| 0.025362 / 0.037411 (-0.012049) \| 0.078534 / 0.014526 (0.064008) \| 0.085304 / 0.176557 (-0.091252) \| 0.139048 / 0.737135 (-0.598087) \| 0.087015 / 0.296338 (-0.209323) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.448506 / 0.215209 (0.233297) \| 4.486694 / 2.077655 (2.409039) \| 2.488022 / 1.504120 (0.983902) \| 2.325321 / 1.541195 (0.784126) \| 2.381311 / 1.468490 (0.912821) \| 0.502102 / 4.584777 (-4.082675) \| 3.018326 / 3.745712 (-0.727386) \| 2.824922 / 5.269862 (-2.444940) \| 1.857414 / 4.565676 (-2.708263) \| 0.057514 / 0.424275 (-0.366761) \| 0.006829 / 0.007607 (-0.000779) \| 0.521939 / 0.226044 (0.295895) \| 5.224393 / 2.268929 (2.955465) \| 2.933132 / 55.444624 (-52.511492) \| 2.661187 / 6.876477 (-4.215290) \| 2.781950 / 2.142072 (0.639878) \| 0.592927 / 4.805227 (-4.212300) \| 0.126685 / 6.500664 (-6.373979) \| 0.064188 / 0.075469 (-0.011281) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.351107 / 1.841788 (-0.490681) \| 18.344453 / 8.074308 (10.270145) \| 13.838788 / 10.191392 (3.647396) \| 0.157881 / 0.680424 (-0.522543) \| 0.016636 / 0.534201 (-0.517565) \| 0.331597 / 0.579283 (-0.247686) \| 0.345573 / 0.434364 (-0.088791) \| 0.397361 / 0.540337 (-0.142976) \| 0.534289 / 1.386936 (-0.852647) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#582e722a76534904c0f3038d32ebb8db88ce9128 \"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) \| 0.006399 / 0.011353 (-0.004954) \| 0.003872 / 0.011008 (-0.007136) \| 0.083722 / 0.038508 (0.045214) \| 0.068845 / 0.023109 (0.045736) \| 0.329112 / 0.275898 (0.053214) \| 0.343295 / 0.323480 (0.019815) \| 0.005137 / 0.007986 (-0.002849) \| 0.003303 / 0.004328 (-0.001026) \| 0.064495 / 0.004250 (0.060245) \| 0.051448 / 0.037052 (0.014395) \| 0.322554 / 0.258489 (0.064065) \| 0.361934 / 0.293841 (0.068093) \| 0.030821 / 0.128546 (-0.097726) \| 0.008482 / 0.075646 (-0.067164) \| 0.288136 / 0.419271 (-0.131135) \| 0.051935 / 0.043533 (0.008402) \| 0.308283 / 0.255139 (0.053144) \| 0.343421 / 0.283200 (0.060221) \| 0.023639 / 0.141683 (-0.118044) \| 1.485442 / 1.452155 (0.033288) \| 1.533282 / 1.492716 (0.040565) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.218163 / 0.018006 (0.200157) \| 0.464473 / 0.000490 (0.463983) \| 0.003097 / 0.000200 (0.002897) \| 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) \| 0.028650 / 0.037411 (-0.008761) \| 0.083295 / 0.014526 (0.068769) \| 0.096468 / 0.176557 (-0.080088) \| 0.152086 / 0.737135 (-0.585050) \| 0.102586 / 0.296338 (-0.193752) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.393038 / 0.215209 (0.177829) \| 3.925514 / 2.077655 (1.847859) \| 1.938419 / 1.504120 (0.434300) \| 1.760265 / 1.541195 (0.219071) \| 1.810024 / 1.468490 (0.341534) \| 0.486232 / 4.584777 (-4.098545) \| 3.618747 / 3.745712 (-0.126965) \| 3.206950 / 5.269862 (-2.062912) \| 1.999240 / 4.565676 (-2.566436) \| 0.056986 / 0.424275 (-0.367289) \| 0.007193 / 0.007607 (-0.000415) \| 0.469313 / 0.226044 (0.243269) \| 4.688670 / 2.268929 (2.419741) \| 2.400332 / 55.444624 (-53.044292) \| 2.074197 / 6.876477 (-4.802279) \| 2.290823 / 2.142072 (0.148751) \| 0.582339 / 4.805227 (-4.222888) \| 0.134127 / 6.500664 (-6.366537) \| 0.061061 / 0.075469 (-0.014408) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.272782 / 1.841788 (-0.569006) \| 19.463375 / 8.074308 (11.389067) \| 14.306819 / 10.191392 (4.115427) \| 0.164608 / 0.680424 (-0.515816) \| 0.018626 / 0.534201 (-0.515575) \| 0.395225 / 0.579283 (-0.184058) \| 0.408984 / 0.434364 (-0.025380) \| 0.463364 / 0.540337 (-0.076974) \| 0.630425 / 1.386936 (-0.756511) \|\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) \| 0.006465 / 0.011353 (-0.004888) \| 0.003975 / 0.011008 (-0.007033) \| 0.063643 / 0.038508 (0.025134) \| 0.075214 / 0.023109 (0.052105) \| 0.361734 / 0.275898 (0.085836) \| 0.396664 / 0.323480 (0.073184) \| 0.005251 / 0.007986 (-0.002735) \| 0.003249 / 0.004328 (-0.001080) \| 0.063841 / 0.004250 (0.059591) \| 0.054504 / 0.037052 (0.017451) \| 0.374791 / 0.258489 (0.116302) \| 0.399205 / 0.293841 (0.105364) \| 0.031355 / 0.128546 (-0.097192) \| 0.008483 / 0.075646 (-0.067163) \| 0.070234 / 0.419271 (-0.349037) \| 0.048336 / 0.043533 (0.004803) \| 0.373484 / 0.255139 (0.118345) \| 0.382174 / 0.283200 (0.098974) \| 0.022560 / 0.141683 (-0.119123) \| 1.449799 / 1.452155 (-0.002355) \| 1.525255 / 1.492716 (0.032539) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228350 / 0.018006 (0.210343) \| 0.444344 / 0.000490 (0.443855) \| 0.003699 / 0.000200 (0.003499) \| 0.000079 / 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) \| 0.030681 / 0.037411 (-0.006731) \| 0.087340 / 0.014526 (0.072814) \| 0.098636 / 0.176557 (-0.077920) \| 0.151665 / 0.737135 (-0.585471) \| 0.100840 / 0.296338 (-0.195498) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417857 / 0.215209 (0.202648) \| 4.168407 / 2.077655 (2.090752) \| 2.201758 / 1.504120 (0.697638) \| 1.997834 / 1.541195 (0.456639) \| 2.127693 / 1.468490 (0.659202) \| 0.486429 / 4.584777 (-4.098348) \| 3.676335 / 3.745712 (-0.069378) \| 3.226268 / 5.269862 (-2.043594) \| 2.027255 / 4.565676 (-2.538422) \| 0.056759 / 0.424275 (-0.367516) \| 0.007628 / 0.007607 (0.000021) \| 0.500482 / 0.226044 (0.274438) \| 4.996236 / 2.268929 (2.727307) \| 2.628884 / 55.444624 (-52.815740) \| 2.347611 / 6.876477 (-4.528866) \| 2.551328 / 2.142072 (0.409255) \| 0.582449 / 4.805227 (-4.222778) \| 0.132844 / 6.500664 (-6.367821) \| 0.061791 / 0.075469 (-0.013678) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.373718 / 1.841788 (-0.468070) \| 19.921217 / 8.074308 (11.846909) \| 14.209642 / 10.191392 (4.018250) \| 0.185334 / 0.680424 (-0.495090) \| 0.018228 / 0.534201 (-0.515973) \| 0.395549 / 0.579283 (-0.183734) \| 0.404446 / 0.434364 (-0.029918) \| 0.472456 / 0.540337 (-0.067882) \| 0.622739 / 1.386936 (-0.764197) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33f736eafa0f77de03aa6894ea4a6c923702e5d1 \"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) \| 0.006007 / 0.011353 (-0.005346) \| 0.003588 / 0.011008 (-0.007420) \| 0.080334 / 0.038508 (0.041826) \| 0.058932 / 0.023109 (0.035823) \| 0.404613 / 0.275898 (0.128715) \| 0.438377 / 0.323480 (0.114897) \| 0.003468 / 0.007986 (-0.004518) \| 0.003702 / 0.004328 (-0.000627) \| 0.062936 / 0.004250 (0.058686) \| 0.047987 / 0.037052 (0.010934) \| 0.411409 / 0.258489 (0.152920) \| 0.450244 / 0.293841 (0.156403) \| 0.027007 / 0.128546 (-0.101539) \| 0.007932 / 0.075646 (-0.067714) \| 0.261390 / 0.419271 (-0.157882) \| 0.044992 / 0.043533 (0.001459) \| 0.409730 / 0.255139 (0.154591) \| 0.433331 / 0.283200 (0.150131) \| 0.020446 / 0.141683 (-0.121237) \| 1.425418 / 1.452155 (-0.026736) \| 1.479242 / 1.492716 (-0.013475) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.187375 / 0.018006 (0.169368) \| 0.428532 / 0.000490 (0.428043) \| 0.003406 / 0.000200 (0.003206) \| 0.000072 / 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) \| 0.024390 / 0.037411 (-0.013022) \| 0.072571 / 0.014526 (0.058045) \| 0.083513 / 0.176557 (-0.093044) \| 0.144395 / 0.737135 (-0.592741) \| 0.084813 / 0.296338 (-0.211526) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.409176 / 0.215209 (0.193967) \| 4.078082 / 2.077655 (2.000428) \| 1.913596 / 1.504120 (0.409476) \| 1.718470 / 1.541195 (0.177275) \| 1.753106 / 1.468490 (0.284616) \| 0.494167 / 4.584777 (-4.090610) \| 3.029531 / 3.745712 (-0.716181) \| 2.807331 / 5.269862 (-2.462531) \| 1.839471 / 4.565676 (-2.726206) \| 0.057169 / 0.424275 (-0.367106) \| 0.006433 / 0.007607 (-0.001175) \| 0.482666 / 0.226044 (0.256621) \| 4.817601 / 2.268929 (2.548673) \| 2.449967 / 55.444624 (-52.994658) \| 2.113891 / 6.876477 (-4.762586) \| 2.399293 / 2.142072 (0.257221) \| 0.578903 / 4.805227 (-4.226324) \| 0.124306 / 6.500664 (-6.376358) \| 0.061572 / 0.075469 (-0.013897) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.254692 / 1.841788 (-0.587096) \| 18.414049 / 8.074308 (10.339741) \| 13.992059 / 10.191392 (3.800667) \| 0.146671 / 0.680424 (-0.533753) \| 0.016925 / 0.534201 (-0.517275) \| 0.333124 / 0.579283 (-0.246159) \| 0.348007 / 0.434364 (-0.086357) \| 0.378519 / 0.540337 (-0.161819) \| 0.532540 / 1.386936 (-0.854396) \|\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) \| 0.006050 / 0.011353 (-0.005303) \| 0.003614 / 0.011008 (-0.007394) \| 0.061707 / 0.038508 (0.023199) \| 0.062874 / 0.023109 (0.039765) \| 0.364760 / 0.275898 (0.088862) \| 0.398136 / 0.323480 (0.074656) \| 0.005598 / 0.007986 (-0.002388) \| 0.002836 / 0.004328 (-0.001493) \| 0.061880 / 0.004250 (0.057630) \| 0.048165 / 0.037052 (0.011113) \| 0.372656 / 0.258489 (0.114167) \| 0.403967 / 0.293841 (0.110126) \| 0.027046 / 0.128546 (-0.101501) \| 0.008091 / 0.075646 (-0.067555) \| 0.066783 / 0.419271 (-0.352489) \| 0.041186 / 0.043533 (-0.002347) \| 0.376009 / 0.255139 (0.120870) \| 0.391769 / 0.283200 (0.108569) \| 0.021020 / 0.141683 (-0.120663) \| 1.514593 / 1.452155 (0.062438) \| 1.548506 / 1.492716 (0.055790) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237610 / 0.018006 (0.219604) \| 0.434274 / 0.000490 (0.433784) \| 0.009720 / 0.000200 (0.009520) \| 0.000098 / 0.000054 (0.000043) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025605 / 0.037411 (-0.011807) \| 0.078971 / 0.014526 (0.064445) \| 0.088154 / 0.176557 (-0.088403) \| 0.139112 / 0.737135 (-0.598023) \| 0.088890 / 0.296338 (-0.207449) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420027 / 0.215209 (0.204818) \| 4.189493 / 2.077655 (2.111838) \| 2.143907 / 1.504120 (0.639787) \| 1.967032 / 1.541195 (0.425837) \| 2.011845 / 1.468490 (0.543355) \| 0.496692 / 4.584777 (-4.088085) \| 3.025456 / 3.745712 (-0.720256) \| 2.828436 / 5.269862 (-2.441426) \| 1.860673 / 4.565676 (-2.705003) \| 0.057199 / 0.424275 (-0.367076) \| 0.006770 / 0.007607 (-0.000838) \| 0.491281 / 0.226044 (0.265236) \| 4.918065 / 2.268929 (2.649136) \| 2.593172 / 55.444624 (-52.851452) \| 2.250750 / 6.876477 (-4.625727) \| 2.406235 / 2.142072 (0.264162) \| 0.588648 / 4.805227 (-4.216579) \| 0.125635 / 6.500664 (-6.375029) \| 0.061697 / 0.075469 (-0.013773) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.374065 / 1.841788 (-0.467722) \| 18.439315 / 8.074308 (10.365007) \| 14.031660 / 10.191392 (3.840268) \| 0.153665 / 0.680424 (-0.526759) \| 0.016980 / 0.534201 (-0.517221) \| 0.331799 / 0.579283 (-0.247484) \| 0.343201 / 0.434364 (-0.091163) \| 0.392445 / 0.540337 (-0.147892) \| 0.530387 / 1.386936 (-0.856549) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33f736eafa0f77de03aa6894ea4a6c923702e5d1 \"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) \| 0.008189 / 0.011353 (-0.003164) \| 0.004598 / 0.011008 (-0.006410) \| 0.102199 / 0.038508 (0.063691) \| 0.077961 / 0.023109 (0.054852) \| 0.364936 / 0.275898 (0.089038) \| 0.402606 / 0.323480 (0.079126) \| 0.005522 / 0.007986 (-0.002464) \| 0.004007 / 0.004328 (-0.000322) \| 0.071560 / 0.004250 (0.067310) \| 0.055818 / 0.037052 (0.018765) \| 0.378394 / 0.258489 (0.119905) \| 0.428990 / 0.293841 (0.135149) \| 0.043142 / 0.128546 (-0.085404) \| 0.013254 / 0.075646 (-0.062392) \| 0.331102 / 0.419271 (-0.088170) \| 0.061407 / 0.043533 (0.017875) \| 0.387397 / 0.255139 (0.132258) \| 0.416062 / 0.283200 (0.132862) \| 0.036330 / 0.141683 (-0.105353) \| 1.735352 / 1.452155 (0.283198) \| 1.773329 / 1.492716 (0.280613) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.188587 / 0.018006 (0.170581) \| 0.519506 / 0.000490 (0.519016) \| 0.004702 / 0.000200 (0.004502) \| 0.000097 / 0.000054 (0.000043) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027152 / 0.037411 (-0.010260) \| 0.094296 / 0.014526 (0.079770) \| 0.098155 / 0.176557 (-0.078402) \| 0.162541 / 0.737135 (-0.574595) \| 0.112092 / 0.296338 (-0.184246) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.537555 / 0.215209 (0.322346) \| 5.486821 / 2.077655 (3.409166) \| 2.377127 / 1.504120 (0.873008) \| 2.073205 / 1.541195 (0.532011) \| 2.075130 / 1.468490 (0.606640) \| 0.783779 / 4.584777 (-3.800998) \| 5.029524 / 3.745712 (1.283812) \| 4.382724 / 5.269862 (-0.887138) \| 2.836180 / 4.565676 (-1.729496) \| 0.108840 / 0.424275 (-0.315435) \| 0.008123 / 0.007607 (0.000516) \| 0.673460 / 0.226044 (0.447416) \| 6.674030 / 2.268929 (4.405102) \| 3.208922 / 55.444624 (-52.235702) \| 2.464908 / 6.876477 (-4.411568) \| 2.661929 / 2.142072 (0.519856) \| 0.962529 / 4.805227 (-3.842698) \| 0.197974 / 6.500664 (-6.302690) \| 0.066656 / 0.075469 (-0.008813) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.430373 / 1.841788 (-0.411415) \| 21.180540 / 8.074308 (13.106232) \| 19.027491 / 10.191392 (8.836099) \| 0.217520 / 0.680424 (-0.462904) \| 0.028038 / 0.534201 (-0.506163) \| 0.435266 / 0.579283 (-0.144017) \| 0.529510 / 0.434364 (0.095147) \| 0.511011 / 0.540337 (-0.029327) \| 0.728940 / 1.386936 (-0.657996) \|\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) \| 0.007883 / 0.011353 (-0.003470) \| 0.004448 / 0.011008 (-0.006560) \| 0.071350 / 0.038508 (0.032842) \| 0.075269 / 0.023109 (0.052160) \| 0.396705 / 0.275898 (0.120807) \| 0.457809 / 0.323480 (0.134329) \| 0.005193 / 0.007986 (-0.002792) \| 0.003695 / 0.004328 (-0.000633) \| 0.078087 / 0.004250 (0.073836) \| 0.054276 / 0.037052 (0.017224) \| 0.412184 / 0.258489 (0.153695) \| 0.452400 / 0.293841 (0.158559) \| 0.049762 / 0.128546 (-0.078784) \| 0.013206 / 0.075646 (-0.062440) \| 0.085985 / 0.419271 (-0.333287) \| 0.058837 / 0.043533 (0.015304) \| 0.432481 / 0.255139 (0.177342) \| 0.433260 / 0.283200 (0.150060) \| 0.031190 / 0.141683 (-0.110493) \| 1.582707 / 1.452155 (0.130552) \| 1.664457 / 1.492716 (0.171741) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223639 / 0.018006 (0.205633) \| 0.524388 / 0.000490 (0.523899) \| 0.005489 / 0.000200 (0.005289) \| 0.000099 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030182 / 0.037411 (-0.007230) \| 0.089309 / 0.014526 (0.074783) \| 0.103306 / 0.176557 (-0.073250) \| 0.162624 / 0.737135 (-0.574511) \| 0.108957 / 0.296338 (-0.187381) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.577423 / 0.215209 (0.362214) \| 5.900154 / 2.077655 (3.822500) \| 2.687369 / 1.504120 (1.183249) \| 2.513061 / 1.541195 (0.971866) \| 2.506453 / 1.468490 (1.037963) \| 0.830838 / 4.584777 (-3.753939) \| 5.032195 / 3.745712 (1.286483) \| 4.396827 / 5.269862 (-0.873035) \| 2.884230 / 4.565676 (-1.681447) \| 0.102239 / 0.424275 (-0.322036) \| 0.008178 / 0.007607 (0.000571) \| 0.710027 / 0.226044 (0.483983) \| 7.149626 / 2.268929 (4.880698) \| 3.403605 / 55.444624 (-52.041019) \| 2.661970 / 6.876477 (-4.214506) \| 2.760227 / 2.142072 (0.618154) \| 1.043981 / 4.805227 (-3.761246) \| 0.195028 / 6.500664 (-6.305636) \| 0.065211 / 0.075469 (-0.010258) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.581265 / 1.841788 (-0.260522) \| 21.640230 / 8.074308 (13.565922) \| 19.031860 / 10.191392 (8.840468) \| 0.196903 / 0.680424 (-0.483520) \| 0.027061 / 0.534201 (-0.507140) \| 0.444995 / 0.579283 (-0.134288) \| 0.528195 / 0.434364 (0.093831) \| 0.521540 / 0.540337 (-0.018797) \| 0.730204 / 1.386936 (-0.656732) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33f736eafa0f77de03aa6894ea4a6c923702e5d1 \"CML watermark\")\n" ]	"2023-08-03T10:18:32"	"2023-08-03T15:08:02"	"2023-08-03T10:24:57"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6115.diff", "html_url": "https://github.com/huggingface/datasets/pull/6115", "merged_at": "2023-08-03T10:24:57Z", "patch_url": "https://github.com/huggingface/datasets/pull/6115.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6115" }	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/6115/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6115/timeline	null	null	true
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" }	[]	open	false	null	[]	null	[]	"2023-08-02T23:18:11"	"2023-08-02T23:20:31"	null	NONE	null	null	null	### 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. ? 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? ### Environment info datasets==2.7.0 python==3.10.8 OS: AWS Linux	{ "+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	null	false
https://api.github.com/repos/huggingface/datasets/issues/6113	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6113/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6113/comments	https://api.github.com/repos/huggingface/datasets/issues/6113/events	https://github.com/huggingface/datasets/issues/6113	1,833,854,030	I_kwDODunzps5tTmRO	6,113	load_dataset() fails with streamlit caching inside docker	{ "avatar_url": "https://avatars.githubusercontent.com/u/987574?v=4", "events_url": "https://api.github.com/users/fierval/events{/privacy}", "followers_url": "https://api.github.com/users/fierval/followers", "following_url": "https://api.github.com/users/fierval/following{/other_user}", "gists_url": "https://api.github.com/users/fierval/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/fierval", "id": 987574, "login": "fierval", "node_id": "MDQ6VXNlcjk4NzU3NA==", "organizations_url": "https://api.github.com/users/fierval/orgs", "received_events_url": "https://api.github.com/users/fierval/received_events", "repos_url": "https://api.github.com/users/fierval/repos", "site_admin": false, "starred_url": "https://api.github.com/users/fierval/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/fierval/subscriptions", "type": "User", "url": "https://api.github.com/users/fierval" }	[]	open	false	null	[]	null	[]	"2023-08-02T20:20:26"	"2023-08-02T20:20:26"	null	NONE	null	null	null	### Describe the bug When calling `load_dataset` in a streamlit application running within a docker container, get a failure with the error message: EmptyDatasetError: The directory at hf://datasets/fetch-rewards/inc-rings-2000@bea27cf60842b3641eae418f38864a2ec4cde684 doesn't contain any data files Traceback: File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 552, in _run_script exec(code, module.__dict__) File "/home/user/app/app.py", line 62, in <module> dashboard() File "/home/user/app/app.py", line 47, in dashboard feat_dict, path_gml = load_data(hf_repo, model_gml_dict[selected_model], hf_token) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 211, in wrapper return cached_func(args, kwargs) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 240, in __call__ return self._get_or_create_cached_value(args, kwargs) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 266, in _get_or_create_cached_value return self._handle_cache_miss(cache, value_key, func_args, func_kwargs) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 320, in _handle_cache_miss computed_value = self._info.func(func_args, **func_kwargs) File "/home/user/app/hf_interface.py", line 16, in load_data hf_dataset = load_dataset(repo_id, use_auth_token=hf_token) File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2109, in load_dataset builder_instance = load_dataset_builder( File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1795, in load_dataset_builder dataset_module = dataset_module_factory( File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1486, in dataset_module_factory raise e1 from None File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1476, in dataset_module_factory ).get_module() File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1032, in get_module else get_data_patterns(base_path, download_config=self.download_config) File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 458, in get_data_patterns raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None ### Steps to reproduce the bug ```python @st.cache_resource def load_data(repo_id: str, hf_token=None): """Load data from HuggingFace Hub """ hf_dataset = load_dataset(repo_id, use_auth_token=hf_token) hf_dataset = hf_dataset.map(lambda x: json.loads(x["ground_truth"]), remove_columns=["ground_truth"]) return hf_dataset ``` ### Expected behavior Expect to load. Note: works fine with datasets==2.13.1 ### Environment info datasets==2.14.2, Ubuntu bionic-based Docker container.	{ "+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/6113/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6113/timeline	null	null	false
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" }	[]	open	false	null	[]	null	[]	"2023-08-02T18:21:21"	"2023-08-02T18:21:21"	null	NONE	null	null	null	### 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	{ "+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	null	false
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" }	[]	open	false	null	[]	null	[]	"2023-08-02T09:17:29"	"2023-08-02T09:17:29"	null	NONE	null	null	null	### 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 ```	{ "+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	null	false
https://api.github.com/repos/huggingface/datasets/issues/6110	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6110/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6110/comments	https://api.github.com/repos/huggingface/datasets/issues/6110/events	https://github.com/huggingface/datasets/issues/6110	1,831,110,633	I_kwDODunzps5tJIfp	6,110	[BUG] Dataset initialized from in-memory data does not create cache.	{ "avatar_url": "https://avatars.githubusercontent.com/u/57797966?v=4", "events_url": "https://api.github.com/users/MattYoon/events{/privacy}", "followers_url": "https://api.github.com/users/MattYoon/followers", "following_url": "https://api.github.com/users/MattYoon/following{/other_user}", "gists_url": "https://api.github.com/users/MattYoon/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/MattYoon", "id": 57797966, "login": "MattYoon", "node_id": "MDQ6VXNlcjU3Nzk3OTY2", "organizations_url": "https://api.github.com/users/MattYoon/orgs", "received_events_url": "https://api.github.com/users/MattYoon/received_events", "repos_url": "https://api.github.com/users/MattYoon/repos", "site_admin": false, "starred_url": "https://api.github.com/users/MattYoon/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/MattYoon/subscriptions", "type": "User", "url": "https://api.github.com/users/MattYoon" }	[]	open	false	null	[]	null	[]	"2023-08-01T11:58:58"	"2023-08-01T12:04:57"	null	NONE	null	null	null	### Describe the bug `Dataset` initialized from in-memory data (dictionary in my case, haven't tested with other types) does not create cache when processed with the `map` method, unlike `Dataset` initialized by other methods such as `load_dataset`. ### Steps to reproduce the bug ```python # below code was run the second time so the map function can be loaded from cache if exists from datasets import load_dataset, Dataset dataset = load_dataset("tatsu-lab/alpaca")['train'] dataset = dataset.map(lambda x: {'input': x['input'] + 'hi'}) # some random map print(len(dataset.cache_files)) # 1 # copy the exact same data but initialize from a dictionary memory_dataset = Dataset.from_dict({ 'instruction': dataset['instruction'], 'input': dataset['input'], 'output': dataset['output'], 'text': dataset['text']}) memory_dataset = memory_dataset.map(lambda x: {'input': x['input'] + 'hi'}) # exact same map print(len(memory_dataset.cache_files)) # Map: 100%\|██████████\| 52002[/52002] # 0 ``` ### Expected behavior The `map` function should create cache regardless of the method the `Dataset` was created. ### Environment info - `datasets` version: 2.14.2 - Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - Huggingface_hub version: 0.14.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3	{ "+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/6110/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6110/timeline	null	null	false
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" }	[]	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" }	[ { "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" } ]	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 " ]	"2023-08-01T08:38:29"	"2023-08-02T07:12:07"	"2023-08-02T07:12:07"	NONE	null	null	null	### 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	{ "+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	false
https://api.github.com/repos/huggingface/datasets/issues/6108	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6108/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6108/comments	https://api.github.com/repos/huggingface/datasets/issues/6108/events	https://github.com/huggingface/datasets/issues/6108	1,830,347,187	I_kwDODunzps5tGOGz	6,108	Loading local datasets got strangely stuck	{ "avatar_url": "https://avatars.githubusercontent.com/u/48412571?v=4", "events_url": "https://api.github.com/users/LoveCatc/events{/privacy}", "followers_url": "https://api.github.com/users/LoveCatc/followers", "following_url": "https://api.github.com/users/LoveCatc/following{/other_user}", "gists_url": "https://api.github.com/users/LoveCatc/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/LoveCatc", "id": 48412571, "login": "LoveCatc", "node_id": "MDQ6VXNlcjQ4NDEyNTcx", "organizations_url": "https://api.github.com/users/LoveCatc/orgs", "received_events_url": "https://api.github.com/users/LoveCatc/received_events", "repos_url": "https://api.github.com/users/LoveCatc/repos", "site_admin": false, "starred_url": "https://api.github.com/users/LoveCatc/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/LoveCatc/subscriptions", "type": "User", "url": "https://api.github.com/users/LoveCatc" }	[]	open	false	null	[]	null	[ "Yesterday I waited for more than 12 hours to make sure it was really stuck instead of proceeding too slow.", "I've had similar weird issues with `load_dataset` as well. Not multiple files, but dataset is quite big, about 50G." ]	"2023-08-01T02:28:06"	"2023-08-03T12:03:30"	null	NONE	null	null	null	### Describe the bug I try to use `load_dataset()` to load several local `.jsonl` files as a dataset. Every line of these files is a json structure only containing one key `text` (yeah it is a dataset for NLP model). The code snippet is as: ```python ds = load_dataset("json", data_files=LIST_OF_FILE_PATHS, num_proc=16)['train'] ``` However, I found that the loading process can get stuck -- the progress bar `Generating train split` no more proceed. When I was trying to find the cause and solution, I found a really strange behavior. If I load the dataset in this way: ```python dlist = list() for _ in LIST_OF_FILE_PATHS: dlist.append(load_dataset("json", data_files=_)['train']) ds = concatenate_datasets(dlist) ``` I can actually successfully load all the files despite its slow speed. But if I load them in batch like above, things go wrong. I did try to use Control-C to trace the stuck point but the program cannot be terminated in this way when `num_proc` is set to `None`. The only thing I can do is use Control-Z to hang it up then kill it. If I use more than 2 cpus, a Control-C would simply cause the following error: ```bash ^C Process ForkPoolWorker-1: Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 314, in _bootstrap self.run() File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 108, in run self._target(self._args, self._kwargs) File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 114, in worker task = get() File "/usr/local/lib/python3.10/dist-packages/multiprocess/queues.py", line 368, in get res = self._reader.recv_bytes() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 224, in recv_bytes buf = self._recv_bytes(maxlength) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv chunk = read(handle, remaining) KeyboardInterrupt Generating train split: 92431 examples [01:23, 1104.25 examples/s] Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1373, in iflatmap_unordered yield queue.get(timeout=0.05) File "<string>", line 2, in get File "/usr/local/lib/python3.10/dist-packages/multiprocess/managers.py", line 818, in _callmethod kind, result = conn.recv() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 258, in recv buf = self._recv_bytes() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv chunk = read(handle, remaining) KeyboardInterrupt During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/mnt/data/liyongyuan/source/batch_load.py", line 11, in <module> a = load_dataset( File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2133, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, *prepare_split_kwargs) File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1842, in _prepare_split for job_id, done, content in iflatmap_unordered( File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 770, in get raise TimeoutError multiprocess.context.TimeoutError ``` I have validated the basic correctness of these `.jsonl` files. They are correctly formatted (or they cannot be loaded singly by `load_dataset`) though some of the json may contain too long text (more than 1e7 characters). I do not know if this could be the problem. And there should not be any bottleneck in system's resource. The whole dataset is ~300GB, and I am using a cloud server with plenty of storage and 1TB ram. Thanks for your efforts and patience! Any suggestion or help would be appreciated. ### Steps to reproduce the bug 1. use load_dataset() with `data_files = LIST_OF_FILES` ### Expected behavior All the files should be smoothly loaded. ### Environment info - Datasets: A private dataset. ~2500 `.jsonl` files. ~300GB in total. Each json structure only contains one key: `text`. Format checked. - `datasets` version: 2.14.2 - Platform: Linux-4.19.91-014.kangaroo.alios7.x86_64-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.15.1 - PyArrow version: 10.0.1.dev0+ga6eabc2b.d20230609 - Pandas version: 1.5.2	{ "+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/6108/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6108/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6107	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6107/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6107/comments	https://api.github.com/repos/huggingface/datasets/issues/6107/events	https://github.com/huggingface/datasets/pull/6107	1,829,625,320	PR_kwDODunzps5W0rLR	6,107	Fix deprecation of use_auth_token in file_utils	{ "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" }	[]	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) \| 0.007678 / 0.011353 (-0.003675) \| 0.004233 / 0.011008 (-0.006776) \| 0.095934 / 0.038508 (0.057426) \| 0.064201 / 0.023109 (0.041092) \| 0.345765 / 0.275898 (0.069867) \| 0.383089 / 0.323480 (0.059609) \| 0.004084 / 0.007986 (-0.003902) \| 0.003311 / 0.004328 (-0.001017) \| 0.072367 / 0.004250 (0.068117) \| 0.048252 / 0.037052 (0.011200) \| 0.338340 / 0.258489 (0.079851) \| 0.391627 / 0.293841 (0.097786) \| 0.045203 / 0.128546 (-0.083343) \| 0.013494 / 0.075646 (-0.062153) \| 0.314097 / 0.419271 (-0.105174) \| 0.058183 / 0.043533 (0.014650) \| 0.353946 / 0.255139 (0.098807) \| 0.385181 / 0.283200 (0.101981) \| 0.033111 / 0.141683 (-0.108572) \| 1.578489 / 1.452155 (0.126335) \| 1.631660 / 1.492716 (0.138944) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.202592 / 0.018006 (0.184586) \| 0.506450 / 0.000490 (0.505961) \| 0.004630 / 0.000200 (0.004430) \| 0.000105 / 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) \| 0.024761 / 0.037411 (-0.012651) \| 0.086295 / 0.014526 (0.071769) \| 0.094063 / 0.176557 (-0.082494) \| 0.154189 / 0.737135 (-0.582947) \| 0.096273 / 0.296338 (-0.200065) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.581731 / 0.215209 (0.366522) \| 5.552020 / 2.077655 (3.474365) \| 2.430800 / 1.504120 (0.926680) \| 2.130864 / 1.541195 (0.589669) \| 2.092802 / 1.468490 (0.624312) \| 0.833956 / 4.584777 (-3.750821) \| 4.840859 / 3.745712 (1.095147) \| 4.267812 / 5.269862 (-1.002050) \| 2.663245 / 4.565676 (-1.902432) \| 0.093195 / 0.424275 (-0.331080) \| 0.007942 / 0.007607 (0.000335) \| 0.651457 / 0.226044 (0.425413) \| 6.782986 / 2.268929 (4.514058) \| 3.103307 / 55.444624 (-52.341318) \| 2.373933 / 6.876477 (-4.502544) \| 2.571613 / 2.142072 (0.429540) \| 0.981389 / 4.805227 (-3.823839) \| 0.199019 / 6.500664 (-6.301645) \| 0.065828 / 0.075469 (-0.009641) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.429778 / 1.841788 (-0.412009) \| 20.967563 / 8.074308 (12.893255) \| 19.329723 / 10.191392 (9.138331) \| 0.222048 / 0.680424 (-0.458376) \| 0.033507 / 0.534201 (-0.500694) \| 0.436801 / 0.579283 (-0.142482) \| 0.530197 / 0.434364 (0.095833) \| 0.491532 / 0.540337 (-0.048805) \| 0.718216 / 1.386936 (-0.668720) \|\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) \| 0.007798 / 0.011353 (-0.003555) \| 0.004748 / 0.011008 (-0.006260) \| 0.070847 / 0.038508 (0.032339) \| 0.069338 / 0.023109 (0.046229) \| 0.400890 / 0.275898 (0.124992) \| 0.429482 / 0.323480 (0.106002) \| 0.006469 / 0.007986 (-0.001517) \| 0.003514 / 0.004328 (-0.000814) \| 0.069049 / 0.004250 (0.064798) \| 0.059800 / 0.037052 (0.022748) \| 0.415644 / 0.258489 (0.157155) \| 0.432562 / 0.293841 (0.138721) \| 0.043778 / 0.128546 (-0.084768) \| 0.015141 / 0.075646 (-0.060506) \| 0.081521 / 0.419271 (-0.337750) \| 0.054692 / 0.043533 (0.011160) \| 0.404497 / 0.255139 (0.149358) \| 0.419783 / 0.283200 (0.136583) \| 0.029588 / 0.141683 (-0.112094) \| 1.593506 / 1.452155 (0.141351) \| 1.615977 / 1.492716 (0.123261) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.270981 / 0.018006 (0.252975) \| 0.522074 / 0.000490 (0.521584) \| 0.026568 / 0.000200 (0.026368) \| 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) \| 0.031551 / 0.037411 (-0.005861) \| 0.086723 / 0.014526 (0.072197) \| 0.103315 / 0.176557 (-0.073242) \| 0.154692 / 0.737135 (-0.582443) \| 0.099472 / 0.296338 (-0.196866) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.570238 / 0.215209 (0.355029) \| 5.655963 / 2.077655 (3.578308) \| 2.662670 / 1.504120 (1.158550) \| 2.380903 / 1.541195 (0.839709) \| 2.409467 / 1.468490 (0.940977) \| 0.828055 / 4.584777 (-3.756722) \| 4.964698 / 3.745712 (1.218986) \| 4.299995 / 5.269862 (-0.969867) \| 2.824162 / 4.565676 (-1.741514) \| 0.095872 / 0.424275 (-0.328403) \| 0.007907 / 0.007607 (0.000300) \| 0.701595 / 0.226044 (0.475551) \| 7.131965 / 2.268929 (4.863036) \| 3.250554 / 55.444624 (-52.194070) \| 2.531916 / 6.876477 (-4.344561) \| 2.717908 / 2.142072 (0.575835) \| 1.014479 / 4.805227 (-3.790748) \| 0.223804 / 6.500664 (-6.276861) \| 0.071893 / 0.075469 (-0.003576) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.541702 / 1.841788 (-0.300086) \| 21.668219 / 8.074308 (13.593911) \| 18.916032 / 10.191392 (8.724640) \| 0.205915 / 0.680424 (-0.474508) \| 0.026356 / 0.534201 (-0.507845) \| 0.429122 / 0.579283 (-0.150161) \| 0.506110 / 0.434364 (0.071746) \| 0.510148 / 0.540337 (-0.030190) \| 0.724699 / 1.386936 (-0.662237) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4ca93ff86551b398c979862e7be7305725a240b \"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) \| 0.006884 / 0.011353 (-0.004469) \| 0.004492 / 0.011008 (-0.006516) \| 0.085439 / 0.038508 (0.046931) \| 0.083905 / 0.023109 (0.060796) \| 0.313604 / 0.275898 (0.037706) \| 0.354683 / 0.323480 (0.031203) \| 0.006535 / 0.007986 (-0.001451) \| 0.004318 / 0.004328 (-0.000011) \| 0.066129 / 0.004250 (0.061879) \| 0.057568 / 0.037052 (0.020516) \| 0.317162 / 0.258489 (0.058672) \| 0.372501 / 0.293841 (0.078660) \| 0.031059 / 0.128546 (-0.097488) \| 0.009013 / 0.075646 (-0.066634) \| 0.288794 / 0.419271 (-0.130478) \| 0.053326 / 0.043533 (0.009793) \| 0.314318 / 0.255139 (0.059179) \| 0.357505 / 0.283200 (0.074305) \| 0.027020 / 0.141683 (-0.114663) \| 1.530653 / 1.452155 (0.078498) \| 1.599782 / 1.492716 (0.107066) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278788 / 0.018006 (0.260782) \| 0.626822 / 0.000490 (0.626333) \| 0.003780 / 0.000200 (0.003580) \| 0.000086 / 0.000054 (0.000032) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031703 / 0.037411 (-0.005708) \| 0.085654 / 0.014526 (0.071128) \| 0.754858 / 0.176557 (0.578301) \| 0.212251 / 0.737135 (-0.524885) \| 0.171344 / 0.296338 (-0.124994) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.382291 / 0.215209 (0.167082) \| 3.825612 / 2.077655 (1.747958) \| 1.874553 / 1.504120 (0.370433) \| 1.712574 / 1.541195 (0.171379) \| 1.791479 / 1.468490 (0.322989) \| 0.481005 / 4.584777 (-4.103772) \| 3.530559 / 3.745712 (-0.215153) \| 3.395305 / 5.269862 (-1.874557) \| 2.133747 / 4.565676 (-2.431930) \| 0.056139 / 0.424275 (-0.368136) \| 0.007424 / 0.007607 (-0.000183) \| 0.458321 / 0.226044 (0.232277) \| 4.577665 / 2.268929 (2.308736) \| 2.380233 / 55.444624 (-53.064392) \| 2.004060 / 6.876477 (-4.872417) \| 2.290712 / 2.142072 (0.148639) \| 0.570157 / 4.805227 (-4.235070) \| 0.131670 / 6.500664 (-6.368994) \| 0.060684 / 0.075469 (-0.014785) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.294929 / 1.841788 (-0.546858) \| 21.386663 / 8.074308 (13.312355) \| 14.389440 / 10.191392 (4.198048) \| 0.171177 / 0.680424 (-0.509247) \| 0.018660 / 0.534201 (-0.515541) \| 0.394385 / 0.579283 (-0.184898) \| 0.424942 / 0.434364 (-0.009422) \| 0.463618 / 0.540337 (-0.076719) \| 0.651499 / 1.386936 (-0.735437) \|\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) \| 0.007079 / 0.011353 (-0.004274) \| 0.004615 / 0.011008 (-0.006393) \| 0.066300 / 0.038508 (0.027792) \| 0.092636 / 0.023109 (0.069527) \| 0.399080 / 0.275898 (0.123182) \| 0.429873 / 0.323480 (0.106393) \| 0.006689 / 0.007986 (-0.001297) \| 0.004358 / 0.004328 (0.000029) \| 0.067155 / 0.004250 (0.062905) \| 0.064040 / 0.037052 (0.026988) \| 0.399905 / 0.258489 (0.141416) \| 0.448237 / 0.293841 (0.154397) \| 0.031985 / 0.128546 (-0.096561) \| 0.009053 / 0.075646 (-0.066593) \| 0.071904 / 0.419271 (-0.347368) \| 0.048759 / 0.043533 (0.005227) \| 0.386797 / 0.255139 (0.131658) \| 0.411240 / 0.283200 (0.128040) \| 0.028568 / 0.141683 (-0.113115) \| 1.501037 / 1.452155 (0.048882) \| 1.594560 / 1.492716 (0.101844) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300756 / 0.018006 (0.282750) \| 0.631220 / 0.000490 (0.630730) \| 0.010163 / 0.000200 (0.009963) \| 0.000144 / 0.000054 (0.000089) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033716 / 0.037411 (-0.003695) \| 0.093562 / 0.014526 (0.079037) \| 0.106975 / 0.176557 (-0.069582) \| 0.161919 / 0.737135 (-0.575216) \| 0.113397 / 0.296338 (-0.182942) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.410392 / 0.215209 (0.195183) \| 4.094411 / 2.077655 (2.016756) \| 2.085868 / 1.504120 (0.581748) \| 1.959589 / 1.541195 (0.418394) \| 2.096683 / 1.468490 (0.628193) \| 0.494593 / 4.584777 (-4.090184) \| 3.854302 / 3.745712 (0.108590) \| 3.742303 / 5.269862 (-1.527558) \| 2.379983 / 4.565676 (-2.185693) \| 0.058640 / 0.424275 (-0.365635) \| 0.008092 / 0.007607 (0.000484) \| 0.486957 / 0.226044 (0.260912) \| 4.855784 / 2.268929 (2.586855) \| 2.654029 / 55.444624 (-52.790595) \| 2.237627 / 6.876477 (-4.638850) \| 2.536955 / 2.142072 (0.394882) \| 0.622398 / 4.805227 (-4.182829) \| 0.139212 / 6.500664 (-6.361452) \| 0.062805 / 0.075469 (-0.012664) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.374862 / 1.841788 (-0.466926) \| 22.797015 / 8.074308 (14.722707) \| 14.393995 / 10.191392 (4.202603) \| 0.196603 / 0.680424 (-0.483821) \| 0.018602 / 0.534201 (-0.515599) \| 0.394568 / 0.579283 (-0.184715) \| 0.408792 / 0.434364 (-0.025572) \| 0.486706 / 0.540337 (-0.053631) \| 0.652365 / 1.386936 (-0.734571) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5713299a88f527ea162a099c2bf2cbceada8fb86 \"CML watermark\")\n" ]	"2023-07-31T16:32:01"	"2023-08-03T10:13:32"	"2023-08-03T10:04:18"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6107.diff", "html_url": "https://github.com/huggingface/datasets/pull/6107", "merged_at": "2023-08-03T10:04:18Z", "patch_url": "https://github.com/huggingface/datasets/pull/6107.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6107" }	Fix issues with the deprecation of `use_auth_token` introduced by: - #5996 in functions: - `get_authentication_headers_for_url` - `request_etag` - `get_from_cache` Currently, `TypeError` is raised: https://github.com/huggingface/datasets-server/actions/runs/5711650666/job/15484685570?pr=1588 ``` FAILED tests/job_runners/config/test_parquet_and_info.py::test__is_too_big_external_files[None-None-False] - TypeError: get_authentication_headers_for_url() got an unexpected keyword argument 'use_auth_token' FAILED tests/job_runners/config/test_parquet_and_info.py::test_fill_builder_info[None-False] - libcommon.exceptions.FileSystemError: Could not read the parquet files: get_authentication_headers_for_url() got an unexpected keyword argument 'use_auth_token' ``` Related to: - #6094	{ "+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/6107/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6107/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6106	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6106/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6106/comments	https://api.github.com/repos/huggingface/datasets/issues/6106/events	https://github.com/huggingface/datasets/issues/6106	1,829,131,223	I_kwDODunzps5tBlPX	6,106	load local json_file as dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/39040787?v=4", "events_url": "https://api.github.com/users/CiaoHe/events{/privacy}", "followers_url": "https://api.github.com/users/CiaoHe/followers", "following_url": "https://api.github.com/users/CiaoHe/following{/other_user}", "gists_url": "https://api.github.com/users/CiaoHe/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/CiaoHe", "id": 39040787, "login": "CiaoHe", "node_id": "MDQ6VXNlcjM5MDQwNzg3", "organizations_url": "https://api.github.com/users/CiaoHe/orgs", "received_events_url": "https://api.github.com/users/CiaoHe/received_events", "repos_url": "https://api.github.com/users/CiaoHe/repos", "site_admin": false, "starred_url": "https://api.github.com/users/CiaoHe/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/CiaoHe/subscriptions", "type": "User", "url": "https://api.github.com/users/CiaoHe" }	[]	open	false	null	[]	null	[]	"2023-07-31T12:53:49"	"2023-07-31T12:53:49"	null	NONE	null	null	null	### Describe the bug I tried to load local json file as dataset but failed to parsing json file because some columns are 'float' type. ### Steps to reproduce the bug 1. load json file with certain columns are 'float' type. For example `data = load_data("json", data_files=JSON_PATH)` 2. Then, the error will be triggered like `ArrowInvalid: Could not convert '-0.2253' with type str: tried to convert to double ### Expected behavior Should allow some columns are 'float' type, at least it should convert those columns to str type. I tried to avoid the error by naively convert the float item to str: ```python # if col type is not str, we need to convert it to str mapping = {} for col in keys: if isinstance(dataset[0][col], str): mapping[col] = [row.get(col) for row in dataset] else: mapping[col] = [str(row.get(col)) for row in dataset] ``` ### Environment info - `datasets` version: 2.14.2 - Platform: Linux-5.4.0-52-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.0 - Pandas version: 2.0.1	{ "+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/6106/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6106/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6105	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6105/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6105/comments	https://api.github.com/repos/huggingface/datasets/issues/6105/events	https://github.com/huggingface/datasets/pull/6105	1,829,008,430	PR_kwDODunzps5WyiJD	6,105	Fix error when loading from GCP bucket	{ "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" }	[]	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) \| 0.006706 / 0.011353 (-0.004647) \| 0.004016 / 0.011008 (-0.006992) \| 0.083696 / 0.038508 (0.045188) \| 0.074340 / 0.023109 (0.051230) \| 0.327338 / 0.275898 (0.051440) \| 0.366663 / 0.323480 (0.043183) \| 0.004052 / 0.007986 (-0.003934) \| 0.003423 / 0.004328 (-0.000906) \| 0.064576 / 0.004250 (0.060326) \| 0.055037 / 0.037052 (0.017985) \| 0.325089 / 0.258489 (0.066600) \| 0.379986 / 0.293841 (0.086145) \| 0.031614 / 0.128546 (-0.096932) \| 0.008553 / 0.075646 (-0.067094) \| 0.287430 / 0.419271 (-0.131841) \| 0.053032 / 0.043533 (0.009499) \| 0.318990 / 0.255139 (0.063851) \| 0.364426 / 0.283200 (0.081226) \| 0.024926 / 0.141683 (-0.116757) \| 1.461835 / 1.452155 (0.009680) \| 1.557172 / 1.492716 (0.064456) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212430 / 0.018006 (0.194424) \| 0.512891 / 0.000490 (0.512402) \| 0.004772 / 0.000200 (0.004572) \| 0.000132 / 0.000054 (0.000078) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027873 / 0.037411 (-0.009538) \| 0.085598 / 0.014526 (0.071072) \| 0.097330 / 0.176557 (-0.079226) \| 0.152235 / 0.737135 (-0.584900) \| 0.097787 / 0.296338 (-0.198552) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.384645 / 0.215209 (0.169436) \| 3.841161 / 2.077655 (1.763506) \| 1.863696 / 1.504120 (0.359577) \| 1.685082 / 1.541195 (0.143887) \| 1.772904 / 1.468490 (0.304414) \| 0.480177 / 4.584777 (-4.104599) \| 3.601537 / 3.745712 (-0.144175) \| 3.273647 / 5.269862 (-1.996214) \| 2.014415 / 4.565676 (-2.551261) \| 0.056668 / 0.424275 (-0.367607) \| 0.007257 / 0.007607 (-0.000350) \| 0.458194 / 0.226044 (0.232150) \| 4.577311 / 2.268929 (2.308382) \| 2.333983 / 55.444624 (-53.110641) \| 1.964508 / 6.876477 (-4.911969) \| 2.193379 / 2.142072 (0.051307) \| 0.577557 / 4.805227 (-4.227670) \| 0.133899 / 6.500664 (-6.366765) \| 0.060804 / 0.075469 (-0.014665) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.249490 / 1.841788 (-0.592298) \| 19.791875 / 8.074308 (11.717567) \| 14.418728 / 10.191392 (4.227336) \| 0.167788 / 0.680424 (-0.512636) \| 0.018993 / 0.534201 (-0.515208) \| 0.396141 / 0.579283 (-0.183142) \| 0.412427 / 0.434364 (-0.021937) \| 0.456718 / 0.540337 (-0.083619) \| 0.641383 / 1.386936 (-0.745553) \|\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) \| 0.006546 / 0.011353 (-0.004807) \| 0.004059 / 0.011008 (-0.006949) \| 0.064523 / 0.038508 (0.026015) \| 0.074988 / 0.023109 (0.051878) \| 0.388932 / 0.275898 (0.113034) \| 0.424496 / 0.323480 (0.101016) \| 0.005226 / 0.007986 (-0.002760) \| 0.003409 / 0.004328 (-0.000920) \| 0.064284 / 0.004250 (0.060034) \| 0.056829 / 0.037052 (0.019777) \| 0.386457 / 0.258489 (0.127968) \| 0.428063 / 0.293841 (0.134222) \| 0.031411 / 0.128546 (-0.097136) \| 0.008577 / 0.075646 (-0.067070) \| 0.070357 / 0.419271 (-0.348915) \| 0.048920 / 0.043533 (0.005388) \| 0.385197 / 0.255139 (0.130058) \| 0.407167 / 0.283200 (0.123967) \| 0.024469 / 0.141683 (-0.117214) \| 1.482733 / 1.452155 (0.030578) \| 1.539027 / 1.492716 (0.046311) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227532 / 0.018006 (0.209526) \| 0.448792 / 0.000490 (0.448302) \| 0.004139 / 0.000200 (0.003939) \| 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) \| 0.031004 / 0.037411 (-0.006408) \| 0.088163 / 0.014526 (0.073637) \| 0.101452 / 0.176557 (-0.075105) \| 0.152907 / 0.737135 (-0.584229) \| 0.102325 / 0.296338 (-0.194014) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418092 / 0.215209 (0.202883) \| 4.162277 / 2.077655 (2.084623) \| 2.232987 / 1.504120 (0.728867) \| 2.143583 / 1.541195 (0.602388) \| 2.246142 / 1.468490 (0.777652) \| 0.490181 / 4.584777 (-4.094596) \| 3.631514 / 3.745712 (-0.114198) \| 3.315025 / 5.269862 (-1.954837) \| 2.101853 / 4.565676 (-2.463823) \| 0.057905 / 0.424275 (-0.366370) \| 0.007686 / 0.007607 (0.000079) \| 0.489965 / 0.226044 (0.263921) \| 4.894375 / 2.268929 (2.625447) \| 2.655459 / 55.444624 (-52.789165) \| 2.262211 / 6.876477 (-4.614266) \| 2.505335 / 2.142072 (0.363263) \| 0.591329 / 4.805227 (-4.213898) \| 0.133554 / 6.500664 (-6.367110) \| 0.061922 / 0.075469 (-0.013547) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.347483 / 1.841788 (-0.494304) \| 20.027011 / 8.074308 (11.952703) \| 14.430737 / 10.191392 (4.239345) \| 0.165767 / 0.680424 (-0.514657) \| 0.018460 / 0.534201 (-0.515741) \| 0.393790 / 0.579283 (-0.185494) \| 0.407213 / 0.434364 (-0.027151) \| 0.474459 / 0.540337 (-0.065879) \| 0.635054 / 1.386936 (-0.751882) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7f575111481e2e2f4d4fc9180771797f69ebcc44 \"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) \| 0.007652 / 0.011353 (-0.003701) \| 0.004581 / 0.011008 (-0.006427) \| 0.101629 / 0.038508 (0.063121) \| 0.090233 / 0.023109 (0.067124) \| 0.392789 / 0.275898 (0.116891) \| 0.432163 / 0.323480 (0.108683) \| 0.004694 / 0.007986 (-0.003292) \| 0.003927 / 0.004328 (-0.000401) \| 0.076533 / 0.004250 (0.072282) \| 0.064442 / 0.037052 (0.027390) \| 0.397539 / 0.258489 (0.139050) \| 0.441323 / 0.293841 (0.147482) \| 0.036278 / 0.128546 (-0.092268) \| 0.009810 / 0.075646 (-0.065836) \| 0.343537 / 0.419271 (-0.075734) \| 0.060273 / 0.043533 (0.016740) \| 0.395023 / 0.255139 (0.139884) \| 0.427210 / 0.283200 (0.144011) \| 0.031717 / 0.141683 (-0.109966) \| 1.771221 / 1.452155 (0.319066) \| 1.896336 / 1.492716 (0.403620) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235081 / 0.018006 (0.217075) \| 0.512781 / 0.000490 (0.512292) \| 0.004920 / 0.000200 (0.004721) \| 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) \| 0.033525 / 0.037411 (-0.003887) \| 0.104416 / 0.014526 (0.089890) \| 0.115695 / 0.176557 (-0.060861) \| 0.182216 / 0.737135 (-0.554919) \| 0.116259 / 0.296338 (-0.180079) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.454817 / 0.215209 (0.239608) \| 4.527753 / 2.077655 (2.450098) \| 2.222273 / 1.504120 (0.718153) \| 2.038448 / 1.541195 (0.497253) \| 2.179444 / 1.468490 (0.710953) \| 0.573665 / 4.584777 (-4.011112) \| 4.504943 / 3.745712 (0.759231) \| 3.848435 / 5.269862 (-1.421427) \| 2.455185 / 4.565676 (-2.110491) \| 0.067985 / 0.424275 (-0.356290) \| 0.008719 / 0.007607 (0.001112) \| 0.552405 / 0.226044 (0.326360) \| 5.515251 / 2.268929 (3.246322) \| 2.851557 / 55.444624 (-52.593067) \| 2.463070 / 6.876477 (-4.413407) \| 2.761596 / 2.142072 (0.619524) \| 0.688561 / 4.805227 (-4.116667) \| 0.159946 / 6.500664 (-6.340718) \| 0.075435 / 0.075469 (-0.000034) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.505178 / 1.841788 (-0.336610) \| 23.555236 / 8.074308 (15.480928) \| 17.272759 / 10.191392 (7.081367) \| 0.206495 / 0.680424 (-0.473928) \| 0.021869 / 0.534201 (-0.512332) \| 0.469271 / 0.579283 (-0.110012) \| 0.469200 / 0.434364 (0.034837) \| 0.542437 / 0.540337 (0.002100) \| 0.792864 / 1.386936 (-0.594072) \|\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) \| 0.008151 / 0.011353 (-0.003202) \| 0.004992 / 0.011008 (-0.006016) \| 0.079545 / 0.038508 (0.041037) \| 0.100234 / 0.023109 (0.077125) \| 0.492791 / 0.275898 (0.216893) \| 0.511315 / 0.323480 (0.187835) \| 0.006878 / 0.007986 (-0.001108) \| 0.003807 / 0.004328 (-0.000522) \| 0.080876 / 0.004250 (0.076625) \| 0.076734 / 0.037052 (0.039681) \| 0.518247 / 0.258489 (0.259758) \| 0.524202 / 0.293841 (0.230361) \| 0.039896 / 0.128546 (-0.088650) \| 0.016581 / 0.075646 (-0.059065) \| 0.101228 / 0.419271 (-0.318043) \| 0.061990 / 0.043533 (0.018457) \| 0.490611 / 0.255139 (0.235472) \| 0.514930 / 0.283200 (0.231730) \| 0.028680 / 0.141683 (-0.113002) \| 1.966215 / 1.452155 (0.514061) \| 2.047757 / 1.492716 (0.555040) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286807 / 0.018006 (0.268801) \| 0.506448 / 0.000490 (0.505959) \| 0.005867 / 0.000200 (0.005667) \| 0.000110 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037141 / 0.037411 (-0.000270) \| 0.113232 / 0.014526 (0.098706) \| 0.121201 / 0.176557 (-0.055356) \| 0.185472 / 0.737135 (-0.551663) \| 0.122896 / 0.296338 (-0.173442) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.514491 / 0.215209 (0.299282) \| 4.942457 / 2.077655 (2.864802) \| 2.533519 / 1.504120 (1.029399) \| 2.371011 / 1.541195 (0.829817) \| 2.495604 / 1.468490 (1.027114) \| 0.576224 / 4.584777 (-4.008553) \| 4.368584 / 3.745712 (0.622872) \| 3.885598 / 5.269862 (-1.384263) \| 2.443596 / 4.565676 (-2.122080) \| 0.068905 / 0.424275 (-0.355371) \| 0.009171 / 0.007607 (0.001564) \| 0.584977 / 0.226044 (0.358932) \| 5.835220 / 2.268929 (3.566291) \| 3.189037 / 55.444624 (-52.255588) \| 2.753228 / 6.876477 (-4.123249) \| 3.009062 / 2.142072 (0.866990) \| 0.690179 / 4.805227 (-4.115048) \| 0.157981 / 6.500664 (-6.342683) \| 0.074518 / 0.075469 (-0.000951) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.599907 / 1.841788 (-0.241880) \| 23.853903 / 8.074308 (15.779595) \| 17.419796 / 10.191392 (7.228404) \| 0.204974 / 0.680424 (-0.475450) \| 0.022014 / 0.534201 (-0.512187) \| 0.473379 / 0.579283 (-0.105905) \| 0.461346 / 0.434364 (0.026982) \| 0.564881 / 0.540337 (0.024543) \| 0.752933 / 1.386936 (-0.634003) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f49c9ca993fa600fae0e327636d52657328e7ffb \"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) \| 0.006547 / 0.011353 (-0.004805) \| 0.004020 / 0.011008 (-0.006988) \| 0.086828 / 0.038508 (0.048320) \| 0.072924 / 0.023109 (0.049815) \| 0.312847 / 0.275898 (0.036949) \| 0.344605 / 0.323480 (0.021125) \| 0.004117 / 0.007986 (-0.003868) \| 0.004365 / 0.004328 (0.000037) \| 0.066755 / 0.004250 (0.062505) \| 0.053248 / 0.037052 (0.016195) \| 0.315744 / 0.258489 (0.057255) \| 0.362426 / 0.293841 (0.068585) \| 0.030732 / 0.128546 (-0.097814) \| 0.008516 / 0.075646 (-0.067130) \| 0.289927 / 0.419271 (-0.129345) \| 0.052115 / 0.043533 (0.008582) \| 0.308026 / 0.255139 (0.052887) \| 0.343115 / 0.283200 (0.059915) \| 0.024131 / 0.141683 (-0.117551) \| 1.464290 / 1.452155 (0.012135) \| 1.559359 / 1.492716 (0.066642) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216744 / 0.018006 (0.198738) \| 0.473156 / 0.000490 (0.472666) \| 0.004176 / 0.000200 (0.003977) \| 0.000093 / 0.000054 (0.000039) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028500 / 0.037411 (-0.008911) \| 0.083892 / 0.014526 (0.069366) \| 0.131851 / 0.176557 (-0.044705) \| 0.162202 / 0.737135 (-0.574933) \| 0.127989 / 0.296338 (-0.168349) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.404555 / 0.215209 (0.189346) \| 4.035989 / 2.077655 (1.958334) \| 2.025174 / 1.504120 (0.521054) \| 1.835785 / 1.541195 (0.294590) \| 1.909819 / 1.468490 (0.441329) \| 0.475352 / 4.584777 (-4.109425) \| 3.548055 / 3.745712 (-0.197657) \| 3.234782 / 5.269862 (-2.035080) \| 2.010305 / 4.565676 (-2.555371) \| 0.056507 / 0.424275 (-0.367768) \| 0.007259 / 0.007607 (-0.000348) \| 0.482021 / 0.226044 (0.255977) \| 4.818559 / 2.268929 (2.549631) \| 2.528765 / 55.444624 (-52.915860) \| 2.159804 / 6.876477 (-4.716673) \| 2.380640 / 2.142072 (0.238567) \| 0.585005 / 4.805227 (-4.220222) \| 0.133811 / 6.500664 (-6.366853) \| 0.060686 / 0.075469 (-0.014783) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.260902 / 1.841788 (-0.580886) \| 19.500215 / 8.074308 (11.425907) \| 14.164698 / 10.191392 (3.973306) \| 0.172492 / 0.680424 (-0.507932) \| 0.018221 / 0.534201 (-0.515980) \| 0.392609 / 0.579283 (-0.186674) \| 0.423265 / 0.434364 (-0.011099) \| 0.454705 / 0.540337 (-0.085633) \| 0.639856 / 1.386936 (-0.747080) \|\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) \| 0.006656 / 0.011353 (-0.004697) \| 0.003903 / 0.011008 (-0.007106) \| 0.063780 / 0.038508 (0.025272) \| 0.076848 / 0.023109 (0.053739) \| 0.379429 / 0.275898 (0.103531) \| 0.442554 / 0.323480 (0.119074) \| 0.005327 / 0.007986 (-0.002658) \| 0.003318 / 0.004328 (-0.001010) \| 0.064307 / 0.004250 (0.060056) \| 0.057183 / 0.037052 (0.020131) \| 0.398163 / 0.258489 (0.139674) \| 0.448532 / 0.293841 (0.154691) \| 0.031322 / 0.128546 (-0.097224) \| 0.008462 / 0.075646 (-0.067184) \| 0.070354 / 0.419271 (-0.348917) \| 0.048420 / 0.043533 (0.004887) \| 0.368304 / 0.255139 (0.113165) \| 0.428786 / 0.283200 (0.145587) \| 0.023921 / 0.141683 (-0.117762) \| 1.499281 / 1.452155 (0.047126) \| 1.554448 / 1.492716 (0.061731) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.238830 / 0.018006 (0.220824) \| 0.464196 / 0.000490 (0.463706) \| 0.004812 / 0.000200 (0.004613) \| 0.000098 / 0.000054 (0.000043) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031642 / 0.037411 (-0.005770) \| 0.089205 / 0.014526 (0.074679) \| 0.101577 / 0.176557 (-0.074980) \| 0.154993 / 0.737135 (-0.582142) \| 0.102935 / 0.296338 (-0.193403) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415218 / 0.215209 (0.200009) \| 4.137711 / 2.077655 (2.060056) \| 2.128757 / 1.504120 (0.624637) \| 1.961086 / 1.541195 (0.419891) \| 2.047552 / 1.468490 (0.579061) \| 0.486953 / 4.584777 (-4.097824) \| 3.587851 / 3.745712 (-0.157861) \| 3.280771 / 5.269862 (-1.989090) \| 2.016980 / 4.565676 (-2.548697) \| 0.057284 / 0.424275 (-0.366991) \| 0.007705 / 0.007607 (0.000097) \| 0.492242 / 0.226044 (0.266197) \| 4.923213 / 2.268929 (2.654285) \| 2.672528 / 55.444624 (-52.772097) \| 2.292862 / 6.876477 (-4.583614) \| 2.517410 / 2.142072 (0.375337) \| 0.614798 / 4.805227 (-4.190429) \| 0.149642 / 6.500664 (-6.351023) \| 0.062898 / 0.075469 (-0.012571) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.323266 / 1.841788 (-0.518522) \| 19.891504 / 8.074308 (11.817196) \| 14.115069 / 10.191392 (3.923677) \| 0.169859 / 0.680424 (-0.510564) \| 0.018538 / 0.534201 (-0.515663) \| 0.398456 / 0.579283 (-0.180827) \| 0.410111 / 0.434364 (-0.024253) \| 0.483198 / 0.540337 (-0.057139) \| 0.639283 / 1.386936 (-0.747653) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01e2194f2aab6aa98686a2069ee5201b69a53c14 \"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) \| 0.007731 / 0.011353 (-0.003622) \| 0.004064 / 0.011008 (-0.006944) \| 0.095261 / 0.038508 (0.056753) \| 0.081594 / 0.023109 (0.058485) \| 0.390413 / 0.275898 (0.114515) \| 0.415542 / 0.323480 (0.092063) \| 0.006031 / 0.007986 (-0.001954) \| 0.003817 / 0.004328 (-0.000512) \| 0.066381 / 0.004250 (0.062131) \| 0.058262 / 0.037052 (0.021210) \| 0.383626 / 0.258489 (0.125137) \| 0.443237 / 0.293841 (0.149396) \| 0.034358 / 0.128546 (-0.094188) \| 0.010002 / 0.075646 (-0.065644) \| 0.317472 / 0.419271 (-0.101800) \| 0.057428 / 0.043533 (0.013895) \| 0.393929 / 0.255139 (0.138790) \| 0.444572 / 0.283200 (0.161373) \| 0.026295 / 0.141683 (-0.115388) \| 1.603639 / 1.452155 (0.151484) \| 1.707750 / 1.492716 (0.215034) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222171 / 0.018006 (0.204165) \| 0.491762 / 0.000490 (0.491272) \| 0.003389 / 0.000200 (0.003189) \| 0.000090 / 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) \| 0.029420 / 0.037411 (-0.007991) \| 0.086201 / 0.014526 (0.071676) \| 0.100150 / 0.176557 (-0.076406) \| 0.162338 / 0.737135 (-0.574797) \| 0.099349 / 0.296338 (-0.196989) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.445976 / 0.215209 (0.230767) \| 4.460197 / 2.077655 (2.382542) \| 2.211767 / 1.504120 (0.707647) \| 1.988740 / 1.541195 (0.447545) \| 2.052289 / 1.468490 (0.583799) \| 0.570321 / 4.584777 (-4.014456) \| 4.148777 / 3.745712 (0.403065) \| 3.750977 / 5.269862 (-1.518885) \| 2.309443 / 4.565676 (-2.256234) \| 0.064552 / 0.424275 (-0.359724) \| 0.008167 / 0.007607 (0.000560) \| 0.523283 / 0.226044 (0.297238) \| 5.349347 / 2.268929 (3.080419) \| 2.710292 / 55.444624 (-52.734332) \| 2.344252 / 6.876477 (-4.532225) \| 2.549903 / 2.142072 (0.407831) \| 0.665942 / 4.805227 (-4.139285) \| 0.154108 / 6.500664 (-6.346556) \| 0.070181 / 0.075469 (-0.005289) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.455733 / 1.841788 (-0.386054) \| 21.846958 / 8.074308 (13.772650) \| 15.133865 / 10.191392 (4.942473) \| 0.199009 / 0.680424 (-0.481415) \| 0.021299 / 0.534201 (-0.512902) \| 0.421555 / 0.579283 (-0.157729) \| 0.437639 / 0.434364 (0.003275) \| 0.498568 / 0.540337 (-0.041769) \| 0.719649 / 1.386936 (-0.667287) \|\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) \| 0.007858 / 0.011353 (-0.003495) \| 0.004629 / 0.011008 (-0.006380) \| 0.075701 / 0.038508 (0.037193) \| 0.084425 / 0.023109 (0.061316) \| 0.436650 / 0.275898 (0.160752) \| 0.466046 / 0.323480 (0.142566) \| 0.006042 / 0.007986 (-0.001944) \| 0.003834 / 0.004328 (-0.000495) \| 0.074729 / 0.004250 (0.070478) \| 0.065983 / 0.037052 (0.028931) \| 0.447239 / 0.258489 (0.188750) \| 0.466728 / 0.293841 (0.172887) \| 0.035814 / 0.128546 (-0.092733) \| 0.009919 / 0.075646 (-0.065727) \| 0.081151 / 0.419271 (-0.338120) \| 0.057256 / 0.043533 (0.013723) \| 0.435609 / 0.255139 (0.180470) \| 0.448901 / 0.283200 (0.165701) \| 0.026325 / 0.141683 (-0.115357) \| 1.745658 / 1.452155 (0.293503) \| 1.804137 / 1.492716 (0.311421) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302551 / 0.018006 (0.284544) \| 0.498438 / 0.000490 (0.497948) \| 0.038562 / 0.000200 (0.038362) \| 0.000411 / 0.000054 (0.000356) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035573 / 0.037411 (-0.001839) \| 0.104957 / 0.014526 (0.090431) \| 0.117208 / 0.176557 (-0.059349) \| 0.178935 / 0.737135 (-0.558200) \| 0.124577 / 0.296338 (-0.171761) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.467076 / 0.215209 (0.251867) \| 4.698852 / 2.077655 (2.621197) \| 2.453389 / 1.504120 (0.949269) \| 2.257378 / 1.541195 (0.716183) \| 2.338615 / 1.468490 (0.870125) \| 0.542379 / 4.584777 (-4.042398) \| 4.066895 / 3.745712 (0.321183) \| 3.689540 / 5.269862 (-1.580321) \| 2.268997 / 4.565676 (-2.296679) \| 0.064754 / 0.424275 (-0.359521) \| 0.008866 / 0.007607 (0.001259) \| 0.546732 / 0.226044 (0.320687) \| 5.487765 / 2.268929 (3.218836) \| 2.974126 / 55.444624 (-52.470498) \| 2.585492 / 6.876477 (-4.290985) \| 2.754417 / 2.142072 (0.612345) \| 0.652045 / 4.805227 (-4.153183) \| 0.145597 / 6.500664 (-6.355067) \| 0.065415 / 0.075469 (-0.010054) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.553970 / 1.841788 (-0.287818) \| 22.300954 / 8.074308 (14.226646) \| 15.640990 / 10.191392 (5.449598) \| 0.170903 / 0.680424 (-0.509521) \| 0.021750 / 0.534201 (-0.512451) \| 0.455316 / 0.579283 (-0.123967) \| 0.455051 / 0.434364 (0.020687) \| 0.536174 / 0.540337 (-0.004164) \| 0.735930 / 1.386936 (-0.651006) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f68139846c26b43631bd235114854f4bf6cb9954 \"CML watermark\")\n" ]	"2023-07-31T11:44:46"	"2023-08-01T10:48:52"	"2023-08-01T10:38:54"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6105.diff", "html_url": "https://github.com/huggingface/datasets/pull/6105", "merged_at": "2023-08-01T10:38:54Z", "patch_url": "https://github.com/huggingface/datasets/pull/6105.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6105" }	Fix `resolve_pattern` for filesystems with tuple protocol. Fix #6100. The bug code lines were introduced by: - #6028	{ "+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/6105/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6105/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6104	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6104/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6104/comments	https://api.github.com/repos/huggingface/datasets/issues/6104/events	https://github.com/huggingface/datasets/issues/6104	1,828,959,107	I_kwDODunzps5tA7OD	6,104	HF Datasets data access is extremely slow even when in memory	{ "avatar_url": "https://avatars.githubusercontent.com/u/36224762?v=4", "events_url": "https://api.github.com/users/NightMachinery/events{/privacy}", "followers_url": "https://api.github.com/users/NightMachinery/followers", "following_url": "https://api.github.com/users/NightMachinery/following{/other_user}", "gists_url": "https://api.github.com/users/NightMachinery/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/NightMachinery", "id": 36224762, "login": "NightMachinery", "node_id": "MDQ6VXNlcjM2MjI0NzYy", "organizations_url": "https://api.github.com/users/NightMachinery/orgs", "received_events_url": "https://api.github.com/users/NightMachinery/received_events", "repos_url": "https://api.github.com/users/NightMachinery/repos", "site_admin": false, "starred_url": "https://api.github.com/users/NightMachinery/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/NightMachinery/subscriptions", "type": "User", "url": "https://api.github.com/users/NightMachinery" }	[]	open	false	null	[]	null	[ "Possibly related:\r\n- https://github.com/pytorch/pytorch/issues/22462" ]	"2023-07-31T11:12:19"	"2023-08-01T11:22:43"	null	CONTRIBUTOR	null	null	null	### Describe the bug Doing a simple `some_dataset[:10]` can take more than a minute. Profiling it: <img width="1280" alt="image" src="https://github.com/huggingface/datasets/assets/36224762/e641fb95-ff02-4072-9016-5416a65f75ab"> `some_dataset` is completely in memory with no disk cache. This is proving fatal to my usage of HF Datasets. Is there a way I can forgo the arrow format and store the dataset as PyTorch tensors so that `_tensorize` is not needed? And is `_consolidate` supposed to take this long? It's faster to produce the dataset from scratch than to access it from HF Datasets! ### Steps to reproduce the bug I have uploaded the dataset that causes this problem [here](https://huggingface.co/datasets/NightMachinery/hf_datasets_bug1). ```python #!/usr/bin/env python3 import sys import time import torch from datasets import load_dataset def main(dataset_name): # Start the timer start_time = time.time() # Load the dataset from Hugging Face Hub dataset = load_dataset(dataset_name) # Set the dataset format as torch dataset.set_format(type="torch") # Perform an identity map dataset = dataset.map(lambda example: example, batched=True, batch_size=20) # End the timer end_time = time.time() # Print the time taken print(f"Time taken: {end_time - start_time:.2f} seconds") if __name__ == "__main__": dataset_name = "NightMachinery/hf_datasets_bug1" print(f"dataset_name: {dataset_name}") main(dataset_name) ``` ### Expected behavior _ ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	{ "+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/6104/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6104/timeline	null	null	false

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