Split (2)

train · 2.4k rows

url string	repository_url string	labels_url string	comments_url string	events_url string	html_url string	id int64	node_id string	number int64	title string	user dict	labels list	state string	locked bool	assignee dict	assignees list	milestone dict	comments list	created_at timestamp[ns, tz=UTC]	updated_at timestamp[ns, tz=UTC]	closed_at timestamp[ns, tz=UTC]	author_association string	type float64	active_lock_reason float64	sub_issues_summary dict	body string	closed_by dict	reactions dict	timeline_url string	performed_via_github_app float64	state_reason string	draft float64	pull_request dict
https://api.github.com/repos/huggingface/datasets/issues/5143	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5143/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5143/comments	https://api.github.com/repos/huggingface/datasets/issues/5143/events	https://github.com/huggingface/datasets/issues/5143	1,416,837,186	I_kwDODunzps5UczhC	5,143	DownloadManager Git LFS support	{ "avatar_url": "https://avatars.githubusercontent.com/u/62820084?v=4", "events_url": "https://api.github.com/users/Muennighoff/events{/privacy}", "followers_url": "https://api.github.com/users/Muennighoff/followers", "following_url": "https://api.github.com/users/Muennighoff/following{/other_user}", "gists_url": "https://api.github.com/users/Muennighoff/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Muennighoff", "id": 62820084, "login": "Muennighoff", "node_id": "MDQ6VXNlcjYyODIwMDg0", "organizations_url": "https://api.github.com/users/Muennighoff/orgs", "received_events_url": "https://api.github.com/users/Muennighoff/received_events", "repos_url": "https://api.github.com/users/Muennighoff/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Muennighoff/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Muennighoff/subscriptions", "type": "User", "url": "https://api.github.com/users/Muennighoff", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	closed	false	null	[]	null	[ "Hey ! Actually it works, just pass the right URL ;)\r\nThe URL must be the one with “/resolve/”\r\n\r\ne.g. https://huggingface.co/datasets/imagenet-1k/resolve/main/data/test_images.tar.gz\r\n\r\nYou can even pass a relative path to the dl_manager instead, like `dl_manager.download(\"data/test_images.tar.gz\")`", "Amazing it works, thanks!" ]	2022-10-20T15:29:29Z	2022-10-20T17:17:10Z	2022-10-20T17:17:10Z	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request Maybe I'm mistaken but the `DownloadManager` does not support extracting git lfs files out of the box right? Using `dl_manager.download()` or `dl_manager.download_and_extract()` still returns lfs files afaict. Is there a good way to write a dataset loading script for a repo with lfs files? ### Motivation / ### Your contribution /	{ "avatar_url": "https://avatars.githubusercontent.com/u/62820084?v=4", "events_url": "https://api.github.com/users/Muennighoff/events{/privacy}", "followers_url": "https://api.github.com/users/Muennighoff/followers", "following_url": "https://api.github.com/users/Muennighoff/following{/other_user}", "gists_url": "https://api.github.com/users/Muennighoff/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Muennighoff", "id": 62820084, "login": "Muennighoff", "node_id": "MDQ6VXNlcjYyODIwMDg0", "organizations_url": "https://api.github.com/users/Muennighoff/orgs", "received_events_url": "https://api.github.com/users/Muennighoff/received_events", "repos_url": "https://api.github.com/users/Muennighoff/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Muennighoff/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Muennighoff/subscriptions", "type": "User", "url": "https://api.github.com/users/Muennighoff", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5143/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5143/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/4597	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4597/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4597/comments	https://api.github.com/repos/huggingface/datasets/issues/4597/events	https://github.com/huggingface/datasets/issues/4597	1,288,672,007	I_kwDODunzps5Mz5MH	4,597	Streaming issue for financial_phrasebank	{ "avatar_url": "https://avatars.githubusercontent.com/u/26859204?v=4", "events_url": "https://api.github.com/users/lewtun/events{/privacy}", "followers_url": "https://api.github.com/users/lewtun/followers", "following_url": "https://api.github.com/users/lewtun/following{/other_user}", "gists_url": "https://api.github.com/users/lewtun/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lewtun", "id": 26859204, "login": "lewtun", "node_id": "MDQ6VXNlcjI2ODU5MjA0", "organizations_url": "https://api.github.com/users/lewtun/orgs", "received_events_url": "https://api.github.com/users/lewtun/received_events", "repos_url": "https://api.github.com/users/lewtun/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lewtun/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lewtun/subscriptions", "type": "User", "url": "https://api.github.com/users/lewtun", "user_view_type": "public" }	[ { "color": "8B51EF", "default": false, "description": "", "id": 4069435429, "name": "hosted-on-google-drive", "node_id": "LA_kwDODunzps7yjqgl", "url": "https://api.github.com/repos/huggingface/datasets/labels/hosted-on-google-drive" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[ "cc @huggingface/datasets: it seems like https://www.researchgate.net/ is flaky for datasets hosting (I put the \"hosted-on-google-drive\" tag since it's the same kind of issue I think)", "Let's see if their license allows hosting their data on the Hub.", "License is Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0).\r\n\r\nWe can host their data on the Hub." ]	2022-06-29T12:45:43Z	2022-07-01T09:29:36Z	2022-07-01T09:29:36Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Link https://huggingface.co/datasets/financial_phrasebank/viewer/sentences_allagree/train ### Description As reported by a community member using [AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/5#62bc217436d0e5d316a768f0), there seems to be a problem streaming this dataset: ``` Server error Status code: 400 Exception: Exception Message: Give up after 5 attempts with ConnectionError ``` ### Owner No	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": 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https://api.github.com/repos/huggingface/datasets/issues/4771	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4771/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4771/comments	https://api.github.com/repos/huggingface/datasets/issues/4771/events	https://github.com/huggingface/datasets/pull/4771	1,322,600,725	PR_kwDODunzps48VjWx	4,771	Remove dummy data generation docs	{ "avatar_url": "https://avatars.githubusercontent.com/u/59462357?v=4", "events_url": "https://api.github.com/users/stevhliu/events{/privacy}", "followers_url": "https://api.github.com/users/stevhliu/followers", "following_url": "https://api.github.com/users/stevhliu/following{/other_user}", "gists_url": "https://api.github.com/users/stevhliu/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/stevhliu", "id": 59462357, "login": "stevhliu", "node_id": "MDQ6VXNlcjU5NDYyMzU3", "organizations_url": "https://api.github.com/users/stevhliu/orgs", "received_events_url": "https://api.github.com/users/stevhliu/received_events", "repos_url": "https://api.github.com/users/stevhliu/repos", "site_admin": false, "starred_url": "https://api.github.com/users/stevhliu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/stevhliu/subscriptions", "type": "User", "url": "https://api.github.com/users/stevhliu", "user_view_type": "public" }	[ { "color": "0075ca", "default": true, "description": "Improvements or additions to documentation", "id": 1935892861, "name": "documentation", "node_id": "MDU6TGFiZWwxOTM1ODkyODYx", "url": "https://api.github.com/repos/huggingface/datasets/labels/documentation" } ]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-07-29T19:20:46Z	2022-08-03T00:04:01Z	2022-08-02T23:50:29Z	MEMBER	null	null	null	This PR removes instructions to generate dummy data since that is no longer necessary for datasets that are uploaded to the Hub instead of our GitHub repo. Close #4744	{ "avatar_url": "https://avatars.githubusercontent.com/u/59462357?v=4", "events_url": "https://api.github.com/users/stevhliu/events{/privacy}", "followers_url": "https://api.github.com/users/stevhliu/followers", "following_url": "https://api.github.com/users/stevhliu/following{/other_user}", "gists_url": "https://api.github.com/users/stevhliu/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/stevhliu", "id": 59462357, "login": "stevhliu", "node_id": "MDQ6VXNlcjU5NDYyMzU3", "organizations_url": "https://api.github.com/users/stevhliu/orgs", "received_events_url": "https://api.github.com/users/stevhliu/received_events", "repos_url": "https://api.github.com/users/stevhliu/repos", "site_admin": false, "starred_url": "https://api.github.com/users/stevhliu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/stevhliu/subscriptions", "type": "User", "url": "https://api.github.com/users/stevhliu", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4771/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4771/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4771.diff", "html_url": "https://github.com/huggingface/datasets/pull/4771", "merged_at": "2022-08-02T23:50:29Z", "patch_url": "https://github.com/huggingface/datasets/pull/4771.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4771" }
https://api.github.com/repos/huggingface/datasets/issues/5205	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5205/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5205/comments	https://api.github.com/repos/huggingface/datasets/issues/5205/events	https://github.com/huggingface/datasets/pull/5205	1,437,221,987	PR_kwDODunzps5CRO33	5,205	Add missing `DownloadConfig.use_auth_token` value	{ "avatar_url": "https://avatars.githubusercontent.com/u/36760800?v=4", "events_url": "https://api.github.com/users/alvarobartt/events{/privacy}", "followers_url": "https://api.github.com/users/alvarobartt/followers", "following_url": "https://api.github.com/users/alvarobartt/following{/other_user}", "gists_url": "https://api.github.com/users/alvarobartt/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alvarobartt", "id": 36760800, "login": "alvarobartt", "node_id": "MDQ6VXNlcjM2NzYwODAw", "organizations_url": "https://api.github.com/users/alvarobartt/orgs", "received_events_url": "https://api.github.com/users/alvarobartt/received_events", "repos_url": "https://api.github.com/users/alvarobartt/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alvarobartt/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvarobartt/subscriptions", "type": "User", "url": "https://api.github.com/users/alvarobartt", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-11-05T23:36:36Z	2022-11-08T08:13:00Z	2022-11-07T16:20:24Z	MEMBER	null	null	null	This PR solves https://github.com/huggingface/datasets/issues/5204 Now the `token` is propagated so that `DownloadConfig.use_auth_token` value is set before trying to download private files from existing datasets in the Hub.	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/5205/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5205/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5205.diff", "html_url": "https://github.com/huggingface/datasets/pull/5205", "merged_at": "2022-11-07T16:20:24Z", "patch_url": "https://github.com/huggingface/datasets/pull/5205.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5205" }
https://api.github.com/repos/huggingface/datasets/issues/6597	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6597/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6597/comments	https://api.github.com/repos/huggingface/datasets/issues/6597/events	https://github.com/huggingface/datasets/issues/6597	2,083,708,521	I_kwDODunzps58Mt5p	6,597	Dataset.push_to_hub of a canonical dataset creates an additional dataset under the user namespace	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[ "It is caused by these code lines: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/dataset_dict.py#L1688-L1694", "Also note the information in the docstring: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/dataset_dict.py#L1582-L1585\r\n\r\n> Also accepts `<dataset_name>`, which will default to the namespace of the logged-in user.\r\n\r\nThis behavior was \"reverted\" by the PR: \r\n- #6519\r\n\r\nWe have therefore contradictory requirements. We should decide:\r\n- whether to support passing dataset_namespace without user/org that defaults to the logged-in user (and not support canonical datasets)\r\n- or vice-versa, to support canonical datasets and not support passing only dataset_name\r\n\r\nAs canonical datasets are \"deprecated\" (and will eventually disappear), I would choose the first option. However, if so, the Space to convert datasets to Parquet will not work for canonical datasets: https://huggingface.co/spaces/albertvillanova/convert-dataset-to-parquet", "IIUC, this could also be \"fixed\" by `create_repo(\"dataset_name\")` not defaulting to `create_repo(\"user/dataset_name\")` (when the user's token is available), which would be consistent with the rest of the `HfApi` ops used in the `push_to_hub` implementation. This is a (small) breaking change for `huggingface_hub`, but justified to make the API more consistent.", "I tag @Wauplin to have his opinion as well.", "Hmm, creating repo with implicit namespace (e.g. `create_repo(\"dataset_name\")`) is a convenient feature used in a lot of integrations. It is not consistent with other HfApi methods specifically because it is the method to create repos. Once the repo is created, the return value provides the explicit repo_id (`namespace/repo_name`) that has to be passed to every `HfApi` method. Otherwise, libraries/scripts would often need to do a `whoami` call to get the namespace before creating a repo.\r\n\r\n Another solution for https://github.com/huggingface/datasets/issues/6597#issuecomment-1893746690 could be that implicit namespace is allowed (same as today) except if the `repo_id` is in a hard-coded list of canonical datasets. This list can be maintained automatically and should be slowly decreasing. Caveat: as a normal user I wouldn't be able to implicitly push to `imagenet-1k` if I wanted to push to `Wauplin/imagenet-1k`. Shouldn't be too problematic, no? Worse case, would need to add a `whoami` call and allow implicit-canonical-name for non-HF users for instance (a bit too over-engineered IMO but doable). ", "As canonical datasets are going to disappear in the following couple of months, I would not make any effort on their support.\r\n\r\nI propose reverting #6519, so that the behavior of `push_to_hub` is aligned with the one described in its dosctring: \"Also accepts `<dataset_name>`, which will default to the namespace of the logged-in user.\"\r\n\r\nI'm opening a PR." ]	2024-01-16T11:27:07Z	2024-02-05T12:29:37Z	2024-02-05T12:29:37Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	While using `Dataset.push_to_hub` of a canonical dataset, an additional dataset was created under my user namespace. ## Steps to reproduce the bug The command: ```python commit_info = ds.push_to_hub( "caner", config_name="default", commit_message="Convert dataset to Parquet", commit_description="Convert dataset to Parquet.", create_pr=True, token=token, ) ``` creates the additional dataset `albertvillanova/caner`.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6597/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6597/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6678	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6678/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6678/comments	https://api.github.com/repos/huggingface/datasets/issues/6678/events	https://github.com/huggingface/datasets/pull/6678	2,141,902,154	PR_kwDODunzps5nQ2ZO	6,678	Release: 2.17.1	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6678). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005070 / 0.011353 (-0.006283) \| 0.003685 / 0.011008 (-0.007323) \| 0.063191 / 0.038508 (0.024683) \| 0.030506 / 0.023109 (0.007397) \| 0.258033 / 0.275898 (-0.017865) \| 0.269790 / 0.323480 (-0.053690) \| 0.004180 / 0.007986 (-0.003805) \| 0.002811 / 0.004328 (-0.001517) \| 0.048718 / 0.004250 (0.044467) \| 0.043473 / 0.037052 (0.006421) \| 0.267306 / 0.258489 (0.008817) \| 0.290315 / 0.293841 (-0.003526) \| 0.027402 / 0.128546 (-0.101144) \| 0.010782 / 0.075646 (-0.064864) \| 0.207243 / 0.419271 (-0.212029) \| 0.035637 / 0.043533 (-0.007896) \| 0.264032 / 0.255139 (0.008893) \| 0.270450 / 0.283200 (-0.012749) \| 0.017407 / 0.141683 (-0.124276) \| 1.107481 / 1.452155 (-0.344674) \| 1.163187 / 1.492716 (-0.329529) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095065 / 0.018006 (0.077059) \| 0.305169 / 0.000490 (0.304680) \| 0.000221 / 0.000200 (0.000021) \| 0.000045 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.017706 / 0.037411 (-0.019706) \| 0.061431 / 0.014526 (0.046905) \| 0.073541 / 0.176557 (-0.103016) \| 0.117326 / 0.737135 (-0.619809) \| 0.074368 / 0.296338 (-0.221971) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284533 / 0.215209 (0.069324) \| 2.775230 / 2.077655 (0.697575) \| 1.455196 / 1.504120 (-0.048924) \| 1.357651 / 1.541195 (-0.183544) \| 1.337477 / 1.468490 (-0.131013) \| 0.567439 / 4.584777 (-4.017338) \| 2.380612 / 3.745712 (-1.365100) \| 2.792305 / 5.269862 (-2.477556) \| 1.726501 / 4.565676 (-2.839176) \| 0.061729 / 0.424275 (-0.362546) \| 0.004928 / 0.007607 (-0.002679) \| 0.331989 / 0.226044 (0.105944) \| 3.301704 / 2.268929 (1.032776) \| 1.805107 / 55.444624 (-53.639518) \| 1.500434 / 6.876477 (-5.376043) \| 1.535548 / 2.142072 (-0.606524) \| 0.639490 / 4.805227 (-4.165737) \| 0.115876 / 6.500664 (-6.384788) \| 0.041895 / 0.075469 (-0.033574) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.993584 / 1.841788 (-0.848203) \| 11.596680 / 8.074308 (3.522371) \| 9.631726 / 10.191392 (-0.559666) \| 0.141153 / 0.680424 (-0.539271) \| 0.014077 / 0.534201 (-0.520124) \| 0.288237 / 0.579283 (-0.291046) \| 0.261213 / 0.434364 (-0.173151) \| 0.323897 / 0.540337 (-0.216441) \| 0.420350 / 1.386936 (-0.966586) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005275 / 0.011353 (-0.006078) \| 0.003739 / 0.011008 (-0.007269) \| 0.049801 / 0.038508 (0.011293) \| 0.030544 / 0.023109 (0.007435) \| 0.264835 / 0.275898 (-0.011063) \| 0.297738 / 0.323480 (-0.025742) \| 0.004487 / 0.007986 (-0.003499) \| 0.002835 / 0.004328 (-0.001493) \| 0.048091 / 0.004250 (0.043841) \| 0.044375 / 0.037052 (0.007322) \| 0.286538 / 0.258489 (0.028049) \| 0.319561 / 0.293841 (0.025720) \| 0.047925 / 0.128546 (-0.080621) \| 0.010816 / 0.075646 (-0.064831) \| 0.057940 / 0.419271 (-0.361331) \| 0.033588 / 0.043533 (-0.009945) \| 0.270075 / 0.255139 (0.014936) \| 0.290441 / 0.283200 (0.007242) \| 0.017173 / 0.141683 (-0.124509) \| 1.164686 / 1.452155 (-0.287469) \| 1.213205 / 1.492716 (-0.279511) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093408 / 0.018006 (0.075402) \| 0.305525 / 0.000490 (0.305036) \| 0.000235 / 0.000200 (0.000035) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021605 / 0.037411 (-0.015806) \| 0.075479 / 0.014526 (0.060953) \| 0.085990 / 0.176557 (-0.090567) \| 0.124783 / 0.737135 (-0.612352) \| 0.089108 / 0.296338 (-0.207230) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.306222 / 0.215209 (0.091013) \| 2.987282 / 2.077655 (0.909627) \| 1.664714 / 1.504120 (0.160594) \| 1.523136 / 1.541195 (-0.018059) \| 1.534112 / 1.468490 (0.065622) \| 0.566347 / 4.584777 (-4.018430) \| 2.438641 / 3.745712 (-1.307071) \| 2.669048 / 5.269862 (-2.600814) \| 1.732935 / 4.565676 (-2.832741) \| 0.063460 / 0.424275 (-0.360815) \| 0.004973 / 0.007607 (-0.002634) \| 0.366233 / 0.226044 (0.140189) \| 3.553578 / 2.268929 (1.284649) \| 1.984343 / 55.444624 (-53.460281) \| 1.711038 / 6.876477 (-5.165439) \| 1.857346 / 2.142072 (-0.284726) \| 0.651077 / 4.805227 (-4.154150) \| 0.118670 / 6.500664 (-6.381994) \| 0.041839 / 0.075469 (-0.033631) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.008230 / 1.841788 (-0.833558) \| 12.047403 / 8.074308 (3.973095) \| 10.039053 / 10.191392 (-0.152339) \| 0.141640 / 0.680424 (-0.538784) \| 0.014758 / 0.534201 (-0.519443) \| 0.285016 / 0.579283 (-0.294267) \| 0.275461 / 0.434364 (-0.158903) \| 0.325535 / 0.540337 (-0.214803) \| 0.415871 / 1.386936 (-0.971065) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d2268261bf0fb3eed8faae6bc1fa20a25b4382c \"CML watermark\")\n" ]	2024-02-19T09:24:29Z	2024-02-19T10:03:00Z	2024-02-19T09:56:52Z	MEMBER	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6678/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6678/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6678.diff", "html_url": "https://github.com/huggingface/datasets/pull/6678", "merged_at": "2024-02-19T09:56:52Z", "patch_url": "https://github.com/huggingface/datasets/pull/6678.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6678" }
https://api.github.com/repos/huggingface/datasets/issues/7376	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7376/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7376/comments	https://api.github.com/repos/huggingface/datasets/issues/7376/events	https://github.com/huggingface/datasets/pull/7376	2,802,621,104	PR_kwDODunzps6IiO9j	7,376	[docs] uv install	{ "avatar_url": "https://avatars.githubusercontent.com/u/59462357?v=4", "events_url": "https://api.github.com/users/stevhliu/events{/privacy}", "followers_url": "https://api.github.com/users/stevhliu/followers", "following_url": "https://api.github.com/users/stevhliu/following{/other_user}", "gists_url": "https://api.github.com/users/stevhliu/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/stevhliu", "id": 59462357, "login": "stevhliu", "node_id": "MDQ6VXNlcjU5NDYyMzU3", "organizations_url": "https://api.github.com/users/stevhliu/orgs", "received_events_url": "https://api.github.com/users/stevhliu/received_events", "repos_url": "https://api.github.com/users/stevhliu/repos", "site_admin": false, "starred_url": "https://api.github.com/users/stevhliu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/stevhliu/subscriptions", "type": "User", "url": "https://api.github.com/users/stevhliu", "user_view_type": "public" }	[]	closed	false	null	[]	null	[]	2025-01-21T19:15:48Z	2025-03-14T20:16:35Z	2025-03-14T20:16:35Z	MEMBER	null	null	null	Proposes adding uv to installation docs (see Slack thread [here](https://huggingface.slack.com/archives/C01N44FJDHT/p1737377177709279) for more context) if you're interested!	{ "avatar_url": "https://avatars.githubusercontent.com/u/59462357?v=4", "events_url": "https://api.github.com/users/stevhliu/events{/privacy}", "followers_url": "https://api.github.com/users/stevhliu/followers", "following_url": "https://api.github.com/users/stevhliu/following{/other_user}", "gists_url": "https://api.github.com/users/stevhliu/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/stevhliu", "id": 59462357, "login": "stevhliu", "node_id": "MDQ6VXNlcjU5NDYyMzU3", "organizations_url": "https://api.github.com/users/stevhliu/orgs", "received_events_url": "https://api.github.com/users/stevhliu/received_events", "repos_url": "https://api.github.com/users/stevhliu/repos", "site_admin": false, "starred_url": "https://api.github.com/users/stevhliu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/stevhliu/subscriptions", "type": "User", "url": "https://api.github.com/users/stevhliu", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7376/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7376/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7376.diff", "html_url": "https://github.com/huggingface/datasets/pull/7376", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/7376.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7376" }
https://api.github.com/repos/huggingface/datasets/issues/5302	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5302/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5302/comments	https://api.github.com/repos/huggingface/datasets/issues/5302/events	https://github.com/huggingface/datasets/pull/5302	1,464,778,901	PR_kwDODunzps5DuJJp	5,302	Improve `use_auth_token` docstring and deprecate `use_auth_token` in `download_and_prepare`	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-11-25T17:09:21Z	2022-12-09T14:20:15Z	2022-12-09T14:17:20Z	COLLABORATOR	null	null	null	Clarify in the docstrings what happens when `use_auth_token` is `None` and deprecate the `use_auth_token` param in `download_and_prepare`.	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5302/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5302/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5302.diff", "html_url": "https://github.com/huggingface/datasets/pull/5302", "merged_at": "2022-12-09T14:17:20Z", "patch_url": "https://github.com/huggingface/datasets/pull/5302.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5302" }
https://api.github.com/repos/huggingface/datasets/issues/5804	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5804/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5804/comments	https://api.github.com/repos/huggingface/datasets/issues/5804/events	https://github.com/huggingface/datasets/pull/5804	1,688,285,666	PR_kwDODunzps5PX0Dk	5,804	Set dev version	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5804). 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) \| 0.006448 / 0.011353 (-0.004905) \| 0.004440 / 0.011008 (-0.006568) \| 0.097837 / 0.038508 (0.059328) \| 0.027754 / 0.023109 (0.004645) \| 0.306462 / 0.275898 (0.030564) \| 0.332454 / 0.323480 (0.008975) \| 0.004984 / 0.007986 (-0.003001) \| 0.004703 / 0.004328 (0.000375) \| 0.075213 / 0.004250 (0.070962) \| 0.036524 / 0.037052 (-0.000529) \| 0.310149 / 0.258489 (0.051659) \| 0.346392 / 0.293841 (0.052552) \| 0.031012 / 0.128546 (-0.097534) \| 0.011598 / 0.075646 (-0.064049) \| 0.323066 / 0.419271 (-0.096206) \| 0.042945 / 0.043533 (-0.000588) \| 0.302286 / 0.255139 (0.047147) \| 0.327813 / 0.283200 (0.044614) \| 0.092540 / 0.141683 (-0.049143) \| 1.532893 / 1.452155 (0.080739) \| 1.556676 / 1.492716 (0.063960) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.195126 / 0.018006 (0.177120) \| 0.399623 / 0.000490 (0.399133) \| 0.003176 / 0.000200 (0.002976) \| 0.000068 / 0.000054 (0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023612 / 0.037411 (-0.013799) \| 0.097794 / 0.014526 (0.083268) \| 0.104665 / 0.176557 (-0.071891) \| 0.167145 / 0.737135 (-0.569990) \| 0.108769 / 0.296338 (-0.187570) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.437818 / 0.215209 (0.222608) \| 4.354896 / 2.077655 (2.277242) \| 2.092832 / 1.504120 (0.588712) \| 1.957630 / 1.541195 (0.416435) \| 2.033135 / 1.468490 (0.564645) \| 0.702316 / 4.584777 (-3.882461) \| 3.448035 / 3.745712 (-0.297678) \| 1.906762 / 5.269862 (-3.363100) \| 1.253274 / 4.565676 (-3.312402) \| 0.082486 / 0.424275 (-0.341789) \| 0.012442 / 0.007607 (0.004835) \| 0.532096 / 0.226044 (0.306052) \| 5.366580 / 2.268929 (3.097652) \| 2.441904 / 55.444624 (-53.002720) \| 2.112116 / 6.876477 (-4.764361) \| 2.185471 / 2.142072 (0.043398) \| 0.797905 / 4.805227 (-4.007322) \| 0.149811 / 6.500664 (-6.350853) \| 0.066507 / 0.075469 (-0.008962) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.206300 / 1.841788 (-0.635487) \| 13.620851 / 8.074308 (5.546543) \| 14.190666 / 10.191392 (3.999274) \| 0.142343 / 0.680424 (-0.538081) \| 0.016867 / 0.534201 (-0.517334) \| 0.381557 / 0.579283 (-0.197726) \| 0.373935 / 0.434364 (-0.060429) \| 0.437856 / 0.540337 (-0.102481) \| 0.525235 / 1.386936 (-0.861701) \|\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) \| 0.006598 / 0.011353 (-0.004755) \| 0.004487 / 0.011008 (-0.006522) \| 0.077582 / 0.038508 (0.039073) \| 0.028008 / 0.023109 (0.004899) \| 0.341602 / 0.275898 (0.065704) \| 0.377105 / 0.323480 (0.053625) \| 0.004999 / 0.007986 (-0.002986) \| 0.004791 / 0.004328 (0.000462) \| 0.076418 / 0.004250 (0.072167) \| 0.038347 / 0.037052 (0.001295) \| 0.343196 / 0.258489 (0.084707) \| 0.382459 / 0.293841 (0.088618) \| 0.030597 / 0.128546 (-0.097950) \| 0.011579 / 0.075646 (-0.064067) \| 0.085876 / 0.419271 (-0.333396) \| 0.043241 / 0.043533 (-0.000292) \| 0.343754 / 0.255139 (0.088615) \| 0.380689 / 0.283200 (0.097489) \| 0.096015 / 0.141683 (-0.045668) \| 1.464419 / 1.452155 (0.012264) \| 1.574010 / 1.492716 (0.081294) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.156433 / 0.018006 (0.138427) \| 0.403179 / 0.000490 (0.402690) \| 0.002415 / 0.000200 (0.002215) \| 0.000082 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024946 / 0.037411 (-0.012465) \| 0.100568 / 0.014526 (0.086042) \| 0.106440 / 0.176557 (-0.070117) \| 0.158457 / 0.737135 (-0.578678) \| 0.110774 / 0.296338 (-0.185564) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434734 / 0.215209 (0.219525) \| 4.343874 / 2.077655 (2.266220) \| 2.059759 / 1.504120 (0.555639) \| 1.855124 / 1.541195 (0.313930) \| 1.908567 / 1.468490 (0.440077) \| 0.695283 / 4.584777 (-3.889494) \| 3.347724 / 3.745712 (-0.397988) \| 2.979498 / 5.269862 (-2.290364) \| 1.532040 / 4.565676 (-3.033636) \| 0.083021 / 0.424275 (-0.341254) \| 0.012522 / 0.007607 (0.004915) \| 0.540934 / 0.226044 (0.314890) \| 5.385690 / 2.268929 (3.116762) \| 2.507409 / 55.444624 (-52.937216) \| 2.160537 / 6.876477 (-4.715939) \| 2.269195 / 2.142072 (0.127123) \| 0.804718 / 4.805227 (-4.000509) \| 0.152432 / 6.500664 (-6.348232) \| 0.068783 / 0.075469 (-0.006686) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.294698 / 1.841788 (-0.547090) \| 14.152792 / 8.074308 (6.078484) \| 14.233132 / 10.191392 (4.041740) \| 0.143655 / 0.680424 (-0.536768) \| 0.016844 / 0.534201 (-0.517357) \| 0.380246 / 0.579283 (-0.199037) \| 0.381730 / 0.434364 (-0.052633) \| 0.456838 / 0.540337 (-0.083499) \| 0.543677 / 1.386936 (-0.843259) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b28d5610887f2e107765f5f1557679184db08214 \"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) \| 0.008586 / 0.011353 (-0.002767) \| 0.005886 / 0.011008 (-0.005122) \| 0.114522 / 0.038508 (0.076014) \| 0.040966 / 0.023109 (0.017857) \| 0.366655 / 0.275898 (0.090757) \| 0.408765 / 0.323480 (0.085285) \| 0.006822 / 0.007986 (-0.001164) \| 0.004508 / 0.004328 (0.000180) \| 0.084715 / 0.004250 (0.080465) \| 0.054007 / 0.037052 (0.016954) \| 0.380500 / 0.258489 (0.122011) \| 0.410377 / 0.293841 (0.116536) \| 0.041040 / 0.128546 (-0.087507) \| 0.013940 / 0.075646 (-0.061707) \| 0.398456 / 0.419271 (-0.020816) \| 0.059315 / 0.043533 (0.015782) \| 0.353640 / 0.255139 (0.098501) \| 0.388682 / 0.283200 (0.105482) \| 0.121744 / 0.141683 (-0.019939) \| 1.729306 / 1.452155 (0.277151) \| 1.824768 / 1.492716 (0.332052) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228806 / 0.018006 (0.210800) \| 0.492790 / 0.000490 (0.492300) \| 0.010815 / 0.000200 (0.010615) \| 0.000372 / 0.000054 (0.000318) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031750 / 0.037411 (-0.005662) \| 0.127160 / 0.014526 (0.112635) \| 0.136717 / 0.176557 (-0.039839) \| 0.205590 / 0.737135 (-0.531545) \| 0.142596 / 0.296338 (-0.153742) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.486419 / 0.215209 (0.271210) \| 4.858572 / 2.077655 (2.780918) \| 2.173867 / 1.504120 (0.669747) \| 1.934619 / 1.541195 (0.393424) \| 2.104185 / 1.468490 (0.635695) \| 0.837913 / 4.584777 (-3.746864) \| 4.552192 / 3.745712 (0.806480) \| 2.565040 / 5.269862 (-2.704822) \| 1.808499 / 4.565676 (-2.757178) \| 0.103283 / 0.424275 (-0.320993) \| 0.015040 / 0.007607 (0.007433) \| 0.602325 / 0.226044 (0.376281) \| 6.038655 / 2.268929 (3.769727) \| 2.759789 / 55.444624 (-52.684835) \| 2.330990 / 6.876477 (-4.545487) \| 2.404111 / 2.142072 (0.262038) \| 1.011637 / 4.805227 (-3.793590) \| 0.202142 / 6.500664 (-6.298522) \| 0.079496 / 0.075469 (0.004026) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.429543 / 1.841788 (-0.412245) \| 18.052409 / 8.074308 (9.978101) \| 16.989154 / 10.191392 (6.797762) \| 0.208981 / 0.680424 (-0.471443) \| 0.020490 / 0.534201 (-0.513711) \| 0.502746 / 0.579283 (-0.076537) \| 0.491769 / 0.434364 (0.057405) \| 0.581970 / 0.540337 (0.041632) \| 0.695816 / 1.386936 (-0.691120) \|\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) \| 0.008449 / 0.011353 (-0.002904) \| 0.006633 / 0.011008 (-0.004375) \| 0.088638 / 0.038508 (0.050130) \| 0.040013 / 0.023109 (0.016904) \| 0.413108 / 0.275898 (0.137210) \| 0.446310 / 0.323480 (0.122830) \| 0.006515 / 0.007986 (-0.001471) \| 0.006223 / 0.004328 (0.001894) \| 0.089823 / 0.004250 (0.085573) \| 0.052029 / 0.037052 (0.014977) \| 0.407263 / 0.258489 (0.148774) \| 0.449416 / 0.293841 (0.155576) \| 0.041810 / 0.128546 (-0.086736) \| 0.014604 / 0.075646 (-0.061042) \| 0.103728 / 0.419271 (-0.315543) \| 0.058212 / 0.043533 (0.014679) \| 0.408936 / 0.255139 (0.153797) \| 0.436727 / 0.283200 (0.153528) \| 0.124344 / 0.141683 (-0.017339) \| 1.752112 / 1.452155 (0.299957) \| 1.859104 / 1.492716 (0.366387) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.231172 / 0.018006 (0.213166) \| 0.502974 / 0.000490 (0.502485) \| 0.005586 / 0.000200 (0.005386) \| 0.000137 / 0.000054 (0.000082) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034097 / 0.037411 (-0.003314) \| 0.133780 / 0.014526 (0.119254) \| 0.142321 / 0.176557 (-0.034236) \| 0.199807 / 0.737135 (-0.537329) \| 0.150073 / 0.296338 (-0.146266) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.515658 / 0.215209 (0.300449) \| 5.129783 / 2.077655 (3.052129) \| 2.534767 / 1.504120 (1.030648) \| 2.352468 / 1.541195 (0.811274) \| 2.430708 / 1.468490 (0.962218) \| 0.850087 / 4.584777 (-3.734690) \| 4.529622 / 3.745712 (0.783910) \| 2.451986 / 5.269862 (-2.817876) \| 1.569568 / 4.565676 (-2.996109) \| 0.102907 / 0.424275 (-0.321368) \| 0.014420 / 0.007607 (0.006813) \| 0.635124 / 0.226044 (0.409080) \| 6.260496 / 2.268929 (3.991568) \| 3.094984 / 55.444624 (-52.349640) \| 2.780629 / 6.876477 (-4.095847) \| 2.947620 / 2.142072 (0.805548) \| 1.002397 / 4.805227 (-3.802830) \| 0.200502 / 6.500664 (-6.300162) \| 0.076577 / 0.075469 (0.001107) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.505958 / 1.841788 (-0.335829) \| 18.364986 / 8.074308 (10.290678) \| 16.707214 / 10.191392 (6.515822) \| 0.210976 / 0.680424 (-0.469447) \| 0.022077 / 0.534201 (-0.512124) \| 0.516174 / 0.579283 (-0.063109) \| 0.502469 / 0.434364 (0.068105) \| 0.626790 / 0.540337 (0.086453) \| 0.747230 / 1.386936 (-0.639706) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bc5fef5b6d91f009e4101684adcb374df2c170f6 \"CML watermark\")\n" ]	2023-04-28T10:10:01Z	2023-04-28T10:18:51Z	2023-04-28T10:10:29Z	MEMBER	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5804/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5804/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5804.diff", "html_url": "https://github.com/huggingface/datasets/pull/5804", "merged_at": "2023-04-28T10:10:29Z", "patch_url": "https://github.com/huggingface/datasets/pull/5804.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5804" }
https://api.github.com/repos/huggingface/datasets/issues/6572	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6572/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6572/comments	https://api.github.com/repos/huggingface/datasets/issues/6572/events	https://github.com/huggingface/datasets/pull/6572	2,072,384,281	PR_kwDODunzps5jlCO5	6,572	Adding option for multipart achive download	{ "avatar_url": "https://avatars.githubusercontent.com/u/66251151?v=4", "events_url": "https://api.github.com/users/jpodivin/events{/privacy}", "followers_url": "https://api.github.com/users/jpodivin/followers", "following_url": "https://api.github.com/users/jpodivin/following{/other_user}", "gists_url": "https://api.github.com/users/jpodivin/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jpodivin", "id": 66251151, "login": "jpodivin", "node_id": "MDQ6VXNlcjY2MjUxMTUx", "organizations_url": "https://api.github.com/users/jpodivin/orgs", "received_events_url": "https://api.github.com/users/jpodivin/received_events", "repos_url": "https://api.github.com/users/jpodivin/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jpodivin/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jpodivin/subscriptions", "type": "User", "url": "https://api.github.com/users/jpodivin", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "On closer examination, this appears to be unnecessary. " ]	2024-01-09T13:35:44Z	2024-02-25T08:13:01Z	2024-02-25T08:13:01Z	NONE	null	null	null	Right now we can only download multiple separate archives or a single file archive, but not multipart archives, such as those produced by `tar --multi-volume`. This PR allows for downloading and extraction of archives split into multiple parts. With the new `multi_part` field of the `DownloadConfig` set, the downloader will first retrieve all the files and attempt to concatenate them before starting extraction. This will obviously fail if files retrieved are actually multiple separate archives, so the option is set to `False` by default. Tests and docs incoming.	{ "avatar_url": "https://avatars.githubusercontent.com/u/66251151?v=4", "events_url": "https://api.github.com/users/jpodivin/events{/privacy}", "followers_url": "https://api.github.com/users/jpodivin/followers", "following_url": "https://api.github.com/users/jpodivin/following{/other_user}", "gists_url": "https://api.github.com/users/jpodivin/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jpodivin", "id": 66251151, "login": "jpodivin", "node_id": "MDQ6VXNlcjY2MjUxMTUx", "organizations_url": "https://api.github.com/users/jpodivin/orgs", "received_events_url": "https://api.github.com/users/jpodivin/received_events", "repos_url": "https://api.github.com/users/jpodivin/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jpodivin/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jpodivin/subscriptions", "type": "User", "url": "https://api.github.com/users/jpodivin", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6572/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6572/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6572.diff", "html_url": "https://github.com/huggingface/datasets/pull/6572", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6572.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6572" }
https://api.github.com/repos/huggingface/datasets/issues/4545	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4545/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4545/comments	https://api.github.com/repos/huggingface/datasets/issues/4545/events	https://github.com/huggingface/datasets/pull/4545	1,280,899,028	PR_kwDODunzps46KV-y	4,545	Make DuplicateKeysError more user friendly [For Issue #2556]	{ "avatar_url": "https://avatars.githubusercontent.com/u/20517962?v=4", "events_url": "https://api.github.com/users/VijayKalmath/events{/privacy}", "followers_url": "https://api.github.com/users/VijayKalmath/followers", "following_url": "https://api.github.com/users/VijayKalmath/following{/other_user}", "gists_url": "https://api.github.com/users/VijayKalmath/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/VijayKalmath", "id": 20517962, "login": "VijayKalmath", "node_id": "MDQ6VXNlcjIwNTE3OTYy", "organizations_url": "https://api.github.com/users/VijayKalmath/orgs", "received_events_url": "https://api.github.com/users/VijayKalmath/received_events", "repos_url": "https://api.github.com/users/VijayKalmath/repos", "site_admin": false, "starred_url": "https://api.github.com/users/VijayKalmath/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/VijayKalmath/subscriptions", "type": "User", "url": "https://api.github.com/users/VijayKalmath", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "> Nice thanks !\r\n> \r\n> After your changes feel free to mark this PR as \"ready for review\" ;)\r\n\r\nMarking PR ready for review.\r\n\r\n@lhoestq Let me know if there is anything else required or if we are good to go ahead and merge.", "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-06-22T21:01:34Z	2022-06-28T09:37:06Z	2022-06-28T09:26:04Z	CONTRIBUTOR	null	null	null	# What does this PR do? ## Summary DuplicateKeysError error does not provide any information regarding the examples which have the same the key. This information is very helpful for debugging the dataset generator script. ## Additions - ## Changes - Changed `DuplicateKeysError Class` in `src/datasets/keyhash.py` to add current index and duplicate_key_indices to error message. - Changed `check_duplicate_keys` function in `src/datasets/arrow_writer.py` to find indices of examples with duplicate hash if duplicate keys are found. ## Deletions - ## To do : - [x] Find way to find and print path `<Path to Dataset>` in Error message ## Issues Addressed : Fixes #2556	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4545/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4545/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4545.diff", "html_url": "https://github.com/huggingface/datasets/pull/4545", "merged_at": "2022-06-28T09:26:04Z", "patch_url": "https://github.com/huggingface/datasets/pull/4545.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4545" }
https://api.github.com/repos/huggingface/datasets/issues/5719	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5719/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5719/comments	https://api.github.com/repos/huggingface/datasets/issues/5719/events	https://github.com/huggingface/datasets/issues/5719	1,659,203,222	I_kwDODunzps5i5W6W	5,719	Array2D feature creates a list of list instead of a numpy array	{ "avatar_url": "https://avatars.githubusercontent.com/u/15215732?v=4", "events_url": "https://api.github.com/users/offchan42/events{/privacy}", "followers_url": "https://api.github.com/users/offchan42/followers", "following_url": "https://api.github.com/users/offchan42/following{/other_user}", "gists_url": "https://api.github.com/users/offchan42/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/offchan42", "id": 15215732, "login": "offchan42", "node_id": "MDQ6VXNlcjE1MjE1NzMy", "organizations_url": "https://api.github.com/users/offchan42/orgs", "received_events_url": "https://api.github.com/users/offchan42/received_events", "repos_url": "https://api.github.com/users/offchan42/repos", "site_admin": false, "starred_url": "https://api.github.com/users/offchan42/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/offchan42/subscriptions", "type": "User", "url": "https://api.github.com/users/offchan42", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi! \r\n\r\nYou need to set the format to `np` before indexing the dataset to get NumPy arrays:\r\n```python\r\nfeatures = Features(dict(seq=Array2D((2,2), 'float32'))) \r\nds = Dataset.from_dict(dict(seq=[np.random.rand(2,2)]), features=features)\r\nds.set_format(\"np\")\r\na = ds[0]['seq']\r\n```\r\n\r\n> I think it should not be the expected behavior especially when I feed a numpy array as input to the data creation function. Why is it converting my array into a list?\r\n\r\nThe same dataset can have examples in different types (Numpy arrays, Torch tensors, Pandas series, etc.), so recovering them all would be slow and impractical. Instead, the design of our formatting API is similar to Arrow's (the lib we use internally to store data on disk/ in RAM), which allows converting a batch of data to Python/Numpy/Pandas in a single call (and uses C++ to do so to make it faster).\r\n\r\n> Also if I change the first dimension of the Array2D shape to None, it's returning array correctly.\r\n\r\nSetting the first dimension to `None` makes it variable-length (allows passing arrays with the first dimensions of differing lengths).\r\n", "Current behavior when indexing the dataset:\r\n- Using `Array((2,2))` returns a list of lists.\r\n- Using `Array((None,2))` returns a numpy array.\r\n\r\nDon't you think this is kind of unexpected behavior from end-user perspective? \r\nAs a user, I expect that when I use `Array2D`, the behavior needs to be consistent even if I specify None or not. It should either return a list or an array. It needs to choose one. Let's say if it always return a list, then I will call `ds.set_format('np')` no problem.\r\n\r\nThe consistency can be in any of these aspects:\r\n1. preserves the type of the input data (in this case, a numpy array)\r\n2. ensure the output type is always the same (it can be either list or array, but it needs to be one of them)\r\n\r\nRight now the API doesn't conform to any of these aspects. But I think it needs to conform to one.", "I thought we made this consistent by returning lists in both scenarios...", "Fixed in #5751 " ]	2023-04-07T21:04:08Z	2023-04-20T15:34:41Z	2023-04-20T15:34:41Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I'm not sure if this is expected behavior or not. When I create a 2D array using `Array2D`, the data has list type instead of numpy array. I think it should not be the expected behavior especially when I feed a numpy array as input to the data creation function. Why is it converting my array into a list? Also if I change the first dimension of the `Array2D` shape to None, it's returning array correctly. ### Steps to reproduce the bug Run this code: ```py from datasets import Dataset, Features, Array2D import numpy as np # you have to change the first dimension of the shape to None to make it return an array features = Features(dict(seq=Array2D((2,2), 'float32'))) ds = Dataset.from_dict(dict(seq=[np.random.rand(2,2)]), features=features) a = ds[0]['seq'] print(a) print(type(a)) ``` The following will be printed in stdout: ``` [[0.8127174377441406, 0.3760348856449127], [0.7510159611701965, 0.4322739541530609]] <class 'list'> ``` ### Expected behavior Each indexed item should be a list or numpy array. Currently, `Array((2,2))` yields a list but `Array((None,2))` yields an array. ### Environment info - `datasets` version: 2.11.0 - Platform: Windows-10-10.0.19045-SP0 - Python version: 3.9.13 - Huggingface_hub version: 0.13.4 - PyArrow version: 11.0.0 - Pandas version: 1.4.4	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5719/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5719/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7466	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7466/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7466/comments	https://api.github.com/repos/huggingface/datasets/issues/7466/events	https://github.com/huggingface/datasets/pull/7466	2,928,661,327	PR_kwDODunzps6PHQyp	7,466	Fix local pdf loading	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7466). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]	2025-03-18T14:09:06Z	2025-03-18T14:11:52Z	2025-03-18T14:09:21Z	MEMBER	null	null	null	fir this error when accessing a local pdf ``` File ~/.pyenv/versions/3.12.2/envs/hf-datasets/lib/python3.12/site-packages/pdfminer/psparser.py:220, in PSBaseParser.seek(self, pos) 218 """Seeks the parser to the given position.""" 219 log.debug("seek: %r", pos) --> 220 self.fp.seek(pos) 221 # reset the status for nextline() 222 self.bufpos = pos ValueError: seek of closed file ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7466/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7466/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7466.diff", "html_url": "https://github.com/huggingface/datasets/pull/7466", "merged_at": "2025-03-18T14:09:21Z", "patch_url": "https://github.com/huggingface/datasets/pull/7466.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7466" }
https://api.github.com/repos/huggingface/datasets/issues/6750	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6750/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6750/comments	https://api.github.com/repos/huggingface/datasets/issues/6750/events	https://github.com/huggingface/datasets/issues/6750	2,203,590,658	I_kwDODunzps6DWCAC	6,750	`load_dataset` requires a network connection for local download?	{ "avatar_url": "https://avatars.githubusercontent.com/u/6306695?v=4", "events_url": "https://api.github.com/users/MiroFurtado/events{/privacy}", "followers_url": "https://api.github.com/users/MiroFurtado/followers", "following_url": "https://api.github.com/users/MiroFurtado/following{/other_user}", "gists_url": "https://api.github.com/users/MiroFurtado/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/MiroFurtado", "id": 6306695, "login": "MiroFurtado", "node_id": "MDQ6VXNlcjYzMDY2OTU=", "organizations_url": "https://api.github.com/users/MiroFurtado/orgs", "received_events_url": "https://api.github.com/users/MiroFurtado/received_events", "repos_url": "https://api.github.com/users/MiroFurtado/repos", "site_admin": false, "starred_url": "https://api.github.com/users/MiroFurtado/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/MiroFurtado/subscriptions", "type": "User", "url": "https://api.github.com/users/MiroFurtado", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Are you using `HF_DATASETS_OFFLINE=1` ?", "> Are you using `HF_DATASETS_OFFLINE=1` ?\r\n\r\nThis doesn't work for me. `datasets=2.18.0`\r\n\r\n`test.py`:\r\n```\r\nimport datasets\r\n\r\ndatasets.utils.logging.set_verbosity_info()\r\n\r\nds = datasets.load_dataset('C-MTEB/AFQMC', revision='b44c3b011063adb25877c13823db83bb193913c4')\r\n\r\nprint(ds)\r\n```\r\n\r\nrun `python test.py`\r\n```\r\nGenerating dataset afqmc (/home/data/.cache/huggingface/datasets/C-MTEB___afqmc/default/0.0.0/b44c3b011063adb25877c13823db83bb193913c4)\r\nDownloading and preparing dataset afqmc/default to /home/data/.cache/huggingface/datasets/C-MTEB___afqmc/default/0.0.0/b44c3b011063adb25877c13823db83bb193913c4...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nhf://datasets/C-MTEB/AFQMC@b44c3b011063adb25877c13823db83bb193913c4/data/validation-00000-of-00001-b8fc393b5ddedac7.parquet not found in cache or force_download set to True, downloading to /home/data/.cache/huggingface/datasets/downloads/78949f93104662359f4f3d5a2f7ec1ae37af5a5af44420a51212ea08c0be966b.incomplete\r\nDownloading data: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 240k/240k [00:01<00:00, 178kB/s]\r\nstoring hf://datasets/C-MTEB/AFQMC@b44c3b011063adb25877c13823db83bb193913c4/data/validation-00000-of-00001-b8fc393b5ddedac7.parquet in cache at /home/data/.cache/huggingface/datasets/downloads/78949f93104662359f4f3d5a2f7ec1ae37af5a5af44420a51212ea08c0be966b\r\ncreating metadata file for /home/data/.cache/huggingface/datasets/downloads/78949f93104662359f4f3d5a2f7ec1ae37af5a5af44420a51212ea08c0be966b\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nGenerating test split\r\nGenerating test split: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3861/3861 [00:00<00:00, 3972.00 examples/s]\r\nGenerating train split\r\nGenerating train split: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 34334/34334 [00:00<00:00, 34355.50 examples/s]\r\nGenerating validation split\r\nGenerating validation split: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 4316/4316 [00:00<00:00, 4477.00 examples/s]\r\nAll the splits matched successfully.\r\nDataset afqmc downloaded and prepared to /home/data/.cache/huggingface/datasets/C-MTEB___afqmc/default/0.0.0/b44c3b011063adb25877c13823db83bb193913c4. Subsequent calls will reuse this data.\r\nDatasetDict({\r\n test: Dataset({\r\n features: ['sentence1', 'sentence2', 'score', 'idx'],\r\n num_rows: 3861\r\n })\r\n train: Dataset({\r\n features: ['sentence1', 'sentence2', 'score', 'idx'],\r\n num_rows: 34334\r\n })\r\n validation: Dataset({\r\n features: ['sentence1', 'sentence2', 'score', 'idx'],\r\n num_rows: 4316\r\n })\r\n})\r\n```\r\n\r\nThen run `HF_DATASETS_OFFLINE=1 python test.py`\r\n```\r\nTraceback (most recent call last):\r\n File \"test.py\", line 9, in <module>\r\n ds = datasets.load_dataset('C-MTEB/AFQMC', revision='b44c3b011063adb25877c13823db83bb193913c4')\r\n File \"/dev/shm/tmp_env/lib/python3.10/site-packages/datasets/load.py\", line 2556, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/dev/shm/tmp_env/lib/python3.10/site-packages/datasets/load.py\", line 2228, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n File \"/dev/shm/tmp_env/lib/python3.10/site-packages/datasets/load.py\", line 1871, in dataset_module_factory\r\n raise ConnectionError(f\"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}\") from None\r\nConnectionError: Couldn't reach the Hugging Face Hub for dataset 'C-MTEB/AFQMC': Offline mode is enabled.\r\n```\r\n\r\n", "I was having similar inexplicable issues.\r\n\r\nDoing this I think helped, but, `datasets` still clearly does not want to respect the cache:\r\n\r\n```python\r\npip install --upgrade datasets # now it is 2.18.0\r\nHF_DATASETS_OFFLINE=\"1\" python blah.py\r\n```\r\n\r\nOr similarly, I must spacify that env var to resuse the cache, IE, no arg to `load_dataset` helps it reuse the cache:\r\n\r\n```python\r\n\r\nimport os\r\nos.environ[\"HF_DATASETS_OFFLINE\"] = \"1\"\r\n\r\nimport logging\r\nlogging.basicConfig(level=logging.DEBUG)\r\n\r\nimport datasets\r\n# >>> datasets.__version__\r\n# '2.18.0'\r\n\r\ndatasets.utils.logging.set_verbosity_info()\r\ndata = datasets.load_dataset(\"c-s-ale/dolly-15k-instruction-alpaca-format\")\r\n```" ]	2024-03-23T01:06:32Z	2024-04-15T15:38:52Z	2024-04-15T15:38:52Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Hi all - I see that in the past a network dependency has been mistakenly introduced into `load_dataset` even for local loads. Is it possible this has happened again? ### Steps to reproduce the bug ``` >>> import datasets >>> datasets.load_dataset("hh-rlhf") Repo card metadata block was not found. Setting CardData to empty. hangs bc i'm firewalled ```` stack trace from ctrl-c: ``` ^CTraceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/load.py", line 2582, in load_dataset builder_instance.download_and_prepare( output_path = get_from_cache( [0/122] File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 532, in get_from_cache response = http_head( File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 419, in http_head response = _request_with_retry( File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 304, in _request_with_retry response = requests.request(method=method.upper(), url=url, timeout=timeout, params) File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/api.py", line 59, in request return session.request(method=method, url=url, kwargs) File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/sessions.py", line 587, in request resp = self.send(prep, send_kwargs) File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/sessions.py", line 701, in send r = adapter.send(request, kwargs) File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/adapters.py", line 487, in send resp = conn.urlopen( File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen httplib_response = self._make_request( File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request self._validate_conn(conn) File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn conn.connect() File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connection.py", line 363, in connect self.sock = conn = self._new_conn() File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn conn = connection.create_connection( File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection sock.connect(sa) KeyboardInterrupt ``` ### Expected behavior loads the dataset ### Environment info ``` > pip show datasets Name: datasets Version: 2.18.0 ``` Python 3.10.2	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6750/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6750/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5905	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5905/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5905/comments	https://api.github.com/repos/huggingface/datasets/issues/5905/events	https://github.com/huggingface/datasets/issues/5905	1,727,541,392	I_kwDODunzps5m-DCQ	5,905	Offer an alternative to Iterable Dataset that allows lazy loading and processing while skipping batches efficiently	{ "avatar_url": "https://avatars.githubusercontent.com/u/48770768?v=4", "events_url": "https://api.github.com/users/bruno-hays/events{/privacy}", "followers_url": "https://api.github.com/users/bruno-hays/followers", "following_url": "https://api.github.com/users/bruno-hays/following{/other_user}", "gists_url": "https://api.github.com/users/bruno-hays/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/bruno-hays", "id": 48770768, "login": "bruno-hays", "node_id": "MDQ6VXNlcjQ4NzcwNzY4", "organizations_url": "https://api.github.com/users/bruno-hays/orgs", "received_events_url": "https://api.github.com/users/bruno-hays/received_events", "repos_url": "https://api.github.com/users/bruno-hays/repos", "site_admin": false, "starred_url": "https://api.github.com/users/bruno-hays/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/bruno-hays/subscriptions", "type": "User", "url": "https://api.github.com/users/bruno-hays", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	[ "We plan to improve this eventually (see https://github.com/huggingface/datasets/issues/5454 and https://github.com/huggingface/datasets/issues/5380).\r\n\r\n> Is it possible to lazily load samples of a mapped dataset ? I'm used to [dataset scripts](https://huggingface.co/docs/datasets/dataset_script), maybe something can be done there.\r\nIf not, I could do it using a plain Pytorch dataset. Then I would need to convert it to a datasets' dataset to get all the features of datasets. Is it something possible ?\r\n\r\nYes, by creating a mapped dataset that stores audio URLs. Indexing a dataset in such format only downloads and decodes the bytes of the accessed samples (without storing them on disk).\r\n\r\nYou can do the following to create this dataset:\r\n```python\r\n\r\ndef gen():\r\n # Generator that yields (audio URL, text) pairs as dict\r\n ...\r\n yield {\"audio\": \"audio_url\", \"text\": \"some text\"}\r\n\r\nfeatures = Features({\"audio\": datasets.Audio(), \"text\": datasets.Value(\"string\")})\r\nds = Dataset.from_generator(gen, features=features)\r\nds[2:5] # downloads and decodes the samples each time they are accessed\r\n```" ]	2023-05-26T12:33:02Z	2023-06-15T13:34:18Z	null	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request I would like a way to resume training from a checkpoint without waiting for a very long time when using an iterable dataset. ### Motivation I am training models on the speech-recognition task. I have very large datasets that I can't comfortably store on a disk and also quite computationally intensive audio processing to do. As a result I want to load data from my remote when it is needed and perform all processing on the fly. I am currently using the iterable dataset feature of _datasets_. It does everything I need with one exception. My issue is that when resuming training at a step n, we have to download all the data and perform the processing of steps < n, just to get the iterable at the right step. In my case it takes almost as long as training for the same steps, which make resuming training from a checkpoint useless in practice. I understand that the nature of iterators make it probably nearly impossible to quickly resume training. I thought about a possible solution nonetheless : I could in fact index my large dataset and make it a mapped dataset. Then I could use set_transform to perform the processing on the fly. Finally, if I'm not mistaken, the _accelerate_ package allows to [skip steps efficiently](https://github.com/huggingface/accelerate/blob/a73898027a211c3f6dc4460351b0ec246aa824aa/src/accelerate/data_loader.py#L827) for a mapped dataset. Is it possible to lazily load samples of a mapped dataset ? I'm used to [dataset scripts](https://huggingface.co/docs/datasets/dataset_script), maybe something can be done there. If not, I could do it using a plain _Pytorch_ dataset. Then I would need to convert it to a _datasets_' dataset to get all the features of _datasets_. Is it something possible ? ### Your contribution I could provide a PR to allow lazy loading of mapped dataset or the conversion of a mapped _Pytorch_ dataset into a _Datasets_ dataset if you think it is an useful new feature.	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/5905/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5905/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7503	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7503/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7503/comments	https://api.github.com/repos/huggingface/datasets/issues/7503/events	https://github.com/huggingface/datasets/issues/7503	2,978,512,625	I_kwDODunzps6xiH7x	7,503	Inconsistency between load_dataset and load_from_disk functionality	{ "avatar_url": "https://avatars.githubusercontent.com/u/60975422?v=4", "events_url": "https://api.github.com/users/zzzzzec/events{/privacy}", "followers_url": "https://api.github.com/users/zzzzzec/followers", "following_url": "https://api.github.com/users/zzzzzec/following{/other_user}", "gists_url": "https://api.github.com/users/zzzzzec/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/zzzzzec", "id": 60975422, "login": "zzzzzec", "node_id": "MDQ6VXNlcjYwOTc1NDIy", "organizations_url": "https://api.github.com/users/zzzzzec/orgs", "received_events_url": "https://api.github.com/users/zzzzzec/received_events", "repos_url": "https://api.github.com/users/zzzzzec/repos", "site_admin": false, "starred_url": "https://api.github.com/users/zzzzzec/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/zzzzzec/subscriptions", "type": "User", "url": "https://api.github.com/users/zzzzzec", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Hi ! you can find more info here: https://github.com/huggingface/datasets/issues/5044#issuecomment-1263714347\n\n> What's the recommended approach for this use case? Should I manually process my gsm8k-new dataset to make it compatible with load_dataset? Is there a standard way to convert between these formats?\n\nYou can use push_to_hub() or to_parquet() for example" ]	2025-04-08T03:46:22Z	2025-04-15T12:39:53Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	## Issue Description I've encountered confusion when using `load_dataset` and `load_from_disk` in the datasets library. Specifically, when working offline with the gsm8k dataset, I can load it using a local path: ```python import datasets ds = datasets.load_dataset('/root/xxx/datasets/gsm8k', 'main') ``` output: ```text DatasetDict({ train: Dataset({ features: ['question', 'answer'], num_rows: 7473 }) test: Dataset({ features: ['question', 'answer'], num_rows: 1319 }) }) ``` This works as expected. However, after processing the dataset (converting answer format from #### to \boxed{}) ```python import datasets ds = datasets.load_dataset('/root/xxx/datasets/gsm8k', 'main') ds_train = ds['train'] ds_test = ds['test'] import re def convert(sample): solution = sample['answer'] solution = re.sub(r'####\s*(\S+)', r'\\boxed{\1}', solution) sample = { 'problem': sample['question'], 'solution': solution } return sample ds_train = ds_train.map(convert, remove_columns=['question', 'answer']) ds_test = ds_test.map(convert,remove_columns=['question', 'answer']) ``` I saved it using save_to_disk: ```python from datasets.dataset_dict import DatasetDict data_dict = DatasetDict({ 'train': ds_train, 'test': ds_test }) data_dict.save_to_disk('/root/xxx/datasets/gsm8k-new') ``` But now I can only load it using load_from_disk: ```python new_ds = load_from_disk('/root/xxx/datasets/gsm8k-new') ``` output: ```text DatasetDict({ train: Dataset({ features: ['problem', 'solution'], num_rows: 7473 }) test: Dataset({ features: ['problem', 'solution'], num_rows: 1319 }) }) ``` Attempting to use load_dataset produces unexpected results: ```python new_ds = load_dataset('/root/xxx/datasets/gsm8k-new') ``` output: ```text DatasetDict({ train: Dataset({ features: ['_data_files', '_fingerprint', '_format_columns', '_format_kwargs', '_format_type', '_output_all_columns', '_split'], num_rows: 1 }) test: Dataset({ features: ['_data_files', '_fingerprint', '_format_columns', '_format_kwargs', '_format_type', '_output_all_columns', '_split'], num_rows: 1 }) }) ``` Questions 1. Why is it designed such that after using `save_to_disk`, the dataset cannot be loaded with `load_dataset`? For small projects with limited code, it might be relatively easy to change all instances of `load_dataset` to `load_from_disk`. However, for complex frameworks like TRL or lighteval, diving into the framework code to change `load_dataset` to `load_from_disk` is extremely tedious and error-prone. Additionally, `load_from_disk` cannot load datasets directly downloaded from the hub, which means that if you need to modify a dataset, you have to choose between using `load_from_disk` or `load_dataset`. This creates an unnecessary dichotomy in the API and complicates workflow when working with modified datasets. 2. What's the recommended approach for this use case? Should I manually process my gsm8k-new dataset to make it compatible with load_dataset? Is there a standard way to convert between these formats? thanks~	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/7503/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7503/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7518	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7518/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7518/comments	https://api.github.com/repos/huggingface/datasets/issues/7518/events	https://github.com/huggingface/datasets/issues/7518	2,996,141,825	I_kwDODunzps6ylX8B	7,518	num_proc parallelization works only for first ~10s.	{ "avatar_url": "https://avatars.githubusercontent.com/u/33901783?v=4", "events_url": "https://api.github.com/users/pshishodiaa/events{/privacy}", "followers_url": "https://api.github.com/users/pshishodiaa/followers", "following_url": "https://api.github.com/users/pshishodiaa/following{/other_user}", "gists_url": "https://api.github.com/users/pshishodiaa/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/pshishodiaa", "id": 33901783, "login": "pshishodiaa", "node_id": "MDQ6VXNlcjMzOTAxNzgz", "organizations_url": "https://api.github.com/users/pshishodiaa/orgs", "received_events_url": "https://api.github.com/users/pshishodiaa/received_events", "repos_url": "https://api.github.com/users/pshishodiaa/repos", "site_admin": false, "starred_url": "https://api.github.com/users/pshishodiaa/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/pshishodiaa/subscriptions", "type": "User", "url": "https://api.github.com/users/pshishodiaa", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Hi, can you check if the processes are still alive ? It's a bit weird because `datasets` does check if processes crash and return an error in that case", "Thank you for reverting quickly. I digged a bit, and realized my disk's IOPS is also limited - which is causing this. will check further and report if it's an issue of hf datasets' side or mine. " ]	2025-04-15T11:44:03Z	2025-04-15T13:12:13Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug When I try to load an already downloaded dataset with num_proc=64, the speed is very high for the first 10-20 seconds acheiving 30-40K samples / s, and 100% utilization for all cores but it soon drops to <= 1000 with almost 0% utilization for most cores. ### Steps to reproduce the bug ``` // download dataset with cli !huggingface-cli download --repo-type dataset timm/imagenet-1k-wds --max-workers 32 from datasets import load_dataset ds = load_dataset("timm/imagenet-1k-wds", num_proc=64) ``` ### Expected behavior 100% core utilization throughout. ### Environment info Azure A100-80GB, 16 cores VM ![Image](https://github.com/user-attachments/assets/69d00fe3-d720-4474-9439-21e046d85034)	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/7518/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7518/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5607	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5607/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5607/comments	https://api.github.com/repos/huggingface/datasets/issues/5607/events	https://github.com/huggingface/datasets/pull/5607	1,609,166,035	PR_kwDODunzps5LQPbG	5,607	Fix outdated `verification_mode` values	{ "avatar_url": "https://avatars.githubusercontent.com/u/16348744?v=4", "events_url": "https://api.github.com/users/polinaeterna/events{/privacy}", "followers_url": "https://api.github.com/users/polinaeterna/followers", "following_url": "https://api.github.com/users/polinaeterna/following{/other_user}", "gists_url": "https://api.github.com/users/polinaeterna/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/polinaeterna", "id": 16348744, "login": "polinaeterna", "node_id": "MDQ6VXNlcjE2MzQ4NzQ0", "organizations_url": "https://api.github.com/users/polinaeterna/orgs", "received_events_url": "https://api.github.com/users/polinaeterna/received_events", "repos_url": "https://api.github.com/users/polinaeterna/repos", "site_admin": false, "starred_url": "https://api.github.com/users/polinaeterna/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/polinaeterna/subscriptions", "type": "User", "url": "https://api.github.com/users/polinaeterna", "user_view_type": "public" }	[]	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) \| 0.006142 / 0.011353 (-0.005211) \| 0.004506 / 0.011008 (-0.006502) \| 0.100224 / 0.038508 (0.061715) \| 0.026988 / 0.023109 (0.003879) \| 0.301625 / 0.275898 (0.025727) \| 0.346337 / 0.323480 (0.022857) \| 0.004642 / 0.007986 (-0.003343) \| 0.003481 / 0.004328 (-0.000847) \| 0.075847 / 0.004250 (0.071597) \| 0.036959 / 0.037052 (-0.000094) \| 0.302697 / 0.258489 (0.044208) \| 0.351917 / 0.293841 (0.058076) \| 0.030719 / 0.128546 (-0.097828) \| 0.011591 / 0.075646 (-0.064056) \| 0.319709 / 0.419271 (-0.099563) \| 0.042000 / 0.043533 (-0.001532) \| 0.306854 / 0.255139 (0.051715) \| 0.326903 / 0.283200 (0.043703) \| 0.082711 / 0.141683 (-0.058972) \| 1.486616 / 1.452155 (0.034461) \| 1.603229 / 1.492716 (0.110513) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.198990 / 0.018006 (0.180983) \| 0.427733 / 0.000490 (0.427243) \| 0.003612 / 0.000200 (0.003412) \| 0.000071 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022932 / 0.037411 (-0.014480) \| 0.096969 / 0.014526 (0.082443) \| 0.105749 / 0.176557 (-0.070807) \| 0.166101 / 0.737135 (-0.571034) \| 0.108646 / 0.296338 (-0.187692) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.428174 / 0.215209 (0.212965) \| 4.271452 / 2.077655 (2.193797) \| 1.907588 / 1.504120 (0.403468) \| 1.680870 / 1.541195 (0.139675) \| 1.761336 / 1.468490 (0.292846) \| 0.700380 / 4.584777 (-3.884396) \| 3.415168 / 3.745712 (-0.330544) \| 1.886122 / 5.269862 (-3.383740) \| 1.276814 / 4.565676 (-3.288863) \| 0.083429 / 0.424275 (-0.340846) \| 0.012988 / 0.007607 (0.005381) \| 0.518821 / 0.226044 (0.292776) \| 5.188284 / 2.268929 (2.919356) \| 2.433084 / 55.444624 (-53.011540) \| 1.988034 / 6.876477 (-4.888443) \| 2.100275 / 2.142072 (-0.041797) \| 0.808252 / 4.805227 (-3.996976) \| 0.158102 / 6.500664 (-6.342562) \| 0.067686 / 0.075469 (-0.007783) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.204171 / 1.841788 (-0.637616) \| 13.548756 / 8.074308 (5.474448) \| 14.339805 / 10.191392 (4.148413) \| 0.142853 / 0.680424 (-0.537571) \| 0.016529 / 0.534201 (-0.517672) \| 0.383800 / 0.579283 (-0.195483) \| 0.380362 / 0.434364 (-0.054002) \| 0.437716 / 0.540337 (-0.102621) \| 0.524306 / 1.386936 (-0.862630) \|\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) \| 0.006730 / 0.011353 (-0.004623) \| 0.004652 / 0.011008 (-0.006356) \| 0.077476 / 0.038508 (0.038968) \| 0.027584 / 0.023109 (0.004475) \| 0.340907 / 0.275898 (0.065009) \| 0.377950 / 0.323480 (0.054470) \| 0.005946 / 0.007986 (-0.002040) \| 0.003548 / 0.004328 (-0.000780) \| 0.076270 / 0.004250 (0.072019) \| 0.037483 / 0.037052 (0.000431) \| 0.346390 / 0.258489 (0.087901) \| 0.384739 / 0.293841 (0.090898) \| 0.031744 / 0.128546 (-0.096802) \| 0.011598 / 0.075646 (-0.064049) \| 0.085651 / 0.419271 (-0.333620) \| 0.047308 / 0.043533 (0.003775) \| 0.344704 / 0.255139 (0.089565) \| 0.363410 / 0.283200 (0.080211) \| 0.095009 / 0.141683 (-0.046674) \| 1.478307 / 1.452155 (0.026152) \| 1.576808 / 1.492716 (0.084092) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.197545 / 0.018006 (0.179539) \| 0.431984 / 0.000490 (0.431494) \| 0.001529 / 0.000200 (0.001329) \| 0.000079 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025452 / 0.037411 (-0.011959) \| 0.100176 / 0.014526 (0.085651) \| 0.108222 / 0.176557 (-0.068335) \| 0.160556 / 0.737135 (-0.576580) \| 0.112748 / 0.296338 (-0.183591) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436326 / 0.215209 (0.221117) \| 4.378443 / 2.077655 (2.300788) \| 2.056001 / 1.504120 (0.551881) \| 1.853406 / 1.541195 (0.312211) \| 1.931645 / 1.468490 (0.463155) \| 0.698340 / 4.584777 (-3.886437) \| 3.368961 / 3.745712 (-0.376751) \| 2.583622 / 5.269862 (-2.686239) \| 1.501274 / 4.565676 (-3.064402) \| 0.083034 / 0.424275 (-0.341241) \| 0.012725 / 0.007607 (0.005117) \| 0.539991 / 0.226044 (0.313947) \| 5.418413 / 2.268929 (3.149485) \| 2.517205 / 55.444624 (-52.927420) \| 2.179332 / 6.876477 (-4.697144) \| 2.215376 / 2.142072 (0.073304) \| 0.806133 / 4.805227 (-3.999094) \| 0.151499 / 6.500664 (-6.349165) \| 0.067270 / 0.075469 (-0.008199) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.308324 / 1.841788 (-0.533464) \| 14.357361 / 8.074308 (6.283053) \| 14.684768 / 10.191392 (4.493376) \| 0.139575 / 0.680424 (-0.540849) \| 0.016409 / 0.534201 (-0.517792) \| 0.374087 / 0.579283 (-0.205196) \| 0.390628 / 0.434364 (-0.043735) \| 0.443102 / 0.540337 (-0.097235) \| 0.536089 / 1.386936 (-0.850847) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#778d4e1c13ece980e706f8c7cb06e8473fd61315 \"CML watermark\")\n" ]	2023-03-03T19:50:29Z	2023-03-09T17:34:13Z	2023-03-09T17:27:07Z	CONTRIBUTOR	null	null	null	~I think it makes sense not to save `dataset_info.json` file to a dataset cache directory when loading dataset with `verification_mode="no_checks"` because otherwise when next time the dataset is loaded without `verification_mode="no_checks"`, it will be loaded successfully, despite some values in info might not correspond to the ones in the repo which was the reason for using `verification_mode="no_checks"` first.~ Updated values of `verification_mode` to the current ones in some places ("none" -> "no_checks", "all" -> "all_checks")	{ "avatar_url": "https://avatars.githubusercontent.com/u/16348744?v=4", "events_url": "https://api.github.com/users/polinaeterna/events{/privacy}", "followers_url": "https://api.github.com/users/polinaeterna/followers", "following_url": "https://api.github.com/users/polinaeterna/following{/other_user}", "gists_url": "https://api.github.com/users/polinaeterna/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/polinaeterna", "id": 16348744, "login": "polinaeterna", "node_id": "MDQ6VXNlcjE2MzQ4NzQ0", "organizations_url": "https://api.github.com/users/polinaeterna/orgs", "received_events_url": "https://api.github.com/users/polinaeterna/received_events", "repos_url": "https://api.github.com/users/polinaeterna/repos", "site_admin": false, "starred_url": "https://api.github.com/users/polinaeterna/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/polinaeterna/subscriptions", "type": "User", "url": "https://api.github.com/users/polinaeterna", "user_view_type": "public" }	{ "+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/5607/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5607/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5607.diff", "html_url": "https://github.com/huggingface/datasets/pull/5607", "merged_at": "2023-03-09T17:27:07Z", "patch_url": "https://github.com/huggingface/datasets/pull/5607.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5607" }
https://api.github.com/repos/huggingface/datasets/issues/4560	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4560/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4560/comments	https://api.github.com/repos/huggingface/datasets/issues/4560/events	https://github.com/huggingface/datasets/pull/4560	1,283,558,873	PR_kwDODunzps46TY9n	4,560	Add evaluation metadata to imagenet-1k	{ "avatar_url": "https://avatars.githubusercontent.com/u/26859204?v=4", "events_url": "https://api.github.com/users/lewtun/events{/privacy}", "followers_url": "https://api.github.com/users/lewtun/followers", "following_url": "https://api.github.com/users/lewtun/following{/other_user}", "gists_url": "https://api.github.com/users/lewtun/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lewtun", "id": 26859204, "login": "lewtun", "node_id": "MDQ6VXNlcjI2ODU5MjA0", "organizations_url": "https://api.github.com/users/lewtun/orgs", "received_events_url": "https://api.github.com/users/lewtun/received_events", "repos_url": "https://api.github.com/users/lewtun/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lewtun/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lewtun/subscriptions", "type": "User", "url": "https://api.github.com/users/lewtun", "user_view_type": "public" }	[ { "color": "0e8a16", "default": false, "description": "Contribution to a dataset script", "id": 4564477500, "name": "dataset contribution", "node_id": "LA_kwDODunzps8AAAABEBBmPA", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20contribution" } ]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "As discussed with @lewtun, we are closing this PR, because it requires first the task names to be aligned between AutoTrain and datasets." ]	2022-06-24T10:12:41Z	2023-09-24T09:35:32Z	2022-09-23T09:37:03Z	MEMBER	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4560/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4560/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4560.diff", "html_url": "https://github.com/huggingface/datasets/pull/4560", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/4560.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4560" }
https://api.github.com/repos/huggingface/datasets/issues/6247	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6247/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6247/comments	https://api.github.com/repos/huggingface/datasets/issues/6247/events	https://github.com/huggingface/datasets/pull/6247	1,901,390,945	PR_kwDODunzps5amAQ1	6,247	Update create_dataset.mdx	{ "avatar_url": "https://avatars.githubusercontent.com/u/76403422?v=4", "events_url": "https://api.github.com/users/EswarDivi/events{/privacy}", "followers_url": "https://api.github.com/users/EswarDivi/followers", "following_url": "https://api.github.com/users/EswarDivi/following{/other_user}", "gists_url": "https://api.github.com/users/EswarDivi/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/EswarDivi", "id": 76403422, "login": "EswarDivi", "node_id": "MDQ6VXNlcjc2NDAzNDIy", "organizations_url": "https://api.github.com/users/EswarDivi/orgs", "received_events_url": "https://api.github.com/users/EswarDivi/received_events", "repos_url": "https://api.github.com/users/EswarDivi/repos", "site_admin": false, "starred_url": "https://api.github.com/users/EswarDivi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/EswarDivi/subscriptions", "type": "User", "url": "https://api.github.com/users/EswarDivi", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008892 / 0.011353 (-0.002461) \| 0.005140 / 0.011008 (-0.005868) \| 0.110951 / 0.038508 (0.072442) \| 0.086159 / 0.023109 (0.063050) \| 0.391117 / 0.275898 (0.115218) \| 0.440884 / 0.323480 (0.117404) \| 0.006562 / 0.007986 (-0.001423) \| 0.003711 / 0.004328 (-0.000618) \| 0.081848 / 0.004250 (0.077598) \| 0.063187 / 0.037052 (0.026135) \| 0.369771 / 0.258489 (0.111282) \| 0.447685 / 0.293841 (0.153844) \| 0.046623 / 0.128546 (-0.081923) \| 0.014024 / 0.075646 (-0.061622) \| 0.418556 / 0.419271 (-0.000715) \| 0.064660 / 0.043533 (0.021127) \| 0.379416 / 0.255139 (0.124277) \| 0.415800 / 0.283200 (0.132600) \| 0.036899 / 0.141683 (-0.104784) \| 1.710280 / 1.452155 (0.258125) \| 1.932326 / 1.492716 (0.439610) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.311351 / 0.018006 (0.293345) \| 0.621121 / 0.000490 (0.620631) \| 0.013677 / 0.000200 (0.013477) \| 0.000543 / 0.000054 (0.000488) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031310 / 0.037411 (-0.006102) \| 0.099546 / 0.014526 (0.085020) \| 0.122100 / 0.176557 (-0.054457) \| 0.186477 / 0.737135 (-0.550659) \| 0.116634 / 0.296338 (-0.179704) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.574639 / 0.215209 (0.359430) \| 5.976678 / 2.077655 (3.899023) \| 2.535482 / 1.504120 (1.031362) \| 2.248873 / 1.541195 (0.707678) \| 2.361696 / 1.468490 (0.893205) \| 0.866700 / 4.584777 (-3.718077) \| 5.298018 / 3.745712 (1.552306) \| 4.753240 / 5.269862 (-0.516622) \| 3.124698 / 4.565676 (-1.440979) \| 0.101852 / 0.424275 (-0.322423) \| 0.009117 / 0.007607 (0.001510) \| 0.723730 / 0.226044 (0.497685) \| 7.172649 / 2.268929 (4.903720) \| 3.400410 / 55.444624 (-52.044214) \| 2.626619 / 6.876477 (-4.249857) \| 2.948692 / 2.142072 (0.806620) \| 0.991589 / 4.805227 (-3.813638) \| 0.208902 / 6.500664 (-6.291762) \| 0.076172 / 0.075469 (0.000703) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.621880 / 1.841788 (-0.219907) \| 22.735673 / 8.074308 (14.661365) \| 20.376990 / 10.191392 (10.185598) \| 0.232219 / 0.680424 (-0.448204) \| 0.028616 / 0.534201 (-0.505585) \| 0.455725 / 0.579283 (-0.123558) \| 0.562796 / 0.434364 (0.128432) \| 0.545344 / 0.540337 (0.005007) \| 0.759440 / 1.386936 (-0.627496) \|\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) \| 0.009845 / 0.011353 (-0.001508) \| 0.005289 / 0.011008 (-0.005719) \| 0.083117 / 0.038508 (0.044609) \| 0.098467 / 0.023109 (0.075357) \| 0.532345 / 0.275898 (0.256447) \| 0.571000 / 0.323480 (0.247520) \| 0.007223 / 0.007986 (-0.000763) \| 0.004442 / 0.004328 (0.000114) \| 0.081710 / 0.004250 (0.077459) \| 0.071132 / 0.037052 (0.034080) \| 0.540093 / 0.258489 (0.281604) \| 0.582244 / 0.293841 (0.288403) \| 0.048509 / 0.128546 (-0.080038) \| 0.013897 / 0.075646 (-0.061749) \| 0.092579 / 0.419271 (-0.326692) \| 0.073409 / 0.043533 (0.029876) \| 0.537369 / 0.255139 (0.282230) \| 0.551403 / 0.283200 (0.268203) \| 0.038847 / 0.141683 (-0.102835) \| 1.940848 / 1.452155 (0.488693) \| 2.045597 / 1.492716 (0.552881) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.303883 / 0.018006 (0.285877) \| 0.600237 / 0.000490 (0.599748) \| 0.006030 / 0.000200 (0.005830) \| 0.000124 / 0.000054 (0.000070) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036633 / 0.037411 (-0.000778) \| 0.105853 / 0.014526 (0.091327) \| 0.126289 / 0.176557 (-0.050267) \| 0.190022 / 0.737135 (-0.547113) \| 0.123251 / 0.296338 (-0.173087) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.711893 / 0.215209 (0.496684) \| 6.979781 / 2.077655 (4.902126) \| 3.491514 / 1.504120 (1.987394) \| 3.268077 / 1.541195 (1.726882) \| 3.241777 / 1.468490 (1.773287) \| 0.875913 / 4.584777 (-3.708864) \| 5.458421 / 3.745712 (1.712709) \| 4.818355 / 5.269862 (-0.451507) \| 3.256046 / 4.565676 (-1.309631) \| 0.095000 / 0.424275 (-0.329275) \| 0.009072 / 0.007607 (0.001465) \| 0.818468 / 0.226044 (0.592424) \| 8.027702 / 2.268929 (5.758773) \| 4.363234 / 55.444624 (-51.081390) \| 3.695269 / 6.876477 (-3.181207) \| 3.902601 / 2.142072 (1.760528) \| 1.039007 / 4.805227 (-3.766220) \| 0.212050 / 6.500664 (-6.288614) \| 0.081438 / 0.075469 (0.005969) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.746945 / 1.841788 (-0.094842) \| 25.274283 / 8.074308 (17.199975) \| 23.514717 / 10.191392 (13.323325) \| 0.232580 / 0.680424 (-0.447843) \| 0.032083 / 0.534201 (-0.502118) \| 0.482873 / 0.579283 (-0.096410) \| 0.585730 / 0.434364 (0.151366) \| 0.602066 / 0.540337 (0.061729) \| 0.796391 / 1.386936 (-0.590546) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0d7cb68fe37dbfd81e5f82e19d8f9847c337788d \"CML watermark\")\n" ]	2023-09-18T17:06:29Z	2023-09-19T18:51:49Z	2023-09-19T18:40:10Z	CONTRIBUTOR	null	null	null	modified , as AudioFolder and ImageFolder not in Dataset Library. ``` from datasets import AudioFolder ``` and ```from datasets import ImageFolder``` to ```from datasets import load_dataset``` ``` cannot import name 'AudioFolder' from 'datasets' (/home/eswardivi/miniconda3/envs/Hugformers/lib/python3.10/site-packages/datasets/__init__.py) ```	{ "avatar_url": 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"confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6247/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6247/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6247.diff", "html_url": "https://github.com/huggingface/datasets/pull/6247", "merged_at": "2023-09-19T18:40:10Z", "patch_url": "https://github.com/huggingface/datasets/pull/6247.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6247" }
https://api.github.com/repos/huggingface/datasets/issues/4746	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4746/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4746/comments	https://api.github.com/repos/huggingface/datasets/issues/4746/events	https://github.com/huggingface/datasets/issues/4746	1,318,486,599	I_kwDODunzps5OloJH	4,746	Dataset Viewer issue for yanekyuk/wikikey	{ "avatar_url": "https://avatars.githubusercontent.com/u/91247690?v=4", "events_url": "https://api.github.com/users/ai-ashok/events{/privacy}", "followers_url": "https://api.github.com/users/ai-ashok/followers", "following_url": "https://api.github.com/users/ai-ashok/following{/other_user}", "gists_url": "https://api.github.com/users/ai-ashok/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ai-ashok", "id": 91247690, "login": "ai-ashok", "node_id": "MDQ6VXNlcjkxMjQ3Njkw", "organizations_url": "https://api.github.com/users/ai-ashok/orgs", "received_events_url": "https://api.github.com/users/ai-ashok/received_events", "repos_url": "https://api.github.com/users/ai-ashok/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ai-ashok/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ai-ashok/subscriptions", "type": "User", "url": "https://api.github.com/users/ai-ashok", "user_view_type": "public" }	[ { "color": "E5583E", "default": false, "description": "Related to the dataset viewer on huggingface.co", "id": 3470211881, "name": "dataset-viewer", "node_id": "LA_kwDODunzps7O1zsp", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset-viewer" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" } ]	null	[ "The dataset is empty, as far as I can tell: there are no files in the repository at https://huggingface.co/datasets/yanekyuk/wikikey/tree/main\r\n\r\nMaybe the viewer can display a better message for empty datasets", "OK. Closing as it's not an error. We will work on making the error message a lot clearer." ]	2022-07-26T16:25:16Z	2022-09-08T08:15:22Z	2022-09-08T08:15:22Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Link _No response_ ### Description _No response_ ### Owner _No response_	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	{ "+1": 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/4746/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4746/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6904	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6904/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6904/comments	https://api.github.com/repos/huggingface/datasets/issues/6904/events	https://github.com/huggingface/datasets/pull/6904	2,302,912,179	PR_kwDODunzps5vzRlD	6,904	Fix decoding multi part extension	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6904). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "takign the liberty to merge this for the viewer and a new dataset being released", "<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) \| 0.005004 / 0.011353 (-0.006349) \| 0.003352 / 0.011008 (-0.007657) \| 0.063035 / 0.038508 (0.024527) \| 0.032031 / 0.023109 (0.008922) \| 0.244801 / 0.275898 (-0.031097) \| 0.270622 / 0.323480 (-0.052857) \| 0.003110 / 0.007986 (-0.004876) \| 0.002629 / 0.004328 (-0.001700) \| 0.048784 / 0.004250 (0.044534) \| 0.045779 / 0.037052 (0.008726) \| 0.258642 / 0.258489 (0.000153) \| 0.291606 / 0.293841 (-0.002235) \| 0.028237 / 0.128546 (-0.100310) \| 0.010184 / 0.075646 (-0.065463) \| 0.202455 / 0.419271 (-0.216816) \| 0.036012 / 0.043533 (-0.007521) \| 0.248209 / 0.255139 (-0.006930) \| 0.267315 / 0.283200 (-0.015884) \| 0.019249 / 0.141683 (-0.122434) \| 1.120420 / 1.452155 (-0.331735) \| 1.169515 / 1.492716 (-0.323201) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095193 / 0.018006 (0.077187) \| 0.300544 / 0.000490 (0.300055) \| 0.000214 / 0.000200 (0.000014) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019001 / 0.037411 (-0.018411) \| 0.061857 / 0.014526 (0.047331) \| 0.073379 / 0.176557 (-0.103178) \| 0.121293 / 0.737135 (-0.615843) \| 0.075665 / 0.296338 (-0.220673) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285153 / 0.215209 (0.069944) \| 2.875527 / 2.077655 (0.797873) \| 1.479851 / 1.504120 (-0.024269) \| 1.360691 / 1.541195 (-0.180504) \| 1.385581 / 1.468490 (-0.082909) \| 0.566312 / 4.584777 (-4.018465) \| 2.400202 / 3.745712 (-1.345510) \| 2.719241 / 5.269862 (-2.550620) \| 1.706469 / 4.565676 (-2.859208) \| 0.062129 / 0.424275 (-0.362146) \| 0.005291 / 0.007607 (-0.002316) \| 0.334585 / 0.226044 (0.108540) \| 3.293347 / 2.268929 (1.024419) \| 1.790490 / 55.444624 (-53.654134) \| 1.505519 / 6.876477 (-5.370958) \| 1.527730 / 2.142072 (-0.614343) \| 0.644554 / 4.805227 (-4.160673) \| 0.119775 / 6.500664 (-6.380889) \| 0.056912 / 0.075469 (-0.018557) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.977512 / 1.841788 (-0.864275) \| 11.293883 / 8.074308 (3.219575) \| 9.669439 / 10.191392 (-0.521953) \| 0.129910 / 0.680424 (-0.550514) \| 0.014322 / 0.534201 (-0.519879) \| 0.284967 / 0.579283 (-0.294316) \| 0.265355 / 0.434364 (-0.169008) \| 0.321965 / 0.540337 (-0.218372) \| 0.415254 / 1.386936 (-0.971682) \|\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) \| 0.005138 / 0.011353 (-0.006215) \| 0.003321 / 0.011008 (-0.007687) \| 0.049731 / 0.038508 (0.011223) \| 0.032307 / 0.023109 (0.009198) \| 0.266331 / 0.275898 (-0.009567) \| 0.290863 / 0.323480 (-0.032617) \| 0.004151 / 0.007986 (-0.003835) \| 0.002684 / 0.004328 (-0.001644) \| 0.048760 / 0.004250 (0.044510) \| 0.042251 / 0.037052 (0.005199) \| 0.280414 / 0.258489 (0.021925) \| 0.305089 / 0.293841 (0.011248) \| 0.029118 / 0.128546 (-0.099428) \| 0.010276 / 0.075646 (-0.065370) \| 0.057790 / 0.419271 (-0.361482) \| 0.033290 / 0.043533 (-0.010243) \| 0.267250 / 0.255139 (0.012111) \| 0.285233 / 0.283200 (0.002034) \| 0.018587 / 0.141683 (-0.123096) \| 1.136198 / 1.452155 (-0.315957) \| 1.185274 / 1.492716 (-0.307442) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096355 / 0.018006 (0.078349) \| 0.301827 / 0.000490 (0.301337) \| 0.000216 / 0.000200 (0.000016) \| 0.000052 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022607 / 0.037411 (-0.014805) \| 0.075724 / 0.014526 (0.061198) \| 0.088197 / 0.176557 (-0.088359) \| 0.127864 / 0.737135 (-0.609271) \| 0.089294 / 0.296338 (-0.207044) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289321 / 0.215209 (0.074112) \| 2.832456 / 2.077655 (0.754802) \| 1.559208 / 1.504120 (0.055088) \| 1.426229 / 1.541195 (-0.114966) \| 1.424564 / 1.468490 (-0.043926) \| 0.557754 / 4.584777 (-4.027023) \| 0.940179 / 3.745712 (-2.805533) \| 2.713640 / 5.269862 (-2.556222) \| 1.697583 / 4.565676 (-2.868093) \| 0.062024 / 0.424275 (-0.362251) \| 0.005270 / 0.007607 (-0.002337) \| 0.339450 / 0.226044 (0.113406) \| 3.333024 / 2.268929 (1.064096) \| 1.946087 / 55.444624 (-53.498537) \| 1.601057 / 6.876477 (-5.275420) \| 1.599862 / 2.142072 (-0.542210) \| 0.642838 / 4.805227 (-4.162390) \| 0.120470 / 6.500664 (-6.380194) \| 0.040815 / 0.075469 (-0.034654) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.012904 / 1.841788 (-0.828884) \| 11.917035 / 8.074308 (3.842727) \| 9.717822 / 10.191392 (-0.473570) \| 0.141730 / 0.680424 (-0.538694) \| 0.015750 / 0.534201 (-0.518451) \| 0.284470 / 0.579283 (-0.294813) \| 0.125662 / 0.434364 (-0.308702) \| 0.380740 / 0.540337 (-0.159598) \| 0.418119 / 1.386936 (-0.968817) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b3f772468b2bbf77a7510e265f9d41e9eb77d53f \"CML watermark\")\n" ]	2024-05-17T14:32:57Z	2024-05-17T14:52:56Z	2024-05-17T14:46:54Z	MEMBER	null	null	null	e.g. a field named `url.txt` should be a treated as text I also included a small fix to support .npz correctly	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6904/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6904/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6904.diff", "html_url": "https://github.com/huggingface/datasets/pull/6904", "merged_at": "2024-05-17T14:46:54Z", "patch_url": "https://github.com/huggingface/datasets/pull/6904.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6904" }
https://api.github.com/repos/huggingface/datasets/issues/5651	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5651/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5651/comments	https://api.github.com/repos/huggingface/datasets/issues/5651/events	https://github.com/huggingface/datasets/issues/5651	1,631,967,509	I_kwDODunzps5hRdkV	5,651	expanduser in save_to_disk	{ "avatar_url": "https://avatars.githubusercontent.com/u/42400165?v=4", "events_url": "https://api.github.com/users/RmZeta2718/events{/privacy}", "followers_url": "https://api.github.com/users/RmZeta2718/followers", "following_url": "https://api.github.com/users/RmZeta2718/following{/other_user}", "gists_url": "https://api.github.com/users/RmZeta2718/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/RmZeta2718", "id": 42400165, "login": "RmZeta2718", "node_id": "MDQ6VXNlcjQyNDAwMTY1", "organizations_url": "https://api.github.com/users/RmZeta2718/orgs", "received_events_url": "https://api.github.com/users/RmZeta2718/received_events", "repos_url": "https://api.github.com/users/RmZeta2718/repos", "site_admin": false, "starred_url": "https://api.github.com/users/RmZeta2718/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/RmZeta2718/subscriptions", "type": "User", "url": "https://api.github.com/users/RmZeta2718", "user_view_type": "public" }	[ { "color": "7057ff", "default": true, "description": "Good for newcomers", "id": 1935892877, "name": "good first issue", "node_id": "MDU6TGFiZWwxOTM1ODkyODc3", "url": "https://api.github.com/repos/huggingface/datasets/labels/good%20first%20issue" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/35114142?v=4", "events_url": "https://api.github.com/users/benjaminbrown038/events{/privacy}", "followers_url": "https://api.github.com/users/benjaminbrown038/followers", "following_url": "https://api.github.com/users/benjaminbrown038/following{/other_user}", "gists_url": "https://api.github.com/users/benjaminbrown038/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/benjaminbrown038", "id": 35114142, "login": "benjaminbrown038", "node_id": "MDQ6VXNlcjM1MTE0MTQy", "organizations_url": "https://api.github.com/users/benjaminbrown038/orgs", "received_events_url": "https://api.github.com/users/benjaminbrown038/received_events", "repos_url": "https://api.github.com/users/benjaminbrown038/repos", "site_admin": false, "starred_url": "https://api.github.com/users/benjaminbrown038/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/benjaminbrown038/subscriptions", "type": "User", "url": "https://api.github.com/users/benjaminbrown038", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/35114142?v=4", "events_url": "https://api.github.com/users/benjaminbrown038/events{/privacy}", "followers_url": "https://api.github.com/users/benjaminbrown038/followers", "following_url": "https://api.github.com/users/benjaminbrown038/following{/other_user}", "gists_url": "https://api.github.com/users/benjaminbrown038/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/benjaminbrown038", "id": 35114142, "login": "benjaminbrown038", "node_id": "MDQ6VXNlcjM1MTE0MTQy", "organizations_url": "https://api.github.com/users/benjaminbrown038/orgs", "received_events_url": "https://api.github.com/users/benjaminbrown038/received_events", "repos_url": "https://api.github.com/users/benjaminbrown038/repos", "site_admin": false, "starred_url": "https://api.github.com/users/benjaminbrown038/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/benjaminbrown038/subscriptions", "type": "User", "url": "https://api.github.com/users/benjaminbrown038", "user_view_type": "public" } ]	null	[ "`save_to_disk` should indeed expand `~`. Marking it as a \"good first issue\".", "#self-assign\r\n\r\nFile path to code: \r\n\r\nhttps://github.com/huggingface/datasets/blob/2.13.0/src/datasets/arrow_dataset.py#L1364\r\n\r\n@RmZeta2718 I created a pull request for this issue. ", "Hello, \r\nIt says `save_to_disk` is deprecated in 2.8.0, so the alternative to this will be `storage_options`? \r\n\r\nhttps://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.save_to_disk", "@ashikshafi08 I think you misunderstood the warning. The method `save_to_disk` is not deprecated only the optional parameter `fs`.\r\nAlso @benjaminbrown038 as I cannot find your PR I would like to work on this if you don't mind.", "@mariosasko It's been several months and the PR is not reviewed. Could you please take a look? I assume this is not complicated and could be merged fairly soon." ]	2023-03-20T12:02:18Z	2023-10-27T14:04:37Z	2023-10-27T14:04:37Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug save_to_disk() does not expand `~` 1. `dataset = load_datasets("any dataset")` 2. `dataset.save_to_disk("~/data")` 3. a folder named "~" created in current folder 4. FileNotFoundError is raised, because the expanded path does not exist (`/home/<user>/data`) related issue https://github.com/huggingface/transformers/issues/10628 ### Steps to reproduce the bug As described above. ### Expected behavior expanduser correctly ### Environment info - datasets 2.10.1 - python 3.10	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5651/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5651/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7469	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7469/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7469/comments	https://api.github.com/repos/huggingface/datasets/issues/7469/events	https://github.com/huggingface/datasets/issues/7469	2,936,606,080	I_kwDODunzps6vCQ2A	7,469	Custom split name with the web interface	{ "avatar_url": "https://avatars.githubusercontent.com/u/15141326?v=4", "events_url": "https://api.github.com/users/vince62s/events{/privacy}", "followers_url": "https://api.github.com/users/vince62s/followers", "following_url": "https://api.github.com/users/vince62s/following{/other_user}", "gists_url": "https://api.github.com/users/vince62s/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/vince62s", "id": 15141326, "login": "vince62s", "node_id": "MDQ6VXNlcjE1MTQxMzI2", "organizations_url": "https://api.github.com/users/vince62s/orgs", "received_events_url": "https://api.github.com/users/vince62s/received_events", "repos_url": "https://api.github.com/users/vince62s/repos", "site_admin": false, "starred_url": "https://api.github.com/users/vince62s/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/vince62s/subscriptions", "type": "User", "url": "https://api.github.com/users/vince62s", "user_view_type": "public" }	[]	closed	false	null	[]	null	[]	2025-03-20T20:45:59Z	2025-03-21T07:20:37Z	2025-03-21T07:20:37Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug According the doc here: https://huggingface.co/docs/hub/datasets-file-names-and-splits#custom-split-name it should infer the split name from the subdir of data or the beg of the name of the files in data. When doing this manually through web upload it does not work. it uses "train" as a unique split. example: https://huggingface.co/datasets/eole-nlp/estimator_chatml ### Steps to reproduce the bug follow the link above ### Expected behavior there should be two splits "mlqe" and "1720_da" ### Environment info website	{ "avatar_url": "https://avatars.githubusercontent.com/u/15141326?v=4", "events_url": "https://api.github.com/users/vince62s/events{/privacy}", "followers_url": "https://api.github.com/users/vince62s/followers", "following_url": "https://api.github.com/users/vince62s/following{/other_user}", "gists_url": "https://api.github.com/users/vince62s/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/vince62s", "id": 15141326, "login": "vince62s", "node_id": "MDQ6VXNlcjE1MTQxMzI2", "organizations_url": "https://api.github.com/users/vince62s/orgs", "received_events_url": "https://api.github.com/users/vince62s/received_events", "repos_url": "https://api.github.com/users/vince62s/repos", "site_admin": false, "starred_url": "https://api.github.com/users/vince62s/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/vince62s/subscriptions", "type": "User", "url": "https://api.github.com/users/vince62s", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7469/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7469/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/4713	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4713/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4713/comments	https://api.github.com/repos/huggingface/datasets/issues/4713/events	https://github.com/huggingface/datasets/pull/4713	1,309,184,756	PR_kwDODunzps47ojC1	4,713	Document installation of sox OS dependency for audio	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-07-19T08:42:35Z	2022-07-21T08:16:59Z	2022-07-21T08:04:15Z	MEMBER	null	null	null	The `sox` OS package needs being installed manually using the distribution package manager. This PR adds this explanation to the docs.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4713/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4713/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4713.diff", "html_url": "https://github.com/huggingface/datasets/pull/4713", "merged_at": "2022-07-21T08:04:15Z", "patch_url": "https://github.com/huggingface/datasets/pull/4713.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4713" }
https://api.github.com/repos/huggingface/datasets/issues/6085	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6085/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6085/comments	https://api.github.com/repos/huggingface/datasets/issues/6085/events	https://github.com/huggingface/datasets/pull/6085	1,824,985,188	PR_kwDODunzps5WlAyA	6,085	Fix `fsspec` download	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	open	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) \| 0.006031 / 0.011353 (-0.005322) \| 0.003579 / 0.011008 (-0.007429) \| 0.080862 / 0.038508 (0.042354) \| 0.056660 / 0.023109 (0.033551) \| 0.388285 / 0.275898 (0.112387) \| 0.422270 / 0.323480 (0.098790) \| 0.004651 / 0.007986 (-0.003335) \| 0.002895 / 0.004328 (-0.001433) \| 0.062767 / 0.004250 (0.058517) \| 0.046491 / 0.037052 (0.009438) \| 0.389918 / 0.258489 (0.131428) \| 0.434650 / 0.293841 (0.140809) \| 0.027265 / 0.128546 (-0.101281) \| 0.007946 / 0.075646 (-0.067701) \| 0.261207 / 0.419271 (-0.158065) \| 0.045057 / 0.043533 (0.001525) \| 0.391977 / 0.255139 (0.136838) \| 0.418525 / 0.283200 (0.135326) \| 0.020705 / 0.141683 (-0.120978) \| 1.459271 / 1.452155 (0.007116) \| 1.516935 / 1.492716 (0.024218) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.174659 / 0.018006 (0.156653) \| 0.429627 / 0.000490 (0.429137) \| 0.003714 / 0.000200 (0.003514) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023255 / 0.037411 (-0.014156) \| 0.073463 / 0.014526 (0.058937) \| 0.083000 / 0.176557 (-0.093557) \| 0.146704 / 0.737135 (-0.590431) \| 0.084419 / 0.296338 (-0.211919) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.392222 / 0.215209 (0.177013) \| 3.902620 / 2.077655 (1.824966) \| 1.903056 / 1.504120 (0.398936) \| 1.753423 / 1.541195 (0.212228) \| 1.874547 / 1.468490 (0.406057) \| 0.495947 / 4.584777 (-4.088829) \| 3.084680 / 3.745712 (-0.661032) \| 4.235064 / 5.269862 (-1.034797) \| 2.626840 / 4.565676 (-1.938837) \| 0.057273 / 0.424275 (-0.367002) \| 0.006457 / 0.007607 (-0.001150) \| 0.466018 / 0.226044 (0.239974) \| 4.648264 / 2.268929 (2.379335) \| 2.520293 / 55.444624 (-52.924331) \| 2.339393 / 6.876477 (-4.537083) \| 2.538848 / 2.142072 (0.396775) \| 0.592018 / 4.805227 (-4.213210) \| 0.125041 / 6.500664 (-6.375623) \| 0.061038 / 0.075469 (-0.014431) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.244285 / 1.841788 (-0.597503) \| 18.411576 / 8.074308 (10.337268) \| 13.850100 / 10.191392 (3.658708) \| 0.131904 / 0.680424 (-0.548520) \| 0.016824 / 0.534201 (-0.517377) \| 0.328931 / 0.579283 (-0.250352) \| 0.364801 / 0.434364 (-0.069563) \| 0.376298 / 0.540337 (-0.164039) \| 0.525045 / 1.386936 (-0.861891) \|\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) \| 0.006059 / 0.011353 (-0.005294) \| 0.003693 / 0.011008 (-0.007315) \| 0.062982 / 0.038508 (0.024473) \| 0.062155 / 0.023109 (0.039046) \| 0.389467 / 0.275898 (0.113568) \| 0.437046 / 0.323480 (0.113566) \| 0.004823 / 0.007986 (-0.003163) \| 0.002935 / 0.004328 (-0.001393) \| 0.062679 / 0.004250 (0.058429) \| 0.049676 / 0.037052 (0.012623) \| 0.418054 / 0.258489 (0.159565) \| 0.442467 / 0.293841 (0.148626) \| 0.027652 / 0.128546 (-0.100895) \| 0.008146 / 0.075646 (-0.067501) \| 0.069414 / 0.419271 (-0.349858) \| 0.042884 / 0.043533 (-0.000649) \| 0.387167 / 0.255139 (0.132028) \| 0.418684 / 0.283200 (0.135484) \| 0.022419 / 0.141683 (-0.119264) \| 1.460606 / 1.452155 (0.008451) \| 1.514204 / 1.492716 (0.021487) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.200523 / 0.018006 (0.182517) \| 0.415970 / 0.000490 (0.415481) \| 0.003202 / 0.000200 (0.003002) \| 0.000069 / 0.000054 (0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025836 / 0.037411 (-0.011575) \| 0.078859 / 0.014526 (0.064333) \| 0.088523 / 0.176557 (-0.088034) \| 0.141572 / 0.737135 (-0.595563) \| 0.090258 / 0.296338 (-0.206080) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.416548 / 0.215209 (0.201339) \| 4.155278 / 2.077655 (2.077623) \| 2.126683 / 1.504120 (0.622563) \| 1.963762 / 1.541195 (0.422568) \| 2.029018 / 1.468490 (0.560528) \| 0.499005 / 4.584777 (-4.085772) \| 3.063503 / 3.745712 (-0.682209) \| 4.250800 / 5.269862 (-1.019061) \| 2.642634 / 4.565676 (-1.923043) \| 0.057815 / 0.424275 (-0.366460) \| 0.006784 / 0.007607 (-0.000823) \| 0.492481 / 0.226044 (0.266437) \| 4.914306 / 2.268929 (2.645377) \| 2.601582 / 55.444624 (-52.843042) \| 2.337863 / 6.876477 (-4.538614) \| 2.462854 / 2.142072 (0.320782) \| 0.593738 / 4.805227 (-4.211489) \| 0.127030 / 6.500664 (-6.373634) \| 0.064206 / 0.075469 (-0.011263) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.326919 / 1.841788 (-0.514868) \| 18.728929 / 8.074308 (10.654621) \| 13.903681 / 10.191392 (3.712289) \| 0.162670 / 0.680424 (-0.517754) \| 0.016913 / 0.534201 (-0.517288) \| 0.337504 / 0.579283 (-0.241779) \| 0.339786 / 0.434364 (-0.094577) \| 0.384955 / 0.540337 (-0.155383) \| 0.514358 / 1.386936 (-0.872578) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5c31b492c45e01c6f4593ada2b84517a75a5c7c \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6085). 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) \| 0.007610 / 0.011353 (-0.003743) \| 0.004616 / 0.011008 (-0.006392) \| 0.100330 / 0.038508 (0.061821) \| 0.084450 / 0.023109 (0.061341) \| 0.386610 / 0.275898 (0.110712) \| 0.418479 / 0.323480 (0.094999) \| 0.006085 / 0.007986 (-0.001900) \| 0.003800 / 0.004328 (-0.000529) \| 0.076248 / 0.004250 (0.071997) \| 0.065175 / 0.037052 (0.028122) \| 0.387154 / 0.258489 (0.128665) \| 0.425484 / 0.293841 (0.131643) \| 0.035946 / 0.128546 (-0.092601) \| 0.009901 / 0.075646 (-0.065745) \| 0.343015 / 0.419271 (-0.076256) \| 0.060965 / 0.043533 (0.017432) \| 0.390585 / 0.255139 (0.135446) \| 0.405873 / 0.283200 (0.122673) \| 0.026929 / 0.141683 (-0.114754) \| 1.767916 / 1.452155 (0.315761) \| 1.893431 / 1.492716 (0.400715) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237888 / 0.018006 (0.219882) \| 0.503949 / 0.000490 (0.503459) \| 0.004769 / 0.000200 (0.004570) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031553 / 0.037411 (-0.005859) \| 0.096950 / 0.014526 (0.082424) \| 0.110374 / 0.176557 (-0.066183) \| 0.176754 / 0.737135 (-0.560381) \| 0.111703 / 0.296338 (-0.184635) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.449232 / 0.215209 (0.234023) \| 4.510247 / 2.077655 (2.432592) \| 2.188547 / 1.504120 (0.684427) \| 2.007530 / 1.541195 (0.466335) \| 2.095650 / 1.468490 (0.627160) \| 0.563262 / 4.584777 (-4.021515) \| 4.062412 / 3.745712 (0.316700) \| 6.338350 / 5.269862 (1.068489) \| 3.844669 / 4.565676 (-0.721008) \| 0.064517 / 0.424275 (-0.359758) \| 0.008536 / 0.007607 (0.000929) \| 0.553872 / 0.226044 (0.327828) \| 5.530311 / 2.268929 (3.261383) \| 2.835109 / 55.444624 (-52.609516) \| 2.493900 / 6.876477 (-4.382577) \| 2.728412 / 2.142072 (0.586340) \| 0.680161 / 4.805227 (-4.125066) \| 0.155831 / 6.500664 (-6.344833) \| 0.070359 / 0.075469 (-0.005110) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.504852 / 1.841788 (-0.336936) \| 22.806335 / 8.074308 (14.732027) \| 16.598386 / 10.191392 (6.406994) \| 0.207857 / 0.680424 (-0.472566) \| 0.021425 / 0.534201 (-0.512776) \| 0.474069 / 0.579283 (-0.105214) \| 0.472263 / 0.434364 (0.037899) \| 0.542195 / 0.540337 (0.001858) \| 0.782871 / 1.386936 (-0.604065) \|\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) \| 0.007443 / 0.011353 (-0.003910) \| 0.004465 / 0.011008 (-0.006544) \| 0.076268 / 0.038508 (0.037759) \| 0.086607 / 0.023109 (0.063498) \| 0.443295 / 0.275898 (0.167397) \| 0.472819 / 0.323480 (0.149339) \| 0.005841 / 0.007986 (-0.002144) \| 0.003727 / 0.004328 (-0.000602) \| 0.076015 / 0.004250 (0.071765) \| 0.063188 / 0.037052 (0.026136) \| 0.450555 / 0.258489 (0.192066) \| 0.478532 / 0.293841 (0.184691) \| 0.036258 / 0.128546 (-0.092288) \| 0.009869 / 0.075646 (-0.065777) \| 0.083786 / 0.419271 (-0.335486) \| 0.056546 / 0.043533 (0.013013) \| 0.449647 / 0.255139 (0.194508) \| 0.457588 / 0.283200 (0.174389) \| 0.027197 / 0.141683 (-0.114486) \| 1.769991 / 1.452155 (0.317836) \| 1.859905 / 1.492716 (0.367189) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.268637 / 0.018006 (0.250631) \| 0.492860 / 0.000490 (0.492370) \| 0.008574 / 0.000200 (0.008374) \| 0.000140 / 0.000054 (0.000085) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037679 / 0.037411 (0.000268) \| 0.108258 / 0.014526 (0.093733) \| 0.117850 / 0.176557 (-0.058707) \| 0.181611 / 0.737135 (-0.555524) \| 0.120901 / 0.296338 (-0.175437) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.485780 / 0.215209 (0.270571) \| 4.851289 / 2.077655 (2.773635) \| 2.486068 / 1.504120 (0.981948) \| 2.299417 / 1.541195 (0.758222) \| 2.387093 / 1.468490 (0.918603) \| 0.568826 / 4.584777 (-4.015951) \| 4.163426 / 3.745712 (0.417713) \| 6.224964 / 5.269862 (0.955102) \| 3.255619 / 4.565676 (-1.310058) \| 0.067081 / 0.424275 (-0.357194) \| 0.009065 / 0.007607 (0.001458) \| 0.580449 / 0.226044 (0.354405) \| 5.786394 / 2.268929 (3.517465) \| 3.057780 / 55.444624 (-52.386844) \| 2.764339 / 6.876477 (-4.112138) \| 2.880718 / 2.142072 (0.738645) \| 0.681376 / 4.805227 (-4.123851) \| 0.157858 / 6.500664 (-6.342806) \| 0.072481 / 0.075469 (-0.002988) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.590704 / 1.841788 (-0.251083) \| 23.141929 / 8.074308 (15.067620) \| 17.001141 / 10.191392 (6.809749) \| 0.203790 / 0.680424 (-0.476634) \| 0.021766 / 0.534201 (-0.512435) \| 0.475309 / 0.579283 (-0.103974) \| 0.466448 / 0.434364 (0.032084) \| 0.551470 / 0.540337 (0.011132) \| 0.727876 / 1.386936 (-0.659060) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#61b19eea7fc5cf484e8cdf41d6ae035f94d8a671 \"CML watermark\")\n" ]	2023-07-27T18:54:47Z	2023-07-27T19:06:13Z	null	COLLABORATOR	null	null	null	Testing `ds = load_dataset("audiofolder", data_files="s3://datasets.huggingface.co/SpeechCommands/v0.01/v0.01_test.tar.gz", storage_options={"anon": True})` and trying to fix the issues raised by `fsspec` ... TODO: fix ``` self.session = aiobotocore.session.AioSession(**self.kwargs) TypeError: __init__() got an unexpected keyword argument 'hf' ``` by "preparing `storage_options`" for the `fsspec` head/get	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/6085/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6085/timeline	null	null	1	{ "diff_url": "https://github.com/huggingface/datasets/pull/6085.diff", "html_url": "https://github.com/huggingface/datasets/pull/6085", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6085.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6085" }
https://api.github.com/repos/huggingface/datasets/issues/6020	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6020/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6020/comments	https://api.github.com/repos/huggingface/datasets/issues/6020/events	https://github.com/huggingface/datasets/issues/6020	1,799,720,536	I_kwDODunzps5rRY5Y	6,020	Inconsistent "The features can't be aligned" error when combining map, multiprocessing, and variable length outputs	{ "avatar_url": "https://avatars.githubusercontent.com/u/38166299?v=4", "events_url": "https://api.github.com/users/kheyer/events{/privacy}", "followers_url": "https://api.github.com/users/kheyer/followers", "following_url": "https://api.github.com/users/kheyer/following{/other_user}", "gists_url": "https://api.github.com/users/kheyer/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/kheyer", "id": 38166299, "login": "kheyer", "node_id": "MDQ6VXNlcjM4MTY2Mjk5", "organizations_url": "https://api.github.com/users/kheyer/orgs", "received_events_url": "https://api.github.com/users/kheyer/received_events", "repos_url": "https://api.github.com/users/kheyer/repos", "site_admin": false, "starred_url": "https://api.github.com/users/kheyer/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kheyer/subscriptions", "type": "User", "url": "https://api.github.com/users/kheyer", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "This scenario currently requires explicitly passing the target features (to avoid the error): \r\n```python\r\nimport datasets\r\n\r\n...\r\n\r\nfeatures = dataset.features\r\nfeatures[\"output\"] = = [{\"test\": datasets.Value(\"int64\")}]\r\ntest2 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=32, features=features)\r\n```", "I just encountered the same error in the same situation (multiprocessing with variable length outputs).\r\n\r\nThe funny (or dangerous?) thing is, that this error only showed up when testing with a small test dataset (16 examples, ValueError with `num_proc` >1) but the same code works fine for the full dataset (~70k examples).\r\n\r\n@mariosasko Any idea on how to do that with a nested feature with lists of variable lengths containing dicts?\r\n\r\nEDIT: Was able to narrow it down: >200 Examples: no error, <150 Examples: Error. \r\nNow idea what to make of this but pretty obvious that this is a bug....", "This error also occurs while concatenating the datasets.", "I'm running into the same error, is there any working workaround for this that doesnt involve using a larger subset or reducing the number of workers? I couldn't get the `features` set mentioned above to work..." ]	2023-07-11T20:40:38Z	2024-10-27T06:30:13Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I'm using a dataset with map and multiprocessing to run a function that returned a variable length list of outputs. This output list may be empty. Normally this is handled fine, but there is an edge case that crops up when using multiprocessing. In some cases, an empty list result ends up in a dataset shard consisting of a single item. This results in a `The features can't be aligned` error that is difficult to debug because it depends on the number of processes/shards used. I've reproduced a minimal example below. My current workaround is to fill empty results with a dummy value that I filter after, but this was a weird error that took a while to track down. ### Steps to reproduce the bug ```python import datasets dataset = datasets.Dataset.from_list([{'idx':i} for i in range(60)]) def test_func(row, idx): if idx==58: return {'output': []} else: return {'output' : [{'test':1}, {'test':2}]} # this works fine test1 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=4) # this fails test2 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=32) >ValueError: The features can't be aligned because the key output of features {'idx': Value(dtype='int64', id=None), 'output': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None)} has unexpected type - Sequence(feature=Value(dtype='null', id=None), length=-1, id=None) (expected either [{'test': Value(dtype='int64', id=None)}] or Value("null"). ``` The error occurs during the check ```python _check_if_features_can_be_aligned([dset.features for dset in dsets]) ``` When the multiprocessing splitting lines up just right with the empty return value, one of the `dset` in `dsets` will have a single item with an empty list value, causing the error. ### Expected behavior Expected behavior is the result would be the same regardless of the `num_proc` value used. ### Environment info Datasets version 2.11.0 Python 3.9.16	null	{ "+1": 5, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 5, "url": "https://api.github.com/repos/huggingface/datasets/issues/6020/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6020/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6325	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6325/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6325/comments	https://api.github.com/repos/huggingface/datasets/issues/6325/events	https://github.com/huggingface/datasets/pull/6325	1,955,420,178	PR_kwDODunzps5dcSM3	6,325	Create battery_analysis.py	{ "avatar_url": "https://avatars.githubusercontent.com/u/130216732?v=4", "events_url": "https://api.github.com/users/vinitkm/events{/privacy}", "followers_url": "https://api.github.com/users/vinitkm/followers", "following_url": "https://api.github.com/users/vinitkm/following{/other_user}", "gists_url": "https://api.github.com/users/vinitkm/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/vinitkm", "id": 130216732, "login": "vinitkm", "node_id": "U_kgDOB8LzHA", "organizations_url": "https://api.github.com/users/vinitkm/orgs", "received_events_url": "https://api.github.com/users/vinitkm/received_events", "repos_url": "https://api.github.com/users/vinitkm/repos", "site_admin": false, "starred_url": "https://api.github.com/users/vinitkm/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/vinitkm/subscriptions", "type": "User", "url": "https://api.github.com/users/vinitkm", "user_view_type": "public" }	[]	closed	false	null	[]	null	[]	2023-10-21T10:06:37Z	2023-10-23T14:55:58Z	2023-10-23T14:55:58Z	NONE	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6325/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6325/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6325.diff", "html_url": "https://github.com/huggingface/datasets/pull/6325", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6325.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6325" }
https://api.github.com/repos/huggingface/datasets/issues/4892	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4892/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4892/comments	https://api.github.com/repos/huggingface/datasets/issues/4892/events	https://github.com/huggingface/datasets/pull/4892	1,350,636,499	PR_kwDODunzps49yCD3	4,892	Add citation to ro_sts and ro_sts_parallel datasets	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4892). All of your documentation changes will be reflected on that endpoint." ]	2022-08-25T09:51:06Z	2022-08-25T10:49:56Z	2022-08-25T10:49:56Z	MEMBER	null	null	null	This PR adds the citation information to `ro_sts_parallel` and `ro_sts_parallel` datasets, once they have replied our request for that information: - https://github.com/dumitrescustefan/RO-STS/issues/4	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4892/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4892/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4892.diff", "html_url": "https://github.com/huggingface/datasets/pull/4892", "merged_at": "2022-08-25T10:49:56Z", "patch_url": "https://github.com/huggingface/datasets/pull/4892.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4892" }
https://api.github.com/repos/huggingface/datasets/issues/5045	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5045/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5045/comments	https://api.github.com/repos/huggingface/datasets/issues/5045/events	https://github.com/huggingface/datasets/issues/5045	1,391,287,609	I_kwDODunzps5S7V05	5,045	Automatically revert to last successful commit to hub when a push_to_hub is interrupted	{ "avatar_url": "https://avatars.githubusercontent.com/u/13120204?v=4", "events_url": "https://api.github.com/users/jorahn/events{/privacy}", "followers_url": "https://api.github.com/users/jorahn/followers", "following_url": "https://api.github.com/users/jorahn/following{/other_user}", "gists_url": "https://api.github.com/users/jorahn/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jorahn", "id": 13120204, "login": "jorahn", "node_id": "MDQ6VXNlcjEzMTIwMjA0", "organizations_url": "https://api.github.com/users/jorahn/orgs", "received_events_url": "https://api.github.com/users/jorahn/received_events", "repos_url": "https://api.github.com/users/jorahn/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jorahn/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jorahn/subscriptions", "type": "User", "url": "https://api.github.com/users/jorahn", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	closed	false	null	[]	null	[ "Could you share the error you got please ? Maybe the full stack trace if you have it ?\r\n\r\nMaybe `push_to_hub` be implemented as a single commit @Wauplin ? This way if it fails, the repo is still at the previous (valid) state instead of ending-up in an invalid/incimplete state.", "> Maybe push_to_hub be implemented as a single commit ? \r\n\r\nI think that would definitely be the way to go. Do you know the reasons why not implementing it like this in the first place ? I guess it is because of not been able to upload all at once with `huggingface_hub` but if there was another reason, please let me know.\r\nAbout pushing all at once, it seems to be a more and more requested feature. I have created this issue https://github.com/huggingface/huggingface_hub/issues/1085 recently but other discussions already happened in the past. The `moon-landing` team is working on it (cc @coyotte508). The `huggingface_hub` integration will come afterwards.\r\n\r\nFor now, maybe it's best to wait for a proper implementation instead of creating a temporary workaround :)\r\n", "> I think that would definitely be the way to go. Do you know the reasons why not implementing it like this in the first place ? I guess it is because of not been able to upload all at once with huggingface_hub but if there was another reason, please let me know.\r\n\r\nIdeally we would want to upload the files iteratively - and then once everything is uploaded we proceed to commit. When we implemented `push_to_hub`, using `upload_file` for each shard was the only option.\r\n\r\nFor more context: for each shard to upload we do:\r\n1. load the arrow shard in memory\r\n2. convert to parquet\r\n3. upload\r\n\r\nSo to avoid OOM we need to upload the files iteratively.\r\n\r\n> For now, maybe it's best to wait for a proper implementation instead of creating a temporary workaround :)\r\n\r\nLet us know if we can help !", "> Ideally we would want to upload the files iteratively - and then once everything is uploaded we proceed to commit. \r\n\r\nOh I see. So maybe this has to be done in an implementation specific to `datasets/` as it is not a very common case (upload a bunch of files on the fly).\r\n\r\nYou can maybe have a look at how `huggingface_hub` is implemented for LFS files (arrow shards are LFS anyway, right?).\r\nIn [`upload_lfs_files`](https://github.com/huggingface/huggingface_hub/blob/e28646c977fc9304a4c3576ce61ff07f9778950b/src/huggingface_hub/_commit_api.py#L164) LFS files are uploaded 1 by 1 (multithreaded) and then [the commit is pushed](https://github.com/huggingface/huggingface_hub/blob/e28646c977fc9304a4c3576ce61ff07f9778950b/src/huggingface_hub/hf_api.py#L1926) to the Hub once all files have been uploaded. This is pretty much what you need, right ?\r\n\r\nI can help you if you have questions how to do it in `datasets`. If that makes sense we could then move the implementation from `datasets` to `huggingface_hub` once it's mature. Next week I'm on holidays but feel free to start without my input.\r\n\r\n(also cc @coyotte508 and @SBrandeis who implemented LFS upload in `hfh`)", "> Could you share the error you got please ? Maybe the full stack trace if you have it ?\r\n\r\nHere’s part of the stack trace, that I can reproduce at the moment from a photo I took (potential typos from OCR):\r\n```\r\nValueError\r\nTraceback (most recent call last)\r\n<ipython-input-4-274613b7d3f5> in <module>\r\nfrom datasets import load dataset\r\nds = load_dataset('jrahn/chessv6', use_auth_token-True)\r\n\r\n/us/local/1ib/python3.7/dist-packages/datasets/table.py in cast_table _to_schema (table, schema)\r\nLine 2005 raise ValueError()\r\n\r\nValueError: Couldn't cast \r\nfen: string \r\nmove: string \r\nres: string \r\neco: string \r\nmove_id: int64\r\nres_num: int64 to\r\n{ 'fen': Value(dtype='string', id=None), \r\n'move': Value(dtype=' string', id=None),\r\n'res': Value(dtype='string', id=None),\r\n'eco': Value(dtype='string', id=None), \r\n'hc': Value(dtype='string', id=None), \r\n'move_ id': Value(dtype='int64', id=None),\r\n'res_num': Value(dtype= 'int64' , id=None) }\r\nbecause column names don't match \r\n```\r\n\r\nThe column 'hc' was removed before the interrupted push_to_hub(). It appears in the column list in curly brackets but not in the column list above.\r\n\r\nLet me know, if I can be of any help." ]	2022-09-29T18:08:12Z	2023-10-16T13:30:49Z	2023-10-16T13:30:49Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	Is your feature request related to a problem? Please describe. I pushed a modification of a large dataset (remove a column) to the hub. The push was interrupted after some files were committed to the repo. This left the dataset to raise an error on load_dataset() (ValueError couldn’t cast … because column names don’t match). Only by specifying the previous (complete) commit as revision=commit_hash in load_data(), I was able to repair this and after a successful, complete push, the dataset loads without error again. Describe the solution you'd like Would it make sense to detect an incomplete push_to_hub() and automatically revert to the previous commit/revision? Describe alternatives you've considered Leave everything as is, the revision parameter in load_dataset() allows to manually fix this problem. Additional context Provide useful defaults	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 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/5045/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5045/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6988	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6988/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6988/comments	https://api.github.com/repos/huggingface/datasets/issues/6988/events	https://github.com/huggingface/datasets/pull/6988	2,364,129,918	PR_kwDODunzps5zDpXX	6,988	[`feat`] Move dataset card creation to method for easier overriding	{ "avatar_url": "https://avatars.githubusercontent.com/u/37621491?v=4", "events_url": "https://api.github.com/users/tomaarsen/events{/privacy}", "followers_url": "https://api.github.com/users/tomaarsen/followers", "following_url": "https://api.github.com/users/tomaarsen/following{/other_user}", "gists_url": "https://api.github.com/users/tomaarsen/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/tomaarsen", "id": 37621491, "login": "tomaarsen", "node_id": "MDQ6VXNlcjM3NjIxNDkx", "organizations_url": "https://api.github.com/users/tomaarsen/orgs", "received_events_url": "https://api.github.com/users/tomaarsen/received_events", "repos_url": "https://api.github.com/users/tomaarsen/repos", "site_admin": false, "starred_url": "https://api.github.com/users/tomaarsen/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/tomaarsen/subscriptions", "type": "User", "url": "https://api.github.com/users/tomaarsen", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6988). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "`Dataset` objects are not made to be subclassed, so I don't think going in that direction is a good idea. In particular there is absolutely no test to make sure it works well, and nothing in the internal has been made to anticipate this use case.\r\n\r\nI'd suggest to use a separate function to push changes to the Dataset card, and call it after `push_to_hub()`. This way people can also use a similar logic with other tools that `datasets`. You can also use composition instead of subclassing.", "Would you consider an alternative where a Dataset instance carries a dataset card template which can be updated?\n\nI don't want to burden my users with having to call another method after `push_to_hub` themselves. If you're not a fan of the template approach above either, then I'll likely subclass `push_to_hub` to once again download the just-uploaded-but-empty dataset card, update it, and reupload it. It'll just be a bit more requests than necessary, but not a big deal overall.\n\n- Tom Aarsen ", "Actually I find the idea of overriding `_create_dataset_card` better than implementing a templating logic. My main concern is that if we go in that direction we better make sure that subclasses of `Dataset` are working well. \r\n\r\nWell if it's been working fine on your side why not, but make sure you test correctly features that could not work because of subclassing (e.g. I'm pretty sure `map()` won't return your subclass of `Dataset`). Or at least the ones that matter for your lib.\r\n\r\nIf it sounds good to you I'm fine with merging your addition to let you override the dataset card.", "> e.g. I'm pretty sure map() won't return your subclass of Dataset\r\n\r\nI understand that there's limitations such as this one. The subclass doesn't have to be robust - I'd just like some simple automatic dataset card generation options directly after generating the dataset. This can be removed if the user does additional steps before pushing the model, e.g. mapping, filtering, saving to disk and uploading the loaded dataset, etc.\r\n\r\n> If it sounds good to you I'm fine with merging your addition to let you override the dataset card.\r\n\r\nThat would be quite useful for me! I appreciate it.\r\n\r\nI'm not very sure what the test failures are caused by, I believe the only change in behaviour is that\r\n```python\r\n DatasetInfosDict({config_name: info_to_dump}).to_dataset_card_data(dataset_card_data)\r\n MetadataConfigs({config_name: metadata_config_to_dump}).to_dataset_card_data(dataset_card_data)\r\n```\r\nare not called when `dataset_card` was already defined. Unless these have side-effects other than updating `dataset_card_data`, it shouldn't be any different than `main`.\r\n\r\n- Tom Aarsen", "Let's try to have this PR merged then !\r\n\r\nIMO your current implementation can be improved since you path both the dataset card data and the dataset card itself, which is redundant. Also I anticipate the failures in the CI to come from your default implementation which doesn't correspond to what it was doing before\r\n\r\n> Unless these have side-effects other than updating dataset_card_data, it shouldn't be any different than main.\r\n\r\nIndeed the dataset_card_data is the value from attribute of the dataset_card from a few lines before your changes, so yes it modifies the dataset_card object too." ]	2024-06-20T10:47:57Z	2024-06-21T16:04:58Z	null	MEMBER	null	null	null	Hello! ## Pull Request overview * Move dataset card creation to method for easier overriding ## Details It's common for me to fully automatically download, reformat, and upload a dataset (e.g. see https://huggingface.co/datasets?other=sentence-transformers), but one aspect that I cannot easily automate is the dataset card generation. This is because during `push_to_hub`, the dataset card is created in 3 lines of code in a much larger method. To automatically generate a dataset card, I need to either: 1. Subclass `Dataset`/`DatasetDict`, copy the entire `push_to_hub` method to override the ~3 lines used to generate the dataset card. This is not viable as the method is likely to change over time. 2. Use `push_to_hub` normally, then separately download the pushed (but empty) dataset card, update it, and reupload the modified dataset. This works fine, but prevents me from being able to return a `Dataset` to my users which will automatically use a nice dataset card. So, in this PR I'm proposing to move the dataset generation into another method so that it can be overridden more easily. For example, imagine the following use case: ````python import json from typing import Any, Dict, Optional from datasets import Dataset, load_dataset from datasets.info import DatasetInfosDict, DatasetInfo from datasets.utils.metadata import MetadataConfigs from huggingface_hub import DatasetCardData, DatasetCard TEMPLATE = r"""--- {dataset_card_data} --- # Dataset Card for {source_dataset_name} with mined hard negatives This dataset is a collection of {column_one}-{column_two}-negative triplets from the {source_dataset_name} dataset. See [{source_dataset_name}](https://huggingface.co/datasets/{source_dataset_id}) for additional information. This dataset can be used directly with Sentence Transformers to train embedding models. ## Mining Parameters The negative samples have been mined using the following parameters: - `range_min`: {range_min}, i.e. we skip the {range_min} most similar samples - `range_max`: {range_max}, i.e. we only look at the top {range_max} most similar samples - `margin`: {margin}, i.e. we require negative similarity + margin < positive similarity, so negative samples can't be more similar than the known true answer - `sampling_strategy`: {sampling_strategy}, i.e. whether to randomly sample from the candidate negatives or take the "top" negatives - `num_negatives`: {num_negatives}, i.e. we mine {num_negatives} negatives per question-answer pair ## Dataset Format - Columns: {column_one}, {column_two}, negative - Column types: str, str, str - Example: ```python {example} ``` """ class HNMDataset(Dataset): @classmethod def from_dict(cls, args, mining_kwargs: Dict[str, Any], kwargs) -> "HNMDataset": dataset = super().from_dict(args, kwargs) dataset.mining_kwargs = mining_kwargs return dataset def _create_dataset_card( self, dataset_card_data: DatasetCardData, dataset_card: Optional[DatasetCard], config_name: str, info_to_dump: DatasetInfo, metadata_config_to_dump: MetadataConfigs, ) -> DatasetCard: if dataset_card: return dataset_card DatasetInfosDict({config_name: info_to_dump}).to_dataset_card_data(dataset_card_data) MetadataConfigs({config_name: metadata_config_to_dump}).to_dataset_card_data(dataset_card_data) dataset_card_data.tags = ["sentence-transformers"] dataset_name = self.mining_kwargs["source_dataset"].info.dataset_name # Very messy, just as an example: dataset_id = list(self.mining_kwargs["source_dataset"].info.download_checksums.keys())[0].removeprefix("hf://datasets/").split("@")[0] content = TEMPLATE.format({ "dataset_card_data": str(dataset_card_data), "source_dataset_name": dataset_name, "source_dataset_id": dataset_id, "range_min": self.mining_kwargs["range_min"], "range_max": self.mining_kwargs["range_max"], "margin": self.mining_kwargs["margin"], "sampling_strategy": self.mining_kwargs["sampling_strategy"], "num_negatives": self.mining_kwargs["num_negatives"], "column_one": self.column_names[0], "column_two": self.column_names[1], "example": json.dumps(self[0], indent=4), }) return DatasetCard(content) source_dataset = load_dataset("sentence-transformers/gooaq", split="train[:100]") dataset = HNMDataset.from_dict({ "query": source_dataset["question"], "answer": source_dataset["answer"], # "negative": ... <- In my case, this column would be 'mined' automatically with these parameters }, mining_kwargs={ "range_min": 10, "range_max": 20, "max_score": 0.9, "margin": 0.1, "sampling_strategy": "random", "num_negatives": 3, "source_dataset": source_dataset, }) dataset.push_to_hub("tomaarsen/mining_demo", private=True) ```` In this script, I've created a subclass which stores some additional information about how the dataset was generated. It's a bit hacky (e.g. setting a `mining_kwargs` parameter in `from_dict` that wasn't created in `__init__`, but that's just a consequence of how the `from_...` methods don't accept kwargs), but it allows me to create a "hard negatives mining" function that returns a dataset which people can use locally like normal, but if they choose to upload it, then it'll automatically include some information, e.g.: https://huggingface.co/datasets/tomaarsen/mining_demo This allows others to actually find this dataset (e.g. via the `sentence-transformers` tag) and get an idea of the quality, source, etc. by looking at the model card. ## Note I'm not fixed on this solution whatsoever: I am also completely fine with other solutions, e.g. a `dataset.set_dataset_card_creator` method that allows me to provide a function without even having to subclass anything. I'm open to all ideas :) cc @albertvillanova @lhoestq cc @LysandreJik - Tom Aarsen	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 1, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6988/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6988/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6988.diff", "html_url": "https://github.com/huggingface/datasets/pull/6988", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6988.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6988" }
https://api.github.com/repos/huggingface/datasets/issues/5236	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5236/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5236/comments	https://api.github.com/repos/huggingface/datasets/issues/5236/events	https://github.com/huggingface/datasets/pull/5236	1,448,190,801	PR_kwDODunzps5C2Hnj	5,236	Handle ArrowNotImplementedError caused by try_type being Image or Audio in cast	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "> Not sure how we can have a test that is relevant for this though - feel free to add one if you have ideas\r\n\r\nYes, this was my reasoning for not adding a test. This change is pretty simple, so I think it's OK not to have a test for it." ]	2022-11-14T14:38:59Z	2022-11-14T16:04:29Z	2022-11-14T16:01:48Z	COLLABORATOR	null	null	null	Handle the `ArrowNotImplementedError` thrown when `try_type` is `Image` or `Audio` and the input array cannot be converted to their storage formats. Reproducer: ```python from datasets import Dataset from PIL import Image import requests ds = Dataset.from_dict({"image": [Image.open(requests.get("https://upload.wikimedia.org/wikipedia/commons/e/e9/Felis_silvestris_silvestris_small_gradual_decrease_of_quality.png", stream=True).raw)]}) ds.map(lambda x: {"image": True}) # ArrowNotImplementedError ``` PS: This could also be fixed by raising `TypeError` in `{Image, Audio}.cast_storage` for unsupported types instead of passing the array to `array_cast.`	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5236/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5236/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5236.diff", "html_url": "https://github.com/huggingface/datasets/pull/5236", "merged_at": "2022-11-14T16:01:48Z", "patch_url": "https://github.com/huggingface/datasets/pull/5236.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5236" }
https://api.github.com/repos/huggingface/datasets/issues/5754	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5754/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5754/comments	https://api.github.com/repos/huggingface/datasets/issues/5754/events	https://github.com/huggingface/datasets/pull/5754	1,668,755,035	PR_kwDODunzps5OWozh	5,754	Minor tqdm fixes	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006479 / 0.011353 (-0.004874) \| 0.004592 / 0.011008 (-0.006416) \| 0.097239 / 0.038508 (0.058731) \| 0.028609 / 0.023109 (0.005499) \| 0.309225 / 0.275898 (0.033327) \| 0.340015 / 0.323480 (0.016535) \| 0.004857 / 0.007986 (-0.003129) \| 0.004649 / 0.004328 (0.000320) \| 0.074770 / 0.004250 (0.070520) \| 0.038351 / 0.037052 (0.001299) \| 0.313360 / 0.258489 (0.054871) \| 0.350256 / 0.293841 (0.056416) \| 0.030770 / 0.128546 (-0.097776) \| 0.011591 / 0.075646 (-0.064055) \| 0.322444 / 0.419271 (-0.096828) \| 0.043704 / 0.043533 (0.000171) \| 0.311790 / 0.255139 (0.056651) \| 0.339183 / 0.283200 (0.055984) \| 0.088041 / 0.141683 (-0.053642) \| 1.490649 / 1.452155 (0.038494) \| 1.561789 / 1.492716 (0.069072) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208984 / 0.018006 (0.190978) \| 0.406105 / 0.000490 (0.405616) \| 0.003152 / 0.000200 (0.002952) \| 0.000074 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022622 / 0.037411 (-0.014790) \| 0.095819 / 0.014526 (0.081294) \| 0.105132 / 0.176557 (-0.071424) \| 0.165684 / 0.737135 (-0.571451) \| 0.106706 / 0.296338 (-0.189632) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426126 / 0.215209 (0.210917) \| 4.233864 / 2.077655 (2.156209) \| 1.918727 / 1.504120 (0.414607) \| 1.729905 / 1.541195 (0.188710) \| 1.760342 / 1.468490 (0.291852) \| 0.695449 / 4.584777 (-3.889328) \| 3.413531 / 3.745712 (-0.332181) \| 1.904557 / 5.269862 (-3.365305) \| 1.270604 / 4.565676 (-3.295072) \| 0.083018 / 0.424275 (-0.341257) \| 0.012760 / 0.007607 (0.005152) \| 0.523991 / 0.226044 (0.297947) \| 5.236132 / 2.268929 (2.967204) \| 2.360959 / 55.444624 (-53.083665) \| 1.996533 / 6.876477 (-4.879943) \| 2.072934 / 2.142072 (-0.069138) \| 0.804133 / 4.805227 (-4.001094) \| 0.150976 / 6.500664 (-6.349688) \| 0.065503 / 0.075469 (-0.009966) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.211828 / 1.841788 (-0.629960) \| 13.657743 / 8.074308 (5.583435) \| 13.887148 / 10.191392 (3.695756) \| 0.145996 / 0.680424 (-0.534428) \| 0.016562 / 0.534201 (-0.517639) \| 0.380359 / 0.579283 (-0.198924) \| 0.388698 / 0.434364 (-0.045666) \| 0.440373 / 0.540337 (-0.099965) \| 0.531753 / 1.386936 (-0.855183) \|\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) \| 0.006444 / 0.011353 (-0.004909) \| 0.004569 / 0.011008 (-0.006439) \| 0.076239 / 0.038508 (0.037731) \| 0.028462 / 0.023109 (0.005352) \| 0.365540 / 0.275898 (0.089642) \| 0.398242 / 0.323480 (0.074762) \| 0.005785 / 0.007986 (-0.002200) \| 0.003346 / 0.004328 (-0.000982) \| 0.076296 / 0.004250 (0.072046) \| 0.039853 / 0.037052 (0.002800) \| 0.367684 / 0.258489 (0.109195) \| 0.409570 / 0.293841 (0.115730) \| 0.030536 / 0.128546 (-0.098010) \| 0.011534 / 0.075646 (-0.064112) \| 0.084962 / 0.419271 (-0.334309) \| 0.042708 / 0.043533 (-0.000825) \| 0.344058 / 0.255139 (0.088919) \| 0.389096 / 0.283200 (0.105897) \| 0.090559 / 0.141683 (-0.051124) \| 1.507101 / 1.452155 (0.054946) \| 1.563977 / 1.492716 (0.071260) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228740 / 0.018006 (0.210734) \| 0.396890 / 0.000490 (0.396400) \| 0.000392 / 0.000200 (0.000192) \| 0.000060 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025052 / 0.037411 (-0.012360) \| 0.099951 / 0.014526 (0.085426) \| 0.106847 / 0.176557 (-0.069710) \| 0.156666 / 0.737135 (-0.580469) \| 0.110344 / 0.296338 (-0.185994) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.442363 / 0.215209 (0.227154) \| 4.429571 / 2.077655 (2.351917) \| 2.076501 / 1.504120 (0.572381) \| 1.875226 / 1.541195 (0.334031) \| 1.909093 / 1.468490 (0.440603) \| 0.703047 / 4.584777 (-3.881730) \| 3.457036 / 3.745712 (-0.288676) \| 2.866648 / 5.269862 (-2.403214) \| 1.524430 / 4.565676 (-3.041246) \| 0.083687 / 0.424275 (-0.340588) \| 0.012251 / 0.007607 (0.004643) \| 0.543945 / 0.226044 (0.317901) \| 5.440559 / 2.268929 (3.171630) \| 2.522924 / 55.444624 (-52.921700) \| 2.188770 / 6.876477 (-4.687707) \| 2.249632 / 2.142072 (0.107559) \| 0.813499 / 4.805227 (-3.991728) \| 0.152861 / 6.500664 (-6.347803) \| 0.067189 / 0.075469 (-0.008280) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.284255 / 1.841788 (-0.557533) \| 14.207864 / 8.074308 (6.133556) \| 14.279691 / 10.191392 (4.088299) \| 0.167027 / 0.680424 (-0.513396) \| 0.016455 / 0.534201 (-0.517746) \| 0.380798 / 0.579283 (-0.198485) \| 0.390013 / 0.434364 (-0.044351) \| 0.445493 / 0.540337 (-0.094845) \| 0.526278 / 1.386936 (-0.860658) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3fdb46c526b9d070df0eb2d56b0ecacdace7cb9a \"CML watermark\")\n" ]	2023-04-14T18:15:14Z	2023-04-20T15:27:58Z	2023-04-20T15:21:00Z	COLLABORATOR	null	null	null	`GeneratorBasedBuilder`'s TQDM bars were not used as context managers. This PR fixes that (missed these bars in https://github.com/huggingface/datasets/pull/5560). Also, this PR modifies the single-proc `save_to_disk` to fix the issue with the TQDM bar not accumulating the progress in the multi-shard setting (again, this bug was introduced by me in the linked PR 😎)	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5754/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5754/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5754.diff", "html_url": "https://github.com/huggingface/datasets/pull/5754", "merged_at": "2023-04-20T15:21:00Z", "patch_url": "https://github.com/huggingface/datasets/pull/5754.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5754" }
https://api.github.com/repos/huggingface/datasets/issues/5776	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5776/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5776/comments	https://api.github.com/repos/huggingface/datasets/issues/5776/events	https://github.com/huggingface/datasets/issues/5776	1,677,116,100	I_kwDODunzps5j9sLE	5,776	Use Pandas' `read_json` in the JSON builder	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[ { "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	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" } ]	null	[]	2023-04-20T17:15:49Z	2023-04-20T17:15:49Z	null	COLLABORATOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	Instead of PyArrow's `read_json`, we should use `pd.read_json` in the JSON builder for consistency with the CSV and SQL builders (e.g., to address https://github.com/huggingface/datasets/issues/5725). In Pandas2.0, to get the same performance, we can set the `engine` to "pyarrow". The issue is that Colab still doesn't install Pandas 2.0 by default, so I think it's best to wait for this to be resolved on their side to avoid downgrading decoding performance in scenarios when Pandas 2.0 is not installed.	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/5776/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5776/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7372	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7372/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7372/comments	https://api.github.com/repos/huggingface/datasets/issues/7372/events	https://github.com/huggingface/datasets/issues/7372	2,791,760,968	I_kwDODunzps6mZuRI	7,372	Inconsistent Behavior Between `load_dataset` and `load_from_disk` When Loading Sharded Datasets	{ "avatar_url": "https://avatars.githubusercontent.com/u/38203359?v=4", "events_url": "https://api.github.com/users/gaohongkui/events{/privacy}", "followers_url": "https://api.github.com/users/gaohongkui/followers", "following_url": "https://api.github.com/users/gaohongkui/following{/other_user}", "gists_url": "https://api.github.com/users/gaohongkui/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/gaohongkui", "id": 38203359, "login": "gaohongkui", "node_id": "MDQ6VXNlcjM4MjAzMzU5", "organizations_url": "https://api.github.com/users/gaohongkui/orgs", "received_events_url": "https://api.github.com/users/gaohongkui/received_events", "repos_url": "https://api.github.com/users/gaohongkui/repos", "site_admin": false, "starred_url": "https://api.github.com/users/gaohongkui/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/gaohongkui/subscriptions", "type": "User", "url": "https://api.github.com/users/gaohongkui", "user_view_type": "public" }	[]	open	false	null	[]	null	[]	2025-01-16T05:47:20Z	2025-01-16T05:47:20Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Description I encountered an inconsistency in behavior between `load_dataset` and `load_from_disk` when loading sharded datasets. Here is a minimal example to reproduce the issue: #### Code 1: Using `load_dataset` ```python from datasets import Dataset, load_dataset # First save with max_shard_size=10 Dataset.from_dict({"id": range(1000)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10) # Second save with max_shard_size=10 Dataset.from_dict({"id": range(500)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10) # Load the DatasetDict loaded_datasetdict = load_dataset("my_sharded_datasetdict") print(loaded_datasetdict) ``` Output: - `train` has 1350 samples. - `test` has 150 samples. #### Code 2: Using `load_from_disk` ```python from datasets import Dataset, load_from_disk # First save with max_shard_size=10 Dataset.from_dict({"id": range(1000)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10) # Second save with max_shard_size=10 Dataset.from_dict({"id": range(500)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10) # Load the DatasetDict loaded_datasetdict = load_from_disk("my_sharded_datasetdict") print(loaded_datasetdict) ``` Output: - `train` has 450 samples. - `test` has 50 samples. ### Expected Behavior I expected both `load_dataset` and `load_from_disk` to load the same dataset, as they are pointing to the same directory. However, the results differ significantly: - `load_dataset` seems to merge all shards, resulting in a combined dataset. - `load_from_disk` only loads the last saved dataset, ignoring previous shards. ### Questions 1. Is this behavior intentional? If so, could you clarify the difference between `load_dataset` and `load_from_disk` in the documentation? 2. If this is not intentional, could this be considered a bug? 3. What is the recommended way to handle cases where multiple datasets are saved to the same directory? Thank you for your time and effort in maintaining this great library! I look forward to your feedback.	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/7372/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7372/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7496	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7496/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7496/comments	https://api.github.com/repos/huggingface/datasets/issues/7496/events	https://github.com/huggingface/datasets/issues/7496	2,967,345,522	I_kwDODunzps6w3hly	7,496	Json builder: Allow features to override problematic Arrow types	{ "avatar_url": "https://avatars.githubusercontent.com/u/1017189?v=4", "events_url": "https://api.github.com/users/edmcman/events{/privacy}", "followers_url": "https://api.github.com/users/edmcman/followers", "following_url": "https://api.github.com/users/edmcman/following{/other_user}", "gists_url": "https://api.github.com/users/edmcman/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/edmcman", "id": 1017189, "login": "edmcman", "node_id": "MDQ6VXNlcjEwMTcxODk=", "organizations_url": "https://api.github.com/users/edmcman/orgs", "received_events_url": "https://api.github.com/users/edmcman/received_events", "repos_url": "https://api.github.com/users/edmcman/repos", "site_admin": false, "starred_url": "https://api.github.com/users/edmcman/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/edmcman/subscriptions", "type": "User", "url": "https://api.github.com/users/edmcman", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	[ "Hi ! It would be cool indeed, currently the JSON data are generally loaded here: \n\nhttps://github.com/huggingface/datasets/blob/90e5bf8a8599b625d6103ee5ac83b98269991141/src/datasets/packaged_modules/json/json.py#L137-L140\n\nMaybe we can pass a Arrow `schema` to avoid errors ?" ]	2025-04-02T19:27:16Z	2025-04-15T13:06:09Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request In the JSON builder, use explicitly requested feature types before or while converting to Arrow. ### Motivation Working with JSON datasets is really hard because of Arrow. At the very least, it seems like it should be possible to work-around these problems by explicitly setting problematic columns's types. But it seems like this is not possible because the features are only used after converting to arrow. Here's a simple example where the Arrow error could potentially be avoided by converting the column to a string: https://colab.research.google.com/drive/16QHRdbUwKSrpwVfGwu8V8AHr8v2dv0dt?usp=sharing ### Your contribution Maybe with some guidance. I'm not very familiar with arrow or pandas.	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/7496/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7496/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6502	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6502/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6502/comments	https://api.github.com/repos/huggingface/datasets/issues/6502/events	https://github.com/huggingface/datasets/pull/6502	2,043,771,731	PR_kwDODunzps5iHPt-	6,502	Pickle support for `torch.Generator` objects	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6502). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005472 / 0.011353 (-0.005881) \| 0.003715 / 0.011008 (-0.007293) \| 0.063257 / 0.038508 (0.024749) \| 0.060683 / 0.023109 (0.037574) \| 0.250885 / 0.275898 (-0.025013) \| 0.271685 / 0.323480 (-0.051795) \| 0.003051 / 0.007986 (-0.004934) \| 0.002799 / 0.004328 (-0.001530) \| 0.049113 / 0.004250 (0.044863) \| 0.038965 / 0.037052 (0.001912) \| 0.252688 / 0.258489 (-0.005801) \| 0.282536 / 0.293841 (-0.011305) \| 0.028722 / 0.128546 (-0.099824) \| 0.010586 / 0.075646 (-0.065060) \| 0.205145 / 0.419271 (-0.214127) \| 0.036996 / 0.043533 (-0.006537) \| 0.248874 / 0.255139 (-0.006265) \| 0.266148 / 0.283200 (-0.017051) \| 0.018540 / 0.141683 (-0.123143) \| 1.120216 / 1.452155 (-0.331938) \| 1.191072 / 1.492716 (-0.301644) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095721 / 0.018006 (0.077714) \| 0.313401 / 0.000490 (0.312911) \| 0.000234 / 0.000200 (0.000034) \| 0.000053 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018604 / 0.037411 (-0.018807) \| 0.061571 / 0.014526 (0.047045) \| 0.075343 / 0.176557 (-0.101213) \| 0.121272 / 0.737135 (-0.615864) \| 0.076448 / 0.296338 (-0.219890) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286885 / 0.215209 (0.071676) \| 2.809100 / 2.077655 (0.731445) \| 1.485365 / 1.504120 (-0.018755) \| 1.367672 / 1.541195 (-0.173523) \| 1.423570 / 1.468490 (-0.044920) \| 0.571063 / 4.584777 (-4.013714) \| 2.385248 / 3.745712 (-1.360464) \| 2.855251 / 5.269862 (-2.414610) \| 1.799371 / 4.565676 (-2.766306) \| 0.063491 / 0.424275 (-0.360784) \| 0.004942 / 0.007607 (-0.002665) \| 0.346181 / 0.226044 (0.120137) \| 3.388123 / 2.268929 (1.119195) \| 1.819093 / 55.444624 (-53.625532) \| 1.552998 / 6.876477 (-5.323479) \| 1.627930 / 2.142072 (-0.514143) \| 0.653438 / 4.805227 (-4.151789) \| 0.123831 / 6.500664 (-6.376833) \| 0.043340 / 0.075469 (-0.032129) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.952167 / 1.841788 (-0.889621) \| 12.149515 / 8.074308 (4.075207) \| 10.665085 / 10.191392 (0.473693) \| 0.127768 / 0.680424 (-0.552656) \| 0.014022 / 0.534201 (-0.520179) \| 0.285959 / 0.579283 (-0.293324) \| 0.269727 / 0.434364 (-0.164637) \| 0.336646 / 0.540337 (-0.203692) \| 0.442932 / 1.386936 (-0.944005) \|\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) \| 0.005351 / 0.011353 (-0.006002) \| 0.003561 / 0.011008 (-0.007448) \| 0.048890 / 0.038508 (0.010382) \| 0.054093 / 0.023109 (0.030984) \| 0.274397 / 0.275898 (-0.001501) \| 0.296980 / 0.323480 (-0.026500) \| 0.004126 / 0.007986 (-0.003860) \| 0.002751 / 0.004328 (-0.001578) \| 0.049131 / 0.004250 (0.044880) \| 0.040769 / 0.037052 (0.003716) \| 0.279147 / 0.258489 (0.020658) \| 0.302014 / 0.293841 (0.008173) \| 0.029847 / 0.128546 (-0.098699) \| 0.010710 / 0.075646 (-0.064936) \| 0.057626 / 0.419271 (-0.361645) \| 0.032801 / 0.043533 (-0.010732) \| 0.272698 / 0.255139 (0.017559) \| 0.289238 / 0.283200 (0.006039) \| 0.017876 / 0.141683 (-0.123807) \| 1.152059 / 1.452155 (-0.300096) \| 1.212289 / 1.492716 (-0.280427) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092914 / 0.018006 (0.074908) \| 0.303092 / 0.000490 (0.302603) \| 0.000214 / 0.000200 (0.000014) \| 0.000058 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022074 / 0.037411 (-0.015337) \| 0.070109 / 0.014526 (0.055583) \| 0.083360 / 0.176557 (-0.093196) \| 0.122445 / 0.737135 (-0.614690) \| 0.083625 / 0.296338 (-0.212714) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282788 / 0.215209 (0.067579) \| 2.789229 / 2.077655 (0.711574) \| 1.571077 / 1.504120 (0.066957) \| 1.452627 / 1.541195 (-0.088567) \| 1.493176 / 1.468490 (0.024686) \| 0.556892 / 4.584777 (-4.027885) \| 2.442771 / 3.745712 (-1.302941) \| 2.826316 / 5.269862 (-2.443545) \| 1.758276 / 4.565676 (-2.807401) \| 0.063039 / 0.424275 (-0.361236) \| 0.004928 / 0.007607 (-0.002679) \| 0.338247 / 0.226044 (0.112202) \| 3.346344 / 2.268929 (1.077416) \| 1.952520 / 55.444624 (-53.492104) \| 1.664520 / 6.876477 (-5.211956) \| 1.701528 / 2.142072 (-0.440544) \| 0.634746 / 4.805227 (-4.170481) \| 0.116879 / 6.500664 (-6.383786) \| 0.040990 / 0.075469 (-0.034479) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969521 / 1.841788 (-0.872267) \| 12.431395 / 8.074308 (4.357087) \| 10.907503 / 10.191392 (0.716111) \| 0.131028 / 0.680424 (-0.549396) \| 0.015239 / 0.534201 (-0.518962) \| 0.290793 / 0.579283 (-0.288490) \| 0.275072 / 0.434364 (-0.159292) \| 0.331036 / 0.540337 (-0.209301) \| 0.567858 / 1.386936 (-0.819078) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#092118fc00f7dd718ab3643739d7b23ff16c9eff \"CML watermark\")\n" ]	2023-12-15T13:55:12Z	2023-12-15T15:04:33Z	2023-12-15T14:58:22Z	COLLABORATOR	null	null	null	Fix for https://discuss.huggingface.co/t/caching-a-dataset-processed-with-randomness/65616	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6502/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6502/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6502.diff", "html_url": "https://github.com/huggingface/datasets/pull/6502", "merged_at": "2023-12-15T14:58:22Z", "patch_url": "https://github.com/huggingface/datasets/pull/6502.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6502" }
https://api.github.com/repos/huggingface/datasets/issues/7486	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7486/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7486/comments	https://api.github.com/repos/huggingface/datasets/issues/7486/events	https://github.com/huggingface/datasets/issues/7486	2,954,042,179	I_kwDODunzps6wExtD	7,486	`shared_datadir` fixture is missing	{ "avatar_url": "https://avatars.githubusercontent.com/u/1289205?v=4", "events_url": "https://api.github.com/users/lahwaacz/events{/privacy}", "followers_url": "https://api.github.com/users/lahwaacz/followers", "following_url": "https://api.github.com/users/lahwaacz/following{/other_user}", "gists_url": "https://api.github.com/users/lahwaacz/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lahwaacz", "id": 1289205, "login": "lahwaacz", "node_id": "MDQ6VXNlcjEyODkyMDU=", "organizations_url": "https://api.github.com/users/lahwaacz/orgs", "received_events_url": "https://api.github.com/users/lahwaacz/received_events", "repos_url": "https://api.github.com/users/lahwaacz/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lahwaacz/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lahwaacz/subscriptions", "type": "User", "url": "https://api.github.com/users/lahwaacz", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "OK I was missing the `pytest-datadir` package. Sorry for the noise!" ]	2025-03-27T18:17:12Z	2025-03-27T19:49:11Z	2025-03-27T19:49:10Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Running the tests for the latest release fails due to missing `shared_datadir` fixture. ### Steps to reproduce the bug Running `pytest` while building a package for Arch Linux leads to these errors: ``` ==================================== ERRORS ==================================== _________ ERROR at setup of test_pdf_feature_encode_example[<lambda>1] _________ [gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8 @require_pdfplumber @pytest.mark.parametrize( "build_example", [ lambda pdf_path: pdf_path, lambda pdf_path: open(pdf_path, "rb").read(), lambda pdf_path: {"path": pdf_path}, lambda pdf_path: {"path": pdf_path, "bytes": None}, lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()}, lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()}, lambda pdf_path: {"bytes": open(pdf_path, "rb").read()}, ], ) def test_pdf_feature_encode_example(shared_datadir, build_example): E fixture 'shared_datadir' not found > available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file > use 'pytest --fixtures [testpath]' for help on them. /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8 _________ ERROR at setup of test_pdf_feature_encode_example[<lambda>2] _________ [gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8 @require_pdfplumber @pytest.mark.parametrize( "build_example", [ lambda pdf_path: pdf_path, lambda pdf_path: open(pdf_path, "rb").read(), lambda pdf_path: {"path": pdf_path}, lambda pdf_path: {"path": pdf_path, "bytes": None}, lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()}, lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()}, lambda pdf_path: {"bytes": open(pdf_path, "rb").read()}, ], ) def test_pdf_feature_encode_example(shared_datadir, build_example): E fixture 'shared_datadir' not found > available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file > use 'pytest --fixtures [testpath]' for help on them. /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8 _________ ERROR at setup of test_pdf_feature_encode_example[<lambda>3] _________ [gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8 @require_pdfplumber @pytest.mark.parametrize( "build_example", [ lambda pdf_path: pdf_path, lambda pdf_path: open(pdf_path, "rb").read(), lambda pdf_path: {"path": pdf_path}, lambda pdf_path: {"path": pdf_path, "bytes": None}, lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()}, lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()}, lambda pdf_path: {"bytes": open(pdf_path, "rb").read()}, ], ) def test_pdf_feature_encode_example(shared_datadir, build_example): E fixture 'shared_datadir' not found > available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file > use 'pytest --fixtures [testpath]' for help on them. /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8 _________ ERROR at setup of test_pdf_feature_encode_example[<lambda>4] _________ [gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8 @require_pdfplumber @pytest.mark.parametrize( "build_example", [ lambda pdf_path: pdf_path, lambda pdf_path: open(pdf_path, "rb").read(), lambda pdf_path: {"path": pdf_path}, lambda pdf_path: {"path": pdf_path, "bytes": None}, lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()}, lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()}, lambda pdf_path: {"bytes": open(pdf_path, "rb").read()}, ], ) def test_pdf_feature_encode_example(shared_datadir, build_example): E fixture 'shared_datadir' not found > available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file > use 'pytest --fixtures [testpath]' for help on them. /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8 _________ ERROR at setup of test_pdf_feature_encode_example[<lambda>5] _________ [gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8 @require_pdfplumber @pytest.mark.parametrize( "build_example", [ lambda pdf_path: pdf_path, lambda pdf_path: open(pdf_path, "rb").read(), lambda pdf_path: {"path": pdf_path}, lambda pdf_path: {"path": pdf_path, "bytes": None}, lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()}, lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()}, lambda pdf_path: {"bytes": open(pdf_path, "rb").read()}, ], ) def test_pdf_feature_encode_example(shared_datadir, build_example): E fixture 'shared_datadir' not found > available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file > use 'pytest --fixtures [testpath]' for help on them. /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8 _________ ERROR at setup of test_pdf_feature_encode_example[<lambda>6] _________ [gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8 @require_pdfplumber @pytest.mark.parametrize( "build_example", [ lambda pdf_path: pdf_path, lambda pdf_path: open(pdf_path, "rb").read(), lambda pdf_path: {"path": pdf_path}, lambda pdf_path: {"path": pdf_path, "bytes": None}, lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()}, lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()}, lambda pdf_path: {"bytes": open(pdf_path, "rb").read()}, ], ) def test_pdf_feature_encode_example(shared_datadir, build_example): E fixture 'shared_datadir' not found > available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file > use 'pytest --fixtures [testpath]' for help on them. /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8 _______________ ERROR at setup of test_dataset_with_pdf_feature ________________ [gw44] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 34 @require_pdfplumber def test_dataset_with_pdf_feature(shared_datadir): E fixture 'shared_datadir' not found > available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file > use 'pytest --fixtures [testpath]' for help on them. /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:34 _________ ERROR at setup of test_pdf_feature_encode_example[<lambda>0] _________ [gw46] linux -- Python 3.13.2 /build/python-datasets/src/datasets-3.5.0/test-env/bin/python file /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py, line 8 @require_pdfplumber @pytest.mark.parametrize( "build_example", [ lambda pdf_path: pdf_path, lambda pdf_path: open(pdf_path, "rb").read(), lambda pdf_path: {"path": pdf_path}, lambda pdf_path: {"path": pdf_path, "bytes": None}, lambda pdf_path: {"path": pdf_path, "bytes": open(pdf_path, "rb").read()}, lambda pdf_path: {"path": None, "bytes": open(pdf_path, "rb").read()}, lambda pdf_path: {"bytes": open(pdf_path, "rb").read()}, ], ) def test_pdf_feature_encode_example(shared_datadir, build_example): E fixture 'shared_datadir' not found > available fixtures: _hf_gated_dataset_repo_txt_data, arrow_file, arrow_path, audio_file, bz2_csv_path, bz2_file, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, ci_hfh_hf_hub_url, ci_hub_config, cleanup_repo, csv2_path, csv_path, data_dir_with_hidden_files, dataset, dataset_dict, disable_implicit_token, disable_tqdm_output, doctest_namespace, geoparquet_path, gz_file, hf_api, hf_gated_dataset_repo_txt_data, hf_private_dataset_repo_txt_data, hf_private_dataset_repo_txt_data_, hf_private_dataset_repo_zipped_img_data, hf_private_dataset_repo_zipped_img_data_, hf_private_dataset_repo_zipped_txt_data, hf_private_dataset_repo_zipped_txt_data_, hf_token, image_file, json_dict_of_lists_path, json_list_of_dicts_path, jsonl2_path, jsonl_312_path, jsonl_gz_path, jsonl_path, jsonl_str_path, lz4_file, mock_fsspec, mockfs, monkeypatch, parquet_path, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, set_ci_hub_access_token, set_sqlalchemy_silence_uber_warning, set_test_cache_config, set_update_download_counts_to_false, seven_zip_file, sqlite_path, tar_file, tar_jsonl_path, tar_nested_jsonl_path, temporary_repo, tensor_file, testrun_uid, text2_path, text_dir, text_dir_with_unsupported_extension, text_file, text_file_content, text_gz_path, text_path, text_path_with_unicode_new_lines, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory, tmpfs, worker_id, xml_file, xz_file, zero_time_out_for_remote_code, zip_csv_path, zip_csv_with_dir_path, zip_file, zip_image_path, zip_jsonl_path, zip_jsonl_with_dir_path, zip_nested_jsonl_path, zip_text_path, zip_text_with_dir_path, zip_unsupported_ext_path, zip_uppercase_csv_path, zstd_file > use 'pytest --fixtures [testpath]' for help on them. /build/python-datasets/src/datasets-3.5.0/tests/features/test_pdf.py:8 ``` ### Expected behavior All fixtures used in tests should be available. ### Environment info Arch Linux build system, building the [python-datasets](https://gitlab.archlinux.org/archlinux/packaging/packages/python-datasets) package. There are actually [many deselected tests](https://gitlab.archlinux.org/archlinux/packaging/packages/python-datasets/-/blob/6f97957f0c326cc7b3da6b7f12326305bcaef374/PKGBUILD#L66-148) which were failing on previous releases, but these errors popped up in 3.5.0.	{ "avatar_url": "https://avatars.githubusercontent.com/u/1289205?v=4", "events_url": "https://api.github.com/users/lahwaacz/events{/privacy}", "followers_url": "https://api.github.com/users/lahwaacz/followers", "following_url": "https://api.github.com/users/lahwaacz/following{/other_user}", "gists_url": "https://api.github.com/users/lahwaacz/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lahwaacz", "id": 1289205, "login": "lahwaacz", "node_id": "MDQ6VXNlcjEyODkyMDU=", "organizations_url": "https://api.github.com/users/lahwaacz/orgs", "received_events_url": "https://api.github.com/users/lahwaacz/received_events", "repos_url": "https://api.github.com/users/lahwaacz/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lahwaacz/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lahwaacz/subscriptions", "type": "User", "url": "https://api.github.com/users/lahwaacz", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7486/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7486/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5550	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5550/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5550/comments	https://api.github.com/repos/huggingface/datasets/issues/5550/events	https://github.com/huggingface/datasets/pull/5550	1,591,409,475	PR_kwDODunzps5KUl5i	5,550	Resolve four broken refs in the docs	{ "avatar_url": "https://avatars.githubusercontent.com/u/37621491?v=4", "events_url": "https://api.github.com/users/tomaarsen/events{/privacy}", "followers_url": "https://api.github.com/users/tomaarsen/followers", "following_url": "https://api.github.com/users/tomaarsen/following{/other_user}", "gists_url": "https://api.github.com/users/tomaarsen/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/tomaarsen", "id": 37621491, "login": "tomaarsen", "node_id": "MDQ6VXNlcjM3NjIxNDkx", "organizations_url": "https://api.github.com/users/tomaarsen/orgs", "received_events_url": "https://api.github.com/users/tomaarsen/received_events", "repos_url": "https://api.github.com/users/tomaarsen/repos", "site_admin": false, "starred_url": "https://api.github.com/users/tomaarsen/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/tomaarsen/subscriptions", "type": "User", "url": "https://api.github.com/users/tomaarsen", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "See the resolved changes [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5550/en/package_reference/main_classes#datasets.Dataset.class_encode_column), [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5550/en/package_reference/main_classes#datasets.Dataset.unique) and [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5550/en/package_reference/main_classes#datasets.DatasetDict.class_encode_column), respectively", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008256 / 0.011353 (-0.003097) \| 0.004400 / 0.011008 (-0.006608) \| 0.098676 / 0.038508 (0.060168) \| 0.028937 / 0.023109 (0.005828) \| 0.302578 / 0.275898 (0.026680) \| 0.334170 / 0.323480 (0.010690) \| 0.006657 / 0.007986 (-0.001329) \| 0.004581 / 0.004328 (0.000253) \| 0.076874 / 0.004250 (0.072624) \| 0.034401 / 0.037052 (-0.002652) \| 0.303928 / 0.258489 (0.045439) \| 0.348421 / 0.293841 (0.054580) \| 0.033303 / 0.128546 (-0.095243) \| 0.011445 / 0.075646 (-0.064202) \| 0.322137 / 0.419271 (-0.097135) \| 0.041072 / 0.043533 (-0.002461) \| 0.306007 / 0.255139 (0.050868) \| 0.325945 / 0.283200 (0.042745) \| 0.086685 / 0.141683 (-0.054998) \| 1.454956 / 1.452155 (0.002801) \| 1.545525 / 1.492716 (0.052809) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.175536 / 0.018006 (0.157530) \| 0.400203 / 0.000490 (0.399713) \| 0.002103 / 0.000200 (0.001903) \| 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) \| 0.022750 / 0.037411 (-0.014661) \| 0.095163 / 0.014526 (0.080637) \| 0.103995 / 0.176557 (-0.072561) \| 0.138806 / 0.737135 (-0.598330) \| 0.105711 / 0.296338 (-0.190628) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.427860 / 0.215209 (0.212651) \| 4.259594 / 2.077655 (2.181940) \| 2.157986 / 1.504120 (0.653866) \| 1.913814 / 1.541195 (0.372619) \| 1.793455 / 1.468490 (0.324965) \| 0.702341 / 4.584777 (-3.882436) \| 3.353086 / 3.745712 (-0.392626) \| 1.856952 / 5.269862 (-3.412909) \| 1.149963 / 4.565676 (-3.415713) \| 0.082926 / 0.424275 (-0.341349) \| 0.012307 / 0.007607 (0.004700) \| 0.524531 / 0.226044 (0.298487) \| 5.254766 / 2.268929 (2.985838) \| 2.590157 / 55.444624 (-52.854468) \| 2.272613 / 6.876477 (-4.603864) \| 2.304367 / 2.142072 (0.162294) \| 0.819298 / 4.805227 (-3.985929) \| 0.152170 / 6.500664 (-6.348494) \| 0.066563 / 0.075469 (-0.008906) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.205054 / 1.841788 (-0.636733) \| 13.729073 / 8.074308 (5.654765) \| 14.061037 / 10.191392 (3.869645) \| 0.138020 / 0.680424 (-0.542404) \| 0.028042 / 0.534201 (-0.506159) \| 0.392260 / 0.579283 (-0.187024) \| 0.405632 / 0.434364 (-0.028732) \| 0.469583 / 0.540337 (-0.070755) \| 0.563110 / 1.386936 (-0.823826) \|\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) \| 0.006513 / 0.011353 (-0.004839) \| 0.004402 / 0.011008 (-0.006606) \| 0.076339 / 0.038508 (0.037831) \| 0.027222 / 0.023109 (0.004112) \| 0.338968 / 0.275898 (0.063070) \| 0.378475 / 0.323480 (0.054995) \| 0.005443 / 0.007986 (-0.002542) \| 0.003312 / 0.004328 (-0.001016) \| 0.075352 / 0.004250 (0.071102) \| 0.034951 / 0.037052 (-0.002102) \| 0.342268 / 0.258489 (0.083779) \| 0.381024 / 0.293841 (0.087183) \| 0.031568 / 0.128546 (-0.096979) \| 0.011558 / 0.075646 (-0.064088) \| 0.085267 / 0.419271 (-0.334005) \| 0.041248 / 0.043533 (-0.002284) \| 0.340422 / 0.255139 (0.085283) \| 0.365497 / 0.283200 (0.082297) \| 0.088278 / 0.141683 (-0.053405) \| 1.479838 / 1.452155 (0.027683) \| 1.554440 / 1.492716 (0.061724) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223240 / 0.018006 (0.205234) \| 0.394771 / 0.000490 (0.394282) \| 0.003022 / 0.000200 (0.002822) \| 0.000071 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024842 / 0.037411 (-0.012570) \| 0.099167 / 0.014526 (0.084641) \| 0.106376 / 0.176557 (-0.070180) \| 0.141397 / 0.737135 (-0.595738) \| 0.110355 / 0.296338 (-0.185983) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.437598 / 0.215209 (0.222389) \| 4.394964 / 2.077655 (2.317310) \| 2.082660 / 1.504120 (0.578540) \| 1.868690 / 1.541195 (0.327496) \| 1.915190 / 1.468490 (0.446700) \| 0.701035 / 4.584777 (-3.883742) \| 3.306594 / 3.745712 (-0.439118) \| 1.842681 / 5.269862 (-3.427181) \| 1.155022 / 4.565676 (-3.410654) \| 0.083310 / 0.424275 (-0.340965) \| 0.012413 / 0.007607 (0.004806) \| 0.543179 / 0.226044 (0.317135) \| 5.445605 / 2.268929 (3.176676) \| 2.545080 / 55.444624 (-52.899544) \| 2.188741 / 6.876477 (-4.687736) \| 2.205561 / 2.142072 (0.063489) \| 0.804967 / 4.805227 (-4.000261) \| 0.151024 / 6.500664 (-6.349640) \| 0.066448 / 0.075469 (-0.009021) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.304671 / 1.841788 (-0.537117) \| 13.996631 / 8.074308 (5.922323) \| 13.617626 / 10.191392 (3.426234) \| 0.141512 / 0.680424 (-0.538912) \| 0.016527 / 0.534201 (-0.517674) \| 0.384981 / 0.579283 (-0.194302) \| 0.385198 / 0.434364 (-0.049166) \| 0.469033 / 0.540337 (-0.071305) \| 0.554738 / 1.386936 (-0.832198) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d09dc897e153fed7c7f459a122fb03faa46688ed \"CML watermark\")\n" ]	2023-02-20T08:52:11Z	2023-02-20T15:16:13Z	2023-02-20T15:09:13Z	MEMBER	null	null	null	Hello! ## Pull Request overview * Resolve 4 broken references in the docs ## The problems Two broken references [here](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.class_encode_column): ![image](https://user-images.githubusercontent.com/37621491/220056232-366b64dc-33c9-461b-8f82-1ac4aa570280.png) --- One broken reference [here](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.unique): ![image](https://user-images.githubusercontent.com/37621491/220057135-2f249d60-c01d-48b5-82bb-5085a7635198.png) --- One missing reference [here](https://huggingface.co/docs/datasets/v2.9.0/en/package_reference/main_classes#datasets.DatasetDict.class_encode_column): ![image](https://user-images.githubusercontent.com/37621491/220057025-4a8e5556-5041-4ec7-b8d8-ed4fdc266495.png) - Tom Aarsen	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5550/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5550/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5550.diff", "html_url": "https://github.com/huggingface/datasets/pull/5550", "merged_at": "2023-02-20T15:09:13Z", "patch_url": "https://github.com/huggingface/datasets/pull/5550.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5550" }
https://api.github.com/repos/huggingface/datasets/issues/5970	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5970/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5970/comments	https://api.github.com/repos/huggingface/datasets/issues/5970/events	https://github.com/huggingface/datasets/issues/5970	1,766,010,356	I_kwDODunzps5pQy30	5,970	description disappearing from Info when Uploading a Dataset Created with `from_dict`	{ "avatar_url": "https://avatars.githubusercontent.com/u/20377292?v=4", "events_url": "https://api.github.com/users/balisujohn/events{/privacy}", "followers_url": "https://api.github.com/users/balisujohn/followers", "following_url": "https://api.github.com/users/balisujohn/following{/other_user}", "gists_url": "https://api.github.com/users/balisujohn/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/balisujohn", "id": 20377292, "login": "balisujohn", "node_id": "MDQ6VXNlcjIwMzc3Mjky", "organizations_url": "https://api.github.com/users/balisujohn/orgs", "received_events_url": "https://api.github.com/users/balisujohn/received_events", "repos_url": "https://api.github.com/users/balisujohn/repos", "site_admin": false, "starred_url": "https://api.github.com/users/balisujohn/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/balisujohn/subscriptions", "type": "User", "url": "https://api.github.com/users/balisujohn", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Here's a minimal way to reproduce the bug, for the sake of convenience.\r\n````\r\nfrom datasets import Dataset, DatasetInfo, load_dataset\r\n\r\n\r\nepisodes_dict = {\"test\":[1,2,3],\"test2\": [1,2,4]}\r\n\r\nhugging_face_dataset = Dataset.from_dict(\r\n episodes_dict, info=DatasetInfo(description=\"test_str\")\r\n)\r\nprint(hugging_face_dataset.info)\r\n\r\nhugging_face_dataset.push_to_hub(\"balisujohn/minari_test\", private=True)\r\n\r\nredownloaded_dataset= load_dataset(\"balisujohn/minari_test\")[\"train\"]\r\n\r\n\r\nprint(redownloaded_dataset.info)\r\n````\r\n", "Thanks for reporting !\r\n\r\nFor now I would recommend uploading a separate JSON file for your metadata.\r\n\r\nAlternatively you can upload a second configuration of the dataset containing your metadata but this feature is not released yet (though you can already use it from [here](https://github.com/huggingface/datasets/pull/5331), it will be released soon)" ]	2023-06-20T19:18:26Z	2023-06-22T14:23:56Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug When uploading a dataset created locally using `from_dict` with a specified `description` field. It appears before upload, but is missing after upload and re-download. ### Steps to reproduce the bug I think the most relevant pattern in the code might be the following lines: ``` description_json_str = json.dumps( { "dataset_id": dataset.spec.dataset_id, "env_name": dataset.spec.env_spec.id, "action_space": serialize_space(dataset.spec.action_space), "observation_space": serialize_space(dataset.spec.observation_space), } ) hugging_face_dataset = Dataset.from_dict( episodes_dict, info=DatasetInfo(description=description_json_str) ) ``` Which comes from this function https://github.com/balisujohn/minarai/blob/8e023727f0a8488c4451651d9f7a79b981412c40/minari/integrations/hugging_face.py#L39 To replicate, clone this branch of my Minari fork https://github.com/balisujohn/minarai/tree/dev-huggingface then run ``` python3.8 -m venv env source env/bin/activate python3 -m pip install -e . python3 -m pip install pytest ``` The change the hugging face repo path in the test called `test_hugging_face_push_and_pull_dataset` in `tests/integrations/test_hugging_face.py` to one you have permissions to write to. Then run: ``` pytest tests/integrations/test_hugging_face.py::test_hugging_face_push_and_pull_dataset ``` ### Expected behavior DATASET INFO BEFORE UPLOADING DatasetInfo(description='{"dataset_id": "dummy-combo-test-v0", "env_name": "DummyComboEnv-v0", "action_space": "{\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [4.0], \\"high\\": [5.0]}]}", "observation_space": "{\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Dict\\", \\"subspaces\\": {\\"component_1\\": {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [-1.0], \\"high\\": [1.0]}, \\"component_2\\": {\\"type\\": \\"Dict\\", \\"subspaces\\": {\\"subcomponent_1\\": {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, \\"subcomponent_2\\": {\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [4.0], \\"high\\": [5.0]}, {\\"type\\": \\"Discrete\\", \\"dtype\\": \\"int64\\", \\"start\\": 0, \\"n\\": 10}]}}}}}]}]}"}', citation='', homepage='', license='', features={'observations': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'component_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'component_2': {'subcomponent_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'subcomponent_2': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Value(dtype='int64', id=None)}}}}}, 'actions': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None)}, 'rewards': Value(dtype='int64', id=None), 'truncations': Value(dtype='bool', id=None), 'terminations': Value(dtype='bool', id=None), 'episode_ids': Value(dtype='int64', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name=None, config_name=None, version=None, splits=None, download_checksums=None, download_size=None, post_processing_size=None, dataset_size=None, size_in_bytes=None) ... DATASET INFO AFTER UPLOADING AND DOWNLOADING DatasetInfo(description='', citation='', homepage='', license='', features={'observations': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'component_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'component_2': {'subcomponent_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'subcomponent_2': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Value(dtype='int64', id=None)}}}}}, 'actions': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None)}, 'rewards': Value(dtype='int64', id=None), 'truncations': Value(dtype='bool', id=None), 'terminations': Value(dtype='bool', id=None), 'episode_ids': Value(dtype='int64', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name=None, config_name=None, version=None, splits={'train': SplitInfo(name='train', num_bytes=4846, num_examples=60, shard_lengths=None, dataset_name='parquet')}, download_checksums={'https://huggingface.co/datasets/balisujohn/minari_test/resolve/8217b614ff9ba5edc1a30c7df430e92a46f65363/data/train-00000-of-00001-7c5900b93b35745e.parquet': {'num_bytes': 9052, 'checksum': None}}, download_size=9052, post_processing_size=None, dataset_size=4846, size_in_bytes=13898) ... ### Environment info - `datasets` version: 2.13.0 - Platform: Linux-5.15.0-75-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.2	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5970/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5970/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5525	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5525/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5525/comments	https://api.github.com/repos/huggingface/datasets/issues/5525/events	https://github.com/huggingface/datasets/issues/5525	1,580,342,729	I_kwDODunzps5eMh3J	5,525	TypeError: Couldn't cast array of type string to null	{ "avatar_url": "https://avatars.githubusercontent.com/u/74564958?v=4", "events_url": "https://api.github.com/users/TJ-Solergibert/events{/privacy}", "followers_url": "https://api.github.com/users/TJ-Solergibert/followers", "following_url": "https://api.github.com/users/TJ-Solergibert/following{/other_user}", "gists_url": "https://api.github.com/users/TJ-Solergibert/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/TJ-Solergibert", "id": 74564958, "login": "TJ-Solergibert", "node_id": "MDQ6VXNlcjc0NTY0OTU4", "organizations_url": "https://api.github.com/users/TJ-Solergibert/orgs", "received_events_url": "https://api.github.com/users/TJ-Solergibert/received_events", "repos_url": "https://api.github.com/users/TJ-Solergibert/repos", "site_admin": false, "starred_url": "https://api.github.com/users/TJ-Solergibert/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/TJ-Solergibert/subscriptions", "type": "User", "url": "https://api.github.com/users/TJ-Solergibert", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Thanks for reporting, @TJ-Solergibert.\r\n\r\nWe cannot access your Colab notebook: `There was an error loading this notebook. Ensure that the file is accessible and try again.`\r\nCould you please make it publicly accessible?\r\n", "I swear it's public, I've checked the settings and I've been able to open it in incognito mode.\r\n\r\nNotebook: https://colab.research.google.com/drive/1JCrS7FlGfu_kFqChMrwKZ_bpabnIMqbP?usp=sharing\r\n\r\nAnyway, this is the code to reproduce the error:\r\n\r\n```python3\r\nfrom datasets import ClassLabel\r\nfrom datasets import load_dataset\r\n\r\neuroparl_ds = load_dataset(\"tj-solergibert/Europarl-ST\")\r\n\r\nsource_lang = \"nl\"\r\nlanguages = list(europarl_ds[\"train\"][0][\"transcriptions\"].keys())\r\nClassLabels = ClassLabel(num_classes = len(languages), names = languages)\r\n\r\ndef map_label2id(example):\r\n example['dest_lang'] = ClassLabels.str2int(example['dest_lang'])\r\n return example\r\n\r\ndef unfold_transcriptions(example):\r\n for lang in languages:\r\n example[lang] = example[\"transcriptions\"][lang]\r\n return example\r\n\r\ndef unroll(batch, src_lang, dest_langs):\r\n source_t, dest_t, dest_l = [], [], []\r\n for lang in dest_langs: \r\n source_t += batch[src_lang]\r\n dest_t += batch[lang]\r\n dest_l += [lang]\r\n return_dict = {\"source_text\": source_t, \"dest_text\": dest_t, \"dest_lang\": dest_l}\r\n return return_dict\r\n\r\ndef preprocess_split(ds_split, src_lang):\r\n dest_langs = [x for x in languages if x != src_lang]\r\n\r\n ds_split = ds_split.map(unroll, fn_kwargs= {\"src_lang\": src_lang, \"dest_langs\": dest_langs}, batched = True, batch_size = 1, remove_columns= list(languages))\r\n ds_split = ds_split.filter(lambda x: x[\"source_text\"] != None and x[\"dest_text\"] != None) # Remove incomplete translations\r\n ds_split = ds_split.filter(lambda x: x[\"source_text\"] != \"None\" and x[\"dest_text\"] != \"None\")\r\n ds_split = ds_split.map(map_label2id) \r\n ds_split = ds_split.cast_column(\"dest_lang\", ClassLabels)\r\n return ds_split\r\n\r\ndef reset_cortas(example):\r\n for lang in languages:\r\n if isinstance(example[lang], str):\r\n if example[lang].isnumeric () or len(example[lang]) <= 5:\r\n example[lang] = \"None\"\r\n return example\r\n\r\ndef clean_dataset(dataset):\r\n # Remove columns\r\n dataset = dataset.remove_columns([\"original_speech\", \"original_language\", \"audio_path\", \"segment_start\", \"segment_end\"])\r\n # Unfold\r\n dataset = dataset.map(unfold_transcriptions, remove_columns = [\"transcriptions\"])\r\n dataset = dataset.map(reset_cortas)\r\n return dataset\r\n\r\nprocessed_europarl = clean_dataset(europarl_ds[\"test\"])\r\nnew_train_ds = preprocess_split(processed_europarl, 'nl')\r\n```", "Thanks, @TJ-Solergibert. I can access your notebook now. Maybe it was just a temporary issue.\r\n\r\nAt first sight, it seems something related to your data: maybe some of the examples do not have all the transcriptions for all the languages. Then, some of them are null when unrolled. And when trying to concatenate with the other rows containing strings, the cast issue is raised (the arrays to be concatenated have different types).\r\n\r\nDo you think this could be the case?", "See, in this example, \"nl\" and \"ro\" transcripts are null:\r\n```python\r\n>>> europarl_ds[\"test\"][:1]\r\n{'original_speech': ['− Señor Presidente, en primer lugar, quisiera felicitar al señor Seeber por el trabajo realizado, porque en su informe se recogen muchas de las preocupaciones manifestadas en esta'],\r\n 'original_language': ['es'],\r\n 'audio_path': ['es/audios/en.20081008.24.3-238.m4a'],\r\n 'segment_start': [0.6200000047683716],\r\n 'segment_end': [11.319999694824219],\r\n 'transcriptions': [{'de': '− Herr Präsident! Zunächst möchte ich Richard Seeber zu der von ihm geleisteten Arbeit gratulieren, denn sein Bericht greift viele der in diesem Haus zum Ausdruck gebrachten Anliegen',\r\n 'en': '− Mr President, firstly I would like to congratulate Mr Seeber on the work he has done, because his report picks up many of the concerns expressed in this',\r\n 'es': '− Señor Presidente, en primer lugar, quisiera felicitar al señor Seeber por el trabajo realizado, porque en su informe se recogen muchas de las preocupaciones manifestadas en esta',\r\n 'fr': '− Monsieur le Président, je voudrais tout d ’ abord féliciter M. Seeber pour le travail qu ’ il a effectué, parce que son rapport reprend beaucoup des inquiétudes exprimées au sein de cette',\r\n 'it': \"− Signor Presidente, mi congratulo innanzi tutto con l'onorevole Seeber per il lavoro svolto, perché la sua relazione accoglie molti dei timori espressi da quest'Aula\",\r\n 'nl': None,\r\n 'pl': '− Panie przewodniczący! Po pierwsze chciałabym pogratulować panu posłowi Seeberowi wykonanej pracy, ponieważ jego sprawozdanie podejmuje szereg podnoszonych w tej Izbie',\r\n 'pt': '− Senhor Presidente, começo por felicitar o senhor deputado Seeber pelo trabalho que desenvolveu em torno deste relatório, que retoma muitas das preocupações expressas nesta',\r\n 'ro': None}]}\r\n```\r\n```python\r\n>>> processed_europarl[0]\r\n{'de': '− Herr Präsident! Zunächst möchte ich Richard Seeber zu der von ihm geleisteten Arbeit gratulieren, denn sein Bericht greift viele der in diesem Haus zum Ausdruck gebrachten Anliegen',\r\n 'en': '− Mr President, firstly I would like to congratulate Mr Seeber on the work he has done, because his report picks up many of the concerns expressed in this',\r\n 'es': '− Señor Presidente, en primer lugar, quisiera felicitar al señor Seeber por el trabajo realizado, porque en su informe se recogen muchas de las preocupaciones manifestadas en esta',\r\n 'fr': '− Monsieur le Président, je voudrais tout d ’ abord féliciter M. Seeber pour le travail qu ’ il a effectué, parce que son rapport reprend beaucoup des inquiétudes exprimées au sein de cette',\r\n 'it': \"− Signor Presidente, mi congratulo innanzi tutto con l'onorevole Seeber per il lavoro svolto, perché la sua relazione accoglie molti dei timori espressi da quest'Aula\",\r\n 'nl': None,\r\n 'pl': '− Panie przewodniczący! Po pierwsze chciałabym pogratulować panu posłowi Seeberowi wykonanej pracy, ponieważ jego sprawozdanie podejmuje szereg podnoszonych w tej Izbie',\r\n 'pt': '− Senhor Presidente, começo por felicitar o senhor deputado Seeber pelo trabalho que desenvolveu em torno deste relatório, que retoma muitas das preocupações expressas nesta',\r\n 'ro': None}\r\n```", "You can fix this issue by forcing the cast of None to str by hand:\r\n- If you replace this line:\r\n```python\r\nsource_t += batch[src_lang]\r\n```\r\n- With this line (because the batch size is 1):\r\n```python\r\nsource_t += [str(batch[src_lang][0])]\r\n```\r\n- Or with this line (if the batch size were larger than 1):\r\n```python\r\nsource_t += [str(text) for text in batch[src_lang]]\r\n```", "Problem solved! Thanks @albertvillanova, now I have even increased the batch size and it's crazy fast :rocket: !" ]	2023-02-10T21:12:36Z	2023-02-14T17:41:08Z	2023-02-14T09:35:49Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Processing a dataset I alredy uploaded to the Hub (https://huggingface.co/datasets/tj-solergibert/Europarl-ST) I found that for some splits and some languages (test split, source_lang = "nl") after applying a map function I get the mentioned error. I alredy tried reseting the shorter strings (reset_cortas function). It only happends with NL, PL, RO and PT. It does not make sense since when processing the other languages I also use the corpus of those that fail and it does not cause any errors. I suspect that the error may be in this direction: We use cast_array_to_feature to support casting to custom types like Audio and Image # Also, when trying type "string", we don't want to convert integers or floats to "string". # We only do it if trying_type is False - since this is what the user asks for. ### Steps to reproduce the bug Here I link a colab notebook to reproduce the error: https://colab.research.google.com/drive/1JCrS7FlGfu_kFqChMrwKZ_bpabnIMqbP?authuser=1#scrollTo=FBAvlhMxIzpA ### Expected behavior Data processing does not fail. A correct example can be seen here: https://huggingface.co/datasets/tj-solergibert/Europarl-ST-processed-mt-en ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.10.147+-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 9.0.0 - Pandas version: 1.3.5	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5525/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5525/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7285	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7285/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7285/comments	https://api.github.com/repos/huggingface/datasets/issues/7285/events	https://github.com/huggingface/datasets/pull/7285	2,644,488,598	PR_kwDODunzps6BV3Gu	7,285	Release v3.1.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/5719745?v=4", "events_url": "https://api.github.com/users/alex-hh/events{/privacy}", "followers_url": "https://api.github.com/users/alex-hh/followers", "following_url": "https://api.github.com/users/alex-hh/following{/other_user}", "gists_url": "https://api.github.com/users/alex-hh/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alex-hh", "id": 5719745, "login": "alex-hh", "node_id": "MDQ6VXNlcjU3MTk3NDU=", "organizations_url": "https://api.github.com/users/alex-hh/orgs", "received_events_url": "https://api.github.com/users/alex-hh/received_events", "repos_url": "https://api.github.com/users/alex-hh/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alex-hh/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alex-hh/subscriptions", "type": "User", "url": "https://api.github.com/users/alex-hh", "user_view_type": "public" }	[]	closed	false	null	[]	null	[]	2024-11-08T16:17:58Z	2024-11-08T16:18:05Z	2024-11-08T16:18:05Z	CONTRIBUTOR	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/5719745?v=4", "events_url": "https://api.github.com/users/alex-hh/events{/privacy}", "followers_url": "https://api.github.com/users/alex-hh/followers", "following_url": "https://api.github.com/users/alex-hh/following{/other_user}", "gists_url": "https://api.github.com/users/alex-hh/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alex-hh", "id": 5719745, "login": "alex-hh", "node_id": "MDQ6VXNlcjU3MTk3NDU=", "organizations_url": "https://api.github.com/users/alex-hh/orgs", "received_events_url": "https://api.github.com/users/alex-hh/received_events", "repos_url": "https://api.github.com/users/alex-hh/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alex-hh/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alex-hh/subscriptions", "type": "User", "url": "https://api.github.com/users/alex-hh", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7285/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7285/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7285.diff", "html_url": "https://github.com/huggingface/datasets/pull/7285", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/7285.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7285" }
https://api.github.com/repos/huggingface/datasets/issues/5188	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5188/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5188/comments	https://api.github.com/repos/huggingface/datasets/issues/5188/events	https://github.com/huggingface/datasets/pull/5188	1,432,477,139	PR_kwDODunzps5CBaoQ	5,188	add: segmentation guide.	{ "avatar_url": "https://avatars.githubusercontent.com/u/22957388?v=4", "events_url": "https://api.github.com/users/sayakpaul/events{/privacy}", "followers_url": "https://api.github.com/users/sayakpaul/followers", "following_url": "https://api.github.com/users/sayakpaul/following{/other_user}", "gists_url": "https://api.github.com/users/sayakpaul/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sayakpaul", "id": 22957388, "login": "sayakpaul", "node_id": "MDQ6VXNlcjIyOTU3Mzg4", "organizations_url": "https://api.github.com/users/sayakpaul/orgs", "received_events_url": "https://api.github.com/users/sayakpaul/received_events", "repos_url": "https://api.github.com/users/sayakpaul/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sayakpaul/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sayakpaul/subscriptions", "type": "User", "url": "https://api.github.com/users/sayakpaul", "user_view_type": "public" }	[ { "color": "0075ca", "default": true, "description": "Improvements or additions to documentation", "id": 1935892861, "name": "documentation", "node_id": "MDU6TGFiZWwxOTM1ODkyODYx", "url": "https://api.github.com/repos/huggingface/datasets/labels/documentation" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/22957388?v=4", "events_url": "https://api.github.com/users/sayakpaul/events{/privacy}", "followers_url": "https://api.github.com/users/sayakpaul/followers", "following_url": "https://api.github.com/users/sayakpaul/following{/other_user}", "gists_url": "https://api.github.com/users/sayakpaul/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sayakpaul", "id": 22957388, "login": "sayakpaul", "node_id": "MDQ6VXNlcjIyOTU3Mzg4", "organizations_url": "https://api.github.com/users/sayakpaul/orgs", "received_events_url": "https://api.github.com/users/sayakpaul/received_events", "repos_url": "https://api.github.com/users/sayakpaul/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sayakpaul/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sayakpaul/subscriptions", "type": "User", "url": "https://api.github.com/users/sayakpaul", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/22957388?v=4", "events_url": "https://api.github.com/users/sayakpaul/events{/privacy}", "followers_url": "https://api.github.com/users/sayakpaul/followers", "following_url": "https://api.github.com/users/sayakpaul/following{/other_user}", "gists_url": "https://api.github.com/users/sayakpaul/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sayakpaul", "id": 22957388, "login": "sayakpaul", "node_id": "MDQ6VXNlcjIyOTU3Mzg4", "organizations_url": "https://api.github.com/users/sayakpaul/orgs", "received_events_url": "https://api.github.com/users/sayakpaul/received_events", "repos_url": "https://api.github.com/users/sayakpaul/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sayakpaul/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sayakpaul/subscriptions", "type": "User", "url": "https://api.github.com/users/sayakpaul", "user_view_type": "public" } ]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks @osanseviero. Am I good to merge? ", "I would wait for a second approval just in case :) ", "Sure :) ", "Merging since the images have been pushed as LFS files ([PR](https://huggingface.co/datasets/huggingface/documentation-images/discussions/8)). " ]	2022-11-02T04:34:36Z	2022-11-04T18:25:57Z	2022-11-04T18:23:34Z	MEMBER	null	null	null	Closes #5181 I have opened a PR on Hub (https://huggingface.co/datasets/huggingface/documentation-images/discussions/5) to include the images in our central Hub repository. Once the PR is merged I will edit the image links. I have also prepared a [Colab Notebook](https://colab.research.google.com/drive/1BMDCfOTBnyshoME5RSxn5iQy-TWeFbOA?usp=sharing) in case anyone wants to play. - [x] Replace the image links	{ "avatar_url": "https://avatars.githubusercontent.com/u/22957388?v=4", "events_url": "https://api.github.com/users/sayakpaul/events{/privacy}", "followers_url": "https://api.github.com/users/sayakpaul/followers", "following_url": "https://api.github.com/users/sayakpaul/following{/other_user}", "gists_url": "https://api.github.com/users/sayakpaul/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sayakpaul", "id": 22957388, "login": "sayakpaul", "node_id": "MDQ6VXNlcjIyOTU3Mzg4", "organizations_url": "https://api.github.com/users/sayakpaul/orgs", "received_events_url": "https://api.github.com/users/sayakpaul/received_events", "repos_url": "https://api.github.com/users/sayakpaul/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sayakpaul/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sayakpaul/subscriptions", "type": "User", "url": "https://api.github.com/users/sayakpaul", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5188/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5188/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5188.diff", "html_url": "https://github.com/huggingface/datasets/pull/5188", "merged_at": "2022-11-04T18:23:34Z", "patch_url": "https://github.com/huggingface/datasets/pull/5188.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5188" }
https://api.github.com/repos/huggingface/datasets/issues/5641	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5641/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5641/comments	https://api.github.com/repos/huggingface/datasets/issues/5641/events	https://github.com/huggingface/datasets/issues/5641	1,625,942,730	I_kwDODunzps5g6erK	5,641	Features cannot be named "self"	{ "avatar_url": "https://avatars.githubusercontent.com/u/14365168?v=4", "events_url": "https://api.github.com/users/alialamiidrissi/events{/privacy}", "followers_url": "https://api.github.com/users/alialamiidrissi/followers", "following_url": "https://api.github.com/users/alialamiidrissi/following{/other_user}", "gists_url": "https://api.github.com/users/alialamiidrissi/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alialamiidrissi", "id": 14365168, "login": "alialamiidrissi", "node_id": "MDQ6VXNlcjE0MzY1MTY4", "organizations_url": "https://api.github.com/users/alialamiidrissi/orgs", "received_events_url": "https://api.github.com/users/alialamiidrissi/received_events", "repos_url": "https://api.github.com/users/alialamiidrissi/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alialamiidrissi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alialamiidrissi/subscriptions", "type": "User", "url": "https://api.github.com/users/alialamiidrissi", "user_view_type": "public" }	[]	closed	false	null	[]	null	[]	2023-03-15T17:16:40Z	2023-03-16T17:14:51Z	2023-03-16T17:14:51Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Hi, I noticed that we cannot create a HuggingFace dataset from Pandas DataFrame with a column named `self`. The error seems to be coming from arguments validation in the `Features.from_dict` function. ### Steps to reproduce the bug ```python import datasets dummy_pandas = pd.DataFrame([0,1,2,3], columns = ["self"]) datasets.arrow_dataset.Dataset.from_pandas(dummy_pandas) ``` ### Expected behavior No error thrown ### Environment info - `datasets` version: 2.8.0 - Python version: 3.9.5 - PyArrow version: 6.0.1 - Pandas version: 1.4.1	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5641/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5641/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6010	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6010/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6010/comments	https://api.github.com/repos/huggingface/datasets/issues/6010/events	https://github.com/huggingface/datasets/issues/6010	1,793,838,152	I_kwDODunzps5q68xI	6,010	Improve `Dataset`'s string representation	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[ { "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	[ "I want to take a shot at this if possible ", "Yes, feel free to work on this.\r\n\r\nYou can check the PyArrow Table `__repr__` and Polars DataFrame `__repr__`/`_repr_html_` implementations for some pointers/ideas.", "@mariosasko are there any other similar issues that I could work on? I see this has been already solved. " ]	2023-07-07T16:38:03Z	2023-09-01T03:45:07Z	null	COLLABORATOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	Currently, `Dataset.__repr__` outputs a dataset's column names and the number of rows. We could improve it by printing its features and the first few rows. We should also implement `_repr_html_` to have a rich HTML representation in notebooks/Streamlit.	null	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6010/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6010/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5610	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5610/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5610/comments	https://api.github.com/repos/huggingface/datasets/issues/5610/events	https://github.com/huggingface/datasets/issues/5610	1,610,698,006	I_kwDODunzps5gAU0W	5,610	use datasets streaming mode in trainer ddp mode cause memory leak	{ "avatar_url": "https://avatars.githubusercontent.com/u/15223544?v=4", "events_url": "https://api.github.com/users/gromzhu/events{/privacy}", "followers_url": "https://api.github.com/users/gromzhu/followers", "following_url": "https://api.github.com/users/gromzhu/following{/other_user}", "gists_url": "https://api.github.com/users/gromzhu/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/gromzhu", "id": 15223544, "login": "gromzhu", "node_id": "MDQ6VXNlcjE1MjIzNTQ0", "organizations_url": "https://api.github.com/users/gromzhu/orgs", "received_events_url": "https://api.github.com/users/gromzhu/received_events", "repos_url": "https://api.github.com/users/gromzhu/repos", "site_admin": false, "starred_url": "https://api.github.com/users/gromzhu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/gromzhu/subscriptions", "type": "User", "url": "https://api.github.com/users/gromzhu", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Same problem, \r\ntransformers 4.28.1\r\ndatasets 2.12.0\r\n\r\nleak around 100Mb per 10 seconds when use dataloader_num_werker > 0 in training argumennts for transformer train, possile bug in transformers repo, but still not found solution :(\r\n", "found an article described a problem, may be helpful for somebody:\r\nhttps://ppwwyyxx.com/blog/2022/Demystify-RAM-Usage-in-Multiprocess-DataLoader/\r\nI confirm, it`s not memory leak, after some time memory growing has stopped", "\"After some time\" - from your description, it sounds like memory growth can happen for 12 hours+, even days, before it stops? That seems very scary." ]	2023-03-06T05:26:49Z	2024-03-07T01:11:32Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug use datasets streaming mode in trainer ddp mode cause memory leak ### Steps to reproduce the bug import os import time import datetime import sys import numpy as np import random import torch from torch.utils.data import Dataset, DataLoader, random_split, RandomSampler, SequentialSampler,DistributedSampler,BatchSampler torch.manual_seed(42) from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config, GPT2Model,DataCollatorForLanguageModeling,AutoModelForCausalLM from transformers import AdamW, get_linear_schedule_with_warmup hf_model_path ='./Wenzhong-GPT2-110M' tokenizer = GPT2Tokenizer.from_pretrained(hf_model_path) tokenizer.add_special_tokens({'pad_token': '<\|pad\|>'}) from datasets import load_dataset gpus=8 max_len = 576 batch_size_node = 17 save_step = 5000 gradient_accumulation = 2 dataloader_num = 4 max_step = 3510001000//batch_size_node//gradient_accumulation//gpus #max_step = -1 print("total_step:%d"%(max_step)) import datasets datasets.version dataset = load_dataset("text", data_files="./gpt_data_v1/",split='train',cache_dir='./dataset_cache',streaming=True) print('load over') shuffled_dataset = dataset.shuffle(seed=42) print('shuffle over') def dataset_tokener(example,max_lenth=max_len): example['text'] = list(map(lambda x : x.strip()+'<\|endoftext\|>',example['text'] )) return tokenizer(example['text'], truncation=True, max_length=max_lenth, padding="longest") new_new_dataset = shuffled_dataset.map(dataset_tokener, batched=True, remove_columns=["text"]) print('map over') configuration = GPT2Config.from_pretrained(hf_model_path, output_hidden_states=False) model = AutoModelForCausalLM.from_pretrained(hf_model_path) model.resize_token_embeddings(len(tokenizer)) seed_val = 42 random.seed(seed_val) np.random.seed(seed_val) torch.manual_seed(seed_val) torch.cuda.manual_seed_all(seed_val) from transformers import Trainer,TrainingArguments import os print("strat train") training_args = TrainingArguments(output_dir="./test_trainer", num_train_epochs=1.0, report_to="none", do_train=True, dataloader_num_workers=dataloader_num, local_rank=int(os.environ.get('LOCAL_RANK', -1)), overwrite_output_dir=True, logging_strategy='steps', logging_first_step=True, logging_dir="./logs", log_on_each_node=False, per_device_train_batch_size=batch_size_node, warmup_ratio=0.03, save_steps=save_step, save_total_limit=5, gradient_accumulation_steps=gradient_accumulation, max_steps=max_step, disable_tqdm=False, data_seed=42 ) trainer = Trainer( model=model, args=training_args, train_dataset=new_new_dataset, eval_dataset=None, tokenizer=tokenizer, data_collator=DataCollatorForLanguageModeling(tokenizer,mlm=False), #compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None, #preprocess_logits_for_metrics=preprocess_logits_for_metrics #if training_args.do_eval and not is_torch_tpu_available() #else None, ) trainer.train(resume_from_checkpoint=True) ### Expected behavior use the train code uppper my dataset ./gpt_data_v1 have 1000 files, each file size is 120mb start cmd is : python -m torch.distributed.launch --nproc_per_node=8 my_train.py here is result: ![image](https://user-images.githubusercontent.com/15223544/223026042-1a81489f-897a-43e4-8339-65a202fd5dc7.png) here is memory usage monitor in 12 hours ![image](https://user-images.githubusercontent.com/15223544/223027076-14e32e8b-9608-4282-9a80-f15d0277026d.png) every dataloader work allocate over 24gb cpu memory according to memory usage monitor in 12 hours,sometime small memory releases, but total memory usage is increase. i think datasets streaming mode should not used so much memery,so maybe somewhere has memory leak. ### Environment info pytorch 1.11.0 py 3.8 cuda 11.3 transformers 4.26.1 datasets 2.9.0	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5610/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5610/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5996	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5996/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5996/comments	https://api.github.com/repos/huggingface/datasets/issues/5996/events	https://github.com/huggingface/datasets/pull/5996	1,779,294,374	PR_kwDODunzps5UKP0i	5,996	Deprecate `use_auth_token` in favor of `token`	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006134 / 0.011353 (-0.005219) \| 0.003816 / 0.011008 (-0.007193) \| 0.098226 / 0.038508 (0.059718) \| 0.036830 / 0.023109 (0.013721) \| 0.314551 / 0.275898 (0.038653) \| 0.372251 / 0.323480 (0.048771) \| 0.004762 / 0.007986 (-0.003224) \| 0.003041 / 0.004328 (-0.001287) \| 0.077651 / 0.004250 (0.073401) \| 0.052445 / 0.037052 (0.015393) \| 0.324632 / 0.258489 (0.066143) \| 0.365724 / 0.293841 (0.071883) \| 0.028069 / 0.128546 (-0.100477) \| 0.008444 / 0.075646 (-0.067203) \| 0.312767 / 0.419271 (-0.106505) \| 0.047773 / 0.043533 (0.004240) \| 0.305317 / 0.255139 (0.050178) \| 0.332007 / 0.283200 (0.048807) \| 0.018985 / 0.141683 (-0.122698) \| 1.538022 / 1.452155 (0.085868) \| 1.575898 / 1.492716 (0.083182) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.204780 / 0.018006 (0.186774) \| 0.428125 / 0.000490 (0.427635) \| 0.003454 / 0.000200 (0.003254) \| 0.000078 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025064 / 0.037411 (-0.012348) \| 0.099419 / 0.014526 (0.084893) \| 0.111068 / 0.176557 (-0.065489) \| 0.169775 / 0.737135 (-0.567361) \| 0.112067 / 0.296338 (-0.184271) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429642 / 0.215209 (0.214433) \| 4.275556 / 2.077655 (2.197901) \| 1.914658 / 1.504120 (0.410539) \| 1.706556 / 1.541195 (0.165361) \| 1.754228 / 1.468490 (0.285738) \| 0.563669 / 4.584777 (-4.021108) \| 3.391501 / 3.745712 (-0.354211) \| 1.791517 / 5.269862 (-3.478345) \| 1.030704 / 4.565676 (-3.534973) \| 0.070882 / 0.424275 (-0.353393) \| 0.011351 / 0.007607 (0.003744) \| 0.529438 / 0.226044 (0.303394) \| 5.294316 / 2.268929 (3.025387) \| 2.344653 / 55.444624 (-53.099972) \| 1.997468 / 6.876477 (-4.879009) \| 2.108932 / 2.142072 (-0.033140) \| 0.676794 / 4.805227 (-4.128433) \| 0.135058 / 6.500664 (-6.365607) \| 0.065857 / 0.075469 (-0.009612) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.231864 / 1.841788 (-0.609924) \| 13.986694 / 8.074308 (5.912386) \| 13.306600 / 10.191392 (3.115208) \| 0.145520 / 0.680424 (-0.534904) \| 0.016717 / 0.534201 (-0.517484) \| 0.366303 / 0.579283 (-0.212980) \| 0.391637 / 0.434364 (-0.042727) \| 0.425445 / 0.540337 (-0.114892) \| 0.507719 / 1.386936 (-0.879217) \|\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) \| 0.006236 / 0.011353 (-0.005116) \| 0.003766 / 0.011008 (-0.007242) \| 0.076794 / 0.038508 (0.038286) \| 0.037210 / 0.023109 (0.014101) \| 0.378387 / 0.275898 (0.102489) \| 0.425456 / 0.323480 (0.101977) \| 0.004694 / 0.007986 (-0.003291) \| 0.002921 / 0.004328 (-0.001407) \| 0.076985 / 0.004250 (0.072735) \| 0.052188 / 0.037052 (0.015136) \| 0.394385 / 0.258489 (0.135896) \| 0.432527 / 0.293841 (0.138686) \| 0.029091 / 0.128546 (-0.099455) \| 0.008364 / 0.075646 (-0.067282) \| 0.082583 / 0.419271 (-0.336689) \| 0.042928 / 0.043533 (-0.000605) \| 0.375321 / 0.255139 (0.120182) \| 0.391719 / 0.283200 (0.108519) \| 0.019388 / 0.141683 (-0.122295) \| 1.550644 / 1.452155 (0.098489) \| 1.604882 / 1.492716 (0.112166) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236859 / 0.018006 (0.218853) \| 0.418528 / 0.000490 (0.418039) \| 0.000388 / 0.000200 (0.000188) \| 0.000059 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025548 / 0.037411 (-0.011863) \| 0.100644 / 0.014526 (0.086118) \| 0.109102 / 0.176557 (-0.067455) \| 0.161694 / 0.737135 (-0.575441) \| 0.112088 / 0.296338 (-0.184250) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.484128 / 0.215209 (0.268919) \| 4.849952 / 2.077655 (2.772297) \| 2.512769 / 1.504120 (1.008649) \| 2.303295 / 1.541195 (0.762100) \| 2.356699 / 1.468490 (0.888209) \| 0.564181 / 4.584777 (-4.020596) \| 3.421393 / 3.745712 (-0.324319) \| 2.570875 / 5.269862 (-2.698987) \| 1.474307 / 4.565676 (-3.091370) \| 0.068035 / 0.424275 (-0.356240) \| 0.011300 / 0.007607 (0.003693) \| 0.587867 / 0.226044 (0.361823) \| 5.862447 / 2.268929 (3.593519) \| 3.004017 / 55.444624 (-52.440607) \| 2.664989 / 6.876477 (-4.211488) \| 2.740020 / 2.142072 (0.597948) \| 0.680840 / 4.805227 (-4.124387) \| 0.137001 / 6.500664 (-6.363663) \| 0.068098 / 0.075469 (-0.007371) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.297362 / 1.841788 (-0.544426) \| 14.207891 / 8.074308 (6.133583) \| 14.087562 / 10.191392 (3.896170) \| 0.149514 / 0.680424 (-0.530910) \| 0.016566 / 0.534201 (-0.517635) \| 0.367602 / 0.579283 (-0.211681) \| 0.400692 / 0.434364 (-0.033671) \| 0.432907 / 0.540337 (-0.107431) \| 0.525924 / 1.386936 (-0.861012) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ec069feaaf6c28d4e4df76d344693b591a74c3f \"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) \| 0.006223 / 0.011353 (-0.005130) \| 0.003672 / 0.011008 (-0.007336) \| 0.097451 / 0.038508 (0.058943) \| 0.036243 / 0.023109 (0.013133) \| 0.375650 / 0.275898 (0.099752) \| 0.431652 / 0.323480 (0.108172) \| 0.004758 / 0.007986 (-0.003227) \| 0.002941 / 0.004328 (-0.001387) \| 0.077383 / 0.004250 (0.073132) \| 0.055342 / 0.037052 (0.018289) \| 0.390335 / 0.258489 (0.131846) \| 0.427867 / 0.293841 (0.134026) \| 0.027619 / 0.128546 (-0.100927) \| 0.008244 / 0.075646 (-0.067402) \| 0.313499 / 0.419271 (-0.105773) \| 0.054987 / 0.043533 (0.011454) \| 0.394044 / 0.255139 (0.138905) \| 0.398784 / 0.283200 (0.115584) \| 0.026499 / 0.141683 (-0.115184) \| 1.496907 / 1.452155 (0.044753) \| 1.554465 / 1.492716 (0.061749) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.241197 / 0.018006 (0.223190) \| 0.427856 / 0.000490 (0.427366) \| 0.006264 / 0.000200 (0.006065) \| 0.000218 / 0.000054 (0.000164) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025550 / 0.037411 (-0.011862) \| 0.104426 / 0.014526 (0.089901) \| 0.110310 / 0.176557 (-0.066246) \| 0.173813 / 0.737135 (-0.563322) \| 0.112129 / 0.296338 (-0.184209) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.458806 / 0.215209 (0.243597) \| 4.576351 / 2.077655 (2.498697) \| 2.265670 / 1.504120 (0.761550) \| 2.073230 / 1.541195 (0.532035) \| 2.135283 / 1.468490 (0.666793) \| 0.562506 / 4.584777 (-4.022271) \| 3.375101 / 3.745712 (-0.370611) \| 1.734393 / 5.269862 (-3.535469) \| 1.026622 / 4.565676 (-3.539054) \| 0.068144 / 0.424275 (-0.356131) \| 0.011092 / 0.007607 (0.003485) \| 0.562779 / 0.226044 (0.336734) \| 5.608256 / 2.268929 (3.339328) \| 2.706468 / 55.444624 (-52.738157) \| 2.381607 / 6.876477 (-4.494869) \| 2.451027 / 2.142072 (0.308954) \| 0.671590 / 4.805227 (-4.133637) \| 0.135749 / 6.500664 (-6.364915) \| 0.065389 / 0.075469 (-0.010080) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.244806 / 1.841788 (-0.596981) \| 14.042150 / 8.074308 (5.967841) \| 14.246612 / 10.191392 (4.055220) \| 0.134309 / 0.680424 (-0.546114) \| 0.017082 / 0.534201 (-0.517119) \| 0.366043 / 0.579283 (-0.213240) \| 0.400748 / 0.434364 (-0.033616) \| 0.425695 / 0.540337 (-0.114643) \| 0.509355 / 1.386936 (-0.877581) \|\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) \| 0.006134 / 0.011353 (-0.005219) \| 0.003980 / 0.011008 (-0.007028) \| 0.078353 / 0.038508 (0.039845) \| 0.038011 / 0.023109 (0.014902) \| 0.375784 / 0.275898 (0.099886) \| 0.433619 / 0.323480 (0.110139) \| 0.004897 / 0.007986 (-0.003088) \| 0.002981 / 0.004328 (-0.001347) \| 0.077362 / 0.004250 (0.073112) \| 0.056108 / 0.037052 (0.019056) \| 0.395984 / 0.258489 (0.137495) \| 0.427397 / 0.293841 (0.133556) \| 0.029325 / 0.128546 (-0.099221) \| 0.008498 / 0.075646 (-0.067148) \| 0.082478 / 0.419271 (-0.336794) \| 0.044085 / 0.043533 (0.000552) \| 0.389923 / 0.255139 (0.134784) \| 0.391180 / 0.283200 (0.107980) \| 0.022452 / 0.141683 (-0.119231) \| 1.507758 / 1.452155 (0.055603) \| 1.530459 / 1.492716 (0.037743) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230928 / 0.018006 (0.212922) \| 0.408484 / 0.000490 (0.407995) \| 0.000806 / 0.000200 (0.000606) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025183 / 0.037411 (-0.012228) \| 0.102292 / 0.014526 (0.087766) \| 0.108142 / 0.176557 (-0.068415) \| 0.161172 / 0.737135 (-0.575963) \| 0.114476 / 0.296338 (-0.181862) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.482978 / 0.215209 (0.267769) \| 4.816103 / 2.077655 (2.738448) \| 2.505567 / 1.504120 (1.001447) \| 2.302598 / 1.541195 (0.761404) \| 2.371238 / 1.468490 (0.902748) \| 0.567467 / 4.584777 (-4.017310) \| 3.363407 / 3.745712 (-0.382306) \| 1.746213 / 5.269862 (-3.523649) \| 1.035468 / 4.565676 (-3.530208) \| 0.068431 / 0.424275 (-0.355844) \| 0.011069 / 0.007607 (0.003462) \| 0.598241 / 0.226044 (0.372196) \| 5.953927 / 2.268929 (3.684999) \| 3.007493 / 55.444624 (-52.437132) \| 2.629399 / 6.876477 (-4.247078) \| 2.737201 / 2.142072 (0.595129) \| 0.682456 / 4.805227 (-4.122771) \| 0.137613 / 6.500664 (-6.363051) \| 0.067941 / 0.075469 (-0.007528) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.306015 / 1.841788 (-0.535772) \| 14.359240 / 8.074308 (6.284932) \| 14.187601 / 10.191392 (3.996209) \| 0.138612 / 0.680424 (-0.541812) \| 0.016708 / 0.534201 (-0.517493) \| 0.366365 / 0.579283 (-0.212918) \| 0.396982 / 0.434364 (-0.037382) \| 0.426939 / 0.540337 (-0.113398) \| 0.520064 / 1.386936 (-0.866872) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#21d0fd041a5eca02d3ee787396216ac613c662ac \"CML watermark\")\n", "They use `token` and emit a deprecation warning if `use_auth_token` is passed instead (see https://github.com/huggingface/transformers/blob/78a2b19fc84ed55c65f4bf20a901edb7ceb73c5f/src/transformers/modeling_utils.py#L1933). \r\n\r\nI think we can update the `examples` scripts after merging this PR.", "> I think we can update the examples scripts after merging this PR.\r\n\r\nWe should do a release before updated in the examples scripts no ? That's why it's an option to not have a deprecation warning until transformers and co are updated with the `token` arg", "> We should do a release before updated in the examples scripts no ? That's why it's an option to not have a deprecation warning until transformers and co are updated with the token arg\r\n\r\nThis would avoid the warning only for the latest `datasets` release. TBH, I don't think this is worth the hassle, considering how simple it is to remove it.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007644 / 0.011353 (-0.003709) \| 0.004667 / 0.011008 (-0.006341) \| 0.117347 / 0.038508 (0.078839) \| 0.050620 / 0.023109 (0.027510) \| 0.415402 / 0.275898 (0.139504) \| 0.485898 / 0.323480 (0.162418) \| 0.005848 / 0.007986 (-0.002138) \| 0.003736 / 0.004328 (-0.000592) \| 0.089798 / 0.004250 (0.085547) \| 0.069344 / 0.037052 (0.032292) \| 0.441684 / 0.258489 (0.183195) \| 0.468972 / 0.293841 (0.175131) \| 0.036637 / 0.128546 (-0.091909) \| 0.010219 / 0.075646 (-0.065427) \| 0.394293 / 0.419271 (-0.024978) \| 0.061462 / 0.043533 (0.017929) \| 0.409448 / 0.255139 (0.154309) \| 0.431557 / 0.283200 (0.148358) \| 0.027795 / 0.141683 (-0.113888) \| 1.837844 / 1.452155 (0.385690) \| 1.862683 / 1.492716 (0.369967) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230500 / 0.018006 (0.212494) \| 0.483139 / 0.000490 (0.482649) \| 0.006517 / 0.000200 (0.006317) \| 0.000143 / 0.000054 (0.000088) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033152 / 0.037411 (-0.004259) \| 0.133673 / 0.014526 (0.119147) \| 0.143853 / 0.176557 (-0.032704) \| 0.215254 / 0.737135 (-0.521882) \| 0.150676 / 0.296338 (-0.145662) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.503796 / 0.215209 (0.288587) \| 5.049981 / 2.077655 (2.972326) \| 2.399427 / 1.504120 (0.895307) \| 2.167635 / 1.541195 (0.626441) \| 2.257448 / 1.468490 (0.788958) \| 0.641298 / 4.584777 (-3.943479) \| 4.828676 / 3.745712 (1.082964) \| 4.346069 / 5.269862 (-0.923793) \| 2.103890 / 4.565676 (-2.461786) \| 0.079115 / 0.424275 (-0.345160) \| 0.013377 / 0.007607 (0.005770) \| 0.621207 / 0.226044 (0.395162) \| 6.190939 / 2.268929 (3.922011) \| 2.920129 / 55.444624 (-52.524495) \| 2.549225 / 6.876477 (-4.327252) \| 2.719221 / 2.142072 (0.577149) \| 0.790949 / 4.805227 (-4.014278) \| 0.172032 / 6.500664 (-6.328632) \| 0.077779 / 0.075469 (0.002310) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.432572 / 1.841788 (-0.409216) \| 21.000031 / 8.074308 (12.925723) \| 17.555093 / 10.191392 (7.363701) \| 0.166646 / 0.680424 (-0.513778) \| 0.020451 / 0.534201 (-0.513750) \| 0.488767 / 0.579283 (-0.090516) \| 0.737036 / 0.434364 (0.302672) \| 0.621694 / 0.540337 (0.081356) \| 0.732074 / 1.386936 (-0.654862) \|\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) \| 0.008198 / 0.011353 (-0.003155) \| 0.004987 / 0.011008 (-0.006021) \| 0.090714 / 0.038508 (0.052206) \| 0.053379 / 0.023109 (0.030270) \| 0.425199 / 0.275898 (0.149301) \| 0.514036 / 0.323480 (0.190556) \| 0.006043 / 0.007986 (-0.001943) \| 0.003888 / 0.004328 (-0.000441) \| 0.088294 / 0.004250 (0.084043) \| 0.073024 / 0.037052 (0.035971) \| 0.435983 / 0.258489 (0.177494) \| 0.514293 / 0.293841 (0.220452) \| 0.039451 / 0.128546 (-0.089095) \| 0.010439 / 0.075646 (-0.065207) \| 0.096885 / 0.419271 (-0.322387) \| 0.060165 / 0.043533 (0.016632) \| 0.421053 / 0.255139 (0.165914) \| 0.455545 / 0.283200 (0.172345) \| 0.027234 / 0.141683 (-0.114449) \| 1.768975 / 1.452155 (0.316820) \| 1.842853 / 1.492716 (0.350137) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278940 / 0.018006 (0.260933) \| 0.480709 / 0.000490 (0.480219) \| 0.000436 / 0.000200 (0.000236) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034900 / 0.037411 (-0.002511) \| 0.144893 / 0.014526 (0.130368) \| 0.149567 / 0.176557 (-0.026989) \| 0.213200 / 0.737135 (-0.523935) \| 0.156735 / 0.296338 (-0.139604) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.535897 / 0.215209 (0.320687) \| 5.336998 / 2.077655 (3.259343) \| 2.685854 / 1.504120 (1.181734) \| 2.470177 / 1.541195 (0.928983) \| 2.547495 / 1.468490 (1.079004) \| 0.642830 / 4.584777 (-3.941947) \| 4.595866 / 3.745712 (0.850154) \| 2.186696 / 5.269862 (-3.083165) \| 1.317969 / 4.565676 (-3.247708) \| 0.079268 / 0.424275 (-0.345007) \| 0.013792 / 0.007607 (0.006185) \| 0.662236 / 0.226044 (0.436192) \| 6.604775 / 2.268929 (4.335847) \| 3.355888 / 55.444624 (-52.088736) \| 2.968911 / 6.876477 (-3.907565) \| 3.121862 / 2.142072 (0.979790) \| 0.794752 / 4.805227 (-4.010475) \| 0.170800 / 6.500664 (-6.329864) \| 0.078393 / 0.075469 (0.002924) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.601605 / 1.841788 (-0.240183) \| 20.743553 / 8.074308 (12.669245) \| 17.543968 / 10.191392 (7.352576) \| 0.221884 / 0.680424 (-0.458540) \| 0.020779 / 0.534201 (-0.513422) \| 0.479677 / 0.579283 (-0.099606) \| 0.516207 / 0.434364 (0.081843) \| 0.564046 / 0.540337 (0.023709) \| 0.711336 / 1.386936 (-0.675600) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#819bb4346434912eb405ce3f3e9f21dc25a2fe85 \"CML watermark\")\n", "Yes, sounds great! Thanks", "yup" ]	2023-06-28T16:26:38Z	2023-07-05T15:22:20Z	2023-07-03T16:03:33Z	COLLABORATOR	null	null	null	... to be consistent with `transformers` and `huggingface_hub`.	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5996/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5996/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5996.diff", "html_url": "https://github.com/huggingface/datasets/pull/5996", "merged_at": "2023-07-03T16:03:33Z", "patch_url": "https://github.com/huggingface/datasets/pull/5996.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5996" }
https://api.github.com/repos/huggingface/datasets/issues/5494	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5494/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5494/comments	https://api.github.com/repos/huggingface/datasets/issues/5494/events	https://github.com/huggingface/datasets/issues/5494	1,566,655,348	I_kwDODunzps5dYUN0	5,494	Update audio installation doc page	{ "avatar_url": "https://avatars.githubusercontent.com/u/16348744?v=4", "events_url": "https://api.github.com/users/polinaeterna/events{/privacy}", "followers_url": "https://api.github.com/users/polinaeterna/followers", "following_url": "https://api.github.com/users/polinaeterna/following{/other_user}", "gists_url": "https://api.github.com/users/polinaeterna/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/polinaeterna", "id": 16348744, "login": "polinaeterna", "node_id": "MDQ6VXNlcjE2MzQ4NzQ0", "organizations_url": "https://api.github.com/users/polinaeterna/orgs", "received_events_url": "https://api.github.com/users/polinaeterna/received_events", "repos_url": "https://api.github.com/users/polinaeterna/repos", "site_admin": false, "starred_url": "https://api.github.com/users/polinaeterna/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/polinaeterna/subscriptions", "type": "User", "url": "https://api.github.com/users/polinaeterna", "user_view_type": "public" }	[ { "color": "0075ca", "default": true, "description": "Improvements or additions to documentation", "id": 1935892861, "name": "documentation", "node_id": "MDU6TGFiZWwxOTM1ODkyODYx", "url": "https://api.github.com/repos/huggingface/datasets/labels/documentation" } ]	closed	false	null	[]	null	[ "Totally agree, the docs should be in sync with our code.\r\n\r\nIndeed to avoid confusing users, I think we should have updated the docs at the same time as this PR:\r\n- #5167", "@albertvillanova yeah sure I should have, but I forgot back then, sorry for that 😶", "No, @polinaeterna, nothing to be sorry about.\r\n\r\nMy comment was for all of us datasets team, as a reminder: when making a PR, but also when reviewing some other's PR, we should not forget to update the corresponding docstring and doc pages. It is something we can improve if we help each other in reminding about it... :hugs: ", "@polinaeterna I think we can close this issue now as we no longer use `torchaudio` for decoding." ]	2023-02-01T19:07:50Z	2023-03-02T16:08:17Z	2023-03-02T16:08:17Z	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	Our [installation documentation page](https://huggingface.co/docs/datasets/installation#audio) says that one can use Datasets for mp3 only with `torchaudio<0.12`. `torchaudio>0.12` is actually supported too but requires a specific version of ffmpeg which is not easily installed on all linux versions but there is a custom ubuntu repo for it, we have insctructions in the code: https://github.com/huggingface/datasets/blob/main/src/datasets/features/audio.py#L327 So we should update the doc page. But first investigate [this issue](5488).	{ "avatar_url": "https://avatars.githubusercontent.com/u/16348744?v=4", "events_url": "https://api.github.com/users/polinaeterna/events{/privacy}", "followers_url": "https://api.github.com/users/polinaeterna/followers", "following_url": "https://api.github.com/users/polinaeterna/following{/other_user}", "gists_url": "https://api.github.com/users/polinaeterna/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/polinaeterna", "id": 16348744, "login": "polinaeterna", "node_id": "MDQ6VXNlcjE2MzQ4NzQ0", "organizations_url": "https://api.github.com/users/polinaeterna/orgs", "received_events_url": "https://api.github.com/users/polinaeterna/received_events", "repos_url": "https://api.github.com/users/polinaeterna/repos", "site_admin": false, "starred_url": "https://api.github.com/users/polinaeterna/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/polinaeterna/subscriptions", "type": "User", "url": "https://api.github.com/users/polinaeterna", "user_view_type": "public" }	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 1, "heart": 1, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 3, "url": "https://api.github.com/repos/huggingface/datasets/issues/5494/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5494/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/4815	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4815/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4815/comments	https://api.github.com/repos/huggingface/datasets/issues/4815/events	https://github.com/huggingface/datasets/issues/4815	1,334,078,303	I_kwDODunzps5PhGtf	4,815	Outdated loading script for OPUS ParaCrawl dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "color": "2edb81", "default": false, "description": "A bug in a dataset script provided in the library", "id": 2067388877, "name": "dataset bug", "node_id": "MDU6TGFiZWwyMDY3Mzg4ODc3", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[]	2022-08-10T05:12:34Z	2022-08-12T14:17:57Z	2022-08-12T14:17:57Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	## Describe the bug Our loading script for OPUS ParaCrawl loads its 7.1 version. Current existing version is 9.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4815/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4815/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/4875	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4875/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4875/comments	https://api.github.com/repos/huggingface/datasets/issues/4875/events	https://github.com/huggingface/datasets/issues/4875	1,348,095,686	I_kwDODunzps5QWk7G	4,875	`_resolve_features` ignores the token	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	[]	open	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" } ]	null	[ "Hi ! Your HF_ENDPOINT seems wrong because of the extra \"/\"\r\n```diff\r\n- os.environ[\"HF_ENDPOINT\"] = \"https://hub-ci.huggingface.co/\"\r\n+ os.environ[\"HF_ENDPOINT\"] = \"https://hub-ci.huggingface.co\"\r\n```\r\n\r\ncan you try again without the extra \"/\" ?", "Oh, yes, sorry, but it's not the issue.\r\n\r\nIn my code, I set `HF_ENDPOINT=https://hub-ci.huggingface.co`. I added `os.environ[\"HF_ENDPOINT\"] = \"https://hub-ci.huggingface.co/\"` afterward just to indicate that we had to have this env var and made a mistake there", "I can't reproduce on my side. I tried using a private dataset repo with a CSV file on hub-ci\r\n\r\nWhat's your version of `huggingface_hub` ?", "I can't reproduce either... Not sure what has occurred, very sorry to have made you lost your time on that ", "I got something similar in https://github.com/huggingface/datasets-server/pull/608. Look how changing the order of the tests (https://github.com/huggingface/datasets-server/pull/608/commits/2c50fe833323de3dfdc76c5cd68639279e0887f8) change the result, which means that something has a side-effect:\r\n- https://github.com/huggingface/datasets-server/actions/runs/3264636253/jobs/5365612918 works\r\n- https://github.com/huggingface/datasets-server/actions/runs/3264651839/jobs/5365654924 does not work\r\n\r\nI still couldn't reproduce it with a simpler script... ", "The issue happens because `extend_module_for_streaming` can't be used several times on packaged builders like `csv` to apply a new auth token. Indeed `extend_module_for_streaming` only applies authentication once, and on subsequent calls does nothing:\r\n\r\nhttps://github.com/huggingface/datasets/blob/07b7c38d9e9c72c74b02524c432ca64d0d3738f4/src/datasets/streaming.py#L62-L64\r\n\r\nThis behavior exists because the authenticatoin wrapper only supports one token. This is an issue for packaged builders which can be used to load several datasets, so it may require several tokens.\r\n\r\nThis can be fixed by storing a dict `token_per_repo_id` instead of `use_auth_token` in the authentication wrapper, and by making it possible to update the authentication wrapper with a new token", "I fixed the datasets-server CI with: https://github.com/huggingface/datasets-server/pull/608\r\n\r\nSee https://github.com/huggingface/datasets-server/actions/runs/3265359326/jobs/5367445018\r\n\r\nThanks for the help @lhoestq !", "> This can be fixed by storing a dict token_per_repo_id instead of use_auth_token in the authentication wrapper, and by making it possible to update the authentication wrapper with a new token\r\n\r\nIf I call the module on the same repo twice: first with authentication, then without authentication, would the second call use authentication anyway? It sounds like a bug: the argument passed to the function would be silently ignored.", "Yes exactly, this is a known bug", "And do you think this bug could be solved as well when fixing this issue?", "yes definitely !" ]	2022-08-23T14:57:36Z	2022-10-17T13:45:47Z	null	COLLABORATOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	## Describe the bug When calling [`_resolve_features()`](https://github.com/huggingface/datasets/blob/54b532a8a2f5353fdb0207578162153f7b2da2ec/src/datasets/iterable_dataset.py#L1255) on a gated dataset, ie. a dataset which requires a token to be loaded, the token seems to be ignored even if it has been provided to `load_dataset` before. ## Steps to reproduce the bug ```python import os os.environ["HF_ENDPOINT"] = "https://hub-ci.huggingface.co/" hf_token = "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" from datasets import load_dataset # public dataset_name = "__DUMMY_DATASETS_SERVER_USER__/repo_csv_data-16612654226756" config_name = "__DUMMY_DATASETS_SERVER_USER__--repo_csv_data-16612654226756" split_name = "train" iterable_dataset = load_dataset( dataset_name, name=config_name, split=split_name, streaming=True, use_auth_token=hf_token, ) iterable_dataset = iterable_dataset._resolve_features() print(iterable_dataset.features) # gated dataset_name = "__DUMMY_DATASETS_SERVER_USER__/repo_csv_data-16612654317644" config_name = "__DUMMY_DATASETS_SERVER_USER__--repo_csv_data-16612654317644" split_name = "train" iterable_dataset = load_dataset( dataset_name, name=config_name, split=split_name, streaming=True, use_auth_token=hf_token, ) try: iterable_dataset = iterable_dataset._resolve_features() except FileNotFoundError as e: print("FAILS") ``` ## Expected results I expect to have the same result on a public dataset and on a gated (or private) dataset, if the token has been provided. ## Actual results An exception is thrown on gated datasets. ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.15.0-1017-aws-x86_64-with-glibc2.35 - Python version: 3.9.6 - PyArrow version: 7.0.0 - Pandas version: 1.4.2	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	{ "+1": 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/4875/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4875/timeline	null	reopened	null	null
https://api.github.com/repos/huggingface/datasets/issues/6469	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6469/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6469/comments	https://api.github.com/repos/huggingface/datasets/issues/6469/events	https://github.com/huggingface/datasets/pull/6469	2,023,695,839	PR_kwDODunzps5hC6xf	6,469	Don't expand_info in HF glob	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6469). All of your documentation changes will be reflected on that endpoint.", "Merging this one for now, but lmk if you had other optimizations in mind for the next version of `huggingface_hub`", "<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) \| 0.004998 / 0.011353 (-0.006355) \| 0.003523 / 0.011008 (-0.007486) \| 0.064932 / 0.038508 (0.026424) \| 0.050107 / 0.023109 (0.026998) \| 0.253715 / 0.275898 (-0.022183) \| 0.275364 / 0.323480 (-0.048116) \| 0.003902 / 0.007986 (-0.004084) \| 0.002716 / 0.004328 (-0.001612) \| 0.048458 / 0.004250 (0.044208) \| 0.037802 / 0.037052 (0.000750) \| 0.262328 / 0.258489 (0.003839) \| 0.285911 / 0.293841 (-0.007930) \| 0.027112 / 0.128546 (-0.101435) \| 0.010780 / 0.075646 (-0.064867) \| 0.206447 / 0.419271 (-0.212824) \| 0.035771 / 0.043533 (-0.007761) \| 0.255031 / 0.255139 (-0.000108) \| 0.270530 / 0.283200 (-0.012670) \| 0.017152 / 0.141683 (-0.124530) \| 1.094734 / 1.452155 (-0.357421) \| 1.163480 / 1.492716 (-0.329237) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092944 / 0.018006 (0.074938) \| 0.301042 / 0.000490 (0.300553) \| 0.000238 / 0.000200 (0.000038) \| 0.000049 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019090 / 0.037411 (-0.018321) \| 0.061046 / 0.014526 (0.046520) \| 0.073330 / 0.176557 (-0.103227) \| 0.121124 / 0.737135 (-0.616012) \| 0.080544 / 0.296338 (-0.215795) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.323866 / 0.215209 (0.108657) \| 2.797727 / 2.077655 (0.720072) \| 1.502994 / 1.504120 (-0.001126) \| 1.376177 / 1.541195 (-0.165018) \| 1.422741 / 1.468490 (-0.045749) \| 0.562990 / 4.584777 (-4.021786) \| 2.431781 / 3.745712 (-1.313931) \| 2.783226 / 5.269862 (-2.486635) \| 1.788055 / 4.565676 (-2.777621) \| 0.064206 / 0.424275 (-0.360069) \| 0.004989 / 0.007607 (-0.002618) \| 0.338282 / 0.226044 (0.112237) \| 3.356226 / 2.268929 (1.087297) \| 1.855644 / 55.444624 (-53.588980) \| 1.580876 / 6.876477 (-5.295601) \| 1.617418 / 2.142072 (-0.524655) \| 0.636816 / 4.805227 (-4.168411) \| 0.117680 / 6.500664 (-6.382985) \| 0.042560 / 0.075469 (-0.032909) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956410 / 1.841788 (-0.885377) \| 11.764886 / 8.074308 (3.690578) \| 10.535801 / 10.191392 (0.344409) \| 0.137797 / 0.680424 (-0.542627) \| 0.014368 / 0.534201 (-0.519833) \| 0.286213 / 0.579283 (-0.293070) \| 0.267093 / 0.434364 (-0.167271) \| 0.334802 / 0.540337 (-0.205535) \| 0.441866 / 1.386936 (-0.945070) \|\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) \| 0.005348 / 0.011353 (-0.006005) \| 0.003551 / 0.011008 (-0.007458) \| 0.049226 / 0.038508 (0.010718) \| 0.052072 / 0.023109 (0.028963) \| 0.268025 / 0.275898 (-0.007873) \| 0.289968 / 0.323480 (-0.033512) \| 0.004034 / 0.007986 (-0.003952) \| 0.002675 / 0.004328 (-0.001653) \| 0.048099 / 0.004250 (0.043848) \| 0.040141 / 0.037052 (0.003089) \| 0.272974 / 0.258489 (0.014485) \| 0.296097 / 0.293841 (0.002256) \| 0.028972 / 0.128546 (-0.099575) \| 0.010689 / 0.075646 (-0.064957) \| 0.057853 / 0.419271 (-0.361418) \| 0.032488 / 0.043533 (-0.011045) \| 0.272018 / 0.255139 (0.016879) \| 0.287179 / 0.283200 (0.003980) \| 0.018446 / 0.141683 (-0.123237) \| 1.140346 / 1.452155 (-0.311809) \| 1.247743 / 1.492716 (-0.244974) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091987 / 0.018006 (0.073980) \| 0.300527 / 0.000490 (0.300037) \| 0.000224 / 0.000200 (0.000024) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021390 / 0.037411 (-0.016021) \| 0.068768 / 0.014526 (0.054242) \| 0.080798 / 0.176557 (-0.095759) \| 0.119081 / 0.737135 (-0.618054) \| 0.082461 / 0.296338 (-0.213878) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286631 / 0.215209 (0.071422) \| 2.804633 / 2.077655 (0.726978) \| 1.574122 / 1.504120 (0.070002) \| 1.459994 / 1.541195 (-0.081201) \| 1.499739 / 1.468490 (0.031249) \| 0.579595 / 4.584777 (-4.005182) \| 2.426407 / 3.745712 (-1.319306) \| 2.917994 / 5.269862 (-2.351868) \| 1.846439 / 4.565676 (-2.719238) \| 0.063274 / 0.424275 (-0.361001) \| 0.005028 / 0.007607 (-0.002579) \| 0.341114 / 0.226044 (0.115070) \| 3.402677 / 2.268929 (1.133748) \| 1.940980 / 55.444624 (-53.503645) \| 1.651902 / 6.876477 (-5.224575) \| 1.677037 / 2.142072 (-0.465036) \| 0.651576 / 4.805227 (-4.153651) \| 0.116398 / 6.500664 (-6.384266) \| 0.041060 / 0.075469 (-0.034409) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973278 / 1.841788 (-0.868509) \| 12.248332 / 8.074308 (4.174024) \| 10.830627 / 10.191392 (0.639235) \| 0.143146 / 0.680424 (-0.537278) \| 0.016249 / 0.534201 (-0.517952) \| 0.298563 / 0.579283 (-0.280720) \| 0.278643 / 0.434364 (-0.155721) \| 0.338206 / 0.540337 (-0.202132) \| 0.589485 / 1.386936 (-0.797451) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#da29ac32c57e079199c173e4404342cc105ed774 \"CML watermark\")\n" ]	2023-12-04T12:00:37Z	2023-12-15T13:18:37Z	2023-12-15T13:12:30Z	MEMBER	null	null	null	Finally fix https://github.com/huggingface/datasets/issues/5537	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6469/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6469/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6469.diff", "html_url": "https://github.com/huggingface/datasets/pull/6469", "merged_at": "2023-12-15T13:12:30Z", "patch_url": "https://github.com/huggingface/datasets/pull/6469.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6469" }
https://api.github.com/repos/huggingface/datasets/issues/4754	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4754/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4754/comments	https://api.github.com/repos/huggingface/datasets/issues/4754/events	https://github.com/huggingface/datasets/pull/4754	1,319,681,541	PR_kwDODunzps48L9p6	4,754	Remove "unkown" language tags	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-07-27T14:50:12Z	2022-07-27T15:03:00Z	2022-07-27T14:51:06Z	MEMBER	null	null	null	Following https://github.com/huggingface/datasets/pull/4753 there was still a "unknown" langauge tag in `wikipedia` so the job at https://github.com/huggingface/datasets/runs/7542567336?check_suite_focus=true failed for wikipedia	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4754/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4754/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4754.diff", "html_url": "https://github.com/huggingface/datasets/pull/4754", "merged_at": "2022-07-27T14:51:06Z", "patch_url": "https://github.com/huggingface/datasets/pull/4754.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4754" }
https://api.github.com/repos/huggingface/datasets/issues/4748	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4748/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4748/comments	https://api.github.com/repos/huggingface/datasets/issues/4748/events	https://github.com/huggingface/datasets/pull/4748	1,318,874,913	PR_kwDODunzps48JTEb	4,748	Add image classification processing guide	{ "avatar_url": "https://avatars.githubusercontent.com/u/59462357?v=4", "events_url": "https://api.github.com/users/stevhliu/events{/privacy}", "followers_url": "https://api.github.com/users/stevhliu/followers", "following_url": "https://api.github.com/users/stevhliu/following{/other_user}", "gists_url": "https://api.github.com/users/stevhliu/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/stevhliu", "id": 59462357, "login": "stevhliu", "node_id": "MDQ6VXNlcjU5NDYyMzU3", "organizations_url": "https://api.github.com/users/stevhliu/orgs", "received_events_url": "https://api.github.com/users/stevhliu/received_events", "repos_url": "https://api.github.com/users/stevhliu/repos", "site_admin": false, "starred_url": "https://api.github.com/users/stevhliu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/stevhliu/subscriptions", "type": "User", "url": "https://api.github.com/users/stevhliu", "user_view_type": "public" }	[ { "color": "0075ca", "default": true, "description": "Improvements or additions to documentation", "id": 1935892861, "name": "documentation", "node_id": "MDU6TGFiZWwxOTM1ODkyODYx", "url": "https://api.github.com/repos/huggingface/datasets/labels/documentation" } ]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-07-27T00:11:11Z	2022-07-27T17:28:21Z	2022-07-27T17:16:12Z	MEMBER	null	null	null	This PR follows up on #4710 to separate the object detection and image classification guides. It expands a little more on the original guide to include a more complete example of loading and transforming a whole dataset.	{ "avatar_url": "https://avatars.githubusercontent.com/u/59462357?v=4", "events_url": "https://api.github.com/users/stevhliu/events{/privacy}", "followers_url": "https://api.github.com/users/stevhliu/followers", "following_url": "https://api.github.com/users/stevhliu/following{/other_user}", "gists_url": "https://api.github.com/users/stevhliu/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/stevhliu", "id": 59462357, "login": "stevhliu", "node_id": "MDQ6VXNlcjU5NDYyMzU3", "organizations_url": "https://api.github.com/users/stevhliu/orgs", "received_events_url": "https://api.github.com/users/stevhliu/received_events", "repos_url": "https://api.github.com/users/stevhliu/repos", "site_admin": false, "starred_url": "https://api.github.com/users/stevhliu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/stevhliu/subscriptions", "type": "User", "url": "https://api.github.com/users/stevhliu", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4748/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4748/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4748.diff", "html_url": "https://github.com/huggingface/datasets/pull/4748", "merged_at": "2022-07-27T17:16:12Z", "patch_url": "https://github.com/huggingface/datasets/pull/4748.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4748" }
https://api.github.com/repos/huggingface/datasets/issues/4937	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4937/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4937/comments	https://api.github.com/repos/huggingface/datasets/issues/4937/events	https://github.com/huggingface/datasets/pull/4937	1,363,426,946	PR_kwDODunzps4-cn6W	4,937	Remove deprecated identical_ok	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-09-06T15:01:24Z	2022-09-06T22:24:09Z	2022-09-06T22:21:57Z	MEMBER	null	null	null	`huggingface-hub` says that the `identical_ok` argument of `HfApi.upload_file` is now deprecated, and will be removed soon. It even has no effect at the moment when it's passed: ```python Args: ... identical_ok (`bool`, optional, defaults to `True`): Deprecated: will be removed in 0.11.0. Changing this value has no effect. ... ``` There was only one occurence of `identical_ok=False` but it's maybe not worth adding a check ti verify if the files were the same. cc @mariosasko	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4937/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4937/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4937.diff", "html_url": "https://github.com/huggingface/datasets/pull/4937", "merged_at": "2022-09-06T22:21:57Z", "patch_url": "https://github.com/huggingface/datasets/pull/4937.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4937" }
https://api.github.com/repos/huggingface/datasets/issues/7515	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7515/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7515/comments	https://api.github.com/repos/huggingface/datasets/issues/7515/events	https://github.com/huggingface/datasets/issues/7515	2,995,082,418	I_kwDODunzps6yhVSy	7,515	`concatenate_datasets` does not preserve Pytorch format for IterableDataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/5140987?v=4", "events_url": "https://api.github.com/users/francescorubbo/events{/privacy}", "followers_url": "https://api.github.com/users/francescorubbo/followers", "following_url": "https://api.github.com/users/francescorubbo/following{/other_user}", "gists_url": "https://api.github.com/users/francescorubbo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/francescorubbo", "id": 5140987, "login": "francescorubbo", "node_id": "MDQ6VXNlcjUxNDA5ODc=", "organizations_url": "https://api.github.com/users/francescorubbo/orgs", "received_events_url": "https://api.github.com/users/francescorubbo/received_events", "repos_url": "https://api.github.com/users/francescorubbo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/francescorubbo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/francescorubbo/subscriptions", "type": "User", "url": "https://api.github.com/users/francescorubbo", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Hi ! Oh indeed it would be cool to return the same format in that case. Would you like to submit a PR ? The function that does the concatenation is here:\n\nhttps://github.com/huggingface/datasets/blob/90e5bf8a8599b625d6103ee5ac83b98269991141/src/datasets/iterable_dataset.py#L3375-L3380", "Thank you for the pointer, @lhoestq ! See #7522 " ]	2025-04-15T04:36:34Z	2025-04-16T02:39:16Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug When concatenating datasets with `concatenate_datasets`, I would expect the resulting combined dataset to be in the same format as the inputs (assuming it's consistent). This is indeed the behavior when combining `Dataset`, but not when combining `IterableDataset`. Specifically, when applying `concatenate_datasets` to a list of `IterableDataset` in Pytorch format (i.e. using `.with_format(Pytorch)`), the output `IterableDataset` is not in Pytorch format. ### Steps to reproduce the bug ``` import datasets ds = datasets.Dataset.from_dict({"a": [1,2,3]}) iterable_ds = ds.to_iterable_dataset() datasets.concatenate_datasets([ds.with_format("torch")]) # <- this preserves Pytorch format datasets.concatenate_datasets([iterable_ds.with_format("torch")]) # <- this does NOT preserves Pytorch format ``` ### Expected behavior Pytorch format should be preserved when combining IterableDataset in Pytorch format. ### Environment info datasets==3.5.0, Python 3.11.11, torch==2.2.2	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7515/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7515/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/4883	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4883/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4883/comments	https://api.github.com/repos/huggingface/datasets/issues/4883/events	https://github.com/huggingface/datasets/issues/4883	1,349,083,235	I_kwDODunzps5QaWBj	4,883	With dataloader RSS memory consumed by HF datasets monotonically increases	{ "avatar_url": "https://avatars.githubusercontent.com/u/3616806?v=4", "events_url": "https://api.github.com/users/apsdehal/events{/privacy}", "followers_url": "https://api.github.com/users/apsdehal/followers", "following_url": "https://api.github.com/users/apsdehal/following{/other_user}", "gists_url": "https://api.github.com/users/apsdehal/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/apsdehal", "id": 3616806, "login": "apsdehal", "node_id": "MDQ6VXNlcjM2MTY4MDY=", "organizations_url": "https://api.github.com/users/apsdehal/orgs", "received_events_url": "https://api.github.com/users/apsdehal/received_events", "repos_url": "https://api.github.com/users/apsdehal/repos", "site_admin": false, "starred_url": "https://api.github.com/users/apsdehal/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/apsdehal/subscriptions", "type": "User", "url": "https://api.github.com/users/apsdehal", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	open	false	null	[]	null	[ "Are you sure there is a leak? How can I see it? You shared the script but not the output which you believe should indicate a leak.\r\n\r\nI modified your reproduction script to print only once per try as your original was printing too much info and you absolutely must add `gc.collect()` when doing any memory measurements, since python's GC is scheduled so you might be measuring the wrong thing. This gives us:\r\n\r\n```\r\nimport psutil\r\nimport os\r\nimport gc\r\nfrom transformers import BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\n\r\nBATCH_SIZE = 32\r\nNUM_TRIES = 100\r\n\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\ndef transform(x):\r\n x.update(tokenizer(x[\"text\"], return_tensors=\"pt\", max_length=64, padding=\"max_length\", truncation=True))\r\n x.pop(\"text\")\r\n x.pop(\"label\")\r\n return x\r\ndataset = load_dataset(\"imdb\", split=\"train\")\r\ndataset.set_transform(transform)\r\ntrain_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4)\r\n\r\nmem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n\r\ncount = 0\r\nwhile count < NUM_TRIES:\r\n for idx, batch in enumerate(train_loader): pass\r\n gc.collect()\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n print(count, mem_after - mem_before)\r\n count += 1\r\n```\r\n\r\nNow running it:\r\n\r\n```\r\n$ python dl-leak.py \r\nReusing dataset imdb (/home/stas/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1)\r\n0 4.43359375\r\n1 4.4453125\r\n2 4.44921875\r\n3 4.44921875\r\n4 4.4609375\r\n5 4.46484375\r\n6 4.46484375\r\n7 4.46484375\r\n8 4.46484375\r\n9 4.46484375\r\n10 4.46484375\r\n11 4.46484375\r\n12 4.46484375\r\n13 4.46484375\r\n14 4.46484375\r\n15 4.46484375\r\n16 4.46484375\r\n```\r\n\r\nIt's normal that at the beginning there is a small growth in memory usage, but after 5 cycles it gets steady.", "Unless of course you're referring the memory growth during the first try. Is that what you're referring to? And since your ds is small it's hard to see the growth - could it be just because some records are longer and it needs to allocate more memory for those?\r\n\r\nThough while experimenting with this I have observed a peculiar thing, if I concatenate 2 datasets, I don't see any growth at all. But that's probably because the program allocated additional peak RSS memory to concatenate and then is re-using the memory\r\n\r\nI basically tried to see if I make the dataset much longer, I'd expect not to see any memory growth once the 780 records of the imdb ds have been processed once.", "It is hard to say if it is directly reproducible in this setup. Maybe it is specific to the images stored in the CM4 case which cause a memory leak. I am still running your script and seeing if I can reproduce that particular leak in this case.", "I was able to reproduce the leak with:\r\n\r\n```\r\nimport psutil\r\nimport os\r\nimport gc\r\nfrom datasets import load_from_disk\r\nimport time\r\n\r\nDATASET_PATH = \"/hf/m4-master/data/cm4/cm4-10000-v0.1\"\r\n\r\ndataset = load_from_disk(DATASET_PATH)\r\n\r\n# truncate to a tiny dataset\r\ndataset = dataset.select(range(1000))\r\n\r\nprint(f\"dataset: {len(dataset)} records\")\r\n\r\nmem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\nfor idx, rec in enumerate(dataset):\r\n if idx % 100 == 0:\r\n gc.collect()\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n print(f\"{idx:4d} {mem_after - mem_before:12.4f}MB\")\r\n```\r\nYou need to adjust the DATASET_PATH record.\r\n\r\nwhich you get from\r\n\r\n```\r\ngsutil -m cp \"gs://hf-science-m4/cm4/cm4-10000-v0.1/dataset.arrow\" \"gs://hf-science-m4/cm4/cm4-10000-v0.1/dataset_info.json\" \"gs://hf-science-m4/cm4/cm4-10000-v0.1/state.json\" .\r\n```\r\n(I assume the hf folks have the perms) - it's a smallish dataset (10k)\r\n\r\nthen you run:\r\n```\r\n$ python ds.py\r\ndataset: 1000 records\r\n 0 1.0156MB\r\n 100 126.3906MB\r\n 200 142.8906MB\r\n 300 168.5586MB\r\n 400 218.3867MB\r\n 500 230.7070MB\r\n 600 238.9570MB\r\n 700 263.3789MB\r\n 800 288.1289MB\r\n 900 300.5039MB\r\n```\r\n\r\nyou should be able to see the leak ", "This issue has nothing to do with `PIL`'s decoder. I removed it and the problem is still there.\r\n\r\nI then traced this leak to this single call: `pa_table.to_pydict()` here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/08a7b389cdd6fb49264a72aa8ccfc49a233494b6/src/datasets/formatting/formatting.py#L138-L140\r\n\r\nI can make it leak much faster by modifying that code to repeat `pa_table.to_pydict()` many times in a row. It shouldn't have that impact:\r\n\r\n```\r\nclass PythonArrowExtractor(BaseArrowExtractor[dict, list, dict]):\r\n def extract_row(self, pa_table: pa.Table) -> dict:\r\n x = [pa_table.to_pydict() for x in range(200)]\r\n return _unnest(pa_table.to_pydict())\r\n```\r\n\r\n@lhoestq - do you know what might be happening inside `pa_table.to_pydict()`, as this is in the `pyarrow` domain. Perhaps you know someone to tag from that project?\r\n\r\nProbably next need to remove `datasets` from the equation and make a reproducible case with just `pyarrow` directly.\r\n\r\nThe problem already happens with `pyarrow==6.0.0` or later (minimum for current `datasets`)\r\n\r\nI'm also trying to dig in with `objgraph` to see if there are any circular references which prevent objects from being freed, but no luck there so far. And I'm pretty sure `to_pydict` is not a python code, so the problem is likely to happen somewhere outside of python's GC.", "This appears to be the same issue I think: https://github.com/huggingface/datasets/issues/4528\r\nI dug into the repro code there and it's the same behavior with the same leak, but it's a pure nlp dataset and thus much faster to work with. \r\n", "I went all the way back to `pyarrow==1.0.0` and `datasets==1.12.0` and the problem is still there. How is it even possible that it wasn't noticed all this time. \r\n\r\nCould it be that the leak is in some 3rd party component `pyarrow` relies on? as while downgrading I have only downgraded the above 2 packages.\r\n", "Also found this warning \r\n\r\n> Be careful: if you don't pass the ArrowArray struct to a consumer,\r\n> array memory will leak. This is a low-level function intended for\r\n> expert users.\r\n\r\nsee: https://github.com/apache/arrow/blob/99b57e84277f24e8ec1ddadbb11ef8b4f43c8c89/python/pyarrow/table.pxi#L2515-L2517\r\n\r\nperhaps something triggers this condition?\r\n\r\nI have no idea if it's related - this is just something that came up during my research.", "Does it crash with OOM at some point? If it doesn't, it isn't a leak, just agressive caching or a custom allocator that doesn't like to give memory back (not uncommon). #4528 looks like it hits a steady state.\r\n\r\nI believe the underlying arrow libs use a custom C allocator. Some of those are designed not to give back to OS, but keep heap memory for themselves to re-use (hitting up the OS involves more expensive mutex locks, contention, etc). The greedy behaviour can be undesirable though. There are likely flags to change the allocator behaviour, and one could likely build without any custom allocators (or use a different one).", "> Does it crash with OOM at some point?\r\n\r\nIn the original setup where we noticed this problem, it was indeed ending in an OOM", "> https://github.com/huggingface/datasets/issues/4528 looks like it hits a steady state.\r\n\r\n@rwightman in the plot I shared, the steady state comes from the `time.sleep(100)` I added in the end of the script, to showcase that even the garbage collector couldn't free that allocated memory.\r\n", "Could this be related to this discussion about a potential memory leak in pyarrow: https://issues.apache.org/jira/browse/ARROW-11007 ?\r\n\r\n(Note: I've tried `import pyarrow; pyarrow.jemalloc_set_decay_ms(0)` and the memory leak is still happening on your toy example)", "> @lhoestq - do you know what might be happening inside pa_table.to_pydict(), as this is in the pyarrow domain. Perhaps you know someone to tag from that project?\r\n\r\n`to_pydict` calls `to_pylist` on each column (i.e. on each PyArrow Array). Then it iterates on the array and calls `as_py` on each element. The `as_py` implementation depends on the data type. For strings I think it simply gets the buffer that contains the binary string data that is defined in C++\r\n\r\nThe Arrow team is pretty responsive at user@arrow.apache.org if it can help\r\n\r\n> Probably next need to remove datasets from the equation and make a reproducible case with just pyarrow directly.\r\n\r\nThat would be ideal indeed. Would be happy to help on this, can you give me access to the bucket so I can try with your data ?", "> That would be ideal indeed. Would be happy to help on this, can you give me access to the bucket so I can try with your data ?\r\n\r\nI added you to the bucket @lhoestq ", "It looks like an issue with memory mapping:\r\n- the amount of memory used in the end corresponds to the size of the dataset\r\n- setting `keep_in_memory=True` in `load_from_disk` loads the dataset in RAM, and doesn't cause any memory leak", "Here is a code to reproduce this issue using only PyArrow and a dummy arrow file:\r\n```python\r\nimport psutil\r\nimport os\r\nimport gc\r\nimport pyarrow as pa\r\nimport time\r\n\r\nARROW_PATH = \"tmp.arrow\"\r\n\r\nif not os.path.exists(ARROW_PATH):\r\n arr = pa.array([b\"a\" * (200 * 1024)] * 1000) # ~200MB\r\n table = pa.table({\"a\": arr})\r\n\r\n with open(ARROW_PATH, \"wb\") as f:\r\n writer = pa.RecordBatchStreamWriter(f, schema=table.schema)\r\n writer.write_table(table)\r\n writer.close()\r\n\r\n\r\ndef memory_mapped_arrow_table_from_file(filename: str) -> pa.Table:\r\n memory_mapped_stream = pa.memory_map(filename)\r\n opened_stream = pa.ipc.open_stream(memory_mapped_stream)\r\n pa_table = opened_stream.read_all()\r\n return pa_table\r\n\r\n\r\ntable = memory_mapped_arrow_table_from_file(ARROW_PATH)\r\narr = table[0]\r\n\r\nmem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\nfor idx, x in enumerate(arr):\r\n if idx % 100 == 0:\r\n gc.collect()\r\n time.sleep(0.1)\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n print(f\"{idx:4d} {mem_after - mem_before:12.4f}MB\")\r\n```\r\nprints\r\n```\r\n 0 0.2500MB\r\n 100 19.8008MB\r\n 200 39.3320MB\r\n 300 58.8633MB\r\n 400 78.3945MB\r\n 500 97.9258MB\r\n 600 117.4570MB\r\n 700 136.9883MB\r\n 800 156.5195MB\r\n 900 176.0508MB\r\n```\r\nNote that this example simply iterates over the `pyarrow.lib.BinaryScalar` objects in the array. Running `.as_py()` is not needed to experience the memory issue.", "@lhoestq that does indeed increase in memory, but if you iterate over array again after the first time, or re-open and remap the same file (repeat `table = memory_mapped_arrow_table_from_file(ARROW_PATH)`) before re-iterating, it doesn't move pas 195MB.... it would appear another step is needed to continue consuming memory past that.. hmmm\r\n\r\nAre the pa_tables held on to anywhere after they are iterated in the real code?\r\n\r\nin my hack, if you do a bunch cut & paste and then change the arr name for each iter \r\n\r\n```\r\ntable = memory_mapped_arrow_table_from_file(ARROW_PATH)\r\narr = table[0]\r\n\r\nfor idx, x in enumerate(arr):\r\n if idx % 100 == 0:\r\n gc.collect()\r\n time.sleep(0.1)\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n print(f\"{idx:4d} {mem_after - mem_before:12.4f}MB\")\r\n\r\ntable = memory_mapped_arrow_table_from_file(ARROW_PATH)\r\narr1 = table[0]\r\n\r\nfor idx, x in enumerate(arr1):\r\n if idx % 100 == 0:\r\n gc.collect()\r\n time.sleep(0.1)\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n print(f\"{idx:4d} {mem_after - mem_before:12.4f}MB\")\r\n\r\ntable = memory_mapped_arrow_table_from_file(ARROW_PATH)\r\narr2 = table[0]\r\n\r\nfor idx, x in enumerate(arr2):\r\n if idx % 100 == 0:\r\n gc.collect()\r\n time.sleep(0.1)\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n print(f\"{idx:4d} {mem_after - mem_before:12.4f}MB\")\r\n```\r\n\r\nit leaks, if all arr are the same name (so prev one gets cleaned up) it does not and goes back to 0, anything that could be holding onto a reference of an intermediary equivalent like arr in the real use case?\r\n\r\n\r\n\r\n", "Yes, we have already established here https://github.com/huggingface/datasets/issues/4883#issuecomment-1232063891 that when one iterates over the whole dataset multiple times, it consumes a bit more memory in the next few repetitions and then remains steady. \r\n\r\nWhich means that when a new iterator is created over the same dataset, all the memory from the previous iterator is re-used.\r\n\r\nSo the leak happens primarily when the iterator is \"drained\" the first time. which tells me that either a circular reference is created somewhere which only gets released when the iterator is destroyed, or there is some global variable that keeps piling up the memory and doesn't release it in time.\r\n\r\nAlso I noticed some `__del__` methods which won't destroy objects automatically and there is usually a warning against using it https://stackoverflow.com/a/1481512/9201239\r\n\r\nThere are also some `weakref`s in the code which too may lead to leaks or weird problems at times.\r\n", "@stas00 my point was, I'm not convinced @lhoestq last example illustrates the leak, but rather the differences between memory mapping and in memory usage patterns. If you destroy arr, memory map impl goes back to 0 each iteration. The amount of memory that 'looks' like it is leaked in first pass differes quite a bit between memory mapped vs in memory, but the underlying issue likely a circular reference, or reference(s) which were not cleaned up that would impact either case, but likely much more visible with mmap.", "Thank you for clarifying, Ross. \r\n\r\nI think we agree that it's almost certain that the `datasets` iterator traps some inner variable that prevents object freeing, since if we create the iterator multiple times (and drain it) after a few runs no new memory is allocated. We could try to dig in more with `objgraph` - my main concern is if the problem happens somewhere outside of python, (i.e. in pyarrow cpp implementation) in which case it'd be much more difficult to trace. \r\n\r\nI wish there was a way on linux to tell the program to free no longer used memory at will.", "FWIW, I revisted some code I had in the works to use HF datasets w/ timm train & val scripts. There is no leak there across multipe epochs. It uses the defaults. \r\n\r\nIt's worth noting that with imagenet `keep_in_memory=True` isn't even an option because the train arrow file is ~140GB and my local memory is less. The virtual address space reflects mmap (> 150GB) and doesn't increase over epochs that I noticed. I have some perf issues to bring up wrt to the current setup, but that's a separate and lower prio discussion to have elsewhere...", "# Notes \r\n\r\nAfter reading many issues and trying many things here is the summary of my learning\r\n\r\nI'm now using @lhoestq repro case as it's pyarrow-isolated: https://github.com/huggingface/datasets/issues/4883#issuecomment-1242034985\r\n\r\n\r\n## 1. pyarrow memory backends\r\n\r\nit has 3 backends, I tried them all with the same results\r\n\r\n```\r\npa.set_memory_pool(pa.jemalloc_memory_pool())\r\npa.set_memory_pool(pa.mimalloc_memory_pool())\r\npa.set_memory_pool(pa.system_memory_pool())\r\n```\r\n\r\n## 2. quick release\r\n\r\nThe `jemalloc` backend supports quick release\r\n\r\n```\r\npa.jemalloc_set_decay_ms(0)\r\n```\r\n\r\nit doesn't make any difference in this case\r\n\r\n## 3. actual memory allocations\r\n\r\nthis is a useful tracer for PA memory allocators\r\n```\r\npa.log_memory_allocations(enable=True)\r\n```\r\n\r\nit nicely reports memory allocations and releases when the arrow file is created the first time.\r\n\r\nbut when we then try to do `enumerate(arr)` this logger reports 0 allocations.\r\n\r\nThis summary also reports no allocations when the script run the second time (post file creation):\r\n```\r\nmem_pool = pa.default_memory_pool()\r\nprint(f\"PyArrow mem pool info: {mem_pool.backend_name} backend, {mem_pool.bytes_allocated()} allocated, \"\r\n f\"{mem_pool.max_memory()} max allocated, \")\r\n\r\nprint(f\"PyArrow total allocated bytes: {pa.total_allocated_bytes()}\")\r\n```\r\n\r\nHowever it's easy to see by using `tracemalloc` which only measures python's memory allocations that it's PA that leaks, since `tracemalloc` shows fixed memory\r\n\r\n(this is bolted on top of the original repro script)\r\n\r\n```\r\nimport tracemalloc\r\ntracemalloc.start()\r\n\r\n[...]\r\nfor idx, x in enumerate(arr):\r\n if idx % 10 == 0:\r\n gc.collect()\r\n time.sleep(0.1)\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / 220\r\n mem_use = pa.total_allocated_bytes() - start_use\r\n mem_peak = pool.max_memory() - start_peak_use\r\n\r\n second_size, second_peak = tracemalloc.get_traced_memory()\r\n mem_diff = (second_size - first_size) / 220\r\n mem_peak_diff = (second_peak - first_peak) / 220\r\n\r\n # pa.jemalloc_memory_pool().release_unused()\r\n # pa.mimalloc_memory_pool().release_unused()\r\n # pa.system_memory_pool().release_unused()\r\n\r\n print(f\"{idx:4d} {mem_after - mem_before:12.4f}MB {mem_diff:12.4f} {mem_peak_diff:12.4f} {memory_mapped_stream.size()/220:4.4}MB {mem_use/220:4.4}MB {mem_peak/220:4.4}MB\")\r\n\r\n```\r\n\r\ngives:\r\n\r\n```\r\n 0 5.4258MB 0.0110 0.0201 195.3MB 0.0MB 0.0MB\r\n 10 25.3672MB 0.0112 0.0202 195.3MB 0.0MB 0.0MB\r\n 20 45.9336MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 30 62.4336MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 40 83.0586MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 50 103.6836MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 60 124.3086MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 70 140.8086MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 80 161.4336MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 90 182.0586MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n```\r\n\r\nthe 3rd and 4th columns are `tracemalloc`'s report.\r\n\r\nthe 5th column is the size of mmaped stream - fixed.\r\n\r\nthe last 2 are the PA's malloc reports - you can see it's totally fixed and 0.\r\n\r\nSo what gives? PA's memory allocator says nothing was allocated and we can see python doesn't allocate any memory either.\r\n\r\nAs someone suggested in one of the PA issues that IPC/GRPC could be the issue. Any suggestions on how debug this one?\r\n\r\nThe main issue is that one can't step through with a python debugger as `arr` is an opaque cpp object binded to python.\r\n\r\nPlease see the next comment for a possible answer.\r\n\r\n# ref-count\r\n\r\nI also traced reference counts and they are all fixed using either `sys.getrefcount(x)` or `len(gc.get_referrers(x))`\r\n\r\nso it's not the python object\r\n\r\n# Important related discussions\r\n\r\nhttps://issues.apache.org/jira/browse/ARROW-11007 - looks very similar to our issue\r\nin particular this part of the report:\r\nhttps://issues.apache.org/jira/browse/ARROW-11007?focusedCommentId=17279642&page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#comment-17279642\r\n", "# There is no leak, just badly communicated linux RSS memory usage stats\r\n\r\nNext, lets revisit @rwightman's suggestion that there is actually no leak.\r\n\r\nAfter all - we are using mmap which will try to map the file to RAM as much as it can and then page out if there is no memory. i.e. MMAP is only fast if you have a lot of CPU RAM.\r\n\r\nSo let's do it:\r\n\r\n# Memory mapping OOM test\r\n\r\nWe first quickly start a cgroups-controlled shell which will instantly kill any program that consumes more than 1GB of memory:\r\n\r\n```\r\n$ systemd-run --user --scope -p MemoryHigh=1G -p MemoryMax=1G -p MemorySwapMax=1G --setenv=\"MEMLIMIT=1GB\" bash\r\n```\r\n\r\nLet's check that it indeed does so. Let's change @lhoestq's script to allocate a 10GB arrow file:\r\n\r\n```\r\n$ python -c 'import pyarrow as pa; pa.array([b\"a\" * (2000 * 1024)] * 5000)'\r\nKilled\r\n```\r\noops, that didn't work, as we tried to allocate 10GB when only 1GB is allowed. This is what we want!\r\n\r\nLet's do a sanity check - can we allocate 0.1GB?\r\n```\r\npython -c 'import pyarrow as pa; pa.array([b\"a\" * (2000 * 1024)] * 50)'\r\n```\r\n\r\nYes. So the limited shell does the right thing. It let's allocate `< 1GB` of RSS RAM.\r\n\r\nNext let's go back to @lhoestq's script but with 10GB arrow file.\r\n\r\nwe change his repro script https://github.com/huggingface/datasets/issues/4883#issuecomment-1242034985 to 50x larger file\r\n```\r\n arr = pa.array([b\"a\" * (2000 * 1024)] * 5000) # ~10000MB\r\n```\r\nwe first have to run into a normal unlimited shell so that we don't get killed (as the script allocates 10GB)\r\n\r\nlet's run the script now in the 1GB-limited shell while running a monitor:\r\n\r\n```\r\n$ htop -F python -s M_RESIDENT -u `whoami`\r\n```\r\n\r\nso we have 2 sources of RSS info just in case.\r\n\r\n```\r\n$ python pyar.py\r\n 0 4.3516MB 0.0103 0.0194 9.766e+03MB 0.0MB 0.0MB\r\n 10 24.3008MB 0.0104 0.0195 9.766e+03MB 0.0MB 0.0MB\r\n[...]\r\n4980 9730.3672MB 0.0108 0.0199 9.766e+03MB 0.0MB 0.0MB\r\n4990 9750.9922MB 0.0108 0.0199 9.766e+03MB 0.0MB 0.0MB\r\nPyArrow mem pool info: jemalloc backend, 0 allocated, 0 max allocated,\r\nPyArrow total allocated bytes: 0\r\n```\r\n\r\nBut wait, it reported 10GB RSS both in `htop` and in our log!\r\n\r\nSo that means it never allocated 10GB otherwise it'd have been killed.\r\n\r\nWhich tells us that there is no leak whatsoever and this is just a really difficult situation where MMAPPED memory is reported as part of RSS which it probably shouldn't. As now we have no way how to measure real memory usage.\r\n\r\nI also attached the script with all the different things I have tried in it, so it should be easy to turn them on/off if you want to reproduce any of my findings.\r\n\r\n[pyar.txt](https://github.com/huggingface/datasets/files/9539430/pyar.txt)\r\n\r\njust rename it to `pyra.py` as gh doesn't let attaching scripts...\r\n\r\n(I have to remember to exit that special mem-limited shell or else I won't be able to do anything serious there.)\r\n\r\n", "The original leak in the multi-modal code is very likely something else. But of course now it'd be very difficult to trace it using mmap.\r\n\r\nI think to debug we have to set `keep_in_memory=True` in `load_from_disk` to load the small dataset in RAM, so there will be no mmap misleading reporting component and then continue searching for another source of a leak.", "To add to what @stas00 found, I'm gonna leave some links to where I believe the confusion came from in pyarrow's APIs, for future reference:\r\n* In the section where they talk about [efficiently writing and reading arrow data](https://arrow.apache.org/docs/dev/python/ipc.html?#efficiently-writing-and-reading-arrow-data), they give an example of how \r\n\r\n> Arrow can directly reference the data mapped from disk and avoid having to allocate its own memory. \r\n\r\nAnd where their example shows 0 RSS memory allocation, the way we used to measure RSS shows 39.6719MB allocated. Here's the script to reproduce:\r\n```\r\nimport psutil\r\nimport os\r\nimport gc\r\nimport pyarrow as pa\r\nimport time\r\nimport sys\r\n\r\n# gc.set_debug(gc.DEBUG_LEAK)\r\n# gc.set_threshold(0,0,0)\r\n\r\n#pa.set_memory_pool(pa.mimalloc_memory_pool())\r\n#pa.set_memory_pool(pa.system_memory_pool())\r\n\r\nimport tracemalloc\r\n\r\n#pa.jemalloc_set_decay_ms(0)\r\n# pa.log_memory_allocations(enable=True)\r\n\r\nBATCH_SIZE = 10000\r\nNUM_BATCHES = 1000\r\nschema = pa.schema([pa.field('nums', pa.int32())])\r\nwith pa.OSFile('bigfile.arrow', 'wb') as sink:\r\n with pa.ipc.new_file(sink, schema) as writer:\r\n for row in range(NUM_BATCHES):\r\n batch = pa.record_batch([pa.array(range(BATCH_SIZE), type=pa.int32())], schema)\r\n writer.write(batch)\r\n\r\nstart_use = pa.total_allocated_bytes()\r\npool = pa.default_memory_pool()\r\nstart_peak_use = pool.max_memory()\r\ntracemalloc.start()\r\nfirst_size, first_peak = tracemalloc.get_traced_memory()\r\nmem_before = psutil.Process(os.getpid()).memory_info().rss / 220\r\n\r\n\r\n# with pa.OSFile('bigfile.arrow', 'rb') as source:\r\n# loaded_array = pa.ipc.open_file(source).read_all()\r\n\r\nwith pa.memory_map('bigfile.arrow', 'rb') as source:\r\n loaded_array = pa.ipc.open_file(source).read_all()\r\n\r\n\r\nprint(\"LEN:\", len(loaded_array))\r\nprint(\"RSS: {}MB\".format(pa.total_allocated_bytes() >> 20))\r\n\r\ngc.collect()\r\ntime.sleep(0.1)\r\nmem_after = psutil.Process(os.getpid()).memory_info().rss / 220\r\nmem_use = pa.total_allocated_bytes() - start_use\r\nmem_peak = pool.max_memory() - start_peak_use\r\nsecond_size, second_peak = tracemalloc.get_traced_memory()\r\nmem_diff = (second_size - first_size) / 220\r\nmem_peak_diff = (second_peak - first_peak) / 220\r\n\r\nidx = 0\r\nprint(f\"{idx:4d} {mem_after - mem_before:12.4f}MB {mem_diff:12.4f} {mem_peak_diff:12.4f} {mem_use/220:4.4}MB {mem_peak/220:4.4}MB\")\r\n```\r\ngives:\r\n```\r\n\r\nLEN: 10000000\r\nRSS: 0MB\r\n 0 39.6719MB 0.0132 0.0529 0.0MB 0.0MB\r\n```\r\nWhich again just proves that we uncorrectly measure RSS, in the case of MMAPPED memory\r\n\r\n\r\n* [The recommended way to do memory profiling from Arrow's docs](https://arrow.apache.org/docs/dev/cpp/memory.html#memory-profiling)\r\n", "@lhoestq, I have been working on a detailed article that shows that MMAP doesn't leak and it's mostly ready. I will share when it's ready.\r\n\r\nThe issue is that we still need to be able to debug memory leaks by turning MMAP off.\r\n\r\nBut, once I tried to show the user that using `load_dataset(... keep_in_memory=True)` is the way to debug an actual memory leak - guess I what I discovered? A potential actual leak.\r\n\r\nHere is the repro:\r\n\r\n```\r\n$ cat ds-mmap.py\r\nfrom datasets import load_dataset\r\nimport gc\r\nimport os\r\nimport psutil\r\n\r\nproc = psutil.Process(os.getpid())\r\ndef mem_read():\r\n gc.collect()\r\n return proc.memory_info().rss / 2*20\r\n\r\ndataset = load_dataset(\"wmt19\", 'cs-en', keep_in_memory=True, streaming=False)['train']\r\n\r\nprint(f\"{'idx':>6} {'RSS':>10} {'Δ RSS':>15}\")\r\nstep = 20000\r\nfor i in range(0, 10step, step):\r\n mem_before = mem_read()\r\n _ = dataset[i:i+step]\r\n mem_after = mem_read()\r\n print(f\"{i:6d} {mem_after:12.4f}MB {mem_after - mem_before:12.4f}MB \")\r\n```\r\n\r\n```\r\npython ds-io.py\r\nReusing dataset wmt19 (/home/stas/.cache/huggingface/datasets/wmt19/cs-en/1.0.0/c3db1bf4240362ed1ef4673b354f468d70aac66d4e67d45f536d493a0840f0d3)\r\n100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 5.66it/s]\r\n idx RSS Δ RSS\r\n 0 1398.4609MB 3.5195MB\r\n 20000 1398.5742MB 0.1133MB\r\n 40000 1398.6016MB 0.0273MB\r\n 60000 1398.6016MB 0.0000MB\r\n 80000 1398.6016MB 0.0000MB\r\n100000 1398.6328MB 0.0312MB\r\n120000 1398.6953MB 0.0625MB\r\n140000 1398.6953MB 0.0000MB\r\n160000 1398.7500MB 0.0547MB\r\n180000 1398.7500MB 0.0000MB\r\n```", "as I suggested on slack perhaps it was due to dataset records length variation, so with your help I wrote another repro with synthetic records which are all identical - which should remove my hypothese from the equation and we should expect 0 incremental growth as we iterate over the datasets. But alas this is not the case. There is a tiny but definite leak-like behavior.\r\n\r\nHere is the new repro:\r\n\r\n```\r\n$ cat ds-synthetic-no-mmap.py\r\nfrom datasets import load_from_disk, Dataset\r\nimport gc\r\nimport sys\r\nimport os\r\nimport psutil\r\n\r\nproc = psutil.Process(os.getpid())\r\ndef mem_read():\r\n gc.collect()\r\n return proc.memory_info().rss / 2*20\r\n\r\nDS_PATH = \"synthetic-ds\"\r\nif not os.path.exists(DS_PATH):\r\n records = 1_000_000\r\n print(\"Creating a synthetic dataset\")\r\n row = dict(foo=[dict(a='a'500, b='b'1000)])\r\n ds = Dataset.from_dict({k: [v] records for k, v in row.items()})\r\n ds.save_to_disk(DS_PATH)\r\n print(\"Done. Please restart the program\")\r\n sys.exit()\r\n\r\ndataset = load_from_disk(DS_PATH, keep_in_memory=True)\r\nprint(f\"Dataset len={len(dataset)}\")\r\n\r\nprint(f\"{'idx':>8} {'RSS':>10} {'Δ RSS':>15}\")\r\nmem_start = 0\r\nstep = 25_000\r\nwarmup_iterations = 4\r\nfor idx, i in enumerate(range(0, len(dataset), step)):\r\n if idx == warmup_iterations: # skip the first few iterations while things get set up\r\n mem_start = mem_read()\r\n mem_before = mem_read()\r\n _ = dataset[i:i+step]\r\n mem_after = mem_read()\r\n print(f\"{i:8d} {mem_after:12.4f}MB {mem_after - mem_before:12.4f}MB\")\r\nmem_end = mem_read()\r\n\r\nprint(f\"Total diff: {mem_end - mem_start:12.4f}MB (after {warmup_iterations} warmup iterations)\")\r\n```\r\n\r\nand the run:\r\n\r\n```\r\n$ python ds-synthetic-no-mmap.py\r\nDataset len=1000000\r\n idx RSS Δ RSS\r\n 0 1601.9258MB 47.9688MB\r\n 25000 1641.6289MB 39.7031MB\r\n 50000 1641.8594MB 0.2305MB\r\n 75000 1642.1289MB 0.2695MB\r\n 100000 1642.1289MB 0.0000MB\r\n 125000 1642.3789MB 0.2500MB\r\n 150000 1642.3789MB 0.0000MB\r\n 175000 1642.6289MB 0.2500MB\r\n 200000 1642.6289MB 0.0000MB\r\n 225000 1642.8789MB 0.2500MB\r\n 250000 1642.8828MB 0.0039MB\r\n 275000 1643.1328MB 0.2500MB\r\n 300000 1643.1328MB 0.0000MB\r\n 325000 1643.3828MB 0.2500MB\r\n 350000 1643.3828MB 0.0000MB\r\n 375000 1643.6328MB 0.2500MB\r\n 400000 1643.6328MB 0.0000MB\r\n 425000 1643.8828MB 0.2500MB\r\n 450000 1643.8828MB 0.0000MB\r\n 475000 1644.1328MB 0.2500MB\r\n 500000 1644.1328MB 0.0000MB\r\n 525000 1644.3828MB 0.2500MB\r\n 550000 1644.3828MB 0.0000MB\r\n 575000 1644.6328MB 0.2500MB\r\n 600000 1644.6328MB 0.0000MB\r\n 625000 1644.8828MB 0.2500MB\r\n 650000 1644.8828MB 0.0000MB\r\n 675000 1645.1328MB 0.2500MB\r\n 700000 1645.1328MB 0.0000MB\r\n 725000 1645.3828MB 0.2500MB\r\n 750000 1645.3828MB 0.0000MB\r\n 775000 1645.6328MB 0.2500MB\r\n 800000 1645.6328MB 0.0000MB\r\n 825000 1645.8828MB 0.2500MB\r\n 850000 1645.8828MB 0.0000MB\r\n 875000 1646.1328MB 0.2500MB\r\n 900000 1646.1328MB 0.0000MB\r\n 925000 1646.3828MB 0.2500MB\r\n 950000 1646.3828MB 0.0000MB\r\n 975000 1646.6328MB 0.2500MB\r\nTotal diff: 4.5039MB (after 4 warmup iterations)\r\n```\r\nso I'm still not sure why we get this.\r\n\r\nAs you can see I started skipping the first few iterations where memory isn't stable yet. As the actual diff is much larger if we count all iterations.\r\n\r\nWhat do you think?", "@stas00 my 2 cents from having looked at a LOT of memory leaks over the years, esp in Python, .3% memory increase over that many iterations of something is difficult to say with certainty it is a leak. \r\n\r\nAlso, just looking at RSS makes it hard to analyze leaks. RSS can stay near constant while you are leaking. RSS is paged in mem, if you have a big leak your RSS might not increase much (leaked mem tends not to get used again so often paged out) while your virtual page allocation could be going through the roof...", "yes, that's true, but unless the leak is big, I'm yet to find another measurement tool.\r\n\r\nTo prove your point here is a very simple IO in a loop program that also reads the same line all over again:\r\n\r\n```\r\n$ cat mmap-no-leak-debug.py\r\nimport gc\r\nimport mmap\r\nimport os\r\nimport psutil\r\nimport sys\r\n\r\nproc = psutil.Process(os.getpid())\r\n\r\nPATH = \"./tmp.txt\"\r\n\r\ndef mem_read():\r\n gc.collect()\r\n return proc.memory_info().rss / 2*20\r\n\r\n# create a large data file with a few long lines\r\nif not os.path.exists(PATH):\r\n with open(PATH, \"w\") as fh:\r\n s = 'a' 227 + \"\\n\" # 128MB\r\n # write ~2GB file\r\n for i in range(16):\r\n fh.write(s)\r\n\r\nprint(f\"{'idx':>4} {'RSS':>10} {'Δ RSS':>12} {'Δ accumulated':>10}\")\r\n\r\ntotal_read = 0\r\ncontent = ''\r\nmem_after = mem_before_acc = mem_after_acc = mem_before = proc.memory_info().rss / 220\r\nprint(f\"{0:4d} {mem_after:10.2f}MB {mem_after - 0:10.2f}MB {0:10.2f}MB\")\r\n\r\nmmap_mode = True if \"--mmap\" in sys.argv else False\r\n\r\nwith open(PATH, \"r\") as fh:\r\n\r\n if mmap_mode:\r\n mm = mmap.mmap(fh.fileno(), 0, access=mmap.ACCESS_READ)\r\n\r\n idx = 0\r\n while True:\r\n idx += 1\r\n mem_before = mem_read()\r\n line = mm.readline() if mmap_mode else fh.readline()\r\n if not line:\r\n break\r\n\r\n #total_read += len(line)\r\n\r\n if \"--accumulate\" in sys.argv:\r\n mem_before_acc = mem_read()\r\n content += str(line)\r\n mem_after_acc = mem_read()\r\n\r\n mem_after = mem_read()\r\n\r\n print(f\"{idx:4d} {mem_after:10.2f}MB {mem_after - mem_before:10.2f}MB {mem_after_acc - mem_before_acc:10.2f}MB\")\r\n```\r\n\r\nit has some other instrumentations to do mmap and accumulate data, but let's ignore that for now.\r\n\r\nHere it is running in a simple non-mmap IO:\r\n\r\n```\r\n$ python mmap-no-leak-debug.py\r\n idx RSS Δ RSS Δ accumulated\r\n 0 12.43MB 12.43MB 0.00MB\r\n 1 269.72MB 257.29MB 0.00MB\r\n 2 269.73MB 0.02MB 0.00MB\r\n 3 269.73MB 0.00MB 0.00MB\r\n 4 269.74MB 0.01MB 0.00MB\r\n 5 269.74MB 0.00MB 0.00MB\r\n 6 269.75MB 0.01MB 0.00MB\r\n 7 269.75MB 0.00MB 0.00MB\r\n 8 269.76MB 0.01MB 0.00MB\r\n 9 269.76MB 0.00MB 0.00MB\r\n 10 269.77MB 0.01MB 0.00MB\r\n 11 269.77MB 0.00MB 0.00MB\r\n 12 269.77MB 0.00MB 0.00MB\r\n 13 269.77MB 0.00MB 0.00MB\r\n 14 269.77MB 0.00MB 0.00MB\r\n 15 269.77MB 0.00MB 0.00MB\r\n 16 146.02MB -123.75MB 0.00MB\r\n```\r\n\r\nas you can see even this super-simplistic program that just performs `readline()` slightly increases in RSS over iterations.\r\n\r\nIf you have a better tool for measurement other than RSS, I'm all ears.", "@stas00 if you aren't using memory maps, you should be able to clearly see the increase in the virtual mem for the process as well. Even then, it could still be challenging to determine if it's leak vs fragmentation due to problematic allocation patterns (not uncommon with Python). Using a better mem allocator like tcmalloc via LD_PRELOAD hooks could reduce impact of fragmentation across both Python and c libs. Not sure that plays nice with any allocator that arrow might use itself though. " ]	2022-08-24T08:42:54Z	2024-01-23T12:42:40Z	null	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	## Describe the bug When the HF datasets is used in conjunction with PyTorch Dataloader, the RSS memory of the process keeps on increasing when it should stay constant. ## Steps to reproduce the bug Run and observe the output of this snippet which logs RSS memory. ```python import psutil import os from transformers import BertTokenizer from datasets import load_dataset from torch.utils.data import DataLoader BATCH_SIZE = 32 NUM_TRIES = 10 tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") def transform(x): x.update(tokenizer(x["text"], return_tensors="pt", max_length=64, padding="max_length", truncation=True)) x.pop("text") x.pop("label") return x dataset = load_dataset("imdb", split="train") dataset.set_transform(transform) train_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4) mem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024) count = 0 while count < NUM_TRIES: for idx, batch in enumerate(train_loader): mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024) print(count, idx, mem_after - mem_before) count += 1 ``` ## Expected results Memory should not increase after initial setup and loading of the dataset ## Actual results Memory continuously increases as can be seen in the log. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 7.0.0	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 3, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 3, "url": "https://api.github.com/repos/huggingface/datasets/issues/4883/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4883/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6220	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6220/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6220/comments	https://api.github.com/repos/huggingface/datasets/issues/6220/events	https://github.com/huggingface/datasets/pull/6220	1,884,285,980	PR_kwDODunzps5ZspRb	6,220	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", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6220). 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) \| 0.005950 / 0.011353 (-0.005403) \| 0.003578 / 0.011008 (-0.007431) \| 0.079327 / 0.038508 (0.040819) \| 0.057862 / 0.023109 (0.034752) \| 0.317288 / 0.275898 (0.041390) \| 0.358210 / 0.323480 (0.034730) \| 0.004685 / 0.007986 (-0.003301) \| 0.002879 / 0.004328 (-0.001450) \| 0.062355 / 0.004250 (0.058105) \| 0.045093 / 0.037052 (0.008041) \| 0.322520 / 0.258489 (0.064031) \| 0.367114 / 0.293841 (0.073273) \| 0.027233 / 0.128546 (-0.101313) \| 0.007941 / 0.075646 (-0.067705) \| 0.260511 / 0.419271 (-0.158761) \| 0.044355 / 0.043533 (0.000822) \| 0.332993 / 0.255139 (0.077854) \| 0.351363 / 0.283200 (0.068163) \| 0.020784 / 0.141683 (-0.120899) \| 1.429044 / 1.452155 (-0.023111) \| 1.489355 / 1.492716 (-0.003362) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.180903 / 0.018006 (0.162897) \| 0.421566 / 0.000490 (0.421077) \| 0.003259 / 0.000200 (0.003059) \| 0.000068 / 0.000054 (0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023765 / 0.037411 (-0.013646) \| 0.072815 / 0.014526 (0.058289) \| 0.084592 / 0.176557 (-0.091965) \| 0.143556 / 0.737135 (-0.593579) \| 0.083591 / 0.296338 (-0.212748) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401896 / 0.215209 (0.186687) \| 4.006344 / 2.077655 (1.928689) \| 2.092280 / 1.504120 (0.588160) \| 1.937828 / 1.541195 (0.396633) \| 2.026901 / 1.468490 (0.558411) \| 0.499999 / 4.584777 (-4.084778) \| 3.008715 / 3.745712 (-0.736997) \| 2.789735 / 5.269862 (-2.480127) \| 1.827319 / 4.565676 (-2.738358) \| 0.057413 / 0.424275 (-0.366862) \| 0.006716 / 0.007607 (-0.000891) \| 0.473061 / 0.226044 (0.247016) \| 4.733256 / 2.268929 (2.464327) \| 2.403922 / 55.444624 (-53.040702) \| 2.017466 / 6.876477 (-4.859011) \| 2.209710 / 2.142072 (0.067638) \| 0.590813 / 4.805227 (-4.214414) \| 0.124760 / 6.500664 (-6.375904) \| 0.060976 / 0.075469 (-0.014494) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.229172 / 1.841788 (-0.612616) \| 17.924644 / 8.074308 (9.850336) \| 13.697347 / 10.191392 (3.505955) \| 0.128258 / 0.680424 (-0.552166) \| 0.016780 / 0.534201 (-0.517421) \| 0.329301 / 0.579283 (-0.249982) \| 0.344527 / 0.434364 (-0.089837) \| 0.379482 / 0.540337 (-0.160855) \| 0.513851 / 1.386936 (-0.873085) \|\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) \| 0.005962 / 0.011353 (-0.005391) \| 0.003613 / 0.011008 (-0.007396) \| 0.062428 / 0.038508 (0.023920) \| 0.058151 / 0.023109 (0.035042) \| 0.452926 / 0.275898 (0.177027) \| 0.489740 / 0.323480 (0.166260) \| 0.006137 / 0.007986 (-0.001848) \| 0.002890 / 0.004328 (-0.001438) \| 0.062880 / 0.004250 (0.058629) \| 0.046175 / 0.037052 (0.009123) \| 0.452416 / 0.258489 (0.193927) \| 0.486047 / 0.293841 (0.192206) \| 0.028517 / 0.128546 (-0.100029) \| 0.008102 / 0.075646 (-0.067544) \| 0.068251 / 0.419271 (-0.351020) \| 0.040569 / 0.043533 (-0.002964) \| 0.461306 / 0.255139 (0.206167) \| 0.477675 / 0.283200 (0.194475) \| 0.020944 / 0.141683 (-0.120739) \| 1.414300 / 1.452155 (-0.037855) \| 1.502108 / 1.492716 (0.009391) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217786 / 0.018006 (0.199780) \| 0.410757 / 0.000490 (0.410267) \| 0.002981 / 0.000200 (0.002781) \| 0.000081 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026846 / 0.037411 (-0.010565) \| 0.080098 / 0.014526 (0.065572) \| 0.090591 / 0.176557 (-0.085965) \| 0.144674 / 0.737135 (-0.592461) \| 0.091287 / 0.296338 (-0.205052) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.458224 / 0.215209 (0.243015) \| 4.590541 / 2.077655 (2.512886) \| 2.511251 / 1.504120 (1.007131) \| 2.329165 / 1.541195 (0.787970) \| 2.379187 / 1.468490 (0.910696) \| 0.507485 / 4.584777 (-4.077292) \| 3.135011 / 3.745712 (-0.610701) \| 2.805913 / 5.269862 (-2.463948) \| 1.851382 / 4.565676 (-2.714295) \| 0.057981 / 0.424275 (-0.366294) \| 0.006557 / 0.007607 (-0.001050) \| 0.532496 / 0.226044 (0.306452) \| 5.348802 / 2.268929 (3.079874) \| 2.993379 / 55.444624 (-52.451245) \| 2.636372 / 6.876477 (-4.240104) \| 2.753219 / 2.142072 (0.611147) \| 0.591989 / 4.805227 (-4.213238) \| 0.126691 / 6.500664 (-6.373973) \| 0.062359 / 0.075469 (-0.013110) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.345498 / 1.841788 (-0.496290) \| 18.335767 / 8.074308 (10.261458) \| 15.115449 / 10.191392 (4.924057) \| 0.147382 / 0.680424 (-0.533041) \| 0.017729 / 0.534201 (-0.516472) \| 0.334337 / 0.579283 (-0.244946) \| 0.359035 / 0.434364 (-0.075329) \| 0.386319 / 0.540337 (-0.154019) \| 0.536378 / 1.386936 (-0.850558) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2b028fd83d74e7701e7b8f2d87e740a989505a7 \"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) \| 0.009136 / 0.011353 (-0.002216) \| 0.005567 / 0.011008 (-0.005442) \| 0.120320 / 0.038508 (0.081812) \| 0.078082 / 0.023109 (0.054973) \| 0.405579 / 0.275898 (0.129681) \| 0.459714 / 0.323480 (0.136234) \| 0.006327 / 0.007986 (-0.001659) \| 0.007187 / 0.004328 (0.002859) \| 0.084373 / 0.004250 (0.080122) \| 0.059727 / 0.037052 (0.022675) \| 0.418918 / 0.258489 (0.160429) \| 0.486767 / 0.293841 (0.192927) \| 0.047715 / 0.128546 (-0.080831) \| 0.014417 / 0.075646 (-0.061229) \| 0.379847 / 0.419271 (-0.039425) \| 0.067472 / 0.043533 (0.023939) \| 0.419304 / 0.255139 (0.164166) \| 0.466260 / 0.283200 (0.183060) \| 0.036872 / 0.141683 (-0.104811) \| 1.876273 / 1.452155 (0.424119) \| 2.043856 / 1.492716 (0.551140) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296266 / 0.018006 (0.278260) \| 0.601843 / 0.000490 (0.601354) \| 0.005663 / 0.000200 (0.005463) \| 0.000102 / 0.000054 (0.000048) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033272 / 0.037411 (-0.004139) \| 0.098839 / 0.014526 (0.084313) \| 0.124658 / 0.176557 (-0.051899) \| 0.190226 / 0.737135 (-0.546909) \| 0.119288 / 0.296338 (-0.177051) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.600878 / 0.215209 (0.385668) \| 6.011749 / 2.077655 (3.934095) \| 2.611809 / 1.504120 (1.107689) \| 2.314985 / 1.541195 (0.773790) \| 2.398988 / 1.468490 (0.930498) \| 0.835577 / 4.584777 (-3.749200) \| 5.482848 / 3.745712 (1.737136) \| 4.965393 / 5.269862 (-0.304469) \| 3.082420 / 4.565676 (-1.483256) \| 0.098048 / 0.424275 (-0.326227) \| 0.009148 / 0.007607 (0.001541) \| 0.725721 / 0.226044 (0.499676) \| 7.297429 / 2.268929 (5.028501) \| 3.558050 / 55.444624 (-51.886575) \| 2.815884 / 6.876477 (-4.060593) \| 3.094103 / 2.142072 (0.952031) \| 1.023617 / 4.805227 (-3.781610) \| 0.222453 / 6.500664 (-6.278211) \| 0.081707 / 0.075469 (0.006238) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.788327 / 1.841788 (-0.053461) \| 25.285829 / 8.074308 (17.211521) \| 21.878811 / 10.191392 (11.687419) \| 0.215494 / 0.680424 (-0.464930) \| 0.032050 / 0.534201 (-0.502151) \| 0.505210 / 0.579283 (-0.074073) \| 0.623545 / 0.434364 (0.189181) \| 0.583342 / 0.540337 (0.043005) \| 0.826497 / 1.386936 (-0.560439) \|\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) \| 0.009640 / 0.011353 (-0.001713) \| 0.005479 / 0.011008 (-0.005529) \| 0.088940 / 0.038508 (0.050432) \| 0.084186 / 0.023109 (0.061077) \| 0.552290 / 0.275898 (0.276392) \| 0.583296 / 0.323480 (0.259816) \| 0.006999 / 0.007986 (-0.000987) \| 0.004597 / 0.004328 (0.000269) \| 0.089407 / 0.004250 (0.085157) \| 0.067210 / 0.037052 (0.030157) \| 0.554968 / 0.258489 (0.296479) \| 0.595635 / 0.293841 (0.301794) \| 0.052245 / 0.128546 (-0.076301) \| 0.015914 / 0.075646 (-0.059733) \| 0.097037 / 0.419271 (-0.322235) \| 0.063954 / 0.043533 (0.020421) \| 0.533752 / 0.255139 (0.278614) \| 0.573789 / 0.283200 (0.290589) \| 0.036526 / 0.141683 (-0.105157) \| 1.867713 / 1.452155 (0.415558) \| 1.996901 / 1.492716 (0.504185) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.414967 / 0.018006 (0.396961) \| 0.632367 / 0.000490 (0.631877) \| 0.064061 / 0.000200 (0.063861) \| 0.000565 / 0.000054 (0.000510) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035953 / 0.037411 (-0.001458) \| 0.112603 / 0.014526 (0.098077) \| 0.126227 / 0.176557 (-0.050330) \| 0.196881 / 0.737135 (-0.540255) \| 0.127635 / 0.296338 (-0.168704) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.674735 / 0.215209 (0.459526) \| 6.614578 / 2.077655 (4.536923) \| 3.208198 / 1.504120 (1.704078) \| 2.870412 / 1.541195 (1.329217) \| 2.979358 / 1.468490 (1.510868) \| 0.872589 / 4.584777 (-3.712187) \| 5.501771 / 3.745712 (1.756059) \| 4.865191 / 5.269862 (-0.404671) \| 3.075281 / 4.565676 (-1.490396) \| 0.098048 / 0.424275 (-0.326227) \| 0.009121 / 0.007607 (0.001514) \| 0.801639 / 0.226044 (0.575595) \| 8.062040 / 2.268929 (5.793111) \| 3.996693 / 55.444624 (-51.447931) \| 3.343770 / 6.876477 (-3.532706) \| 3.555977 / 2.142072 (1.413904) \| 1.035050 / 4.805227 (-3.770177) \| 0.227552 / 6.500664 (-6.273112) \| 0.097733 / 0.075469 (0.022264) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.897210 / 1.841788 (0.055422) \| 25.762459 / 8.074308 (17.688151) \| 22.771290 / 10.191392 (12.579898) \| 0.252650 / 0.680424 (-0.427773) \| 0.032534 / 0.534201 (-0.501667) \| 0.521047 / 0.579283 (-0.058236) \| 0.620850 / 0.434364 (0.186486) \| 0.612750 / 0.540337 (0.072413) \| 0.837486 / 1.386936 (-0.549451) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1f522e5bdd73c45f7ba0a03f2ecd4e7de7351f2e \"CML watermark\")\n" ]	2023-09-06T15:40:33Z	2023-09-06T15:52:33Z	2023-09-06T15:41:13Z	MEMBER	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6220/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6220/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6220.diff", "html_url": "https://github.com/huggingface/datasets/pull/6220", "merged_at": "2023-09-06T15:41:13Z", "patch_url": "https://github.com/huggingface/datasets/pull/6220.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6220" }
https://api.github.com/repos/huggingface/datasets/issues/6491	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6491/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6491/comments	https://api.github.com/repos/huggingface/datasets/issues/6491/events	https://github.com/huggingface/datasets/pull/6491	2,037,690,643	PR_kwDODunzps5hyiTY	6,491	Fix metrics dead link	{ "avatar_url": "https://avatars.githubusercontent.com/u/45557362?v=4", "events_url": "https://api.github.com/users/qgallouedec/events{/privacy}", "followers_url": "https://api.github.com/users/qgallouedec/followers", "following_url": "https://api.github.com/users/qgallouedec/following{/other_user}", "gists_url": "https://api.github.com/users/qgallouedec/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/qgallouedec", "id": 45557362, "login": "qgallouedec", "node_id": "MDQ6VXNlcjQ1NTU3MzYy", "organizations_url": "https://api.github.com/users/qgallouedec/orgs", "received_events_url": "https://api.github.com/users/qgallouedec/received_events", "repos_url": "https://api.github.com/users/qgallouedec/repos", "site_admin": false, "starred_url": "https://api.github.com/users/qgallouedec/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/qgallouedec/subscriptions", "type": "User", "url": "https://api.github.com/users/qgallouedec", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6491). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005193 / 0.011353 (-0.006160) \| 0.003246 / 0.011008 (-0.007762) \| 0.063053 / 0.038508 (0.024545) \| 0.049636 / 0.023109 (0.026527) \| 0.240990 / 0.275898 (-0.034908) \| 0.263732 / 0.323480 (-0.059747) \| 0.004062 / 0.007986 (-0.003923) \| 0.002681 / 0.004328 (-0.001648) \| 0.048527 / 0.004250 (0.044277) \| 0.044159 / 0.037052 (0.007107) \| 0.248031 / 0.258489 (-0.010458) \| 0.275705 / 0.293841 (-0.018136) \| 0.028210 / 0.128546 (-0.100336) \| 0.010314 / 0.075646 (-0.065332) \| 0.209887 / 0.419271 (-0.209384) \| 0.035649 / 0.043533 (-0.007884) \| 0.251321 / 0.255139 (-0.003818) \| 0.266672 / 0.283200 (-0.016528) \| 0.017382 / 0.141683 (-0.124301) \| 1.088937 / 1.452155 (-0.363217) \| 1.143692 / 1.492716 (-0.349024) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092558 / 0.018006 (0.074552) \| 0.301648 / 0.000490 (0.301159) \| 0.000208 / 0.000200 (0.000008) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018305 / 0.037411 (-0.019106) \| 0.059836 / 0.014526 (0.045310) \| 0.072926 / 0.176557 (-0.103631) \| 0.119826 / 0.737135 (-0.617309) \| 0.074357 / 0.296338 (-0.221982) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279051 / 0.215209 (0.063842) \| 2.711402 / 2.077655 (0.633747) \| 1.431782 / 1.504120 (-0.072338) \| 1.316592 / 1.541195 (-0.224603) \| 1.352062 / 1.468490 (-0.116428) \| 0.562553 / 4.584777 (-4.022224) \| 2.387719 / 3.745712 (-1.357993) \| 2.693330 / 5.269862 (-2.576532) \| 1.682040 / 4.565676 (-2.883636) \| 0.061832 / 0.424275 (-0.362443) \| 0.005066 / 0.007607 (-0.002541) \| 0.332730 / 0.226044 (0.106685) \| 3.315503 / 2.268929 (1.046575) \| 1.787129 / 55.444624 (-53.657496) \| 1.508955 / 6.876477 (-5.367522) \| 1.512620 / 2.142072 (-0.629453) \| 0.637120 / 4.805227 (-4.168107) \| 0.116005 / 6.500664 (-6.384660) \| 0.041973 / 0.075469 (-0.033496) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.936996 / 1.841788 (-0.904792) \| 11.485975 / 8.074308 (3.411667) \| 10.604481 / 10.191392 (0.413089) \| 0.130803 / 0.680424 (-0.549621) \| 0.014561 / 0.534201 (-0.519640) \| 0.285905 / 0.579283 (-0.293378) \| 0.271573 / 0.434364 (-0.162791) \| 0.329206 / 0.540337 (-0.211132) \| 0.411977 / 1.386936 (-0.974959) \|\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) \| 0.005333 / 0.011353 (-0.006020) \| 0.003519 / 0.011008 (-0.007489) \| 0.050880 / 0.038508 (0.012372) \| 0.053681 / 0.023109 (0.030571) \| 0.269359 / 0.275898 (-0.006539) \| 0.291498 / 0.323480 (-0.031982) \| 0.004006 / 0.007986 (-0.003979) \| 0.002676 / 0.004328 (-0.001653) \| 0.049652 / 0.004250 (0.045401) \| 0.040588 / 0.037052 (0.003536) \| 0.271701 / 0.258489 (0.013212) \| 0.308384 / 0.293841 (0.014543) \| 0.028713 / 0.128546 (-0.099833) \| 0.010423 / 0.075646 (-0.065223) \| 0.058099 / 0.419271 (-0.361172) \| 0.032372 / 0.043533 (-0.011161) \| 0.269395 / 0.255139 (0.014256) \| 0.292252 / 0.283200 (0.009052) \| 0.020038 / 0.141683 (-0.121645) \| 1.124761 / 1.452155 (-0.327393) \| 1.177609 / 1.492716 (-0.315107) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092187 / 0.018006 (0.074181) \| 0.301936 / 0.000490 (0.301446) \| 0.000230 / 0.000200 (0.000030) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022932 / 0.037411 (-0.014480) \| 0.076552 / 0.014526 (0.062027) \| 0.088729 / 0.176557 (-0.087827) \| 0.127198 / 0.737135 (-0.609937) \| 0.091902 / 0.296338 (-0.204436) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299964 / 0.215209 (0.084755) \| 2.929352 / 2.077655 (0.851697) \| 1.598715 / 1.504120 (0.094595) \| 1.462438 / 1.541195 (-0.078756) \| 1.474308 / 1.468490 (0.005818) \| 0.567120 / 4.584777 (-4.017657) \| 2.481757 / 3.745712 (-1.263955) \| 2.795375 / 5.269862 (-2.474487) \| 1.740346 / 4.565676 (-2.825331) \| 0.064048 / 0.424275 (-0.360227) \| 0.004995 / 0.007607 (-0.002612) \| 0.349084 / 0.226044 (0.123040) \| 3.417679 / 2.268929 (1.148750) \| 1.910615 / 55.444624 (-53.534009) \| 1.694120 / 6.876477 (-5.182356) \| 1.658654 / 2.142072 (-0.483419) \| 0.638158 / 4.805227 (-4.167069) \| 0.115509 / 6.500664 (-6.385156) \| 0.040650 / 0.075469 (-0.034819) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.988081 / 1.841788 (-0.853707) \| 12.210089 / 8.074308 (4.135781) \| 11.090203 / 10.191392 (0.898811) \| 0.131861 / 0.680424 (-0.548563) \| 0.015461 / 0.534201 (-0.518740) \| 0.287737 / 0.579283 (-0.291546) \| 0.284170 / 0.434364 (-0.150194) \| 0.324949 / 0.540337 (-0.215388) \| 0.414912 / 1.386936 (-0.972024) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf71653947cecd84050daf0448dc5a73c2c071f3 \"CML watermark\")\n" ]	2023-12-12T12:51:49Z	2023-12-21T15:15:08Z	2023-12-21T15:08:53Z	MEMBER	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6491/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6491/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6491.diff", "html_url": "https://github.com/huggingface/datasets/pull/6491", "merged_at": "2023-12-21T15:08:53Z", "patch_url": "https://github.com/huggingface/datasets/pull/6491.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6491" }
https://api.github.com/repos/huggingface/datasets/issues/7398	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7398/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7398/comments	https://api.github.com/repos/huggingface/datasets/issues/7398/events	https://github.com/huggingface/datasets/pull/7398	2,853,097,869	PR_kwDODunzps6LNoDk	7,398	Release: 3.3.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7398). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]	2025-02-14T09:15:03Z	2025-02-14T09:57:39Z	2025-02-14T09:57:37Z	MEMBER	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7398/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7398/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7398.diff", "html_url": "https://github.com/huggingface/datasets/pull/7398", "merged_at": "2025-02-14T09:57:37Z", "patch_url": "https://github.com/huggingface/datasets/pull/7398.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7398" }
https://api.github.com/repos/huggingface/datasets/issues/6269	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6269/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6269/comments	https://api.github.com/repos/huggingface/datasets/issues/6269/events	https://github.com/huggingface/datasets/pull/6269	1,919,572,790	PR_kwDODunzps5bjbDc	6,269	Reduce the number of commits in `push_to_hub`	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005864 / 0.011353 (-0.005489) \| 0.003535 / 0.011008 (-0.007474) \| 0.080732 / 0.038508 (0.042224) \| 0.057072 / 0.023109 (0.033963) \| 0.334342 / 0.275898 (0.058444) \| 0.361345 / 0.323480 (0.037865) \| 0.003290 / 0.007986 (-0.004696) \| 0.003794 / 0.004328 (-0.000534) \| 0.063414 / 0.004250 (0.059163) \| 0.046901 / 0.037052 (0.009848) \| 0.335973 / 0.258489 (0.077484) \| 0.377929 / 0.293841 (0.084088) \| 0.027199 / 0.128546 (-0.101348) \| 0.008049 / 0.075646 (-0.067597) \| 0.261810 / 0.419271 (-0.157462) \| 0.044669 / 0.043533 (0.001136) \| 0.333600 / 0.255139 (0.078461) \| 0.356362 / 0.283200 (0.073162) \| 0.020325 / 0.141683 (-0.121358) \| 1.458138 / 1.452155 (0.005984) \| 1.505923 / 1.492716 (0.013207) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216456 / 0.018006 (0.198450) \| 0.421750 / 0.000490 (0.421261) \| 0.007359 / 0.000200 (0.007159) \| 0.000246 / 0.000054 (0.000191) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023400 / 0.037411 (-0.014012) \| 0.073363 / 0.014526 (0.058838) \| 0.083533 / 0.176557 (-0.093023) \| 0.144045 / 0.737135 (-0.593090) \| 0.084050 / 0.296338 (-0.212288) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.398354 / 0.215209 (0.183145) \| 3.982875 / 2.077655 (1.905220) \| 2.047299 / 1.504120 (0.543180) \| 1.873780 / 1.541195 (0.332585) \| 1.977044 / 1.468490 (0.508554) \| 0.497038 / 4.584777 (-4.087739) \| 3.039743 / 3.745712 (-0.705969) \| 2.832885 / 5.269862 (-2.436977) \| 1.827300 / 4.565676 (-2.738377) \| 0.057503 / 0.424275 (-0.366772) \| 0.006272 / 0.007607 (-0.001335) \| 0.468681 / 0.226044 (0.242637) \| 4.696551 / 2.268929 (2.427622) \| 2.413805 / 55.444624 (-53.030819) \| 2.157199 / 6.876477 (-4.719278) \| 2.345986 / 2.142072 (0.203914) \| 0.584632 / 4.805227 (-4.220595) \| 0.124684 / 6.500664 (-6.375980) \| 0.060090 / 0.075469 (-0.015379) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.293551 / 1.841788 (-0.548236) \| 17.198292 / 8.074308 (9.123984) \| 13.677910 / 10.191392 (3.486518) \| 0.146633 / 0.680424 (-0.533791) \| 0.016711 / 0.534201 (-0.517490) \| 0.331644 / 0.579283 (-0.247639) \| 0.360148 / 0.434364 (-0.074215) \| 0.381194 / 0.540337 (-0.159143) \| 0.537952 / 1.386936 (-0.848984) \|\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) \| 0.006020 / 0.011353 (-0.005333) \| 0.003557 / 0.011008 (-0.007451) \| 0.061926 / 0.038508 (0.023418) \| 0.056246 / 0.023109 (0.033137) \| 0.446679 / 0.275898 (0.170781) \| 0.479843 / 0.323480 (0.156363) \| 0.004656 / 0.007986 (-0.003330) \| 0.002823 / 0.004328 (-0.001505) \| 0.061366 / 0.004250 (0.057115) \| 0.045793 / 0.037052 (0.008740) \| 0.460807 / 0.258489 (0.202318) \| 0.485467 / 0.293841 (0.191626) \| 0.028555 / 0.128546 (-0.099991) \| 0.007973 / 0.075646 (-0.067674) \| 0.068305 / 0.419271 (-0.350966) \| 0.040844 / 0.043533 (-0.002689) \| 0.463715 / 0.255139 (0.208576) \| 0.474553 / 0.283200 (0.191354) \| 0.019959 / 0.141683 (-0.121723) \| 1.432527 / 1.452155 (-0.019628) \| 1.485410 / 1.492716 (-0.007307) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.205555 / 0.018006 (0.187549) \| 0.408271 / 0.000490 (0.407781) \| 0.004325 / 0.000200 (0.004125) \| 0.000076 / 0.000054 (0.000022) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026338 / 0.037411 (-0.011074) \| 0.080534 / 0.014526 (0.066008) \| 0.093935 / 0.176557 (-0.082622) \| 0.146446 / 0.737135 (-0.590689) \| 0.092890 / 0.296338 (-0.203448) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.463879 / 0.215209 (0.248670) \| 4.646411 / 2.077655 (2.568756) \| 2.567320 / 1.504120 (1.063200) \| 2.384376 / 1.541195 (0.843181) \| 2.412738 / 1.468490 (0.944248) \| 0.510240 / 4.584777 (-4.074537) \| 3.094988 / 3.745712 (-0.650724) \| 2.837700 / 5.269862 (-2.432161) \| 1.850163 / 4.565676 (-2.715513) \| 0.059320 / 0.424275 (-0.364955) \| 0.006330 / 0.007607 (-0.001277) \| 0.537770 / 0.226044 (0.311726) \| 5.385556 / 2.268929 (3.116627) \| 3.036088 / 55.444624 (-52.408536) \| 2.650464 / 6.876477 (-4.226013) \| 2.755676 / 2.142072 (0.613603) \| 0.607353 / 4.805227 (-4.197875) \| 0.124589 / 6.500664 (-6.376075) \| 0.060778 / 0.075469 (-0.014691) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.343243 / 1.841788 (-0.498545) \| 17.630281 / 8.074308 (9.555973) \| 14.401219 / 10.191392 (4.209827) \| 0.143252 / 0.680424 (-0.537172) \| 0.017880 / 0.534201 (-0.516321) \| 0.337391 / 0.579283 (-0.241892) \| 0.373531 / 0.434364 (-0.060833) \| 0.398408 / 0.540337 (-0.141929) \| 0.558925 / 1.386936 (-0.828011) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a8f511638b486b9f83b17fd69a505fe606ad257b \"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) \| 0.006552 / 0.011353 (-0.004801) \| 0.003853 / 0.011008 (-0.007155) \| 0.077673 / 0.038508 (0.039165) \| 0.066043 / 0.023109 (0.042934) \| 0.289858 / 0.275898 (0.013960) \| 0.299009 / 0.323480 (-0.024471) \| 0.004806 / 0.007986 (-0.003179) \| 0.003517 / 0.004328 (-0.000811) \| 0.058227 / 0.004250 (0.053977) \| 0.052134 / 0.037052 (0.015082) \| 0.328800 / 0.258489 (0.070311) \| 0.317616 / 0.293841 (0.023776) \| 0.028344 / 0.128546 (-0.100202) \| 0.007853 / 0.075646 (-0.067794) \| 0.291207 / 0.419271 (-0.128065) \| 0.052977 / 0.043533 (0.009444) \| 0.287548 / 0.255139 (0.032409) \| 0.307647 / 0.283200 (0.024448) \| 0.023899 / 0.141683 (-0.117784) \| 1.382267 / 1.452155 (-0.069888) \| 1.589915 / 1.492716 (0.097199) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246244 / 0.018006 (0.228238) \| 0.478255 / 0.000490 (0.477766) \| 0.014115 / 0.000200 (0.013915) \| 0.000305 / 0.000054 (0.000250) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027033 / 0.037411 (-0.010378) \| 0.073988 / 0.014526 (0.059462) \| 0.088337 / 0.176557 (-0.088219) \| 0.144067 / 0.737135 (-0.593069) \| 0.091295 / 0.296338 (-0.205043) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.365904 / 0.215209 (0.150695) \| 3.537330 / 2.077655 (1.459675) \| 1.678341 / 1.504120 (0.174221) \| 1.530297 / 1.541195 (-0.010898) \| 1.605634 / 1.468490 (0.137144) \| 0.437461 / 4.584777 (-4.147316) \| 3.419040 / 3.745712 (-0.326672) \| 3.203549 / 5.269862 (-2.066312) \| 1.913214 / 4.565676 (-2.652463) \| 0.052675 / 0.424275 (-0.371600) \| 0.006681 / 0.007607 (-0.000926) \| 0.429269 / 0.226044 (0.203225) \| 4.214051 / 2.268929 (1.945122) \| 2.217928 / 55.444624 (-53.226696) \| 1.842679 / 6.876477 (-5.033798) \| 1.867961 / 2.142072 (-0.274111) \| 0.550566 / 4.805227 (-4.254661) \| 0.118015 / 6.500664 (-6.382649) \| 0.054749 / 0.075469 (-0.020720) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.170547 / 1.841788 (-0.671241) \| 18.410567 / 8.074308 (10.336259) \| 12.729992 / 10.191392 (2.538600) \| 0.160426 / 0.680424 (-0.519998) \| 0.021259 / 0.534201 (-0.512942) \| 0.369573 / 0.579283 (-0.209710) \| 0.440350 / 0.434364 (0.005986) \| 0.443755 / 0.540337 (-0.096582) \| 0.645614 / 1.386936 (-0.741322) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005913 / 0.011353 (-0.005440) \| 0.003542 / 0.011008 (-0.007466) \| 0.057621 / 0.038508 (0.019113) \| 0.065822 / 0.023109 (0.042713) \| 0.390847 / 0.275898 (0.114949) \| 0.393127 / 0.323480 (0.069647) \| 0.005040 / 0.007986 (-0.002945) \| 0.002944 / 0.004328 (-0.001384) \| 0.069058 / 0.004250 (0.064808) \| 0.051594 / 0.037052 (0.014542) \| 0.383745 / 0.258489 (0.125256) \| 0.414372 / 0.293841 (0.120531) \| 0.030038 / 0.128546 (-0.098508) \| 0.008109 / 0.075646 (-0.067538) \| 0.065444 / 0.419271 (-0.353828) \| 0.045974 / 0.043533 (0.002441) \| 0.401695 / 0.255139 (0.146556) \| 0.417834 / 0.283200 (0.134635) \| 0.020137 / 0.141683 (-0.121546) \| 1.452130 / 1.452155 (-0.000025) \| 1.455259 / 1.492716 (-0.037458) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228262 / 0.018006 (0.210255) \| 0.455155 / 0.000490 (0.454665) \| 0.006667 / 0.000200 (0.006467) \| 0.000207 / 0.000054 (0.000153) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030159 / 0.037411 (-0.007252) \| 0.098478 / 0.014526 (0.083952) \| 0.101409 / 0.176557 (-0.075147) \| 0.148689 / 0.737135 (-0.588446) \| 0.103067 / 0.296338 (-0.193272) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.444095 / 0.215209 (0.228886) \| 3.991588 / 2.077655 (1.913934) \| 2.147845 / 1.504120 (0.643725) \| 2.007871 / 1.541195 (0.466676) \| 2.042074 / 1.468490 (0.573584) \| 0.451592 / 4.584777 (-4.133185) \| 3.439400 / 3.745712 (-0.306312) \| 3.107756 / 5.269862 (-2.162106) \| 1.909785 / 4.565676 (-2.655891) \| 0.051718 / 0.424275 (-0.372558) \| 0.006597 / 0.007607 (-0.001010) \| 0.480822 / 0.226044 (0.254777) \| 4.913235 / 2.268929 (2.644307) \| 2.631882 / 55.444624 (-52.812742) \| 2.397209 / 6.876477 (-4.479267) \| 2.487191 / 2.142072 (0.345119) \| 0.566321 / 4.805227 (-4.238906) \| 0.121741 / 6.500664 (-6.378924) \| 0.053399 / 0.075469 (-0.022070) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.256599 / 1.841788 (-0.585189) \| 18.891127 / 8.074308 (10.816819) \| 13.219662 / 10.191392 (3.028270) \| 0.154570 / 0.680424 (-0.525854) \| 0.022599 / 0.534201 (-0.511602) \| 0.361998 / 0.579283 (-0.217286) \| 0.413287 / 0.434364 (-0.021077) \| 0.464867 / 0.540337 (-0.075470) \| 0.638880 / 1.386936 (-0.748056) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#869e6bc775cf4dff1b92834426e1a286b104432b \"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) \| 0.010625 / 0.011353 (-0.000728) \| 0.005129 / 0.011008 (-0.005879) \| 0.119975 / 0.038508 (0.081467) \| 0.100128 / 0.023109 (0.077019) \| 0.448678 / 0.275898 (0.172780) \| 0.533150 / 0.323480 (0.209670) \| 0.005881 / 0.007986 (-0.002105) \| 0.007451 / 0.004328 (0.003123) \| 0.090792 / 0.004250 (0.086542) \| 0.073416 / 0.037052 (0.036363) \| 0.455395 / 0.258489 (0.196906) \| 0.497572 / 0.293841 (0.203731) \| 0.053112 / 0.128546 (-0.075434) \| 0.014619 / 0.075646 (-0.061027) \| 0.388023 / 0.419271 (-0.031248) \| 0.074004 / 0.043533 (0.030471) \| 0.435319 / 0.255139 (0.180180) \| 0.465985 / 0.283200 (0.182785) \| 0.046991 / 0.141683 (-0.094692) \| 1.895717 / 1.452155 (0.443563) \| 2.086600 / 1.492716 (0.593884) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.334412 / 0.018006 (0.316406) \| 0.645510 / 0.000490 (0.645020) \| 0.019175 / 0.000200 (0.018975) \| 0.000429 / 0.000054 (0.000374) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034385 / 0.037411 (-0.003026) \| 0.108939 / 0.014526 (0.094413) \| 0.125937 / 0.176557 (-0.050619) \| 0.205643 / 0.737135 (-0.531493) \| 0.127662 / 0.296338 (-0.168676) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.674093 / 0.215209 (0.458884) \| 6.646554 / 2.077655 (4.568900) \| 2.837698 / 1.504120 (1.333578) \| 2.397199 / 1.541195 (0.856004) \| 2.485856 / 1.468490 (1.017366) \| 0.955142 / 4.584777 (-3.629635) \| 5.667462 / 3.745712 (1.921750) \| 5.354129 / 5.269862 (0.084268) \| 3.301609 / 4.565676 (-1.264068) \| 0.106051 / 0.424275 (-0.318224) \| 0.009287 / 0.007607 (0.001680) \| 0.766678 / 0.226044 (0.540634) \| 7.786701 / 2.268929 (5.517772) \| 3.665463 / 55.444624 (-51.779161) \| 2.982912 / 6.876477 (-3.893564) \| 3.053363 / 2.142072 (0.911290) \| 1.141090 / 4.805227 (-3.664137) \| 0.223975 / 6.500664 (-6.276689) \| 0.093024 / 0.075469 (0.017555) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.728175 / 1.841788 (-0.113613) \| 25.640134 / 8.074308 (17.565826) \| 22.124769 / 10.191392 (11.933377) \| 0.237489 / 0.680424 (-0.442935) \| 0.030353 / 0.534201 (-0.503848) \| 0.509371 / 0.579283 (-0.069913) \| 0.642320 / 0.434364 (0.207956) \| 0.576889 / 0.540337 (0.036552) \| 0.899377 / 1.386936 (-0.487559) \|\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) \| 0.010846 / 0.011353 (-0.000507) \| 0.005876 / 0.011008 (-0.005132) \| 0.090810 / 0.038508 (0.052302) \| 0.106651 / 0.023109 (0.083542) \| 0.551064 / 0.275898 (0.275166) \| 0.608328 / 0.323480 (0.284848) \| 0.007563 / 0.007986 (-0.000423) \| 0.004595 / 0.004328 (0.000267) \| 0.089125 / 0.004250 (0.084874) \| 0.076577 / 0.037052 (0.039525) \| 0.579970 / 0.258489 (0.321481) \| 0.620214 / 0.293841 (0.326373) \| 0.052577 / 0.128546 (-0.075970) \| 0.013734 / 0.075646 (-0.061912) \| 0.099825 / 0.419271 (-0.319447) \| 0.068391 / 0.043533 (0.024858) \| 0.564733 / 0.255139 (0.309594) \| 0.593925 / 0.283200 (0.310726) \| 0.037201 / 0.141683 (-0.104482) \| 1.880969 / 1.452155 (0.428815) \| 2.065094 / 1.492716 (0.572377) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426148 / 0.018006 (0.408141) \| 0.673935 / 0.000490 (0.673445) \| 0.124190 / 0.000200 (0.123990) \| 0.001219 / 0.000054 (0.001164) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.040280 / 0.037411 (0.002868) \| 0.122042 / 0.014526 (0.107516) \| 0.131333 / 0.176557 (-0.045223) \| 0.203039 / 0.737135 (-0.534096) \| 0.134851 / 0.296338 (-0.161487) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.684599 / 0.215209 (0.469390) \| 6.727529 / 2.077655 (4.649874) \| 3.255228 / 1.504120 (1.751108) \| 2.925865 / 1.541195 (1.384670) \| 2.978762 / 1.468490 (1.510272) \| 0.931769 / 4.584777 (-3.653008) \| 5.988956 / 3.745712 (2.243244) \| 5.228049 / 5.269862 (-0.041812) \| 3.341470 / 4.565676 (-1.224206) \| 0.106737 / 0.424275 (-0.317539) \| 0.009847 / 0.007607 (0.002240) \| 0.813954 / 0.226044 (0.587909) \| 8.137071 / 2.268929 (5.868143) \| 4.140725 / 55.444624 (-51.303899) \| 3.500579 / 6.876477 (-3.375898) \| 3.623120 / 2.142072 (1.481047) \| 1.096634 / 4.805227 (-3.708593) \| 0.236938 / 6.500664 (-6.263726) \| 0.083099 / 0.075469 (0.007630) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.856112 / 1.841788 (0.014324) \| 26.531325 / 8.074308 (18.457017) \| 24.435866 / 10.191392 (14.244474) \| 0.264093 / 0.680424 (-0.416331) \| 0.034872 / 0.534201 (-0.499329) \| 0.520682 / 0.579283 (-0.058601) \| 0.635010 / 0.434364 (0.200646) \| 0.645451 / 0.540337 (0.105113) \| 0.914616 / 1.386936 (-0.472320) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d8c29b9416371283e8aaabee235a91b2f45a05ee \"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) \| 0.005928 / 0.011353 (-0.005425) \| 0.003633 / 0.011008 (-0.007375) \| 0.079554 / 0.038508 (0.041046) \| 0.057093 / 0.023109 (0.033984) \| 0.311374 / 0.275898 (0.035476) \| 0.343778 / 0.323480 (0.020298) \| 0.004634 / 0.007986 (-0.003352) \| 0.002886 / 0.004328 (-0.001443) \| 0.061888 / 0.004250 (0.057637) \| 0.045895 / 0.037052 (0.008843) \| 0.316447 / 0.258489 (0.057958) \| 0.358141 / 0.293841 (0.064300) \| 0.027247 / 0.128546 (-0.101300) \| 0.007947 / 0.075646 (-0.067699) \| 0.259070 / 0.419271 (-0.160201) \| 0.043802 / 0.043533 (0.000269) \| 0.315453 / 0.255139 (0.060314) \| 0.335282 / 0.283200 (0.052082) \| 0.021096 / 0.141683 (-0.120587) \| 1.443219 / 1.452155 (-0.008936) \| 1.523140 / 1.492716 (0.030423) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222957 / 0.018006 (0.204951) \| 0.414611 / 0.000490 (0.414122) \| 0.008354 / 0.000200 (0.008154) \| 0.000249 / 0.000054 (0.000195) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023880 / 0.037411 (-0.013532) \| 0.074523 / 0.014526 (0.059997) \| 0.084803 / 0.176557 (-0.091754) \| 0.146701 / 0.737135 (-0.590435) \| 0.084990 / 0.296338 (-0.211348) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.397736 / 0.215209 (0.182527) \| 3.961740 / 2.077655 (1.884086) \| 1.909014 / 1.504120 (0.404894) \| 1.823026 / 1.541195 (0.281831) \| 1.966235 / 1.468490 (0.497745) \| 0.498056 / 4.584777 (-4.086721) \| 3.041408 / 3.745712 (-0.704304) \| 2.998010 / 5.269862 (-2.271852) \| 1.887293 / 4.565676 (-2.678384) \| 0.057096 / 0.424275 (-0.367179) \| 0.006338 / 0.007607 (-0.001269) \| 0.465166 / 0.226044 (0.239122) \| 4.667710 / 2.268929 (2.398781) \| 2.480798 / 55.444624 (-52.963826) \| 2.270701 / 6.876477 (-4.605776) \| 2.376470 / 2.142072 (0.234397) \| 0.579873 / 4.805227 (-4.225355) \| 0.125032 / 6.500664 (-6.375632) \| 0.061057 / 0.075469 (-0.014412) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.229916 / 1.841788 (-0.611872) \| 17.829628 / 8.074308 (9.755320) \| 13.860184 / 10.191392 (3.668792) \| 0.143507 / 0.680424 (-0.536917) \| 0.016943 / 0.534201 (-0.517258) \| 0.350106 / 0.579283 (-0.229178) \| 0.364547 / 0.434364 (-0.069817) \| 0.398889 / 0.540337 (-0.141448) \| 0.557948 / 1.386936 (-0.828988) \|\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) \| 0.006052 / 0.011353 (-0.005301) \| 0.003636 / 0.011008 (-0.007372) \| 0.062705 / 0.038508 (0.024197) \| 0.057753 / 0.023109 (0.034644) \| 0.453219 / 0.275898 (0.177321) \| 0.485179 / 0.323480 (0.161699) \| 0.004886 / 0.007986 (-0.003100) \| 0.002838 / 0.004328 (-0.001490) \| 0.062593 / 0.004250 (0.058343) \| 0.047476 / 0.037052 (0.010423) \| 0.454266 / 0.258489 (0.195777) \| 0.487939 / 0.293841 (0.194098) \| 0.028124 / 0.128546 (-0.100422) \| 0.008000 / 0.075646 (-0.067647) \| 0.068335 / 0.419271 (-0.350937) \| 0.040491 / 0.043533 (-0.003042) \| 0.457868 / 0.255139 (0.202729) \| 0.476355 / 0.283200 (0.193155) \| 0.019557 / 0.141683 (-0.122126) \| 1.507111 / 1.452155 (0.054956) \| 1.569720 / 1.492716 (0.077003) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.209205 / 0.018006 (0.191199) \| 0.411782 / 0.000490 (0.411292) \| 0.003544 / 0.000200 (0.003344) \| 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) \| 0.026569 / 0.037411 (-0.010842) \| 0.081213 / 0.014526 (0.066687) \| 0.090971 / 0.176557 (-0.085585) \| 0.145287 / 0.737135 (-0.591849) \| 0.091792 / 0.296338 (-0.204546) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.458329 / 0.215209 (0.243120) \| 4.574463 / 2.077655 (2.496808) \| 2.516693 / 1.504120 (1.012573) \| 2.329463 / 1.541195 (0.788269) \| 2.386704 / 1.468490 (0.918214) \| 0.503526 / 4.584777 (-4.081251) \| 3.113382 / 3.745712 (-0.632331) \| 2.872538 / 5.269862 (-2.397323) \| 1.865483 / 4.565676 (-2.700194) \| 0.058292 / 0.424275 (-0.365983) \| 0.006434 / 0.007607 (-0.001173) \| 0.530804 / 0.226044 (0.304760) \| 5.312666 / 2.268929 (3.043738) \| 2.992569 / 55.444624 (-52.452055) \| 2.611524 / 6.876477 (-4.264953) \| 2.779569 / 2.142072 (0.637497) \| 0.595200 / 4.805227 (-4.210028) \| 0.123957 / 6.500664 (-6.376707) \| 0.060601 / 0.075469 (-0.014868) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.345536 / 1.841788 (-0.496252) \| 18.183827 / 8.074308 (10.109519) \| 14.814084 / 10.191392 (4.622692) \| 0.145305 / 0.680424 (-0.535119) \| 0.018812 / 0.534201 (-0.515389) \| 0.334793 / 0.579283 (-0.244490) \| 0.375331 / 0.434364 (-0.059033) \| 0.392499 / 0.540337 (-0.147839) \| 0.563286 / 1.386936 (-0.823650) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1e186f0b7fe851f1f474020f0d6b1dc35114f994 \"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) \| 0.008922 / 0.011353 (-0.002431) \| 0.005169 / 0.011008 (-0.005840) \| 0.106275 / 0.038508 (0.067767) \| 0.076446 / 0.023109 (0.053337) \| 0.400207 / 0.275898 (0.124309) \| 0.476262 / 0.323480 (0.152782) \| 0.006032 / 0.007986 (-0.001954) \| 0.004266 / 0.004328 (-0.000063) \| 0.083518 / 0.004250 (0.079267) \| 0.059644 / 0.037052 (0.022592) \| 0.409094 / 0.258489 (0.150605) \| 0.470400 / 0.293841 (0.176559) \| 0.050161 / 0.128546 (-0.078385) \| 0.013580 / 0.075646 (-0.062066) \| 0.375047 / 0.419271 (-0.044224) \| 0.068319 / 0.043533 (0.024786) \| 0.433765 / 0.255139 (0.178626) \| 0.449221 / 0.283200 (0.166021) \| 0.037636 / 0.141683 (-0.104047) \| 1.825855 / 1.452155 (0.373700) \| 1.889665 / 1.492716 (0.396948) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.319622 / 0.018006 (0.301616) \| 0.588878 / 0.000490 (0.588388) \| 0.017790 / 0.000200 (0.017590) \| 0.000532 / 0.000054 (0.000477) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031152 / 0.037411 (-0.006259) \| 0.093808 / 0.014526 (0.079282) \| 0.119296 / 0.176557 (-0.057261) \| 0.181845 / 0.737135 (-0.555291) \| 0.108527 / 0.296338 (-0.187811) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.575106 / 0.215209 (0.359896) \| 5.776322 / 2.077655 (3.698668) \| 2.592913 / 1.504120 (1.088793) \| 2.389481 / 1.541195 (0.848286) \| 2.390117 / 1.468490 (0.921627) \| 0.852420 / 4.584777 (-3.732357) \| 5.474171 / 3.745712 (1.728459) \| 4.967188 / 5.269862 (-0.302674) \| 3.053712 / 4.565676 (-1.511965) \| 0.098128 / 0.424275 (-0.326147) \| 0.008722 / 0.007607 (0.001115) \| 0.699838 / 0.226044 (0.473794) \| 7.103622 / 2.268929 (4.834693) \| 3.359326 / 55.444624 (-52.085299) \| 2.733943 / 6.876477 (-4.142534) \| 2.770001 / 2.142072 (0.627929) \| 1.058217 / 4.805227 (-3.747011) \| 0.215845 / 6.500664 (-6.284820) \| 0.078532 / 0.075469 (0.003063) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.633173 / 1.841788 (-0.208614) \| 23.795045 / 8.074308 (15.720737) \| 21.094433 / 10.191392 (10.903041) \| 0.234522 / 0.680424 (-0.445902) \| 0.033632 / 0.534201 (-0.500569) \| 0.496701 / 0.579283 (-0.082582) \| 0.626861 / 0.434364 (0.192497) \| 0.558267 / 0.540337 (0.017930) \| 0.807461 / 1.386936 (-0.579475) \|\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) \| 0.009136 / 0.011353 (-0.002217) \| 0.005425 / 0.011008 (-0.005584) \| 0.081478 / 0.038508 (0.042970) \| 0.077240 / 0.023109 (0.054130) \| 0.512156 / 0.275898 (0.236258) \| 0.561593 / 0.323480 (0.238113) \| 0.006499 / 0.007986 (-0.001486) \| 0.004080 / 0.004328 (-0.000248) \| 0.082121 / 0.004250 (0.077870) \| 0.063774 / 0.037052 (0.026722) \| 0.509801 / 0.258489 (0.251312) \| 0.572826 / 0.293841 (0.278985) \| 0.050969 / 0.128546 (-0.077578) \| 0.014876 / 0.075646 (-0.060771) \| 0.094815 / 0.419271 (-0.324456) \| 0.063904 / 0.043533 (0.020371) \| 0.530572 / 0.255139 (0.275433) \| 0.545940 / 0.283200 (0.262741) \| 0.036729 / 0.141683 (-0.104954) \| 1.799493 / 1.452155 (0.347339) \| 1.931955 / 1.492716 (0.439239) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291405 / 0.018006 (0.273398) \| 0.590257 / 0.000490 (0.589767) \| 0.008394 / 0.000200 (0.008194) \| 0.000112 / 0.000054 (0.000058) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037613 / 0.037411 (0.000201) \| 0.103136 / 0.014526 (0.088610) \| 0.121744 / 0.176557 (-0.054813) \| 0.198503 / 0.737135 (-0.538632) \| 0.120183 / 0.296338 (-0.176156) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.659872 / 0.215209 (0.444663) \| 6.616775 / 2.077655 (4.539120) \| 3.031679 / 1.504120 (1.527559) \| 2.743489 / 1.541195 (1.202294) \| 2.786786 / 1.468490 (1.318296) \| 0.866625 / 4.584777 (-3.718152) \| 5.637705 / 3.745712 (1.891993) \| 4.702563 / 5.269862 (-0.567298) \| 3.017797 / 4.565676 (-1.547879) \| 0.100107 / 0.424275 (-0.324169) \| 0.008443 / 0.007607 (0.000836) \| 0.791385 / 0.226044 (0.565341) \| 7.869504 / 2.268929 (5.600576) \| 3.856634 / 55.444624 (-51.587991) \| 3.140089 / 6.876477 (-3.736388) \| 3.489339 / 2.142072 (1.347267) \| 1.132170 / 4.805227 (-3.673058) \| 0.219630 / 6.500664 (-6.281034) \| 0.082289 / 0.075469 (0.006820) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.781902 / 1.841788 (-0.059885) \| 24.912604 / 8.074308 (16.838296) \| 21.626512 / 10.191392 (11.435120) \| 0.228194 / 0.680424 (-0.452230) \| 0.032799 / 0.534201 (-0.501402) \| 0.483683 / 0.579283 (-0.095600) \| 0.604966 / 0.434364 (0.170602) \| 0.617278 / 0.540337 (0.076940) \| 0.887337 / 1.386936 (-0.499599) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#579c31fda7182ca6fc33ab1e95db9e3a21fb5518 \"CML watermark\")\n", "I used [this](https://colab.research.google.com/drive/1q2FYnkJFDMM3OZbhnYeZkfzmBa6ksofQ?usp=sharing) Colab to test the new `push_to_hub` on a large dataset (55 GB). It works great. \r\n\r\nOne thing that could be improved is the performance of `dataset.data.nbytes` - it takes ≈ 3 minutes to compute for the dataset in question (50k array chunks per column). It probably makes sense to store larger chunks locally. But this can be addressed in a subsequent PR.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007190 / 0.011353 (-0.004163) \| 0.004394 / 0.011008 (-0.006614) \| 0.085506 / 0.038508 (0.046998) \| 0.092177 / 0.023109 (0.069068) \| 0.351636 / 0.275898 (0.075738) \| 0.389716 / 0.323480 (0.066236) \| 0.004443 / 0.007986 (-0.003543) \| 0.003641 / 0.004328 (-0.000687) \| 0.066578 / 0.004250 (0.062328) \| 0.061399 / 0.037052 (0.024346) \| 0.356008 / 0.258489 (0.097519) \| 0.398677 / 0.293841 (0.104836) \| 0.031958 / 0.128546 (-0.096588) \| 0.008857 / 0.075646 (-0.066789) \| 0.289613 / 0.419271 (-0.129659) \| 0.053555 / 0.043533 (0.010022) \| 0.349268 / 0.255139 (0.094129) \| 0.368666 / 0.283200 (0.085466) \| 0.028267 / 0.141683 (-0.113416) \| 1.502857 / 1.452155 (0.050702) \| 1.598422 / 1.492716 (0.105705) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.319938 / 0.018006 (0.301931) \| 0.566925 / 0.000490 (0.566435) \| 0.014625 / 0.000200 (0.014425) \| 0.000372 / 0.000054 (0.000318) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030156 / 0.037411 (-0.007255) \| 0.083128 / 0.014526 (0.068602) \| 0.101435 / 0.176557 (-0.075122) \| 0.158971 / 0.737135 (-0.578165) \| 0.101488 / 0.296338 (-0.194851) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.383904 / 0.215209 (0.168695) \| 3.829201 / 2.077655 (1.751546) \| 1.815224 / 1.504120 (0.311104) \| 1.647865 / 1.541195 (0.106670) \| 1.738411 / 1.468490 (0.269921) \| 0.484963 / 4.584777 (-4.099814) \| 3.494811 / 3.745712 (-0.250901) \| 3.505811 / 5.269862 (-1.764051) \| 2.115467 / 4.565676 (-2.450210) \| 0.057271 / 0.424275 (-0.367004) \| 0.007285 / 0.007607 (-0.000322) \| 0.467162 / 0.226044 (0.241118) \| 4.661572 / 2.268929 (2.392643) \| 2.330443 / 55.444624 (-53.114182) \| 1.986116 / 6.876477 (-4.890361) \| 2.055350 / 2.142072 (-0.086723) \| 0.580369 / 4.805227 (-4.224858) \| 0.132700 / 6.500664 (-6.367964) \| 0.061219 / 0.075469 (-0.014251) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.270843 / 1.841788 (-0.570945) \| 19.870723 / 8.074308 (11.796415) \| 14.368932 / 10.191392 (4.177540) \| 0.167345 / 0.680424 (-0.513079) \| 0.018358 / 0.534201 (-0.515843) \| 0.390833 / 0.579283 (-0.188450) \| 0.419884 / 0.434364 (-0.014480) \| 0.465683 / 0.540337 (-0.074655) \| 0.646101 / 1.386936 (-0.740835) \|\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) \| 0.007027 / 0.011353 (-0.004326) \| 0.004578 / 0.011008 (-0.006430) \| 0.066468 / 0.038508 (0.027960) \| 0.081576 / 0.023109 (0.058466) \| 0.414928 / 0.275898 (0.139030) \| 0.452130 / 0.323480 (0.128651) \| 0.005861 / 0.007986 (-0.002124) \| 0.003740 / 0.004328 (-0.000588) \| 0.066943 / 0.004250 (0.062692) \| 0.060100 / 0.037052 (0.023048) \| 0.418697 / 0.258489 (0.160208) \| 0.466604 / 0.293841 (0.172764) \| 0.031887 / 0.128546 (-0.096660) \| 0.009119 / 0.075646 (-0.066527) \| 0.072285 / 0.419271 (-0.346986) \| 0.047599 / 0.043533 (0.004066) \| 0.410791 / 0.255139 (0.155652) \| 0.434182 / 0.283200 (0.150982) \| 0.024799 / 0.141683 (-0.116884) \| 1.500310 / 1.452155 (0.048155) \| 1.567151 / 1.492716 (0.074434) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.322482 / 0.018006 (0.304476) \| 0.550234 / 0.000490 (0.549744) \| 0.007796 / 0.000200 (0.007596) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036013 / 0.037411 (-0.001398) \| 0.098482 / 0.014526 (0.083956) \| 0.111641 / 0.176557 (-0.064916) \| 0.166251 / 0.737135 (-0.570884) \| 0.112426 / 0.296338 (-0.183912) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429181 / 0.215209 (0.213972) \| 4.273126 / 2.077655 (2.195472) \| 2.277440 / 1.504120 (0.773321) \| 2.112567 / 1.541195 (0.571372) \| 2.224118 / 1.468490 (0.755628) \| 0.488876 / 4.584777 (-4.095901) \| 3.711638 / 3.745712 (-0.034074) \| 3.480995 / 5.269862 (-1.788867) \| 2.122114 / 4.565676 (-2.443563) \| 0.057538 / 0.424275 (-0.366737) \| 0.007416 / 0.007607 (-0.000191) \| 0.506881 / 0.226044 (0.280836) \| 5.067601 / 2.268929 (2.798672) \| 2.769216 / 55.444624 (-52.675408) \| 2.420448 / 6.876477 (-4.456029) \| 2.694225 / 2.142072 (0.552153) \| 0.588911 / 4.805227 (-4.216316) \| 0.133542 / 6.500664 (-6.367122) \| 0.061135 / 0.075469 (-0.014334) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.378029 / 1.841788 (-0.463758) \| 20.660942 / 8.074308 (12.586634) \| 15.725969 / 10.191392 (5.534577) \| 0.169078 / 0.680424 (-0.511346) \| 0.020540 / 0.534201 (-0.513661) \| 0.399409 / 0.579283 (-0.179874) \| 0.432572 / 0.434364 (-0.001792) \| 0.477106 / 0.540337 (-0.063231) \| 0.675593 / 1.386936 (-0.711343) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9764c49d8bfdad5439e16faa6c52e510feabf6fa \"CML watermark\")\n", "@lhoestq \r\n\r\n> single commit can fail (time out) if there are too many operations so we might have to do multi commits anyway in that case\r\n\r\nMultiple commits complicate the logic significantly. Maybe, let's keep things simple and emit a warning if there are more than 100 additions (we can suggest increasing `max_shard_size` in that case). Additionally, we can set the default `max_shard_size` to a higher value, e.g., 5GB. I think handling up to 500GB of data in the default case seems reasonable. In rare cases where this is a problem, one could increase the default `max_shard_size` even further (if RAM is not a limiting factor) or use `to_parquet` + `huggingface_hub` (we could have a docstring or a doc note that explains this).\r\n\r\nNote that we split the dataset based on the Arrow data size, which means Parquet shards will be considerably smaller unless there are binary fields such as image JPEGs in the dataset, which are hard to compress efficiently.\r\n\r\n> how to let users resume a push_to_hub that failed mid-way because of a connection error for example\r\n\r\nThey can resume by rerunning the failed `push_to_hub`.\r\n\r\n`preupload_lfs_files` will be instant in that scenario, as explained in https://github.com/huggingface/huggingface_hub/pull/1699#discussion_r1342446406", "> Multiple commits complicate the logic significantly. Maybe, let's keep things simple and emit a warning if there are more than 100 additions (we can suggest increasing max_shard_size in that case)\r\n\r\nI don't think we can do that, many people are uploading files with 100+ files and it would break their workflow", "Indeed, we should not break this, considering the number of datasets with more than 100 shards on the Hub (over 1k)", "<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) \| 0.006834 / 0.011353 (-0.004519) \| 0.004424 / 0.011008 (-0.006584) \| 0.085199 / 0.038508 (0.046691) \| 0.080237 / 0.023109 (0.057128) \| 0.308800 / 0.275898 (0.032902) \| 0.346314 / 0.323480 (0.022835) \| 0.004399 / 0.007986 (-0.003586) \| 0.003773 / 0.004328 (-0.000556) \| 0.065886 / 0.004250 (0.061636) \| 0.057830 / 0.037052 (0.020777) \| 0.312035 / 0.258489 (0.053546) \| 0.362646 / 0.293841 (0.068805) \| 0.031223 / 0.128546 (-0.097323) \| 0.008851 / 0.075646 (-0.066795) \| 0.288264 / 0.419271 (-0.131007) \| 0.052600 / 0.043533 (0.009067) \| 0.316127 / 0.255139 (0.060988) \| 0.328539 / 0.283200 (0.045340) \| 0.026068 / 0.141683 (-0.115615) \| 1.458928 / 1.452155 (0.006773) \| 1.547619 / 1.492716 (0.054902) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274382 / 0.018006 (0.256375) \| 0.591192 / 0.000490 (0.590703) \| 0.009290 / 0.000200 (0.009090) \| 0.000327 / 0.000054 (0.000273) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031428 / 0.037411 (-0.005983) \| 0.087523 / 0.014526 (0.072997) \| 0.101427 / 0.176557 (-0.075130) \| 0.159228 / 0.737135 (-0.577907) \| 0.101430 / 0.296338 (-0.194909) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.393914 / 0.215209 (0.178705) \| 3.917323 / 2.077655 (1.839668) \| 1.940577 / 1.504120 (0.436457) \| 1.760996 / 1.541195 (0.219801) \| 1.865858 / 1.468490 (0.397368) \| 0.488920 / 4.584777 (-4.095857) \| 3.513465 / 3.745712 (-0.232248) \| 3.506600 / 5.269862 (-1.763261) \| 2.072583 / 4.565676 (-2.493093) \| 0.058256 / 0.424275 (-0.366019) \| 0.007420 / 0.007607 (-0.000187) \| 0.467241 / 0.226044 (0.241197) \| 4.671470 / 2.268929 (2.402542) \| 2.422717 / 55.444624 (-53.021908) \| 2.069501 / 6.876477 (-4.806975) \| 2.159257 / 2.142072 (0.017184) \| 0.583808 / 4.805227 (-4.221419) \| 0.134160 / 6.500664 (-6.366504) \| 0.068855 / 0.075469 (-0.006614) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.305299 / 1.841788 (-0.536488) \| 19.913902 / 8.074308 (11.839593) \| 14.708057 / 10.191392 (4.516665) \| 0.160113 / 0.680424 (-0.520311) \| 0.018431 / 0.534201 (-0.515770) \| 0.396147 / 0.579283 (-0.183136) \| 0.411738 / 0.434364 (-0.022626) \| 0.459297 / 0.540337 (-0.081041) \| 0.636599 / 1.386936 (-0.750337) \|\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) \| 0.006936 / 0.011353 (-0.004417) \| 0.004290 / 0.011008 (-0.006718) \| 0.065754 / 0.038508 (0.027246) \| 0.080655 / 0.023109 (0.057546) \| 0.399701 / 0.275898 (0.123803) \| 0.435999 / 0.323480 (0.112519) \| 0.005690 / 0.007986 (-0.002295) \| 0.003580 / 0.004328 (-0.000748) \| 0.065685 / 0.004250 (0.061434) \| 0.059299 / 0.037052 (0.022246) \| 0.404295 / 0.258489 (0.145806) \| 0.438745 / 0.293841 (0.144904) \| 0.032241 / 0.128546 (-0.096305) \| 0.008699 / 0.075646 (-0.066947) \| 0.072053 / 0.419271 (-0.347218) \| 0.047489 / 0.043533 (0.003956) \| 0.395638 / 0.255139 (0.140499) \| 0.417224 / 0.283200 (0.134025) \| 0.022734 / 0.141683 (-0.118949) \| 1.507519 / 1.452155 (0.055364) \| 1.570459 / 1.492716 (0.077743) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.260442 / 0.018006 (0.242435) \| 0.551933 / 0.000490 (0.551444) \| 0.005240 / 0.000200 (0.005040) \| 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) \| 0.033718 / 0.037411 (-0.003694) \| 0.095710 / 0.014526 (0.081184) \| 0.109970 / 0.176557 (-0.066586) \| 0.167930 / 0.737135 (-0.569205) \| 0.109977 / 0.296338 (-0.186362) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430067 / 0.215209 (0.214857) \| 4.292564 / 2.077655 (2.214910) \| 2.313511 / 1.504120 (0.809391) \| 2.158153 / 1.541195 (0.616959) \| 2.262486 / 1.468490 (0.793996) \| 0.492376 / 4.584777 (-4.092401) \| 3.622287 / 3.745712 (-0.123425) \| 3.380162 / 5.269862 (-1.889699) \| 2.111874 / 4.565676 (-2.453803) \| 0.057882 / 0.424275 (-0.366393) \| 0.007317 / 0.007607 (-0.000290) \| 0.504722 / 0.226044 (0.278678) \| 5.039009 / 2.268929 (2.770080) \| 2.772162 / 55.444624 (-52.672463) \| 2.430928 / 6.876477 (-4.445549) \| 2.666556 / 2.142072 (0.524484) \| 0.586722 / 4.805227 (-4.218505) \| 0.133780 / 6.500664 (-6.366884) \| 0.060269 / 0.075469 (-0.015200) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.339064 / 1.841788 (-0.502724) \| 20.743931 / 8.074308 (12.669623) \| 15.491066 / 10.191392 (5.299674) \| 0.159236 / 0.680424 (-0.521188) \| 0.020722 / 0.534201 (-0.513479) \| 0.399440 / 0.579283 (-0.179843) \| 0.424501 / 0.434364 (-0.009863) \| 0.474026 / 0.540337 (-0.066311) \| 0.685239 / 1.386936 (-0.701697) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#58406f61c52e7ff064ac6c19ebdb3e5247c70862 \"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) \| 0.005930 / 0.011353 (-0.005422) \| 0.003496 / 0.011008 (-0.007512) \| 0.079631 / 0.038508 (0.041123) \| 0.058250 / 0.023109 (0.035141) \| 0.310108 / 0.275898 (0.034210) \| 0.352747 / 0.323480 (0.029267) \| 0.005367 / 0.007986 (-0.002619) \| 0.002943 / 0.004328 (-0.001386) \| 0.062449 / 0.004250 (0.058199) \| 0.046433 / 0.037052 (0.009381) \| 0.311020 / 0.258489 (0.052531) \| 0.361033 / 0.293841 (0.067192) \| 0.027419 / 0.128546 (-0.101128) \| 0.008073 / 0.075646 (-0.067574) \| 0.261403 / 0.419271 (-0.157869) \| 0.045059 / 0.043533 (0.001527) \| 0.310622 / 0.255139 (0.055483) \| 0.344361 / 0.283200 (0.061161) \| 0.020561 / 0.141683 (-0.121122) \| 1.427409 / 1.452155 (-0.024746) \| 1.506612 / 1.492716 (0.013896) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.234095 / 0.018006 (0.216089) \| 0.432603 / 0.000490 (0.432113) \| 0.010283 / 0.000200 (0.010083) \| 0.000289 / 0.000054 (0.000235) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024263 / 0.037411 (-0.013148) \| 0.073672 / 0.014526 (0.059146) \| 0.084080 / 0.176557 (-0.092476) \| 0.146679 / 0.737135 (-0.590457) \| 0.084337 / 0.296338 (-0.212001) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434297 / 0.215209 (0.219088) \| 4.358287 / 2.077655 (2.280633) \| 2.268461 / 1.504120 (0.764341) \| 2.107924 / 1.541195 (0.566729) \| 2.165136 / 1.468490 (0.696646) \| 0.498421 / 4.584777 (-4.086356) \| 3.094414 / 3.745712 (-0.651298) \| 2.991511 / 5.269862 (-2.278351) \| 1.998052 / 4.565676 (-2.567624) \| 0.057363 / 0.424275 (-0.366912) \| 0.006405 / 0.007607 (-0.001203) \| 0.508396 / 0.226044 (0.282351) \| 5.104756 / 2.268929 (2.835828) \| 2.720462 / 55.444624 (-52.724163) \| 2.391840 / 6.876477 (-4.484637) \| 2.443063 / 2.142072 (0.300991) \| 0.590015 / 4.805227 (-4.215212) \| 0.125414 / 6.500664 (-6.375250) \| 0.061122 / 0.075469 (-0.014347) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.221883 / 1.841788 (-0.619904) \| 17.788248 / 8.074308 (9.713940) \| 13.753315 / 10.191392 (3.561923) \| 0.146388 / 0.680424 (-0.534036) \| 0.017038 / 0.534201 (-0.517163) \| 0.339162 / 0.579283 (-0.240121) \| 0.372054 / 0.434364 (-0.062309) \| 0.381507 / 0.540337 (-0.158830) \| 0.538603 / 1.386936 (-0.848333) \|\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) \| 0.006044 / 0.011353 (-0.005309) \| 0.003654 / 0.011008 (-0.007354) \| 0.062956 / 0.038508 (0.024448) \| 0.061325 / 0.023109 (0.038216) \| 0.450006 / 0.275898 (0.174108) \| 0.474560 / 0.323480 (0.151080) \| 0.004846 / 0.007986 (-0.003140) \| 0.002904 / 0.004328 (-0.001425) \| 0.064206 / 0.004250 (0.059956) \| 0.047850 / 0.037052 (0.010798) \| 0.448431 / 0.258489 (0.189942) \| 0.481363 / 0.293841 (0.187523) \| 0.028622 / 0.128546 (-0.099925) \| 0.008255 / 0.075646 (-0.067391) \| 0.068461 / 0.419271 (-0.350810) \| 0.040234 / 0.043533 (-0.003299) \| 0.447396 / 0.255139 (0.192257) \| 0.465383 / 0.283200 (0.182184) \| 0.021864 / 0.141683 (-0.119819) \| 1.402197 / 1.452155 (-0.049957) \| 1.475337 / 1.492716 (-0.017379) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227093 / 0.018006 (0.209087) \| 0.407908 / 0.000490 (0.407419) \| 0.006709 / 0.000200 (0.006509) \| 0.000076 / 0.000054 (0.000022) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026560 / 0.037411 (-0.010851) \| 0.080926 / 0.014526 (0.066400) \| 0.091531 / 0.176557 (-0.085026) \| 0.145742 / 0.737135 (-0.591393) \| 0.092203 / 0.296338 (-0.204135) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.473029 / 0.215209 (0.257820) \| 4.703613 / 2.077655 (2.625958) \| 2.642622 / 1.504120 (1.138502) \| 2.465376 / 1.541195 (0.924181) \| 2.510125 / 1.468490 (1.041635) \| 0.512606 / 4.584777 (-4.072171) \| 3.132127 / 3.745712 (-0.613585) \| 2.890098 / 5.269862 (-2.379763) \| 1.908140 / 4.565676 (-2.657537) \| 0.058938 / 0.424275 (-0.365337) \| 0.006486 / 0.007607 (-0.001121) \| 0.542279 / 0.226044 (0.316235) \| 5.435621 / 2.268929 (3.166693) \| 3.083943 / 55.444624 (-52.360681) \| 2.761575 / 6.876477 (-4.114901) \| 2.919672 / 2.142072 (0.777599) \| 0.608022 / 4.805227 (-4.197205) \| 0.126821 / 6.500664 (-6.373843) \| 0.061374 / 0.075469 (-0.014095) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.348848 / 1.841788 (-0.492940) \| 18.323507 / 8.074308 (10.249199) \| 14.713411 / 10.191392 (4.522019) \| 0.155277 / 0.680424 (-0.525146) \| 0.017739 / 0.534201 (-0.516462) \| 0.337357 / 0.579283 (-0.241926) \| 0.376519 / 0.434364 (-0.057844) \| 0.398011 / 0.540337 (-0.142327) \| 0.589797 / 1.386936 (-0.797139) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#26d8bfca337e01bd78d5590d5e49c6d8d022a3ff \"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) \| 0.007823 / 0.011353 (-0.003530) \| 0.004136 / 0.011008 (-0.006872) \| 0.087282 / 0.038508 (0.048774) \| 0.086352 / 0.023109 (0.063243) \| 0.328107 / 0.275898 (0.052209) \| 0.368717 / 0.323480 (0.045237) \| 0.005452 / 0.007986 (-0.002533) \| 0.003460 / 0.004328 (-0.000868) \| 0.064360 / 0.004250 (0.060110) \| 0.062215 / 0.037052 (0.025162) \| 0.334666 / 0.258489 (0.076177) \| 0.388688 / 0.293841 (0.094847) \| 0.031093 / 0.128546 (-0.097454) \| 0.008510 / 0.075646 (-0.067137) \| 0.295965 / 0.419271 (-0.123306) \| 0.052858 / 0.043533 (0.009325) \| 0.320104 / 0.255139 (0.064965) \| 0.346761 / 0.283200 (0.063562) \| 0.024864 / 0.141683 (-0.116819) \| 1.483164 / 1.452155 (0.031010) \| 1.580363 / 1.492716 (0.087647) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.243523 / 0.018006 (0.225516) \| 0.459741 / 0.000490 (0.459251) \| 0.010508 / 0.000200 (0.010308) \| 0.000384 / 0.000054 (0.000330) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029896 / 0.037411 (-0.007515) \| 0.089150 / 0.014526 (0.074624) \| 0.098855 / 0.176557 (-0.077702) \| 0.154469 / 0.737135 (-0.582667) \| 0.099546 / 0.296338 (-0.196792) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.403547 / 0.215209 (0.188338) \| 4.036711 / 2.077655 (1.959056) \| 2.030882 / 1.504120 (0.526762) \| 1.850432 / 1.541195 (0.309238) \| 1.924248 / 1.468490 (0.455758) \| 0.493153 / 4.584777 (-4.091624) \| 3.634074 / 3.745712 (-0.111638) \| 3.546145 / 5.269862 (-1.723717) \| 2.120819 / 4.565676 (-2.444858) \| 0.057137 / 0.424275 (-0.367138) \| 0.007454 / 0.007607 (-0.000153) \| 0.481687 / 0.226044 (0.255642) \| 4.813203 / 2.268929 (2.544275) \| 2.481260 / 55.444624 (-52.963364) \| 2.194185 / 6.876477 (-4.682292) \| 2.255381 / 2.142072 (0.113308) \| 0.575160 / 4.805227 (-4.230068) \| 0.132310 / 6.500664 (-6.368355) \| 0.061917 / 0.075469 (-0.013553) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.265722 / 1.841788 (-0.576066) \| 19.949624 / 8.074308 (11.875315) \| 14.804356 / 10.191392 (4.612964) \| 0.170485 / 0.680424 (-0.509939) \| 0.018831 / 0.534201 (-0.515370) \| 0.407051 / 0.579283 (-0.172233) \| 0.420560 / 0.434364 (-0.013804) \| 0.470721 / 0.540337 (-0.069616) \| 0.651665 / 1.386936 (-0.735271) \|\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) \| 0.007113 / 0.011353 (-0.004240) \| 0.004186 / 0.011008 (-0.006822) \| 0.065082 / 0.038508 (0.026574) \| 0.080275 / 0.023109 (0.057166) \| 0.393460 / 0.275898 (0.117562) \| 0.426702 / 0.323480 (0.103223) \| 0.005639 / 0.007986 (-0.002347) \| 0.003492 / 0.004328 (-0.000836) \| 0.065774 / 0.004250 (0.061523) \| 0.059708 / 0.037052 (0.022656) \| 0.395598 / 0.258489 (0.137109) \| 0.437088 / 0.293841 (0.143247) \| 0.033165 / 0.128546 (-0.095381) \| 0.008559 / 0.075646 (-0.067087) \| 0.071782 / 0.419271 (-0.347490) \| 0.048672 / 0.043533 (0.005139) \| 0.393883 / 0.255139 (0.138744) \| 0.412817 / 0.283200 (0.129617) \| 0.024115 / 0.141683 (-0.117568) \| 1.522752 / 1.452155 (0.070597) \| 1.577311 / 1.492716 (0.084595) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225569 / 0.018006 (0.207563) \| 0.460310 / 0.000490 (0.459820) \| 0.004733 / 0.000200 (0.004533) \| 0.000115 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035241 / 0.037411 (-0.002170) \| 0.098092 / 0.014526 (0.083566) \| 0.108025 / 0.176557 (-0.068531) \| 0.162910 / 0.737135 (-0.574225) \| 0.108649 / 0.296338 (-0.187689) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441723 / 0.215209 (0.226514) \| 4.400656 / 2.077655 (2.323001) \| 2.413588 / 1.504120 (0.909468) \| 2.261890 / 1.541195 (0.720696) \| 2.420878 / 1.468490 (0.952388) \| 0.496456 / 4.584777 (-4.088321) \| 3.679930 / 3.745712 (-0.065782) \| 3.390539 / 5.269862 (-1.879322) \| 2.109599 / 4.565676 (-2.456078) \| 0.058896 / 0.424275 (-0.365379) \| 0.007483 / 0.007607 (-0.000125) \| 0.521108 / 0.226044 (0.295064) \| 5.209468 / 2.268929 (2.940540) \| 2.948595 / 55.444624 (-52.496029) \| 2.658864 / 6.876477 (-4.217613) \| 2.913653 / 2.142072 (0.771580) \| 0.602776 / 4.805227 (-4.202451) \| 0.136166 / 6.500664 (-6.364498) \| 0.063812 / 0.075469 (-0.011657) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.350306 / 1.841788 (-0.491482) \| 20.453980 / 8.074308 (12.379672) \| 15.758719 / 10.191392 (5.567327) \| 0.165847 / 0.680424 (-0.514577) \| 0.020254 / 0.534201 (-0.513947) \| 0.400006 / 0.579283 (-0.179277) \| 0.440336 / 0.434364 (0.005972) \| 0.480122 / 0.540337 (-0.060215) \| 0.688994 / 1.386936 (-0.697942) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#997082a2a3c599ea1b23a70759d3af98a78f7f33 \"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) \| 0.008633 / 0.011353 (-0.002720) \| 0.004851 / 0.011008 (-0.006157) \| 0.100647 / 0.038508 (0.062139) \| 0.084701 / 0.023109 (0.061592) \| 0.410489 / 0.275898 (0.134590) \| 0.440231 / 0.323480 (0.116751) \| 0.004679 / 0.007986 (-0.003307) \| 0.004172 / 0.004328 (-0.000157) \| 0.079911 / 0.004250 (0.075661) \| 0.069537 / 0.037052 (0.032485) \| 0.423506 / 0.258489 (0.165017) \| 0.466098 / 0.293841 (0.172257) \| 0.048773 / 0.128546 (-0.079773) \| 0.014446 / 0.075646 (-0.061200) \| 0.342776 / 0.419271 (-0.076495) \| 0.065672 / 0.043533 (0.022139) \| 0.411845 / 0.255139 (0.156706) \| 0.466662 / 0.283200 (0.183462) \| 0.035752 / 0.141683 (-0.105931) \| 1.684956 / 1.452155 (0.232801) \| 1.832173 / 1.492716 (0.339456) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.250744 / 0.018006 (0.232738) \| 0.528860 / 0.000490 (0.528371) \| 0.013301 / 0.000200 (0.013101) \| 0.000413 / 0.000054 (0.000359) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032376 / 0.037411 (-0.005035) \| 0.094630 / 0.014526 (0.080104) \| 0.107163 / 0.176557 (-0.069394) \| 0.172503 / 0.737135 (-0.564633) \| 0.108407 / 0.296338 (-0.187932) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.671251 / 0.215209 (0.456042) \| 6.235361 / 2.077655 (4.157706) \| 2.650328 / 1.504120 (1.146208) \| 2.341199 / 1.541195 (0.800004) \| 2.368803 / 1.468490 (0.900313) \| 0.841347 / 4.584777 (-3.743430) \| 5.042508 / 3.745712 (1.296796) \| 4.807565 / 5.269862 (-0.462296) \| 3.007420 / 4.565676 (-1.558257) \| 0.099953 / 0.424275 (-0.324322) \| 0.008412 / 0.007607 (0.000805) \| 0.747803 / 0.226044 (0.521759) \| 7.481245 / 2.268929 (5.212316) \| 3.416157 / 55.444624 (-52.028467) \| 2.724608 / 6.876477 (-4.151869) \| 2.832982 / 2.142072 (0.690910) \| 1.072423 / 4.805227 (-3.732804) \| 0.211314 / 6.500664 (-6.289351) \| 0.074098 / 0.075469 (-0.001371) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.566010 / 1.841788 (-0.275778) \| 23.137708 / 8.074308 (15.063400) \| 21.440132 / 10.191392 (11.248740) \| 0.230713 / 0.680424 (-0.449711) \| 0.028271 / 0.534201 (-0.505930) \| 0.450821 / 0.579283 (-0.128463) \| 0.548399 / 0.434364 (0.114035) \| 0.543588 / 0.540337 (0.003250) \| 0.805522 / 1.386936 (-0.581414) \|\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) \| 0.008969 / 0.011353 (-0.002384) \| 0.004793 / 0.011008 (-0.006216) \| 0.075804 / 0.038508 (0.037296) \| 0.079893 / 0.023109 (0.056783) \| 0.464358 / 0.275898 (0.188460) \| 0.507243 / 0.323480 (0.183763) \| 0.005945 / 0.007986 (-0.002040) \| 0.005341 / 0.004328 (0.001012) \| 0.077952 / 0.004250 (0.073701) \| 0.059965 / 0.037052 (0.022913) \| 0.478947 / 0.258489 (0.220458) \| 0.528444 / 0.293841 (0.234603) \| 0.052878 / 0.128546 (-0.075668) \| 0.013939 / 0.075646 (-0.061707) \| 0.087351 / 0.419271 (-0.331920) \| 0.058448 / 0.043533 (0.014916) \| 0.478664 / 0.255139 (0.223525) \| 0.491239 / 0.283200 (0.208039) \| 0.032674 / 0.141683 (-0.109008) \| 1.753911 / 1.452155 (0.301756) \| 1.858923 / 1.492716 (0.366206) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.239278 / 0.018006 (0.221271) \| 0.507372 / 0.000490 (0.506882) \| 0.005489 / 0.000200 (0.005289) \| 0.000142 / 0.000054 (0.000087) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032919 / 0.037411 (-0.004493) \| 0.097726 / 0.014526 (0.083200) \| 0.119159 / 0.176557 (-0.057398) \| 0.174545 / 0.737135 (-0.562590) \| 0.115319 / 0.296338 (-0.181020) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.627107 / 0.215209 (0.411898) \| 6.211925 / 2.077655 (4.134270) \| 2.731484 / 1.504120 (1.227365) \| 2.488847 / 1.541195 (0.947652) \| 2.372445 / 1.468490 (0.903955) \| 0.822663 / 4.584777 (-3.762114) \| 4.924001 / 3.745712 (1.178289) \| 4.371161 / 5.269862 (-0.898700) \| 2.850314 / 4.565676 (-1.715363) \| 0.099156 / 0.424275 (-0.325119) \| 0.007941 / 0.007607 (0.000334) \| 0.721539 / 0.226044 (0.495495) \| 7.260874 / 2.268929 (4.991946) \| 3.351072 / 55.444624 (-52.093552) \| 2.757115 / 6.876477 (-4.119362) \| 2.858899 / 2.142072 (0.716827) \| 0.994054 / 4.805227 (-3.811173) \| 0.209186 / 6.500664 (-6.291478) \| 0.072070 / 0.075469 (-0.003399) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.748073 / 1.841788 (-0.093714) \| 23.514638 / 8.074308 (15.440330) \| 20.372037 / 10.191392 (10.180645) \| 0.220020 / 0.680424 (-0.460404) \| 0.057130 / 0.534201 (-0.477071) \| 0.458204 / 0.579283 (-0.121079) \| 0.600509 / 0.434364 (0.166145) \| 0.557100 / 0.540337 (0.016762) \| 0.814360 / 1.386936 (-0.572576) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#072f0ceafde60c16516fe1297e4aba981fc56052 \"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) \| 0.007341 / 0.011353 (-0.004012) \| 0.004606 / 0.011008 (-0.006402) \| 0.087903 / 0.038508 (0.049395) \| 0.094090 / 0.023109 (0.070981) \| 0.322278 / 0.275898 (0.046380) \| 0.356770 / 0.323480 (0.033290) \| 0.005988 / 0.007986 (-0.001997) \| 0.003667 / 0.004328 (-0.000662) \| 0.066105 / 0.004250 (0.061854) \| 0.061220 / 0.037052 (0.024167) \| 0.331190 / 0.258489 (0.072701) \| 0.381402 / 0.293841 (0.087561) \| 0.032261 / 0.128546 (-0.096285) \| 0.009281 / 0.075646 (-0.066366) \| 0.293694 / 0.419271 (-0.125577) \| 0.055041 / 0.043533 (0.011508) \| 0.318080 / 0.255139 (0.062941) \| 0.348763 / 0.283200 (0.065563) \| 0.027379 / 0.141683 (-0.114304) \| 1.496294 / 1.452155 (0.044139) \| 1.581942 / 1.492716 (0.089226) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.307592 / 0.018006 (0.289586) \| 0.591805 / 0.000490 (0.591316) \| 0.017082 / 0.000200 (0.016882) \| 0.000721 / 0.000054 (0.000666) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032157 / 0.037411 (-0.005254) \| 0.096249 / 0.014526 (0.081724) \| 0.106656 / 0.176557 (-0.069901) \| 0.162966 / 0.737135 (-0.574169) \| 0.107068 / 0.296338 (-0.189271) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.409083 / 0.215209 (0.193874) \| 4.044307 / 2.077655 (1.966652) \| 2.062887 / 1.504120 (0.558767) \| 1.900568 / 1.541195 (0.359373) \| 2.011862 / 1.468490 (0.543372) \| 0.489250 / 4.584777 (-4.095527) \| 3.519531 / 3.745712 (-0.226182) \| 3.631713 / 5.269862 (-1.638149) \| 2.163967 / 4.565676 (-2.401709) \| 0.057723 / 0.424275 (-0.366552) \| 0.007474 / 0.007607 (-0.000133) \| 0.479562 / 0.226044 (0.253517) \| 4.799825 / 2.268929 (2.530897) \| 2.530036 / 55.444624 (-52.914588) \| 2.195344 / 6.876477 (-4.681133) \| 2.341046 / 2.142072 (0.198974) \| 0.625105 / 4.805227 (-4.180122) \| 0.132823 / 6.500664 (-6.367841) \| 0.061721 / 0.075469 (-0.013748) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.301313 / 1.841788 (-0.540475) \| 21.218468 / 8.074308 (13.144159) \| 15.466347 / 10.191392 (5.274955) \| 0.166115 / 0.680424 (-0.514309) \| 0.018866 / 0.534201 (-0.515335) \| 0.399307 / 0.579283 (-0.179976) \| 0.430537 / 0.434364 (-0.003827) \| 0.467110 / 0.540337 (-0.073228) \| 0.645686 / 1.386936 (-0.741250) \|\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) \| 0.007288 / 0.011353 (-0.004065) \| 0.004298 / 0.011008 (-0.006710) \| 0.065515 / 0.038508 (0.027007) \| 0.089948 / 0.023109 (0.066839) \| 0.410121 / 0.275898 (0.134223) \| 0.449312 / 0.323480 (0.125832) \| 0.006749 / 0.007986 (-0.001237) \| 0.003927 / 0.004328 (-0.000401) \| 0.065321 / 0.004250 (0.061071) \| 0.062480 / 0.037052 (0.025428) \| 0.410796 / 0.258489 (0.152307) \| 0.457356 / 0.293841 (0.163515) \| 0.032632 / 0.128546 (-0.095914) \| 0.008798 / 0.075646 (-0.066849) \| 0.075936 / 0.419271 (-0.343335) \| 0.048402 / 0.043533 (0.004869) \| 0.403385 / 0.255139 (0.148246) \| 0.426094 / 0.283200 (0.142895) \| 0.025326 / 0.141683 (-0.116357) \| 1.551550 / 1.452155 (0.099395) \| 1.628622 / 1.492716 (0.135905) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279689 / 0.018006 (0.261682) \| 0.583754 / 0.000490 (0.583265) \| 0.006579 / 0.000200 (0.006379) \| 0.000096 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034906 / 0.037411 (-0.002505) \| 0.099232 / 0.014526 (0.084706) \| 0.113093 / 0.176557 (-0.063464) \| 0.165499 / 0.737135 (-0.571636) \| 0.113398 / 0.296338 (-0.182941) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.439154 / 0.215209 (0.223945) \| 4.377041 / 2.077655 (2.299387) \| 2.395058 / 1.504120 (0.890938) \| 2.233359 / 1.541195 (0.692164) \| 2.357281 / 1.468490 (0.888791) \| 0.486036 / 4.584777 (-4.098741) \| 3.568794 / 3.745712 (-0.176918) \| 3.485421 / 5.269862 (-1.784440) \| 2.174325 / 4.565676 (-2.391351) \| 0.057855 / 0.424275 (-0.366420) \| 0.007545 / 0.007607 (-0.000062) \| 0.516853 / 0.226044 (0.290808) \| 5.173340 / 2.268929 (2.904412) \| 2.931475 / 55.444624 (-52.513149) \| 2.566814 / 6.876477 (-4.309663) \| 2.873304 / 2.142072 (0.731232) \| 0.597072 / 4.805227 (-4.208155) \| 0.133589 / 6.500664 (-6.367075) \| 0.061882 / 0.075469 (-0.013587) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.382845 / 1.841788 (-0.458943) \| 21.608316 / 8.074308 (13.534008) \| 15.702152 / 10.191392 (5.510759) \| 0.190629 / 0.680424 (-0.489795) \| 0.020572 / 0.534201 (-0.513629) \| 0.396207 / 0.579283 (-0.183076) \| 0.421184 / 0.434364 (-0.013180) \| 0.477700 / 0.540337 (-0.062638) \| 0.690828 / 1.386936 (-0.696108) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5e7374b453911cda5e0f866ad45b51c3fbe267c9 \"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) \| 0.008450 / 0.011353 (-0.002903) \| 0.004958 / 0.011008 (-0.006051) \| 0.105397 / 0.038508 (0.066889) \| 0.079508 / 0.023109 (0.056399) \| 0.403050 / 0.275898 (0.127152) \| 0.443679 / 0.323480 (0.120199) \| 0.004654 / 0.007986 (-0.003332) \| 0.005629 / 0.004328 (0.001301) \| 0.078755 / 0.004250 (0.074505) \| 0.055694 / 0.037052 (0.018642) \| 0.409952 / 0.258489 (0.151463) \| 0.454931 / 0.293841 (0.161090) \| 0.045124 / 0.128546 (-0.083422) \| 0.014031 / 0.075646 (-0.061616) \| 0.347340 / 0.419271 (-0.071931) \| 0.064359 / 0.043533 (0.020826) \| 0.414158 / 0.255139 (0.159019) \| 0.428442 / 0.283200 (0.145243) \| 0.033726 / 0.141683 (-0.107957) \| 1.770483 / 1.452155 (0.318328) \| 1.795267 / 1.492716 (0.302551) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.251020 / 0.018006 (0.233014) \| 0.507066 / 0.000490 (0.506576) \| 0.015751 / 0.000200 (0.015551) \| 0.000531 / 0.000054 (0.000477) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028897 / 0.037411 (-0.008515) \| 0.087393 / 0.014526 (0.072867) \| 0.097365 / 0.176557 (-0.079192) \| 0.164833 / 0.737135 (-0.572303) \| 0.101281 / 0.296338 (-0.195058) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.610806 / 0.215209 (0.395597) \| 6.011697 / 2.077655 (3.934042) \| 2.544268 / 1.504120 (1.040148) \| 2.127103 / 1.541195 (0.585908) \| 2.133330 / 1.468490 (0.664839) \| 0.860964 / 4.584777 (-3.723813) \| 4.982374 / 3.745712 (1.236662) \| 5.073026 / 5.269862 (-0.196836) \| 3.033056 / 4.565676 (-1.532621) \| 0.118835 / 0.424275 (-0.305440) \| 0.010122 / 0.007607 (0.002515) \| 0.805807 / 0.226044 (0.579763) \| 7.839166 / 2.268929 (5.570238) \| 3.512405 / 55.444624 (-51.932219) \| 2.767578 / 6.876477 (-4.108898) \| 2.936885 / 2.142072 (0.794813) \| 1.058533 / 4.805227 (-3.746695) \| 0.222260 / 6.500664 (-6.278404) \| 0.073890 / 0.075469 (-0.001580) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.628307 / 1.841788 (-0.213480) \| 22.827116 / 8.074308 (14.752808) \| 21.809759 / 10.191392 (11.618367) \| 0.220637 / 0.680424 (-0.459786) \| 0.028030 / 0.534201 (-0.506171) \| 0.448620 / 0.579283 (-0.130663) \| 0.540442 / 0.434364 (0.106078) \| 0.548601 / 0.540337 (0.008264) \| 0.770387 / 1.386936 (-0.616549) \|\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) \| 0.009198 / 0.011353 (-0.002155) \| 0.004935 / 0.011008 (-0.006073) \| 0.079095 / 0.038508 (0.040587) \| 0.090490 / 0.023109 (0.067381) \| 0.453374 / 0.275898 (0.177476) \| 0.519483 / 0.323480 (0.196003) \| 0.006539 / 0.007986 (-0.001447) \| 0.004160 / 0.004328 (-0.000169) \| 0.078433 / 0.004250 (0.074182) \| 0.068022 / 0.037052 (0.030969) \| 0.467686 / 0.258489 (0.209197) \| 0.523863 / 0.293841 (0.230022) \| 0.050926 / 0.128546 (-0.077620) \| 0.013664 / 0.075646 (-0.061982) \| 0.088787 / 0.419271 (-0.330485) \| 0.060503 / 0.043533 (0.016971) \| 0.474692 / 0.255139 (0.219553) \| 0.516461 / 0.283200 (0.233261) \| 0.034482 / 0.141683 (-0.107200) \| 1.747939 / 1.452155 (0.295784) \| 1.915212 / 1.492716 (0.422496) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.247400 / 0.018006 (0.229394) \| 0.516829 / 0.000490 (0.516339) \| 0.005770 / 0.000200 (0.005570) \| 0.000121 / 0.000054 (0.000067) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034334 / 0.037411 (-0.003077) \| 0.102397 / 0.014526 (0.087871) \| 0.114187 / 0.176557 (-0.062370) \| 0.171093 / 0.737135 (-0.566043) \| 0.117281 / 0.296338 (-0.179058) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.635710 / 0.215209 (0.420501) \| 6.400656 / 2.077655 (4.323002) \| 2.896896 / 1.504120 (1.392776) \| 2.682890 / 1.541195 (1.141696) \| 2.656445 / 1.468490 (1.187955) \| 1.044244 / 4.584777 (-3.540533) \| 5.393212 / 3.745712 (1.647500) \| 4.592928 / 5.269862 (-0.676934) \| 2.798525 / 4.565676 (-1.767151) \| 0.103720 / 0.424275 (-0.320555) \| 0.010196 / 0.007607 (0.002589) \| 0.762756 / 0.226044 (0.536711) \| 7.232939 / 2.268929 (4.964011) \| 3.714015 / 55.444624 (-51.730609) \| 3.050766 / 6.876477 (-3.825711) \| 3.149715 / 2.142072 (1.007643) \| 1.058827 / 4.805227 (-3.746400) \| 0.214079 / 6.500664 (-6.286585) \| 0.076712 / 0.075469 (0.001243) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.701032 / 1.841788 (-0.140755) \| 23.742023 / 8.074308 (15.667715) \| 22.486043 / 10.191392 (12.294651) \| 0.249757 / 0.680424 (-0.430667) \| 0.031714 / 0.534201 (-0.502486) \| 0.479914 / 0.579283 (-0.099369) \| 0.593315 / 0.434364 (0.158951) \| 0.562897 / 0.540337 (0.022560) \| 0.826636 / 1.386936 (-0.560300) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#429f9c69d1813ec643c316313b69ff23aaf208f6 \"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) \| 0.007816 / 0.011353 (-0.003537) \| 0.004541 / 0.011008 (-0.006467) \| 0.097256 / 0.038508 (0.058748) \| 0.081376 / 0.023109 (0.058267) \| 0.356635 / 0.275898 (0.080737) \| 0.394969 / 0.323480 (0.071489) \| 0.004670 / 0.007986 (-0.003316) \| 0.003537 / 0.004328 (-0.000791) \| 0.075564 / 0.004250 (0.071314) \| 0.063459 / 0.037052 (0.026407) \| 0.363846 / 0.258489 (0.105357) \| 0.416337 / 0.293841 (0.122496) \| 0.036690 / 0.128546 (-0.091857) \| 0.009653 / 0.075646 (-0.065993) \| 0.337265 / 0.419271 (-0.082007) \| 0.061446 / 0.043533 (0.017913) \| 0.359190 / 0.255139 (0.104051) \| 0.385866 / 0.283200 (0.102666) \| 0.030474 / 0.141683 (-0.111209) \| 1.796903 / 1.452155 (0.344748) \| 1.852332 / 1.492716 (0.359616) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.264008 / 0.018006 (0.246002) \| 0.507387 / 0.000490 (0.506897) \| 0.012309 / 0.000200 (0.012109) \| 0.000377 / 0.000054 (0.000323) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033224 / 0.037411 (-0.004188) \| 0.097136 / 0.014526 (0.082610) \| 0.113035 / 0.176557 (-0.063522) \| 0.181778 / 0.737135 (-0.555357) \| 0.130511 / 0.296338 (-0.165827) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.444512 / 0.215209 (0.229303) \| 4.453285 / 2.077655 (2.375631) \| 2.154123 / 1.504120 (0.650003) \| 1.955451 / 1.541195 (0.414256) \| 2.015089 / 1.468490 (0.546599) \| 0.567824 / 4.584777 (-4.016953) \| 4.083084 / 3.745712 (0.337371) \| 3.912417 / 5.269862 (-1.357445) \| 2.366197 / 4.565676 (-2.199480) \| 0.066468 / 0.424275 (-0.357807) \| 0.008478 / 0.007607 (0.000870) \| 0.531196 / 0.226044 (0.305152) \| 5.311285 / 2.268929 (3.042356) \| 2.743252 / 55.444624 (-52.701372) \| 2.322353 / 6.876477 (-4.554124) \| 2.368168 / 2.142072 (0.226095) \| 0.679223 / 4.805227 (-4.126004) \| 0.152401 / 6.500664 (-6.348263) \| 0.071954 / 0.075469 (-0.003515) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.489114 / 1.841788 (-0.352674) \| 22.114956 / 8.074308 (14.040648) \| 16.072564 / 10.191392 (5.881172) \| 0.164303 / 0.680424 (-0.516121) \| 0.021317 / 0.534201 (-0.512884) \| 0.460250 / 0.579283 (-0.119033) \| 0.467554 / 0.434364 (0.033190) \| 0.539773 / 0.540337 (-0.000564) \| 0.751904 / 1.386936 (-0.635032) \|\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) \| 0.007520 / 0.011353 (-0.003833) \| 0.004487 / 0.011008 (-0.006521) \| 0.075074 / 0.038508 (0.036566) \| 0.083135 / 0.023109 (0.060026) \| 0.474052 / 0.275898 (0.198154) \| 0.524051 / 0.323480 (0.200571) \| 0.006192 / 0.007986 (-0.001793) \| 0.003835 / 0.004328 (-0.000494) \| 0.074643 / 0.004250 (0.070392) \| 0.065334 / 0.037052 (0.028282) \| 0.507033 / 0.258489 (0.248544) \| 0.519846 / 0.293841 (0.226005) \| 0.036985 / 0.128546 (-0.091561) \| 0.009828 / 0.075646 (-0.065818) \| 0.082992 / 0.419271 (-0.336279) \| 0.055942 / 0.043533 (0.012409) \| 0.480652 / 0.255139 (0.225513) \| 0.503683 / 0.283200 (0.220483) \| 0.025560 / 0.141683 (-0.116123) \| 1.801390 / 1.452155 (0.349235) \| 1.892929 / 1.492716 (0.400213) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246771 / 0.018006 (0.228765) \| 0.498901 / 0.000490 (0.498411) \| 0.008186 / 0.000200 (0.007986) \| 0.000166 / 0.000054 (0.000112) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038666 / 0.037411 (0.001254) \| 0.110317 / 0.014526 (0.095791) \| 0.122995 / 0.176557 (-0.053562) \| 0.185355 / 0.737135 (-0.551781) \| 0.123720 / 0.296338 (-0.172619) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.508421 / 0.215209 (0.293212) \| 5.046464 / 2.077655 (2.968809) \| 2.660004 / 1.504120 (1.155884) \| 2.482841 / 1.541195 (0.941646) \| 2.573941 / 1.468490 (1.105451) \| 0.565702 / 4.584777 (-4.019075) \| 4.197895 / 3.745712 (0.452183) \| 3.755480 / 5.269862 (-1.514381) \| 2.308066 / 4.565676 (-2.257610) \| 0.066559 / 0.424275 (-0.357716) \| 0.008436 / 0.007607 (0.000829) \| 0.589858 / 0.226044 (0.363814) \| 5.873488 / 2.268929 (3.604559) \| 3.241810 / 55.444624 (-52.202814) \| 2.789831 / 6.876477 (-4.086645) \| 3.008989 / 2.142072 (0.866917) \| 0.679624 / 4.805227 (-4.125603) \| 0.150868 / 6.500664 (-6.349796) \| 0.068581 / 0.075469 (-0.006889) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.582955 / 1.841788 (-0.258833) \| 22.684969 / 8.074308 (14.610661) \| 16.829855 / 10.191392 (6.638463) \| 0.201599 / 0.680424 (-0.478825) \| 0.023261 / 0.534201 (-0.510940) \| 0.465009 / 0.579283 (-0.114274) \| 0.497701 / 0.434364 (0.063337) \| 0.557822 / 0.540337 (0.017485) \| 0.803234 / 1.386936 (-0.583702) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9241c1070b5c9021705c17b12548b6fea75782d8 \"CML watermark\")\n", "Well done! :clap: :fire: ", "<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) \| 0.008866 / 0.011353 (-0.002487) \| 0.005910 / 0.011008 (-0.005098) \| 0.099916 / 0.038508 (0.061408) \| 0.085787 / 0.023109 (0.062678) \| 0.391028 / 0.275898 (0.115130) \| 0.412689 / 0.323480 (0.089209) \| 0.006527 / 0.007986 (-0.001459) \| 0.004629 / 0.004328 (0.000301) \| 0.084627 / 0.004250 (0.080377) \| 0.063404 / 0.037052 (0.026352) \| 0.408923 / 0.258489 (0.150434) \| 0.437130 / 0.293841 (0.143289) \| 0.050256 / 0.128546 (-0.078290) \| 0.013914 / 0.075646 (-0.061732) \| 0.350893 / 0.419271 (-0.068379) \| 0.067931 / 0.043533 (0.024398) \| 0.383807 / 0.255139 (0.128668) \| 0.424150 / 0.283200 (0.140950) \| 0.039978 / 0.141683 (-0.101705) \| 1.697631 / 1.452155 (0.245476) \| 1.925568 / 1.492716 (0.432851) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.315417 / 0.018006 (0.297410) \| 0.607050 / 0.000490 (0.606560) \| 0.017314 / 0.000200 (0.017114) \| 0.000514 / 0.000054 (0.000459) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032994 / 0.037411 (-0.004417) \| 0.103993 / 0.014526 (0.089467) \| 0.125369 / 0.176557 (-0.051187) \| 0.185984 / 0.737135 (-0.551151) \| 0.139192 / 0.296338 (-0.157146) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.639769 / 0.215209 (0.424560) \| 6.236187 / 2.077655 (4.158532) \| 2.775777 / 1.504120 (1.271657) \| 2.599683 / 1.541195 (1.058488) \| 2.780064 / 1.468490 (1.311574) \| 1.107247 / 4.584777 (-3.477530) \| 5.724223 / 3.745712 (1.978511) \| 5.284786 / 5.269862 (0.014925) \| 3.342465 / 4.565676 (-1.223211) \| 0.107685 / 0.424275 (-0.316590) \| 0.009237 / 0.007607 (0.001630) \| 0.760282 / 0.226044 (0.534238) \| 7.570859 / 2.268929 (5.301930) \| 3.572498 / 55.444624 (-51.872126) \| 2.997482 / 6.876477 (-3.878995) \| 2.910001 / 2.142072 (0.767929) \| 1.249272 / 4.805227 (-3.555955) \| 0.229425 / 6.500664 (-6.271239) \| 0.091974 / 0.075469 (0.016505) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.663859 / 1.841788 (-0.177929) \| 25.283961 / 8.074308 (17.209653) \| 20.793389 / 10.191392 (10.601997) \| 0.239263 / 0.680424 (-0.441161) \| 0.028808 / 0.534201 (-0.505393) \| 0.521045 / 0.579283 (-0.058238) \| 0.602451 / 0.434364 (0.168087) \| 0.544536 / 0.540337 (0.004198) \| 0.819732 / 1.386936 (-0.567204) \|\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) \| 0.008970 / 0.011353 (-0.002383) \| 0.009663 / 0.011008 (-0.001345) \| 0.083471 / 0.038508 (0.044963) \| 0.090695 / 0.023109 (0.067585) \| 0.562539 / 0.275898 (0.286641) \| 0.572092 / 0.323480 (0.248612) \| 0.007269 / 0.007986 (-0.000717) \| 0.004664 / 0.004328 (0.000335) \| 0.084212 / 0.004250 (0.079961) \| 0.072716 / 0.037052 (0.035664) \| 0.559810 / 0.258489 (0.301320) \| 0.574296 / 0.293841 (0.280455) \| 0.048555 / 0.128546 (-0.079991) \| 0.015901 / 0.075646 (-0.059746) \| 0.107815 / 0.419271 (-0.311456) \| 0.065404 / 0.043533 (0.021871) \| 0.544787 / 0.255139 (0.289648) \| 0.586993 / 0.283200 (0.303794) \| 0.042613 / 0.141683 (-0.099069) \| 1.919266 / 1.452155 (0.467111) \| 2.095189 / 1.492716 (0.602473) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298512 / 0.018006 (0.280506) \| 0.597745 / 0.000490 (0.597256) \| 0.008806 / 0.000200 (0.008606) \| 0.000119 / 0.000054 (0.000064) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.039420 / 0.037411 (0.002009) \| 0.111378 / 0.014526 (0.096852) \| 0.136421 / 0.176557 (-0.040135) \| 0.192006 / 0.737135 (-0.545129) \| 0.130037 / 0.296338 (-0.166301) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.679169 / 0.215209 (0.463960) \| 6.750881 / 2.077655 (4.673226) \| 3.220411 / 1.504120 (1.716291) \| 2.851988 / 1.541195 (1.310794) \| 2.974247 / 1.468490 (1.505757) \| 0.892593 / 4.584777 (-3.692184) \| 5.659975 / 3.745712 (1.914263) \| 5.172641 / 5.269862 (-0.097220) \| 3.308429 / 4.565676 (-1.257248) \| 0.100580 / 0.424275 (-0.323695) \| 0.009320 / 0.007607 (0.001713) \| 0.833290 / 0.226044 (0.607245) \| 8.091847 / 2.268929 (5.822918) \| 4.023734 / 55.444624 (-51.420890) \| 3.441583 / 6.876477 (-3.434894) \| 3.763562 / 2.142072 (1.621489) \| 1.055105 / 4.805227 (-3.750122) \| 0.239218 / 6.500664 (-6.261446) \| 0.081922 / 0.075469 (0.006453) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.796495 / 1.841788 (-0.045293) \| 25.942492 / 8.074308 (17.868184) \| 23.211617 / 10.191392 (13.020225) \| 0.256054 / 0.680424 (-0.424370) \| 0.030491 / 0.534201 (-0.503710) \| 0.520474 / 0.579283 (-0.058809) \| 0.626331 / 0.434364 (0.191967) \| 0.619897 / 0.540337 (0.079560) \| 0.900833 / 1.386936 (-0.486103) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e74f80255700c4b8cde383a426c4b2def6db1253 \"CML watermark\")\n", "Congrats on merging this! 👏 " ]	2023-09-29T16:22:31Z	2023-10-16T16:03:18Z	2023-10-16T13:30:46Z	COLLABORATOR	null	null	null	Reduces the number of commits in `push_to_hub` by using the `preupload` API from https://github.com/huggingface/huggingface_hub/pull/1699. Each commit contains a maximum of 50 uploaded files. A shard's fingerprint no longer needs to be added as a suffix to support resuming an upload, meaning the shards' naming scheme is the same as the initial one. Also, it adds support for the following params: `create_pr`, `commit_message` and `revision` (`branch` deprecated; unlike the previous implementation, this one creates a branch if the branch does not exist to be consistent with `transformers`). (Nit) This implementation keeps the markdown section of the generated README.md empty to enable importing the card template (when the card is accessed on the Hub). Fixes https://github.com/huggingface/datasets/issues/5492, fixes https://github.com/huggingface/datasets/issues/6257, fixes https://github.com/huggingface/datasets/issues/5045, fixes https://github.com/huggingface/datasets/issues/6271 TODO: - [x] set the minimal version to the next `hfh` release (once it's published)	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 3, "laugh": 0, "rocket": 1, "total_count": 4, "url": "https://api.github.com/repos/huggingface/datasets/issues/6269/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6269/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6269.diff", "html_url": "https://github.com/huggingface/datasets/pull/6269", "merged_at": "2023-10-16T13:30:46Z", "patch_url": "https://github.com/huggingface/datasets/pull/6269.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6269" }
https://api.github.com/repos/huggingface/datasets/issues/6749	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6749/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6749/comments	https://api.github.com/repos/huggingface/datasets/issues/6749/events	https://github.com/huggingface/datasets/pull/6749	2,202,310,116	PR_kwDODunzps5qeoSk	6,749	Fix fsspec tqdm callback	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6749). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005017 / 0.011353 (-0.006336) \| 0.002958 / 0.011008 (-0.008050) \| 0.063455 / 0.038508 (0.024946) \| 0.028206 / 0.023109 (0.005096) \| 0.230884 / 0.275898 (-0.045014) \| 0.252688 / 0.323480 (-0.070792) \| 0.002995 / 0.007986 (-0.004991) \| 0.002613 / 0.004328 (-0.001716) \| 0.046477 / 0.004250 (0.042226) \| 0.040662 / 0.037052 (0.003609) \| 0.241824 / 0.258489 (-0.016665) \| 0.269063 / 0.293841 (-0.024778) \| 0.027336 / 0.128546 (-0.101210) \| 0.010614 / 0.075646 (-0.065032) \| 0.216087 / 0.419271 (-0.203184) \| 0.035667 / 0.043533 (-0.007866) \| 0.238657 / 0.255139 (-0.016482) \| 0.253433 / 0.283200 (-0.029767) \| 0.017433 / 0.141683 (-0.124250) \| 1.120856 / 1.452155 (-0.331299) \| 1.157415 / 1.492716 (-0.335302) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088028 / 0.018006 (0.070022) \| 0.277368 / 0.000490 (0.276878) \| 0.000204 / 0.000200 (0.000004) \| 0.000049 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.017956 / 0.037411 (-0.019455) \| 0.061061 / 0.014526 (0.046535) \| 0.073323 / 0.176557 (-0.103234) \| 0.119254 / 0.737135 (-0.617881) \| 0.074308 / 0.296338 (-0.222031) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285118 / 0.215209 (0.069908) \| 2.785796 / 2.077655 (0.708142) \| 1.476436 / 1.504120 (-0.027684) \| 1.356505 / 1.541195 (-0.184690) \| 1.362505 / 1.468490 (-0.105985) \| 0.554064 / 4.584777 (-4.030713) \| 2.395774 / 3.745712 (-1.349938) \| 2.713703 / 5.269862 (-2.556159) \| 1.701020 / 4.565676 (-2.864657) \| 0.062370 / 0.424275 (-0.361905) \| 0.004944 / 0.007607 (-0.002663) \| 0.327948 / 0.226044 (0.101904) \| 3.243739 / 2.268929 (0.974811) \| 1.803881 / 55.444624 (-53.640743) \| 1.551635 / 6.876477 (-5.324841) \| 1.560627 / 2.142072 (-0.581446) \| 0.628187 / 4.805227 (-4.177040) \| 0.115824 / 6.500664 (-6.384840) \| 0.041655 / 0.075469 (-0.033814) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.968797 / 1.841788 (-0.872991) \| 11.220905 / 8.074308 (3.146597) \| 9.322584 / 10.191392 (-0.868808) \| 0.139629 / 0.680424 (-0.540795) \| 0.013823 / 0.534201 (-0.520378) \| 0.286700 / 0.579283 (-0.292583) \| 0.263517 / 0.434364 (-0.170847) \| 0.341264 / 0.540337 (-0.199074) \| 0.418834 / 1.386936 (-0.968102) \|\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) \| 0.005404 / 0.011353 (-0.005949) \| 0.003630 / 0.011008 (-0.007378) \| 0.048977 / 0.038508 (0.010469) \| 0.029980 / 0.023109 (0.006871) \| 0.274671 / 0.275898 (-0.001227) \| 0.295671 / 0.323480 (-0.027808) \| 0.004230 / 0.007986 (-0.003756) \| 0.002656 / 0.004328 (-0.001672) \| 0.048603 / 0.004250 (0.044353) \| 0.044323 / 0.037052 (0.007271) \| 0.286499 / 0.258489 (0.028010) \| 0.313199 / 0.293841 (0.019358) \| 0.030079 / 0.128546 (-0.098468) \| 0.010480 / 0.075646 (-0.065166) \| 0.058226 / 0.419271 (-0.361045) \| 0.054920 / 0.043533 (0.011387) \| 0.274921 / 0.255139 (0.019783) \| 0.296559 / 0.283200 (0.013360) \| 0.019164 / 0.141683 (-0.122519) \| 1.154703 / 1.452155 (-0.297452) \| 1.207015 / 1.492716 (-0.285701) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089368 / 0.018006 (0.071362) \| 0.301196 / 0.000490 (0.300706) \| 0.000208 / 0.000200 (0.000008) \| 0.000047 / 0.000054 (-0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021355 / 0.037411 (-0.016056) \| 0.074688 / 0.014526 (0.060162) \| 0.085840 / 0.176557 (-0.090716) \| 0.125784 / 0.737135 (-0.611351) \| 0.087103 / 0.296338 (-0.209235) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296727 / 0.215209 (0.081518) \| 2.884922 / 2.077655 (0.807267) \| 1.586515 / 1.504120 (0.082395) \| 1.474417 / 1.541195 (-0.066777) \| 1.492105 / 1.468490 (0.023615) \| 0.570016 / 4.584777 (-4.014761) \| 2.435760 / 3.745712 (-1.309952) \| 2.657999 / 5.269862 (-2.611863) \| 1.740160 / 4.565676 (-2.825516) \| 0.063743 / 0.424275 (-0.360532) \| 0.005048 / 0.007607 (-0.002559) \| 0.341279 / 0.226044 (0.115235) \| 3.396185 / 2.268929 (1.127256) \| 1.952825 / 55.444624 (-53.491800) \| 1.676669 / 6.876477 (-5.199808) \| 1.773158 / 2.142072 (-0.368915) \| 0.650664 / 4.805227 (-4.154563) \| 0.116815 / 6.500664 (-6.383849) \| 0.040813 / 0.075469 (-0.034656) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.999836 / 1.841788 (-0.841952) \| 11.854540 / 8.074308 (3.780232) \| 10.245516 / 10.191392 (0.054124) \| 0.141235 / 0.680424 (-0.539189) \| 0.015562 / 0.534201 (-0.518639) \| 0.287556 / 0.579283 (-0.291727) \| 0.274946 / 0.434364 (-0.159418) \| 0.324652 / 0.540337 (-0.215685) \| 0.449204 / 1.386936 (-0.937733) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ed2b406d045349dad16738985c947fe743260710 \"CML watermark\")\n" ]	2024-03-22T11:44:11Z	2024-03-22T14:51:45Z	2024-03-22T14:45:39Z	MEMBER	null	null	null	Following changes at https://github.com/fsspec/filesystem_spec/pull/1497 for `fsspec>=2024.2.0`	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6749/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6749/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6749.diff", "html_url": "https://github.com/huggingface/datasets/pull/6749", "merged_at": "2024-03-22T14:45:39Z", "patch_url": "https://github.com/huggingface/datasets/pull/6749.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6749" }
https://api.github.com/repos/huggingface/datasets/issues/7203	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7203/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7203/comments	https://api.github.com/repos/huggingface/datasets/issues/7203/events	https://github.com/huggingface/datasets/pull/7203	2,573,154,222	PR_kwDODunzps598skr	7,203	with_format docstring	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7203). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]	2024-10-08T13:05:19Z	2024-10-08T13:13:12Z	2024-10-08T13:13:05Z	MEMBER	null	null	null	reported at https://github.com/huggingface/datasets/issues/3444	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7203/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7203/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7203.diff", "html_url": "https://github.com/huggingface/datasets/pull/7203", "merged_at": "2024-10-08T13:13:05Z", "patch_url": "https://github.com/huggingface/datasets/pull/7203.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7203" }
https://api.github.com/repos/huggingface/datasets/issues/6929	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6929/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6929/comments	https://api.github.com/repos/huggingface/datasets/issues/6929/events	https://github.com/huggingface/datasets/issues/6929	2,322,980,077	I_kwDODunzps6Kddzt	6,929	Avoid downloading the whole dataset when only README.me has been touched on hub.	{ "avatar_url": "https://avatars.githubusercontent.com/u/73740254?v=4", "events_url": "https://api.github.com/users/zinc75/events{/privacy}", "followers_url": "https://api.github.com/users/zinc75/followers", "following_url": "https://api.github.com/users/zinc75/following{/other_user}", "gists_url": "https://api.github.com/users/zinc75/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/zinc75", "id": 73740254, "login": "zinc75", "node_id": "MDQ6VXNlcjczNzQwMjU0", "organizations_url": "https://api.github.com/users/zinc75/orgs", "received_events_url": "https://api.github.com/users/zinc75/received_events", "repos_url": "https://api.github.com/users/zinc75/repos", "site_admin": false, "starred_url": "https://api.github.com/users/zinc75/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/zinc75/subscriptions", "type": "User", "url": "https://api.github.com/users/zinc75", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	[ "you're right, we're tackling this here: https://github.com/huggingface/dataset-viewer/issues/2757", "@severo : great !" ]	2024-05-29T10:36:06Z	2024-05-29T20:51:56Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request `datasets.load_dataset()` triggers a new download of the whole dataset when the README.md file has been touched on huggingface hub, even if data files / parquet files are the exact same. I think the current behaviour of the load_dataset function is triggered whenever a change of the hash of latest commit on huggingface hub, but is there a clever way to only download again the dataset if and only if data is modified ? ### Motivation The current behaviour is a waste of network bandwidth / disk space / research time. ### Your contribution I don't have time to submit a PR, but I hope a simple solution will emerge from this issue !	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 1, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6929/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6929/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5078	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5078/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5078/comments	https://api.github.com/repos/huggingface/datasets/issues/5078/events	https://github.com/huggingface/datasets/pull/5078	1,398,335,148	PR_kwDODunzps5APjkH	5,078	Fix header level in Audio docs	{ "avatar_url": "https://avatars.githubusercontent.com/u/59462357?v=4", "events_url": "https://api.github.com/users/stevhliu/events{/privacy}", "followers_url": "https://api.github.com/users/stevhliu/followers", "following_url": "https://api.github.com/users/stevhliu/following{/other_user}", "gists_url": "https://api.github.com/users/stevhliu/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/stevhliu", "id": 59462357, "login": "stevhliu", "node_id": "MDQ6VXNlcjU5NDYyMzU3", "organizations_url": "https://api.github.com/users/stevhliu/orgs", "received_events_url": "https://api.github.com/users/stevhliu/received_events", "repos_url": "https://api.github.com/users/stevhliu/repos", "site_admin": false, "starred_url": "https://api.github.com/users/stevhliu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/stevhliu/subscriptions", "type": "User", "url": "https://api.github.com/users/stevhliu", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-10-05T20:22:44Z	2022-10-06T08:12:23Z	2022-10-06T08:09:41Z	MEMBER	null	null	null	Fixes header level so `Dataset features` is the doc title instead of `The Audio type`: ![Screen Shot 2022-10-05 at 1 22 02 PM](https://user-images.githubusercontent.com/59462357/194155840-eeb5d62f-f4eb-411e-b281-8494c5fffdce.png)	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5078/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5078/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5078.diff", "html_url": "https://github.com/huggingface/datasets/pull/5078", "merged_at": "2022-10-06T08:09:41Z", "patch_url": "https://github.com/huggingface/datasets/pull/5078.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5078" }
https://api.github.com/repos/huggingface/datasets/issues/5166	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5166/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5166/comments	https://api.github.com/repos/huggingface/datasets/issues/5166/events	https://github.com/huggingface/datasets/pull/5166	1,423,629,582	PR_kwDODunzps5Bj5IQ	5,166	Support dill 0.3.6	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "I think it hasn't been merged ? https://github.com/uqfoundation/dill/pull/501\r\n\r\nThough I can see that the CI is green because it uses dill 0.3.1.1 - we should probably fix the dill version in both CIs:\r\n- use 0.3.1.1 for the CI with the minimum requirements\r\n- use latest for the CI with the latest requirements", "I have noticed our CI uses `dill-0.3.1.1`, so not really testing dill 0.3.6...", "The dill version in our CI is due to `apache-beam`...", "I've tested locally: we need a specific fix for 0.3.6 (different from the previous ones)...", "I think we can force the version of dill to be whatever we want in the CI - no matter what beam says. The alternative would be to run beam tests separately but it's more work", "@lhoestq I tried the easiest solution: force dill==0.3.6 ignoring the requirement of apache-beam. But it doesn't work:\r\n- For example, for `tests/test_builder.py::test_beam_based_builder_download_and_prepare_as_parquet`:\r\n```\r\n @dill.dill.register(dill.dill.ModuleType)\r\n def save_module(pickler, obj):\r\n if dill.dill.is_dill(pickler) and obj is pickler._main:\r\n return old_save_module(pickler, obj)\r\n else:\r\n> dill.dill.log.info('M2: %s' % obj)\r\nE AttributeError: module 'dill._dill' has no attribute 'log'\r\n\r\nvenv/lib/python3.9/site-packages/apache_beam/internal/dill_pickler.py:170: AttributeError\r\n```\r\n - Apache Beam registers some dill functions (`save_module`) which are incompatible with dill 0.3.6 (in 0.3.6 'dill._dill' has no attribute 'log' but 'logger')\r\n - This has an impact in CI tests using either Apache Beam or `multiprocess` (even without using Apache Beam!):\r\n```\r\nFAILED tests/test_beam.py::BeamBuilderTest::test_download_and_prepare - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_beam.py::BeamBuilderTest::test_nested_features - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_filter_multiprocessing_in_memory - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_filter_multiprocessing_on_disk - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_builder.py::test_beam_based_download_and_prepare - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_in_memory - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_on_disk - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_builder.py::test_beam_based_as_dataset - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_multiprocessing_in_memory - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_multiprocessing_on_disk - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_builder.py::test_beam_based_builder_download_and_prepare_as_parquet - AttributeError: module 'dill._dill' has no attribute 'log'\r\n```\r\n\r\nI guess we should implement the other option: run beam tests separately.\r\n\r\nI'm opening another PR for the CI refactoring.", "Ah crap >< maybe only install apache_beam for the \"minimum requirements\" CI", "@lhoestq if we install apache-beam only in the \"minimum requirements\" CI, then this other PR should be merged first:\r\n- #5168 \r\n\r\nOtherwise, our CI for \"latest\" will fail because it will try to run the beam tests (because PyTorch is installed but indeed apache-beam is not installed).", "One of the test is failing because we set \r\n```python\r\n# google colab doesn't allow to pickle loggers\r\n# so we want to make sure each tests passes without pickling the logger\r\ndef reduce_ex(self):\r\n raise pickle.PicklingError()\r\n\r\ndatasets.arrow_dataset.logger.__reduce_ex__ = reduce_ex\r\n```\r\nin `test_arrow_dataset.py` to avoid pickling the logger because it used to fail on google colab.\r\n\r\nNow pickling the logger seems to be working on google colab again - so you can remove it, and it should fix some tests", "For the other 2 errors:\r\n- FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_in_memory - _pickle.PicklingError: Can't pickle <class 'unittest.mock.MagicMock'>: it's not the same object as unittest.mock.MagicMock\r\n- FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_on_disk - _pickle.PicklingError: Can't pickle <class 'unittest.mock.MagicMock'>: it's not the same object as unittest.mock.MagicMock\r\n\r\nI have implemented a pickable MagicMock." ]	2022-10-26T08:24:59Z	2022-10-28T05:41:05Z	2022-10-28T05:38:14Z	MEMBER	null	null	null	This PR: - ~~Unpins dill to allow installing dill>=0.3.6~~ - ~~Removes the fix on dill for >=0.3.6 because they implemented a deterministic mode (to be confirmed by @anivegesana)~~ - Pins dill<0.3.7 to allow latest dill 0.3.6 - Implements a fix for dill `save_function` for dill 0.3.6 - Additionally had to implement a fix for dill `save_code` and `_save_regex` for dill 0.3.6 - Fixes the CI so that the latest dill version is tested (besides the minimum 0.3.1.1 required by apache-beam 2.42.0) Fix #5162.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5166/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5166/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5166.diff", "html_url": "https://github.com/huggingface/datasets/pull/5166", "merged_at": "2022-10-28T05:38:14Z", "patch_url": "https://github.com/huggingface/datasets/pull/5166.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5166" }
https://api.github.com/repos/huggingface/datasets/issues/7432	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7432/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7432/comments	https://api.github.com/repos/huggingface/datasets/issues/7432/events	https://github.com/huggingface/datasets/pull/7432	2,887,717,289	PR_kwDODunzps6M-DI0	7,432	Fix type annotation	{ "avatar_url": "https://avatars.githubusercontent.com/u/730137?v=4", "events_url": "https://api.github.com/users/NeilGirdhar/events{/privacy}", "followers_url": "https://api.github.com/users/NeilGirdhar/followers", "following_url": "https://api.github.com/users/NeilGirdhar/following{/other_user}", "gists_url": "https://api.github.com/users/NeilGirdhar/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/NeilGirdhar", "id": 730137, "login": "NeilGirdhar", "node_id": "MDQ6VXNlcjczMDEzNw==", "organizations_url": "https://api.github.com/users/NeilGirdhar/orgs", "received_events_url": "https://api.github.com/users/NeilGirdhar/received_events", "repos_url": "https://api.github.com/users/NeilGirdhar/repos", "site_admin": false, "starred_url": "https://api.github.com/users/NeilGirdhar/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/NeilGirdhar/subscriptions", "type": "User", "url": "https://api.github.com/users/NeilGirdhar", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Thanks ! There is https://github.com/huggingface/datasets/pull/7426 already that fixes the issue, I'm closing your PR if you don't mind" ]	2025-02-28T17:28:20Z	2025-03-04T15:53:03Z	2025-03-04T15:53:03Z	CONTRIBUTOR	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7432/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7432/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7432.diff", "html_url": "https://github.com/huggingface/datasets/pull/7432", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/7432.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7432" }
https://api.github.com/repos/huggingface/datasets/issues/7151	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7151/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7151/comments	https://api.github.com/repos/huggingface/datasets/issues/7151/events	https://github.com/huggingface/datasets/pull/7151	2,527,577,048	PR_kwDODunzps57kyY4	7,151	Align filename prefix splitting with WebDataset library	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[]	2024-09-16T06:07:39Z	2024-09-16T15:26:36Z	2024-09-16T15:26:34Z	MEMBER	null	null	null	Align filename prefix splitting with WebDataset library. This PR uses the same `base_plus_ext` function as the one used by the `webdataset` library. Fix #7150. Related to #7144.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7151/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7151/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7151.diff", "html_url": "https://github.com/huggingface/datasets/pull/7151", "merged_at": "2024-09-16T15:26:34Z", "patch_url": "https://github.com/huggingface/datasets/pull/7151.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7151" }
https://api.github.com/repos/huggingface/datasets/issues/4818	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4818/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4818/comments	https://api.github.com/repos/huggingface/datasets/issues/4818/events	https://github.com/huggingface/datasets/pull/4818	1,334,941,810	PR_kwDODunzps48-W7a	4,818	Add add cc-by-sa-2.5 license tag	{ "avatar_url": "https://avatars.githubusercontent.com/u/16348744?v=4", "events_url": "https://api.github.com/users/polinaeterna/events{/privacy}", "followers_url": "https://api.github.com/users/polinaeterna/followers", "following_url": "https://api.github.com/users/polinaeterna/following{/other_user}", "gists_url": "https://api.github.com/users/polinaeterna/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/polinaeterna", "id": 16348744, "login": "polinaeterna", "node_id": "MDQ6VXNlcjE2MzQ4NzQ0", "organizations_url": "https://api.github.com/users/polinaeterna/orgs", "received_events_url": "https://api.github.com/users/polinaeterna/received_events", "repos_url": "https://api.github.com/users/polinaeterna/repos", "site_admin": false, "starred_url": "https://api.github.com/users/polinaeterna/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/polinaeterna/subscriptions", "type": "User", "url": "https://api.github.com/users/polinaeterna", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4818). All of your documentation changes will be reflected on that endpoint.", "I think we can close this PR because the `standard_licenses.tsv` file was removed from this repo and we no longer perform any dataset card validation." ]	2022-08-10T17:18:39Z	2022-10-04T13:47:24Z	2022-10-04T13:47:24Z	CONTRIBUTOR	null	null	null	- [ ] add it to moon-landing - [ ] add it to hub-docs	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4818/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4818/timeline	null	null	1	{ "diff_url": "https://github.com/huggingface/datasets/pull/4818.diff", "html_url": "https://github.com/huggingface/datasets/pull/4818", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/4818.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4818" }
https://api.github.com/repos/huggingface/datasets/issues/5930	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5930/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5930/comments	https://api.github.com/repos/huggingface/datasets/issues/5930/events	https://github.com/huggingface/datasets/issues/5930	1,745,184,395	I_kwDODunzps5oBWaL	5,930	loading private custom dataset script - authentication error	{ "avatar_url": "https://avatars.githubusercontent.com/u/103381497?v=4", "events_url": "https://api.github.com/users/flckv/events{/privacy}", "followers_url": "https://api.github.com/users/flckv/followers", "following_url": "https://api.github.com/users/flckv/following{/other_user}", "gists_url": "https://api.github.com/users/flckv/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/flckv", "id": 103381497, "login": "flckv", "node_id": "U_kgDOBil5-Q", "organizations_url": "https://api.github.com/users/flckv/orgs", "received_events_url": "https://api.github.com/users/flckv/received_events", "repos_url": "https://api.github.com/users/flckv/repos", "site_admin": false, "starred_url": "https://api.github.com/users/flckv/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/flckv/subscriptions", "type": "User", "url": "https://api.github.com/users/flckv", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "This issue seems to have been resolved, so I'm closing it." ]	2023-06-07T06:58:23Z	2023-06-15T14:49:21Z	2023-06-15T14:49:20Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Train model with my custom dataset stored in HuggingFace and loaded with the loading script requires authentication but I am not sure how ? I am logged in in the terminal, in the browser. I receive this error: /python3.8/site-packages/datasets/utils/file_utils.py", line 566, in get_from_cache raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})") ConnectionError: Couldn't reach https://huggingface.co/datasets/fkov/s/blob/main/data/s/train/labels `(ConnectionError('Unauthorized for URL `https://huggingface.co/datasets/fkov/s/blob/main/data/s/train/labels. Please use the parameter ``use_auth_token=True`` after logging in with ``huggingface-cli login``')) when I added: `use_auth_token=True` and logged in via terminal then I received error: or the same error in different format: raise ConnectionError(f"`Couldn't reach {url} (error {response.status_code}`)") ConnectionError: Couldn't reach https://huggingface.co/datasets/fkov/s/blob/main/data/s/train/labels (`error 401`) ### Steps to reproduce the bug 1. cloned transformers library locally: https://huggingface.co/docs/transformers/v4.15.0/examples : > git clone https://github.com/huggingface/transformers > cd transformers > pip install . > cd /transformers/examples/pytorch/audio-classification > pip install -r requirements.txt 2. created loading script > https://huggingface.co/docs/datasets/dataset_script added next to dataset: 3. uploaded private custom dataset with loading script to HuggingFace > https://huggingface.co/docs/datasets/dataset_script 4. added dataset loading script to local directory in the above cloned transformers library: > cd /transformers/examples/pytorch/audio-classification 5. logged in to HuggingFace on local terminal with : > huggingface-cli login 6. run the model with the custom dataset stored on HuggingFace with code: https://github.com/huggingface/transformers/blob/main/examples/pytorch/audio-classification/README.md cd /transformers/examples/pytorch/audio-classification > python run_audio_classification.py \ > --model_name_or_path facebook/wav2vec2-base \ > --output_dir l/users/flck/outputs/wav2vec2-base-s \ > --overwrite_output_dir \ > --dataset_name s \ > --dataset_config_name s \ > --remove_unused_columns False \ > --do_train \ > --do_eval \ > --fp16 \ > --learning_rate 3e-5 \ > --max_length_seconds 1 \ > --attention_mask False \ > --warmup_ratio 0.1 \ > --num_train_epochs 5 \ > --per_device_train_batch_size 32 \ > --gradient_accumulation_steps 4 \ > --per_device_eval_batch_size 32 \ > --dataloader_num_workers 4 \ > --logging_strategy steps \ > --logging_steps 10 \ > --evaluation_strategy epoch \ > --save_strategy epoch \ > --load_best_model_at_end True \ > --metric_for_best_model accuracy \ > --save_total_limit 3 \ > --seed 0 \ > --push_to_hub \ > --use_auth_token=True ### Expected behavior Be able to train a model the https://github.com/huggingface/transformers/blob/main/examples/pytorch/audio-classification/ run_audio_classification.py with private custom dataset stored on HuggingFace. ### Environment info - datasets version: 2.12.0 - `transformers` version: 4.30.0.dev0 - Platform: Linux-5.4.204-ql-generic-12.0-19-x86_64-with-glibc2.17 - Python version: 3.8.12 - Huggingface_hub version: 0.15.1 - Safetensors version: 0.3.1 - PyTorch version (GPU?): 2.0.1+cu117 (True) Versions of relevant libraries: [pip3] numpy==1.24.3 [pip3] torch==2.0.1 [pip3] torchaudio==2.0.2 [conda] numpy 1.24.3 pypi_0 pypi [conda] torch 2.0.1 pypi_0 pypi [conda] torchaudio 2.0.2 pypi_0 pypi	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5930/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5930/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5984	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5984/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5984/comments	https://api.github.com/repos/huggingface/datasets/issues/5984/events	https://github.com/huggingface/datasets/issues/5984	1,771,571,458	I_kwDODunzps5pmAkC	5,984	AutoSharding IterableDataset's when num_workers > 1	{ "avatar_url": "https://avatars.githubusercontent.com/u/25594384?v=4", "events_url": "https://api.github.com/users/mathephysicist/events{/privacy}", "followers_url": "https://api.github.com/users/mathephysicist/followers", "following_url": "https://api.github.com/users/mathephysicist/following{/other_user}", "gists_url": "https://api.github.com/users/mathephysicist/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mathephysicist", "id": 25594384, "login": "mathephysicist", "node_id": "MDQ6VXNlcjI1NTk0Mzg0", "organizations_url": "https://api.github.com/users/mathephysicist/orgs", "received_events_url": "https://api.github.com/users/mathephysicist/received_events", "repos_url": "https://api.github.com/users/mathephysicist/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mathephysicist/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mathephysicist/subscriptions", "type": "User", "url": "https://api.github.com/users/mathephysicist", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	[ "For this to be possible, we would have to switch from the \"Streaming\" Arrow format to the \"Random Access\" (IPC/Feather) format, which allows reading arbitrary record batches (explained [here](https://arrow.apache.org/docs/python/ipc.html)). We could then use these batches to construct shards.\r\n\r\n@lhoestq @albertvillanova Do you think this use case is worth the switch? Also, we currently shard files, not inner row groups/chunks. Should we also support sharding row groups (e.g. if the number of input files is 1)?\r\n\r\nPS: I don't expect significant speed-up for local, uncompressed Arrow files.", "Alternatively we could support multiprocessing map for iterable datasets and let the user do the CPU intensive task there ?\r\n\r\nThis way it would work on arrow data but also on any iterable dataset", "> For this to be possible, we would have to switch from the \"Streaming\" Arrow format to the \"Random Access\" (IPC/Feather) format, which allows reading arbitrary record batches (explained [here](https://arrow.apache.org/docs/python/ipc.html)). We could then use these batches to construct shards.\r\n> \r\n> @lhoestq @albertvillanova Do you think this use case is worth the switch? Also, we currently shard files, not inner row groups/chunks. Should we also support sharding row groups (e.g. if the number of input files is 1)?\r\n> \r\n> PS: I don't expect significant speed-up for local, uncompressed Arrow files.\r\n\r\nCould you explain why you'd need to change the arrow format?\r\n\r\nWhen we use streaming datasets we simply determine the number of worker shards and then add some modulo logic at the appropriate place. Worst case scenario, you'd skip streaming entries according to the number of shards.\r\n\r\nFor PyTorch, I'd be happy to provide an implementation or a sketch thereof, if you point me toward what the testing requirements would be for such a PR.", "> Could you explain why you'd need to change the arrow format?\r\n\r\nThis way workers have random access to the location of the file where its dataset subset starts. Currently we're using the Arrow streaming format which doesn't include the metadata of the record batches offsets. This is needed here to efficiently split a dataset made of one single file.", "> > Could you explain why you'd need to change the arrow format?\r\n> \r\n> This way workers have random access to the location of the file where its dataset subset starts. Currently we're using the Arrow streaming format which doesn't include the metadata of the record batches offsets. This is needed here to efficiently split a dataset made of one single file.\r\n\r\nI guess I don't understand why you'd need to subset the dataset in the first place. \r\nIt seems sufficient to figure out how to offset or skip rows.\r\n\r\nFor instance, using pyArrow, you could use RecordBatchStreamReader to zero-copy iterate over records with read_next_batch and then only initiate the next step for records modulo worker shard.\r\nThat's one way to do it, where of course you'd need to account for gpu sharding as well.\r\n\r\n\r\nOtherwise, how did you implement worker/node/GPU sharding for iterable/streaming data where you do not have index information or prior splits (e.g. files)?", "> For instance, using pyArrow, you could use RecordBatchStreamReader to zero-copy iterate over records with read_next_batch and then only initiate the next step for records modulo worker shard.\r\n\r\nThat works indeed ! And what we meant is that you can make it even faster to instantiate. Indeed using RecordBatchStreamReader you need to get the list of all the record batches in each worker, whereas you could just get the list of record batches per worker if you use the record batches locations in the Arrow IPC file footer. This would be especially appreciated to have a fast instantiation in case you have tens of thousands of Arrow files for example.", "Any recent updates on this ? ", "I would also appreciate this feature" ]	2023-06-23T14:34:20Z	2024-03-22T15:01:14Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request Minimal Example ``` import torch from datasets import IterableDataset d = IterableDataset.from_file(<file_name>) dl = torch.utils.data.dataloader.DataLoader(d,num_workers=3) for sample in dl: print(sample) ``` Warning: Too many dataloader workers: 2 (max is dataset.n_shards=1). Stopping 1 dataloader workers. To parallelize data loading, we give each process some shards (or data sources) to process. Therefore it's unnecessary to have a number of workers greater than dataset.n_shards=1. To enable more parallelism, please split the dataset in more files than 1. Expected Behavior: Dataset is sharded each cpu uses subset (contiguously - so you can do checkpoint loading/saving) ### Motivation I have a lot of unused cpu's and would like to be able to shard iterable datasets with pytorch's dataloader when num_workers > 1. This is for a very large single file. I am aware that we can use the `split_dataset_by_node` to ensure that each node (for distributed) gets different shards, but we should extend it so that this also continues for multiple workers. ### Your contribution If someone points me to what needs to change, I can create a PR.	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/5984/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5984/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6916	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6916/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6916/comments	https://api.github.com/repos/huggingface/datasets/issues/6916/events	https://github.com/huggingface/datasets/issues/6916	2,311,675,564	I_kwDODunzps6JyV6s	6,916	```push_to_hub()``` - Prevent Automatic Generation of Splits	{ "avatar_url": "https://avatars.githubusercontent.com/u/29337128?v=4", "events_url": "https://api.github.com/users/jetlime/events{/privacy}", "followers_url": "https://api.github.com/users/jetlime/followers", "following_url": "https://api.github.com/users/jetlime/following{/other_user}", "gists_url": "https://api.github.com/users/jetlime/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jetlime", "id": 29337128, "login": "jetlime", "node_id": "MDQ6VXNlcjI5MzM3MTI4", "organizations_url": "https://api.github.com/users/jetlime/orgs", "received_events_url": "https://api.github.com/users/jetlime/received_events", "repos_url": "https://api.github.com/users/jetlime/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jetlime/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jetlime/subscriptions", "type": "User", "url": "https://api.github.com/users/jetlime", "user_view_type": "public" }	[]	closed	false	null	[]	null	[]	2024-05-22T23:52:15Z	2024-05-23T00:07:53Z	2024-05-23T00:07:53Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I currently have a dataset which has not been splited. When pushing the dataset to my hugging face dataset repository, it is split into a testing and training set. How can I prevent the split from happening? ### Steps to reproduce the bug 1. Have a unsplit dataset ```python Dataset({ features: ['input', 'output', 'Attack', '__index_level_0__'], num_rows: 944685 }) ``` 2. Push it to huggingface ```python dataset.push_to_hub(dataset_name) ``` 3. On the hugging face dataset repo, the dataset then appears to be splited: ![image](https://github.com/huggingface/datasets/assets/29337128/b4fbc141-42b0-4f49-98df-dd479648fe09) 4. Indeed, when loading the dataset from this repo, the dataset is split in two testing and training set. ```python from datasets import load_dataset, Dataset dataset = load_dataset("Jetlime/NF-CSE-CIC-IDS2018-v2", streaming=True) dataset ``` output: ``` IterableDatasetDict({ train: IterableDataset({ features: ['input', 'output', 'Attack', '__index_level_0__'], n_shards: 2 }) test: IterableDataset({ features: ['input', 'output', 'Attack', '__index_level_0__'], n_shards: 1 }) ``` ### Expected behavior The dataset shall not be splited, as not requested. ### Environment info - `datasets` version: 2.19.1 - Platform: Linux-6.2.0-35-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.23.0 - PyArrow version: 15.0.2 - Pandas version: 2.2.2 - `fsspec` version: 2024.3.1	{ "avatar_url": "https://avatars.githubusercontent.com/u/29337128?v=4", "events_url": "https://api.github.com/users/jetlime/events{/privacy}", "followers_url": "https://api.github.com/users/jetlime/followers", "following_url": "https://api.github.com/users/jetlime/following{/other_user}", "gists_url": "https://api.github.com/users/jetlime/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jetlime", "id": 29337128, "login": "jetlime", "node_id": "MDQ6VXNlcjI5MzM3MTI4", "organizations_url": "https://api.github.com/users/jetlime/orgs", "received_events_url": "https://api.github.com/users/jetlime/received_events", "repos_url": "https://api.github.com/users/jetlime/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jetlime/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jetlime/subscriptions", "type": "User", "url": "https://api.github.com/users/jetlime", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6916/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6916/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5262	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5262/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5262/comments	https://api.github.com/repos/huggingface/datasets/issues/5262/events	https://github.com/huggingface/datasets/issues/5262	1,455,171,100	I_kwDODunzps5WvCYc	5,262	AttributeError: 'Value' object has no attribute 'names'	{ "avatar_url": "https://avatars.githubusercontent.com/u/102913847?v=4", "events_url": "https://api.github.com/users/emnaboughariou/events{/privacy}", "followers_url": "https://api.github.com/users/emnaboughariou/followers", "following_url": "https://api.github.com/users/emnaboughariou/following{/other_user}", "gists_url": "https://api.github.com/users/emnaboughariou/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/emnaboughariou", "id": 102913847, "login": "emnaboughariou", "node_id": "U_kgDOBiJXNw", "organizations_url": "https://api.github.com/users/emnaboughariou/orgs", "received_events_url": "https://api.github.com/users/emnaboughariou/received_events", "repos_url": "https://api.github.com/users/emnaboughariou/repos", "site_admin": false, "starred_url": "https://api.github.com/users/emnaboughariou/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/emnaboughariou/subscriptions", "type": "User", "url": "https://api.github.com/users/emnaboughariou", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi ! It looks like your \"isDif\" column is a Sequence of Value(\"string\"), not a Sequence of ClassLabel.\r\n\r\nYou can convert your Value(\"string\") feature type to a ClassLabel feature type this way:\r\n```python\r\nfrom datasets import ClassLabel, Sequence\r\n\r\n# provide the label_names yourself\r\nlabel_names = [...]\r\n# OR get them from the dataset\r\nlabel_names = sorted(set(label for labels in raw_datasets[\"train\"][\"isDif\"] for label in labels))\r\n\r\n# Cast to ClassLabel\r\nraw_datasets = raw_datasets.cast_column(\"isDif\", Sequence(ClassLabel(names=label_names)))\r\n```\r\n", "thank you \r\nit works 💯 " ]	2022-11-18T13:58:42Z	2022-11-22T10:09:24Z	2022-11-22T10:09:23Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	Hello I'm trying to build a model for custom token classification I already followed the token classification course on huggingface while adapting the code to my work, this message occures : 'Value' object has no attribute 'names' Here's my code: `raw_datasets` generates DatasetDict({ train: Dataset({ features: ['isDisf', 'pos', 'tokens', 'id'], num_rows: 14 }) }) `raw_datasets["train"][3]["isDisf"]` generates ['B_RM', 'I_RM', 'I_RM', 'B_RP', 'I_RP', 'O', 'O'] `dis_feature = raw_datasets["train"].features["isDisf"] dis_feature` generates Sequence(feature=Value(dtype='string', id=None), length=-1, id=None) and `label_names = dis_feature.feature.names label_names` generates AttributeError Traceback (most recent call last) [<ipython-input-28-972fd54a869a>](https://localhost:8080/#) in <module> ----> 1 label_names = dis_feature.feature.names 2 label_names AttributeError: 'Value' object has AttributeError: 'Value' object has no attribute 'names' Thank you for your help	{ "avatar_url": "https://avatars.githubusercontent.com/u/102913847?v=4", "events_url": "https://api.github.com/users/emnaboughariou/events{/privacy}", "followers_url": "https://api.github.com/users/emnaboughariou/followers", "following_url": "https://api.github.com/users/emnaboughariou/following{/other_user}", "gists_url": "https://api.github.com/users/emnaboughariou/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/emnaboughariou", "id": 102913847, "login": "emnaboughariou", "node_id": "U_kgDOBiJXNw", "organizations_url": "https://api.github.com/users/emnaboughariou/orgs", "received_events_url": "https://api.github.com/users/emnaboughariou/received_events", "repos_url": "https://api.github.com/users/emnaboughariou/repos", "site_admin": false, "starred_url": "https://api.github.com/users/emnaboughariou/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/emnaboughariou/subscriptions", "type": "User", "url": "https://api.github.com/users/emnaboughariou", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5262/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5262/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5385	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5385/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5385/comments	https://api.github.com/repos/huggingface/datasets/issues/5385/events	https://github.com/huggingface/datasets/issues/5385	1,508,535,532	I_kwDODunzps5Z6mzs	5,385	Is `fs=` deprecated in `load_from_disk()` as well?	{ "avatar_url": "https://avatars.githubusercontent.com/u/15098095?v=4", "events_url": "https://api.github.com/users/dconathan/events{/privacy}", "followers_url": "https://api.github.com/users/dconathan/followers", "following_url": "https://api.github.com/users/dconathan/following{/other_user}", "gists_url": "https://api.github.com/users/dconathan/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/dconathan", "id": 15098095, "login": "dconathan", "node_id": "MDQ6VXNlcjE1MDk4MDk1", "organizations_url": "https://api.github.com/users/dconathan/orgs", "received_events_url": "https://api.github.com/users/dconathan/received_events", "repos_url": "https://api.github.com/users/dconathan/repos", "site_admin": false, "starred_url": "https://api.github.com/users/dconathan/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/dconathan/subscriptions", "type": "User", "url": "https://api.github.com/users/dconathan", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR? ", "> Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR?\r\n\r\nYeah I can do that sometime next week. Should the storage_options be a new arg here? I’ll look around for anywhere else where fs is an arg.", "Closed by #5393." ]	2022-12-22T21:00:45Z	2023-01-23T10:50:05Z	2023-01-23T10:50:04Z	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug The `fs=` argument was deprecated from `Dataset.save_to_disk` and `Dataset.load_from_disk` in favor of automagically figuring it out via fsspec: https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/arrow_dataset.py#L1339-L1340 Is there a reason the same thing shouldn't also apply to `datasets.load.load_from_disk()` as well ? https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/load.py#L1779 ### Steps to reproduce the bug n/a ### Expected behavior n/a ### Environment info n/a	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5385/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5385/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6879	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6879/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6879/comments	https://api.github.com/repos/huggingface/datasets/issues/6879/events	https://github.com/huggingface/datasets/issues/6879	2,282,968,259	I_kwDODunzps6IE1TD	6,879	Batched mapping does not raise an error if values for an existing column are empty	{ "avatar_url": "https://avatars.githubusercontent.com/u/208336?v=4", "events_url": "https://api.github.com/users/felix-schneider/events{/privacy}", "followers_url": "https://api.github.com/users/felix-schneider/followers", "following_url": "https://api.github.com/users/felix-schneider/following{/other_user}", "gists_url": "https://api.github.com/users/felix-schneider/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/felix-schneider", "id": 208336, "login": "felix-schneider", "node_id": "MDQ6VXNlcjIwODMzNg==", "organizations_url": "https://api.github.com/users/felix-schneider/orgs", "received_events_url": "https://api.github.com/users/felix-schneider/received_events", "repos_url": "https://api.github.com/users/felix-schneider/repos", "site_admin": false, "starred_url": "https://api.github.com/users/felix-schneider/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/felix-schneider/subscriptions", "type": "User", "url": "https://api.github.com/users/felix-schneider", "user_view_type": "public" }	[]	open	false	null	[]	null	[]	2024-05-07T11:02:40Z	2024-05-07T11:02:40Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Using `Dataset.map(fn, batched=True)` allows resizing the dataset by returning a dict of lists, all of which must be the same size. If they are not the same size, an error like `pyarrow.lib.ArrowInvalid: Column 1 named x expected length 1 but got length 0` is raised. This is not the case if the function returns an empty list for an existing column in the dataset. In that case, the dataset is silently resized to 0 rows. ### Steps to reproduce the bug MWE: ``` import datasets data = datasets.Dataset.from_dict({"test": [1]}) def mapping_fn(examples): return {"test": [], "y": [1]} data = data.map(mapping_fn, batched=True) print(len(data)) ``` Note that when returning `"x": []`, the error is raised correctly, also when returning `"test": [1,2]`. ### Expected behavior Expected an exception: `pyarrow.lib.ArrowInvalid: Column 1 named test expected length 1 but got length 0` or `pyarrow.lib.ArrowInvalid: Column 2 named y expected length 0 but got length 1`. Any exception would be acceptable. ### Environment info - `datasets` version: 2.19.1 - Platform: Linux-5.4.0-153-generic-x86_64-with-glibc2.31 - Python version: 3.11.8 - `huggingface_hub` version: 0.22.2 - PyArrow version: 15.0.2 - Pandas version: 2.2.1 - `fsspec` version: 2024.2.0	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6879/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6879/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7010	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7010/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7010/comments	https://api.github.com/repos/huggingface/datasets/issues/7010/events	https://github.com/huggingface/datasets/issues/7010	2,379,777,480	I_kwDODunzps6N2IXI	7,010	Re-enable raising error from huggingface-hub FutureWarning in CI	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "color": "d4c5f9", "default": false, "description": "Maintenance tasks", "id": 4296013012, "name": "maintenance", "node_id": "LA_kwDODunzps8AAAABAA_01A", "url": "https://api.github.com/repos/huggingface/datasets/labels/maintenance" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[]	2024-06-28T07:23:40Z	2024-06-28T12:19:30Z	2024-06-28T12:19:29Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	Re-enable raising error from huggingface-hub FutureWarning in CI, which was disabled by PR: - #6876 Note that this can only be done once transformers releases the fix: - https://github.com/huggingface/transformers/pull/31007	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7010/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7010/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6063	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6063/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6063/comments	https://api.github.com/repos/huggingface/datasets/issues/6063/events	https://github.com/huggingface/datasets/pull/6063	1,818,679,485	PR_kwDODunzps5WPtxi	6,063	Release: 2.14.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The 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) \| 0.007703 / 0.011353 (-0.003650) \| 0.004699 / 0.011008 (-0.006309) \| 0.090195 / 0.038508 (0.051687) \| 0.119165 / 0.023109 (0.096056) \| 0.361435 / 0.275898 (0.085537) \| 0.404429 / 0.323480 (0.080949) \| 0.006172 / 0.007986 (-0.001814) \| 0.003932 / 0.004328 (-0.000397) \| 0.068384 / 0.004250 (0.064133) \| 0.066730 / 0.037052 (0.029678) \| 0.360978 / 0.258489 (0.102489) \| 0.401301 / 0.293841 (0.107460) \| 0.032836 / 0.128546 (-0.095710) \| 0.010821 / 0.075646 (-0.064825) \| 0.294526 / 0.419271 (-0.124745) \| 0.068751 / 0.043533 (0.025218) \| 0.368427 / 0.255139 (0.113288) \| 0.376969 / 0.283200 (0.093770) \| 0.040538 / 0.141683 (-0.101145) \| 1.509966 / 1.452155 (0.057811) \| 1.564885 / 1.492716 (0.072169) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292243 / 0.018006 (0.274237) \| 0.662067 / 0.000490 (0.661577) \| 0.004966 / 0.000200 (0.004766) \| 0.000103 / 0.000054 (0.000048) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029050 / 0.037411 (-0.008361) \| 0.099880 / 0.014526 (0.085354) \| 0.109277 / 0.176557 (-0.067280) \| 0.167877 / 0.737135 (-0.569258) \| 0.110770 / 0.296338 (-0.185569) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.395742 / 0.215209 (0.180533) \| 3.944152 / 2.077655 (1.866498) \| 1.875295 / 1.504120 (0.371175) \| 1.705088 / 1.541195 (0.163893) \| 1.884443 / 1.468490 (0.415953) \| 0.497243 / 4.584777 (-4.087534) \| 3.749287 / 3.745712 (0.003575) \| 4.418826 / 5.269862 (-0.851035) \| 2.481149 / 4.565676 (-2.084528) \| 0.058260 / 0.424275 (-0.366015) \| 0.007744 / 0.007607 (0.000137) \| 0.472531 / 0.226044 (0.246486) \| 4.716022 / 2.268929 (2.447094) \| 2.480446 / 55.444624 (-52.964179) \| 2.163098 / 6.876477 (-4.713379) \| 2.217555 / 2.142072 (0.075482) \| 0.601965 / 4.805227 (-4.203262) \| 0.139364 / 6.500664 (-6.361301) \| 0.067097 / 0.075469 (-0.008372) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.330537 / 1.841788 (-0.511251) \| 22.176270 / 8.074308 (14.101962) \| 16.224981 / 10.191392 (6.033589) \| 0.173708 / 0.680424 (-0.506715) \| 0.019402 / 0.534201 (-0.514799) \| 0.401994 / 0.579283 (-0.177289) \| 0.432597 / 0.434364 (-0.001767) \| 0.489933 / 0.540337 (-0.050404) \| 0.672334 / 1.386936 (-0.714602) \|\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) \| 0.008622 / 0.011353 (-0.002731) \| 0.004609 / 0.011008 (-0.006399) \| 0.067791 / 0.038508 (0.029283) \| 0.112770 / 0.023109 (0.089661) \| 0.380939 / 0.275898 (0.105041) \| 0.416940 / 0.323480 (0.093460) \| 0.006170 / 0.007986 (-0.001815) \| 0.003876 / 0.004328 (-0.000452) \| 0.066227 / 0.004250 (0.061976) \| 0.073132 / 0.037052 (0.036080) \| 0.390120 / 0.258489 (0.131631) \| 0.420893 / 0.293841 (0.127052) \| 0.033235 / 0.128546 (-0.095311) \| 0.009659 / 0.075646 (-0.065987) \| 0.072668 / 0.419271 (-0.346604) \| 0.051333 / 0.043533 (0.007801) \| 0.393828 / 0.255139 (0.138689) \| 0.412376 / 0.283200 (0.129176) \| 0.027760 / 0.141683 (-0.113923) \| 1.494369 / 1.452155 (0.042214) \| 1.592862 / 1.492716 (0.100145) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.345376 / 0.018006 (0.327369) \| 0.609399 / 0.000490 (0.608909) \| 0.000546 / 0.000200 (0.000346) \| 0.000061 / 0.000054 (0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035601 / 0.037411 (-0.001810) \| 0.106527 / 0.014526 (0.092001) \| 0.114388 / 0.176557 (-0.062168) \| 0.175607 / 0.737135 (-0.561529) \| 0.113009 / 0.296338 (-0.183329) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417237 / 0.215209 (0.202028) \| 4.136329 / 2.077655 (2.058675) \| 2.147134 / 1.504120 (0.643014) \| 2.009501 / 1.541195 (0.468306) \| 2.139499 / 1.468490 (0.671009) \| 0.491593 / 4.584777 (-4.093184) \| 3.766734 / 3.745712 (0.021022) \| 5.652446 / 5.269862 (0.382585) \| 3.021654 / 4.565676 (-1.544022) \| 0.058458 / 0.424275 (-0.365817) \| 0.008271 / 0.007607 (0.000664) \| 0.488229 / 0.226044 (0.262184) \| 4.861343 / 2.268929 (2.592415) \| 2.694142 / 55.444624 (-52.750482) \| 2.489130 / 6.876477 (-4.387346) \| 2.679376 / 2.142072 (0.537304) \| 0.589959 / 4.805227 (-4.215268) \| 0.137939 / 6.500664 (-6.362725) \| 0.066833 / 0.075469 (-0.008636) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.444871 / 1.841788 (-0.396916) \| 22.874961 / 8.074308 (14.800653) \| 15.842130 / 10.191392 (5.650738) \| 0.175529 / 0.680424 (-0.504895) \| 0.019024 / 0.534201 (-0.515177) \| 0.406551 / 0.579283 (-0.172732) \| 0.430335 / 0.434364 (-0.004029) \| 0.475750 / 0.540337 (-0.064587) \| 0.624836 / 1.386936 (-0.762100) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dabbb7467f49fd22ae1a43cc577eb43008d63ee8 \"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) \| 0.006068 / 0.011353 (-0.005285) \| 0.003694 / 0.011008 (-0.007315) \| 0.080321 / 0.038508 (0.041813) \| 0.061738 / 0.023109 (0.038629) \| 0.329675 / 0.275898 (0.053777) \| 0.364008 / 0.323480 (0.040528) \| 0.004722 / 0.007986 (-0.003263) \| 0.002857 / 0.004328 (-0.001471) \| 0.062447 / 0.004250 (0.058197) \| 0.047006 / 0.037052 (0.009953) \| 0.335730 / 0.258489 (0.077241) \| 0.373047 / 0.293841 (0.079206) \| 0.027273 / 0.128546 (-0.101274) \| 0.007979 / 0.075646 (-0.067667) \| 0.262693 / 0.419271 (-0.156579) \| 0.045416 / 0.043533 (0.001883) \| 0.340774 / 0.255139 (0.085635) \| 0.359667 / 0.283200 (0.076468) \| 0.020848 / 0.141683 (-0.120835) \| 1.450110 / 1.452155 (-0.002045) \| 1.489511 / 1.492716 (-0.003206) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.185090 / 0.018006 (0.167084) \| 0.429823 / 0.000490 (0.429334) \| 0.000703 / 0.000200 (0.000503) \| 0.000058 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024398 / 0.037411 (-0.013013) \| 0.072983 / 0.014526 (0.058457) \| 0.084012 / 0.176557 (-0.092544) \| 0.146160 / 0.737135 (-0.590975) \| 0.084068 / 0.296338 (-0.212270) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.432204 / 0.215209 (0.216995) \| 4.320593 / 2.077655 (2.242939) \| 2.261260 / 1.504120 (0.757140) \| 2.087148 / 1.541195 (0.545954) \| 2.144520 / 1.468490 (0.676029) \| 0.501477 / 4.584777 (-4.083300) \| 3.119557 / 3.745712 (-0.626156) \| 3.572527 / 5.269862 (-1.697335) \| 2.208836 / 4.565676 (-2.356840) \| 0.057232 / 0.424275 (-0.367043) \| 0.006494 / 0.007607 (-0.001113) \| 0.508135 / 0.226044 (0.282091) \| 5.090416 / 2.268929 (2.821488) \| 2.739800 / 55.444624 (-52.704824) \| 2.416105 / 6.876477 (-4.460372) \| 2.616037 / 2.142072 (0.473965) \| 0.583730 / 4.805227 (-4.221497) \| 0.124312 / 6.500664 (-6.376352) \| 0.060760 / 0.075469 (-0.014709) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.256097 / 1.841788 (-0.585691) \| 18.326073 / 8.074308 (10.251765) \| 13.859173 / 10.191392 (3.667781) \| 0.143639 / 0.680424 (-0.536785) \| 0.016649 / 0.534201 (-0.517552) \| 0.331671 / 0.579283 (-0.247612) \| 0.365370 / 0.434364 (-0.068994) \| 0.392753 / 0.540337 (-0.147584) \| 0.549302 / 1.386936 (-0.837634) \|\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) \| 0.006054 / 0.011353 (-0.005299) \| 0.003641 / 0.011008 (-0.007367) \| 0.063109 / 0.038508 (0.024601) \| 0.060482 / 0.023109 (0.037372) \| 0.404047 / 0.275898 (0.128149) \| 0.425436 / 0.323480 (0.101956) \| 0.004603 / 0.007986 (-0.003382) \| 0.002905 / 0.004328 (-0.001423) \| 0.063207 / 0.004250 (0.058956) \| 0.048248 / 0.037052 (0.011196) \| 0.404325 / 0.258489 (0.145836) \| 0.432652 / 0.293841 (0.138811) \| 0.027630 / 0.128546 (-0.100916) \| 0.008062 / 0.075646 (-0.067584) \| 0.068367 / 0.419271 (-0.350905) \| 0.042169 / 0.043533 (-0.001364) \| 0.384903 / 0.255139 (0.129764) \| 0.418617 / 0.283200 (0.135417) \| 0.020767 / 0.141683 (-0.120915) \| 1.463606 / 1.452155 (0.011451) \| 1.512081 / 1.492716 (0.019365) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229601 / 0.018006 (0.211594) \| 0.417878 / 0.000490 (0.417388) \| 0.000373 / 0.000200 (0.000173) \| 0.000053 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026354 / 0.037411 (-0.011057) \| 0.078100 / 0.014526 (0.063574) \| 0.087122 / 0.176557 (-0.089434) \| 0.140017 / 0.737135 (-0.597118) \| 0.089923 / 0.296338 (-0.206415) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422405 / 0.215209 (0.207196) \| 4.237383 / 2.077655 (2.159728) \| 2.161104 / 1.504120 (0.656984) \| 1.982337 / 1.541195 (0.441142) \| 2.050216 / 1.468490 (0.581726) \| 0.499281 / 4.584777 (-4.085496) \| 2.996953 / 3.745712 (-0.748759) \| 5.027069 / 5.269862 (-0.242792) \| 2.804703 / 4.565676 (-1.760974) \| 0.057707 / 0.424275 (-0.366568) \| 0.006809 / 0.007607 (-0.000798) \| 0.495196 / 0.226044 (0.269152) \| 4.946593 / 2.268929 (2.677665) \| 2.598965 / 55.444624 (-52.845660) \| 2.349871 / 6.876477 (-4.526606) \| 2.451665 / 2.142072 (0.309593) \| 0.592314 / 4.805227 (-4.212913) \| 0.125685 / 6.500664 (-6.374979) \| 0.063252 / 0.075469 (-0.012217) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.325422 / 1.841788 (-0.516366) \| 18.521059 / 8.074308 (10.446751) \| 14.046757 / 10.191392 (3.855365) \| 0.133009 / 0.680424 (-0.547415) \| 0.017097 / 0.534201 (-0.517104) \| 0.339804 / 0.579283 (-0.239479) \| 0.345464 / 0.434364 (-0.088900) \| 0.387623 / 0.540337 (-0.152714) \| 0.519880 / 1.386936 (-0.867056) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#88896a7b28610ace95e444b94f9a4bc332cc1ee3 \"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) \| 0.008671 / 0.011353 (-0.002682) \| 0.004681 / 0.011008 (-0.006327) \| 0.107517 / 0.038508 (0.069008) \| 0.078846 / 0.023109 (0.055737) \| 0.449745 / 0.275898 (0.173847) \| 0.504075 / 0.323480 (0.180596) \| 0.005837 / 0.007986 (-0.002148) \| 0.004031 / 0.004328 (-0.000297) \| 0.092021 / 0.004250 (0.087771) \| 0.065954 / 0.037052 (0.028902) \| 0.442082 / 0.258489 (0.183593) \| 0.529349 / 0.293841 (0.235508) \| 0.052527 / 0.128546 (-0.076019) \| 0.013854 / 0.075646 (-0.061792) \| 0.367315 / 0.419271 (-0.051956) \| 0.068731 / 0.043533 (0.025199) \| 0.494733 / 0.255139 (0.239594) \| 0.472801 / 0.283200 (0.189601) \| 0.036791 / 0.141683 (-0.104892) \| 1.877648 / 1.452155 (0.425493) \| 1.928399 / 1.492716 (0.435683) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.231910 / 0.018006 (0.213904) \| 0.553464 / 0.000490 (0.552974) \| 0.011915 / 0.000200 (0.011715) \| 0.000378 / 0.000054 (0.000324) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028232 / 0.037411 (-0.009179) \| 0.091441 / 0.014526 (0.076916) \| 0.110394 / 0.176557 (-0.066162) \| 0.187638 / 0.737135 (-0.549497) \| 0.111810 / 0.296338 (-0.184529) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.599987 / 0.215209 (0.384778) \| 6.008709 / 2.077655 (3.931054) \| 2.518769 / 1.504120 (1.014650) \| 2.197029 / 1.541195 (0.655834) \| 2.217165 / 1.468490 (0.748675) \| 0.894939 / 4.584777 (-3.689837) \| 5.001217 / 3.745712 (1.255505) \| 4.636482 / 5.269862 (-0.633379) \| 3.237613 / 4.565676 (-1.328063) \| 0.104227 / 0.424275 (-0.320048) \| 0.008504 / 0.007607 (0.000897) \| 0.750190 / 0.226044 (0.524145) \| 7.514571 / 2.268929 (5.245642) \| 3.358003 / 55.444624 (-52.086621) \| 2.585649 / 6.876477 (-4.290827) \| 2.731129 / 2.142072 (0.589056) \| 1.088828 / 4.805227 (-3.716400) \| 0.217308 / 6.500664 (-6.283356) \| 0.076410 / 0.075469 (0.000941) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.620087 / 1.841788 (-0.221701) \| 23.145743 / 8.074308 (15.071435) \| 20.583403 / 10.191392 (10.392011) \| 0.225467 / 0.680424 (-0.454956) \| 0.029063 / 0.534201 (-0.505138) \| 0.480563 / 0.579283 (-0.098720) \| 0.539083 / 0.434364 (0.104719) \| 0.563787 / 0.540337 (0.023449) \| 0.782902 / 1.386936 (-0.604034) \|\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) \| 0.010113 / 0.011353 (-0.001239) \| 0.004997 / 0.011008 (-0.006011) \| 0.082974 / 0.038508 (0.044466) \| 0.090375 / 0.023109 (0.067266) \| 0.440273 / 0.275898 (0.164375) \| 0.476939 / 0.323480 (0.153459) \| 0.005955 / 0.007986 (-0.002031) \| 0.004375 / 0.004328 (0.000046) \| 0.080459 / 0.004250 (0.076209) \| 0.061787 / 0.037052 (0.024734) \| 0.477211 / 0.258489 (0.218722) \| 0.487164 / 0.293841 (0.193323) \| 0.054198 / 0.128546 (-0.074348) \| 0.013945 / 0.075646 (-0.061701) \| 0.093006 / 0.419271 (-0.326266) \| 0.062685 / 0.043533 (0.019152) \| 0.461373 / 0.255139 (0.206234) \| 0.475766 / 0.283200 (0.192567) \| 0.032059 / 0.141683 (-0.109623) \| 1.857989 / 1.452155 (0.405834) \| 1.837993 / 1.492716 (0.345277) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.243048 / 0.018006 (0.225042) \| 0.535850 / 0.000490 (0.535360) \| 0.007204 / 0.000200 (0.007004) \| 0.000104 / 0.000054 (0.000049) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032584 / 0.037411 (-0.004827) \| 0.098151 / 0.014526 (0.083625) \| 0.109691 / 0.176557 (-0.066866) \| 0.172803 / 0.737135 (-0.564333) \| 0.110469 / 0.296338 (-0.185869) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.635086 / 0.215209 (0.419877) \| 6.500864 / 2.077655 (4.423210) \| 2.996727 / 1.504120 (1.492607) \| 2.537218 / 1.541195 (0.996023) \| 2.572310 / 1.468490 (1.103820) \| 0.870868 / 4.584777 (-3.713909) \| 4.989744 / 3.745712 (1.244032) \| 4.422174 / 5.269862 (-0.847687) \| 2.935874 / 4.565676 (-1.629803) \| 0.097118 / 0.424275 (-0.327157) \| 0.009360 / 0.007607 (0.001753) \| 0.790447 / 0.226044 (0.564403) \| 7.859519 / 2.268929 (5.590591) \| 3.975616 / 55.444624 (-51.469009) \| 3.018271 / 6.876477 (-3.858206) \| 3.111173 / 2.142072 (0.969101) \| 1.085577 / 4.805227 (-3.719651) \| 0.225719 / 6.500664 (-6.274945) \| 0.080576 / 0.075469 (0.005107) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.802284 / 1.841788 (-0.039504) \| 23.487921 / 8.074308 (15.413613) \| 20.595171 / 10.191392 (10.403779) \| 0.196610 / 0.680424 (-0.483814) \| 0.027483 / 0.534201 (-0.506718) \| 0.485840 / 0.579283 (-0.093443) \| 0.542661 / 0.434364 (0.108297) \| 0.580602 / 0.540337 (0.040265) \| 0.768195 / 1.386936 (-0.618741) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#88896a7b28610ace95e444b94f9a4bc332cc1ee3 \"CML watermark\")\n" ]	2023-07-24T15:41:19Z	2023-07-24T16:05:16Z	2023-07-24T15:47:51Z	MEMBER	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", 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"merged_at": "2023-07-24T15:47:51Z", "patch_url": "https://github.com/huggingface/datasets/pull/6063.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6063" }
https://api.github.com/repos/huggingface/datasets/issues/6994	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6994/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6994/comments	https://api.github.com/repos/huggingface/datasets/issues/6994/events	https://github.com/huggingface/datasets/pull/6994	2,370,491,689	PR_kwDODunzps5zYYXr	6,994	Fix incorrect rank value in data splitting	{ "avatar_url": "https://avatars.githubusercontent.com/u/18402347?v=4", "events_url": "https://api.github.com/users/yzhangcs/events{/privacy}", "followers_url": "https://api.github.com/users/yzhangcs/followers", "following_url": "https://api.github.com/users/yzhangcs/following{/other_user}", "gists_url": "https://api.github.com/users/yzhangcs/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/yzhangcs", "id": 18402347, "login": "yzhangcs", "node_id": "MDQ6VXNlcjE4NDAyMzQ3", "organizations_url": "https://api.github.com/users/yzhangcs/orgs", "received_events_url": "https://api.github.com/users/yzhangcs/received_events", "repos_url": "https://api.github.com/users/yzhangcs/repos", "site_admin": false, "starred_url": "https://api.github.com/users/yzhangcs/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/yzhangcs/subscriptions", "type": "User", "url": "https://api.github.com/users/yzhangcs", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Sure~", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6994). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005538 / 0.011353 (-0.005815) \| 0.003997 / 0.011008 (-0.007011) \| 0.063444 / 0.038508 (0.024935) \| 0.032552 / 0.023109 (0.009442) \| 0.266574 / 0.275898 (-0.009324) \| 0.282841 / 0.323480 (-0.040639) \| 0.004279 / 0.007986 (-0.003706) \| 0.002788 / 0.004328 (-0.001540) \| 0.049226 / 0.004250 (0.044976) \| 0.044688 / 0.037052 (0.007636) \| 0.275464 / 0.258489 (0.016975) \| 0.305278 / 0.293841 (0.011437) \| 0.030097 / 0.128546 (-0.098450) \| 0.012237 / 0.075646 (-0.063410) \| 0.205526 / 0.419271 (-0.213745) \| 0.036145 / 0.043533 (-0.007388) \| 0.267395 / 0.255139 (0.012256) \| 0.289149 / 0.283200 (0.005949) \| 0.019044 / 0.141683 (-0.122639) \| 1.162294 / 1.452155 (-0.289861) \| 1.183642 / 1.492716 (-0.309074) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.139125 / 0.018006 (0.121119) \| 0.301743 / 0.000490 (0.301253) \| 0.000260 / 0.000200 (0.000061) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019494 / 0.037411 (-0.017917) \| 0.063078 / 0.014526 (0.048552) \| 0.076989 / 0.176557 (-0.099567) \| 0.121363 / 0.737135 (-0.615773) \| 0.080040 / 0.296338 (-0.216298) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284401 / 0.215209 (0.069192) \| 2.805397 / 2.077655 (0.727742) \| 1.555609 / 1.504120 (0.051489) \| 1.405662 / 1.541195 (-0.135533) \| 1.459492 / 1.468490 (-0.008999) \| 0.718376 / 4.584777 (-3.866401) \| 2.395918 / 3.745712 (-1.349794) \| 2.976753 / 5.269862 (-2.293108) \| 1.883938 / 4.565676 (-2.681738) \| 0.078867 / 0.424275 (-0.345408) \| 0.005207 / 0.007607 (-0.002400) \| 0.335178 / 0.226044 (0.109133) \| 3.313414 / 2.268929 (1.044485) \| 1.856929 / 55.444624 (-53.587696) \| 1.565319 / 6.876477 (-5.311158) \| 1.592723 / 2.142072 (-0.549350) \| 0.793621 / 4.805227 (-4.011606) \| 0.134208 / 6.500664 (-6.366456) \| 0.042853 / 0.075469 (-0.032616) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981553 / 1.841788 (-0.860235) \| 11.810438 / 8.074308 (3.736130) \| 9.529874 / 10.191392 (-0.661518) \| 0.142216 / 0.680424 (-0.538207) \| 0.014303 / 0.534201 (-0.519898) \| 0.304600 / 0.579283 (-0.274684) \| 0.261869 / 0.434364 (-0.172495) \| 0.347301 / 0.540337 (-0.193036) \| 0.437395 / 1.386936 (-0.949541) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005881 / 0.011353 (-0.005472) \| 0.004039 / 0.011008 (-0.006969) \| 0.050241 / 0.038508 (0.011733) \| 0.032670 / 0.023109 (0.009561) \| 0.264940 / 0.275898 (-0.010959) \| 0.287105 / 0.323480 (-0.036374) \| 0.004844 / 0.007986 (-0.003142) \| 0.002867 / 0.004328 (-0.001462) \| 0.048083 / 0.004250 (0.043833) \| 0.040965 / 0.037052 (0.003913) \| 0.274390 / 0.258489 (0.015901) \| 0.312107 / 0.293841 (0.018266) \| 0.031714 / 0.128546 (-0.096832) \| 0.012603 / 0.075646 (-0.063043) \| 0.060698 / 0.419271 (-0.358573) \| 0.033130 / 0.043533 (-0.010402) \| 0.264444 / 0.255139 (0.009305) \| 0.282797 / 0.283200 (-0.000403) \| 0.027872 / 0.141683 (-0.113811) \| 1.139026 / 1.452155 (-0.313129) \| 1.181431 / 1.492716 (-0.311285) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097314 / 0.018006 (0.079308) \| 0.301326 / 0.000490 (0.300836) \| 0.000215 / 0.000200 (0.000015) \| 0.000049 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023394 / 0.037411 (-0.014018) \| 0.076270 / 0.014526 (0.061744) \| 0.089065 / 0.176557 (-0.087491) \| 0.129996 / 0.737135 (-0.607139) \| 0.089642 / 0.296338 (-0.206697) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295390 / 0.215209 (0.080181) \| 2.877849 / 2.077655 (0.800194) \| 1.537129 / 1.504120 (0.033009) \| 1.409441 / 1.541195 (-0.131754) \| 1.432468 / 1.468490 (-0.036023) \| 0.718054 / 4.584777 (-3.866722) \| 0.930872 / 3.745712 (-2.814841) \| 2.841028 / 5.269862 (-2.428834) \| 1.921990 / 4.565676 (-2.643686) \| 0.077638 / 0.424275 (-0.346637) \| 0.005494 / 0.007607 (-0.002113) \| 0.336331 / 0.226044 (0.110287) \| 3.330490 / 2.268929 (1.061561) \| 1.887994 / 55.444624 (-53.556630) \| 1.593332 / 6.876477 (-5.283144) \| 1.726956 / 2.142072 (-0.415116) \| 0.783612 / 4.805227 (-4.021615) \| 0.129926 / 6.500664 (-6.370738) \| 0.040792 / 0.075469 (-0.034677) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980274 / 1.841788 (-0.861514) \| 12.193871 / 8.074308 (4.119563) \| 10.348934 / 10.191392 (0.157542) \| 0.141584 / 0.680424 (-0.538840) \| 0.015737 / 0.534201 (-0.518464) \| 0.300725 / 0.579283 (-0.278558) \| 0.127190 / 0.434364 (-0.307174) \| 0.341142 / 0.540337 (-0.199196) \| 0.459523 / 1.386936 (-0.927413) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#637246baf96f07b19b193ed101f34b65cb35cffb \"CML watermark\")\n" ]	2024-06-24T15:07:47Z	2024-06-26T04:37:35Z	2024-06-25T16:19:17Z	CONTRIBUTOR	null	null	null	Fix #6990.	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6994/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6994/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6994.diff", "html_url": "https://github.com/huggingface/datasets/pull/6994", "merged_at": "2024-06-25T16:19:17Z", "patch_url": "https://github.com/huggingface/datasets/pull/6994.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6994" }
https://api.github.com/repos/huggingface/datasets/issues/7502	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7502/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7502/comments	https://api.github.com/repos/huggingface/datasets/issues/7502/events	https://github.com/huggingface/datasets/issues/7502	2,977,453,814	I_kwDODunzps6xeFb2	7,502	`load_dataset` of size 40GB creates a cache of >720GB	{ "avatar_url": "https://avatars.githubusercontent.com/u/61748653?v=4", "events_url": "https://api.github.com/users/pietrolesci/events{/privacy}", "followers_url": "https://api.github.com/users/pietrolesci/followers", "following_url": "https://api.github.com/users/pietrolesci/following{/other_user}", "gists_url": "https://api.github.com/users/pietrolesci/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/pietrolesci", "id": 61748653, "login": "pietrolesci", "node_id": "MDQ6VXNlcjYxNzQ4NjUz", "organizations_url": "https://api.github.com/users/pietrolesci/orgs", "received_events_url": "https://api.github.com/users/pietrolesci/received_events", "repos_url": "https://api.github.com/users/pietrolesci/repos", "site_admin": false, "starred_url": "https://api.github.com/users/pietrolesci/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/pietrolesci/subscriptions", "type": "User", "url": "https://api.github.com/users/pietrolesci", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi ! Parquet is a compressed format. When you load a dataset, it uncompresses the Parquet data into Arrow data on your disk. That's why you can indeed end up with 720GB of uncompressed data on disk. The uncompression is needed to enable performant dataset objects (especially for random access).\n\nTo save some storage you can instead load the dataset with `streaming=True`. This way you get an `IterableDataset` that reads the Parquet data iteratively without ever writing to disk.\n\nPS: `ReadInstruction` might not be implemented for `streaming=True`, if it's the case you can use `ds.take()` and `ds.skip()` instead", "Hi @lhoestq, thanks a lot for your answer. This makes perfect sense. I will try using the streaming mode. Closing the issue." ]	2025-04-07T16:52:34Z	2025-04-15T15:22:12Z	2025-04-15T15:22:11Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	Hi there, I am trying to load a dataset from the Hugging Face Hub and split it into train and validation splits. Somehow, when I try to do it with `load_dataset`, it exhausts my disk quota. So, I tried manually downloading the parquet files from the hub and loading them as follows: ```python ds = DatasetDict( { "train": load_dataset( "parquet", data_dir=f"{local_dir}/{tok}", cache_dir=cache_dir, num_proc=min(12, os.cpu_count()), # type: ignore split=ReadInstruction("train", from_=0, to=NUM_TRAIN, unit="abs"), # type: ignore ), "validation": load_dataset( "parquet", data_dir=f"{local_dir}/{tok}", cache_dir=cache_dir, num_proc=min(12, os.cpu_count()), # type: ignore split=ReadInstruction("train", from_=NUM_TRAIN, unit="abs"), # type: ignore ) } ) ``` which still strangely creates 720GB of cache. In addition, if I remove the raw parquet file folder (`f"{local_dir}/{tok}"` in this example), I am not able to load anything. So, I am left wondering what this cache is doing. Am I missing something? Is there a solution to this problem? Thanks a lot in advance for your help! A related issue: https://github.com/huggingface/transformers/issues/10204#issue-809007443. --- Python: 3.11.11 datasets: 3.5.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/61748653?v=4", "events_url": "https://api.github.com/users/pietrolesci/events{/privacy}", "followers_url": "https://api.github.com/users/pietrolesci/followers", "following_url": "https://api.github.com/users/pietrolesci/following{/other_user}", "gists_url": "https://api.github.com/users/pietrolesci/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/pietrolesci", "id": 61748653, "login": "pietrolesci", "node_id": "MDQ6VXNlcjYxNzQ4NjUz", "organizations_url": "https://api.github.com/users/pietrolesci/orgs", "received_events_url": "https://api.github.com/users/pietrolesci/received_events", "repos_url": "https://api.github.com/users/pietrolesci/repos", "site_admin": false, "starred_url": "https://api.github.com/users/pietrolesci/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/pietrolesci/subscriptions", "type": "User", "url": "https://api.github.com/users/pietrolesci", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7502/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7502/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6615	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6615/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6615/comments	https://api.github.com/repos/huggingface/datasets/issues/6615/events	https://github.com/huggingface/datasets/issues/6615	2,098,951,409	I_kwDODunzps59G3Tx	6,615	...	{ "avatar_url": "https://avatars.githubusercontent.com/u/22179777?v=4", "events_url": "https://api.github.com/users/ftkeys/events{/privacy}", "followers_url": "https://api.github.com/users/ftkeys/followers", "following_url": "https://api.github.com/users/ftkeys/following{/other_user}", "gists_url": "https://api.github.com/users/ftkeys/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ftkeys", "id": 22179777, "login": "ftkeys", "node_id": "MDQ6VXNlcjIyMTc5Nzc3", "organizations_url": "https://api.github.com/users/ftkeys/orgs", "received_events_url": "https://api.github.com/users/ftkeys/received_events", "repos_url": "https://api.github.com/users/ftkeys/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ftkeys/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ftkeys/subscriptions", "type": "User", "url": "https://api.github.com/users/ftkeys", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Sorry I posted in the wrong repo, please delete.. thanks!" ]	2024-01-24T19:37:03Z	2024-01-24T19:42:30Z	2024-01-24T19:40:11Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	...	{ "avatar_url": "https://avatars.githubusercontent.com/u/22179777?v=4", "events_url": "https://api.github.com/users/ftkeys/events{/privacy}", "followers_url": "https://api.github.com/users/ftkeys/followers", "following_url": "https://api.github.com/users/ftkeys/following{/other_user}", "gists_url": "https://api.github.com/users/ftkeys/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ftkeys", "id": 22179777, "login": "ftkeys", "node_id": "MDQ6VXNlcjIyMTc5Nzc3", "organizations_url": "https://api.github.com/users/ftkeys/orgs", "received_events_url": "https://api.github.com/users/ftkeys/received_events", "repos_url": "https://api.github.com/users/ftkeys/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ftkeys/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ftkeys/subscriptions", "type": "User", "url": "https://api.github.com/users/ftkeys", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6615/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6615/timeline	null	not_planned	null	null
https://api.github.com/repos/huggingface/datasets/issues/6128	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6128/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6128/comments	https://api.github.com/repos/huggingface/datasets/issues/6128/events	https://github.com/huggingface/datasets/issues/6128	1,841,545,493	I_kwDODunzps5tw8EV	6,128	IndexError: Invalid key: 88 is out of bounds for size 0	{ "avatar_url": "https://avatars.githubusercontent.com/u/38727343?v=4", "events_url": "https://api.github.com/users/TomasAndersonFang/events{/privacy}", "followers_url": "https://api.github.com/users/TomasAndersonFang/followers", "following_url": "https://api.github.com/users/TomasAndersonFang/following{/other_user}", "gists_url": "https://api.github.com/users/TomasAndersonFang/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/TomasAndersonFang", "id": 38727343, "login": "TomasAndersonFang", "node_id": "MDQ6VXNlcjM4NzI3MzQz", "organizations_url": "https://api.github.com/users/TomasAndersonFang/orgs", "received_events_url": "https://api.github.com/users/TomasAndersonFang/received_events", "repos_url": "https://api.github.com/users/TomasAndersonFang/repos", "site_admin": false, "starred_url": "https://api.github.com/users/TomasAndersonFang/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/TomasAndersonFang/subscriptions", "type": "User", "url": "https://api.github.com/users/TomasAndersonFang", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi @TomasAndersonFang,\r\n\r\nHave you tried instead to use `torch_compile` in `transformers.TrainingArguments`? https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.torch_compile", "> \r\n\r\nI tried this and got the following error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 324, in _compile\r\n out_code = transform_code_object(code, transform)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py\", line 445, in transform_code_object\r\n transformations(instructions, code_options)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 311, in transform\r\n tracer.run()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 1726, in run\r\n super().run()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 576, in run\r\n and self.step()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 540, in step\r\n getattr(self, inst.opname)(inst)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 1030, in LOAD_ATTR\r\n result = BuiltinVariable(getattr).call_function(\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py\", line 566, in call_function\r\n result = handler(tx, args, kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py\", line 931, in call_getattr\r\n return obj.var_getattr(tx, name).add_options(options)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/nn_module.py\", line 124, in var_getattr\r\n subobj = inspect.getattr_static(base, name)\r\n File \"/apps/Arch/software/Python/3.10.8-GCCcore-12.2.0/lib/python3.10/inspect.py\", line 1777, in getattr_static\r\n raise AttributeError(attr)\r\nAttributeError: config\r\n\r\nfrom user code:\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/peft/peft_model.py\", line 909, in forward\r\n if self.base_model.config.model_type == \"mpt\":\r\n\r\nSet torch._dynamo.config.verbose=True for more information\r\n\r\n\r\nYou can suppress this exception and fall back to eager by setting:\r\n torch._dynamo.config.suppress_errors = True\r\n\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/llm-copt/fine-tune/falcon/falcon_sft.py\", line 228, in <module>\r\n main()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/llm-copt/fine-tune/falcon/falcon_sft.py\", line 221, in main\r\n trainer.train()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 1539, in train\r\n return inner_training_loop(\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 1809, in _inner_training_loop\r\n tr_loss_step = self.training_step(model, inputs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 2654, in training_step\r\n loss = self.compute_loss(model, inputs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 2679, in compute_loss\r\n outputs = model(inputs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\r\n return forward_call(args, *kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py\", line 82, in forward\r\n return self.dynamo_ctx(self._orig_mod.forward)(args, *kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py\", line 209, in _fn\r\n return fn(args, *kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/accelerate/utils/operations.py\", line 581, in forward\r\n return model_forward(args, *kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/accelerate/utils/operations.py\", line 569, in __call__\r\n return convert_to_fp32(self.model_forward(args, *kwargs))\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/amp/autocast_mode.py\", line 14, in decorate_autocast\r\n return func(args, *kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py\", line 337, in catch_errors\r\n return callback(frame, cache_size, hooks)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 404, in _convert_frame\r\n result = inner_convert(frame, cache_size, hooks)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 104, in _fn\r\n return fn(args, *kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 262, in _convert_frame_assert\r\n return _compile(\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/utils.py\", line 163, in time_wrapper\r\n r = func(args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 394, in _compile\r\n raise InternalTorchDynamoError() from e\r\ntorch._dynamo.exc.InternalTorchDynamoError\r\n```", "Hi @TomasAndersonFang,\r\n\r\nI guess in this case it may be an issue with `transformers` (or `PyTorch`). I would recommend you open an issue on their repo.", "@albertvillanova Thanks for your recommendation. I'll do it", "@TomasAndersonFang were you able to find a solution to this issue? I would highly appreciate any help. \r\n\r\nThanks!" ]	2023-08-08T15:32:08Z	2023-12-26T07:51:57Z	2023-08-11T13:35:09Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug This bug generates when I use torch.compile(model) in my code, which seems to raise an error in datasets lib. ### Steps to reproduce the bug I use the following code to fine-tune Falcon on my private dataset. ```python import transformers from transformers import ( AutoModelForCausalLM, AutoTokenizer, AutoConfig, DataCollatorForSeq2Seq, Trainer, Seq2SeqTrainer, HfArgumentParser, Seq2SeqTrainingArguments, BitsAndBytesConfig, ) from peft import ( LoraConfig, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training, set_peft_model_state_dict, ) import torch import os import evaluate import functools from datasets import load_dataset import bitsandbytes as bnb import logging import json import copy from typing import Dict, Optional, Sequence from dataclasses import dataclass, field # Lora settings LORA_R = 8 LORA_ALPHA = 16 LORA_DROPOUT= 0.05 LORA_TARGET_MODULES = ["query_key_value"] @dataclass class ModelArguments: model_name_or_path: Optional[str] = field(default="Salesforce/codegen2-7B") @dataclass class DataArguments: data_path: str = field(default=None, metadata={"help": "Path to the training data."}) train_file: str = field(default=None, metadata={"help": "Path to the evaluation data."}) eval_file: str = field(default=None, metadata={"help": "Path to the evaluation data."}) cache_path: str = field(default=None, metadata={"help": "Path to the cache directory."}) num_proc: int = field(default=4, metadata={"help": "Number of processes to use for data preprocessing."}) @dataclass class TrainingArguments(transformers.TrainingArguments): # cache_dir: Optional[str] = field(default=None) optim: str = field(default="adamw_torch") model_max_length: int = field( default=512, metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."}, ) is_lora: bool = field(default=True, metadata={"help": "Whether to use LORA."}) def tokenize(text, tokenizer, max_seq_len=512, add_eos_token=True): result = tokenizer( text, truncation=True, max_length=max_seq_len, padding=False, return_tensors=None, ) if ( result["input_ids"][-1] != tokenizer.eos_token_id and len(result["input_ids"]) < max_seq_len and add_eos_token ): result["input_ids"].append(tokenizer.eos_token_id) result["attention_mask"].append(1) if add_eos_token and len(result["input_ids"]) >= max_seq_len: result["input_ids"][max_seq_len - 1] = tokenizer.eos_token_id result["attention_mask"][max_seq_len - 1] = 1 result["labels"] = result["input_ids"].copy() return result def main(): parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments)) model_args, data_args, training_args = parser.parse_args_into_dataclasses() config = AutoConfig.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, trust_remote_code=True, ) if training_args.is_lora: model = AutoModelForCausalLM.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, torch_dtype=torch.float16, trust_remote_code=True, load_in_8bit=True, quantization_config=BitsAndBytesConfig( load_in_8bit=True, llm_int8_threshold=6.0 ), ) model = prepare_model_for_int8_training(model) config = LoraConfig( r=LORA_R, lora_alpha=LORA_ALPHA, target_modules=LORA_TARGET_MODULES, lora_dropout=LORA_DROPOUT, bias="none", task_type="CAUSAL_LM", ) model = get_peft_model(model, config) else: model = AutoModelForCausalLM.from_pretrained( model_args.model_name_or_path, torch_dtype=torch.float16, cache_dir=data_args.cache_path, trust_remote_code=True, ) model.config.use_cache = False def print_trainable_parameters(model): """ Prints the number of trainable parameters in the model. """ trainable_params = 0 all_param = 0 for _, param in model.named_parameters(): all_param += param.numel() if param.requires_grad: trainable_params += param.numel() print( f"trainable params: {trainable_params} \|\| all params: {all_param} \|\| trainable%: {100 * trainable_params / all_param}" ) print_trainable_parameters(model) tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, model_max_length=training_args.model_max_length, padding_side="left", use_fast=True, trust_remote_code=True, ) tokenizer.pad_token = tokenizer.eos_token # Load dataset def generate_and_tokenize_prompt(sample): input_text = sample["input"] target_text = sample["output"] + tokenizer.eos_token full_text = input_text + target_text tokenized_full_text = tokenize(full_text, tokenizer, max_seq_len=512) tokenized_input_text = tokenize(input_text, tokenizer, max_seq_len=512) input_len = len(tokenized_input_text["input_ids"]) - 1 # -1 for eos token tokenized_full_text["labels"] = [-100] * input_len + tokenized_full_text["labels"][input_len:] return tokenized_full_text data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file if data_args.eval_file is not None: data_files["eval"] = data_args.eval_file dataset = load_dataset(data_args.data_path, data_files=data_files) train_dataset = dataset["train"] eval_dataset = dataset["eval"] train_dataset = train_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc) eval_dataset = eval_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc) data_collator = DataCollatorForSeq2Seq(tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True) # Evaluation metrics def compute_metrics(eval_preds, tokenizer): metric = evaluate.load('exact_match') preds, labels = eval_preds # In case the model returns more than the prediction logits if isinstance(preds, tuple): preds = preds[0] decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=False) # Replace -100s in the labels as we can't decode them labels[labels == -100] = tokenizer.pad_token_id decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True, clean_up_tokenization_spaces=False) # Some simple post-processing decoded_preds = [pred.strip() for pred in decoded_preds] decoded_labels = [label.strip() for label in decoded_labels] result = metric.compute(predictions=decoded_preds, references=decoded_labels) return {'exact_match': result['exact_match']} compute_metrics_fn = functools.partial(compute_metrics, tokenizer=tokenizer) model = torch.compile(model) # Training trainer = Trainer( model=model, train_dataset=train_dataset, eval_dataset=eval_dataset, args=training_args, data_collator=data_collator, compute_metrics=compute_metrics_fn, ) trainer.train() trainer.save_state() trainer.save_model(output_dir=training_args.output_dir) tokenizer.save_pretrained(save_directory=training_args.output_dir) if __name__ == "__main__": main() ``` When I didn't use `torch.cpmpile(model)`, my code worked well. But when I added this line to my code, It produced the following error: ``` Traceback (most recent call last): File "falcon_sft.py", line 230, in <module> main() File "falcon_sft.py", line 223, in main trainer.train() File "python3.10/site-packages/transformers/trainer.py", line 1539, in train return inner_training_loop( File "python3.10/site-packages/transformers/trainer.py", line 1787, in _inner_training_loop for step, inputs in enumerate(epoch_iterator): File "python3.10/site-packages/accelerate/data_loader.py", line 384, in __iter__ current_batch = next(dataloader_iter) File "python3.10/site-packages/torch/utils/data/dataloader.py", line 633, in __next__ data = self._next_data() File "python3.10/site-packages/torch/utils/data/dataloader.py", line 677, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = self.dataset.__getitems__(possibly_batched_index) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2807, in __getitems__ batch = self.__getitem__(keys) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__ return self._getitem(key) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2787, in _getitem pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) File "python3.10/site-packages/datasets/formatting/formatting.py", line 583, in query_table _check_valid_index_key(key, size) File "python3.10/site-packages/datasets/formatting/formatting.py", line 536, in _check_valid_index_key _check_valid_index_key(int(max(key)), size=size) File "python3.10/site-packages/datasets/formatting/formatting.py", line 526, in _check_valid_index_key raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") IndexError: Invalid key: 88 is out of bounds for size 0 ``` So I'm confused about why this error was generated, and how to fix it. Is this error produced by datasets or `torch.compile`? ### Expected behavior I want to use `torch.compile` in my code. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.10.8 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	{ "avatar_url": "https://avatars.githubusercontent.com/u/38727343?v=4", "events_url": "https://api.github.com/users/TomasAndersonFang/events{/privacy}", "followers_url": "https://api.github.com/users/TomasAndersonFang/followers", "following_url": "https://api.github.com/users/TomasAndersonFang/following{/other_user}", "gists_url": "https://api.github.com/users/TomasAndersonFang/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/TomasAndersonFang", "id": 38727343, "login": "TomasAndersonFang", "node_id": "MDQ6VXNlcjM4NzI3MzQz", "organizations_url": "https://api.github.com/users/TomasAndersonFang/orgs", "received_events_url": "https://api.github.com/users/TomasAndersonFang/received_events", "repos_url": "https://api.github.com/users/TomasAndersonFang/repos", "site_admin": false, "starred_url": "https://api.github.com/users/TomasAndersonFang/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/TomasAndersonFang/subscriptions", "type": "User", "url": "https://api.github.com/users/TomasAndersonFang", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6128/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6128/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7484	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7484/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7484/comments	https://api.github.com/repos/huggingface/datasets/issues/7484/events	https://github.com/huggingface/datasets/pull/7484	2,953,677,168	PR_kwDODunzps6Qbevn	7,484	release: 3.5.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7484). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]	2025-03-27T16:33:27Z	2025-03-27T16:35:44Z	2025-03-27T16:34:22Z	MEMBER	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7484/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7484/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7484.diff", "html_url": "https://github.com/huggingface/datasets/pull/7484", "merged_at": "2025-03-27T16:34:22Z", "patch_url": "https://github.com/huggingface/datasets/pull/7484.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7484" }
https://api.github.com/repos/huggingface/datasets/issues/5009	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5009/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5009/comments	https://api.github.com/repos/huggingface/datasets/issues/5009/events	https://github.com/huggingface/datasets/issues/5009	1,381,194,067	I_kwDODunzps5SU1lT	5,009	Error loading StonyBrookNLP/tellmewhy dataset from hub even though local copy loads correctly	{ "avatar_url": "https://avatars.githubusercontent.com/u/4996184?v=4", "events_url": "https://api.github.com/users/ykl7/events{/privacy}", "followers_url": "https://api.github.com/users/ykl7/followers", "following_url": "https://api.github.com/users/ykl7/following{/other_user}", "gists_url": "https://api.github.com/users/ykl7/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ykl7", "id": 4996184, "login": "ykl7", "node_id": "MDQ6VXNlcjQ5OTYxODQ=", "organizations_url": "https://api.github.com/users/ykl7/orgs", "received_events_url": "https://api.github.com/users/ykl7/received_events", "repos_url": "https://api.github.com/users/ykl7/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ykl7/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ykl7/subscriptions", "type": "User", "url": "https://api.github.com/users/ykl7", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	null	[]	null	[ "I think this is because some columns are mostly empty lists. In particular the train and validation splits only have empty lists for `val_ann`. Therefore the type inference doesn't know which type is inside (or it would have to scan the other splits first before knowing).\r\n\r\nYou can fix that by specifying the features types explicitly.\r\nThen you can save the feature types inside the dataset repository, so that you won't need to specify the features in subsequent calls:\r\n```python\r\nfrom datasets import load_dataset, Features, Sequence, Value\r\nfrom datasets.info import DatasetInfosDict\r\n\r\nfeatures = Features({\r\n 'narrative': Value('string'),\r\n 'question': Value('string'),\r\n 'original_sentence_for_question': Value('string'),\r\n 'narrative_lexical_overlap': Value('float64'),\r\n 'is_ques_answerable': Value('string'),\r\n 'answer': Value('string'),\r\n 'is_ques_answerable_annotator': Value('string'),\r\n 'original_narrative_form': Sequence(Value('string')),\r\n 'question_meta': Value('string'),\r\n 'helpful_sentences': Sequence(Value('int64')),\r\n 'human_eval': Value('bool'),\r\n 'val_ann': Sequence(Value('int64')),\r\n 'gram_ann': Sequence(Value('int64'))\r\n})\r\nds = load_dataset('StonyBrookNLP/tellmewhy', features=features)\r\nDatasetInfosDict({\"default\": ds[\"train\"].info}).write_to_directory(\"path/to/local/tellmewhy\")\r\n```\r\nand then after pushing the change to the dataset repository on the Hub, `load_dataset(\"StonyBrookNLP/tellmewhy\")` will work directly`", "(Note that specifying explicit types will be made easier with https://github.com/huggingface/datasets/pull/4926)", "`gram_ann` and `val_ann` are annotations that only exist for part of the test set. I wanted to keep all the columns consistent across all files, so I added them to train and validation as well. I'll check if removing them from those files is still compliant with this repo. Otherwise, I will do as you suggested. Thanks @lhoestq !", "@lhoestq I followed the exact steps you described but it seems like I'm getting the same error unfortunately. Any other ideas? Thanks in advance", "Hi ! If you move `dataset_infos.json` from `data/` to the root of your dataset repository if should work :)", "I tried that and pushed to the [hub](https://huggingface.co/datasets/StonyBrookNLP/tellmewhy/tree/main). Now, there is a new error.\r\n```\r\n File \"/home/yklal95/tellmewhy/src/prepare_data.py\", line 67, in main\r\n dataset = load_dataset('StonyBrookNLP/tellmewhy')\r\n File \"/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/load.py\", line 1746, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py\", line 704, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py\", line 775, in _download_and_prepare\r\n verify_checksums(\r\n File \"/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/utils/info_utils.py\", line 33, in verify_checksums\r\n raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\ndatasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'/home/yklal95/tellmewhy/data/test.json', '/home/yklal95/tellmewhy/data/validation.json', '/home/yklal95/tellmewhy/data/train.json'}\r\n```\r\nNo changes were made to any of the other files and they are still on the hub. Let me know if you have any ideas @lhoestq Thanks!", "Oh I see - the code I gave you returns local paths instead of URLs to store metadata about files to download.\r\nI opened a PR in your repo here to remove this: https://huggingface.co/datasets/StonyBrookNLP/tellmewhy/discussions/1\r\nsorry for the inconvenience !", "It works now! Thanks a lot @lhoestq " ]	2022-09-21T16:23:06Z	2022-09-29T13:07:29Z	2022-09-29T13:07:29Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	## Describe the bug I have added a new dataset with the identifier `StonyBrookNLP/tellmewhy` to the hub. When I load the individual files using my local copy using `dataset = datasets.load_dataset("json", data_files="data/train.jsonl")`, it loads the dataset correctly. However, when I try to load it from the hub, I get an error (pasted below). Additionally, `dataset = datasets.load_dataset("json", data_dir="data/")` throws the same error. ## Steps to reproduce the bug ```python dataset = datasets.load_dataset('StonyBrookNLP/tellmewhy') ``` ## Expected results Successfully load the `StonyBrookNLP/tellmewhy` dataset. ## Actual results ``` Using custom data configuration StonyBrookNLP--tellmewhy-82712924092694ff Downloading and preparing dataset json/StonyBrookNLP--tellmewhy to /home/yklal95/.cache/huggingface/datasets/StonyBrookNLP___json/StonyBrookNLP--tellmewhy-82712924092694ff/0.0.0/a3e658c4731e59120d44081ac10bf85dc7e1388126b92338344ce9661907f253... Downloading data files: 100%\|██████████████████████████████\| 3/3 [00:00<00:00, 957.46it/s] Extracting data files: 100%\|███████████████████████████████\| 3/3 [00:00<00:00, 299.14it/s] Traceback (most recent call last): File "/home/yklal95/tmw-generalization/src/load_datasets.py", line 17, in <module> main(args) File "/home/yklal95/tmw-generalization/src/load_datasets.py", line 11, in main dataset = datasets.load_dataset(args.dataset_name) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/load.py", line 1746, in load_dataset builder_instance.download_and_prepare( File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py", line 793, in _download_and_prepare self._prepare_split(split_generator, *prepare_split_kwargs) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py", line 1277, in _prepare_split writer.write_table(table) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/arrow_writer.py", line 524, in write_table pa_table = table_cast(pa_table, self._schema) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 2005, in table_cast return cast_table_to_schema(table, schema) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1969, in cast_table_to_schema arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1969, in <listcomp> arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1681, in wrapper return pa.chunked_array([func(chunk, args, *kwargs) for chunk in array.chunks]) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1681, in <listcomp> return pa.chunked_array([func(chunk, args, *kwargs) for chunk in array.chunks]) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1822, in cast_array_to_feature casted_values = _c(array.values, feature.feature) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1683, in wrapper return func(array, args, *kwargs) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1853, in cast_array_to_feature return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1683, in wrapper return func(array, args, **kwargs) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1761, in array_cast raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}") TypeError: Couldn't cast array of type int64 to null ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-4.15.0-121-generic-x86_64-with-glibc2.27 - Python version: 3.9.13 - PyArrow version: 9.0.0 - Pandas version: 1.5.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/4996184?v=4", "events_url": "https://api.github.com/users/ykl7/events{/privacy}", "followers_url": "https://api.github.com/users/ykl7/followers", "following_url": "https://api.github.com/users/ykl7/following{/other_user}", "gists_url": "https://api.github.com/users/ykl7/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ykl7", "id": 4996184, "login": "ykl7", "node_id": "MDQ6VXNlcjQ5OTYxODQ=", "organizations_url": "https://api.github.com/users/ykl7/orgs", "received_events_url": "https://api.github.com/users/ykl7/received_events", "repos_url": "https://api.github.com/users/ykl7/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ykl7/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ykl7/subscriptions", "type": "User", "url": "https://api.github.com/users/ykl7", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5009/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5009/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6096	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6096/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6096/comments	https://api.github.com/repos/huggingface/datasets/issues/6096/events	https://github.com/huggingface/datasets/pull/6096	1,826,731,091	PR_kwDODunzps5Wq9Hb	6,096	Add `fsspec` support for `to_json`, `to_csv`, and `to_parquet`	{ "avatar_url": "https://avatars.githubusercontent.com/u/36760800?v=4", "events_url": "https://api.github.com/users/alvarobartt/events{/privacy}", "followers_url": "https://api.github.com/users/alvarobartt/followers", "following_url": "https://api.github.com/users/alvarobartt/following{/other_user}", "gists_url": "https://api.github.com/users/alvarobartt/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alvarobartt", "id": 36760800, "login": "alvarobartt", "node_id": "MDQ6VXNlcjM2NzYwODAw", "organizations_url": "https://api.github.com/users/alvarobartt/orgs", "received_events_url": "https://api.github.com/users/alvarobartt/received_events", "repos_url": "https://api.github.com/users/alvarobartt/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alvarobartt/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvarobartt/subscriptions", "type": "User", "url": "https://api.github.com/users/alvarobartt", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6096). All of your documentation changes will be reflected on that endpoint.", "Hi here @lhoestq @mariosasko I just realised this PR is still open, should we close it in case this is something not to include within `datasets`, or should we merge? Let me know whatever you decide 🤗", "<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) \| 0.005011 / 0.011353 (-0.006342) \| 0.003203 / 0.011008 (-0.007806) \| 0.064033 / 0.038508 (0.025524) \| 0.029152 / 0.023109 (0.006043) \| 0.242884 / 0.275898 (-0.033014) \| 0.263517 / 0.323480 (-0.059963) \| 0.004088 / 0.007986 (-0.003898) \| 0.002570 / 0.004328 (-0.001759) \| 0.049061 / 0.004250 (0.044811) \| 0.040170 / 0.037052 (0.003117) \| 0.263305 / 0.258489 (0.004816) \| 0.286255 / 0.293841 (-0.007586) \| 0.028206 / 0.128546 (-0.100340) \| 0.010337 / 0.075646 (-0.065309) \| 0.206235 / 0.419271 (-0.213036) \| 0.038182 / 0.043533 (-0.005351) \| 0.246486 / 0.255139 (-0.008653) \| 0.263077 / 0.283200 (-0.020122) \| 0.017850 / 0.141683 (-0.123833) \| 1.173921 / 1.452155 (-0.278234) \| 1.255583 / 1.492716 (-0.237133) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090278 / 0.018006 (0.072272) \| 0.298146 / 0.000490 (0.297657) \| 0.000215 / 0.000200 (0.000015) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018021 / 0.037411 (-0.019390) \| 0.061434 / 0.014526 (0.046908) \| 0.072617 / 0.176557 (-0.103939) \| 0.119063 / 0.737135 (-0.618072) \| 0.073997 / 0.296338 (-0.222341) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288496 / 0.215209 (0.073287) \| 2.794943 / 2.077655 (0.717288) \| 1.538299 / 1.504120 (0.034179) \| 1.399164 / 1.541195 (-0.142031) \| 1.419104 / 1.468490 (-0.049386) \| 0.566147 / 4.584777 (-4.018630) \| 2.386687 / 3.745712 (-1.359025) \| 2.723584 / 5.269862 (-2.546278) \| 1.699161 / 4.565676 (-2.866515) \| 0.062526 / 0.424275 (-0.361750) \| 0.004927 / 0.007607 (-0.002680) \| 0.345132 / 0.226044 (0.119087) \| 3.389634 / 2.268929 (1.120706) \| 1.898012 / 55.444624 (-53.546612) \| 1.599050 / 6.876477 (-5.277427) \| 1.614289 / 2.142072 (-0.527783) \| 0.656716 / 4.805227 (-4.148511) \| 0.118480 / 6.500664 (-6.382184) \| 0.041913 / 0.075469 (-0.033557) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.968676 / 1.841788 (-0.873111) \| 11.184668 / 8.074308 (3.110360) \| 9.249912 / 10.191392 (-0.941480) \| 0.141139 / 0.680424 (-0.539285) \| 0.014207 / 0.534201 (-0.519994) \| 0.287603 / 0.579283 (-0.291680) \| 0.262792 / 0.434364 (-0.171572) \| 0.340239 / 0.540337 (-0.200099) \| 0.437471 / 1.386936 (-0.949465) \|\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) \| 0.005268 / 0.011353 (-0.006085) \| 0.003142 / 0.011008 (-0.007866) \| 0.049333 / 0.038508 (0.010825) \| 0.029558 / 0.023109 (0.006449) \| 0.270716 / 0.275898 (-0.005182) \| 0.293834 / 0.323480 (-0.029646) \| 0.004285 / 0.007986 (-0.003701) \| 0.002703 / 0.004328 (-0.001626) \| 0.048857 / 0.004250 (0.044607) \| 0.043456 / 0.037052 (0.006404) \| 0.286058 / 0.258489 (0.027569) \| 0.313491 / 0.293841 (0.019650) \| 0.029336 / 0.128546 (-0.099210) \| 0.010287 / 0.075646 (-0.065360) \| 0.057753 / 0.419271 (-0.361518) \| 0.050867 / 0.043533 (0.007334) \| 0.271717 / 0.255139 (0.016578) \| 0.291468 / 0.283200 (0.008268) \| 0.018668 / 0.141683 (-0.123015) \| 1.137399 / 1.452155 (-0.314755) \| 1.186315 / 1.492716 (-0.306401) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090289 / 0.018006 (0.072283) \| 0.297987 / 0.000490 (0.297497) \| 0.000227 / 0.000200 (0.000027) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021012 / 0.037411 (-0.016399) \| 0.075046 / 0.014526 (0.060520) \| 0.085295 / 0.176557 (-0.091261) \| 0.123879 / 0.737135 (-0.613257) \| 0.086572 / 0.296338 (-0.209766) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293350 / 0.215209 (0.078141) \| 2.875958 / 2.077655 (0.798304) \| 1.586460 / 1.504120 (0.082340) \| 1.467950 / 1.541195 (-0.073245) \| 1.453478 / 1.468490 (-0.015012) \| 0.566083 / 4.584777 (-4.018693) \| 2.462582 / 3.745712 (-1.283130) \| 2.609367 / 5.269862 (-2.660495) \| 1.709691 / 4.565676 (-2.855985) \| 0.062928 / 0.424275 (-0.361347) \| 0.005040 / 0.007607 (-0.002567) \| 0.337997 / 0.226044 (0.111952) \| 3.347235 / 2.268929 (1.078306) \| 1.923940 / 55.444624 (-53.520684) \| 1.657731 / 6.876477 (-5.218746) \| 1.747469 / 2.142072 (-0.394604) \| 0.657061 / 4.805227 (-4.148167) \| 0.116655 / 6.500664 (-6.384009) \| 0.040363 / 0.075469 (-0.035106) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.011171 / 1.841788 (-0.830617) \| 11.705905 / 8.074308 (3.631597) \| 10.064391 / 10.191392 (-0.127001) \| 0.141681 / 0.680424 (-0.538743) \| 0.014763 / 0.534201 (-0.519438) \| 0.286425 / 0.579283 (-0.292858) \| 0.271036 / 0.434364 (-0.163328) \| 0.321393 / 0.540337 (-0.218944) \| 0.424539 / 1.386936 (-0.962397) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e52f4d0cf1cb89a9719b42fff13a891f92d51e04 \"CML watermark\")\n", "Thanks @alvarobartt. \r\n\r\nI am linking this PR to the corresponding issue (on the right column, under \"Development\") and closing the issue.\r\n\r\nFor future contributions, please add to the PR description the word \"fix\" followed by the issue number, e.g.:\r\n```\r\nFix #6086.\r\n```\r\n- I have edited the PR description to add this.", "> Thanks @alvarobartt.\r\n> \r\n> I am linking this PR to the corresponding issue (on the right column, under \"Development\") and closing the issue.\r\n> \r\n> For future contributions, please add to the PR description the word \"fix\" followed by the issue number, e.g.:\r\n> \r\n> ```\r\n> Fix #6086.\r\n> ```\r\n> \r\n> * I have edited the PR description to add this.\r\n\r\nHi @albertvillanova, fair, I missed that, thanks for the edit and the heads up!" ]	2023-07-28T16:36:59Z	2024-05-28T07:40:30Z	2024-03-06T11:12:42Z	MEMBER	null	null	null	Hi to whoever is reading this! 🤗 (Most likely @mariosasko) ## What's in this PR? This PR replaces the `open` from Python with `fsspec.open` and adds the argument `storage_options` for the methods `to_json`, `to_csv`, and `to_parquet`, to allow users to export any 🤗`Dataset` into a file in a file-system as requested at #6086. ## What's missing in this PR? As per `to_json`, `to_csv`, and `to_parquet` docstrings for the recently included `storage_options` arg, I've scoped it to 2.15.0, so we should check that before merging in case we want to scope that for 2.14.2 instead. Additionally, should we also add `fsspec` support for the `from_csv`, `from_json`, and `from_parquet` methods? If you want me to do so @mariosasko just let me know and I'll create another PR to support that too! Fix #6086.	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6096/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6096/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6096.diff", "html_url": "https://github.com/huggingface/datasets/pull/6096", "merged_at": "2024-03-06T11:12:42Z", "patch_url": "https://github.com/huggingface/datasets/pull/6096.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6096" }
https://api.github.com/repos/huggingface/datasets/issues/4783	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4783/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4783/comments	https://api.github.com/repos/huggingface/datasets/issues/4783/events	https://github.com/huggingface/datasets/pull/4783	1,326,375,011	PR_kwDODunzps48iHey	4,783	Docs for creating a loading script for image datasets	{ "avatar_url": "https://avatars.githubusercontent.com/u/59462357?v=4", "events_url": "https://api.github.com/users/stevhliu/events{/privacy}", "followers_url": "https://api.github.com/users/stevhliu/followers", "following_url": "https://api.github.com/users/stevhliu/following{/other_user}", "gists_url": "https://api.github.com/users/stevhliu/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/stevhliu", "id": 59462357, "login": "stevhliu", "node_id": "MDQ6VXNlcjU5NDYyMzU3", "organizations_url": "https://api.github.com/users/stevhliu/orgs", "received_events_url": "https://api.github.com/users/stevhliu/received_events", "repos_url": "https://api.github.com/users/stevhliu/repos", "site_admin": false, "starred_url": "https://api.github.com/users/stevhliu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/stevhliu/subscriptions", "type": "User", "url": "https://api.github.com/users/stevhliu", "user_view_type": "public" }	[ { "color": "0075ca", "default": true, "description": "Improvements or additions to documentation", "id": 1935892861, "name": "documentation", "node_id": "MDU6TGFiZWwxOTM1ODkyODYx", "url": "https://api.github.com/repos/huggingface/datasets/labels/documentation" } ]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "IMO it would make more sense to add a \"Create image dataset\" page with two main sections - a no-code approach with `imagefolder` + metadata (preferred way), and with a loading script (advanced). It should be clear when to choose which. If we leave this as-is, the user who jumps straight to the Vision section could be under the impression that writing a loading script is the preferred way to share a vision dataset due to how this subsection starts:\r\n```\r\nWrite a dataset loading script to share a dataset.\r\n```\r\n \r\nAlso, I think a note explaining how to make a dataset gated/disable the viewer to hide the data would be beneficial (it's pretty common to require submitting a form to access a CV dataset).", "Great suggestion @mariosasko! I added your suggestions, let me know what you think. For gated dataset access, I just added a tip referring users to the relevant docs since it's more of a Hub feature than `datasets` feature.", "Thanks, looks much better now :). I would also move the sections explaining how to create an `imagefolder` for the specific task from the [loading page](https://raw.githubusercontent.com/huggingface/datasets/main/docs/source/image_load.mdx) to this one. IMO it makes more sense to have the basic info (imagefolder structure + `load_dataset` call) there + a link to this page for info on how to create an image folder dataset.", "Good idea! Moved everything about `imagefolder` + metadata to the create an image dataset section since the `load_dataset` call is the same for different computer vision tasks. ", "Thanks for all the feedbacks! 🥰\r\n\r\nWhat do you think about creating how to share an `ImageFolder` dataset in a separate PR? I think we should create a new section under `Vision` for how to share an image dataset.", "I love it thanks ! I think moving forward we can use CSV instead of JSON Lines in the docs ;)" ]	2022-08-02T20:36:03Z	2022-09-09T17:08:14Z	2022-09-07T19:07:34Z	MEMBER	null	null	null	This PR is a first draft of creating a loading script for image datasets. Feel free to let me know if there are any specificities I'm missing for this. 🙂 To do: - [x] Document how to create different configurations.	{ "avatar_url": "https://avatars.githubusercontent.com/u/59462357?v=4", "events_url": "https://api.github.com/users/stevhliu/events{/privacy}", "followers_url": "https://api.github.com/users/stevhliu/followers", "following_url": "https://api.github.com/users/stevhliu/following{/other_user}", "gists_url": "https://api.github.com/users/stevhliu/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/stevhliu", "id": 59462357, "login": "stevhliu", "node_id": "MDQ6VXNlcjU5NDYyMzU3", "organizations_url": "https://api.github.com/users/stevhliu/orgs", "received_events_url": "https://api.github.com/users/stevhliu/received_events", "repos_url": "https://api.github.com/users/stevhliu/repos", "site_admin": false, "starred_url": "https://api.github.com/users/stevhliu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/stevhliu/subscriptions", "type": "User", "url": "https://api.github.com/users/stevhliu", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4783/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4783/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4783.diff", "html_url": "https://github.com/huggingface/datasets/pull/4783", "merged_at": "2022-09-07T19:07:34Z", "patch_url": "https://github.com/huggingface/datasets/pull/4783.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4783" }
https://api.github.com/repos/huggingface/datasets/issues/5894	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5894/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5894/comments	https://api.github.com/repos/huggingface/datasets/issues/5894/events	https://github.com/huggingface/datasets/pull/5894	1,724,774,910	PR_kwDODunzps5RSjot	5,894	Force overwrite existing filesystem protocol	{ "avatar_url": "https://avatars.githubusercontent.com/u/24520725?v=4", "events_url": "https://api.github.com/users/baskrahmer/events{/privacy}", "followers_url": "https://api.github.com/users/baskrahmer/followers", "following_url": "https://api.github.com/users/baskrahmer/following{/other_user}", "gists_url": "https://api.github.com/users/baskrahmer/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/baskrahmer", "id": 24520725, "login": "baskrahmer", "node_id": "MDQ6VXNlcjI0NTIwNzI1", "organizations_url": "https://api.github.com/users/baskrahmer/orgs", "received_events_url": "https://api.github.com/users/baskrahmer/received_events", "repos_url": "https://api.github.com/users/baskrahmer/repos", "site_admin": false, "starred_url": "https://api.github.com/users/baskrahmer/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/baskrahmer/subscriptions", "type": "User", "url": "https://api.github.com/users/baskrahmer", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009139 / 0.011353 (-0.002214) \| 0.005634 / 0.011008 (-0.005374) \| 0.129587 / 0.038508 (0.091079) \| 0.038298 / 0.023109 (0.015189) \| 0.428149 / 0.275898 (0.152251) \| 0.443744 / 0.323480 (0.120264) \| 0.007501 / 0.007986 (-0.000485) \| 0.005999 / 0.004328 (0.001671) \| 0.100796 / 0.004250 (0.096546) \| 0.053236 / 0.037052 (0.016184) \| 0.423868 / 0.258489 (0.165379) \| 0.460110 / 0.293841 (0.166269) \| 0.041255 / 0.128546 (-0.087291) \| 0.013790 / 0.075646 (-0.061856) \| 0.438398 / 0.419271 (0.019127) \| 0.063086 / 0.043533 (0.019553) \| 0.414826 / 0.255139 (0.159687) \| 0.460652 / 0.283200 (0.177453) \| 0.121223 / 0.141683 (-0.020460) \| 1.754430 / 1.452155 (0.302275) \| 1.900037 / 1.492716 (0.407320) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027222 / 0.018006 (0.009216) \| 0.617666 / 0.000490 (0.617176) \| 0.022443 / 0.000200 (0.022243) \| 0.000820 / 0.000054 (0.000766) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030397 / 0.037411 (-0.007014) \| 0.125732 / 0.014526 (0.111206) \| 0.149805 / 0.176557 (-0.026752) \| 0.234048 / 0.737135 (-0.503087) \| 0.143108 / 0.296338 (-0.153231) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.631189 / 0.215209 (0.415980) \| 6.182871 / 2.077655 (4.105216) \| 2.635730 / 1.504120 (1.131610) \| 2.231429 / 1.541195 (0.690235) \| 2.438360 / 1.468490 (0.969870) \| 0.861170 / 4.584777 (-3.723607) \| 5.785984 / 3.745712 (2.040272) \| 2.758358 / 5.269862 (-2.511504) \| 1.678095 / 4.565676 (-2.887582) \| 0.105961 / 0.424275 (-0.318314) \| 0.013659 / 0.007607 (0.006052) \| 0.762943 / 0.226044 (0.536898) \| 7.774399 / 2.268929 (5.505471) \| 3.319027 / 55.444624 (-52.125598) \| 2.700248 / 6.876477 (-4.176229) \| 3.008581 / 2.142072 (0.866509) \| 1.122522 / 4.805227 (-3.682705) \| 0.214832 / 6.500664 (-6.285832) \| 0.085281 / 0.075469 (0.009811) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.647610 / 1.841788 (-0.194177) \| 18.178316 / 8.074308 (10.104008) \| 21.199177 / 10.191392 (11.007785) \| 0.247063 / 0.680424 (-0.433361) \| 0.030443 / 0.534201 (-0.503758) \| 0.512527 / 0.579283 (-0.066757) \| 0.640758 / 0.434364 (0.206394) \| 0.639986 / 0.540337 (0.099649) \| 0.760113 / 1.386936 (-0.626823) \|\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) \| 0.008293 / 0.011353 (-0.003060) \| 0.005360 / 0.011008 (-0.005648) \| 0.102932 / 0.038508 (0.064424) \| 0.037457 / 0.023109 (0.014347) \| 0.444114 / 0.275898 (0.168216) \| 0.512855 / 0.323480 (0.189375) \| 0.007030 / 0.007986 (-0.000956) \| 0.004954 / 0.004328 (0.000625) \| 0.095757 / 0.004250 (0.091507) \| 0.051239 / 0.037052 (0.014187) \| 0.471118 / 0.258489 (0.212629) \| 0.517764 / 0.293841 (0.223923) \| 0.041953 / 0.128546 (-0.086593) \| 0.013748 / 0.075646 (-0.061898) \| 0.118089 / 0.419271 (-0.301182) \| 0.060159 / 0.043533 (0.016626) \| 0.466011 / 0.255139 (0.210872) \| 0.489180 / 0.283200 (0.205980) \| 0.123250 / 0.141683 (-0.018433) \| 1.714738 / 1.452155 (0.262584) \| 1.838571 / 1.492716 (0.345855) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.267792 / 0.018006 (0.249785) \| 0.624313 / 0.000490 (0.623824) \| 0.007315 / 0.000200 (0.007115) \| 0.000136 / 0.000054 (0.000082) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033751 / 0.037411 (-0.003661) \| 0.122819 / 0.014526 (0.108293) \| 0.148270 / 0.176557 (-0.028286) \| 0.198581 / 0.737135 (-0.538554) \| 0.144845 / 0.296338 (-0.151494) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.620631 / 0.215209 (0.405422) \| 6.224665 / 2.077655 (4.147010) \| 2.856592 / 1.504120 (1.352473) \| 2.525089 / 1.541195 (0.983894) \| 2.600198 / 1.468490 (1.131708) \| 0.872038 / 4.584777 (-3.712739) \| 5.571650 / 3.745712 (1.825937) \| 5.907643 / 5.269862 (0.637782) \| 2.348770 / 4.565676 (-2.216906) \| 0.111665 / 0.424275 (-0.312610) \| 0.013886 / 0.007607 (0.006278) \| 0.762154 / 0.226044 (0.536109) \| 7.792686 / 2.268929 (5.523758) \| 3.601122 / 55.444624 (-51.843503) \| 2.939412 / 6.876477 (-3.937064) \| 2.973430 / 2.142072 (0.831358) \| 1.065016 / 4.805227 (-3.740211) \| 0.221701 / 6.500664 (-6.278963) \| 0.088157 / 0.075469 (0.012688) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.771061 / 1.841788 (-0.070727) \| 18.826926 / 8.074308 (10.752618) \| 21.283830 / 10.191392 (11.092438) \| 0.239233 / 0.680424 (-0.441191) \| 0.026159 / 0.534201 (-0.508042) \| 0.487074 / 0.579283 (-0.092209) \| 0.623241 / 0.434364 (0.188877) \| 0.600506 / 0.540337 (0.060169) \| 0.691271 / 1.386936 (-0.695665) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1bbe2c3496498a6415765b517ac4bc600a02ad06 \"CML watermark\")\n" ]	2023-05-24T21:41:53Z	2023-05-25T06:52:08Z	2023-05-25T06:42:33Z	CONTRIBUTOR	null	null	null	Fix #5876	{ "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": 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}	https://api.github.com/repos/huggingface/datasets/issues/5894/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5894.diff", "html_url": "https://github.com/huggingface/datasets/pull/5894", "merged_at": "2023-05-25T06:42:33Z", "patch_url": "https://github.com/huggingface/datasets/pull/5894.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5894" }
https://api.github.com/repos/huggingface/datasets/issues/6381	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6381/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6381/comments	https://api.github.com/repos/huggingface/datasets/issues/6381/events	https://github.com/huggingface/datasets/pull/6381	1,975,028,470	PR_kwDODunzps5eeYty	6,381	Add my dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/103646675?v=4", "events_url": "https://api.github.com/users/keyur536/events{/privacy}", "followers_url": "https://api.github.com/users/keyur536/followers", "following_url": "https://api.github.com/users/keyur536/following{/other_user}", "gists_url": "https://api.github.com/users/keyur536/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/keyur536", "id": 103646675, "login": "keyur536", "node_id": "U_kgDOBi2F0w", "organizations_url": "https://api.github.com/users/keyur536/orgs", "received_events_url": "https://api.github.com/users/keyur536/received_events", "repos_url": "https://api.github.com/users/keyur536/repos", "site_admin": false, "starred_url": "https://api.github.com/users/keyur536/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/keyur536/subscriptions", "type": "User", "url": "https://api.github.com/users/keyur536", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi! We do not host datasets in this repo. Instead, you should use `dataset.push_to_hub` to upload the dataset to the HF Hub.", "@mariosasko could you provide me proper guide to push data on HF hub ", "You can find this info here: https://huggingface.co/docs/datasets/upload_dataset. Also, check https://huggingface.co/docs/datasets/loading for how to load a local dataset (before pushing it to the Hub)." ]	2023-11-02T20:59:52Z	2023-11-08T14:37:46Z	2023-11-06T15:50:14Z	NONE	null	null	null	## medical data Description: This dataset, named "medical data," is a collection of text data from various sources, carefully curated and cleaned for use in natural language processing (NLP) tasks. It consists of a diverse range of text, including articles, books, and online content, covering topics from science to literature. Citation: If applicable, please include a citation for this dataset to give credit to the original sources or contributors. Key Features: - Language: The text is primarily in English, but it may include content in other languages as well. - Use Cases: This dataset is suitable for text classification, language modeling, sentiment analysis, and other NLP tasks. Usage: To access this dataset, use the `load_your_dataset` function provided in the `your_dataset.py` script within this repository. You can specify the dataset split you need, such as "train," "test," or "validation," to get the data for your specific task. Contributors: - [Keyur Chaudhari] Contact: If you have any questions or need assistance regarding this dataset, please feel free to contact [keyurchaudhari536@gmail.com]. Please note that this dataset is shared under a specific license, which can be found in the [LICENSE](link to your dataset's license) file. Make sure to review and adhere to the terms of the license when using this dataset for your projects.	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6381/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6381/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6381.diff", "html_url": "https://github.com/huggingface/datasets/pull/6381", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6381.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6381" }
https://api.github.com/repos/huggingface/datasets/issues/5051	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5051/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5051/comments	https://api.github.com/repos/huggingface/datasets/issues/5051/events	https://github.com/huggingface/datasets/pull/5051	1,392,559,503	PR_kwDODunzps4_8drw	5,051	Revert task removal in folder-based builders	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-09-30T14:50:03Z	2022-10-03T12:23:35Z	2022-10-03T12:21:31Z	COLLABORATOR	null	null	null	Reverts the removal of `task_templates` in the folder-based builders. I also added the `AudioClassifaction` task for consistency. This is needed to fix https://github.com/huggingface/transformers/issues/19177. I think we should soon deprecate and remove the current task API (and investigate if it's possible to integrate the `train eval index` API), but we need to update the Transformers examples before that so we don't break them. cc @NielsRogge	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5051/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5051/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5051.diff", "html_url": "https://github.com/huggingface/datasets/pull/5051", "merged_at": "2022-10-03T12:21:31Z", "patch_url": "https://github.com/huggingface/datasets/pull/5051.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5051" }
https://api.github.com/repos/huggingface/datasets/issues/5056	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5056/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5056/comments	https://api.github.com/repos/huggingface/datasets/issues/5056/events	https://github.com/huggingface/datasets/pull/5056	1,394,713,173	PR_kwDODunzps5ADfxN	5,056	Fix broken URL's (GEM)	{ "avatar_url": "https://avatars.githubusercontent.com/u/6687858?v=4", "events_url": "https://api.github.com/users/manandey/events{/privacy}", "followers_url": "https://api.github.com/users/manandey/followers", "following_url": "https://api.github.com/users/manandey/following{/other_user}", "gists_url": "https://api.github.com/users/manandey/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/manandey", "id": 6687858, "login": "manandey", "node_id": "MDQ6VXNlcjY2ODc4NTg=", "organizations_url": "https://api.github.com/users/manandey/orgs", "received_events_url": "https://api.github.com/users/manandey/received_events", "repos_url": "https://api.github.com/users/manandey/repos", "site_admin": false, "starred_url": "https://api.github.com/users/manandey/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/manandey/subscriptions", "type": "User", "url": "https://api.github.com/users/manandey", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5056). All of your documentation changes will be reflected on that endpoint.", "Thanks, @manandey. We have removed all dataset scripts from this repo. Subsequent PRs should be opened directly on the Hugging Face Hub." ]	2022-10-03T13:13:22Z	2022-10-04T13:49:00Z	2022-10-04T13:48:59Z	CONTRIBUTOR	null	null	null	This PR fixes the broken URL's in GEM. cc. @lhoestq, @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", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5056/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5056/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5056.diff", "html_url": "https://github.com/huggingface/datasets/pull/5056", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5056.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5056" }
https://api.github.com/repos/huggingface/datasets/issues/5000	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5000/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5000/comments	https://api.github.com/repos/huggingface/datasets/issues/5000/events	https://github.com/huggingface/datasets/issues/5000	1,379,709,398	I_kwDODunzps5SPLHW	5,000	Dataset Viewer issue for asapp/slue	{ "avatar_url": "https://avatars.githubusercontent.com/u/56092571?v=4", "events_url": "https://api.github.com/users/fwu-asapp/events{/privacy}", "followers_url": "https://api.github.com/users/fwu-asapp/followers", "following_url": "https://api.github.com/users/fwu-asapp/following{/other_user}", "gists_url": "https://api.github.com/users/fwu-asapp/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/fwu-asapp", "id": 56092571, "login": "fwu-asapp", "node_id": "MDQ6VXNlcjU2MDkyNTcx", "organizations_url": "https://api.github.com/users/fwu-asapp/orgs", "received_events_url": "https://api.github.com/users/fwu-asapp/received_events", "repos_url": "https://api.github.com/users/fwu-asapp/repos", "site_admin": false, "starred_url": "https://api.github.com/users/fwu-asapp/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/fwu-asapp/subscriptions", "type": "User", "url": "https://api.github.com/users/fwu-asapp", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "<img width=\"519\" alt=\"Capture d’écran 2022-09-20 à 22 33 47\" src=\"https://user-images.githubusercontent.com/1676121/191358952-1220cb7d-745a-4203-a66b-3c707b25038f.png\">\r\n\r\n```\r\nNot found.\r\n\r\nError code: SplitsResponseNotFound\r\n```\r\n\r\nhttps://datasets-server.huggingface.co/splits?dataset=asapp/slue\r\n\r\n```json\r\n{\"error\":\"Not found.\"}\r\n```", "I just launched a refresh. It's weird, I don't see any entry for this dataset in the cache, it's a bug on our side. In order to try to understand what happened, did you change the visibility status from private to public, by any chance?", "The dataset is being refreshed, please retry later.\r\n\r\n<img width=\"802\" alt=\"Capture d’écran 2022-09-20 à 22 39 46\" src=\"https://user-images.githubusercontent.com/1676121/191360072-7cc86486-4e84-4b47-8f9a-4a69fe84a5ac.png\">\r\n", "OK. We now have an issue because the dataset cannot be streamed, and the dataset viewer relies on it.\r\n\r\nMaybe @huggingface/datasets can help:\r\n\r\n```\r\nError code: StreamingRowsError\r\nException: NotImplementedError\r\nMessage: Extraction protocol for TAR archives like 'https://public-dataset-model-store.awsdev.asapp.com/users/sshon/public/slue/slue-voxpopuli_v0.2_blind.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.\r\nTraceback: Traceback (most recent call last):\r\n File \"/src/services/worker/src/worker/responses/first_rows.py\", line 337, in get_first_rows_response\r\n rows = get_rows(dataset, config, split, streaming=True, rows_max_number=rows_max_number, hf_token=hf_token)\r\n File \"/src/services/worker/src/worker/utils.py\", line 123, in decorator\r\n return func(args, *kwargs)\r\n File \"/src/services/worker/src/worker/responses/first_rows.py\", line 65, in get_rows\r\n ds = load_dataset(\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 1739, in load_dataset\r\n return builder_instance.as_streaming_dataset(split=split)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py\", line 1025, in as_streaming_dataset\r\n splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}\r\n File \"/tmp/modules-cache/datasets_modules/datasets/asapp--slue/adaa0c78233e1a1df9c2f054e690ec5fc3eaf453bd76b80fe5cbe5728e55d9b1/slue.py\", line 189, in _split_generators\r\n dl_dir = dl_manager.download_and_extract(_DL_URLS[config_name])\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 944, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 907, in extract\r\n urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 385, in map_nested\r\n return function(data_struct)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 912, in _extract\r\n protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 390, in _get_extraction_protocol\r\n raise NotImplementedError(\r\n NotImplementedError: Extraction protocol for TAR archives like 'https://public-dataset-model-store.awsdev.asapp.com/users/sshon/public/slue/slue-voxpopuli_v0.2_blind.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.\r\n```", "Thanks @severo, \r\n\r\nDo I have to modify the python script to support streaming so that it can be previewed?\r\nIs there a document somewhere that I can follow?\r\n", "Hi @fwu-asapp thanks for reporting, and thanks @severo for the investigation.\r\n\r\nAs explained by @severo, the preview requires that your dataset loading script supports streaming.\r\n\r\nThere are several options here:\r\n- the easiest would be to replace the source files, archived using ZIP instead TAR: the TAR format does not allow random access while streaming, but only sequential access; the ZIP files support streaming out of the box.\r\n- alternatively, to stream TAR archives you can use `dl_manager.iter_archive`: the only prerequisite is that your \"index\" files (.tsv) should have been archived before their corresponding audio files, so while iterating the content of the TAR archive, the metadata files appear first. I think this is the case for voxpopuli tar but not for voxceleb.\r\n- if your .tsv files were not archived before their corresponding audio files (I think this is the case for voxceleb), then you should extract the .tsv files and host them separately (you can host them on the same Hugging Face Hub).\r\n - you can take as example, e.g.: https://huggingface.co/datasets/vivos/blob/main/vivos.py\r\n\r\nAs an advanced approach, you can handle both streaming and non-streaming cases separately.\r\n- as for example: https://huggingface.co/datasets/librispeech_asr/blob/main/librispeech_asr.py or https://huggingface.co/datasets/google/fleurs/blob/main/fleurs.py\r\n\r\nSee related discussion:\r\n- https://github.com/huggingface/datasets/issues/4697#issuecomment-1191502492", "Thanks @albertvillanova for your clarification. I'll talk to my collaborators to see if we can replace those files. Let me just close this issue for now.", "FYI, after replacing the source files with the ZIP ones, the dataset viewer works well. Thanks again to @severo and @albertvillanova for your help!", "Great! And thank you for sharing that interesting dataset!" ]	2022-09-20T16:45:45Z	2022-09-27T07:04:03Z	2022-09-21T07:24:07Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Link https://huggingface.co/datasets/asapp/slue/viewer/ ### Description Hi, I wonder how to get the dataset viewer of our slue dataset to work. Best, Felix ### Owner Yes	{ "avatar_url": "https://avatars.githubusercontent.com/u/56092571?v=4", "events_url": "https://api.github.com/users/fwu-asapp/events{/privacy}", "followers_url": "https://api.github.com/users/fwu-asapp/followers", "following_url": "https://api.github.com/users/fwu-asapp/following{/other_user}", "gists_url": "https://api.github.com/users/fwu-asapp/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/fwu-asapp", "id": 56092571, "login": "fwu-asapp", "node_id": "MDQ6VXNlcjU2MDkyNTcx", "organizations_url": "https://api.github.com/users/fwu-asapp/orgs", "received_events_url": "https://api.github.com/users/fwu-asapp/received_events", "repos_url": "https://api.github.com/users/fwu-asapp/repos", "site_admin": false, "starred_url": "https://api.github.com/users/fwu-asapp/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/fwu-asapp/subscriptions", "type": "User", "url": "https://api.github.com/users/fwu-asapp", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5000/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5000/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6619	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6619/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6619/comments	https://api.github.com/repos/huggingface/datasets/issues/6619/events	https://github.com/huggingface/datasets/pull/6619	2,102,407,478	PR_kwDODunzps5lK2VY	6,619	Migrate from `setup.cfg` to `pyproject.toml`	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6619). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005066 / 0.011353 (-0.006287) \| 0.003678 / 0.011008 (-0.007330) \| 0.063057 / 0.038508 (0.024549) \| 0.031250 / 0.023109 (0.008140) \| 0.248856 / 0.275898 (-0.027042) \| 0.266932 / 0.323480 (-0.056548) \| 0.003814 / 0.007986 (-0.004172) \| 0.002843 / 0.004328 (-0.001485) \| 0.049210 / 0.004250 (0.044959) \| 0.041514 / 0.037052 (0.004462) \| 0.264874 / 0.258489 (0.006385) \| 0.288834 / 0.293841 (-0.005007) \| 0.027457 / 0.128546 (-0.101089) \| 0.011071 / 0.075646 (-0.064575) \| 0.206433 / 0.419271 (-0.212839) \| 0.035381 / 0.043533 (-0.008152) \| 0.246829 / 0.255139 (-0.008310) \| 0.271094 / 0.283200 (-0.012106) \| 0.017790 / 0.141683 (-0.123893) \| 1.134618 / 1.452155 (-0.317536) \| 1.182600 / 1.492716 (-0.310116) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094970 / 0.018006 (0.076964) \| 0.306438 / 0.000490 (0.305949) \| 0.000212 / 0.000200 (0.000012) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.017786 / 0.037411 (-0.019625) \| 0.060652 / 0.014526 (0.046127) \| 0.072619 / 0.176557 (-0.103937) \| 0.119460 / 0.737135 (-0.617676) \| 0.073580 / 0.296338 (-0.222759) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279304 / 0.215209 (0.064095) \| 2.747179 / 2.077655 (0.669524) \| 1.438291 / 1.504120 (-0.065829) \| 1.313405 / 1.541195 (-0.227789) \| 1.354569 / 1.468490 (-0.113921) \| 0.578375 / 4.584777 (-4.006402) \| 2.424576 / 3.745712 (-1.321136) \| 2.831513 / 5.269862 (-2.438348) \| 1.756062 / 4.565676 (-2.809614) \| 0.064460 / 0.424275 (-0.359815) \| 0.005065 / 0.007607 (-0.002542) \| 0.335003 / 0.226044 (0.108958) \| 3.310500 / 2.268929 (1.041571) \| 1.778017 / 55.444624 (-53.666607) \| 1.504743 / 6.876477 (-5.371734) \| 1.532843 / 2.142072 (-0.609229) \| 0.662110 / 4.805227 (-4.143118) \| 0.118239 / 6.500664 (-6.382425) \| 0.042135 / 0.075469 (-0.033335) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945650 / 1.841788 (-0.896137) \| 11.623179 / 8.074308 (3.548871) \| 10.927315 / 10.191392 (0.735923) \| 0.131050 / 0.680424 (-0.549374) \| 0.014725 / 0.534201 (-0.519476) \| 0.290716 / 0.579283 (-0.288567) \| 0.272357 / 0.434364 (-0.162007) \| 0.323274 / 0.540337 (-0.217064) \| 0.426692 / 1.386936 (-0.960244) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005478 / 0.011353 (-0.005875) \| 0.003618 / 0.011008 (-0.007390) \| 0.049599 / 0.038508 (0.011091) \| 0.030814 / 0.023109 (0.007705) \| 0.273663 / 0.275898 (-0.002235) \| 0.292099 / 0.323480 (-0.031381) \| 0.004196 / 0.007986 (-0.003790) \| 0.002779 / 0.004328 (-0.001550) \| 0.047812 / 0.004250 (0.043562) \| 0.045095 / 0.037052 (0.008043) \| 0.286288 / 0.258489 (0.027799) \| 0.314125 / 0.293841 (0.020284) \| 0.047940 / 0.128546 (-0.080606) \| 0.010714 / 0.075646 (-0.064932) \| 0.057453 / 0.419271 (-0.361819) \| 0.033482 / 0.043533 (-0.010051) \| 0.273391 / 0.255139 (0.018252) \| 0.284936 / 0.283200 (0.001736) \| 0.017805 / 0.141683 (-0.123878) \| 1.148303 / 1.452155 (-0.303852) \| 1.185268 / 1.492716 (-0.307448) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092442 / 0.018006 (0.074436) \| 0.309908 / 0.000490 (0.309418) \| 0.000213 / 0.000200 (0.000013) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022874 / 0.037411 (-0.014537) \| 0.078238 / 0.014526 (0.063712) \| 0.088844 / 0.176557 (-0.087713) \| 0.127054 / 0.737135 (-0.610081) \| 0.089809 / 0.296338 (-0.206530) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292360 / 0.215209 (0.077151) \| 2.842700 / 2.077655 (0.765045) \| 1.571071 / 1.504120 (0.066951) \| 1.450773 / 1.541195 (-0.090422) \| 1.467090 / 1.468490 (-0.001400) \| 0.583529 / 4.584777 (-4.001248) \| 2.469284 / 3.745712 (-1.276428) \| 2.844426 / 5.269862 (-2.425435) \| 1.773336 / 4.565676 (-2.792341) \| 0.064585 / 0.424275 (-0.359690) \| 0.005098 / 0.007607 (-0.002509) \| 0.342816 / 0.226044 (0.116771) \| 3.363309 / 2.268929 (1.094381) \| 1.922834 / 55.444624 (-53.521790) \| 1.649702 / 6.876477 (-5.226774) \| 1.672727 / 2.142072 (-0.469345) \| 0.665015 / 4.805227 (-4.140212) \| 0.124764 / 6.500664 (-6.375900) \| 0.041564 / 0.075469 (-0.033905) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.988970 / 1.841788 (-0.852818) \| 12.148983 / 8.074308 (4.074675) \| 11.132697 / 10.191392 (0.941305) \| 0.131596 / 0.680424 (-0.548828) \| 0.015700 / 0.534201 (-0.518501) \| 0.288819 / 0.579283 (-0.290464) \| 0.276692 / 0.434364 (-0.157672) \| 0.330260 / 0.540337 (-0.210078) \| 0.421612 / 1.386936 (-0.965324) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d627fb8357f39d78d79e704712609c7b34bdeba4 \"CML watermark\")\n" ]	2024-01-26T15:27:10Z	2024-01-26T15:53:40Z	2024-01-26T15:47:32Z	COLLABORATOR	null	null	null	Based on https://github.com/huggingface/huggingface_hub/pull/1971 in `hfh`	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6619/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6619/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6619.diff", "html_url": "https://github.com/huggingface/datasets/pull/6619", "merged_at": "2024-01-26T15:47:32Z", "patch_url": "https://github.com/huggingface/datasets/pull/6619.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6619" }
https://api.github.com/repos/huggingface/datasets/issues/5883	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5883/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5883/comments	https://api.github.com/repos/huggingface/datasets/issues/5883/events	https://github.com/huggingface/datasets/pull/5883	1,719,527,597	PR_kwDODunzps5RAkYi	5,883	Fix string-encoding, make `batch_size` optional, and minor improvements in `Dataset.to_tf_dataset`	{ "avatar_url": "https://avatars.githubusercontent.com/u/36760800?v=4", "events_url": "https://api.github.com/users/alvarobartt/events{/privacy}", "followers_url": "https://api.github.com/users/alvarobartt/followers", "following_url": "https://api.github.com/users/alvarobartt/following{/other_user}", "gists_url": "https://api.github.com/users/alvarobartt/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alvarobartt", "id": 36760800, "login": "alvarobartt", "node_id": "MDQ6VXNlcjM2NzYwODAw", "organizations_url": "https://api.github.com/users/alvarobartt/orgs", "received_events_url": "https://api.github.com/users/alvarobartt/received_events", "repos_url": "https://api.github.com/users/alvarobartt/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alvarobartt/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvarobartt/subscriptions", "type": "User", "url": "https://api.github.com/users/alvarobartt", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "To showcase the current issue, here's a Colab Gist, that shows that the `imdb` dataset cannot be read/iterated, since one or more samples contain a non-ascii character that is being converted to `numpy.bytes_`, and so on fails.\r\n\r\nColab Gist at https://gist.github.com/alvarobartt/1746959d1abb9a33e0c593f3bd82a2fb\r\n\r\nAlso, here's a quick sample of what's happening:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"imdb\", split=\"train\")\r\ntfds = ds.to_tf_dataset(batch_size=16)\r\nfor batch in tfds:\r\n print(batch)\r\n>>> UnicodeEncodeError: 'ascii' codec can't encode character '\\xe9' in position 0: ordinal not in range(128)\r\n```\r\n\r\nA more detailed version of it:\r\n\r\n```python\r\nfrom datasets import Dataset\r\n\r\nds = Dataset.from_dict(\r\n {\r\n \"a\": [1],\r\n \"b\": [\"é\"],\r\n }\r\n)\r\ntfds = ds.to_tf_dataset(batch_size=1)\r\nfor batch in tfds:\r\n print(batch)\r\n>>> UnicodeEncodeError: 'ascii' codec can't encode character '\\xe9' in position 0: ordinal not in range(128)\r\n```\r\n\r\nThe original issue comes from https://github.com/tensorflow/tensorflow/blob/388d952114e59a1aeda440ed4737b29f8b7c6e8a/tensorflow/python/ops/script_ops.py#LL234C4-L234C4, which could easily be solved by replacing that line with `return result.astype(np.unicode_)` but they are mentioning that it may lead to issues.\r\n\r\nEven the following fails in `numpy`:\r\n\r\n```python\r\nimport numpy as np\r\n\r\nx = np.array([\"é\"]).astype(np.bytes_)\r\n```", "cc. @lhoestq :hugs:", "cc @Rocketknight1 ", "> Nice ! Could you add some tests to make sure that batch_size=None works as expected ?\r\n\r\nSure, I'll add the tests for everything, including the string-encoding issue to make sure it's solved!", "Thanks for the review @lhoestq and @Rocketknight1! I do understand that processing it in batches is always more efficient than processing it one-by-one, it was just to make `batch_size` optional. What we can do is default it to a certain batch size e.g. 16 as before, and that's it, but I think it can still remain optional.", "@Rocketknight1 then I'll add the integration tests for the optional `batch_size` as well as for the encoding of non-ASCII compatible characters 😄 Do we set the default `batch_size` to 16 instead of `None`?", "@alvarobartt I think 16 is a reasonable default, yep!", "I think default should be None, not 16.\r\nUsers won't expect to have it batched by default.", "Then I'll leave it as is, and add the unit/integration tests, thanks @Rocketknight1 and @lhoestq ", "Hi @Rocketknight1 @lhoestq! So the string-encoding issue is already solved, but I've got one doubt about the `batch_size` being optional in the multiprocessing approach, since in that case I assume the `batch_size` should be mandatory, for the moment I'm assuming it is/should be mandatory, but let me know if you want me to add a check to disallow `batch_size=None` when `num_workers>1`. Thanks!", "> To showcase the current issue, here's a Colab Gist, that shows that the `imdb` dataset cannot be read/iterated, since one or more samples contain a non-ascii character that is being converted to `numpy.bytes_`, and so on fails.\r\n> \r\n> Colab Gist at https://gist.github.com/alvarobartt/1746959d1abb9a33e0c593f3bd82a2fb\r\n\r\nI've used the Colab shared above for testing purposes, and it works fine, plus the unit/integration tests are passing. I've also trained a `KerasNLP` model with incoming data from 🤗`datasets` with no issue at all!", "> in the multiprocessing approach, since in that case I assume the batch_size should be mandatory,\r\n\r\nNo I think they're quite orthogonal, no need to have it mandatory", "> No I think they're quite orthogonal, no need to have it mandatory\r\n\r\nBut it will break if `batch_size=None` as the multiprocessing approach will aim to prepare batches and distribute those to every worker, and assuming `batch_size=1` when `batch_size=None` I guess is not a good assumption, right?", "Ah I see. Multiprocessing should support batch_size=None indeed. If you have ideas you can do it in this PR, or raise a NotImplementedError and we can see later", "Sure @lhoestq, I can add a `NotImplementedError` for the moment, and prepare the next PR straight-away to tackle the multiprocessing approach with `batch_size=None`, but not sure if that may eventually collide with @Rocketknight1 PR at https://github.com/huggingface/datasets/pull/5863", "Yes, let me merge the PR at #5863 after this one, and then we can open another to improve the behaviour with multiprocessing and `batch_size=None`!", "Sure @Rocketknight1 makes complete sense to me! Do you want me to add the `raise NotImplementedError` and then we merge this PR? Or you prefer to directly merge the current?", "`raise NotImplementedError` for now with an error telling the user that multiprocessing needs them to specify a batch size, I think!", "Since you recently approved @Rocketknight1, are we ready to merge? Thanks 🤗", "Ah actually it looks like `minimal_tf_collate_fn` doesn't support batch_size=None", "Hi @lhoestq so I didn't include the call to `collate_fn`, as we won't need to collate the incoming data e.g. \"str\" should remain a \"str\" not a [\"str\"], and the `minimal_collate_fn` was indeed putting everything into a list, so the output was not un-batched, but batched with size 1", "What if the user passes a collate_fn ? The torch DataLoader still applies it if batch_size=None for example.\r\n\r\nDoes my last change look of to you ? If so I think we can merge", "> What if the user passes a collate_fn ? The torch DataLoader still applies it if batch_size=None for example.\r\n> \r\n> Does my last change look of to you ? If so I think we can merge\r\n\r\nI think we're good, since it won't batch it under the scenario of `str` being provided instead of `List[str]`, and the unit/integration tests are passing, so I'm OK to merge. Maybe we can double check with Matt? cc @Rocketknight1 ", "Yes, and sorry for the delay! I'm happy to merge.", "<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) \| 0.006555 / 0.011353 (-0.004798) \| 0.004521 / 0.011008 (-0.006487) \| 0.096633 / 0.038508 (0.058125) \| 0.032859 / 0.023109 (0.009750) \| 0.294632 / 0.275898 (0.018734) \| 0.325140 / 0.323480 (0.001660) \| 0.005676 / 0.007986 (-0.002310) \| 0.005252 / 0.004328 (0.000924) \| 0.074349 / 0.004250 (0.070099) \| 0.045836 / 0.037052 (0.008784) \| 0.302919 / 0.258489 (0.044430) \| 0.340686 / 0.293841 (0.046845) \| 0.028398 / 0.128546 (-0.100148) \| 0.008942 / 0.075646 (-0.066704) \| 0.326994 / 0.419271 (-0.092278) \| 0.049556 / 0.043533 (0.006023) \| 0.293883 / 0.255139 (0.038744) \| 0.316522 / 0.283200 (0.033322) \| 0.097385 / 0.141683 (-0.044298) \| 1.405334 / 1.452155 (-0.046821) \| 1.521529 / 1.492716 (0.028812) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212269 / 0.018006 (0.194263) \| 0.445692 / 0.000490 (0.445203) \| 0.004930 / 0.000200 (0.004730) \| 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) \| 0.026907 / 0.037411 (-0.010504) \| 0.108607 / 0.014526 (0.094081) \| 0.116806 / 0.176557 (-0.059751) \| 0.178428 / 0.737135 (-0.558707) \| 0.122326 / 0.296338 (-0.174012) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.404211 / 0.215209 (0.189002) \| 4.045374 / 2.077655 (1.967719) \| 1.877237 / 1.504120 (0.373117) \| 1.706276 / 1.541195 (0.165081) \| 1.750610 / 1.468490 (0.282120) \| 0.522331 / 4.584777 (-4.062446) \| 3.742286 / 3.745712 (-0.003426) \| 1.791285 / 5.269862 (-3.478577) \| 1.043872 / 4.565676 (-3.521805) \| 0.065176 / 0.424275 (-0.359099) \| 0.011821 / 0.007607 (0.004214) \| 0.507374 / 0.226044 (0.281329) \| 5.088803 / 2.268929 (2.819875) \| 2.282742 / 55.444624 (-53.161882) \| 1.950737 / 6.876477 (-4.925740) \| 2.042262 / 2.142072 (-0.099810) \| 0.636525 / 4.805227 (-4.168702) \| 0.140837 / 6.500664 (-6.359827) \| 0.063223 / 0.075469 (-0.012246) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.188070 / 1.841788 (-0.653718) \| 14.622681 / 8.074308 (6.548372) \| 13.247988 / 10.191392 (3.056596) \| 0.165858 / 0.680424 (-0.514566) \| 0.017476 / 0.534201 (-0.516725) \| 0.391973 / 0.579283 (-0.187310) \| 0.433326 / 0.434364 (-0.001038) \| 0.467163 / 0.540337 (-0.073175) \| 0.568359 / 1.386936 (-0.818577) \|\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) \| 0.006076 / 0.011353 (-0.005276) \| 0.004439 / 0.011008 (-0.006570) \| 0.074496 / 0.038508 (0.035988) \| 0.031396 / 0.023109 (0.008287) \| 0.372237 / 0.275898 (0.096339) \| 0.403412 / 0.323480 (0.079932) \| 0.005430 / 0.007986 (-0.002555) \| 0.003846 / 0.004328 (-0.000483) \| 0.074403 / 0.004250 (0.070153) \| 0.045398 / 0.037052 (0.008346) \| 0.394133 / 0.258489 (0.135644) \| 0.421769 / 0.293841 (0.127928) \| 0.027936 / 0.128546 (-0.100610) \| 0.008962 / 0.075646 (-0.066685) \| 0.083158 / 0.419271 (-0.336113) \| 0.044863 / 0.043533 (0.001331) \| 0.393834 / 0.255139 (0.138695) \| 0.391537 / 0.283200 (0.108337) \| 0.097971 / 0.141683 (-0.043712) \| 1.496632 / 1.452155 (0.044477) \| 1.585511 / 1.492716 (0.092795) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010094 / 0.018006 (-0.007913) \| 0.437811 / 0.000490 (0.437321) \| 0.000963 / 0.000200 (0.000763) \| 0.000084 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028864 / 0.037411 (-0.008547) \| 0.112480 / 0.014526 (0.097954) \| 0.120938 / 0.176557 (-0.055619) \| 0.170888 / 0.737135 (-0.566247) \| 0.125903 / 0.296338 (-0.170435) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426716 / 0.215209 (0.211507) \| 4.238380 / 2.077655 (2.160725) \| 2.052889 / 1.504120 (0.548769) \| 1.871043 / 1.541195 (0.329848) \| 1.890405 / 1.468490 (0.421915) \| 0.522059 / 4.584777 (-4.062718) \| 3.813331 / 3.745712 (0.067619) \| 2.891651 / 5.269862 (-2.378210) \| 1.323836 / 4.565676 (-3.241841) \| 0.065124 / 0.424275 (-0.359151) \| 0.011498 / 0.007607 (0.003891) \| 0.525102 / 0.226044 (0.299057) \| 5.245190 / 2.268929 (2.976261) \| 2.531149 / 55.444624 (-52.913476) \| 2.197323 / 6.876477 (-4.679153) \| 2.197314 / 2.142072 (0.055241) \| 0.633423 / 4.805227 (-4.171804) \| 0.140248 / 6.500664 (-6.360416) \| 0.064432 / 0.075469 (-0.011037) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.270639 / 1.841788 (-0.571149) \| 14.856678 / 8.074308 (6.782369) \| 14.337631 / 10.191392 (4.146239) \| 0.195319 / 0.680424 (-0.485105) \| 0.017628 / 0.534201 (-0.516573) \| 0.393984 / 0.579283 (-0.185299) \| 0.421987 / 0.434364 (-0.012376) \| 0.459245 / 0.540337 (-0.081092) \| 0.557786 / 1.386936 (-0.829150) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a129219a48c1b07c06d4bc1db32c317bf513089d \"CML watermark\")\n", "Will you eventually need help with your PR @Rocketknight1? I'll be happy to help if needed 😄 ", "<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) \| 0.007577 / 0.011353 (-0.003776) \| 0.004960 / 0.011008 (-0.006048) \| 0.113622 / 0.038508 (0.075114) \| 0.037981 / 0.023109 (0.014872) \| 0.355312 / 0.275898 (0.079414) \| 0.393384 / 0.323480 (0.069904) \| 0.006575 / 0.007986 (-0.001411) \| 0.005941 / 0.004328 (0.001612) \| 0.085976 / 0.004250 (0.081726) \| 0.053784 / 0.037052 (0.016732) \| 0.369358 / 0.258489 (0.110869) \| 0.399402 / 0.293841 (0.105561) \| 0.032155 / 0.128546 (-0.096391) \| 0.010448 / 0.075646 (-0.065199) \| 0.389009 / 0.419271 (-0.030263) \| 0.057377 / 0.043533 (0.013844) \| 0.354968 / 0.255139 (0.099829) \| 0.382404 / 0.283200 (0.099204) \| 0.111056 / 0.141683 (-0.030627) \| 1.807986 / 1.452155 (0.355832) \| 1.866070 / 1.492716 (0.373354) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.244439 / 0.018006 (0.226432) \| 0.491942 / 0.000490 (0.491452) \| 0.001910 / 0.000200 (0.001710) \| 0.000112 / 0.000054 (0.000058) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031024 / 0.037411 (-0.006387) \| 0.129674 / 0.014526 (0.115148) \| 0.142974 / 0.176557 (-0.033583) \| 0.213568 / 0.737135 (-0.523568) \| 0.147794 / 0.296338 (-0.148545) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.480333 / 0.215209 (0.265124) \| 4.792901 / 2.077655 (2.715246) \| 2.233145 / 1.504120 (0.729025) \| 2.036291 / 1.541195 (0.495096) \| 2.109631 / 1.468490 (0.641140) \| 0.624546 / 4.584777 (-3.960231) \| 4.543511 / 3.745712 (0.797799) \| 3.961345 / 5.269862 (-1.308517) \| 1.903634 / 4.565676 (-2.662042) \| 0.076584 / 0.424275 (-0.347691) \| 0.014590 / 0.007607 (0.006983) \| 0.593195 / 0.226044 (0.367151) \| 5.928740 / 2.268929 (3.659811) \| 2.781164 / 55.444624 (-52.663460) \| 2.364303 / 6.876477 (-4.512173) \| 2.510139 / 2.142072 (0.368067) \| 0.770886 / 4.805227 (-4.034341) \| 0.167995 / 6.500664 (-6.332669) \| 0.076622 / 0.075469 (0.001153) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.402398 / 1.841788 (-0.439390) \| 17.921233 / 8.074308 (9.846925) \| 17.036738 / 10.191392 (6.845346) \| 0.168997 / 0.680424 (-0.511427) \| 0.020259 / 0.534201 (-0.513941) \| 0.465322 / 0.579283 (-0.113962) \| 0.500435 / 0.434364 (0.066071) \| 0.546846 / 0.540337 (0.006509) \| 0.658130 / 1.386936 (-0.728806) \|\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) \| 0.007624 / 0.011353 (-0.003729) \| 0.005265 / 0.011008 (-0.005744) \| 0.086886 / 0.038508 (0.048377) \| 0.038235 / 0.023109 (0.015126) \| 0.463969 / 0.275898 (0.188071) \| 0.502451 / 0.323480 (0.178971) \| 0.006285 / 0.007986 (-0.001701) \| 0.004525 / 0.004328 (0.000197) \| 0.086557 / 0.004250 (0.082307) \| 0.052414 / 0.037052 (0.015362) \| 0.482167 / 0.258489 (0.223678) \| 0.513684 / 0.293841 (0.219843) \| 0.032929 / 0.128546 (-0.095618) \| 0.010249 / 0.075646 (-0.065397) \| 0.093377 / 0.419271 (-0.325895) \| 0.054114 / 0.043533 (0.010582) \| 0.466116 / 0.255139 (0.210977) \| 0.488977 / 0.283200 (0.205777) \| 0.115446 / 0.141683 (-0.026237) \| 1.762912 / 1.452155 (0.310757) \| 1.874191 / 1.492716 (0.381475) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012666 / 0.018006 (-0.005341) \| 0.485962 / 0.000490 (0.485473) \| 0.002621 / 0.000200 (0.002421) \| 0.000128 / 0.000054 (0.000074) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033661 / 0.037411 (-0.003751) \| 0.135395 / 0.014526 (0.120869) \| 0.147230 / 0.176557 (-0.029326) \| 0.205847 / 0.737135 (-0.531288) \| 0.151496 / 0.296338 (-0.144842) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.514097 / 0.215209 (0.298887) \| 5.134093 / 2.077655 (3.056438) \| 2.496775 / 1.504120 (0.992655) \| 2.268078 / 1.541195 (0.726883) \| 2.342153 / 1.468490 (0.873663) \| 0.623130 / 4.584777 (-3.961647) \| 4.601787 / 3.745712 (0.856075) \| 3.414249 / 5.269862 (-1.855613) \| 1.849603 / 4.565676 (-2.716073) \| 0.078350 / 0.424275 (-0.345925) \| 0.013785 / 0.007607 (0.006178) \| 0.638783 / 0.226044 (0.412739) \| 6.378356 / 2.268929 (4.109427) \| 3.072867 / 55.444624 (-52.371757) \| 2.668123 / 6.876477 (-4.208354) \| 2.693905 / 2.142072 (0.551833) \| 0.764583 / 4.805227 (-4.040644) \| 0.166854 / 6.500664 (-6.333810) \| 0.076883 / 0.075469 (0.001414) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.502003 / 1.841788 (-0.339784) \| 18.674205 / 8.074308 (10.599897) \| 16.837759 / 10.191392 (6.646367) \| 0.176995 / 0.680424 (-0.503428) \| 0.020126 / 0.534201 (-0.514075) \| 0.464480 / 0.579283 (-0.114803) \| 0.516477 / 0.434364 (0.082113) \| 0.549818 / 0.540337 (0.009481) \| 0.659927 / 1.386936 (-0.727009) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a129219a48c1b07c06d4bc1db32c317bf513089d \"CML watermark\")\n", "@alvarobartt Yes, I'll ping you for a review once it's ready!" ]	2023-05-22T11:51:07Z	2023-06-08T11:09:03Z	2023-06-06T16:49:15Z	MEMBER	null	null	null	## What's in this PR? This PR addresses some minor fixes and general improvements in the `to_tf_dataset` method of `datasets.Dataset`, to convert a 🤗HuggingFace Dataset as a TensorFlow Dataset. The main bug solved in this PR comes with the string-encoding, since for safety purposes the internal conversion of `numpy.arrays` when `dtype` is unicode/string, is to convert it into `numpy.bytes`, more information in the docstring of https://github.com/tensorflow/tensorflow/blob/388d952114e59a1aeda440ed4737b29f8b7c6e8a/tensorflow/python/ops/script_ops.py#L210. That's triggered when using `tensorflow.numpy_function` as it's applying another type cast besides the one that `datasets` does, so the casting is applied at least twice per entry/batch. So this means that the definition of the `numpy.unicode_` dtype when the data in the batch is a string, is ignored, and replaced by `numpy.bytes_`. Besides that, some other minor things have been fixed: * Made `batch_size` an optional parameter in `to_tf_dataset` * Map the `tensorflow` output dtypes just once, and not in every `tf.function` call during `map` * Keep `numpy` formatting in the `datasets.Dataset` if already formatted like it, no need to format it again as `numpy` * Docstring indentation in `dataset_to_tf` and `multiprocess_dataset_to_tf` ## What's missing in this PR? I can include some integration tests if needed, to validate that `batch_size` is optional, and that the tensors in the TF-Dataset can be looped over with no issues as before.	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5883/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5883/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5883.diff", "html_url": "https://github.com/huggingface/datasets/pull/5883", "merged_at": "2023-06-06T16:49:15Z", "patch_url": "https://github.com/huggingface/datasets/pull/5883.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5883" }
https://api.github.com/repos/huggingface/datasets/issues/7515	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7515/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7515/comments	https://api.github.com/repos/huggingface/datasets/issues/7515/events	https://github.com/huggingface/datasets/issues/7515	2,995,082,418	I_kwDODunzps6yhVSy	7,515	`concatenate_datasets` does not preserve Pytorch format for IterableDataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/5140987?v=4", "events_url": "https://api.github.com/users/francescorubbo/events{/privacy}", "followers_url": "https://api.github.com/users/francescorubbo/followers", "following_url": "https://api.github.com/users/francescorubbo/following{/other_user}", "gists_url": "https://api.github.com/users/francescorubbo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/francescorubbo", "id": 5140987, "login": "francescorubbo", "node_id": "MDQ6VXNlcjUxNDA5ODc=", "organizations_url": "https://api.github.com/users/francescorubbo/orgs", "received_events_url": "https://api.github.com/users/francescorubbo/received_events", "repos_url": "https://api.github.com/users/francescorubbo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/francescorubbo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/francescorubbo/subscriptions", "type": "User", "url": "https://api.github.com/users/francescorubbo", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Hi ! Oh indeed it would be cool to return the same format in that case. Would you like to submit a PR ? The function that does the concatenation is here:\n\nhttps://github.com/huggingface/datasets/blob/90e5bf8a8599b625d6103ee5ac83b98269991141/src/datasets/iterable_dataset.py#L3375-L3380", "Thank you for the pointer, @lhoestq ! See #7522 " ]	2025-04-15T04:36:34Z	2025-04-16T02:39:16Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug When concatenating datasets with `concatenate_datasets`, I would expect the resulting combined dataset to be in the same format as the inputs (assuming it's consistent). This is indeed the behavior when combining `Dataset`, but not when combining `IterableDataset`. Specifically, when applying `concatenate_datasets` to a list of `IterableDataset` in Pytorch format (i.e. using `.with_format(Pytorch)`), the output `IterableDataset` is not in Pytorch format. ### Steps to reproduce the bug ``` import datasets ds = datasets.Dataset.from_dict({"a": [1,2,3]}) iterable_ds = ds.to_iterable_dataset() datasets.concatenate_datasets([ds.with_format("torch")]) # <- this preserves Pytorch format datasets.concatenate_datasets([iterable_ds.with_format("torch")]) # <- this does NOT preserves Pytorch format ``` ### Expected behavior Pytorch format should be preserved when combining IterableDataset in Pytorch format. ### Environment info datasets==3.5.0, Python 3.11.11, torch==2.2.2	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7515/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7515/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5160	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5160/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5160/comments	https://api.github.com/repos/huggingface/datasets/issues/5160/events	https://github.com/huggingface/datasets/issues/5160	1,422,193,938	I_kwDODunzps5UxPUS	5,160	Automatically add filename for image/audio folder	{ "avatar_url": "https://avatars.githubusercontent.com/u/23423619?v=4", "events_url": "https://api.github.com/users/patrickvonplaten/events{/privacy}", "followers_url": "https://api.github.com/users/patrickvonplaten/followers", "following_url": "https://api.github.com/users/patrickvonplaten/following{/other_user}", "gists_url": "https://api.github.com/users/patrickvonplaten/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/patrickvonplaten", "id": 23423619, "login": "patrickvonplaten", "node_id": "MDQ6VXNlcjIzNDIzNjE5", "organizations_url": "https://api.github.com/users/patrickvonplaten/orgs", "received_events_url": "https://api.github.com/users/patrickvonplaten/received_events", "repos_url": "https://api.github.com/users/patrickvonplaten/repos", "site_admin": false, "starred_url": "https://api.github.com/users/patrickvonplaten/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/patrickvonplaten/subscriptions", "type": "User", "url": "https://api.github.com/users/patrickvonplaten", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/16348744?v=4", "events_url": "https://api.github.com/users/polinaeterna/events{/privacy}", "followers_url": "https://api.github.com/users/polinaeterna/followers", "following_url": "https://api.github.com/users/polinaeterna/following{/other_user}", "gists_url": "https://api.github.com/users/polinaeterna/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/polinaeterna", "id": 16348744, "login": "polinaeterna", "node_id": "MDQ6VXNlcjE2MzQ4NzQ0", "organizations_url": "https://api.github.com/users/polinaeterna/orgs", "received_events_url": "https://api.github.com/users/polinaeterna/received_events", "repos_url": "https://api.github.com/users/polinaeterna/repos", "site_admin": false, "starred_url": "https://api.github.com/users/polinaeterna/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/polinaeterna/subscriptions", "type": "User", "url": "https://api.github.com/users/polinaeterna", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/16348744?v=4", "events_url": "https://api.github.com/users/polinaeterna/events{/privacy}", "followers_url": "https://api.github.com/users/polinaeterna/followers", "following_url": "https://api.github.com/users/polinaeterna/following{/other_user}", "gists_url": "https://api.github.com/users/polinaeterna/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/polinaeterna", "id": 16348744, "login": "polinaeterna", "node_id": "MDQ6VXNlcjE2MzQ4NzQ0", "organizations_url": "https://api.github.com/users/polinaeterna/orgs", "received_events_url": "https://api.github.com/users/polinaeterna/received_events", "repos_url": "https://api.github.com/users/polinaeterna/repos", "site_admin": false, "starred_url": "https://api.github.com/users/polinaeterna/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/polinaeterna/subscriptions", "type": "User", "url": "https://api.github.com/users/polinaeterna", "user_view_type": "public" }, { "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }, { "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" } ]	null	[ "Also cc @anton-l ", "BTW the exact same holds true for the audio folder", "I'm fine with adding a new column with the file name personally. Not sure how breaking this is though", "@patrickvonplaten do you mean just filename or full relative path inside the repo?\r\nI think it shouldn't be breaking, at least I cannot come up with any case where it is. Maybe @mariosasko can?\r\n\r\nalso I think that the problem here and in general is that Image/AudioFolder has default configuration which implies automatic label creation if there is not metadata file. It can be changed when you load the dataset with `load_dataset` but not on it's Hub page. \r\n\r\n", "> also I think that the problem here and in general Image/AudioFolder has default configuration which implies automatic label creation if there is not metadata file\r\n\r\nYea I agree it's often the wrong default. We can also imagine adding the builder's parameters as YAML in the repo.", "@lhoestq yes I also got the idea of some YAML config! not sure of what priority it is though.", "but it would actually also solve this issue: https://github.com/huggingface/datasets/issues/5153", "I meant just the file name (no path) that would already be super helpful IMO :-) (maybe dir+filename if there are dirs in the folder)", "@patrickvonplaten one more time, to be sure I understand you.\r\nFor example, we have data structure like this:\r\n```\r\n├─ data/\r\n│ └─ subdir/\r\n│ └── cats/\r\n│ ├── 0.jpg\r\n│ ├── 1.jpg\r\n│ └── 2.jpg\r\n│ └── dogs/\r\n│ ├── 0.jpg\r\n│ ├── 1.jpg\r\n│ └── 2.jpg\r\n└── another_subdir/\r\n ├── 10.jpg\r\n ├── 11.jpg\r\n └── 12.jpg\r\n```\r\nIs it okay to provide `\"data/subdir/cats/0.jpg\"`, `\"data/subdir/dogs/0.jpg\"`, `\"data/another_subdir/10.jpg\"`?\r\nI think providing just filenames might be confusing if they are not unique, as in this example. ", "Yes I think the relative path as you proposed makes a lot of sense :-) " ]	2022-10-25T09:56:49Z	2022-10-26T16:51:46Z	null	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request When creating a custom audio of image dataset, it would be great to automatically have access to the filename. It should be both: a) Automatically displayed in the viewer b) Automatically added as a column to the dataset when doing `load_dataset` In `diffusers` our test rely quite heavily on images and audio files now and it's a bit tedious at the moment to download specific images from a datasets repo. E.g. we have a dataset of images for tests in `diffusers`: https://huggingface.co/datasets/hf-internal-testing/diffusers-images where it would be extremely nice to have direct access to the filename both visually on the datasets page (@severo ) as well as via the `load_datasets` function. We currently have some akward functionality to download images by path name: https://github.com/huggingface/diffusers/blob/2fb8fafa4b761f6fc144cf75a6f6f0ea6af3a1c1/src/diffusers/utils/testing_utils.py#L131 It would be much nicer to just go over `load_dataset(...)` ### Motivation Intuitively the filename is something people understand directly. E.g if you upload a folder of images online, it's nice if you recognize the image as well as the filename next to it directly and that you're able to use it right away. The label on the other hand is less intuitive to understand as you haven't added it yourself. ### Your contribution Not sure if I have the time to add it myself anytime soon, but it would help us a lot for `diffusers`.	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/5160/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5160/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6299	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6299/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6299/comments	https://api.github.com/repos/huggingface/datasets/issues/6299/events	https://github.com/huggingface/datasets/issues/6299	1,939,649,238	I_kwDODunzps5znLLW	6,299	Support for newer versions of JAX	{ "avatar_url": "https://avatars.githubusercontent.com/u/25456859?v=4", "events_url": "https://api.github.com/users/ddrous/events{/privacy}", "followers_url": "https://api.github.com/users/ddrous/followers", "following_url": "https://api.github.com/users/ddrous/following{/other_user}", "gists_url": "https://api.github.com/users/ddrous/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ddrous", "id": 25456859, "login": "ddrous", "node_id": "MDQ6VXNlcjI1NDU2ODU5", "organizations_url": "https://api.github.com/users/ddrous/orgs", "received_events_url": "https://api.github.com/users/ddrous/received_events", "repos_url": "https://api.github.com/users/ddrous/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ddrous/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ddrous/subscriptions", "type": "User", "url": "https://api.github.com/users/ddrous", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	closed	false	null	[]	null	[]	2023-10-12T10:03:46Z	2023-10-12T16:28:59Z	2023-10-12T16:28:59Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request Hi, I like your idea of adapting the datasets library to be usable with JAX. Thank you for that. However, in your [setup.py](https://github.com/huggingface/datasets/blob/main/setup.py), you enforce old versions of JAX <= 0.3... It is very cumbersome ! What is the rationale for such a limitation ? Can you remove it please ? Thanks, ### Motivation This library is unusable with new versions of JAX ? ### Your contribution Yes.	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6299/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6299/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6953	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6953/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6953/comments	https://api.github.com/repos/huggingface/datasets/issues/6953/events	https://github.com/huggingface/datasets/issues/6953	2,333,366,120	I_kwDODunzps6LFFdo	6,953	Remove canonical datasets from docs	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "color": "0075ca", "default": true, "description": "Improvements or additions to documentation", "id": 1935892861, "name": "documentation", "node_id": "MDU6TGFiZWwxOTM1ODkyODYx", "url": "https://api.github.com/repos/huggingface/datasets/labels/documentation" } ]	closed	false	null	[]	null	[ "Canonical datasets are no longer mentioned in the docs." ]	2024-06-04T12:09:03Z	2024-07-01T11:31:25Z	2024-07-01T11:31:25Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	Remove canonical datasets from docs, now that we no longer have canonical datasets.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6953/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6953/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5099	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5099/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5099/comments	https://api.github.com/repos/huggingface/datasets/issues/5099/events	https://github.com/huggingface/datasets/issues/5099	1,404,370,191	I_kwDODunzps5TtP0P	5,099	datasets doesn't support # in data paths	{ "avatar_url": "https://avatars.githubusercontent.com/u/44069155?v=4", "events_url": "https://api.github.com/users/loubnabnl/events{/privacy}", "followers_url": "https://api.github.com/users/loubnabnl/followers", "following_url": "https://api.github.com/users/loubnabnl/following{/other_user}", "gists_url": "https://api.github.com/users/loubnabnl/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/loubnabnl", "id": 44069155, "login": "loubnabnl", "node_id": "MDQ6VXNlcjQ0MDY5MTU1", "organizations_url": 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"https://api.github.com/users/riccardobucco/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/riccardobucco/subscriptions", "type": "User", "url": "https://api.github.com/users/riccardobucco", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/9295277?v=4", "events_url": "https://api.github.com/users/riccardobucco/events{/privacy}", "followers_url": "https://api.github.com/users/riccardobucco/followers", "following_url": "https://api.github.com/users/riccardobucco/following{/other_user}", "gists_url": "https://api.github.com/users/riccardobucco/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/riccardobucco", "id": 9295277, "login": "riccardobucco", "node_id": "MDQ6VXNlcjkyOTUyNzc=", "organizations_url": "https://api.github.com/users/riccardobucco/orgs", "received_events_url": "https://api.github.com/users/riccardobucco/received_events", "repos_url": "https://api.github.com/users/riccardobucco/repos", "site_admin": false, "starred_url": "https://api.github.com/users/riccardobucco/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/riccardobucco/subscriptions", "type": "User", "url": "https://api.github.com/users/riccardobucco", "user_view_type": "public" } ]	null	[ "`datasets` doesn't seem to urlencode the directory names here\r\n\r\nhttps://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/utils/file_utils.py#L109-L111\r\n\r\nfor example we should have\r\n```python\r\nfrom datasets.utils.file_utils import hf_hub_url\r\n\r\nurl = hf_hub_url(\"loubnabnl/bigcode_csharp\", \"data/c#/data_0003.jsonl\")\r\nprint(url)\r\n# Currently returns\r\n# https://huggingface.co/datasets/loubnabnl/bigcode_csharp/resolve/main/data/c#/data_0003.jsonl\r\n# while it should be \r\n# https://huggingface.co/datasets/loubnabnl/bigcode_csharp/resolve/main/data/c%23/data_0003.jsonl\r\n```", "I'll work on this :)", "@loubnabnl The dataset you linked in the description of the bug does not work and returns a 404. Where can I find the dataset to reproduce the bug?", "I think you can create a dataset repository on the Hub with a dummy file containing a `#`", "Ah sorry it was private I just made it public, I can also help with this if needed", "@lhoestq Should I url encode also repo_id and revision parameters? I'm not sure what are the valid characters there.\r\n\r\nPersonally, I would be cautious and only url encode the path parameter.", "These are possible solutions (assuming `from urllib.parse import quote`):\r\n\r\n1) url encode only the path parameter:\r\n```\r\n# src/datasets/utils/file_utils.py\r\ndef hf_hub_url(repo_id: str, path: str, revision: Optional[str] = None) -> str:\r\n revision = revision or config.HUB_DEFAULT_VERSION\r\n return config.HUB_DATASETS_URL.format(repo_id=repo_id, path=quote(path), revision=revision)\r\n```\r\n2) url encode all parameters:\r\n```\r\n# src/datasets/utils/file_utils.py\r\ndef hf_hub_url(repo_id: str, path: str, revision: Optional[str] = None) -> str:\r\n revision = revision or config.HUB_DEFAULT_VERSION\r\n return config.HUB_DATASETS_URL.format(repo_id=quote(repo_id), path=quote(path), revision=quote(revision))\r\n```\r\n3) url encode the whole url:\r\n```\r\n# src/datasets/config.py\r\nHUB_DATASETS_PATH = \"/datasets/{repo_id}/resolve/{revision}/{path}\"\r\nHUB_DATASETS_URL = HF_ENDPOINT + HUB_DATASETS_PATH\r\n```\r\n```\r\n# src/datasets/utils/file_utils.py\r\ndef hf_hub_url(repo_id: str, path: str, revision: Optional[str] = None) -> str:\r\n revision = revision or config.HUB_DEFAULT_VERSION\r\n return config.HF_ENDPOINT + quote(config.HUB_DATASETS_PATH.format(repo_id=repo_id, path=path, revision=revision))\r\n```", "repo_id can only contain alphanumeric characters and _- so it doesn't need to be encoded.\r\n\r\nHowever I agree it's a good idea to also apply `quote` to the revision as well as in 2. !", "Should be fixed by https://github.com/huggingface/datasets/issues/5099 - we'll do a release later today" ]	2022-10-11T10:05:32Z	2022-10-13T13:14:20Z	2022-10-13T13:14:20Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	## Describe the bug dataset files with `#` symbol their paths aren't read correctly. ## Steps to reproduce the bug The data in folder `c#`of this [dataset](https://huggingface.co/datasets/loubnabnl/bigcode_csharp) can't be loaded. While the folder `c_sharp` with the same data is loaded properly ```python ds = load_dataset('loubnabnl/bigcode_csharp', split="train", data_files=["data/c#/*"]) ``` ``` FileNotFoundError: Couldn't find file at https://huggingface.co/datasets/loubnabnl/bigcode_csharp/resolve/27a3166cff4bb18e11919cafa6f169c0f57483de/data/c#/data_0003.jsonl ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.5.2 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.13 - PyArrow version: 9.0.0 - Pandas version: 1.4.3 cc @lhoestq	{ "avatar_url": "https://avatars.githubusercontent.com/u/44069155?v=4", "events_url": "https://api.github.com/users/loubnabnl/events{/privacy}", "followers_url": "https://api.github.com/users/loubnabnl/followers", "following_url": "https://api.github.com/users/loubnabnl/following{/other_user}", "gists_url": "https://api.github.com/users/loubnabnl/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/loubnabnl", "id": 44069155, "login": "loubnabnl", "node_id": "MDQ6VXNlcjQ0MDY5MTU1", "organizations_url": "https://api.github.com/users/loubnabnl/orgs", "received_events_url": "https://api.github.com/users/loubnabnl/received_events", "repos_url": "https://api.github.com/users/loubnabnl/repos", "site_admin": false, "starred_url": "https://api.github.com/users/loubnabnl/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/loubnabnl/subscriptions", "type": "User", "url": "https://api.github.com/users/loubnabnl", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5099/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5099/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7209	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7209/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7209/comments	https://api.github.com/repos/huggingface/datasets/issues/7209/events	https://github.com/huggingface/datasets/pull/7209	2,575,526,651	PR_kwDODunzps5-D6_P	7,209	Preserve features in iterable dataset.filter	{ "avatar_url": "https://avatars.githubusercontent.com/u/5719745?v=4", "events_url": "https://api.github.com/users/alex-hh/events{/privacy}", "followers_url": "https://api.github.com/users/alex-hh/followers", "following_url": "https://api.github.com/users/alex-hh/following{/other_user}", "gists_url": "https://api.github.com/users/alex-hh/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alex-hh", "id": 5719745, "login": "alex-hh", "node_id": "MDQ6VXNlcjU3MTk3NDU=", "organizations_url": "https://api.github.com/users/alex-hh/orgs", "received_events_url": "https://api.github.com/users/alex-hh/received_events", "repos_url": "https://api.github.com/users/alex-hh/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alex-hh/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alex-hh/subscriptions", "type": "User", "url": "https://api.github.com/users/alex-hh", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Yes your assumption on concatenate/interleave is ok imo.\r\n\r\nIt seems the TypedExamplesIterable can slow down things, it should take formatting into account to not convert numpy arrays to python lists\r\n\r\nright now it's slow (unrelatedly to your PR):\r\n\r\n```python\r\n>>> ds = Dataset.from_dict({\"a\": np.zeros((1000, 32, 32))}).to_iterable_dataset().with_format(\"np\")\r\n>>> filtered_ds = ds.filter(lambda x: True)\r\n>>> %time sum(1 for _ in ds)\r\nCPU times: user 175 ms, sys: 8.1 ms, total: 183 ms\r\nWall time: 184 ms\r\n1000\r\n>>> %time sum(1 for _ in filtered_ds)\r\nCPU times: user 4.1 s, sys: 8.41 ms, total: 4.1 s\r\nWall time: 4.12 s\r\n1000\r\n```", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7209). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> It seems the TypedExamplesIterable can slow down things, it should take formatting into account to not convert numpy arrays to python lists\r\n\r\nShould be fixed by updated #7207 I hope!" ]	2024-10-09T10:42:05Z	2024-10-16T11:27:22Z	2024-10-09T16:04:07Z	CONTRIBUTOR	null	null	null	Fixes example in #7208 - I'm not sure what other checks I should do? @lhoestq I also haven't thought hard about the concatenate / interleaving example iterables but think this might work assuming that features are either all identical or None?	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7209/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7209/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7209.diff", "html_url": "https://github.com/huggingface/datasets/pull/7209", "merged_at": "2024-10-09T16:04:07Z", "patch_url": "https://github.com/huggingface/datasets/pull/7209.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7209" }
https://api.github.com/repos/huggingface/datasets/issues/5064	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5064/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5064/comments	https://api.github.com/repos/huggingface/datasets/issues/5064/events	https://github.com/huggingface/datasets/pull/5064	1,395,978,143	PR_kwDODunzps5AHsP0	5,064	Align signature of create/delete_repo with latest hfh	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-10-04T09:54:53Z	2022-10-07T17:02:11Z	2022-10-07T16:59:30Z	MEMBER	null	null	null	This PR aligns the signature of `create_repo`/`delete_repo` with the current one in hfh, by removing deprecated `name` and `organization`, and using `repo_id` instead. Related to: - #5063 CC: @lhoestq	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5064/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5064/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5064.diff", "html_url": "https://github.com/huggingface/datasets/pull/5064", "merged_at": "2022-10-07T16:59:30Z", "patch_url": "https://github.com/huggingface/datasets/pull/5064.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5064" }
https://api.github.com/repos/huggingface/datasets/issues/6273	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6273/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6273/comments	https://api.github.com/repos/huggingface/datasets/issues/6273/events	https://github.com/huggingface/datasets/issues/6273	1,920,922,260	I_kwDODunzps5yfvKU	6,273	Broken Link to PubMed Abstracts dataset .	{ "avatar_url": "https://avatars.githubusercontent.com/u/100606327?v=4", "events_url": "https://api.github.com/users/sameemqureshi/events{/privacy}", "followers_url": "https://api.github.com/users/sameemqureshi/followers", "following_url": "https://api.github.com/users/sameemqureshi/following{/other_user}", "gists_url": "https://api.github.com/users/sameemqureshi/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sameemqureshi", "id": 100606327, "login": "sameemqureshi", "node_id": "U_kgDOBf8hdw", "organizations_url": "https://api.github.com/users/sameemqureshi/orgs", "received_events_url": "https://api.github.com/users/sameemqureshi/received_events", "repos_url": "https://api.github.com/users/sameemqureshi/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sameemqureshi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sameemqureshi/subscriptions", "type": "User", "url": "https://api.github.com/users/sameemqureshi", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "This has already been reported in the HF Course repo (https://github.com/huggingface/course/issues/623).", "@lhoestq @albertvillanova @lewtun I don't think we are allowed to host these data files on the Hub (due to DMCA), which means the only option is to use a different dataset in the course (and to re-record the video 🙂), no?", "Keeping the video is maybe fine, we can add a note on youtube to suggest to load a dataset with a different name. Maybe C4 ? And update the code snippets on the website ?", "Maybe you want to try it with the PUBMED dataset that I reproduced based on the The [PubMed Abstract GitHub Site](http://github.com/thoppe/The-Pile-PubMed) and uploaded on the HuggingFace:\r\n\r\n```\r\nfrom datasets import load_dataset\r\npubmed_dataset = load_dataset(\"hwang2006/PUBMED_title_abstracts_2020_baseline\")\r\npubmed_dataset\r\n\r\n#Downloading data: 100%\r\n#7.98G/7.98G [11:47<00:00, 9.68MB/s]\r\n#Generating train split: 17722096/0 [00:36<00:00, 505376.37 examples/s]\r\n\r\n#DatasetDict({\r\n# train: Dataset({\r\n# features: ['meta', 'text'],\r\n# num_rows: 17722096\r\n# })\r\n#})\r\n```", "孔令涛说感谢感谢" ]	2023-10-01T19:08:48Z	2024-04-28T02:30:42Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug The link provided for the dataset is broken, data_files = [https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst](url) The ### Steps to reproduce the bug Steps to reproduce: 1) Head over to [https://huggingface.co/learn/nlp-course/chapter5/4?fw=pt#big-data-datasets-to-the-rescue](url) 2) In the Section "What is the Pile?", you can see a code snippet that contains the broken link. ### Expected behavior The link should Redirect to the "PubMed Abstracts dataset" as expected . ### Environment info .	null	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6273/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6273/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5437	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5437/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5437/comments	https://api.github.com/repos/huggingface/datasets/issues/5437/events	https://github.com/huggingface/datasets/issues/5437	1,536,837,144	I_kwDODunzps5bmkYY	5,437	Can't load png dataset with 4 channel (RGBA)	{ "avatar_url": "https://avatars.githubusercontent.com/u/41611046?v=4", "events_url": "https://api.github.com/users/WiNE-iNEFF/events{/privacy}", "followers_url": "https://api.github.com/users/WiNE-iNEFF/followers", "following_url": "https://api.github.com/users/WiNE-iNEFF/following{/other_user}", "gists_url": "https://api.github.com/users/WiNE-iNEFF/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/WiNE-iNEFF", "id": 41611046, "login": "WiNE-iNEFF", "node_id": "MDQ6VXNlcjQxNjExMDQ2", "organizations_url": "https://api.github.com/users/WiNE-iNEFF/orgs", "received_events_url": "https://api.github.com/users/WiNE-iNEFF/received_events", "repos_url": "https://api.github.com/users/WiNE-iNEFF/repos", "site_admin": false, "starred_url": "https://api.github.com/users/WiNE-iNEFF/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/WiNE-iNEFF/subscriptions", "type": "User", "url": "https://api.github.com/users/WiNE-iNEFF", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi! Can you please share the directory structure of your image folder and the `load_dataset` call? We decode images with Pillow, and Pillow supports RGBA PNGs, so this shouldn't be a problem.\r\n\r\n", "> Hi! Can you please share the directory structure of your image folder and the `load_dataset` call? We decode images with Pillow, and Pillow supports RGBA PNGs, so this shouldn't be a problem.\n> \n> \n\nI have only 1 folder that I use in the load_dataset function with the name \"IMGDATA\" and all my 9000 images are located in this folder.\n`\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"IMGDATA\")\n`\nAt the same time, using another data set with images consisting of 3 RGB channels, everything works", "Okay, I figured out what was wrong. When uploading my dataset via Google Drive, the images broke and Pillow couldn't open them. As a result, I solved the problem by downloading the ZIP archive" ]	2023-01-17T18:22:27Z	2023-01-18T20:20:15Z	2023-01-18T20:20:15Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	I try to create dataset which contains about 9000 png images 64x64 in size, and they are all 4-channel (RGBA). When trying to use load_dataset() then a dataset is created from only 2 images. What exactly interferes I can not understand.![Screenshot_20230117_212213.jpg](https://user-images.githubusercontent.com/41611046/212980147-9aa68e30-76e9-4b61-a937-c2fdabd56564.jpg)	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5437/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5437/timeline	null	completed	null	null

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