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/5737	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5737/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5737/comments	https://api.github.com/repos/huggingface/datasets/issues/5737/events	https://github.com/huggingface/datasets/issues/5737	1,662,919,811	I_kwDODunzps5jHiSD	5,737	ClassLabel Error	{ "avatar_url": "https://avatars.githubusercontent.com/u/10896776?v=4", "events_url": "https://api.github.com/users/mrcaelumn/events{/privacy}", "followers_url": "https://api.github.com/users/mrcaelumn/followers", "following_url": "https://api.github.com/users/mrcaelumn/following{/other_user}", "gists_url": "https://api.github.com/users/mrcaelumn/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mrcaelumn", "id": 10896776, "login": "mrcaelumn", "node_id": "MDQ6VXNlcjEwODk2Nzc2", "organizations_url": "https://api.github.com/users/mrcaelumn/orgs", "received_events_url": "https://api.github.com/users/mrcaelumn/received_events", "repos_url": "https://api.github.com/users/mrcaelumn/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mrcaelumn/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mrcaelumn/subscriptions", "type": "User", "url": "https://api.github.com/users/mrcaelumn", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi, you can use the `cast_column` function to change the feature type from a `Value(int64)` to `ClassLabel`:\r\n\r\n```py\r\ndataset = dataset.cast_column(\"label\", ClassLabel(names=[\"label_1\", \"label_2\", \"label_3\"]))\r\nprint(dataset.features)\r\n{'text': Value(dtype='string', id=None),\r\n 'label': ClassLabel(names=['label_1', 'label_2', 'label_3'], id=None)}\r\n```", "thank you @stevhliu, its worked. " ]	2023-04-11T17:14:13Z	2023-04-13T16:49:57Z	2023-04-13T16:49:57Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I still getting the error "call() takes 1 positional argument but 2 were given" even after ensuring that the value being passed to the label object is a single value and that the ClassLabel object has been created with the correct number of label classes ### Steps to reproduce the bug from datasets import ClassLabel, Dataset 1. Create the ClassLabel object with 3 label values and their corresponding names label_test = ClassLabel(num_classes=3, names=["label_1", "label_2", "label_3"]) 2. Define a dictionary with text and label fields data = { 'text': ['text_1', 'text_2', 'text_3'], 'label': [1, 2, 3], } 3. Create a Hugging Face dataset from the dictionary dataset = Dataset.from_dict(data) print(dataset.features) 4. Map the label values to their corresponding label names using the label object dataset = dataset.map(lambda example: {'text': example['text'], 'label': label_test(example['label'])}) 5. Print the resulting dataset print(dataset) ### Expected behavior I hope my label type is class label instead int. ### Environment info python 3.9 google colab	{ "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/5737/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5737/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/4979	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4979/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4979/comments	https://api.github.com/repos/huggingface/datasets/issues/4979/events	https://github.com/huggingface/datasets/pull/4979	1,374,820,758	PR_kwDODunzps4_CouM	4,979	Fix missing tags in dataset cards	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-09-15T16:51:03Z	2022-09-22T12:37:55Z	2022-09-15T17:12:09Z	MEMBER	null	null	null	Fix missing tags in dataset cards: - amazon_us_reviews - art - discofuse - indic_glue - ubuntu_dialogs_corpus This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task. Related to: - #4833 - #4891 - #4896 - #4908 - #4921 - #4931	{ "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/4979/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4979/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4979.diff", "html_url": "https://github.com/huggingface/datasets/pull/4979", "merged_at": "2022-09-15T17:12:09Z", "patch_url": "https://github.com/huggingface/datasets/pull/4979.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4979" }
https://api.github.com/repos/huggingface/datasets/issues/7157	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7157/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7157/comments	https://api.github.com/repos/huggingface/datasets/issues/7157/events	https://github.com/huggingface/datasets/pull/7157	2,540,354,890	PR_kwDODunzps58P-1R	7,157	Fix zero proba interleave datasets	{ "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_7157). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]	2024-09-21T15:19:14Z	2024-09-24T14:33:54Z	2024-09-24T14:33:54Z	MEMBER	null	null	null	fix https://github.com/huggingface/datasets/issues/7147	{ "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/7157/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7157/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7157.diff", "html_url": "https://github.com/huggingface/datasets/pull/7157", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/7157.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7157" }
https://api.github.com/repos/huggingface/datasets/issues/6901	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6901/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6901/comments	https://api.github.com/repos/huggingface/datasets/issues/6901/events	https://github.com/huggingface/datasets/issues/6901	2,300,167,465	I_kwDODunzps6JGcUp	6,901	HTTPError 403 raised by CLI convert_to_parquet when creating script branch on 3rd party repos	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[]	2024-05-16T11:40:22Z	2024-05-16T12:51:06Z	2024-05-16T12:51:06Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	CLI convert_to_parquet cannot create "script" branch on 3rd party repos. It can only create it on repos where the user executing the script has write access. Otherwise, a 403 Forbidden HTTPError is raised: ``` Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py", line 304, in hf_raise_for_status response.raise_for_status() File "/usr/local/lib/python3.10/dist-packages/requests/models.py", line 1021, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://huggingface.co/api/datasets/ORG/DATASET/branch/script The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/usr/local/bin/datasets-cli", line 8, in <module> sys.exit(main()) File "/usr/local/lib/python3.10/dist-packages/datasets/commands/datasets_cli.py", line 41, in main service.run() File "/usr/local/lib/python3.10/dist-packages/datasets/commands/convert_to_parquet.py", line 92, in run create_branch(dataset_id, branch="script", repo_type="dataset", token=token, exist_ok=True) File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn return fn(args, *kwargs) File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py", line 5503, in create_branch hf_raise_for_status(response) File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py", line 367, in hf_raise_for_status raise HfHubHTTPError(message, response=response) from e huggingface_hub.utils._errors.HfHubHTTPError: (Request ID: Root=1-6645ee0d-4db1ed8a1fbe04956be15897;139a6e23-df7d-4f62-b5ba-adb6d8e6e696) 403 Forbidden: Forbidden: cannot write to script. Cannot access content at: https://huggingface.co/api/datasets/ORG/DATASET/branch/script. If you are trying to create or update content,make sure you have a token with the `write` role. ```	{ "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/6901/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6901/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5135	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5135/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5135/comments	https://api.github.com/repos/huggingface/datasets/issues/5135/events	https://github.com/huggingface/datasets/issues/5135	1,414,413,519	I_kwDODunzps5UTjzP	5,135	Update docs once dataset scripts transferred to the Hub	{ "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	{ "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	[]	2022-10-19T06:58:19Z	2022-10-20T08:10:01Z	2022-10-20T08:10:01Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	## Describe the bug As discussed in: - https://github.com/huggingface/hub-docs/pull/423#pullrequestreview-1146083701 we should update our docs once dataset scripts have been transferred to the Hub (and removed from GitHub): - #4974 Concretely: - [x] Datasets on GitHub (legacy): https://huggingface.co/docs/datasets/main/en/share#datasets-on-github-legacy - [x] ADD_NEW_DATASET: https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md - ... This PR complements the work of: - #5067 This PR is a follow-up of PRs: - #3777 CC: @julien-c	{ "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/5135/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5135/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6972	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6972/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6972/comments	https://api.github.com/repos/huggingface/datasets/issues/6972/events	https://github.com/huggingface/datasets/pull/6972	2,353,531,912	PR_kwDODunzps5yfa_e	6,972	Fix webdataset pickling	{ "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_6972). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005195 / 0.011353 (-0.006157) \| 0.003734 / 0.011008 (-0.007275) \| 0.063087 / 0.038508 (0.024579) \| 0.031467 / 0.023109 (0.008358) \| 0.245183 / 0.275898 (-0.030715) \| 0.280071 / 0.323480 (-0.043409) \| 0.003205 / 0.007986 (-0.004780) \| 0.003311 / 0.004328 (-0.001018) \| 0.049967 / 0.004250 (0.045717) \| 0.044927 / 0.037052 (0.007875) \| 0.262244 / 0.258489 (0.003755) \| 0.284549 / 0.293841 (-0.009292) \| 0.027595 / 0.128546 (-0.100952) \| 0.010521 / 0.075646 (-0.065126) \| 0.206928 / 0.419271 (-0.212343) \| 0.036179 / 0.043533 (-0.007354) \| 0.254256 / 0.255139 (-0.000883) \| 0.272733 / 0.283200 (-0.010467) \| 0.020456 / 0.141683 (-0.121226) \| 1.118527 / 1.452155 (-0.333628) \| 1.152741 / 1.492716 (-0.339975) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096642 / 0.018006 (0.078636) \| 0.306981 / 0.000490 (0.306491) \| 0.000220 / 0.000200 (0.000020) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019031 / 0.037411 (-0.018380) \| 0.063960 / 0.014526 (0.049435) \| 0.074428 / 0.176557 (-0.102129) \| 0.121226 / 0.737135 (-0.615909) \| 0.077111 / 0.296338 (-0.219228) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279830 / 0.215209 (0.064621) \| 2.748243 / 2.077655 (0.670588) \| 1.481554 / 1.504120 (-0.022566) \| 1.355015 / 1.541195 (-0.186180) \| 1.379655 / 1.468490 (-0.088835) \| 0.560378 / 4.584777 (-4.024399) \| 2.407241 / 3.745712 (-1.338471) \| 2.837090 / 5.269862 (-2.432771) \| 1.767084 / 4.565676 (-2.798593) \| 0.063517 / 0.424275 (-0.360758) \| 0.005024 / 0.007607 (-0.002584) \| 0.334845 / 0.226044 (0.108800) \| 3.290712 / 2.268929 (1.021783) \| 1.836923 / 55.444624 (-53.607702) \| 1.543671 / 6.876477 (-5.332806) \| 1.582319 / 2.142072 (-0.559754) \| 0.637689 / 4.805227 (-4.167538) \| 0.119515 / 6.500664 (-6.381149) \| 0.042191 / 0.075469 (-0.033278) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980018 / 1.841788 (-0.861770) \| 11.620211 / 8.074308 (3.545903) \| 9.697799 / 10.191392 (-0.493593) \| 0.131733 / 0.680424 (-0.548691) \| 0.014007 / 0.534201 (-0.520193) \| 0.286046 / 0.579283 (-0.293237) \| 0.264776 / 0.434364 (-0.169588) \| 0.325041 / 0.540337 (-0.215296) \| 0.452740 / 1.386936 (-0.934196) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005603 / 0.011353 (-0.005750) \| 0.003810 / 0.011008 (-0.007199) \| 0.050773 / 0.038508 (0.012265) \| 0.032601 / 0.023109 (0.009492) \| 0.268035 / 0.275898 (-0.007863) \| 0.292614 / 0.323480 (-0.030866) \| 0.005076 / 0.007986 (-0.002910) \| 0.004487 / 0.004328 (0.000159) \| 0.049988 / 0.004250 (0.045737) \| 0.040258 / 0.037052 (0.003205) \| 0.284145 / 0.258489 (0.025656) \| 0.318291 / 0.293841 (0.024450) \| 0.029672 / 0.128546 (-0.098875) \| 0.010534 / 0.075646 (-0.065113) \| 0.059020 / 0.419271 (-0.360252) \| 0.033451 / 0.043533 (-0.010082) \| 0.270220 / 0.255139 (0.015081) \| 0.290500 / 0.283200 (0.007300) \| 0.017123 / 0.141683 (-0.124560) \| 1.130870 / 1.452155 (-0.321285) \| 1.160038 / 1.492716 (-0.332678) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097045 / 0.018006 (0.079039) \| 0.314573 / 0.000490 (0.314083) \| 0.000203 / 0.000200 (0.000003) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022396 / 0.037411 (-0.015015) \| 0.079393 / 0.014526 (0.064867) \| 0.088460 / 0.176557 (-0.088097) \| 0.128050 / 0.737135 (-0.609085) \| 0.093070 / 0.296338 (-0.203268) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293858 / 0.215209 (0.078649) \| 2.819956 / 2.077655 (0.742301) \| 1.540181 / 1.504120 (0.036061) \| 1.419671 / 1.541195 (-0.121524) \| 1.441594 / 1.468490 (-0.026897) \| 0.565200 / 4.584777 (-4.019577) \| 0.963967 / 3.745712 (-2.781745) \| 2.752137 / 5.269862 (-2.517725) \| 1.779239 / 4.565676 (-2.786438) \| 0.063787 / 0.424275 (-0.360488) \| 0.005344 / 0.007607 (-0.002263) \| 0.344283 / 0.226044 (0.118239) \| 3.353263 / 2.268929 (1.084334) \| 1.898678 / 55.444624 (-53.545947) \| 1.607868 / 6.876477 (-5.268609) \| 1.781938 / 2.142072 (-0.360134) \| 0.652119 / 4.805227 (-4.153108) \| 0.117883 / 6.500664 (-6.382781) \| 0.048811 / 0.075469 (-0.026658) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.013154 / 1.841788 (-0.828634) \| 12.421963 / 8.074308 (4.347655) \| 10.352056 / 10.191392 (0.160664) \| 0.143784 / 0.680424 (-0.536640) \| 0.016370 / 0.534201 (-0.517831) \| 0.283668 / 0.579283 (-0.295615) \| 0.127070 / 0.434364 (-0.307294) \| 0.326199 / 0.540337 (-0.214138) \| 0.432776 / 1.386936 (-0.954160) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5e72fb13b4824dcb27aedb807e4e28c420dec244 \"CML watermark\")\n" ]	2024-06-14T14:43:02Z	2024-06-14T15:43:43Z	2024-06-14T15:37:35Z	MEMBER	null	null	null	...by making tracked iterables picklable. This is important to make streaming datasets compatible with multiprocessing e.g. for parallel data 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" }	{ "+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/6972/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6972/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6972.diff", "html_url": "https://github.com/huggingface/datasets/pull/6972", "merged_at": "2024-06-14T15:37:35Z", "patch_url": "https://github.com/huggingface/datasets/pull/6972.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6972" }
https://api.github.com/repos/huggingface/datasets/issues/5656	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5656/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5656/comments	https://api.github.com/repos/huggingface/datasets/issues/5656/events	https://github.com/huggingface/datasets/pull/5656	1,634,156,563	PR_kwDODunzps5Mjxoo	5,656	Fix `fsspec.open` when using an HTTP proxy	{ "avatar_url": "https://avatars.githubusercontent.com/u/3905501?v=4", "events_url": "https://api.github.com/users/bryant1410/events{/privacy}", "followers_url": "https://api.github.com/users/bryant1410/followers", "following_url": "https://api.github.com/users/bryant1410/following{/other_user}", "gists_url": "https://api.github.com/users/bryant1410/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/bryant1410", "id": 3905501, "login": "bryant1410", "node_id": "MDQ6VXNlcjM5MDU1MDE=", "organizations_url": "https://api.github.com/users/bryant1410/orgs", "received_events_url": "https://api.github.com/users/bryant1410/received_events", "repos_url": "https://api.github.com/users/bryant1410/repos", "site_admin": false, "starred_url": "https://api.github.com/users/bryant1410/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/bryant1410/subscriptions", "type": "User", "url": "https://api.github.com/users/bryant1410", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007980 / 0.011353 (-0.003373) \| 0.005351 / 0.011008 (-0.005657) \| 0.096325 / 0.038508 (0.057817) \| 0.034204 / 0.023109 (0.011095) \| 0.328080 / 0.275898 (0.052182) \| 0.361519 / 0.323480 (0.038039) \| 0.005954 / 0.007986 (-0.002032) \| 0.004106 / 0.004328 (-0.000222) \| 0.072827 / 0.004250 (0.068576) \| 0.050522 / 0.037052 (0.013470) \| 0.326975 / 0.258489 (0.068486) \| 0.373180 / 0.293841 (0.079339) \| 0.037024 / 0.128546 (-0.091522) \| 0.012347 / 0.075646 (-0.063299) \| 0.332341 / 0.419271 (-0.086931) \| 0.050695 / 0.043533 (0.007162) \| 0.328298 / 0.255139 (0.073159) \| 0.352808 / 0.283200 (0.069608) \| 0.101637 / 0.141683 (-0.040046) \| 1.435172 / 1.452155 (-0.016982) \| 1.529797 / 1.492716 (0.037080) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.305727 / 0.018006 (0.287721) \| 0.583951 / 0.000490 (0.583462) \| 0.011699 / 0.000200 (0.011499) \| 0.000345 / 0.000054 (0.000290) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027917 / 0.037411 (-0.009495) \| 0.107698 / 0.014526 (0.093173) \| 0.120572 / 0.176557 (-0.055985) \| 0.176066 / 0.737135 (-0.561069) \| 0.125348 / 0.296338 (-0.170991) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.411980 / 0.215209 (0.196771) \| 4.113135 / 2.077655 (2.035480) \| 1.868725 / 1.504120 (0.364605) \| 1.677422 / 1.541195 (0.136227) \| 1.796759 / 1.468490 (0.328269) \| 0.701957 / 4.584777 (-3.882820) \| 3.830742 / 3.745712 (0.085030) \| 2.170444 / 5.269862 (-3.099418) \| 1.345097 / 4.565676 (-3.220580) \| 0.086661 / 0.424275 (-0.337614) \| 0.013073 / 0.007607 (0.005466) \| 0.519150 / 0.226044 (0.293106) \| 5.193447 / 2.268929 (2.924518) \| 2.391155 / 55.444624 (-53.053470) \| 2.076610 / 6.876477 (-4.799867) \| 2.245557 / 2.142072 (0.103484) \| 0.846496 / 4.805227 (-3.958731) \| 0.169246 / 6.500664 (-6.331418) \| 0.066360 / 0.075469 (-0.009109) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.196344 / 1.841788 (-0.645444) \| 15.640363 / 8.074308 (7.566055) \| 14.936144 / 10.191392 (4.744752) \| 0.163613 / 0.680424 (-0.516811) \| 0.017900 / 0.534201 (-0.516301) \| 0.425377 / 0.579283 (-0.153906) \| 0.431119 / 0.434364 (-0.003245) \| 0.513669 / 0.540337 (-0.026669) \| 0.592970 / 1.386936 (-0.793966) \|\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) \| 0.007958 / 0.011353 (-0.003395) \| 0.005707 / 0.011008 (-0.005301) \| 0.075377 / 0.038508 (0.036869) \| 0.037126 / 0.023109 (0.014016) \| 0.344589 / 0.275898 (0.068691) \| 0.381060 / 0.323480 (0.057580) \| 0.006592 / 0.007986 (-0.001393) \| 0.004479 / 0.004328 (0.000151) \| 0.074456 / 0.004250 (0.070206) \| 0.054087 / 0.037052 (0.017035) \| 0.344942 / 0.258489 (0.086453) \| 0.393174 / 0.293841 (0.099333) \| 0.037926 / 0.128546 (-0.090620) \| 0.012638 / 0.075646 (-0.063009) \| 0.087743 / 0.419271 (-0.331529) \| 0.050081 / 0.043533 (0.006548) \| 0.340406 / 0.255139 (0.085267) \| 0.361487 / 0.283200 (0.078287) \| 0.108546 / 0.141683 (-0.033137) \| 1.424626 / 1.452155 (-0.027529) \| 1.553958 / 1.492716 (0.061242) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.329922 / 0.018006 (0.311916) \| 0.523239 / 0.000490 (0.522749) \| 0.012164 / 0.000200 (0.011964) \| 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) \| 0.031935 / 0.037411 (-0.005477) \| 0.115680 / 0.014526 (0.101154) \| 0.130062 / 0.176557 (-0.046494) \| 0.180679 / 0.737135 (-0.556457) \| 0.135548 / 0.296338 (-0.160790) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429648 / 0.215209 (0.214439) \| 4.303342 / 2.077655 (2.225687) \| 1.999395 / 1.504120 (0.495275) \| 1.810354 / 1.541195 (0.269160) \| 1.963132 / 1.468490 (0.494642) \| 0.701654 / 4.584777 (-3.883122) \| 3.844687 / 3.745712 (0.098975) \| 2.153425 / 5.269862 (-3.116436) \| 1.351541 / 4.565676 (-3.214135) \| 0.086292 / 0.424275 (-0.337983) \| 0.012491 / 0.007607 (0.004883) \| 0.523144 / 0.226044 (0.297099) \| 5.243283 / 2.268929 (2.974355) \| 2.465849 / 55.444624 (-52.978775) \| 2.154505 / 6.876477 (-4.721972) \| 2.245500 / 2.142072 (0.103428) \| 0.838902 / 4.805227 (-3.966326) \| 0.169441 / 6.500664 (-6.331223) \| 0.065631 / 0.075469 (-0.009838) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.262175 / 1.841788 (-0.579612) \| 15.424650 / 8.074308 (7.350342) \| 15.000718 / 10.191392 (4.809326) \| 0.186328 / 0.680424 (-0.494096) \| 0.018076 / 0.534201 (-0.516125) \| 0.433458 / 0.579283 (-0.145825) \| 0.424213 / 0.434364 (-0.010151) \| 0.546568 / 0.540337 (0.006231) \| 0.643529 / 1.386936 (-0.743407) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ea7298bf121d7ae8079f0a59deb67c2fa1d4df6a \"CML watermark\")\n" ]	2023-03-21T15:23:29Z	2023-03-23T14:14:50Z	2023-03-23T13:15:46Z	CONTRIBUTOR	null	null	null	Most HTTP(S) downloads from this library support proxy automatically by reading the `HTTP_PROXY` environment variable (et al.) because `requests` is widely used. However, in some parts of the code, `fsspec` is used, which in turn uses `aiohttp` for HTTP(S) requests (as opposed to `requests`), which in turn doesn't support reading proxy env variables by default. This PR enables reading them automatically. Read [aiohttp docs on using proxies](https://docs.aiohttp.org/en/stable/client_advanced.html?highlight=trust_env#proxy-support). For context, [the Python library requests](https://requests.readthedocs.io/en/latest/user/advanced/?highlight=http_proxy#proxies) and [the official Python library via `urllib.urlopen` support this automatically by default](https://docs.python.org/3/library/urllib.request.html#urllib.request.urlopen). Many (most common ones?) programs also do the same, including cURL, APT, Wget, and many others.	{ "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/5656/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5656/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5656.diff", "html_url": "https://github.com/huggingface/datasets/pull/5656", "merged_at": "2023-03-23T13:15:46Z", "patch_url": "https://github.com/huggingface/datasets/pull/5656.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5656" }
https://api.github.com/repos/huggingface/datasets/issues/6908	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6908/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6908/comments	https://api.github.com/repos/huggingface/datasets/issues/6908/events	https://github.com/huggingface/datasets/issues/6908	2,304,958,116	I_kwDODunzps6JYt6k	6,908	Fail to load "stas/c4-en-10k" dataset since 2.16 version	{ "avatar_url": "https://avatars.githubusercontent.com/u/38173059?v=4", "events_url": "https://api.github.com/users/guch8017/events{/privacy}", "followers_url": "https://api.github.com/users/guch8017/followers", "following_url": "https://api.github.com/users/guch8017/following{/other_user}", "gists_url": "https://api.github.com/users/guch8017/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/guch8017", "id": 38173059, "login": "guch8017", "node_id": "MDQ6VXNlcjM4MTczMDU5", "organizations_url": "https://api.github.com/users/guch8017/orgs", "received_events_url": "https://api.github.com/users/guch8017/received_events", "repos_url": "https://api.github.com/users/guch8017/repos", "site_admin": false, "starred_url": "https://api.github.com/users/guch8017/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/guch8017/subscriptions", "type": "User", "url": "https://api.github.com/users/guch8017", "user_view_type": "public" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[ "I am not able to reproduce the error with datasets 2.19.1:\r\n```python\r\nIn [1]: from datasets import load_dataset; ds = load_dataset(\"stas/c4-en-10k\", streaming=True); item = next(iter(ds[\"train\"])); item\r\nOut[1]: {'text': 'Beginners BBQ Class Taking Place in Missoula!\\nDo you want to get better at making delicious BBQ? You will have the opportunity, put this on your calendar now. Thursday, September 22nd join World Class BBQ Champion, Tony Balay from Lonestar Smoke Rangers. He will be teaching a beginner level class for everyone who wants to get better with their culinary skills.\\nHe will teach you everything you need to know to compete in a KCBS BBQ competition, including techniques, recipes, timelines, meat selection and trimming, plus smoker and fire information.\\nThe cost to be in the class is $35 per person, and for spectators it is free. Included in the cost will be either a t-shirt or apron and you will be tasting samples of each meat that is prepared.'}\r\n\r\nIn [2]: from datasets import load_dataset; ds = load_dataset(\"stas/c4-en-10k\", download_mode=\"force_redownload\"); ds\r\nDownloading data: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 13.3M/13.3M [00:00<00:00, 18.7MB/s]\r\nGenerating train split: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 10000/10000 [00:00<00:00, 78548.55 examples/s]\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['text'],\r\n num_rows: 10000\r\n })\r\n})\r\n```\r\n\r\nLooking at your error traceback, I notice that the code line numbers do not correspond to the ones of datasets 2.19.1.\r\n\r\nAdditionally, I can't reproduce the issue with `HfFileSystem`:\r\n```python\r\nIn [1]: from huggingface_hub import HfFileSystem\r\n\r\nIn [2]: fs = HfFileSystem()\r\n\r\nIn [3]: with fs.open(\"datasets/stas/c4-en-10k/c4-en-10k.py\", \"rb\") as f:\r\n ...: data = f.read()\r\n ...: \r\n\r\nIn [4]: data[:20]\r\nOut[4]: b'# coding=utf-8\\n# Cop'\r\n```\r\n\r\nCould you please verify the `datasets` and `huggingface_hub` versions you are indeed using?\r\n```python\r\nimport datasets; print(datasets.__version__)\r\n\r\nimport huggingface_hub; print(huggingface_hub.__version__)\r\n```", "Thanks for your reply! After I update the datasets version from 2.15.0 back to 2.19.1 again, it seems everything work well. Sorry for bordering you!" ]	2024-05-20T02:43:59Z	2024-05-24T10:58:09Z	2024-05-24T10:58:09Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug When update datasets library to version 2.16+ ( I test it on 2.16, 2.19.0 and 2.19.1), using the following code to load stas/c4-en-10k dataset ```python from datasets import load_dataset, Dataset dataset = load_dataset('stas/c4-en-10k') ``` and then it raise UnicodeDecodeError like ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home//conda3/envs/watermark/lib/python3.10/site-packages/datasets/load.py", line 2523, in load_dataset builder_instance = load_dataset_builder( File "/home//conda3/envs/watermark/lib/python3.10/site-packages/datasets/load.py", line 2195, in load_dataset_builder dataset_module = dataset_module_factory( File "/home//conda3/envs/watermark/lib/python3.10/site-packages/datasets/load.py", line 1846, in dataset_module_factory raise e1 from None File "/home//conda3/envs/watermark/lib/python3.10/site-packages/datasets/load.py", line 1798, in dataset_module_factory can_load_config_from_parquet_export = "DEFAULT_CONFIG_NAME" not in f.read() File "/home/*/conda3/envs/watermark/lib/python3.10/codecs.py", line 322, in decode (result, consumed) = self._buffer_decode(data, self.errors, final) UnicodeDecodeError: 'utf-8' codec can't decode byte 0x8b in position 1: invalid start byte ``` I found that fs.open loads a gzip file and parses it like plain text using utf-8 encoder. ```python fs = HfFileSystem('https://huggingface.co') fs.open("datasets/stas/c4-en-10k/c4-en-10k.py", "rb") data = fs.read() # data is gzip bytes begin with b'\x1f\x8b\x08\x00\x00\tn\x88\x00...' data2 = unzip_gzip_bytes(data) # data2 is what we want: '# coding=utf-8\n# Copyright 2020 The HuggingFace Datasets...' ``` ### Steps to reproduce the bug 1. Install datasets between version 2.16 and 2.19 2. Use `datasets.load_dataset` method to load `stas/c4-en-10k` dataset. ### Expected behavior Load dataset normally. ### Environment info Platform = Linux-5.4.0-159-generic-x86_64-with-glibc2.35 Python = 3.10.14 Datasets = 2.19	{ "avatar_url": "https://avatars.githubusercontent.com/u/38173059?v=4", "events_url": "https://api.github.com/users/guch8017/events{/privacy}", "followers_url": "https://api.github.com/users/guch8017/followers", "following_url": "https://api.github.com/users/guch8017/following{/other_user}", "gists_url": "https://api.github.com/users/guch8017/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/guch8017", "id": 38173059, "login": "guch8017", "node_id": "MDQ6VXNlcjM4MTczMDU5", "organizations_url": "https://api.github.com/users/guch8017/orgs", "received_events_url": "https://api.github.com/users/guch8017/received_events", "repos_url": "https://api.github.com/users/guch8017/repos", "site_admin": false, "starred_url": "https://api.github.com/users/guch8017/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/guch8017/subscriptions", "type": "User", "url": "https://api.github.com/users/guch8017", "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/6908/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6908/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5013	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5013/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5013/comments	https://api.github.com/repos/huggingface/datasets/issues/5013/events	https://github.com/huggingface/datasets/issues/5013	1,383,415,971	I_kwDODunzps5SdUCj	5,013	would huggingface like publish cpp binding for datasets package ?	{ "avatar_url": "https://avatars.githubusercontent.com/u/6143404?v=4", "events_url": "https://api.github.com/users/mullerhai/events{/privacy}", "followers_url": "https://api.github.com/users/mullerhai/followers", "following_url": "https://api.github.com/users/mullerhai/following{/other_user}", "gists_url": "https://api.github.com/users/mullerhai/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mullerhai", "id": 6143404, "login": "mullerhai", "node_id": "MDQ6VXNlcjYxNDM0MDQ=", "organizations_url": "https://api.github.com/users/mullerhai/orgs", "received_events_url": "https://api.github.com/users/mullerhai/received_events", "repos_url": "https://api.github.com/users/mullerhai/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mullerhai/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mullerhai/subscriptions", "type": "User", "url": "https://api.github.com/users/mullerhai", "user_view_type": "public" }	[ { "color": "ffffff", "default": true, "description": "This will not be worked on", "id": 1935892913, "name": "wontfix", "node_id": "MDU6TGFiZWwxOTM1ODkyOTEz", "url": "https://api.github.com/repos/huggingface/datasets/labels/wontfix" } ]	closed	false	null	[]	null	[ "Hi ! Can you share more information about your use case ? How could it help you to have cpp bindings versus using the python libraries ?", "> Hi ! Can you share more information about your use case ? How could it help you to have cpp bindings versus using the python libraries ?\r\n\r\nfor example ,the huggingface load_model() and load_dataset() can execute in cpp env", "If it's a viable option for you, you can check [tch-rs](https://github.com/LaurentMazare/tch-rs) to load models in Rust. Regarding datasets, you can first download them in python and then use Arrow C++ or Rust to load them", "If you are more adventurous, another option is to embed python calls inside c++ e.g. with `pybind11`.", "> pybind11\r\n\r\nI think it is not the best solution" ]	2022-09-23T07:42:49Z	2023-02-24T16:20:57Z	2023-02-24T16:20:57Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	HI: I use cpp env libtorch, I like use hugggingface ,but huggingface not cpp binding, would you like publish cpp binding for it. thanks	{ "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/5013/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5013/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5779	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5779/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5779/comments	https://api.github.com/repos/huggingface/datasets/issues/5779/events	https://github.com/huggingface/datasets/pull/5779	1,678,669,865	PR_kwDODunzps5O3sHp	5,779	Call fs.makedirs in save_to_disk	{ "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) \| 0.007490 / 0.011353 (-0.003862) \| 0.004957 / 0.011008 (-0.006051) \| 0.096952 / 0.038508 (0.058444) \| 0.034125 / 0.023109 (0.011016) \| 0.301926 / 0.275898 (0.026028) \| 0.330538 / 0.323480 (0.007058) \| 0.005999 / 0.007986 (-0.001987) \| 0.003948 / 0.004328 (-0.000380) \| 0.073024 / 0.004250 (0.068773) \| 0.050020 / 0.037052 (0.012967) \| 0.299987 / 0.258489 (0.041498) \| 0.336077 / 0.293841 (0.042237) \| 0.035781 / 0.128546 (-0.092765) \| 0.012159 / 0.075646 (-0.063487) \| 0.333311 / 0.419271 (-0.085960) \| 0.059925 / 0.043533 (0.016392) \| 0.297772 / 0.255139 (0.042633) \| 0.313447 / 0.283200 (0.030247) \| 0.100991 / 0.141683 (-0.040692) \| 1.472182 / 1.452155 (0.020027) \| 1.553010 / 1.492716 (0.060294) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.214222 / 0.018006 (0.196216) \| 0.441579 / 0.000490 (0.441090) \| 0.001030 / 0.000200 (0.000830) \| 0.000194 / 0.000054 (0.000140) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026149 / 0.037411 (-0.011262) \| 0.107324 / 0.014526 (0.092798) \| 0.113390 / 0.176557 (-0.063167) \| 0.170282 / 0.737135 (-0.566854) \| 0.120601 / 0.296338 (-0.175737) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.411795 / 0.215209 (0.196585) \| 4.091412 / 2.077655 (2.013757) \| 1.819597 / 1.504120 (0.315477) \| 1.623413 / 1.541195 (0.082218) \| 1.658959 / 1.468490 (0.190469) \| 0.697671 / 4.584777 (-3.887106) \| 3.868855 / 3.745712 (0.123143) \| 3.220448 / 5.269862 (-2.049414) \| 1.796472 / 4.565676 (-2.769204) \| 0.085817 / 0.424275 (-0.338458) \| 0.012422 / 0.007607 (0.004815) \| 0.520302 / 0.226044 (0.294258) \| 5.062477 / 2.268929 (2.793548) \| 2.275065 / 55.444624 (-53.169560) \| 1.936717 / 6.876477 (-4.939759) \| 2.069924 / 2.142072 (-0.072148) \| 0.838964 / 4.805227 (-3.966264) \| 0.170632 / 6.500664 (-6.330032) \| 0.066011 / 0.075469 (-0.009458) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.190673 / 1.841788 (-0.651114) \| 14.679478 / 8.074308 (6.605169) \| 14.099743 / 10.191392 (3.908351) \| 0.142556 / 0.680424 (-0.537868) \| 0.017601 / 0.534201 (-0.516600) \| 0.421301 / 0.579283 (-0.157982) \| 0.418035 / 0.434364 (-0.016329) \| 0.503799 / 0.540337 (-0.036539) \| 0.588809 / 1.386936 (-0.798127) \|\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) \| 0.007556 / 0.011353 (-0.003797) \| 0.005283 / 0.011008 (-0.005725) \| 0.075616 / 0.038508 (0.037107) \| 0.034127 / 0.023109 (0.011018) \| 0.345145 / 0.275898 (0.069247) \| 0.377490 / 0.323480 (0.054010) \| 0.006532 / 0.007986 (-0.001454) \| 0.004145 / 0.004328 (-0.000183) \| 0.074724 / 0.004250 (0.070473) \| 0.048658 / 0.037052 (0.011605) \| 0.339989 / 0.258489 (0.081500) \| 0.398240 / 0.293841 (0.104399) \| 0.037433 / 0.128546 (-0.091114) \| 0.012410 / 0.075646 (-0.063237) \| 0.088110 / 0.419271 (-0.331162) \| 0.050635 / 0.043533 (0.007103) \| 0.351878 / 0.255139 (0.096739) \| 0.365707 / 0.283200 (0.082508) \| 0.104342 / 0.141683 (-0.037341) \| 1.438009 / 1.452155 (-0.014145) \| 1.533616 / 1.492716 (0.040900) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225570 / 0.018006 (0.207563) \| 0.442482 / 0.000490 (0.441992) \| 0.000402 / 0.000200 (0.000202) \| 0.000063 / 0.000054 (0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030348 / 0.037411 (-0.007063) \| 0.111402 / 0.014526 (0.096877) \| 0.123365 / 0.176557 (-0.053192) \| 0.175604 / 0.737135 (-0.561531) \| 0.128458 / 0.296338 (-0.167881) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426054 / 0.215209 (0.210845) \| 4.255050 / 2.077655 (2.177395) \| 2.039568 / 1.504120 (0.535448) \| 1.856842 / 1.541195 (0.315647) \| 1.923792 / 1.468490 (0.455301) \| 0.701023 / 4.584777 (-3.883754) \| 3.746632 / 3.745712 (0.000920) \| 2.055563 / 5.269862 (-3.214298) \| 1.308068 / 4.565676 (-3.257608) \| 0.085524 / 0.424275 (-0.338751) \| 0.012103 / 0.007607 (0.004496) \| 0.522929 / 0.226044 (0.296885) \| 5.258133 / 2.268929 (2.989205) \| 2.458440 / 55.444624 (-52.986185) \| 2.141681 / 6.876477 (-4.734796) \| 2.258667 / 2.142072 (0.116595) \| 0.842533 / 4.805227 (-3.962694) \| 0.168089 / 6.500664 (-6.332575) \| 0.063707 / 0.075469 (-0.011762) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.312252 / 1.841788 (-0.529536) \| 14.939185 / 8.074308 (6.864877) \| 14.479845 / 10.191392 (4.288453) \| 0.162557 / 0.680424 (-0.517867) \| 0.017660 / 0.534201 (-0.516541) \| 0.423261 / 0.579283 (-0.156023) \| 0.417693 / 0.434364 (-0.016671) \| 0.495440 / 0.540337 (-0.044897) \| 0.589932 / 1.386936 (-0.797004) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4e3c86574155961097b367d5cddda5bd13c42b09 \"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) \| 0.008796 / 0.011353 (-0.002557) \| 0.005828 / 0.011008 (-0.005180) \| 0.118629 / 0.038508 (0.080121) \| 0.042435 / 0.023109 (0.019326) \| 0.383780 / 0.275898 (0.107882) \| 0.420344 / 0.323480 (0.096864) \| 0.006855 / 0.007986 (-0.001130) \| 0.006290 / 0.004328 (0.001962) \| 0.087160 / 0.004250 (0.082910) \| 0.057568 / 0.037052 (0.020516) \| 0.378761 / 0.258489 (0.120272) \| 0.426496 / 0.293841 (0.132655) \| 0.041772 / 0.128546 (-0.086774) \| 0.014226 / 0.075646 (-0.061420) \| 0.400097 / 0.419271 (-0.019174) \| 0.060402 / 0.043533 (0.016870) \| 0.381955 / 0.255139 (0.126816) \| 0.399110 / 0.283200 (0.115911) \| 0.124608 / 0.141683 (-0.017075) \| 1.737856 / 1.452155 (0.285702) \| 1.829034 / 1.492716 (0.336318) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.219941 / 0.018006 (0.201934) \| 0.497156 / 0.000490 (0.496666) \| 0.005094 / 0.000200 (0.004894) \| 0.000097 / 0.000054 (0.000043) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032144 / 0.037411 (-0.005268) \| 0.131782 / 0.014526 (0.117256) \| 0.141543 / 0.176557 (-0.035014) \| 0.211419 / 0.737135 (-0.525716) \| 0.147338 / 0.296338 (-0.149001) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.478345 / 0.215209 (0.263136) \| 4.749506 / 2.077655 (2.671851) \| 2.195794 / 1.504120 (0.691674) \| 1.978126 / 1.541195 (0.436932) \| 2.059941 / 1.468490 (0.591451) \| 0.821959 / 4.584777 (-3.762818) \| 5.737479 / 3.745712 (1.991767) \| 2.507125 / 5.269862 (-2.762737) \| 2.051772 / 4.565676 (-2.513905) \| 0.100619 / 0.424275 (-0.323656) \| 0.014437 / 0.007607 (0.006830) \| 0.599484 / 0.226044 (0.373440) \| 5.977579 / 2.268929 (3.708651) \| 2.708143 / 55.444624 (-52.736482) \| 2.320279 / 6.876477 (-4.556198) \| 2.510172 / 2.142072 (0.368100) \| 1.006279 / 4.805227 (-3.798948) \| 0.199812 / 6.500664 (-6.300853) \| 0.077967 / 0.075469 (0.002498) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.510171 / 1.841788 (-0.331616) \| 21.099446 / 8.074308 (13.025138) \| 17.634225 / 10.191392 (7.442833) \| 0.223506 / 0.680424 (-0.456918) \| 0.023845 / 0.534201 (-0.510356) \| 0.613489 / 0.579283 (0.034206) \| 0.685735 / 0.434364 (0.251371) \| 0.652485 / 0.540337 (0.112148) \| 0.734756 / 1.386936 (-0.652180) \|\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) \| 0.008444 / 0.011353 (-0.002909) \| 0.005789 / 0.011008 (-0.005220) \| 0.088297 / 0.038508 (0.049789) \| 0.040847 / 0.023109 (0.017737) \| 0.411748 / 0.275898 (0.135850) \| 0.452320 / 0.323480 (0.128841) \| 0.006689 / 0.007986 (-0.001296) \| 0.006029 / 0.004328 (0.001701) \| 0.086080 / 0.004250 (0.081830) \| 0.053310 / 0.037052 (0.016257) \| 0.402568 / 0.258489 (0.144079) \| 0.459047 / 0.293841 (0.165206) \| 0.041203 / 0.128546 (-0.087343) \| 0.014216 / 0.075646 (-0.061431) \| 0.102729 / 0.419271 (-0.316543) \| 0.057170 / 0.043533 (0.013637) \| 0.407137 / 0.255139 (0.151998) \| 0.429703 / 0.283200 (0.146503) \| 0.123528 / 0.141683 (-0.018155) \| 1.690026 / 1.452155 (0.237872) \| 1.797793 / 1.492716 (0.305077) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.264581 / 0.018006 (0.246575) \| 0.498981 / 0.000490 (0.498492) \| 0.000462 / 0.000200 (0.000262) \| 0.000096 / 0.000054 (0.000041) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034613 / 0.037411 (-0.002798) \| 0.136596 / 0.014526 (0.122070) \| 0.142183 / 0.176557 (-0.034374) \| 0.201816 / 0.737135 (-0.535320) \| 0.148843 / 0.296338 (-0.147496) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.506708 / 0.215209 (0.291499) \| 5.042829 / 2.077655 (2.965175) \| 2.448414 / 1.504120 (0.944295) \| 2.213251 / 1.541195 (0.672056) \| 2.255805 / 1.468490 (0.787315) \| 0.829929 / 4.584777 (-3.754848) \| 5.145717 / 3.745712 (1.400004) \| 2.493947 / 5.269862 (-2.775915) \| 1.676171 / 4.565676 (-2.889506) \| 0.102097 / 0.424275 (-0.322178) \| 0.014545 / 0.007607 (0.006938) \| 0.635473 / 0.226044 (0.409429) \| 6.306767 / 2.268929 (4.037839) \| 3.050284 / 55.444624 (-52.394341) \| 2.653175 / 6.876477 (-4.223302) \| 2.850569 / 2.142072 (0.708496) \| 1.355280 / 4.805227 (-3.449947) \| 0.248112 / 6.500664 (-6.252552) \| 0.091993 / 0.075469 (0.016524) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.837509 / 1.841788 (-0.004279) \| 21.268838 / 8.074308 (13.194530) \| 17.338053 / 10.191392 (7.146660) \| 0.232263 / 0.680424 (-0.448161) \| 0.029093 / 0.534201 (-0.505108) \| 0.651056 / 0.579283 (0.071773) \| 0.617623 / 0.434364 (0.183259) \| 0.773921 / 0.540337 (0.233584) \| 0.705118 / 1.386936 (-0.681818) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#35846fd54fa16aa72ff344d15c98b5e08c5effe0 \"CML watermark\")\n" ]	2023-04-21T15:04:28Z	2023-04-26T12:20:01Z	2023-04-26T12:11:15Z	MEMBER	null	null	null	We need to call `fs.makedirs` when saving a dataset using `save_to_disk`, because some fs implementations have actual directories (S3 and others don't) Close https://github.com/huggingface/datasets/issues/5775	{ "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/5779/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5779/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5779.diff", "html_url": "https://github.com/huggingface/datasets/pull/5779", "merged_at": "2023-04-26T12:11:15Z", "patch_url": "https://github.com/huggingface/datasets/pull/5779.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5779" }
https://api.github.com/repos/huggingface/datasets/issues/5094	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5094/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5094/comments	https://api.github.com/repos/huggingface/datasets/issues/5094/events	https://github.com/huggingface/datasets/issues/5094	1,403,214,950	I_kwDODunzps5To1xm	5,094	Multiprocessing with `Dataset.map` and `PyTorch` results in deadlock	{ "avatar_url": "https://avatars.githubusercontent.com/u/36822895?v=4", "events_url": "https://api.github.com/users/RR-28023/events{/privacy}", "followers_url": "https://api.github.com/users/RR-28023/followers", "following_url": "https://api.github.com/users/RR-28023/following{/other_user}", "gists_url": "https://api.github.com/users/RR-28023/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/RR-28023", "id": 36822895, "login": "RR-28023", "node_id": "MDQ6VXNlcjM2ODIyODk1", "organizations_url": "https://api.github.com/users/RR-28023/orgs", "received_events_url": "https://api.github.com/users/RR-28023/received_events", "repos_url": "https://api.github.com/users/RR-28023/repos", "site_admin": false, "starred_url": "https://api.github.com/users/RR-28023/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/RR-28023/subscriptions", "type": "User", "url": "https://api.github.com/users/RR-28023", "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	[ "Hi ! Could it be an Out of Memory issue that could have killed one of the processes ? can you check your memory ?", "Hi! I don't think it is a memory issue. I'm monitoring the main and spawn python processes and threads with `htop` and the memory does not peak. Besides, the example I've posted above should not be that demanding in terms of memory, right? (I have 32GB of RAM). ", "Indeed it should be fine. I couldn't reproduce the error though - I ran your script on my side and it works fine. What version of pytorch are you using ?", "Interesting.. I'm using `torch 1.12.1`", "I also tried on colab and it works fine 🤔 \r\nMaybe something is wrong with your installation of pytorch ?", "Oh actually I just saw that you're using python 3.9\r\n\r\nThis could be related to https://github.com/huggingface/datasets/issues/4113\r\n\r\nWe'll fix that as soon as we can, in the meantime you can try to use use single process, or use an older version of python maybe ?", "I tried with python 3.7 and the issue persists. In collab, which also uses 3.7 I don't get the issue, so yes I guess is something on mu side... will post it here if I manage to fix it", "Hi! Which version of transformers are you using? I test the code on Colab (so python 3.7) with transformers 4.23.1, torch 1.12.1 and pyarrow 9.0.0 (also 6.x), it worked without stuck.", "Hi, I have the same problem in use datasets.IterableDatasetDict.map()\r\nmy pytorch is 2.0.0a0+gitc263bd4\r\nmy python is 3.8.16(default, Jun 12 2023, 17:37:21)\r\nwork on aarch64 in 16 node, each node with 4*nVidia-A100-40G\r\nevery node have 4 process execute code as ↓\r\n\r\n```\r\nfrom datasets import load_dataset, interleave_datasets, IterableDatasetDict, concatenate_datasets\r\n```\r\n...\r\n```\r\n model_args.cache_dir = '/home/scx/.cache'\r\n for dataset_name in data_args.datasets_name:\r\n train_datasets.append(\r\n load_dataset(\r\n dataset_name,\r\n cache_dir=model_args.cache_dir,\r\n use_auth_token=True if model_args.use_auth_token else None,\r\n streaming=data_args.streaming,\r\n split='train'\r\n ).select_columns('text')\r\n )\r\n valid_datasets.append(\r\n load_dataset(\r\n dataset_name,\r\n cache_dir=model_args.cache_dir,\r\n use_auth_token=True if model_args.use_auth_token else None,\r\n streaming=data_args.streaming,\r\n split='validation'\r\n ).select_columns('text')\r\n )\r\n train_dataset = interleave_datasets(train_datasets,\r\n probabilities=data_args.datasets_probabilities, \r\n seed=training_args.seed,\r\n stopping_strategy='all_exhausted')\r\n raw_datasets = IterableDatasetDict({'train': train_dataset, 'validation': valid_dataset})\r\n```\r\n...\r\n\r\n```\r\n tokenized_datasets = None\r\n with training_args.main_process_first(desc=\"dataset map tokenization\"):\r\n if not data_args.streaming:\r\n tokenized_datasets = raw_datasets.map(\r\n tokenize_function,\r\n batched=True,\r\n num_proc=data_args.preprocessing_num_workers,\r\n load_from_cache_file=not data_args.overwrite_cache,\r\n desc=\"Running tokenizer on dataset\",\r\n remove_columns=column_names,\r\n )\r\n else:\r\n #TODO 20230722\r\n logger.info('{}: {}'.format(__file__, 'tokenized_datasets = raw_datasets.map('))\r\n logger.info('len raw_datasets: {}'.format(len(raw_datasets.items())))\r\n logger.info('raw_datasets:{}'.format(raw_datasets.items()))\r\n tokenized_datasets = raw_datasets.map(\r\n tokenize_function,\r\n batched=True,\r\n batch_size=1000,\r\n remove_columns=column_names\r\n )\r\n logger.info('map ok!')\r\n logger.info('show train: {}'.format(next(iter(tokenized_datasets['train']))))\r\n logger.info('ok')\r\n # ### RAW CODE ###\r\n # tokenized_datasets = raw_datasets.map(\r\n # tokenize_function,\r\n # batched=True,\r\n # batch_size=1000,\r\n # remove_columns=column_names\r\n # )\r\n #TODO 20230722\r\n logger.info(\"Finish tokenization\")\r\n```\r\nthe output of my code is\r\n```\r\n07/22/2023 21:57:09 - INFO - __main__ - /demo/run_blue_space.py: tokenized_datasets = raw_datasets.map(\r\n07/22/2023 21:57:09 - INFO - __main__ - len raw_datasets: 2\r\n07/22/2023 21:57:09 - INFO - __main__ - raw_datasets:dict_items([('train', <datasets.iterable_dataset.IterableDataset object at 0x4005ee301190>), ('validation', <datasets.iterable_dataset.IterableDataset object at 0x4005ee5427f0>)])\r\n07/22/2023 21:57:09 - INFO - __main__ - map ok!\r\n07/22/2023 22:01:07 - INFO - __main__ - show train: {'input_ids': [14608, 26797, 31891, 34260, 12227, 33207, 5, 5, 31632, 26797, 31891, 34260, 12227, 33207, 7398, 28561, 31236, 31177, 31253, 33558, 31556, 31377, 72, 20732, 32383, 32295, 14027, 31178, 53, 61, 53, 55, 31189, 31146, 31321, 31235, 53, 61, 56, 58, 31189, 31145, 72, 53, 61, 58, 54, 31189, 54, 31245, 53, 60, 31224, 31896, 31178, 28561, 29331, 20732, 31888, 32637, 4426, 2824, 72, 53, 61, 60, 55, 31189, 53, 54, 31245, 53, 31224, 31896, 31178, 28561, 29331, 26137, 20732, 4426, 2824, 73, 54, 52, 52, 52, 31189, 61, 31245, 59, 31224, 31896, 31178, 29331, 28561, 20732, 4426, 2824, 73, 5], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\r\n07/22/2023 22:01:07 - INFO - __main__ - ok\r\n```\r\n\r\n", "@bio-punk `IterableDatasetDict.map` does not support multiprocessing (only `DatasetDict.map` and `Dataset.map` do), so please open a new issue as this doesn't seem to be related to the original issue. ", "Closing as this issue doesn't seem to be related to `datasets`." ]	2022-10-10T13:50:56Z	2023-07-24T15:29:13Z	2023-07-24T15:29:13Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	## Describe the bug There seems to be an issue with using multiprocessing with `datasets.Dataset.map` (i.e. setting `num_proc` to a value greater than one) combined with a function that uses `torch` under the hood. The subprocesses that `datasets.Dataset.map` spawns [a this step](https://github.com/huggingface/datasets/blob/1b935dab9d2f171a8c6294269421fe967eb55e34/src/datasets/arrow_dataset.py#L2663) go into wait mode forever. ## Steps to reproduce the bug The below code goes into deadlock when `NUMBER_OF_PROCESSES` is greater than one. ```python NUMBER_OF_PROCESSES = 2 from transformers import AutoTokenizer, AutoModel from datasets import load_dataset dataset = load_dataset("glue", "mrpc", split="train") tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2") model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2") model.to("cpu") def cls_pooling(model_output): return model_output.last_hidden_state[:, 0] def generate_embeddings_batched(examples): sentences_batch = list(examples['sentence1']) encoded_input = tokenizer( sentences_batch, padding=True, truncation=True, return_tensors="pt" ) encoded_input = {k: v.to("cpu") for k, v in encoded_input.items()} model_output = model(**encoded_input) embeddings = cls_pooling(model_output) examples['embeddings'] = embeddings.detach().cpu().numpy() # 64, 384 return examples embeddings_dataset = dataset.map( generate_embeddings_batched, batched=True, batch_size=10, num_proc=NUMBER_OF_PROCESSES ) ``` While debugging it I've seen that it gets "stuck" when calling `torch.nn.Embedding.forward` but some testing shows that the same happens with other functions from `torch.nn`. ## Environment info - Platform: Linux-5.14.0-1052-oem-x86_64-with-glibc2.31 - Python version: 3.9.14 - PyArrow version: 9.0.0 - Pandas version: 1.5.0 Not sure if this is a HF problem, a PyTorch problem or something I'm doing wrong.. Thanks!	{ "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/5094/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5094/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/4661	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4661/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4661/comments	https://api.github.com/repos/huggingface/datasets/issues/4661/events	https://github.com/huggingface/datasets/issues/4661	1,298,374,944	I_kwDODunzps5NY6Eg	4,661	Concurrency bug when using same cache among several jobs	{ "avatar_url": "https://avatars.githubusercontent.com/u/17202292?v=4", "events_url": "https://api.github.com/users/ioana-blue/events{/privacy}", "followers_url": "https://api.github.com/users/ioana-blue/followers", "following_url": "https://api.github.com/users/ioana-blue/following{/other_user}", "gists_url": "https://api.github.com/users/ioana-blue/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ioana-blue", "id": 17202292, "login": "ioana-blue", "node_id": "MDQ6VXNlcjE3MjAyMjky", "organizations_url": "https://api.github.com/users/ioana-blue/orgs", "received_events_url": "https://api.github.com/users/ioana-blue/received_events", "repos_url": "https://api.github.com/users/ioana-blue/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ioana-blue/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ioana-blue/subscriptions", "type": "User", "url": "https://api.github.com/users/ioana-blue", "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	[ "I can confirm that if I run one job first that processes the dataset, then I can run any jobs in parallel with no problem (no write-concurrency anymore...). ", "Hi! That's weird. It seems like the error points to the `mkstemp` function, but the official docs state the following:\r\n```\r\nThere are no race conditions in the file’s creation, assuming that the platform properly implements the [os.O_EXCL](https://docs.python.org/3/library/os.html#os.O_EXCL) flag for [os.open()](https://docs.python.org/3/library/os.html#os.open)\r\n```\r\nSo this could mean your platform doesn't support that flag.\r\n\r\n~~Can you please check if wrapping the temp file creation (the line `tmp_file = tempfile.NamedTemporaryFile(\"wb\", dir=os.path.dirname(cache_file_name), delete=False)` in `_map_single`) with the `multiprocess.Lock` fixes the issue?~~\r\nPerhaps wrapping the temp file creation in `_map_single` with `filelock` could work:\r\n```python\r\nwith FileLock(lock_path):\r\n tmp_file = tempfile.NamedTemporaryFile(\"wb\", dir=os.path.dirname(cache_file_name), delete=False)\r\n```\r\nCan you please check if that helps?", "Edit: while writing my comment I took the time the read previous comments. By wrapping `dl_manager.download_and_extract` with a FileLock it works like a charm ! Thx @mariosasko \n\nOS : MacOS 14.7.4 (intel)\nPython : 3.12\ndatasets : 3.5.0\n\nAdding to this, I had a similar problem when 2 process concurrently load different subsets of the same dataset that needs to be extracted. The use case is similar as OP : running a benchmark.\n\nThe dataloader needs to download and extract a zip file, with ~20 ~100Mo files.\n\nWhen 2 processes executes `load_dataset(\"TESTLOAD.py\", name=\"a\", trust_remote_code=True)` at the same time it is fine (there must some lock on `(\"TESTLOAD.py\", \"a\")`).\nBut when running `load_dataset(\"TESTLOAD.py\", name=\"a\", trust_remote_code=True)` and `load_dataset(\"TESTLOAD.py\", name=\"b\", trust_remote_code=True)` (cf. `test.py`.\n\nHere is what I managed to understand as a table using the scripts below. Step 3 is attested by the `os.listdir` in `TESTLOAD.py`.\n\n\| steps \| process a \| process b \|\n\|---\|---\|---\|\n\| 1 \| download \| wait \|\n\| 2 \| end of download \| wait \|\n\| 3 \| extracting \| FAIL to open a not yet extracted file \|\n\| 4 \| end of extraction \| KO \|\n\| 5 \| OK \| KO \|\n\n<details>\n\n<summary>TESTLOAD.py (dataloader)</summary>\n\n```python\nimport os\nimport datasets\n# from filelock import FileLock\n_URL = \"/Users/ygallina/Documents/dr-benchmark/GSC-v1.1_big.zip\"\n\nclass TESTLOAD(datasets.GeneratorBasedBuilder):\n\n\tBUILDER_CONFIGS = [\n\t\tdatasets.BuilderConfig(name='a'),\n\t\tdatasets.BuilderConfig(name='b')\n\t]\n\n\tdef _info(self):\n\t\tfeatures = datasets.Features({\n\t\t\t\"id\": datasets.Value(\"string\"),\n\t\t})\n\t\treturn datasets.DatasetInfo(features=features)\n\n\tdef _split_generators(self, dl_manager):\n\t\tc2p = {'a': \"Medline_GSC_en_fr_man.xml\", 'b': \"Medline_GSC_en_es_man.xml\"}\n\t\t# with FileLock(\"path/to/tmp.lock\"):\n\t\tdata_dir = dl_manager.download_and_extract(_URL)\n\n\t\tprint(os.listdir(data_dir))\n\n\t\tdata_dir = data_dir + \"/\" + c2p[self.config.name]\n\t\treturn [datasets.SplitGenerator(\n\t\t\tname=datasets.Split.TRAIN,\n\t\t\tgen_kwargs={\"data_dir\": data_dir}\n\t\t)]\n\n\tdef _generate_examples(self, data_dir):\n\t\tf = open(data_dir)\n\t\tf.close()\n\t\tyield 0, {'id': data_dir}\n```\n\n</details>\n\n\n<details>\n\n<summary>test.py (main file)</summary>\n\nCommands to execute the test\n```bash\nrm -rf \"test\"\nHF_HOME=\"test\" python test.py\n```\n\n```python\nimport os\nfrom datasets import load_dataset\n\n# Forking to make sure access will be concurrent\n# Waiting to fork after imports (because it takes a while)\nchild = os.fork()\n\nif child:\n print('child')\n ds = load_dataset('TESTLOAD.py', 'a', trust_remote_code=True)\n print(f\"child, {ds['train'][0]}\")\nelse:\n print('parent')\n ds = load_dataset('TESTLOAD.py', 'b', trust_remote_code=True)\n print(f\"parent, {ds['train'][0]}\")\n```\n\n</details>" ]	2022-07-08T01:58:11Z	2025-04-10T13:21:23Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	## Describe the bug I used to see this bug with an older version of the datasets. It seems to persist. This is my concrete scenario: I launch several evaluation jobs on a cluster in which I share the file system and I share the cache directory used by huggingface libraries. The evaluation jobs read the same .csv files. If my jobs get all scheduled pretty much at the same time, there are all kinds of weird concurrency errors. Sometime it crashes silently. This time I got lucky that it crashed with a stack trace that I can share and maybe you get to the bottom of this. If you don't have a similar setup available, it may be hard to reproduce as you really need two jobs accessing the same file at the same time to see this type of bug. ## Steps to reproduce the bug I'm running a modified version of `run_glue.py` script adapted to my use case. I've seen the same problem when running some glue datasets as well (so it's not specific to loading the datasets from csv files). ## Expected results No crash, concurrent access to the (intermediate) files just fine. ## Actual results Crashes due to races/concurrency bugs. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-4.18.0-348.23.1.el8_5.x86_64-x86_64-with-glibc2.10 - Python version: 3.8.5 - PyArrow version: 8.0.0 - Pandas version: 1.1.0 Stack trace that I just got with the crash (I've obfuscated some names, it should still be quite informative): ``` Running tokenizer on dataset: 0%\| \| 0/3 [00:00<?, ?ba/s] Traceback (most recent call last): File "../../src/models//run_****.py", line 600, in <module> main() File "../../src/models//run_***.py", line 444, in main raw_datasets = raw_datasets.map( File "/***//envs/tr-crt/lib/python3.8/site-packages/datasets/dataset_dict.py", line 770, in map { File "/***//envs/tr-crt/lib/python3.8/site-packages/datasets/dataset_dict.py", line 771, in <dictcomp> k: dataset.map( File "/***//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2376, in map return self._map_single( File "/****/envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 551, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) File "/****//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 518, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) File "/****/envs/tr-crt/lib/python3.8/site-packages/datasets/fingerprint.py", line 458, in wrapper out = func(self, args, kwargs) File "/***//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2776, in _map_single buf_writer, writer, tmp_file = init_buffer_and_writer() File "/***//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2696, in init_buffer_and_writer tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(cache_file_name), delete=False) File "/***//envs/tr-crt/lib/python3.8/tempfile.py", line 541, in NamedTemporaryFile (fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type) File "/***//envs/tr-crt/lib/python3.8/tempfile.py", line 250, in _mkstemp_inner fd = _os.open(file, flags, 0o600) FileNotFoundError: [Errno 2] No such file or directory: '/*****/cache-transformers//transformers/csv/default-ef9cd184210742a7/0.0.0/51cce309a08df9c4d82ffd9363bbe090bf173197fc01a71b034e8594995a1a58/tmps8l6j5yc' ``` As I ran 100s of experiments last year for an empirical paper, I ran into this type of bugs several times. I found several bandaid/work-arounds, e.g., run one job first that caches the dataset => eliminate concurrency; OR use unique caches => eliminate concurrency (but increase storage space), etc. and it all works fine. I'd like to help you fixing this bug as it's really annoying to always apply the work arounds. Let me know what other info from my side could help you figure out the issue. Thanks for your help!	null	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 1, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/4661/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4661/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/4778	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4778/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4778/comments	https://api.github.com/repos/huggingface/datasets/issues/4778/events	https://github.com/huggingface/datasets/pull/4778	1,324,928,750	PR_kwDODunzps48dRPh	4,778	Update local loading script 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 docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4778). All of your documentation changes will be reflected on that endpoint.", "I would rather have a section in the docs that explains how to modify the script of an existing dataset (`inspect_dataset` + modification + `load_dataset`) instead of focusing on the GH datasets bundled with the source (only applicable for devs).", "Good idea! I went with @mariosasko's suggestion to use `inspect_dataset` instead of cloning a dataset repository since it's a good opportunity to show off more of the library's lesser-known functions if that's ok with everyone :)", "One advantage of cloning the repo is that it fetches potential data files referenced inside a script using relative paths, so if we decide to use `inspect_dataset`, we should at least add a tip to explain this limitation and how to circumvent it.", "Oh you're right. Calling `load_dataset` on the modified script without having the files that come with it is not ideal. I agree it should be `git clone` instead - and inspect is for inspection only ^^'" ]	2022-08-01T20:21:07Z	2022-08-23T16:32:26Z	2022-08-23T16:32:22Z	MEMBER	null	null	null	This PR clarifies the local loading script section to include how to load a dataset after you've modified the local loading script (closes #4732).	{ "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/4778/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4778/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4778.diff", "html_url": "https://github.com/huggingface/datasets/pull/4778", "merged_at": "2022-08-23T16:32:22Z", "patch_url": "https://github.com/huggingface/datasets/pull/4778.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4778" }
https://api.github.com/repos/huggingface/datasets/issues/4620	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4620/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4620/comments	https://api.github.com/repos/huggingface/datasets/issues/4620/events	https://github.com/huggingface/datasets/issues/4620	1,292,797,878	I_kwDODunzps5NDoe2	4,620	Data type is not recognized when using datetime.time	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	[ { "color": "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/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	[ "cc @mariosasko ", "Hi, thanks for reporting! I'm investigating the issue." ]	2022-07-04T08:13:38Z	2022-07-07T13:57:11Z	2022-07-07T13:57:11Z	COLLABORATOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	## Describe the bug Creating a dataset from a pandas dataframe with `datetime.time` format generates an error. ## Steps to reproduce the bug ```python import pandas as pd from datetime import time from datasets import Dataset df = pd.DataFrame({"feature_name": [time(1, 1, 1)]}) dataset = Dataset.from_pandas(df) ``` ## Expected results The dataset should be created. ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 823, in from_pandas return cls(table, info=info, split=split) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 679, in __init__ inferred_features = Features.from_arrow_schema(arrow_table.schema) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1551, in from_arrow_schema obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1551, in <dictcomp> obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1315, in generate_from_arrow_type return Value(dtype=_arrow_to_datasets_dtype(pa_type)) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 83, in _arrow_to_datasets_dtype return f"time64[{arrow_type.unit}]" AttributeError: 'pyarrow.lib.DataType' object has no attribute 'unit' ``` ## Environment info - `datasets` version: 2.3.3.dev0 - Platform: Linux-5.13.0-1031-aws-x86_64-with-glibc2.31 - Python version: 3.9.6 - PyArrow version: 7.0.0 - Pandas version: 1.4.2	{ "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/4620/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4620/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6270	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6270/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6270/comments	https://api.github.com/repos/huggingface/datasets/issues/6270/events	https://github.com/huggingface/datasets/issues/6270	1,920,329,373	I_kwDODunzps5ydead	6,270	Dataset.from_generator raises with sharded gen_args	{ "avatar_url": "https://avatars.githubusercontent.com/u/53510?v=4", "events_url": "https://api.github.com/users/hartmans/events{/privacy}", "followers_url": "https://api.github.com/users/hartmans/followers", "following_url": "https://api.github.com/users/hartmans/following{/other_user}", "gists_url": "https://api.github.com/users/hartmans/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/hartmans", "id": 53510, "login": "hartmans", "node_id": "MDQ6VXNlcjUzNTEw", "organizations_url": "https://api.github.com/users/hartmans/orgs", "received_events_url": "https://api.github.com/users/hartmans/received_events", "repos_url": "https://api.github.com/users/hartmans/repos", "site_admin": false, "starred_url": "https://api.github.com/users/hartmans/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/hartmans/subscriptions", "type": "User", "url": "https://api.github.com/users/hartmans", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "`gen_kwargs` should be a `dict`, as stated in the docstring, but you are passing a `list`.\r\n\r\nSo, to fix the error, replace the list of dicts with a dict of lists (and slightly modify the generator function):\r\n```python\r\nfrom pathlib import Path\r\nimport datasets\r\n\r\ndef process_yaml(files):\r\n for f in files:\r\n # process\r\n yield dict(...)\r\n\r\n\r\nif __name__ == '__main__':\r\n import sys\r\n dir = Path(sys.argv[0]).parent\r\n ds = datasets.Dataset.from_generator(process_yaml, gen_kwargs={'files': [f for f in dir.glob('.yml')]})\r\n ds.to_json('training.jsonl')\r\n```", "That runs, and because my dataset is small, it's what I did to get past the problem.\r\nHowever, it does not produce a sharded dataset. From the doc string I expect there ought to be a way to call from_generator such that num_shards in the resulting data set is equal to the number of items in the list.\r\nThe part of the doc string that your suggestion is not responsive to is:\r\n` You can define a sharded dataset by passing the list of shards in g\r\nen_kwargs.\r\n`\r\n\r\nWhat your suggestion does is calls the generator once, with the list argument, and produces a single shard dataset.\r\n", "The sharding mentioned here refers to using this function with `num_proc` (multiprocessing splits the `kwargs` into shards and passes them to the generator function)\r\n\r\n> That runs, and because my dataset is small, it's what I did to get past the problem.\r\n\r\n`from_generator` generates a memory-mapped dataset (can be larger than RAM), so the dataset size should not be an issue unless the generator function's implementation does not properly free the memory.\r\n", "It sounds like you are saying that num_proc affects the form of gen_kwargs.\r\nAre you saying that for non-zero num_proc gen_kwargs should be a list whose length is the same as num_proc?\r\nOr are you saying that for non-zero num_proc, gen_kwargs should be a dict whose elements are lists the length of num_proc?\r\n", "I ran some tests. So, it looks like with num_proc greater than 1, gen_kwargs is expected to be a dict of lists. It calls the generator also with a dict of lists, but the lists are split.\r\nI.E. if my original has `gen_kwargs=dict(a=[0,1,2])`, then my generator might get called with `gen_kwalrgs=dict([0])`.\r\nThat all makes sense, but I definitely think there is room for improvement in the doc string here.\r\nIn order to suggest improvements to the doc string, I need to look at how the gen_kwargs are split, and figure out if:\r\n num_proc needs to exactly equal the length of the lists\r\n* num_proc needs to evenly divide the length of the lists\r\n* Or there's no required relationship.\r\nI'll look into that and then propose an improved doc string if no one else gets to it first.", "Okay, that was fun; I took a dive through the dataset code and feel like I have a much better understanding.\r\nHere is my understanding of the behavior:\r\n* max_proc is an upper limit on the number of shards that `from_generator` produces\r\n* If `max_proc` is greater than 1, then all lists in gen_kwargs must be the same length\r\n* If the lists in gen_kwargs are shorter than num_proc elements, num_proc will be reduced and a warning produced. Put another way, `min(list_length, num_shards)` shards will be produced\r\n* The members of the lists in gen_kwargs will be partitioned among the created jobs.\r\nTo validate the above, take a look at\r\n`_number_of_shards_in_gen_kwargs` and `_distribute_shards` and `_split_gen_kwargs` in utils/sharding.py.\r\nI've also chased down starting at from_generator all the way through to GeneratorBuilder and the calls to the functions in sharding.py.\r\nTomorrow I'll take a look at the contributing guidelines and see what's involved in putting together a PR to improve the doc string." ]	2023-09-30T16:50:06Z	2023-10-11T20:29:12Z	2023-10-11T20:29:11Z	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug According to the docs of Datasets.from_generator: ``` gen_kwargs(`dict`, optional): Keyword arguments to be passed to the `generator` callable. You can define a sharded dataset by passing the list of shards in `gen_kwargs`. ``` So I'd expect that if gen_kwargs was a list, then my generator would be called once for each element in the list with the dict in the list for that element. It doesn't work that way though. ### Steps to reproduce the bug ```python #!/usr/bin/python from pathlib import Path import datasets def process_yaml(file): yield dict(example=42) if __name__ == '__main__': import sys dir = Path(sys.argv[0]).parent ds = datasets.Dataset.from_generator(process_yaml, gen_kwargs=[{'file':f} for f in dir.glob('.yml')], ) ds.to_json('training.jsonl') ``` ``` Generating train split: 0 examples [00:00, ? examples/s] Traceback (most recent call last): File "/tmp/dataset_bug.py", line 13, in <module> ds = datasets.Dataset.from_generator(process_yaml, gen_kwargs=[{'file':f} for f in dir.glob('.yml')], ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 1072, in from_generator ).read() ^^^^^^ File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/io/generator.py", line 47, in read self.builder.download_and_prepare( File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 1717, in _download_and_prepare super()._download_and_prepare( File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, prepare_split_kwargs) File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 1555, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 1656, in _prepare_split_single generator = self._generate_examples(gen_kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: datasets.packaged_modules.generator.generator.Generator._generate_examples() argument after ** must be a ``` mapping, not list ### Expected behavior I would expect that process_yaml would be called once for each yaml file in the directory where the script is run. I also tried with the list being in gen_kwargs, but in that case process_yaml gets called with a list. ### Environment info - `datasets` version: 2.14.6.dev0 (git commit 0cc77d7f45c7369; also tested with 2.14.0) - Platform: Linux-6.1.0-10-amd64-x86_64-with-glibc2.36 - Python version: 3.11.2 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6270/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6270/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6152	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6152/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6152/comments	https://api.github.com/repos/huggingface/datasets/issues/6152/events	https://github.com/huggingface/datasets/issues/6152	1,852,494,646	I_kwDODunzps5uatM2	6,152	FolderBase Dataset automatically resolves under current directory when data_dir is not specified	{ "avatar_url": "https://avatars.githubusercontent.com/u/11533479?v=4", "events_url": "https://api.github.com/users/npuichigo/events{/privacy}", "followers_url": "https://api.github.com/users/npuichigo/followers", "following_url": "https://api.github.com/users/npuichigo/following{/other_user}", "gists_url": "https://api.github.com/users/npuichigo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/npuichigo", "id": 11533479, "login": "npuichigo", "node_id": "MDQ6VXNlcjExNTMzNDc5", "organizations_url": "https://api.github.com/users/npuichigo/orgs", "received_events_url": "https://api.github.com/users/npuichigo/received_events", "repos_url": "https://api.github.com/users/npuichigo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/npuichigo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/npuichigo/subscriptions", "type": "User", "url": "https://api.github.com/users/npuichigo", "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" } ]	open	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/126772439?v=4", "events_url": "https://api.github.com/users/debrupf2946/events{/privacy}", "followers_url": "https://api.github.com/users/debrupf2946/followers", "following_url": "https://api.github.com/users/debrupf2946/following{/other_user}", "gists_url": "https://api.github.com/users/debrupf2946/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/debrupf2946", "id": 126772439, "login": "debrupf2946", "node_id": "U_kgDOB45k1w", "organizations_url": "https://api.github.com/users/debrupf2946/orgs", "received_events_url": "https://api.github.com/users/debrupf2946/received_events", "repos_url": "https://api.github.com/users/debrupf2946/repos", "site_admin": false, "starred_url": "https://api.github.com/users/debrupf2946/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/debrupf2946/subscriptions", "type": "User", "url": "https://api.github.com/users/debrupf2946", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/126772439?v=4", "events_url": "https://api.github.com/users/debrupf2946/events{/privacy}", "followers_url": "https://api.github.com/users/debrupf2946/followers", "following_url": "https://api.github.com/users/debrupf2946/following{/other_user}", "gists_url": "https://api.github.com/users/debrupf2946/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/debrupf2946", "id": 126772439, "login": "debrupf2946", "node_id": "U_kgDOB45k1w", "organizations_url": "https://api.github.com/users/debrupf2946/orgs", "received_events_url": "https://api.github.com/users/debrupf2946/received_events", "repos_url": "https://api.github.com/users/debrupf2946/repos", "site_admin": false, "starred_url": "https://api.github.com/users/debrupf2946/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/debrupf2946/subscriptions", "type": "User", "url": "https://api.github.com/users/debrupf2946", "user_view_type": "public" } ]	null	[ "@lhoestq ", "Makes sense, I guess this can be fixed in the load_dataset_builder method.\r\nIt concerns every packaged builder I think (see values in `_PACKAGED_DATASETS_MODULES`)", "I think the behavior is related to these lines, which short circuited the error handling.\r\nhttps://github.com/huggingface/datasets/blob/664a1cb72ea1e6ef7c47e671e2686ca4a35e8d63/src/datasets/load.py#L946-L952\r\n\r\nSo should data_dir be checked here or still delegating to actual `DatasetModule`? In that case, how to properly set `data_files` here.", "This is location in PackagedDatasetModuleFactory.get_module seems the be the right place to check if at least data_dir or data_files are passed", "@mariosasko can you please assign this issue to me,I want to work on this", "#self-assign", "@mariosasko is this issue still open? i would love to kickstart my journey to open source with this issue!\r\nRegards\r\nzutarich", "@zutarich It is unless @debrupf2946 is working on it.", "#self-assign", "I am working and will open a pull request soon @Etelis \r\n", "@mariosasko can i take this up? ", "#self-assign", "Yes, feel free to work on this :)", "i think its working as expected . Heres the log i get for the same line -\r\n\r\n![image](https://github.com/huggingface/datasets/assets/63234112/8a857ec5-8dd0-4b01-b3a7-7c93444b9558)\r\n", "#self-assign", "Hey! I saw that PR #7314 fixed the issue when no matching data files are found, which is great. But I think the builder still defaults to the current directory when `data_dir` isn't specified, which could lead to unexpected behavior.\n\nIf no one’s actively working on that part, I’d be happy to pick it up and propose a fix. Let me know if that works!\n", "Cool, feel free to ping me if you open a PR @aniyagnik :)" ]	2023-08-16T04:38:09Z	2025-04-08T13:13:03Z	null	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug FolderBase Dataset automatically resolves under current directory when data_dir is not specified. For example: ``` load_dataset("audiofolder") ``` takes long time to resolve and collect data_files from current directory. But I think it should reach out to this line for error handling https://github.com/huggingface/datasets/blob/cb8c5de5145c7e7eee65391cb7f4d92f0d565d62/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L58-L59 ### Steps to reproduce the bug ``` load_dataset("audiofolder") ``` ### Expected behavior Error report ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.15.0-78-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3	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/6152/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6152/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7474	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7474/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7474/comments	https://api.github.com/repos/huggingface/datasets/issues/7474/events	https://github.com/huggingface/datasets/pull/7474	2,945,066,258	PR_kwDODunzps6P91lM	7,474	Remove conditions for Python < 3.9	{ "avatar_url": "https://avatars.githubusercontent.com/u/17618148?v=4", "events_url": "https://api.github.com/users/cyyever/events{/privacy}", "followers_url": "https://api.github.com/users/cyyever/followers", "following_url": "https://api.github.com/users/cyyever/following{/other_user}", "gists_url": "https://api.github.com/users/cyyever/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/cyyever", "id": 17618148, "login": "cyyever", "node_id": "MDQ6VXNlcjE3NjE4MTQ4", "organizations_url": "https://api.github.com/users/cyyever/orgs", "received_events_url": "https://api.github.com/users/cyyever/received_events", "repos_url": "https://api.github.com/users/cyyever/repos", "site_admin": false, "starred_url": "https://api.github.com/users/cyyever/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/cyyever/subscriptions", "type": "User", "url": "https://api.github.com/users/cyyever", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7474). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Thanks ! can you run `make style` to fix code formatting ? then we can merge", "@lhoestq Done" ]	2025-03-25T03:08:04Z	2025-04-16T00:11:06Z	2025-04-15T16:07:55Z	CONTRIBUTOR	null	null	null	This PR remove conditions for Python < 3.9.	{ "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/7474/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7474/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7474.diff", "html_url": "https://github.com/huggingface/datasets/pull/7474", "merged_at": "2025-04-15T16:07:54Z", "patch_url": "https://github.com/huggingface/datasets/pull/7474.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7474" }
https://api.github.com/repos/huggingface/datasets/issues/6513	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6513/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6513/comments	https://api.github.com/repos/huggingface/datasets/issues/6513/events	https://github.com/huggingface/datasets/issues/6513	2,048,869,151	I_kwDODunzps56H0Mf	6,513	Support huggingface-hub 0.20.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[]	2023-12-19T15:15:46Z	2023-12-20T08:44:45Z	2023-12-20T08:44:45Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	CI to test the support of `huggingface-hub` 0.20.0: https://github.com/huggingface/datasets/compare/main...ci-test-huggingface-hub-v0.20.0.rc1 We need to merge: - #6510 - #6512 - #6516	{ "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/6513/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6513/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6337	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6337/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6337/comments	https://api.github.com/repos/huggingface/datasets/issues/6337/events	https://github.com/huggingface/datasets/pull/6337	1,956,875,259	PR_kwDODunzps5dg9Uu	6,337	Pin supported upper version of fsspec	{ "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	[ "<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) \| 0.006915 / 0.011353 (-0.004438) \| 0.004110 / 0.011008 (-0.006898) \| 0.084392 / 0.038508 (0.045884) \| 0.079649 / 0.023109 (0.056540) \| 0.305760 / 0.275898 (0.029862) \| 0.343968 / 0.323480 (0.020488) \| 0.005402 / 0.007986 (-0.002584) \| 0.003342 / 0.004328 (-0.000986) \| 0.064774 / 0.004250 (0.060523) \| 0.055919 / 0.037052 (0.018866) \| 0.315194 / 0.258489 (0.056705) \| 0.355014 / 0.293841 (0.061173) \| 0.032140 / 0.128546 (-0.096406) \| 0.008865 / 0.075646 (-0.066781) \| 0.287684 / 0.419271 (-0.131588) \| 0.053504 / 0.043533 (0.009971) \| 0.306852 / 0.255139 (0.051713) \| 0.331125 / 0.283200 (0.047925) \| 0.023476 / 0.141683 (-0.118207) \| 1.506590 / 1.452155 (0.054435) \| 1.574508 / 1.492716 (0.081792) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.239987 / 0.018006 (0.221981) \| 0.459144 / 0.000490 (0.458654) \| 0.008509 / 0.000200 (0.008309) \| 0.000335 / 0.000054 (0.000280) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028353 / 0.037411 (-0.009058) \| 0.082345 / 0.014526 (0.067819) \| 0.499524 / 0.176557 (0.322967) \| 0.152896 / 0.737135 (-0.584239) \| 0.096978 / 0.296338 (-0.199360) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.404855 / 0.215209 (0.189646) \| 4.053103 / 2.077655 (1.975448) \| 2.069638 / 1.504120 (0.565518) \| 1.917354 / 1.541195 (0.376159) \| 2.035816 / 1.468490 (0.567326) \| 0.480358 / 4.584777 (-4.104419) \| 3.594316 / 3.745712 (-0.151396) \| 3.582952 / 5.269862 (-1.686910) \| 2.101142 / 4.565676 (-2.464535) \| 0.057004 / 0.424275 (-0.367271) \| 0.007715 / 0.007607 (0.000108) \| 0.487417 / 0.226044 (0.261372) \| 4.863100 / 2.268929 (2.594172) \| 2.569038 / 55.444624 (-52.875587) \| 2.187167 / 6.876477 (-4.689310) \| 2.270034 / 2.142072 (0.127962) \| 0.578095 / 4.805227 (-4.227132) \| 0.133283 / 6.500664 (-6.367381) \| 0.060164 / 0.075469 (-0.015305) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.269120 / 1.841788 (-0.572667) \| 19.493072 / 8.074308 (11.418764) \| 14.560576 / 10.191392 (4.369184) \| 0.167440 / 0.680424 (-0.512984) \| 0.018493 / 0.534201 (-0.515708) \| 0.392774 / 0.579283 (-0.186509) \| 0.420903 / 0.434364 (-0.013461) \| 0.461904 / 0.540337 (-0.078433) \| 0.643104 / 1.386936 (-0.743832) \|\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) \| 0.006985 / 0.011353 (-0.004368) \| 0.004246 / 0.011008 (-0.006762) \| 0.066246 / 0.038508 (0.027738) \| 0.080757 / 0.023109 (0.057648) \| 0.391774 / 0.275898 (0.115876) \| 0.424957 / 0.323480 (0.101478) \| 0.005575 / 0.007986 (-0.002411) \| 0.003447 / 0.004328 (-0.000881) \| 0.066565 / 0.004250 (0.062315) \| 0.057597 / 0.037052 (0.020544) \| 0.394663 / 0.258489 (0.136174) \| 0.430310 / 0.293841 (0.136469) \| 0.032746 / 0.128546 (-0.095800) \| 0.008783 / 0.075646 (-0.066863) \| 0.071940 / 0.419271 (-0.347331) \| 0.048877 / 0.043533 (0.005344) \| 0.390269 / 0.255139 (0.135130) \| 0.411867 / 0.283200 (0.128668) \| 0.024101 / 0.141683 (-0.117582) \| 1.507370 / 1.452155 (0.055215) \| 1.585810 / 1.492716 (0.093093) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222796 / 0.018006 (0.204790) \| 0.459035 / 0.000490 (0.458546) \| 0.005322 / 0.000200 (0.005122) \| 0.000099 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033237 / 0.037411 (-0.004174) \| 0.098244 / 0.014526 (0.083718) \| 0.106654 / 0.176557 (-0.069903) \| 0.159675 / 0.737135 (-0.577460) \| 0.108470 / 0.296338 (-0.187869) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429085 / 0.215209 (0.213876) \| 4.281206 / 2.077655 (2.203551) \| 2.320492 / 1.504120 (0.816372) \| 2.153218 / 1.541195 (0.612024) \| 2.287122 / 1.468490 (0.818632) \| 0.497307 / 4.584777 (-4.087470) \| 3.799541 / 3.745712 (0.053828) \| 3.380053 / 5.269862 (-1.889809) \| 2.100009 / 4.565676 (-2.465667) \| 0.057988 / 0.424275 (-0.366287) \| 0.007381 / 0.007607 (-0.000226) \| 0.506843 / 0.226044 (0.280798) \| 5.071286 / 2.268929 (2.802357) \| 2.750487 / 55.444624 (-52.694137) \| 2.415613 / 6.876477 (-4.460864) \| 2.667144 / 2.142072 (0.525072) \| 0.624889 / 4.805227 (-4.180338) \| 0.134191 / 6.500664 (-6.366473) \| 0.060704 / 0.075469 (-0.014765) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.353074 / 1.841788 (-0.488714) \| 20.507074 / 8.074308 (12.432766) \| 14.911788 / 10.191392 (4.720396) \| 0.149248 / 0.680424 (-0.531176) \| 0.020593 / 0.534201 (-0.513608) \| 0.398458 / 0.579283 (-0.180825) \| 0.434846 / 0.434364 (0.000482) \| 0.478853 / 0.540337 (-0.061484) \| 0.648072 / 1.386936 (-0.738864) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b30c72a2d3d9c191a590e0f0a6b3a6363ab15e8f \"CML watermark\")\n", "In particular I expect fsspec to do another breaking change in the next release (switch to glob.glob)", "_The documentation is not available anymore as the PR was closed or merged._", "see https://github.com/huggingface/datasets/pull/6338", "Yes, unfortunately breaking changes are quite usual from their part.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006099 / 0.011353 (-0.005253) \| 0.003672 / 0.011008 (-0.007336) \| 0.083095 / 0.038508 (0.044587) \| 0.059607 / 0.023109 (0.036498) \| 0.319591 / 0.275898 (0.043693) \| 0.351945 / 0.323480 (0.028465) \| 0.004785 / 0.007986 (-0.003201) \| 0.002965 / 0.004328 (-0.001364) \| 0.062907 / 0.004250 (0.058657) \| 0.049122 / 0.037052 (0.012070) \| 0.344641 / 0.258489 (0.086152) \| 0.361519 / 0.293841 (0.067678) \| 0.027254 / 0.128546 (-0.101292) \| 0.008081 / 0.075646 (-0.067565) \| 0.261569 / 0.419271 (-0.157702) \| 0.045101 / 0.043533 (0.001568) \| 0.313645 / 0.255139 (0.058506) \| 0.337843 / 0.283200 (0.054644) \| 0.020968 / 0.141683 (-0.120715) \| 1.438450 / 1.452155 (-0.013705) \| 1.507567 / 1.492716 (0.014850) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230826 / 0.018006 (0.212820) \| 0.434363 / 0.000490 (0.433873) \| 0.008210 / 0.000200 (0.008010) \| 0.000212 / 0.000054 (0.000157) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025278 / 0.037411 (-0.012133) \| 0.073659 / 0.014526 (0.059133) \| 0.085147 / 0.176557 (-0.091409) \| 0.145451 / 0.737135 (-0.591684) \| 0.086400 / 0.296338 (-0.209939) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429887 / 0.215209 (0.214678) \| 4.292626 / 2.077655 (2.214971) \| 2.266824 / 1.504120 (0.762704) \| 2.090472 / 1.541195 (0.549277) \| 2.186477 / 1.468490 (0.717987) \| 0.503684 / 4.584777 (-4.081093) \| 3.100791 / 3.745712 (-0.644921) \| 3.008938 / 5.269862 (-2.260923) \| 1.885559 / 4.565676 (-2.680118) \| 0.057434 / 0.424275 (-0.366841) \| 0.006639 / 0.007607 (-0.000969) \| 0.506579 / 0.226044 (0.280535) \| 5.058905 / 2.268929 (2.789977) \| 2.708321 / 55.444624 (-52.736304) \| 2.367388 / 6.876477 (-4.509089) \| 2.422660 / 2.142072 (0.280587) \| 0.587562 / 4.805227 (-4.217665) \| 0.125260 / 6.500664 (-6.375404) \| 0.061856 / 0.075469 (-0.013613) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.280495 / 1.841788 (-0.561292) \| 17.968873 / 8.074308 (9.894565) \| 13.922838 / 10.191392 (3.731446) \| 0.149907 / 0.680424 (-0.530517) \| 0.016736 / 0.534201 (-0.517465) \| 0.333417 / 0.579283 (-0.245866) \| 0.367710 / 0.434364 (-0.066654) \| 0.389648 / 0.540337 (-0.150690) \| 0.535625 / 1.386936 (-0.851311) \|\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) \| 0.006237 / 0.011353 (-0.005116) \| 0.003787 / 0.011008 (-0.007221) \| 0.062536 / 0.038508 (0.024028) \| 0.062335 / 0.023109 (0.039226) \| 0.455209 / 0.275898 (0.179311) \| 0.488961 / 0.323480 (0.165482) \| 0.004875 / 0.007986 (-0.003111) \| 0.002961 / 0.004328 (-0.001368) \| 0.063045 / 0.004250 (0.058795) \| 0.048624 / 0.037052 (0.011571) \| 0.455743 / 0.258489 (0.197254) \| 0.494024 / 0.293841 (0.200183) \| 0.028690 / 0.128546 (-0.099856) \| 0.008147 / 0.075646 (-0.067499) \| 0.069479 / 0.419271 (-0.349792) \| 0.041613 / 0.043533 (-0.001919) \| 0.460472 / 0.255139 (0.205333) \| 0.475606 / 0.283200 (0.192406) \| 0.020600 / 0.141683 (-0.121083) \| 1.464960 / 1.452155 (0.012805) \| 1.540942 / 1.492716 (0.048226) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.214558 / 0.018006 (0.196552) \| 0.410482 / 0.000490 (0.409992) \| 0.005539 / 0.000200 (0.005339) \| 0.000076 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027044 / 0.037411 (-0.010367) \| 0.081512 / 0.014526 (0.066986) \| 0.101963 / 0.176557 (-0.074593) \| 0.146686 / 0.737135 (-0.590449) \| 0.092676 / 0.296338 (-0.203663) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.468766 / 0.215209 (0.253557) \| 4.680514 / 2.077655 (2.602859) \| 2.562454 / 1.504120 (1.058334) \| 2.383692 / 1.541195 (0.842497) \| 2.481820 / 1.468490 (1.013330) \| 0.509122 / 4.584777 (-4.075655) \| 3.201597 / 3.745712 (-0.544115) \| 2.853539 / 5.269862 (-2.416323) \| 1.891535 / 4.565676 (-2.674141) \| 0.058594 / 0.424275 (-0.365681) \| 0.006448 / 0.007607 (-0.001159) \| 0.535950 / 0.226044 (0.309906) \| 5.388239 / 2.268929 (3.119311) \| 2.999986 / 55.444624 (-52.444638) \| 2.733291 / 6.876477 (-4.143186) \| 2.841548 / 2.142072 (0.699475) \| 0.602388 / 4.805227 (-4.202840) \| 0.126369 / 6.500664 (-6.374295) \| 0.061519 / 0.075469 (-0.013951) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.322746 / 1.841788 (-0.519042) \| 17.940825 / 8.074308 (9.866517) \| 14.679559 / 10.191392 (4.488167) \| 0.146481 / 0.680424 (-0.533943) \| 0.018060 / 0.534201 (-0.516141) \| 0.334924 / 0.579283 (-0.244359) \| 0.384735 / 0.434364 (-0.049629) \| 0.391834 / 0.540337 (-0.148503) \| 0.540011 / 1.386936 (-0.846925) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d82f3c2264436ef60fac8c397fb11c80175c5132 \"CML watermark\")\n" ]	2023-10-23T10:44:16Z	2023-10-23T12:13:20Z	2023-10-23T12:04:36Z	MEMBER	null	null	null	Pin upper version of `fsspec` to avoid disruptions introduced by breaking changes (and the need of urgent patch releases with hotfixes) on each release on their side. See: - #6331 - #6210 - #5731 - #5617 - #5447 I propose that we explicitly test, introduce fixes and support each new `fsspec` version release. CC: @LysandreJik	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6337/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6337/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6337.diff", "html_url": "https://github.com/huggingface/datasets/pull/6337", "merged_at": "2023-10-23T12:04:36Z", "patch_url": "https://github.com/huggingface/datasets/pull/6337.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6337" }
https://api.github.com/repos/huggingface/datasets/issues/7094	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7094/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7094/comments	https://api.github.com/repos/huggingface/datasets/issues/7094/events	https://github.com/huggingface/datasets/pull/7094	2,454,418,130	PR_kwDODunzps53w2b7	7,094	Add Arabic Docs to Datasets	{ "avatar_url": "https://avatars.githubusercontent.com/u/53489256?v=4", "events_url": "https://api.github.com/users/AhmedAlmaghz/events{/privacy}", "followers_url": "https://api.github.com/users/AhmedAlmaghz/followers", "following_url": "https://api.github.com/users/AhmedAlmaghz/following{/other_user}", "gists_url": "https://api.github.com/users/AhmedAlmaghz/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/AhmedAlmaghz", "id": 53489256, "login": "AhmedAlmaghz", "node_id": "MDQ6VXNlcjUzNDg5MjU2", "organizations_url": "https://api.github.com/users/AhmedAlmaghz/orgs", "received_events_url": "https://api.github.com/users/AhmedAlmaghz/received_events", "repos_url": "https://api.github.com/users/AhmedAlmaghz/repos", "site_admin": false, "starred_url": "https://api.github.com/users/AhmedAlmaghz/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/AhmedAlmaghz/subscriptions", "type": "User", "url": "https://api.github.com/users/AhmedAlmaghz", "user_view_type": "public" }	[]	open	false	null	[]	null	[]	2024-08-07T21:53:06Z	2024-08-07T21:53:06Z	null	NONE	null	null	null	Translate Docs into Arabic issue-number : #7093 [Arabic Docs](https://github.com/AhmedAlmaghz/datasets/blob/main/docs/source/ar/index.mdx) [English Docs](https://github.com/AhmedAlmaghz/datasets/blob/main/docs/source/en/index.mdx) @stevhliu	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/7094/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7094/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7094.diff", "html_url": "https://github.com/huggingface/datasets/pull/7094", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/7094.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7094" }
https://api.github.com/repos/huggingface/datasets/issues/5096	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5096/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5096/comments	https://api.github.com/repos/huggingface/datasets/issues/5096/events	https://github.com/huggingface/datasets/issues/5096	1,403,379,816	I_kwDODunzps5TpeBo	5,096	Transfer some canonical datasets under an organization 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": "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	{ "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	[ "The transfer of the dummy dataset to the dummy org works as expected:\r\n```python\r\nIn [1]: from datasets import load_dataset; ds = load_dataset(\"dummy_canonical_dataset\", download_mode=\"force_redownload\"); ds\r\nDownloading builder script: 100%\|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2.98k/2.98k [00:00<00:00, 2.01MB/s]\r\nDownloading and preparing dataset dummy_canonical_dataset/default (download: 411 bytes, generated: 385 bytes, post-processed: Unknown size, total: 796 bytes) to .../.cache/huggingface/datasets/dummy_canonical_dataset/default/1.0.0/100870c358637e269fee140585e61e1472d5075a9bf6f866719934c725e55fb4...\r\nDownloading data: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 411/411 [00:00<00:00, 293kB/s]\r\nDataset dummy_canonical_dataset downloaded and prepared to .../.cache/huggingface/datasets/dummy_canonical_dataset/default/1.0.0/100870c358637e269fee140585e61e1472d5075a9bf6f866719934c725e55fb4. Subsequent calls will reuse this data.\r\n100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:00<00:00, 304.16it/s]\r\nOut[1]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['langs', 'ner_tags', 'tokens'],\r\n num_rows: 3\r\n })\r\n})\r\n\r\nIn [2]: from datasets import load_dataset; ds = load_dataset(\"dummy-canonical-org/dummy_canonical_dataset\"); ds\r\nDownloading builder script: 100%\|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2.98k/2.98k [00:00<00:00, 1.57MB/s]\r\nDownloading and preparing dataset dummy_canonical_dataset/default to .../.cache/huggingface/datasets/dummy-canonical-org___dummy_canonical_dataset/default/1.0.0/100870c358637e269fee140585e61e1472d5075a9bf6f866719934c725e55fb4...\r\nDataset dummy_canonical_dataset downloaded and prepared to .../.cache/huggingface/datasets/dummy-canonical-org___dummy_canonical_dataset/default/1.0.0/100870c358637e269fee140585e61e1472d5075a9bf6f866719934c725e55fb4. Subsequent calls will reuse this data.\r\n100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:00<00:00, 362.48it/s]\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['langs', 'ner_tags', 'tokens'],\r\n num_rows: 3\r\n })\r\n})\r\n```", "Cool ! 🚀 ", "Maybe we should be a bit more proactive with these transfers. There are only ≈70 canonical models, so reaching that number with datasets would be great, too. It's not easy considering the current number of ≈750 canonical datasets, but doable.\r\n\r\nFor instance, it shouldn't be too hard to transfer these datasets (partial list; all of them have more than > 1k downloads):\r\n\r\n<details>\r\n\r\n<summary> Datasets to transfer </summary>\r\n\r\n```\r\nquickdraw -> google\r\nopenai_humaneval -> openai\r\nc4 -> allenai/c4 (the canonical version reads data from the org version)\r\nmbpp -> google (ask jaaustin (author) where to transfer the dataset)\r\ncompetition_math -> hendrycks (author)\r\ngsm8k -> openai\r\nai2_arc -> allenai\r\nimdb -> stanfordai\r\ngreek_legal_code -> chrispap (author)\r\nspider -> Yale-LILY\r\nsquad and squad_v2 -> rajpurkarlab (or rajpurkar, a member of the org and one of the authors)\r\ncppe-5 -> rishitdagli\r\nnews_commentary -> Helsinki-NLP\r\njfleg -> keisks (author)\r\npubmed_qa -> qiaojin (author)\r\nmedmcqa -> infinitylogesh (author)\r\ncifar10 and cifar100 -> UniversityofToronto\r\ncc100 -> gwenzek (author)\r\nasset -> facebook\r\nblbooks -> BritishLibraryLabs\r\ncapes -> FLSRDS (maybe the author?)\r\ncc_news -> fhamborg (author)\r\nclue -> CLUE benchmark\r\ncoqa -> stanfordnlp\r\nlambada -> germank (author)\r\nlibrispeech_asr -> openslr\r\ndrop -> allenai\r\nduorc -> salesforce (ask amritasaha87 (author) where to transfer)\r\nglue -> nyu-mll ?\r\ngo_emotions -> google\r\ncommonsense_qa -> tau\r\ndbpedia_14 -> JensLehmann (author?)\r\ndiscofuse -> google\r\nmc4 -> allenai/c4\r\nopenbookqa -> allenai\r\nropes -> allene\r\ntrivia_qa -> mandarjoshi (author)\r\nwikiann -> afshinrahimi (author)\r\nxtreme -> google\r\nxscr -> INK-USC\r\nyelp_review_full -> Yelp\r\ntruthful_qa -> jacobhilton22 (author)\r\nbigbench -> google\r\nxnli -> facebook\r\nsciq -> allenai\r\nsst2 -> stanfordnlp\r\nblimp -> alexwarstadt (author)\r\ntweet_eval -> cardiffnlp\r\nbeans -> AI-Lab-Makerere\r\nlex_glue -> coastalcph\r\namericas_nli -> abteen (author)\r\nopus_euconst -> tiedeman (author)\r\nmedical_questions_pairs -> curaihealth\r\nweb_questions -> joberant (author)\r\nanli -> facebook\r\nrace -> CarnegieMellonCS\r\nklue -> klue\r\nwino_bias -> uclanlp\r\nwiki_qa -> microsoft\r\nxcopa -> cambridgeltl\r\nindic_glue -> ai4bharat\r\nboolq -> google\r\nadversarial_qa -> mbartolo (author)\r\nnq_open -> google\r\nsnli -> stanfordnlp\r\nstsb_multi_mt -> PhilipMay (author)\r\nmulti_nli -> sleepinyourhat (author)\r\npaws -> google\r\npaws-x -> google\r\nms_marco - microsoft\r\nxquad -> deepmind\r\nnarrativeqa -> deepmind\r\nkilt_tasks -> facebook\r\nhate_speech_offensive -> tdavidson (author)\r\nwiki40b -> google\r\ncovost2 -> facebook\r\ncommon_gen -> INKLAB\r\nmulti_eurlex -> kiddothe2b (author)\r\nexams -> mhardalov (author)\r\ntiny_shakespeare -> karpathy (author)\r\nblbooksgenre -> BritishLibraryLabs ?\r\nfood101 -> ethz ?\r\nscitail -> allenai\r\nbillsum -> FiscalNote\r\nimppres -> facebook\r\nquartz -> allenai\r\nqasc -> allenai\r\nquail -> textmachinelab\r\nwiki_lingua -> esdurmus\r\ncos_e -> salesforce ?\r\ncivil_comments -> google ? (create a “jigsaw” org) \r\nxquad_r -> google\r\nwikitext-> metamind (or salesforce)\r\n\r\n// deprecate c4 and mc4 in favor of allenai/c4 (add a dataset script to the org version to make it easier to use?)\r\n```\r\n</details>\r\n\r\nAlso, a space that allows users to claim the existing canonical datasets (for themselves or their organizations) could be nice.\r\n\r\nWDYT?", "Next week I can take care of some of them :) In most cases we just need to send an email to ask them if they're ok with it.\r\nLet's coordinate on slack ?", "Yup, sounds good to me!", "I can also continuing working on this if we agree this has become a priority now.", "cool stuff! \r\n\r\nthis morning on my side i moved huggingface.co/ctrl (a not very used model) to its rightful entity", "As a previous step before transferring the datasets, we decided we should convert them to Parquet, so that the viewer does not stop working (the viewer does not support datasets with scripts). \r\n\r\nDatasets converted to Parquet:\r\n- [x] adversarial_qa\r\n- [x] ai2_arc\r\n- [x] americas_nli\r\n- [x] anli\r\n- [x] asset\r\n- [x] beans\r\n- [ ] bigbench\r\n- [x] billsum\r\n- [ ] blbooks: it was already transferred to: TheBritishLibrary/blbooks\r\n- [ ] blbooksgenre: it was already transferred to: TheBritishLibrary/blbooksgenre\r\n- [x] blimp\r\n- [x] boolq\r\n- [ ] c4\r\n- [x] capes\r\n- [ ] cc100\r\n- [x] cc_news\r\n- [x] cifar10\r\n- [x] cifar100\r\n- [x] civil_comments\r\n- [x] clue\r\n- [x] common_gen\r\n- [x] commonsense_qa\r\n- [ ] competition_math: it was already transferred to: hendrycks/competition_math\r\n- [x] coqa\r\n- [x] cos_e\r\n- [ ] covost2: it requires manual download\r\n- [x] cppe-5\r\n- [x] dbpedia_14\r\n- [x] discofuse\r\n- [x] drop\r\n- [x] duorc\r\n- [x] exams\r\n- [x] food101\r\n- [x] glue\r\n- [x] go_emotions\r\n- [x] greek_legal_code\r\n- [x] gsm8k\r\n- [x] hate_speech_offensive\r\n- [x] imdb\r\n- [x] imppres\r\n- [x] indic_glue\r\n- [x] jfleg\r\n- [x] kilt_tasks\r\n- [x] klue\r\n- [x] lambada\r\n- [x] lex_glue\r\n- [ ] librispeech_asr\r\n- [x] mbpp\r\n- [ ] mc4\r\n- [x] medical_questions_pairs\r\n- [x] medmcqa\r\n- [x] ms_marco\r\n- [ ] multi_eurlex\r\n- [x] multi_nli\r\n- [ ] narrativeqa\r\n- [ ] news_commentary\r\n- [x] nq_open\r\n- [x] openai_humaneval\r\n- [x] openbookqa\r\n- [ ] opus_euconst\r\n- [x] paws\r\n- [x] paws-x\r\n- [x] pubmed_qa\r\n- [x] qasc\r\n- [x] quail\r\n- [x] quartz\r\n- [ ] quickdraw\r\n- [x] race\r\n- [x] ropes\r\n- [x] sciq\r\n- [x] scitail\r\n- [ ] snli\r\n- [x] spider\r\n- [x] squad\r\n- [x] squad_v2\r\n- [x] sst2\r\n- [x] stsb_multi_mt\r\n- [x] tiny_shakespeare\r\n- [x] trivia_qa\r\n- [x] truthful_qa\r\n- [x] tweet_eval\r\n- [x] web_questions\r\n- [ ] wiki40b\r\n- [x] wiki_lingua\r\n- [x] wiki_qa\r\n- [ ] wikiann\r\n- [x] wikitext\r\n- [x] wino_bias\r\n- [x] xcopa\r\n- [x] xcsr\r\n- [x] xnli\r\n- [x] xquad\r\n- [x] xquad_r\r\n- [ ] xtreme\r\n- [x] yelp_review_full\r\n", "For `c4` and `mc4` I was thinking of adding the corresponding configs to `allenai/c4` and redirect `c4` and `mc4` to `allenai/c4`. I'll open a PR on `allenai/c4` if it's good for you", "@davanstrien and @lhoestq, I have shared with you this spreadsheet: https://docs.google.com/spreadsheets/d/1GvNTd1UxmtTvEFOK-Eq6E3Str4FUWQuWZsEN0WVFirs/edit?usp=sharing\r\n\r\nThis way we can take datasets by batches to contact the authors and transfer to the organizations.", "We have already transferred all canonical datasets under organization/user namespaces." ]	2022-10-10T15:44:31Z	2024-06-24T06:06:28Z	2024-06-24T06:02:45Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	As discussed during our @huggingface/datasets meeting, we are planning to move some "canonical" dataset scripts under their corresponding organization namespace (if this does not exist). On the contrary, if the dataset already exists under the organization namespace, we are deprecating the canonical one (and eventually delete it). First, we should test it using a dummy dataset/organization. TODO: - [x] Test with a dummy dataset - [x] Create dummy canonical dataset: https://huggingface.co/datasets/dummy_canonical_dataset - [x] Create dummy organization: https://huggingface.co/dummy-canonical-org - [x] Transfer dummy canonical dataset to dummy organization - [ ] Transfer datasets - [x] babi_qa => facebook - [x] blbooks => TheBritishLibrary/blbooks - [x] blbooksgenre => TheBritishLibrary/blbooksgenre - [x] common_gen => allenai - [x] commonsense_qa => tau - [x] competition_math => hendrycks/competition_math - [x] cord19 => allenai - [x] emotion => dair-ai - [ ] gem => GEM - [x] hellaswag => Rowan - [x] hendrycks_test => cais/mmlu - [x] indonlu => indonlp - [ ] multilingual_librispeech => facebook - It already exists "facebook/multilingual_librispeech" - [ ] oscar => oscar-corpus - [x] peer_read => allenai - [x] qasper => allenai - [x] reddit => webis/tldr-17 - [x] russian_super_glue => russiannlp - [x] rvl_cdip => aharley - [x] s2orc => allenai - [x] scicite => allenai - [x] scifact => allenai - [x] scitldr => allenai - [x] swiss_judgment_prediction => rcds - [x] the_pile => EleutherAI - [ ] wmt14, wmt15, wmt16, wmt17, wmt18, wmt19,... => wmt - [ ] Deprecate (and eventually remove) datasets that cannot be transferred because they already exist - [x] banking77 => PolyAI - [x] common_voice => mozilla-foundation - [x] german_legal_entity_recognition => elenanereiss - ... EDIT: the list above is continuously being updated	{ "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": 2, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/5096/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5096/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7512	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7512/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7512/comments	https://api.github.com/repos/huggingface/datasets/issues/7512/events	https://github.com/huggingface/datasets/issues/7512	2,994,043,544	I_kwDODunzps6ydXqY	7,512	.map() fails if function uses pyvista	{ "avatar_url": "https://avatars.githubusercontent.com/u/11832922?v=4", "events_url": "https://api.github.com/users/el-hult/events{/privacy}", "followers_url": "https://api.github.com/users/el-hult/followers", "following_url": "https://api.github.com/users/el-hult/following{/other_user}", "gists_url": "https://api.github.com/users/el-hult/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/el-hult", "id": 11832922, "login": "el-hult", "node_id": "MDQ6VXNlcjExODMyOTIy", "organizations_url": "https://api.github.com/users/el-hult/orgs", "received_events_url": "https://api.github.com/users/el-hult/received_events", "repos_url": "https://api.github.com/users/el-hult/repos", "site_admin": false, "starred_url": "https://api.github.com/users/el-hult/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/el-hult/subscriptions", "type": "User", "url": "https://api.github.com/users/el-hult", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "I found a similar (?) issue in https://github.com/huggingface/datasets/issues/6435, where someone had issues with forks and CUDA. According to https://huggingface.co/docs/datasets/main/en/process#multiprocessing we should do \n\n```\nfrom multiprocess import set_start_method\nset_start_method(\"spawn\")\n```\n\nto avoid the fork. The updated code\n\n```python\nimport numpy as np\nimport pyvista as pv\nimport datasets\nimport multiprocess\n\ndata = [{\"coords\": np.random.rand(5, 3)} for _ in range(3)]\n\ndef render_point(example):\n plotter = pv.Plotter(off_screen=True)\n cloud = pv.PolyData(example[\"coords\"])\n plotter.add_mesh(cloud)\n img = plotter.screenshot(return_img=True)\n return {\"image\": img}\n\n\n# breaks if num_proc>1\nmultiprocess.set_start_method(\"spawn\")\nds = datasets.Dataset.from_list(data).map(render_point, num_proc=2)\n```\n\ninstead fails with `TypeError: fork_exec() takes exactly 23 arguments (21 given)` which also seems like a bug to me." ]	2025-04-14T19:43:02Z	2025-04-14T20:01:53Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Using PyVista inside a .map() produces a crash with `objc[78796]: +[NSResponder initialize] may have been in progress in another thread when fork() was called. We cannot safely call it or ignore it in the fork() child process. Crashing instead. Set a breakpoint on objc_initializeAfterForkError to debug.` ### Steps to reproduce the bug Run ```python import numpy as np import pyvista as pv import datasets data = [{"coords": np.random.rand(5, 3)} for _ in range(3)] def render_point(example): plotter = pv.Plotter(off_screen=True) cloud = pv.PolyData(example["coords"]) plotter.add_mesh(cloud) img = plotter.screenshot(return_img=True) return {"image": img} # breaks if num_proc>1 ds = datasets.Dataset.from_list(data).map(render_point, num_proc=2) ``` ### Expected behavior It should work. Just like when I use a process pool to make it work. ```python import numpy as np import pyvista as pv import multiprocessing data = [{"coords": np.random.rand(5, 3)} for _ in range(3)] def render_point(example): plotter = pv.Plotter(off_screen=True) cloud = pv.PolyData(example["coords"]) plotter.add_mesh(cloud) img = plotter.screenshot(return_img=True) return {"image": img} if __name__ == "__main__": with multiprocessing.Pool(processes=2) as pool: results = pool.map(render_point, data) print(results[0]["image"].shape) ``` ### Environment info - `datasets` version: 3.3.2 - Platform: macOS-15.3.2-arm64-arm-64bit - Python version: 3.11.10 - `huggingface_hub` version: 0.28.1 - PyArrow version: 18.1.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.10.0 And then I suppose pyvista info is good to have. ```python import pyvista as pv print(pv.Report()) ``` gives -------------------------------------------------------------------------------- Date: Mon Apr 14 21:42:08 2025 CEST OS : Darwin (macOS 15.3.2) CPU(s) : 10 Machine : arm64 Architecture : 64bit RAM : 32.0 GiB Environment : IPython File system : apfs GPU Vendor : Apple GPU Renderer : Apple M1 Max GPU Version : 4.1 Metal - 89.3 MathText Support : True Python 3.11.10 (main, Oct 7 2024, 23:25:27) [Clang 18.1.8 ] pyvista : 0.44.2 vtk : 9.4.0 numpy : 2.2.2 matplotlib : 3.10.0 scooby : 0.10.0 pooch : 1.8.2 pillow : 11.1.0 imageio : 2.36.1 PyQt5 : 5.15.11 IPython : 8.30.0 scipy : 1.14.1 tqdm : 4.67.1 jupyterlab : 4.3.5 nest_asyncio : 1.6.0 --------------------------------------------------------------------------------	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/7512/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7512/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5655	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5655/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5655/comments	https://api.github.com/repos/huggingface/datasets/issues/5655/events	https://github.com/huggingface/datasets/pull/5655	1,634,030,017	PR_kwDODunzps5MjWYy	5,655	Improve features decoding in to_iterable_dataset	{ "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) \| 0.009691 / 0.011353 (-0.001662) \| 0.006160 / 0.011008 (-0.004848) \| 0.127528 / 0.038508 (0.089020) \| 0.034445 / 0.023109 (0.011335) \| 0.391483 / 0.275898 (0.115585) \| 0.425922 / 0.323480 (0.102442) \| 0.006621 / 0.007986 (-0.001365) \| 0.004550 / 0.004328 (0.000221) \| 0.099134 / 0.004250 (0.094884) \| 0.051089 / 0.037052 (0.014037) \| 0.398675 / 0.258489 (0.140186) \| 0.456740 / 0.293841 (0.162899) \| 0.052279 / 0.128546 (-0.076267) \| 0.020878 / 0.075646 (-0.054768) \| 0.414954 / 0.419271 (-0.004317) \| 0.061903 / 0.043533 (0.018370) \| 0.393088 / 0.255139 (0.137949) \| 0.410289 / 0.283200 (0.127089) \| 0.101684 / 0.141683 (-0.039998) \| 1.747102 / 1.452155 (0.294947) \| 1.896976 / 1.492716 (0.404260) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.203193 / 0.018006 (0.185187) \| 0.495011 / 0.000490 (0.494521) \| 0.006290 / 0.000200 (0.006090) \| 0.000098 / 0.000054 (0.000043) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034840 / 0.037411 (-0.002571) \| 0.122529 / 0.014526 (0.108003) \| 0.133870 / 0.176557 (-0.042686) \| 0.207771 / 0.737135 (-0.529364) \| 0.141441 / 0.296338 (-0.154897) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.604190 / 0.215209 (0.388981) \| 6.040295 / 2.077655 (3.962641) \| 2.405703 / 1.504120 (0.901583) \| 2.062767 / 1.541195 (0.521572) \| 2.079313 / 1.468490 (0.610823) \| 1.240107 / 4.584777 (-3.344670) \| 5.316583 / 3.745712 (1.570871) \| 3.104758 / 5.269862 (-2.165103) \| 2.056489 / 4.565676 (-2.509187) \| 0.149060 / 0.424275 (-0.275215) \| 0.014467 / 0.007607 (0.006860) \| 0.736882 / 0.226044 (0.510838) \| 7.324142 / 2.268929 (5.055213) \| 3.048752 / 55.444624 (-52.395872) \| 2.385013 / 6.876477 (-4.491463) \| 2.457478 / 2.142072 (0.315405) \| 1.459276 / 4.805227 (-3.345951) \| 0.253882 / 6.500664 (-6.246782) \| 0.076756 / 0.075469 (0.001287) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.499166 / 1.841788 (-0.342622) \| 17.294165 / 8.074308 (9.219857) \| 20.385668 / 10.191392 (10.194276) \| 0.254633 / 0.680424 (-0.425791) \| 0.026253 / 0.534201 (-0.507948) \| 0.532928 / 0.579283 (-0.046355) \| 0.606095 / 0.434364 (0.171731) \| 0.615025 / 0.540337 (0.074687) \| 0.728651 / 1.386936 (-0.658285) \|\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) \| 0.009376 / 0.011353 (-0.001977) \| 0.005981 / 0.011008 (-0.005027) \| 0.109898 / 0.038508 (0.071390) \| 0.033746 / 0.023109 (0.010637) \| 0.410226 / 0.275898 (0.134328) \| 0.470606 / 0.323480 (0.147126) \| 0.006706 / 0.007986 (-0.001279) \| 0.004482 / 0.004328 (0.000153) \| 0.092280 / 0.004250 (0.088030) \| 0.047988 / 0.037052 (0.010935) \| 0.430628 / 0.258489 (0.172139) \| 0.480668 / 0.293841 (0.186827) \| 0.052099 / 0.128546 (-0.076447) \| 0.018743 / 0.075646 (-0.056903) \| 0.112204 / 0.419271 (-0.307068) \| 0.059838 / 0.043533 (0.016305) \| 0.418230 / 0.255139 (0.163091) \| 0.451568 / 0.283200 (0.168368) \| 0.107026 / 0.141683 (-0.034657) \| 1.708111 / 1.452155 (0.255956) \| 1.839268 / 1.492716 (0.346552) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229558 / 0.018006 (0.211552) \| 0.488099 / 0.000490 (0.487609) \| 0.004643 / 0.000200 (0.004443) \| 0.000107 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030461 / 0.037411 (-0.006951) \| 0.120993 / 0.014526 (0.106467) \| 0.130874 / 0.176557 (-0.045682) \| 0.193550 / 0.737135 (-0.543585) \| 0.138164 / 0.296338 (-0.158174) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.635709 / 0.215209 (0.420500) \| 6.225112 / 2.077655 (4.147457) \| 2.639584 / 1.504120 (1.135465) \| 2.254487 / 1.541195 (0.713293) \| 2.280478 / 1.468490 (0.811988) \| 1.205712 / 4.584777 (-3.379065) \| 5.367845 / 3.745712 (1.622133) \| 3.020207 / 5.269862 (-2.249655) \| 2.001897 / 4.565676 (-2.563779) \| 0.149582 / 0.424275 (-0.274693) \| 0.014867 / 0.007607 (0.007260) \| 0.759050 / 0.226044 (0.533006) \| 7.692969 / 2.268929 (5.424041) \| 3.274009 / 55.444624 (-52.170615) \| 2.635529 / 6.876477 (-4.240948) \| 2.672960 / 2.142072 (0.530888) \| 1.426487 / 4.805227 (-3.378740) \| 0.253368 / 6.500664 (-6.247296) \| 0.078650 / 0.075469 (0.003181) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.620265 / 1.841788 (-0.221523) \| 17.674168 / 8.074308 (9.599860) \| 21.120528 / 10.191392 (10.929136) \| 0.244205 / 0.680424 (-0.436218) \| 0.029646 / 0.534201 (-0.504555) \| 0.510948 / 0.579283 (-0.068335) \| 0.586255 / 0.434364 (0.151891) \| 0.589286 / 0.540337 (0.048949) \| 0.736561 / 1.386936 (-0.650375) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#de5fe9ae5df84c489e08dcbdc3d2d20272b312c3 \"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) \| 0.007778 / 0.011353 (-0.003575) \| 0.005432 / 0.011008 (-0.005577) \| 0.098776 / 0.038508 (0.060268) \| 0.035196 / 0.023109 (0.012087) \| 0.305646 / 0.275898 (0.029748) \| 0.342661 / 0.323480 (0.019181) \| 0.006513 / 0.007986 (-0.001472) \| 0.005897 / 0.004328 (0.001568) \| 0.075797 / 0.004250 (0.071547) \| 0.056060 / 0.037052 (0.019007) \| 0.306645 / 0.258489 (0.048156) \| 0.352447 / 0.293841 (0.058606) \| 0.037304 / 0.128546 (-0.091242) \| 0.012514 / 0.075646 (-0.063132) \| 0.334949 / 0.419271 (-0.084323) \| 0.051600 / 0.043533 (0.008067) \| 0.302302 / 0.255139 (0.047163) \| 0.322238 / 0.283200 (0.039038) \| 0.106896 / 0.141683 (-0.034787) \| 1.483163 / 1.452155 (0.031008) \| 1.587483 / 1.492716 (0.094767) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292318 / 0.018006 (0.274312) \| 0.541541 / 0.000490 (0.541051) \| 0.008342 / 0.000200 (0.008142) \| 0.000339 / 0.000054 (0.000285) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028287 / 0.037411 (-0.009124) \| 0.107775 / 0.014526 (0.093250) \| 0.119112 / 0.176557 (-0.057445) \| 0.174002 / 0.737135 (-0.563134) \| 0.126531 / 0.296338 (-0.169808) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401684 / 0.215209 (0.186475) \| 4.024708 / 2.077655 (1.947053) \| 1.812763 / 1.504120 (0.308643) \| 1.629540 / 1.541195 (0.088345) \| 1.731733 / 1.468490 (0.263243) \| 0.711066 / 4.584777 (-3.873711) \| 3.867499 / 3.745712 (0.121786) \| 3.615968 / 5.269862 (-1.653893) \| 1.876077 / 4.565676 (-2.689600) \| 0.087003 / 0.424275 (-0.337272) \| 0.012445 / 0.007607 (0.004838) \| 0.499106 / 0.226044 (0.273061) \| 4.975920 / 2.268929 (2.706992) \| 2.279074 / 55.444624 (-53.165550) \| 1.952311 / 6.876477 (-4.924166) \| 2.167480 / 2.142072 (0.025408) \| 0.855882 / 4.805227 (-3.949346) \| 0.171378 / 6.500664 (-6.329287) \| 0.066731 / 0.075469 (-0.008738) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.184226 / 1.841788 (-0.657561) \| 15.383396 / 8.074308 (7.309088) \| 15.069783 / 10.191392 (4.878391) \| 0.161489 / 0.680424 (-0.518935) \| 0.017763 / 0.534201 (-0.516438) \| 0.427103 / 0.579283 (-0.152180) \| 0.434295 / 0.434364 (-0.000069) \| 0.496848 / 0.540337 (-0.043489) \| 0.592572 / 1.386936 (-0.794364) \|\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) \| 0.008014 / 0.011353 (-0.003339) \| 0.005607 / 0.011008 (-0.005401) \| 0.076826 / 0.038508 (0.038318) \| 0.035283 / 0.023109 (0.012174) \| 0.347809 / 0.275898 (0.071911) \| 0.382482 / 0.323480 (0.059003) \| 0.006276 / 0.007986 (-0.001709) \| 0.005978 / 0.004328 (0.001650) \| 0.074938 / 0.004250 (0.070687) \| 0.054323 / 0.037052 (0.017271) \| 0.344027 / 0.258489 (0.085538) \| 0.397623 / 0.293841 (0.103783) \| 0.037851 / 0.128546 (-0.090695) \| 0.012649 / 0.075646 (-0.062997) \| 0.086169 / 0.419271 (-0.333103) \| 0.051510 / 0.043533 (0.007977) \| 0.341112 / 0.255139 (0.085973) \| 0.357957 / 0.283200 (0.074757) \| 0.110949 / 0.141683 (-0.030734) \| 1.479573 / 1.452155 (0.027419) \| 1.578572 / 1.492716 (0.085855) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.310678 / 0.018006 (0.292672) \| 0.525504 / 0.000490 (0.525015) \| 0.000447 / 0.000200 (0.000247) \| 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) \| 0.031262 / 0.037411 (-0.006149) \| 0.113801 / 0.014526 (0.099275) \| 0.124967 / 0.176557 (-0.051590) \| 0.175226 / 0.737135 (-0.561909) \| 0.129377 / 0.296338 (-0.166962) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420672 / 0.215209 (0.205463) \| 4.181337 / 2.077655 (2.103682) \| 1.985524 / 1.504120 (0.481404) \| 1.803468 / 1.541195 (0.262273) \| 1.952915 / 1.468490 (0.484425) \| 0.710928 / 4.584777 (-3.873849) \| 3.886245 / 3.745712 (0.140533) \| 3.737837 / 5.269862 (-1.532024) \| 1.806859 / 4.565676 (-2.758818) \| 0.088461 / 0.424275 (-0.335814) \| 0.013125 / 0.007607 (0.005518) \| 0.522410 / 0.226044 (0.296365) \| 5.232591 / 2.268929 (2.963663) \| 2.451188 / 55.444624 (-52.993437) \| 2.127725 / 6.876477 (-4.748751) \| 2.232859 / 2.142072 (0.090786) \| 0.854257 / 4.805227 (-3.950970) \| 0.171004 / 6.500664 (-6.329661) \| 0.066724 / 0.075469 (-0.008746) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.257700 / 1.841788 (-0.584088) \| 15.738605 / 8.074308 (7.664297) \| 15.021698 / 10.191392 (4.830306) \| 0.147422 / 0.680424 (-0.533002) \| 0.017928 / 0.534201 (-0.516273) \| 0.428121 / 0.579283 (-0.151162) \| 0.432056 / 0.434364 (-0.002308) \| 0.498318 / 0.540337 (-0.042020) \| 0.591040 / 1.386936 (-0.795896) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ac74267032ef3608779a8c8c4361b95a83ecbcb \"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) \| 0.007014 / 0.011353 (-0.004339) \| 0.004792 / 0.011008 (-0.006216) \| 0.099822 / 0.038508 (0.061314) \| 0.029333 / 0.023109 (0.006224) \| 0.306453 / 0.275898 (0.030555) \| 0.344598 / 0.323480 (0.021118) \| 0.005121 / 0.007986 (-0.002865) \| 0.004850 / 0.004328 (0.000522) \| 0.076668 / 0.004250 (0.072417) \| 0.039980 / 0.037052 (0.002927) \| 0.312276 / 0.258489 (0.053787) \| 0.354722 / 0.293841 (0.060881) \| 0.031653 / 0.128546 (-0.096893) \| 0.011743 / 0.075646 (-0.063903) \| 0.322998 / 0.419271 (-0.096274) \| 0.042813 / 0.043533 (-0.000720) \| 0.308855 / 0.255139 (0.053716) \| 0.332650 / 0.283200 (0.049451) \| 0.087155 / 0.141683 (-0.054528) \| 1.454946 / 1.452155 (0.002791) \| 1.550589 / 1.492716 (0.057873) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.192921 / 0.018006 (0.174914) \| 0.411155 / 0.000490 (0.410666) \| 0.004779 / 0.000200 (0.004579) \| 0.000071 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024462 / 0.037411 (-0.012950) \| 0.100320 / 0.014526 (0.085794) \| 0.105509 / 0.176557 (-0.071048) \| 0.168533 / 0.737135 (-0.568602) \| 0.110018 / 0.296338 (-0.186321) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415025 / 0.215209 (0.199816) \| 4.144583 / 2.077655 (2.066928) \| 1.871627 / 1.504120 (0.367507) \| 1.671638 / 1.541195 (0.130443) \| 1.734458 / 1.468490 (0.265968) \| 0.693435 / 4.584777 (-3.891342) \| 3.487999 / 3.745712 (-0.257713) \| 3.196553 / 5.269862 (-2.073308) \| 1.628499 / 4.565676 (-2.937178) \| 0.082999 / 0.424275 (-0.341276) \| 0.012822 / 0.007607 (0.005215) \| 0.514904 / 0.226044 (0.288860) \| 5.157525 / 2.268929 (2.888596) \| 2.313093 / 55.444624 (-53.131531) \| 1.968335 / 6.876477 (-4.908142) \| 2.083462 / 2.142072 (-0.058610) \| 0.804485 / 4.805227 (-4.000742) \| 0.152290 / 6.500664 (-6.348374) \| 0.066813 / 0.075469 (-0.008656) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.210370 / 1.841788 (-0.631418) \| 14.261779 / 8.074308 (6.187471) \| 14.268121 / 10.191392 (4.076729) \| 0.149216 / 0.680424 (-0.531207) \| 0.016529 / 0.534201 (-0.517672) \| 0.378814 / 0.579283 (-0.200469) \| 0.386304 / 0.434364 (-0.048060) \| 0.439653 / 0.540337 (-0.100684) \| 0.523658 / 1.386936 (-0.863278) \|\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) \| 0.006979 / 0.011353 (-0.004374) \| 0.004718 / 0.011008 (-0.006290) \| 0.077023 / 0.038508 (0.038514) \| 0.029080 / 0.023109 (0.005971) \| 0.343145 / 0.275898 (0.067247) \| 0.380633 / 0.323480 (0.057153) \| 0.006057 / 0.007986 (-0.001928) \| 0.003541 / 0.004328 (-0.000788) \| 0.075773 / 0.004250 (0.071523) \| 0.039112 / 0.037052 (0.002060) \| 0.342355 / 0.258489 (0.083866) \| 0.386002 / 0.293841 (0.092161) \| 0.033238 / 0.128546 (-0.095308) \| 0.011696 / 0.075646 (-0.063950) \| 0.086178 / 0.419271 (-0.333093) \| 0.045219 / 0.043533 (0.001686) \| 0.360710 / 0.255139 (0.105571) \| 0.367490 / 0.283200 (0.084290) \| 0.093041 / 0.141683 (-0.048642) \| 1.523670 / 1.452155 (0.071516) \| 1.595280 / 1.492716 (0.102564) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235888 / 0.018006 (0.217882) \| 0.410205 / 0.000490 (0.409715) \| 0.000405 / 0.000200 (0.000205) \| 0.000059 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025752 / 0.037411 (-0.011659) \| 0.103343 / 0.014526 (0.088818) \| 0.108722 / 0.176557 (-0.067834) \| 0.159241 / 0.737135 (-0.577894) \| 0.113684 / 0.296338 (-0.182654) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441809 / 0.215209 (0.226600) \| 4.410893 / 2.077655 (2.333238) \| 2.104061 / 1.504120 (0.599941) \| 1.854016 / 1.541195 (0.312821) \| 1.947100 / 1.468490 (0.478610) \| 0.697682 / 4.584777 (-3.887095) \| 3.467513 / 3.745712 (-0.278199) \| 1.911603 / 5.269862 (-3.358258) \| 1.187479 / 4.565676 (-3.378197) \| 0.083153 / 0.424275 (-0.341122) \| 0.012651 / 0.007607 (0.005044) \| 0.542081 / 0.226044 (0.316036) \| 5.444622 / 2.268929 (3.175693) \| 2.524236 / 55.444624 (-52.920388) \| 2.190463 / 6.876477 (-4.686014) \| 2.265764 / 2.142072 (0.123691) \| 0.810778 / 4.805227 (-3.994450) \| 0.152459 / 6.500664 (-6.348205) \| 0.067815 / 0.075469 (-0.007654) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.334388 / 1.841788 (-0.507400) \| 14.640459 / 8.074308 (6.566151) \| 14.714874 / 10.191392 (4.523482) \| 0.153479 / 0.680424 (-0.526945) \| 0.016709 / 0.534201 (-0.517492) \| 0.379427 / 0.579283 (-0.199856) \| 0.391602 / 0.434364 (-0.042762) \| 0.438297 / 0.540337 (-0.102041) \| 0.524170 / 1.386936 (-0.862766) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b277cef5cb56c0c506eda082fb69fddb839156a1 \"CML watermark\")\n" ]	2023-03-21T14:18:09Z	2023-03-23T13:19:27Z	2023-03-23T13:12:25Z	MEMBER	null	null	null	Following discussion at https://github.com/huggingface/datasets/pull/5589 Right now `to_iterable_dataset` on images/audio hurts iterable dataset performance a lot (e.g. x4 slower because it encodes+decodes images/audios unnecessarily). I fixed it by providing a generator that yields undecoded examples	{ "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/5655/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5655/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5655.diff", "html_url": "https://github.com/huggingface/datasets/pull/5655", "merged_at": "2023-03-23T13:12:25Z", "patch_url": "https://github.com/huggingface/datasets/pull/5655.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5655" }
https://api.github.com/repos/huggingface/datasets/issues/5907	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5907/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5907/comments	https://api.github.com/repos/huggingface/datasets/issues/5907/events	https://github.com/huggingface/datasets/pull/5907	1,728,648,560	PR_kwDODunzps5RfqUU	5,907	Add `flatten_indices` to `DatasetDict`	{ "avatar_url": "https://avatars.githubusercontent.com/u/29152154?v=4", "events_url": "https://api.github.com/users/maximxlss/events{/privacy}", "followers_url": "https://api.github.com/users/maximxlss/followers", "following_url": "https://api.github.com/users/maximxlss/following{/other_user}", "gists_url": "https://api.github.com/users/maximxlss/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/maximxlss", "id": 29152154, "login": "maximxlss", "node_id": "MDQ6VXNlcjI5MTUyMTU0", "organizations_url": "https://api.github.com/users/maximxlss/orgs", "received_events_url": "https://api.github.com/users/maximxlss/received_events", "repos_url": "https://api.github.com/users/maximxlss/repos", "site_admin": false, "starred_url": "https://api.github.com/users/maximxlss/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/maximxlss/subscriptions", "type": "User", "url": "https://api.github.com/users/maximxlss", "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) \| 0.006192 / 0.011353 (-0.005161) \| 0.004410 / 0.011008 (-0.006598) \| 0.095990 / 0.038508 (0.057482) \| 0.032662 / 0.023109 (0.009553) \| 0.322827 / 0.275898 (0.046929) \| 0.352542 / 0.323480 (0.029062) \| 0.005398 / 0.007986 (-0.002588) \| 0.003926 / 0.004328 (-0.000403) \| 0.075131 / 0.004250 (0.070880) \| 0.046205 / 0.037052 (0.009153) \| 0.330957 / 0.258489 (0.072468) \| 0.360166 / 0.293841 (0.066325) \| 0.027880 / 0.128546 (-0.100666) \| 0.008813 / 0.075646 (-0.066833) \| 0.327316 / 0.419271 (-0.091955) \| 0.050071 / 0.043533 (0.006539) \| 0.319939 / 0.255139 (0.064800) \| 0.331593 / 0.283200 (0.048393) \| 0.096745 / 0.141683 (-0.044938) \| 1.445165 / 1.452155 (-0.006990) \| 1.515538 / 1.492716 (0.022821) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.209365 / 0.018006 (0.191358) \| 0.437007 / 0.000490 (0.436518) \| 0.003207 / 0.000200 (0.003007) \| 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) \| 0.027261 / 0.037411 (-0.010151) \| 0.105101 / 0.014526 (0.090575) \| 0.117163 / 0.176557 (-0.059394) \| 0.176237 / 0.737135 (-0.560898) \| 0.122559 / 0.296338 (-0.173779) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.406792 / 0.215209 (0.191583) \| 4.060831 / 2.077655 (1.983176) \| 1.829691 / 1.504120 (0.325571) \| 1.633155 / 1.541195 (0.091960) \| 1.704817 / 1.468490 (0.236327) \| 0.525325 / 4.584777 (-4.059452) \| 3.752907 / 3.745712 (0.007194) \| 1.857513 / 5.269862 (-3.412349) \| 1.222237 / 4.565676 (-3.343439) \| 0.065941 / 0.424275 (-0.358334) \| 0.012498 / 0.007607 (0.004891) \| 0.495009 / 0.226044 (0.268965) \| 4.968074 / 2.268929 (2.699145) \| 2.277898 / 55.444624 (-53.166727) \| 1.936656 / 6.876477 (-4.939821) \| 1.970698 / 2.142072 (-0.171374) \| 0.635221 / 4.805227 (-4.170006) \| 0.140539 / 6.500664 (-6.360125) \| 0.064111 / 0.075469 (-0.011358) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.238151 / 1.841788 (-0.603637) \| 14.681262 / 8.074308 (6.606954) \| 13.405525 / 10.191392 (3.214133) \| 0.163225 / 0.680424 (-0.517199) \| 0.017282 / 0.534201 (-0.516918) \| 0.395526 / 0.579283 (-0.183757) \| 0.429156 / 0.434364 (-0.005208) \| 0.470806 / 0.540337 (-0.069531) \| 0.571290 / 1.386936 (-0.815646) \|\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) \| 0.004388 / 0.011008 (-0.006621) \| 0.075004 / 0.038508 (0.036496) \| 0.032904 / 0.023109 (0.009795) \| 0.375360 / 0.275898 (0.099462) \| 0.413684 / 0.323480 (0.090204) \| 0.005854 / 0.007986 (-0.002132) \| 0.005504 / 0.004328 (0.001175) \| 0.075049 / 0.004250 (0.070799) \| 0.047973 / 0.037052 (0.010920) \| 0.377943 / 0.258489 (0.119454) \| 0.427039 / 0.293841 (0.133198) \| 0.028248 / 0.128546 (-0.100298) \| 0.008972 / 0.075646 (-0.066674) \| 0.081848 / 0.419271 (-0.337424) \| 0.047935 / 0.043533 (0.004402) \| 0.377980 / 0.255139 (0.122841) \| 0.407856 / 0.283200 (0.124656) \| 0.103454 / 0.141683 (-0.038229) \| 1.469051 / 1.452155 (0.016896) \| 1.590657 / 1.492716 (0.097941) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.192380 / 0.018006 (0.174374) \| 0.440995 / 0.000490 (0.440505) \| 0.004082 / 0.000200 (0.003882) \| 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) \| 0.029584 / 0.037411 (-0.007828) \| 0.110051 / 0.014526 (0.095525) \| 0.121196 / 0.176557 (-0.055361) \| 0.172249 / 0.737135 (-0.564886) \| 0.125380 / 0.296338 (-0.170958) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.435218 / 0.215209 (0.220009) \| 4.354811 / 2.077655 (2.277156) \| 2.102050 / 1.504120 (0.597930) \| 1.913454 / 1.541195 (0.372260) \| 1.974624 / 1.468490 (0.506134) \| 0.529975 / 4.584777 (-4.054802) \| 3.801605 / 3.745712 (0.055893) \| 3.162408 / 5.269862 (-2.107454) \| 1.599576 / 4.565676 (-2.966101) \| 0.066710 / 0.424275 (-0.357565) \| 0.012158 / 0.007607 (0.004551) \| 0.549187 / 0.226044 (0.323142) \| 5.489930 / 2.268929 (3.221002) \| 2.646787 / 55.444624 (-52.797837) \| 2.311915 / 6.876477 (-4.564562) \| 2.335645 / 2.142072 (0.193572) \| 0.641067 / 4.805227 (-4.164160) \| 0.142227 / 6.500664 (-6.358437) \| 0.065303 / 0.075469 (-0.010166) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.283209 / 1.841788 (-0.558579) \| 15.241809 / 8.074308 (7.167501) \| 14.131471 / 10.191392 (3.940079) \| 0.143921 / 0.680424 (-0.536503) \| 0.017497 / 0.534201 (-0.516704) \| 0.402236 / 0.579283 (-0.177047) \| 0.418917 / 0.434364 (-0.015447) \| 0.461745 / 0.540337 (-0.078593) \| 0.560212 / 1.386936 (-0.826724) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7098922130cabfbfa6b8a3885ff2e6f032d6203d \"CML watermark\")\n" ]	2023-05-27T10:55:44Z	2023-06-01T11:46:35Z	2023-06-01T11:39:36Z	CONTRIBUTOR	null	null	null	Add `flatten_indices` to `DatasetDict` for convinience	{ "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": 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"https://github.com/huggingface/datasets/pull/5907.diff", "html_url": "https://github.com/huggingface/datasets/pull/5907", "merged_at": "2023-06-01T11:39:35Z", "patch_url": "https://github.com/huggingface/datasets/pull/5907.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5907" }
https://api.github.com/repos/huggingface/datasets/issues/5639	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5639/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5639/comments	https://api.github.com/repos/huggingface/datasets/issues/5639/events	https://github.com/huggingface/datasets/issues/5639	1,625,737,098	I_kwDODunzps5g5seK	5,639	Parquet file wrongly recognized as zip prevents loading a dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/22726840?v=4", "events_url": "https://api.github.com/users/clefourrier/events{/privacy}", "followers_url": "https://api.github.com/users/clefourrier/followers", "following_url": "https://api.github.com/users/clefourrier/following{/other_user}", "gists_url": "https://api.github.com/users/clefourrier/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/clefourrier", "id": 22726840, "login": "clefourrier", "node_id": "MDQ6VXNlcjIyNzI2ODQw", "organizations_url": "https://api.github.com/users/clefourrier/orgs", "received_events_url": "https://api.github.com/users/clefourrier/received_events", "repos_url": "https://api.github.com/users/clefourrier/repos", "site_admin": false, "starred_url": "https://api.github.com/users/clefourrier/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/clefourrier/subscriptions", "type": "User", "url": "https://api.github.com/users/clefourrier", "user_view_type": "public" }	[]	closed	false	null	[]	null	[]	2023-03-15T15:20:45Z	2023-03-16T13:40:14Z	2023-03-16T13:40:14Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug When trying to `load_dataset_builder` for `HuggingFaceGECLM/StackExchange_Mar2023`, extraction fails, because parquet file [devops-00000-of-00001-22fe902fd8702892.parquet](https://huggingface.co/datasets/HuggingFaceGECLM/StackExchange_Mar2023/resolve/1f8c9a2ab6f7d0f9ae904b8b922e4384592ae1a5/data/devops-00000-of-00001-22fe902fd8702892.parquet) is wrongly identified by python as being a zip not a parquet. (Full thread on [Slack](https://huggingface.slack.com/archives/C02V51Q3800/p1678890880803599)) ### Steps to reproduce the bug ```python from datasets import load_dataset_builder ds = load_dataset_builder("HuggingFaceGECLM/StackExchange_Mar2023") ``` ### Expected behavior Loading the file normally. ### Environment info - `datasets` version: 2.3.2 - Platform: Linux-5.14.0-1058-oem-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 8.0.0 - Pandas version: 1.4.3	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5639/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5639/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7141	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7141/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7141/comments	https://api.github.com/repos/huggingface/datasets/issues/7141/events	https://github.com/huggingface/datasets/issues/7141	2,510,797,653	I_kwDODunzps6Vp7tV	7,141	Older datasets throwing safety errors with 2.21.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/1050316?v=4", "events_url": "https://api.github.com/users/alvations/events{/privacy}", "followers_url": "https://api.github.com/users/alvations/followers", "following_url": "https://api.github.com/users/alvations/following{/other_user}", "gists_url": "https://api.github.com/users/alvations/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alvations", "id": 1050316, "login": "alvations", "node_id": "MDQ6VXNlcjEwNTAzMTY=", "organizations_url": "https://api.github.com/users/alvations/orgs", "received_events_url": "https://api.github.com/users/alvations/received_events", "repos_url": "https://api.github.com/users/alvations/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alvations/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvations/subscriptions", "type": "User", "url": "https://api.github.com/users/alvations", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "I am also getting this error with this dataset: https://huggingface.co/datasets/google/IFEval", "Me too, didn't have this issue few hours ago.", "same observation. I even downgraded `datasets==2.20.0` and `huggingface_hub==0.23.5` leading me to believe it's an issue on the server.\r\n\r\nany known workarounds?\r\n", "Not a good idea, but commenting out the whole security block at `/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py` is a temporary workaround:\r\n\r\n```\r\n #security = kwargs.pop(\"security\", None)\r\n #if security is not None:\r\n # security = BlobSecurityInfo(\r\n # safe=security[\"safe\"], av_scan=security[\"avScan\"], pickle_import_scan=security[\"pickleImportScan\"]\r\n # )\r\n #self.security = security\r\n```\r\n", "Uploading a dataset to Huggingface also results in the following error in the Dataset Preview:\r\n```\r\nThe full dataset viewer is not available (click to read why). Only showing a preview of the rows.\r\n'safe'\r\nError code: UnexpectedError\r\nNeed help to make the dataset viewer work? Make sure to review [how to configure the dataset viewer](link1), and [open a discussion](link2) for direct support.\r\n```\r\nI used jsonl format for the dataset in this case. Same exact dataset worked previously.", "Same issue here. Even reverting to older version of `datasets` (e.g., `2.19.0`) results in same error:\r\n\r\n```python\r\n>>> datasets.load_dataset('allenai/ai2_arc', 'ARC-Easy')\r\n\r\nFile \"/Users/lucas/miniforge3/envs/oe-eval-internal/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 3048, in <listcomp>\r\n RepoFile(path_info) if path_info[\"type\"] == \"file\" else RepoFolder(path_info)\r\n File \"/Users/lucas/miniforge3/envs/oe-eval-internal/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 534, in __init__\r\n safe=security[\"safe\"], av_scan=security[\"avScan\"], pickle_import_scan=security[\"pickleImportScan\"]\r\nKeyError: 'safe'\r\n```", "i just had this issue a few minutes ago, crawled the internet and found nothing. came here to open an issue and found this. it is really frustrating. anyone found a fix?", "hi, me and my team have the same problem", "Yeah, this just suddenly appeared without client-side code changes, within the last hours.\r\n\r\nHere's a patch to fix the issue temporarily:\r\n```python\r\nimport huggingface_hub\r\ndef patched_repofolder_init(self, kwargs):\r\n self.path = kwargs.pop(\"path\")\r\n self.tree_id = kwargs.pop(\"oid\")\r\n last_commit = kwargs.pop(\"lastCommit\", None) or kwargs.pop(\"last_commit\", None)\r\n if last_commit is not None:\r\n last_commit = huggingface_hub.hf_api.LastCommitInfo(\r\n oid=last_commit[\"id\"],\r\n title=last_commit[\"title\"],\r\n date=huggingface_hub.utils.parse_datetime(last_commit[\"date\"]),\r\n )\r\n self.last_commit = last_commit\r\n\r\n\r\ndef patched_repo_file_init(self, kwargs):\r\n self.path = kwargs.pop(\"path\")\r\n self.size = kwargs.pop(\"size\")\r\n self.blob_id = kwargs.pop(\"oid\")\r\n lfs = kwargs.pop(\"lfs\", None)\r\n if lfs is not None:\r\n lfs = huggingface_hub.hf_api.BlobLfsInfo(size=lfs[\"size\"], sha256=lfs[\"oid\"], pointer_size=lfs[\"pointerSize\"])\r\n self.lfs = lfs\r\n last_commit = kwargs.pop(\"lastCommit\", None) or kwargs.pop(\"last_commit\", None)\r\n if last_commit is not None:\r\n last_commit = huggingface_hub.hf_api.LastCommitInfo(\r\n oid=last_commit[\"id\"],\r\n title=last_commit[\"title\"],\r\n date=huggingface_hub.utils.parse_datetime(last_commit[\"date\"]),\r\n )\r\n self.last_commit = last_commit\r\n self.security = None\r\n\r\n # backwards compatibility\r\n self.rfilename = self.path\r\n self.lastCommit = self.last_commit\r\n\r\n\r\nhuggingface_hub.hf_api.RepoFile.__init__ = patched_repo_file_init\r\nhuggingface_hub.hf_api.RepoFolder.__init__ = patched_repofolder_init\r\n```\r\n", "Also discussed here:\r\nhttps://discuss.huggingface.co/t/i-keep-getting-keyerror-safe-when-loading-my-datasets/105669/1", "i'm thinking this should be a server issue, i mean no client code was changed on my end. so weird!", "As far as I can tell, this seems to be happening with all datasets that use RepoFolder (probably represents most datasets on huggingface, right?)", "> Here is a temporary fix for the problem: https://discuss.huggingface.co/t/i-keep-getting-keyerror-safe-when-loading-my-datasets/105669/12?u=mlscientist\r\n\r\nthis doesn't seem to work!", "In case you are using Colab or similar, remember to restart your session after modyfing the hf_api.py file", "No need to modify the file directly, just monkey-patch.\r\n\r\nI'm now more sure that the error appears because the backend expects the api code to look like it does on `main`. If `RepoFile` and `RepoFolder` look about like they look on main, they work again.\r\n\r\nIf not fixed like above, a secondary error that will appear is \r\n```\r\n return self.info(path, expand_info=False)[\"type\"] == \"directory\"\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n\r\n \"tree_id\": path_info.tree_id,\r\n ^^^^^^^^^^^^^^^^^\r\nAttributeError: 'RepoFolder' object has no attribute 'tree_id'\r\n```\r\n", "We've reverted the deployment, please let us know if the issue still persists!", "thanks @muellerzr!" ]	2024-09-06T16:26:30Z	2024-09-06T21:14:14Z	2024-09-06T19:09:29Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug The dataset loading was throwing some safety errors for this popular dataset `wmt14`. [in]: ``` import datasets # train_data = datasets.load_dataset("wmt14", "de-en", split="train") train_data = datasets.load_dataset("wmt14", "de-en", split="train") val_data = datasets.load_dataset("wmt14", "de-en", split="validation[:10%]") ``` [out]: ``` --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-9-445f0ecc4817>](https://localhost:8080/#) in <cell line: 4>() 2 3 # train_data = datasets.load_dataset("wmt14", "de-en", split="train") ----> 4 train_data = datasets.load_dataset("wmt14", "de-en", split="train") 5 val_data = datasets.load_dataset("wmt14", "de-en", split="validation[:10%]") 12 frames [/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py](https://localhost:8080/#) in __init__(self, **kwargs) 636 if security is not None: 637 security = BlobSecurityInfo( --> 638 safe=security["safe"], av_scan=security["avScan"], pickle_import_scan=security["pickleImportScan"] 639 ) 640 self.security = security KeyError: 'safe' ``` ### Steps to reproduce the bug See above. ### Expected behavior Dataset properly loaded. ### Environment info version: 2.21.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/7831895?v=4", "events_url": "https://api.github.com/users/muellerzr/events{/privacy}", "followers_url": "https://api.github.com/users/muellerzr/followers", "following_url": "https://api.github.com/users/muellerzr/following{/other_user}", "gists_url": "https://api.github.com/users/muellerzr/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/muellerzr", "id": 7831895, "login": "muellerzr", "node_id": "MDQ6VXNlcjc4MzE4OTU=", "organizations_url": "https://api.github.com/users/muellerzr/orgs", "received_events_url": "https://api.github.com/users/muellerzr/received_events", "repos_url": "https://api.github.com/users/muellerzr/repos", "site_admin": false, "starred_url": "https://api.github.com/users/muellerzr/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/muellerzr/subscriptions", "type": "User", "url": "https://api.github.com/users/muellerzr", "user_view_type": "public" }	{ "+1": 26, "-1": 0, "confused": 3, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 29, "url": "https://api.github.com/repos/huggingface/datasets/issues/7141/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7141/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7418	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7418/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7418/comments	https://api.github.com/repos/huggingface/datasets/issues/7418/events	https://github.com/huggingface/datasets/issues/7418	2,868,701,471	I_kwDODunzps6q_Okf	7,418	pyarrow.lib.arrowinvalid: cannot mix list and non-list, non-null values with map function	{ "avatar_url": "https://avatars.githubusercontent.com/u/15705569?v=4", "events_url": "https://api.github.com/users/alexxchen/events{/privacy}", "followers_url": "https://api.github.com/users/alexxchen/followers", "following_url": "https://api.github.com/users/alexxchen/following{/other_user}", "gists_url": "https://api.github.com/users/alexxchen/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alexxchen", "id": 15705569, "login": "alexxchen", "node_id": "MDQ6VXNlcjE1NzA1NTY5", "organizations_url": "https://api.github.com/users/alexxchen/orgs", "received_events_url": "https://api.github.com/users/alexxchen/received_events", "repos_url": "https://api.github.com/users/alexxchen/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alexxchen/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alexxchen/subscriptions", "type": "User", "url": "https://api.github.com/users/alexxchen", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "@lhoestq ", "Can you try passing text: None for the image object ? Pyarrow expects all the objects to have the exact same type, in particular the dicttionaries in \"content\" should all have the keys \"type\" and \"text\"", "The following modification on system prompt works, but it is different from the usual way to use it.\n```\ndef make_conversation(example):\n prompt = []\n\n prompt.append({\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\": system_prompt}]})\n prompt.append(\n {\n \"role\": \"user\", \n \"content\": [\n {\"type\": \"image\"},\n {\"type\": \"text\", \"text\": example[\"problem\"]},\n ]\n }\n )\n return {\"prompt\": prompt}\n```", "Good to know ! But yes Arrow / Parquet have this typing limitation (which is great to ensure data integrity, but constraining at the same time). It's is really blocking you, feel free to ping the arrow team / community if they plan to have a Union type or a JSON type" ]	2025-02-21T10:58:06Z	2025-02-25T15:26:46Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Encounter pyarrow.lib.arrowinvalid error with map function in some example when loading the dataset ### Steps to reproduce the bug ``` from datasets import load_dataset from PIL import Image, PngImagePlugin dataset = load_dataset("leonardPKU/GEOQA_R1V_Train_8K") system_prompt="You are a helpful AI Assistant" def make_conversation(example): prompt = [] prompt.append({"role": "system", "content": system_prompt}) prompt.append( { "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": example["problem"]}, ] } ) return {"prompt": prompt} def check_data_types(example): for key, value in example.items(): if key == 'image': if not isinstance(value, PngImagePlugin.PngImageFile): print(value) if key == "problem" or key == "solution": if not isinstance(value, str): print(value) return example dataset = dataset.map(check_data_types) dataset = dataset.map(make_conversation) ``` ### Expected behavior Successfully process the dataset with map ### Environment info datasets==3.3.1	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7418/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7418/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6089	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6089/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6089/comments	https://api.github.com/repos/huggingface/datasets/issues/6089/events	https://github.com/huggingface/datasets/issues/6089	1,825,761,476	I_kwDODunzps5s0ujE	6,089	AssertionError: daemonic processes are not allowed to have children	{ "avatar_url": "https://avatars.githubusercontent.com/u/138426806?v=4", "events_url": "https://api.github.com/users/codingl2k1/events{/privacy}", "followers_url": "https://api.github.com/users/codingl2k1/followers", "following_url": "https://api.github.com/users/codingl2k1/following{/other_user}", "gists_url": "https://api.github.com/users/codingl2k1/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/codingl2k1", "id": 138426806, "login": "codingl2k1", "node_id": "U_kgDOCEA5tg", "organizations_url": "https://api.github.com/users/codingl2k1/orgs", "received_events_url": "https://api.github.com/users/codingl2k1/received_events", "repos_url": "https://api.github.com/users/codingl2k1/repos", "site_admin": false, "starred_url": "https://api.github.com/users/codingl2k1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/codingl2k1/subscriptions", "type": "User", "url": "https://api.github.com/users/codingl2k1", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "We could add a \"threads\" parallel backend to `datasets.parallel.parallel_backend` to support downloading with threads but note that `download_and_extract` also decompresses archives, and this is a CPU-intensive task, which is not ideal for (Python) threads (good for IO-intensive tasks).", "> We could add a \"threads\" parallel backend to `datasets.parallel.parallel_backend` to support downloading with threads but note that `download_and_extract` also decompresses archives, and this is a CPU-intensive task, which is not ideal for (Python) threads (good for IO-intensive tasks).\r\n\r\nGreat! Download takes more time than extract, multiple threads can download in parallel, which can speed up a lot." ]	2023-07-28T06:04:00Z	2023-07-31T02:34:02Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug When I load_dataset with num_proc > 0 in a deamon process, I got an error: ```python File "/Users/codingl2k1/Work/datasets/src/datasets/download/download_manager.py", line 564, in download_and_extract return self.extract(self.download(url_or_urls)) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/download/download_manager.py", line 427, in download downloaded_path_or_paths = map_nested( ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 468, in map_nested mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/utils/experimental.py", line 40, in _inner_fn return fn(args, *kwargs) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/parallel/parallel.py", line 34, in parallel_map return _map_with_multiprocessing_pool( ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/parallel/parallel.py", line 64, in _map_with_multiprocessing_pool with Pool(num_proc, initargs=initargs, initializer=initializer) as pool: ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/context.py", line 119, in Pool return Pool(processes, initializer, initargs, maxtasksperchild, ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 215, in __init__ self._repopulate_pool() ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 306, in _repopulate_pool return self._repopulate_pool_static(self._ctx, self.Process, ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 329, in _repopulate_pool_static w.start() File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/process.py", line 118, in start assert not _current_process._config.get('daemon'), ^^^^^^^^^^^^^^^^^ AssertionError: daemonic processes are not allowed to have children ``` The download is io-intensive computing, may be datasets can replece the multi processing pool by a multi threading pool if in a deamon process. ### Steps to reproduce the bug 1. start a deamon process 2. run load_dataset with num_proc > 0 ### Expected behavior No error. ### Environment info Python 3.11.4 datasets latest master	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/6089/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6089/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7377	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7377/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7377/comments	https://api.github.com/repos/huggingface/datasets/issues/7377/events	https://github.com/huggingface/datasets/issues/7377	2,802,723,285	I_kwDODunzps6nDinV	7,377	Support for sparse arrays with the Arrow Sparse Tensor format?	{ "avatar_url": "https://avatars.githubusercontent.com/u/3231217?v=4", "events_url": "https://api.github.com/users/JulesGM/events{/privacy}", "followers_url": "https://api.github.com/users/JulesGM/followers", "following_url": "https://api.github.com/users/JulesGM/following{/other_user}", "gists_url": "https://api.github.com/users/JulesGM/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/JulesGM", "id": 3231217, "login": "JulesGM", "node_id": "MDQ6VXNlcjMyMzEyMTc=", "organizations_url": "https://api.github.com/users/JulesGM/orgs", "received_events_url": "https://api.github.com/users/JulesGM/received_events", "repos_url": "https://api.github.com/users/JulesGM/repos", "site_admin": false, "starred_url": "https://api.github.com/users/JulesGM/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/JulesGM/subscriptions", "type": "User", "url": "https://api.github.com/users/JulesGM", "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 ! Unfortunately the Sparse Tensor structure in Arrow is not part of the Arrow format (yes it's confusing...), so it's not possible to use it in `datasets`. It's a separate structure that doesn't correspond to any type or extension type in Arrow.\n\nThe Arrow community recently added an extension type for fixed shape tensors at https://arrow.apache.org/docs/format/CanonicalExtensions.html#fixed-shape-tensor, it should be possible to contribute an extension type for sparse tensors as well." ]	2025-01-21T20:14:35Z	2025-01-30T14:06:45Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request AI in biology is becoming a big thing. One thing that would be a huge benefit to the field that Huggingface Datasets doesn't currently have is native support for sparse arrays. Arrow has support for sparse tensors. https://arrow.apache.org/docs/format/Other.html#sparse-tensor It would be a big deal if Hugging Face Datasets supported sparse tensors as a feature type, natively. ### Motivation This is important for example in the field of transcriptomics (modeling and understanding gene expression), because a large fraction of the genes are not expressed (zero). More generally, in science, sparse arrays are very common, so adding support for them would be very benefitial, it would make just using Hugging Face Dataset objects a lot more straightforward and clean. ### Your contribution We can discuss this further once the team comments of what they think about the feature, and if there were previous attempts at making it work, and understanding their evaluation of how hard it would be. My intuition is that it should be fairly straightforward, as the Arrow backend already supports it.	null	{ "+1": 2, "-1": 0, "confused": 0, "eyes": 2, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 4, "url": "https://api.github.com/repos/huggingface/datasets/issues/7377/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7377/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5861	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5861/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5861/comments	https://api.github.com/repos/huggingface/datasets/issues/5861/events	https://github.com/huggingface/datasets/pull/5861	1,709,807,340	PR_kwDODunzps5Qf55q	5,861	Better error message when combining dataset dicts instead of datasets	{ "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) \| 0.007167 / 0.011353 (-0.004185) \| 0.004914 / 0.011008 (-0.006094) \| 0.096858 / 0.038508 (0.058350) \| 0.033468 / 0.023109 (0.010359) \| 0.297276 / 0.275898 (0.021378) \| 0.344289 / 0.323480 (0.020809) \| 0.005703 / 0.007986 (-0.002282) \| 0.003972 / 0.004328 (-0.000357) \| 0.075191 / 0.004250 (0.070940) \| 0.046247 / 0.037052 (0.009194) \| 0.317857 / 0.258489 (0.059368) \| 0.347263 / 0.293841 (0.053422) \| 0.035017 / 0.128546 (-0.093529) \| 0.012036 / 0.075646 (-0.063611) \| 0.332522 / 0.419271 (-0.086750) \| 0.050188 / 0.043533 (0.006655) \| 0.296627 / 0.255139 (0.041488) \| 0.319196 / 0.283200 (0.035997) \| 0.101100 / 0.141683 (-0.040583) \| 1.484536 / 1.452155 (0.032382) \| 1.606364 / 1.492716 (0.113648) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.203954 / 0.018006 (0.185948) \| 0.436505 / 0.000490 (0.436015) \| 0.003853 / 0.000200 (0.003654) \| 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) \| 0.025834 / 0.037411 (-0.011578) \| 0.105759 / 0.014526 (0.091233) \| 0.114289 / 0.176557 (-0.062268) \| 0.174388 / 0.737135 (-0.562748) \| 0.122248 / 0.296338 (-0.174090) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.404218 / 0.215209 (0.189009) \| 4.027900 / 2.077655 (1.950245) \| 1.854757 / 1.504120 (0.350637) \| 1.668882 / 1.541195 (0.127687) \| 1.731451 / 1.468490 (0.262961) \| 0.707843 / 4.584777 (-3.876934) \| 3.756386 / 3.745712 (0.010674) \| 2.067751 / 5.269862 (-3.202110) \| 1.313039 / 4.565676 (-3.252638) \| 0.086442 / 0.424275 (-0.337833) \| 0.012329 / 0.007607 (0.004722) \| 0.505964 / 0.226044 (0.279919) \| 5.050788 / 2.268929 (2.781860) \| 2.353936 / 55.444624 (-53.090688) \| 2.055560 / 6.876477 (-4.820917) \| 2.162948 / 2.142072 (0.020876) \| 0.850532 / 4.805227 (-3.954696) \| 0.168560 / 6.500664 (-6.332104) \| 0.063143 / 0.075469 (-0.012326) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.182723 / 1.841788 (-0.659065) \| 14.779342 / 8.074308 (6.705034) \| 14.461572 / 10.191392 (4.270180) \| 0.163120 / 0.680424 (-0.517303) \| 0.017978 / 0.534201 (-0.516223) \| 0.419168 / 0.579283 (-0.160115) \| 0.420955 / 0.434364 (-0.013409) \| 0.509710 / 0.540337 (-0.030628) \| 0.619586 / 1.386936 (-0.767350) \|\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) \| 0.006804 / 0.011353 (-0.004549) \| 0.005136 / 0.011008 (-0.005872) \| 0.074910 / 0.038508 (0.036402) \| 0.032552 / 0.023109 (0.009443) \| 0.374998 / 0.275898 (0.099100) \| 0.399219 / 0.323480 (0.075739) \| 0.005615 / 0.007986 (-0.002371) \| 0.004118 / 0.004328 (-0.000210) \| 0.074219 / 0.004250 (0.069969) \| 0.045924 / 0.037052 (0.008871) \| 0.383228 / 0.258489 (0.124739) \| 0.407195 / 0.293841 (0.113354) \| 0.035460 / 0.128546 (-0.093086) \| 0.012460 / 0.075646 (-0.063187) \| 0.087077 / 0.419271 (-0.332195) \| 0.050507 / 0.043533 (0.006974) \| 0.369001 / 0.255139 (0.113862) \| 0.385761 / 0.283200 (0.102561) \| 0.106999 / 0.141683 (-0.034684) \| 1.465456 / 1.452155 (0.013302) \| 1.556962 / 1.492716 (0.064246) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.214926 / 0.018006 (0.196920) \| 0.436893 / 0.000490 (0.436403) \| 0.003388 / 0.000200 (0.003188) \| 0.000093 / 0.000054 (0.000038) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029919 / 0.037411 (-0.007492) \| 0.110859 / 0.014526 (0.096333) \| 0.120617 / 0.176557 (-0.055939) \| 0.171781 / 0.737135 (-0.565355) \| 0.125627 / 0.296338 (-0.170712) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436024 / 0.215209 (0.220815) \| 4.359167 / 2.077655 (2.281512) \| 2.188399 / 1.504120 (0.684279) \| 2.001196 / 1.541195 (0.460001) \| 2.023710 / 1.468490 (0.555220) \| 0.713799 / 4.584777 (-3.870978) \| 3.832217 / 3.745712 (0.086504) \| 3.269351 / 5.269862 (-2.000510) \| 1.534608 / 4.565676 (-3.031068) \| 0.088505 / 0.424275 (-0.335770) \| 0.012345 / 0.007607 (0.004738) \| 0.542446 / 0.226044 (0.316401) \| 5.377757 / 2.268929 (3.108828) \| 2.659837 / 55.444624 (-52.784787) \| 2.272356 / 6.876477 (-4.604120) \| 2.297289 / 2.142072 (0.155217) \| 0.855276 / 4.805227 (-3.949952) \| 0.170666 / 6.500664 (-6.329998) \| 0.064549 / 0.075469 (-0.010920) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.255938 / 1.841788 (-0.585850) \| 15.151471 / 8.074308 (7.077163) \| 12.905762 / 10.191392 (2.714370) \| 0.162425 / 0.680424 (-0.517999) \| 0.017504 / 0.534201 (-0.516697) \| 0.448671 / 0.579283 (-0.130612) \| 0.422424 / 0.434364 (-0.011940) \| 0.551772 / 0.540337 (0.011434) \| 0.649115 / 1.386936 (-0.737821) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#be73d9f192149727c5542ff257df81b03024fa39 \"CML watermark\")\n", "Having those different checks helps providing an appropriate error message.\r\n\r\nIf the input is a dict, we suggest to select a split. If the input lists is a mix of iterable and non-iterable, we mention that it must be one or the other.", "<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) \| 0.006559 / 0.011353 (-0.004794) \| 0.004569 / 0.011008 (-0.006439) \| 0.104503 / 0.038508 (0.065995) \| 0.028220 / 0.023109 (0.005111) \| 0.365507 / 0.275898 (0.089609) \| 0.400238 / 0.323480 (0.076758) \| 0.004968 / 0.007986 (-0.003017) \| 0.003271 / 0.004328 (-0.001057) \| 0.082804 / 0.004250 (0.078554) \| 0.036299 / 0.037052 (-0.000754) \| 0.361201 / 0.258489 (0.102712) \| 0.410962 / 0.293841 (0.117121) \| 0.030423 / 0.128546 (-0.098123) \| 0.011612 / 0.075646 (-0.064034) \| 0.331820 / 0.419271 (-0.087452) \| 0.043822 / 0.043533 (0.000289) \| 0.356242 / 0.255139 (0.101103) \| 0.393035 / 0.283200 (0.109836) \| 0.088426 / 0.141683 (-0.053257) \| 1.484139 / 1.452155 (0.031984) \| 1.566712 / 1.492716 (0.073995) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.195887 / 0.018006 (0.177880) \| 0.402720 / 0.000490 (0.402231) \| 0.003516 / 0.000200 (0.003316) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023270 / 0.037411 (-0.014141) \| 0.095834 / 0.014526 (0.081308) \| 0.102924 / 0.176557 (-0.073632) \| 0.161397 / 0.737135 (-0.575738) \| 0.105225 / 0.296338 (-0.191114) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.451701 / 0.215209 (0.236491) \| 4.495171 / 2.077655 (2.417517) \| 2.223203 / 1.504120 (0.719083) \| 2.035533 / 1.541195 (0.494338) \| 2.076182 / 1.468490 (0.607692) \| 0.697317 / 4.584777 (-3.887460) \| 3.406309 / 3.745712 (-0.339403) \| 1.847179 / 5.269862 (-3.422683) \| 1.158762 / 4.565676 (-3.406914) \| 0.083067 / 0.424275 (-0.341208) \| 0.012453 / 0.007607 (0.004846) \| 0.546502 / 0.226044 (0.320458) \| 5.455712 / 2.268929 (3.186784) \| 2.654142 / 55.444624 (-52.790483) \| 2.298722 / 6.876477 (-4.577755) \| 2.383467 / 2.142072 (0.241395) \| 0.805950 / 4.805227 (-3.999278) \| 0.152479 / 6.500664 (-6.348185) \| 0.066784 / 0.075469 (-0.008685) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.239129 / 1.841788 (-0.602659) \| 13.603707 / 8.074308 (5.529398) \| 14.062004 / 10.191392 (3.870612) \| 0.130928 / 0.680424 (-0.549495) \| 0.016907 / 0.534201 (-0.517294) \| 0.381614 / 0.579283 (-0.197670) \| 0.386770 / 0.434364 (-0.047594) \| 0.455792 / 0.540337 (-0.084545) \| 0.526092 / 1.386936 (-0.860844) \|\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) \| 0.006202 / 0.011353 (-0.005151) \| 0.004478 / 0.011008 (-0.006531) \| 0.076492 / 0.038508 (0.037984) \| 0.026703 / 0.023109 (0.003594) \| 0.355134 / 0.275898 (0.079236) \| 0.391207 / 0.323480 (0.067727) \| 0.004852 / 0.007986 (-0.003133) \| 0.003271 / 0.004328 (-0.001057) \| 0.075080 / 0.004250 (0.070830) \| 0.038803 / 0.037052 (0.001750) \| 0.359530 / 0.258489 (0.101041) \| 0.409044 / 0.293841 (0.115203) \| 0.030366 / 0.128546 (-0.098180) \| 0.011544 / 0.075646 (-0.064102) \| 0.084849 / 0.419271 (-0.334423) \| 0.040076 / 0.043533 (-0.003457) \| 0.357359 / 0.255139 (0.102220) \| 0.384075 / 0.283200 (0.100875) \| 0.089130 / 0.141683 (-0.052552) \| 1.520400 / 1.452155 (0.068246) \| 1.604403 / 1.492716 (0.111687) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.257127 / 0.018006 (0.239121) \| 0.403691 / 0.000490 (0.403202) \| 0.006894 / 0.000200 (0.006694) \| 0.000088 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024653 / 0.037411 (-0.012758) \| 0.098834 / 0.014526 (0.084309) \| 0.107276 / 0.176557 (-0.069281) \| 0.158256 / 0.737135 (-0.578879) \| 0.111339 / 0.296338 (-0.184999) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.445006 / 0.215209 (0.229797) \| 4.452953 / 2.077655 (2.375299) \| 2.168291 / 1.504120 (0.664171) \| 1.969457 / 1.541195 (0.428262) \| 2.003505 / 1.468490 (0.535015) \| 0.695857 / 4.584777 (-3.888920) \| 3.433424 / 3.745712 (-0.312288) \| 2.466977 / 5.269862 (-2.802885) \| 1.528167 / 4.565676 (-3.037509) \| 0.082425 / 0.424275 (-0.341850) \| 0.012470 / 0.007607 (0.004863) \| 0.559039 / 0.226044 (0.332995) \| 5.609496 / 2.268929 (3.340568) \| 2.602898 / 55.444624 (-52.841726) \| 2.273971 / 6.876477 (-4.602506) \| 2.303370 / 2.142072 (0.161298) \| 0.803875 / 4.805227 (-4.001352) \| 0.151069 / 6.500664 (-6.349595) \| 0.067956 / 0.075469 (-0.007513) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.334443 / 1.841788 (-0.507345) \| 13.773252 / 8.074308 (5.698944) \| 13.007042 / 10.191392 (2.815650) \| 0.127939 / 0.680424 (-0.552485) \| 0.016412 / 0.534201 (-0.517789) \| 0.374744 / 0.579283 (-0.204539) \| 0.396912 / 0.434364 (-0.037452) \| 0.443197 / 0.540337 (-0.097140) \| 0.528338 / 1.386936 (-0.858598) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#51d9f2a3064aa89a780e3d02c6cc34000c51c4fb \"CML watermark\")\n", "Just modified it to use only one loop. I think I managed to keep it readable as well", "<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) \| 0.007382 / 0.011353 (-0.003971) \| 0.005143 / 0.011008 (-0.005865) \| 0.097635 / 0.038508 (0.059127) \| 0.034726 / 0.023109 (0.011616) \| 0.315556 / 0.275898 (0.039658) \| 0.355951 / 0.323480 (0.032472) \| 0.006055 / 0.007986 (-0.001931) \| 0.004264 / 0.004328 (-0.000065) \| 0.073636 / 0.004250 (0.069386) \| 0.050480 / 0.037052 (0.013428) \| 0.316031 / 0.258489 (0.057542) \| 0.363933 / 0.293841 (0.070092) \| 0.035138 / 0.128546 (-0.093408) \| 0.012407 / 0.075646 (-0.063239) \| 0.333677 / 0.419271 (-0.085595) \| 0.050586 / 0.043533 (0.007053) \| 0.309507 / 0.255139 (0.054369) \| 0.327043 / 0.283200 (0.043844) \| 0.108975 / 0.141683 (-0.032708) \| 1.447778 / 1.452155 (-0.004377) \| 1.519971 / 1.492716 (0.027255) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.248770 / 0.018006 (0.230764) \| 0.603036 / 0.000490 (0.602546) \| 0.000383 / 0.000200 (0.000183) \| 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) \| 0.027094 / 0.037411 (-0.010317) \| 0.104427 / 0.014526 (0.089901) \| 0.120627 / 0.176557 (-0.055929) \| 0.178790 / 0.737135 (-0.558346) \| 0.124877 / 0.296338 (-0.171461) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.414442 / 0.215209 (0.199233) \| 4.138009 / 2.077655 (2.060355) \| 1.964642 / 1.504120 (0.460523) \| 1.775940 / 1.541195 (0.234745) \| 1.899719 / 1.468490 (0.431228) \| 0.695406 / 4.584777 (-3.889371) \| 3.760470 / 3.745712 (0.014758) \| 3.906958 / 5.269862 (-1.362904) \| 2.028164 / 4.565676 (-2.537513) \| 0.086704 / 0.424275 (-0.337571) \| 0.012465 / 0.007607 (0.004857) \| 0.512336 / 0.226044 (0.286292) \| 5.108587 / 2.268929 (2.839659) \| 2.435273 / 55.444624 (-53.009352) \| 2.142387 / 6.876477 (-4.734090) \| 2.258234 / 2.142072 (0.116162) \| 0.854035 / 4.805227 (-3.951193) \| 0.170443 / 6.500664 (-6.330222) \| 0.065762 / 0.075469 (-0.009707) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.187529 / 1.841788 (-0.654259) \| 15.151164 / 8.074308 (7.076856) \| 14.577545 / 10.191392 (4.386153) \| 0.166973 / 0.680424 (-0.513450) \| 0.017883 / 0.534201 (-0.516318) \| 0.427607 / 0.579283 (-0.151676) \| 0.417050 / 0.434364 (-0.017314) \| 0.508116 / 0.540337 (-0.032221) \| 0.590173 / 1.386936 (-0.796763) \|\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) \| 0.007499 / 0.011353 (-0.003854) \| 0.005195 / 0.011008 (-0.005813) \| 0.073600 / 0.038508 (0.035091) \| 0.033574 / 0.023109 (0.010464) \| 0.377506 / 0.275898 (0.101608) \| 0.432752 / 0.323480 (0.109272) \| 0.006042 / 0.007986 (-0.001944) \| 0.006427 / 0.004328 (0.002098) \| 0.071666 / 0.004250 (0.067416) \| 0.053243 / 0.037052 (0.016190) \| 0.363972 / 0.258489 (0.105483) \| 0.454988 / 0.293841 (0.161147) \| 0.035118 / 0.128546 (-0.093428) \| 0.012395 / 0.075646 (-0.063251) \| 0.084308 / 0.419271 (-0.334963) \| 0.048589 / 0.043533 (0.005057) \| 0.368036 / 0.255139 (0.112897) \| 0.399414 / 0.283200 (0.116215) \| 0.109043 / 0.141683 (-0.032640) \| 1.462972 / 1.452155 (0.010817) \| 1.574443 / 1.492716 (0.081726) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.215107 / 0.018006 (0.197101) \| 0.550255 / 0.000490 (0.549765) \| 0.004630 / 0.000200 (0.004430) \| 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) \| 0.029948 / 0.037411 (-0.007463) \| 0.111866 / 0.014526 (0.097340) \| 0.126559 / 0.176557 (-0.049997) \| 0.181443 / 0.737135 (-0.555693) \| 0.130559 / 0.296338 (-0.165779) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441410 / 0.215209 (0.226201) \| 4.403406 / 2.077655 (2.325752) \| 2.180276 / 1.504120 (0.676156) \| 2.003729 / 1.541195 (0.462534) \| 2.079394 / 1.468490 (0.610904) \| 0.706061 / 4.584777 (-3.878716) \| 3.805668 / 3.745712 (0.059956) \| 3.864941 / 5.269862 (-1.404921) \| 1.970468 / 4.565676 (-2.595208) \| 0.086033 / 0.424275 (-0.338242) \| 0.012261 / 0.007607 (0.004654) \| 0.550427 / 0.226044 (0.324383) \| 5.542270 / 2.268929 (3.273342) \| 2.717047 / 55.444624 (-52.727577) \| 2.449022 / 6.876477 (-4.427455) \| 2.549567 / 2.142072 (0.407495) \| 0.854981 / 4.805227 (-3.950247) \| 0.169756 / 6.500664 (-6.330908) \| 0.067082 / 0.075469 (-0.008387) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.281369 / 1.841788 (-0.560419) \| 15.445090 / 8.074308 (7.370781) \| 13.205652 / 10.191392 (3.014260) \| 0.170070 / 0.680424 (-0.510354) \| 0.017815 / 0.534201 (-0.516385) \| 0.425193 / 0.579283 (-0.154090) \| 0.425205 / 0.434364 (-0.009159) \| 0.493561 / 0.540337 (-0.046776) \| 0.588994 / 1.386936 (-0.797942) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e427105fc68fce04d0f3c74efb942cbf3a65d166 \"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) \| 0.006345 / 0.011353 (-0.005008) \| 0.004330 / 0.011008 (-0.006678) \| 0.096327 / 0.038508 (0.057819) \| 0.032964 / 0.023109 (0.009855) \| 0.335600 / 0.275898 (0.059702) \| 0.365635 / 0.323480 (0.042155) \| 0.005435 / 0.007986 (-0.002551) \| 0.005005 / 0.004328 (0.000677) \| 0.071107 / 0.004250 (0.066856) \| 0.044363 / 0.037052 (0.007311) \| 0.339988 / 0.258489 (0.081498) \| 0.375575 / 0.293841 (0.081734) \| 0.028343 / 0.128546 (-0.100203) \| 0.008587 / 0.075646 (-0.067059) \| 0.324349 / 0.419271 (-0.094922) \| 0.050105 / 0.043533 (0.006573) \| 0.327398 / 0.255139 (0.072259) \| 0.348479 / 0.283200 (0.065279) \| 0.102357 / 0.141683 (-0.039326) \| 1.419905 / 1.452155 (-0.032250) \| 1.534887 / 1.492716 (0.042171) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212418 / 0.018006 (0.194412) \| 0.433183 / 0.000490 (0.432693) \| 0.000595 / 0.000200 (0.000395) \| 0.000062 / 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) \| 0.027520 / 0.037411 (-0.009891) \| 0.109503 / 0.014526 (0.094977) \| 0.118202 / 0.176557 (-0.058355) \| 0.177236 / 0.737135 (-0.559899) \| 0.123736 / 0.296338 (-0.172602) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.405734 / 0.215209 (0.190525) \| 4.039566 / 2.077655 (1.961911) \| 1.838211 / 1.504120 (0.334091) \| 1.652650 / 1.541195 (0.111456) \| 1.753488 / 1.468490 (0.284998) \| 0.525258 / 4.584777 (-4.059519) \| 3.704509 / 3.745712 (-0.041203) \| 1.826794 / 5.269862 (-3.443067) \| 1.236361 / 4.565676 (-3.329315) \| 0.065619 / 0.424275 (-0.358656) \| 0.011606 / 0.007607 (0.003999) \| 0.505954 / 0.226044 (0.279910) \| 5.054140 / 2.268929 (2.785211) \| 2.352587 / 55.444624 (-53.092037) \| 2.050601 / 6.876477 (-4.825875) \| 2.097222 / 2.142072 (-0.044850) \| 0.641044 / 4.805227 (-4.164183) \| 0.140676 / 6.500664 (-6.359988) \| 0.063217 / 0.075469 (-0.012253) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.177750 / 1.841788 (-0.664038) \| 14.819346 / 8.074308 (6.745038) \| 14.085937 / 10.191392 (3.894545) \| 0.168618 / 0.680424 (-0.511806) \| 0.017189 / 0.534201 (-0.517011) \| 0.393415 / 0.579283 (-0.185868) \| 0.422879 / 0.434364 (-0.011485) \| 0.477289 / 0.540337 (-0.063048) \| 0.569078 / 1.386936 (-0.817858) \|\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) \| 0.006502 / 0.011353 (-0.004850) \| 0.004640 / 0.011008 (-0.006368) \| 0.073272 / 0.038508 (0.034764) \| 0.033225 / 0.023109 (0.010116) \| 0.359165 / 0.275898 (0.083267) \| 0.391659 / 0.323480 (0.068179) \| 0.005684 / 0.007986 (-0.002302) \| 0.004045 / 0.004328 (-0.000284) \| 0.072880 / 0.004250 (0.068629) \| 0.046260 / 0.037052 (0.009208) \| 0.361772 / 0.258489 (0.103283) \| 0.402905 / 0.293841 (0.109064) \| 0.027732 / 0.128546 (-0.100814) \| 0.008864 / 0.075646 (-0.066783) \| 0.081961 / 0.419271 (-0.337310) \| 0.046170 / 0.043533 (0.002637) \| 0.364198 / 0.255139 (0.109059) \| 0.387468 / 0.283200 (0.104269) \| 0.105456 / 0.141683 (-0.036227) \| 1.457176 / 1.452155 (0.005021) \| 1.564899 / 1.492716 (0.072183) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.179129 / 0.018006 (0.161123) \| 0.439699 / 0.000490 (0.439209) \| 0.002882 / 0.000200 (0.002682) \| 0.000090 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029123 / 0.037411 (-0.008288) \| 0.112046 / 0.014526 (0.097520) \| 0.122773 / 0.176557 (-0.053784) \| 0.178404 / 0.737135 (-0.558732) \| 0.127904 / 0.296338 (-0.168434) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440413 / 0.215209 (0.225204) \| 4.407334 / 2.077655 (2.329680) \| 2.112932 / 1.504120 (0.608812) \| 1.911034 / 1.541195 (0.369840) \| 2.057168 / 1.468490 (0.588677) \| 0.525472 / 4.584777 (-4.059305) \| 3.738894 / 3.745712 (-0.006818) \| 1.807592 / 5.269862 (-3.462270) \| 1.053837 / 4.565676 (-3.511839) \| 0.066203 / 0.424275 (-0.358072) \| 0.011965 / 0.007607 (0.004358) \| 0.541137 / 0.226044 (0.315093) \| 5.415040 / 2.268929 (3.146112) \| 2.580476 / 55.444624 (-52.864148) \| 2.234144 / 6.876477 (-4.642333) \| 2.306014 / 2.142072 (0.163942) \| 0.644221 / 4.805227 (-4.161006) \| 0.142870 / 6.500664 (-6.357794) \| 0.065015 / 0.075469 (-0.010454) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.303465 / 1.841788 (-0.538323) \| 14.949683 / 8.074308 (6.875375) \| 14.370871 / 10.191392 (4.179478) \| 0.142714 / 0.680424 (-0.537710) \| 0.017372 / 0.534201 (-0.516829) \| 0.403898 / 0.579283 (-0.175385) \| 0.424781 / 0.434364 (-0.009583) \| 0.465984 / 0.540337 (-0.074353) \| 0.570863 / 1.386936 (-0.816074) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#22d1d533e8ab831b1aa1aab3e7d3c72ba42a83e8 \"CML watermark\")\n" ]	2023-05-15T10:36:24Z	2023-05-23T10:40:13Z	2023-05-23T10:32:58Z	MEMBER	null	null	null	close https://github.com/huggingface/datasets/issues/5851	{ "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/5861/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5861/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5861.diff", "html_url": "https://github.com/huggingface/datasets/pull/5861", "merged_at": "2023-05-23T10:32:58Z", "patch_url": "https://github.com/huggingface/datasets/pull/5861.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5861" }
https://api.github.com/repos/huggingface/datasets/issues/7462	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7462/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7462/comments	https://api.github.com/repos/huggingface/datasets/issues/7462/events	https://github.com/huggingface/datasets/pull/7462	2,925,612,945	PR_kwDODunzps6O9EA1	7,462	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_7462). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]	2025-03-17T16:00:53Z	2025-03-17T16:03:31Z	2025-03-17T16:01:08Z	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/7462/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7462/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7462.diff", "html_url": "https://github.com/huggingface/datasets/pull/7462", "merged_at": "2025-03-17T16:01:08Z", "patch_url": "https://github.com/huggingface/datasets/pull/7462.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7462" }
https://api.github.com/repos/huggingface/datasets/issues/6326	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6326/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6326/comments	https://api.github.com/repos/huggingface/datasets/issues/6326/events	https://github.com/huggingface/datasets/pull/6326	1,955,420,536	PR_kwDODunzps5dcSRa	6,326	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	[]	2023-10-21T10:07:48Z	2023-10-23T14:56:20Z	2023-10-23T14:56:20Z	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/6326/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6326/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6326.diff", "html_url": "https://github.com/huggingface/datasets/pull/6326", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6326.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6326" }
https://api.github.com/repos/huggingface/datasets/issues/5389	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5389/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5389/comments	https://api.github.com/repos/huggingface/datasets/issues/5389/events	https://github.com/huggingface/datasets/pull/5389	1,509,348,626	PR_kwDODunzps5GHsOo	5,389	Fix link in `load_dataset` docstring	{ "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==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) \| 0.008935 / 0.011353 (-0.002417) \| 0.004582 / 0.011008 (-0.006426) \| 0.100950 / 0.038508 (0.062442) \| 0.030305 / 0.023109 (0.007196) \| 0.299759 / 0.275898 (0.023861) \| 0.378577 / 0.323480 (0.055097) \| 0.007834 / 0.007986 (-0.000152) \| 0.003399 / 0.004328 (-0.000930) \| 0.078568 / 0.004250 (0.074318) \| 0.037990 / 0.037052 (0.000938) \| 0.313025 / 0.258489 (0.054536) \| 0.359543 / 0.293841 (0.065702) \| 0.033631 / 0.128546 (-0.094916) \| 0.011681 / 0.075646 (-0.063966) \| 0.324542 / 0.419271 (-0.094729) \| 0.041014 / 0.043533 (-0.002519) \| 0.302884 / 0.255139 (0.047745) \| 0.337059 / 0.283200 (0.053859) \| 0.089403 / 0.141683 (-0.052280) \| 1.491262 / 1.452155 (0.039108) \| 1.521626 / 1.492716 (0.028910) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.172627 / 0.018006 (0.154621) \| 0.419406 / 0.000490 (0.418917) \| 0.001974 / 0.000200 (0.001775) \| 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) \| 0.023598 / 0.037411 (-0.013814) \| 0.098127 / 0.014526 (0.083601) \| 0.105611 / 0.176557 (-0.070946) \| 0.142612 / 0.737135 (-0.594523) \| 0.121687 / 0.296338 (-0.174651) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418512 / 0.215209 (0.203303) \| 4.173099 / 2.077655 (2.095444) \| 1.865900 / 1.504120 (0.361780) \| 1.664053 / 1.541195 (0.122858) \| 1.726289 / 1.468490 (0.257799) \| 0.693214 / 4.584777 (-3.891563) \| 3.499982 / 3.745712 (-0.245730) \| 1.894278 / 5.269862 (-3.375583) \| 1.178214 / 4.565676 (-3.387463) \| 0.082391 / 0.424275 (-0.341884) \| 0.012486 / 0.007607 (0.004878) \| 0.532190 / 0.226044 (0.306145) \| 5.286612 / 2.268929 (3.017684) \| 2.316680 / 55.444624 (-53.127944) \| 1.964020 / 6.876477 (-4.912457) \| 2.016457 / 2.142072 (-0.125616) \| 0.812290 / 4.805227 (-3.992937) \| 0.149102 / 6.500664 (-6.351562) \| 0.064215 / 0.075469 (-0.011254) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.281919 / 1.841788 (-0.559869) \| 14.107509 / 8.074308 (6.033201) \| 13.892369 / 10.191392 (3.700977) \| 0.146164 / 0.680424 (-0.534260) \| 0.028740 / 0.534201 (-0.505460) \| 0.395218 / 0.579283 (-0.184066) \| 0.406321 / 0.434364 (-0.028043) \| 0.460880 / 0.540337 (-0.079458) \| 0.545975 / 1.386936 (-0.840961) \|\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) \| 0.006797 / 0.011353 (-0.004556) \| 0.004522 / 0.011008 (-0.006486) \| 0.098440 / 0.038508 (0.059932) \| 0.027722 / 0.023109 (0.004613) \| 0.423995 / 0.275898 (0.148097) \| 0.456164 / 0.323480 (0.132684) \| 0.005156 / 0.007986 (-0.002830) \| 0.003439 / 0.004328 (-0.000889) \| 0.075307 / 0.004250 (0.071057) \| 0.039599 / 0.037052 (0.002547) \| 0.423671 / 0.258489 (0.165181) \| 0.463841 / 0.293841 (0.170001) \| 0.032473 / 0.128546 (-0.096073) \| 0.011674 / 0.075646 (-0.063972) \| 0.320548 / 0.419271 (-0.098723) \| 0.041618 / 0.043533 (-0.001915) \| 0.426133 / 0.255139 (0.170994) \| 0.443018 / 0.283200 (0.159819) \| 0.091103 / 0.141683 (-0.050579) \| 1.468758 / 1.452155 (0.016604) \| 1.532695 / 1.492716 (0.039978) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.255314 / 0.018006 (0.237308) \| 0.422982 / 0.000490 (0.422492) \| 0.015405 / 0.000200 (0.015205) \| 0.000103 / 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) \| 0.025260 / 0.037411 (-0.012152) \| 0.102062 / 0.014526 (0.087537) \| 0.108161 / 0.176557 (-0.068395) \| 0.144205 / 0.737135 (-0.592930) \| 0.111686 / 0.296338 (-0.184653) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.482633 / 0.215209 (0.267424) \| 4.824777 / 2.077655 (2.747123) \| 2.488626 / 1.504120 (0.984506) \| 2.285410 / 1.541195 (0.744215) \| 2.336793 / 1.468490 (0.868303) \| 0.701894 / 4.584777 (-3.882883) \| 3.506908 / 3.745712 (-0.238804) \| 3.399789 / 5.269862 (-1.870072) \| 1.536359 / 4.565676 (-3.029317) \| 0.083621 / 0.424275 (-0.340655) \| 0.012702 / 0.007607 (0.005094) \| 0.581259 / 0.226044 (0.355215) \| 5.829640 / 2.268929 (3.560711) \| 2.932201 / 55.444624 (-52.512424) \| 2.577175 / 6.876477 (-4.299301) \| 2.621782 / 2.142072 (0.479710) \| 0.812074 / 4.805227 (-3.993153) \| 0.152840 / 6.500664 (-6.347824) \| 0.067982 / 0.075469 (-0.007487) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.274915 / 1.841788 (-0.566873) \| 14.345800 / 8.074308 (6.271492) \| 14.242475 / 10.191392 (4.051083) \| 0.143636 / 0.680424 (-0.536788) \| 0.016824 / 0.534201 (-0.517377) \| 0.376449 / 0.579283 (-0.202834) \| 0.394219 / 0.434364 (-0.040145) \| 0.435368 / 0.540337 (-0.104969) \| 0.518393 / 1.386936 (-0.868544) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#187e4faa978fef267a055f6988564f922e51eaa4 \"CML watermark\")\n", "I also fixed the rest of the links that point to the markdown files. \r\n\r\nPS: the CI failures are unrelated ", "<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) \| 0.008641 / 0.011353 (-0.002712) \| 0.004560 / 0.011008 (-0.006448) \| 0.100559 / 0.038508 (0.062051) \| 0.029744 / 0.023109 (0.006635) \| 0.300580 / 0.275898 (0.024682) \| 0.359100 / 0.323480 (0.035620) \| 0.007016 / 0.007986 (-0.000970) \| 0.003393 / 0.004328 (-0.000936) \| 0.078649 / 0.004250 (0.074399) \| 0.038138 / 0.037052 (0.001086) \| 0.307730 / 0.258489 (0.049241) \| 0.347678 / 0.293841 (0.053837) \| 0.033630 / 0.128546 (-0.094917) \| 0.011452 / 0.075646 (-0.064194) \| 0.320903 / 0.419271 (-0.098369) \| 0.042659 / 0.043533 (-0.000874) \| 0.298886 / 0.255139 (0.043747) \| 0.324371 / 0.283200 (0.041171) \| 0.092582 / 0.141683 (-0.049101) \| 1.490017 / 1.452155 (0.037863) \| 1.512825 / 1.492716 (0.020109) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.178965 / 0.018006 (0.160958) \| 0.420001 / 0.000490 (0.419512) \| 0.002686 / 0.000200 (0.002486) \| 0.000071 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023568 / 0.037411 (-0.013843) \| 0.097027 / 0.014526 (0.082502) \| 0.104721 / 0.176557 (-0.071836) \| 0.148757 / 0.737135 (-0.588378) \| 0.110849 / 0.296338 (-0.185489) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415034 / 0.215209 (0.199825) \| 4.155249 / 2.077655 (2.077594) \| 1.837027 / 1.504120 (0.332907) \| 1.627754 / 1.541195 (0.086559) \| 1.687958 / 1.468490 (0.219468) \| 0.699542 / 4.584777 (-3.885235) \| 3.376707 / 3.745712 (-0.369005) \| 2.900778 / 5.269862 (-2.369083) \| 1.556168 / 4.565676 (-3.009508) \| 0.082438 / 0.424275 (-0.341837) \| 0.012339 / 0.007607 (0.004732) \| 0.524952 / 0.226044 (0.298907) \| 5.269852 / 2.268929 (3.000924) \| 2.278770 / 55.444624 (-53.165854) \| 1.917987 / 6.876477 (-4.958490) \| 1.955000 / 2.142072 (-0.187072) \| 0.821169 / 4.805227 (-3.984058) \| 0.149019 / 6.500664 (-6.351645) \| 0.064604 / 0.075469 (-0.010865) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.199768 / 1.841788 (-0.642020) \| 13.760897 / 8.074308 (5.686589) \| 13.911550 / 10.191392 (3.720158) \| 0.161727 / 0.680424 (-0.518697) \| 0.028615 / 0.534201 (-0.505586) \| 0.393917 / 0.579283 (-0.185366) \| 0.392524 / 0.434364 (-0.041840) \| 0.451763 / 0.540337 (-0.088574) \| 0.536880 / 1.386936 (-0.850056) \|\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) \| 0.006407 / 0.011353 (-0.004946) \| 0.004420 / 0.011008 (-0.006588) \| 0.097244 / 0.038508 (0.058736) \| 0.027114 / 0.023109 (0.004005) \| 0.412512 / 0.275898 (0.136614) \| 0.448189 / 0.323480 (0.124709) \| 0.005831 / 0.007986 (-0.002155) \| 0.005423 / 0.004328 (0.001095) \| 0.076051 / 0.004250 (0.071801) \| 0.038828 / 0.037052 (0.001776) \| 0.414586 / 0.258489 (0.156097) \| 0.457196 / 0.293841 (0.163355) \| 0.031615 / 0.128546 (-0.096931) \| 0.011542 / 0.075646 (-0.064104) \| 0.316967 / 0.419271 (-0.102304) \| 0.041278 / 0.043533 (-0.002254) \| 0.411371 / 0.255139 (0.156232) \| 0.436376 / 0.283200 (0.153177) \| 0.090212 / 0.141683 (-0.051471) \| 1.461831 / 1.452155 (0.009677) \| 1.606515 / 1.492716 (0.113799) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.221453 / 0.018006 (0.203447) \| 0.404140 / 0.000490 (0.403650) \| 0.000422 / 0.000200 (0.000222) \| 0.000060 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024588 / 0.037411 (-0.012824) \| 0.098604 / 0.014526 (0.084078) \| 0.113682 / 0.176557 (-0.062874) \| 0.141141 / 0.737135 (-0.595994) \| 0.110069 / 0.296338 (-0.186270) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.477267 / 0.215209 (0.262058) \| 4.775086 / 2.077655 (2.697431) \| 2.445449 / 1.504120 (0.941329) \| 2.242220 / 1.541195 (0.701025) \| 2.303542 / 1.468490 (0.835051) \| 0.693448 / 4.584777 (-3.891329) \| 3.413319 / 3.745712 (-0.332393) \| 3.052734 / 5.269862 (-2.217127) \| 1.434075 / 4.565676 (-3.131602) \| 0.082429 / 0.424275 (-0.341846) \| 0.012594 / 0.007607 (0.004987) \| 0.584259 / 0.226044 (0.358214) \| 5.865098 / 2.268929 (3.596169) \| 2.926301 / 55.444624 (-52.518324) \| 2.572555 / 6.876477 (-4.303921) \| 2.608584 / 2.142072 (0.466512) \| 0.805029 / 4.805227 (-4.000198) \| 0.151247 / 6.500664 (-6.349417) \| 0.067142 / 0.075469 (-0.008327) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.285454 / 1.841788 (-0.556334) \| 14.296425 / 8.074308 (6.222117) \| 14.147278 / 10.191392 (3.955886) \| 0.151698 / 0.680424 (-0.528726) \| 0.016876 / 0.534201 (-0.517325) \| 0.383302 / 0.579283 (-0.195981) \| 0.388461 / 0.434364 (-0.045902) \| 0.438286 / 0.540337 (-0.102051) \| 0.525249 / 1.386936 (-0.861687) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2a3b2f04f1fd62249ac43c534761ce151ad5c269 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008677 / 0.011353 (-0.002676) \| 0.004863 / 0.011008 (-0.006145) \| 0.096606 / 0.038508 (0.058098) \| 0.034004 / 0.023109 (0.010895) \| 0.296362 / 0.275898 (0.020464) \| 0.323445 / 0.323480 (-0.000035) \| 0.007341 / 0.007986 (-0.000644) \| 0.005518 / 0.004328 (0.001189) \| 0.073584 / 0.004250 (0.069334) \| 0.041471 / 0.037052 (0.004419) \| 0.302183 / 0.258489 (0.043694) \| 0.339369 / 0.293841 (0.045528) \| 0.037375 / 0.128546 (-0.091171) \| 0.011827 / 0.075646 (-0.063819) \| 0.330723 / 0.419271 (-0.088549) \| 0.048751 / 0.043533 (0.005218) \| 0.298370 / 0.255139 (0.043231) \| 0.317781 / 0.283200 (0.034582) \| 0.097488 / 0.141683 (-0.044195) \| 1.456242 / 1.452155 (0.004088) \| 1.530149 / 1.492716 (0.037433) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207053 / 0.018006 (0.189046) \| 0.438165 / 0.000490 (0.437675) \| 0.001161 / 0.000200 (0.000961) \| 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) \| 0.025353 / 0.037411 (-0.012059) \| 0.105536 / 0.014526 (0.091010) \| 0.116122 / 0.176557 (-0.060434) \| 0.151605 / 0.737135 (-0.585530) \| 0.121777 / 0.296338 (-0.174561) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.402780 / 0.215209 (0.187571) \| 4.017882 / 2.077655 (1.940227) \| 1.813111 / 1.504120 (0.308991) \| 1.620000 / 1.541195 (0.078805) \| 1.649186 / 1.468490 (0.180696) \| 0.687523 / 4.584777 (-3.897254) \| 3.712595 / 3.745712 (-0.033117) \| 2.038535 / 5.269862 (-3.231326) \| 1.414794 / 4.565676 (-3.150882) \| 0.083357 / 0.424275 (-0.340918) \| 0.012032 / 0.007607 (0.004425) \| 0.502899 / 0.226044 (0.276854) \| 5.038914 / 2.268929 (2.769985) \| 2.250476 / 55.444624 (-53.194148) \| 1.919954 / 6.876477 (-4.956523) \| 1.930928 / 2.142072 (-0.211144) \| 0.826634 / 4.805227 (-3.978593) \| 0.161599 / 6.500664 (-6.339066) \| 0.061356 / 0.075469 (-0.014113) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.228998 / 1.841788 (-0.612790) \| 14.587914 / 8.074308 (6.513606) \| 14.237514 / 10.191392 (4.046122) \| 0.190913 / 0.680424 (-0.489510) \| 0.029104 / 0.534201 (-0.505097) \| 0.436160 / 0.579283 (-0.143123) \| 0.431464 / 0.434364 (-0.002900) \| 0.511670 / 0.540337 (-0.028668) \| 0.609046 / 1.386936 (-0.777890) \|\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) \| 0.006980 / 0.011353 (-0.004373) \| 0.005260 / 0.011008 (-0.005748) \| 0.095288 / 0.038508 (0.056780) \| 0.032465 / 0.023109 (0.009356) \| 0.410799 / 0.275898 (0.134901) \| 0.423814 / 0.323480 (0.100334) \| 0.005533 / 0.007986 (-0.002452) \| 0.005764 / 0.004328 (0.001436) \| 0.070713 / 0.004250 (0.066462) \| 0.048193 / 0.037052 (0.011141) \| 0.405742 / 0.258489 (0.147253) \| 0.458773 / 0.293841 (0.164932) \| 0.036415 / 0.128546 (-0.092131) \| 0.012192 / 0.075646 (-0.063454) \| 0.330655 / 0.419271 (-0.088617) \| 0.055945 / 0.043533 (0.012412) \| 0.407497 / 0.255139 (0.152358) \| 0.421496 / 0.283200 (0.138296) \| 0.106285 / 0.141683 (-0.035398) \| 1.459837 / 1.452155 (0.007683) \| 1.573147 / 1.492716 (0.080431) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.205776 / 0.018006 (0.187770) \| 0.441523 / 0.000490 (0.441033) \| 0.003073 / 0.000200 (0.002873) \| 0.000092 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029207 / 0.037411 (-0.008205) \| 0.110295 / 0.014526 (0.095770) \| 0.130233 / 0.176557 (-0.046324) \| 0.157489 / 0.737135 (-0.579647) \| 0.125374 / 0.296338 (-0.170965) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440942 / 0.215209 (0.225733) \| 4.389647 / 2.077655 (2.311992) \| 2.234883 / 1.504120 (0.730763) \| 2.029510 / 1.541195 (0.488315) \| 2.082503 / 1.468490 (0.614013) \| 0.698046 / 4.584777 (-3.886731) \| 3.769127 / 3.745712 (0.023415) \| 2.058511 / 5.269862 (-3.211351) \| 1.324302 / 4.565676 (-3.241375) \| 0.085695 / 0.424275 (-0.338580) \| 0.012122 / 0.007607 (0.004515) \| 0.552406 / 0.226044 (0.326362) \| 5.527073 / 2.268929 (3.258145) \| 2.711354 / 55.444624 (-52.733270) \| 2.328848 / 6.876477 (-4.547629) \| 2.340750 / 2.142072 (0.198678) \| 0.846300 / 4.805227 (-3.958927) \| 0.167465 / 6.500664 (-6.333199) \| 0.063419 / 0.075469 (-0.012050) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.262452 / 1.841788 (-0.579336) \| 15.043537 / 8.074308 (6.969229) \| 14.212563 / 10.191392 (4.021171) \| 0.170229 / 0.680424 (-0.510194) \| 0.017696 / 0.534201 (-0.516505) \| 0.423194 / 0.579283 (-0.156089) \| 0.430908 / 0.434364 (-0.003456) \| 0.491733 / 0.540337 (-0.048604) \| 0.599267 / 1.386936 (-0.787669) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2a3b2f04f1fd62249ac43c534761ce151ad5c269 \"CML watermark\")\n", "Program enthusiastic " ]	2022-12-23T13:26:31Z	2023-01-25T19:00:43Z	2023-01-24T16:33:38Z	COLLABORATOR	null	null	null	Fix https://github.com/huggingface/datasets/issues/5387, fix https://github.com/huggingface/datasets/issues/4566	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": 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"rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5389/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5389/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5389.diff", "html_url": "https://github.com/huggingface/datasets/pull/5389", "merged_at": "2023-01-24T16:33:38Z", "patch_url": "https://github.com/huggingface/datasets/pull/5389.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5389" }
https://api.github.com/repos/huggingface/datasets/issues/7009	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7009/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7009/comments	https://api.github.com/repos/huggingface/datasets/issues/7009/events	https://github.com/huggingface/datasets/pull/7009	2,379,619,132	PR_kwDODunzps5z2Xe6	7,009	Support ruff 0.5.0 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" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7009). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005481 / 0.011353 (-0.005872) \| 0.003580 / 0.011008 (-0.007428) \| 0.062682 / 0.038508 (0.024174) \| 0.031125 / 0.023109 (0.008015) \| 0.239443 / 0.275898 (-0.036455) \| 0.262950 / 0.323480 (-0.060529) \| 0.003129 / 0.007986 (-0.004857) \| 0.003393 / 0.004328 (-0.000935) \| 0.048765 / 0.004250 (0.044514) \| 0.044363 / 0.037052 (0.007311) \| 0.248632 / 0.258489 (-0.009857) \| 0.285056 / 0.293841 (-0.008785) \| 0.029674 / 0.128546 (-0.098872) \| 0.011963 / 0.075646 (-0.063684) \| 0.204122 / 0.419271 (-0.215150) \| 0.035867 / 0.043533 (-0.007665) \| 0.245422 / 0.255139 (-0.009717) \| 0.267165 / 0.283200 (-0.016035) \| 0.018556 / 0.141683 (-0.123127) \| 1.132112 / 1.452155 (-0.320043) \| 1.173512 / 1.492716 (-0.319204) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092749 / 0.018006 (0.074743) \| 0.298946 / 0.000490 (0.298457) \| 0.000211 / 0.000200 (0.000011) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019496 / 0.037411 (-0.017915) \| 0.062209 / 0.014526 (0.047683) \| 0.074656 / 0.176557 (-0.101901) \| 0.121238 / 0.737135 (-0.615897) \| 0.075810 / 0.296338 (-0.220528) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278089 / 0.215209 (0.062880) \| 2.725602 / 2.077655 (0.647948) \| 1.413346 / 1.504120 (-0.090774) \| 1.290352 / 1.541195 (-0.250843) \| 1.306732 / 1.468490 (-0.161758) \| 0.713945 / 4.584777 (-3.870832) \| 2.380131 / 3.745712 (-1.365581) \| 2.804548 / 5.269862 (-2.465314) \| 1.896506 / 4.565676 (-2.669170) \| 0.078303 / 0.424275 (-0.345972) \| 0.005475 / 0.007607 (-0.002132) \| 0.340162 / 0.226044 (0.114117) \| 3.355732 / 2.268929 (1.086803) \| 1.776012 / 55.444624 (-53.668613) \| 1.507006 / 6.876477 (-5.369471) \| 1.607234 / 2.142072 (-0.534838) \| 0.796458 / 4.805227 (-4.008769) \| 0.135888 / 6.500664 (-6.364776) \| 0.042352 / 0.075469 (-0.033118) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.988337 / 1.841788 (-0.853450) \| 11.299311 / 8.074308 (3.225003) \| 9.166845 / 10.191392 (-1.024547) \| 0.140351 / 0.680424 (-0.540073) \| 0.013932 / 0.534201 (-0.520269) \| 0.302157 / 0.579283 (-0.277126) \| 0.259355 / 0.434364 (-0.175009) \| 0.339850 / 0.540337 (-0.200488) \| 0.465345 / 1.386936 (-0.921591) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005707 / 0.011353 (-0.005646) \| 0.003846 / 0.011008 (-0.007162) \| 0.050100 / 0.038508 (0.011591) \| 0.031810 / 0.023109 (0.008701) \| 0.265120 / 0.275898 (-0.010778) \| 0.286635 / 0.323480 (-0.036845) \| 0.004329 / 0.007986 (-0.003657) \| 0.002757 / 0.004328 (-0.001571) \| 0.050864 / 0.004250 (0.046614) \| 0.039872 / 0.037052 (0.002820) \| 0.277675 / 0.258489 (0.019186) \| 0.310251 / 0.293841 (0.016410) \| 0.032458 / 0.128546 (-0.096088) \| 0.012072 / 0.075646 (-0.063574) \| 0.060539 / 0.419271 (-0.358733) \| 0.033772 / 0.043533 (-0.009761) \| 0.265992 / 0.255139 (0.010853) \| 0.286152 / 0.283200 (0.002953) \| 0.018210 / 0.141683 (-0.123473) \| 1.151461 / 1.452155 (-0.300694) \| 1.199998 / 1.492716 (-0.292718) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094109 / 0.018006 (0.076103) \| 0.298190 / 0.000490 (0.297701) \| 0.000199 / 0.000200 (-0.000001) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022431 / 0.037411 (-0.014980) \| 0.076319 / 0.014526 (0.061794) \| 0.090023 / 0.176557 (-0.086533) \| 0.128189 / 0.737135 (-0.608946) \| 0.089564 / 0.296338 (-0.206774) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298887 / 0.215209 (0.083678) \| 2.928580 / 2.077655 (0.850926) \| 1.565379 / 1.504120 (0.061259) \| 1.424704 / 1.541195 (-0.116490) \| 1.446336 / 1.468490 (-0.022154) \| 0.716348 / 4.584777 (-3.868429) \| 0.967465 / 3.745712 (-2.778247) \| 2.967318 / 5.269862 (-2.302544) \| 1.918878 / 4.565676 (-2.646798) \| 0.077167 / 0.424275 (-0.347108) \| 0.005271 / 0.007607 (-0.002336) \| 0.342376 / 0.226044 (0.116332) \| 3.386044 / 2.268929 (1.117115) \| 1.915308 / 55.444624 (-53.529316) \| 1.612729 / 6.876477 (-5.263748) \| 1.621278 / 2.142072 (-0.520794) \| 0.804639 / 4.805227 (-4.000589) \| 0.132596 / 6.500664 (-6.368069) \| 0.041075 / 0.075469 (-0.034394) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.996521 / 1.841788 (-0.845267) \| 12.328856 / 8.074308 (4.254548) \| 10.585154 / 10.191392 (0.393762) \| 0.131720 / 0.680424 (-0.548704) \| 0.016777 / 0.534201 (-0.517424) \| 0.300424 / 0.579283 (-0.278860) \| 0.128526 / 0.434364 (-0.305838) \| 0.339961 / 0.540337 (-0.200377) \| 0.441661 / 1.386936 (-0.945275) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a16477ddf8f96e590e9597225a5d180cce343f26 \"CML watermark\")\n" ]	2024-06-28T05:37:36Z	2024-06-28T07:17:26Z	2024-06-28T07:11:17Z	MEMBER	null	null	null	Support ruff 0.5.0 in CI and revert: - #7007 Fix #7008.	{ "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/7009/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7009/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7009.diff", "html_url": "https://github.com/huggingface/datasets/pull/7009", "merged_at": "2024-06-28T07:11:17Z", "patch_url": "https://github.com/huggingface/datasets/pull/7009.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7009" }
https://api.github.com/repos/huggingface/datasets/issues/7384	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7384/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7384/comments	https://api.github.com/repos/huggingface/datasets/issues/7384/events	https://github.com/huggingface/datasets/pull/7384	2,828,208,828	PR_kwDODunzps6J4wVi	7,384	Support async functions in map()	{ "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_7384). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "example of what you can do with it:\r\n\r\n```python\r\nimport aiohttp\r\nfrom huggingface_hub import get_token\r\n\r\nfrom datasets import Dataset\r\n\r\n\r\nAPI_URL = \"https://api-inference.huggingface.co/models/microsoft/Phi-3-mini-4k-instruct/v1/chat/completions\"\r\nPROMPT = \"What is this text mainly about ? Here is the text:\\n\\n```\\n{Problem}\\n```\\n\\nReply in one or two words.\"\r\n\r\nasync def query(example):\r\n headers = {\"Authorization\": f\"Bearer {get_token()}\", \"Content-Type\": \"application/json\"}\r\n json = {\"messages\": [{\"role\": \"user\", \"content\": PROMPT.format(Problem=example[\"Problem\"])}], \"max_tokens\": 20, \"seed\": 42}\r\n async with aiohttp.ClientSession() as session, session.post(API_URL, headers=headers, json=json) as response:\r\n output = await response.json()\r\n return {\"output\": output[\"choices\"][0][\"message\"][\"content\"]}\r\n\r\nds = Dataset.from_dict({\"Problem\": [\"1 + 1\"] * 10})\r\nds = ds.map(query)\r\nprint(ds[0])\r\n# {'Problem': '1 + 1', 'output': 'Arithmetic'}\r\n```" ]	2025-02-03T18:18:40Z	2025-02-13T14:01:13Z	2025-02-13T14:00:06Z	MEMBER	null	null	null	e.g. to download images or call an inference API like HF or vLLM ```python import asyncio import random from datasets import Dataset async def f(x): await asyncio.sleep(random.random()) ds = Dataset.from_dict({"data": range(100)}) ds.map(f) # Map: 100%\|█████████████████████████████\| 100/100 [00:01<00:00, 99.81 examples/s] ``` TODO - [x] clean code (right now it's a big copy paste) - [x] batched - [x] Dataset.map() - [x] IterableDataset.map() - [x] Dataset.filter() - [x] IterableDataset.filter() - [x] test - [x] docs	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 3, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 3, "url": "https://api.github.com/repos/huggingface/datasets/issues/7384/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7384/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7384.diff", "html_url": "https://github.com/huggingface/datasets/pull/7384", "merged_at": "2025-02-13T14:00:06Z", "patch_url": "https://github.com/huggingface/datasets/pull/7384.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7384" }
https://api.github.com/repos/huggingface/datasets/issues/6687	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6687/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6687/comments	https://api.github.com/repos/huggingface/datasets/issues/6687/events	https://github.com/huggingface/datasets/pull/6687	2,148,554,178	PR_kwDODunzps5nnqBB	6,687	fsspec: support fsspec>=2023.12.0 glob changes	{ "avatar_url": "https://avatars.githubusercontent.com/u/651988?v=4", "events_url": "https://api.github.com/users/pmrowla/events{/privacy}", "followers_url": "https://api.github.com/users/pmrowla/followers", "following_url": "https://api.github.com/users/pmrowla/following{/other_user}", "gists_url": "https://api.github.com/users/pmrowla/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/pmrowla", "id": 651988, "login": "pmrowla", "node_id": "MDQ6VXNlcjY1MTk4OA==", "organizations_url": "https://api.github.com/users/pmrowla/orgs", "received_events_url": "https://api.github.com/users/pmrowla/received_events", "repos_url": "https://api.github.com/users/pmrowla/repos", "site_admin": false, "starred_url": "https://api.github.com/users/pmrowla/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/pmrowla/subscriptions", "type": "User", "url": "https://api.github.com/users/pmrowla", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6687). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Looking into the CI failure, this PR is incompatible with `huggingface-hub>=0.20.0`. It looks like there were several changes made to HfFileSystem in 0.20.0, @lhoestq any ideas on what the issue might be in particular?\r\n\r\na bisect indicates that it's related to https://github.com/huggingface/huggingface_hub/pull/1815", "It looks like huggingface-hub's `HfFileSystem.glob` is broken for exact string matches (that don't contain glob wildcards) when combining `huggingface-hub>=0.20.0` and `fsspec>=2023.12.0`.\r\n\r\nI did a quick test with huggingface-hub `main`, and adding this test case to `tests/test_hf_filesystem::HfFileSystemTests::test_glob` (https://github.com/huggingface/huggingface_hub/blob/main/tests/test_hf_file_system.py) passes with `fsspec==2023.10.0` and fails with `fsspec==2023.12.0`\r\n```python\r\n self.assertEqual(\r\n sorted(self.hffs.glob(self.hf_path + \"/.gitattributes\")),\r\n sorted([self.hf_path + \"/.gitattributes\"]),\r\n )\r\n\r\n```\r\n\r\nthe `hffs.glob()` call with a pattern that does not contain any wildcards returns an empty list:\r\n```\r\nE AssertionError: Lists differ: [] != ['datasets/__DUMMY_TRANSFORMERS_USER__/rep[35 chars]tes']\r\nE\r\nE Second list contains 1 additional elements.\r\nE First extra element 0:\r\nE 'datasets/__DUMMY_TRANSFORMERS_USER__/repo-7d0ae9-17091013467064/.gitattributes'\r\nE\r\nE - []\r\nE + ['datasets/__DUMMY_TRANSFORMERS_USER__/repo-7d0ae9-17091013467064/.gitattributes']\r\n```\r\n(and with the compatible/passing older fsspec versions the glob call returns the single exact file match as expected)\r\n\r\nSo it looks like the CI failure here isn't directly related to this PR. The failing patterns that don't contain any `*` wildcards are generated by `datasets` with or without this PR, but now that this PR installs the incompatible fsspec version, the underlying `HfFileSystem.glob()` call ends up failing.", "I just opened https://github.com/huggingface/huggingface_hub/pull/2056 to fix this.\r\n\r\nDo you mind if I continue this PR to run the CI against `huggingface_hub@main` until the fix is released ?\r\n\r\nEDIT: the fix has been released in `huggingface_hub` 0.21.2 - I removed my commits that were using `huggingface_hub@main`", "I just added two additional patterns to cover cases like `test-data/xxx.csv` and `data-test/xxx.csv`", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005461 / 0.011353 (-0.005892) \| 0.003861 / 0.011008 (-0.007148) \| 0.063252 / 0.038508 (0.024744) \| 0.031474 / 0.023109 (0.008364) \| 0.250321 / 0.275898 (-0.025577) \| 0.275198 / 0.323480 (-0.048282) \| 0.003275 / 0.007986 (-0.004710) \| 0.002874 / 0.004328 (-0.001454) \| 0.049499 / 0.004250 (0.045248) \| 0.045334 / 0.037052 (0.008282) \| 0.266347 / 0.258489 (0.007858) \| 0.308974 / 0.293841 (0.015133) \| 0.027742 / 0.128546 (-0.100804) \| 0.010274 / 0.075646 (-0.065373) \| 0.207516 / 0.419271 (-0.211755) \| 0.036538 / 0.043533 (-0.006995) \| 0.247949 / 0.255139 (-0.007190) \| 0.268986 / 0.283200 (-0.014214) \| 0.019842 / 0.141683 (-0.121841) \| 1.117547 / 1.452155 (-0.334607) \| 1.175813 / 1.492716 (-0.316903) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.103661 / 0.018006 (0.085655) \| 0.331023 / 0.000490 (0.330534) \| 0.000240 / 0.000200 (0.000040) \| 0.000041 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019767 / 0.037411 (-0.017645) \| 0.061500 / 0.014526 (0.046974) \| 0.075899 / 0.176557 (-0.100658) \| 0.122240 / 0.737135 (-0.614895) \| 0.074621 / 0.296338 (-0.221717) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287501 / 0.215209 (0.072292) \| 2.794737 / 2.077655 (0.717082) \| 1.505362 / 1.504120 (0.001242) \| 1.379481 / 1.541195 (-0.161713) \| 1.394836 / 1.468490 (-0.073654) \| 0.545803 / 4.584777 (-4.038974) \| 2.364167 / 3.745712 (-1.381545) \| 2.800923 / 5.269862 (-2.468939) \| 1.723910 / 4.565676 (-2.841766) \| 0.061270 / 0.424275 (-0.363005) \| 0.005006 / 0.007607 (-0.002601) \| 0.334952 / 0.226044 (0.108908) \| 3.367122 / 2.268929 (1.098194) \| 1.839822 / 55.444624 (-53.604803) \| 1.553774 / 6.876477 (-5.322703) \| 1.583585 / 2.142072 (-0.558488) \| 0.624680 / 4.805227 (-4.180547) \| 0.116364 / 6.500664 (-6.384300) \| 0.042412 / 0.075469 (-0.033057) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975207 / 1.841788 (-0.866580) \| 11.843126 / 8.074308 (3.768818) \| 9.418537 / 10.191392 (-0.772855) \| 0.130648 / 0.680424 (-0.549775) \| 0.013747 / 0.534201 (-0.520454) \| 0.288195 / 0.579283 (-0.291088) \| 0.269861 / 0.434364 (-0.164503) \| 0.326732 / 0.540337 (-0.213606) \| 0.441256 / 1.386936 (-0.945680) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005185 / 0.011353 (-0.006168) \| 0.003836 / 0.011008 (-0.007172) \| 0.050057 / 0.038508 (0.011549) \| 0.030929 / 0.023109 (0.007820) \| 0.263558 / 0.275898 (-0.012340) \| 0.284553 / 0.323480 (-0.038927) \| 0.004331 / 0.007986 (-0.003655) \| 0.002815 / 0.004328 (-0.001513) \| 0.050187 / 0.004250 (0.045936) \| 0.048431 / 0.037052 (0.011379) \| 0.271005 / 0.258489 (0.012515) \| 0.304749 / 0.293841 (0.010908) \| 0.029286 / 0.128546 (-0.099260) \| 0.010598 / 0.075646 (-0.065048) \| 0.058111 / 0.419271 (-0.361160) \| 0.053665 / 0.043533 (0.010132) \| 0.257574 / 0.255139 (0.002436) \| 0.285802 / 0.283200 (0.002602) \| 0.018917 / 0.141683 (-0.122766) \| 1.206517 / 1.452155 (-0.245638) \| 1.220572 / 1.492716 (-0.272144) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.122466 / 0.018006 (0.104460) \| 0.567887 / 0.000490 (0.567397) \| 0.000321 / 0.000200 (0.000121) \| 0.000049 / 0.000054 (-0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022120 / 0.037411 (-0.015292) \| 0.075456 / 0.014526 (0.060931) \| 0.086385 / 0.176557 (-0.090171) \| 0.126045 / 0.737135 (-0.611091) \| 0.087502 / 0.296338 (-0.208837) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304847 / 0.215209 (0.089638) \| 3.008095 / 2.077655 (0.930441) \| 1.726178 / 1.504120 (0.222058) \| 1.592332 / 1.541195 (0.051138) \| 1.603714 / 1.468490 (0.135224) \| 0.576875 / 4.584777 (-4.007902) \| 2.450884 / 3.745712 (-1.294828) \| 2.719073 / 5.269862 (-2.550789) \| 1.775261 / 4.565676 (-2.790415) \| 0.063144 / 0.424275 (-0.361131) \| 0.005122 / 0.007607 (-0.002485) \| 0.350004 / 0.226044 (0.123960) \| 3.467146 / 2.268929 (1.198218) \| 2.062907 / 55.444624 (-53.381717) \| 1.798793 / 6.876477 (-5.077684) \| 1.921204 / 2.142072 (-0.220868) \| 0.651832 / 4.805227 (-4.153396) \| 0.122326 / 6.500664 (-6.378338) \| 0.041396 / 0.075469 (-0.034073) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.024859 / 1.841788 (-0.816928) \| 12.569744 / 8.074308 (4.495436) \| 10.448487 / 10.191392 (0.257095) \| 0.131529 / 0.680424 (-0.548895) \| 0.014853 / 0.534201 (-0.519348) \| 0.287683 / 0.579283 (-0.291600) \| 0.289814 / 0.434364 (-0.144550) \| 0.323935 / 0.540337 (-0.216403) \| 0.425208 / 1.386936 (-0.961728) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ba71e92c59c9bd9d1ee6168691977f0c4728ed6e \"CML watermark\")\n", "> EDIT: the fix has been released in `huggingface_hub` 0.21.2 - I removed my commits that were using `huggingface_hub@main`\r\n\r\nPlease note that people using `huggingface_hub` < 0.21.2 and latest `fsspec` will have issues when using `datasets`:\r\n- https://github.com/huggingface/lighteval/actions/runs/8139147047/job/22241658122?pr=86\r\n- https://github.com/huggingface/lighteval/pull/84\r\n\r\nCC: @clefourrier \r\n" ]	2024-02-22T08:59:32Z	2024-03-04T12:59:42Z	2024-02-29T15:12:17Z	CONTRIBUTOR	null	null	null	- adds support for the `fs.glob` changes introduced in `fsspec==2023.12.0` and unpins the current upper bound Should close #6644 Should close #6645 The `test_data_files` glob/pattern tests pass for me in: - `fsspec==2023.10.0` (the pinned max version in datasets `main`) - `fsspec==2023.12.0` (#6644) - `fsspec==2024.2.0` (#6645)	{ "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": 5, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 5, "url": "https://api.github.com/repos/huggingface/datasets/issues/6687/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6687/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6687.diff", "html_url": "https://github.com/huggingface/datasets/pull/6687", "merged_at": "2024-02-29T15:12:17Z", "patch_url": "https://github.com/huggingface/datasets/pull/6687.patch", 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https://api.github.com/repos/huggingface/datasets/issues/5602	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5602/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5602/comments	https://api.github.com/repos/huggingface/datasets/issues/5602/events	https://github.com/huggingface/datasets/pull/5602	1,607,054,110	PR_kwDODunzps5LJGfa	5,602	Return dict structure if columns are lists - to_tf_dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/22614925?v=4", "events_url": "https://api.github.com/users/amyeroberts/events{/privacy}", "followers_url": "https://api.github.com/users/amyeroberts/followers", "following_url": "https://api.github.com/users/amyeroberts/following{/other_user}", "gists_url": "https://api.github.com/users/amyeroberts/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/amyeroberts", "id": 22614925, "login": "amyeroberts", "node_id": "MDQ6VXNlcjIyNjE0OTI1", "organizations_url": "https://api.github.com/users/amyeroberts/orgs", "received_events_url": "https://api.github.com/users/amyeroberts/received_events", "repos_url": "https://api.github.com/users/amyeroberts/repos", "site_admin": false, "starred_url": "https://api.github.com/users/amyeroberts/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/amyeroberts/subscriptions", "type": "User", "url": "https://api.github.com/users/amyeroberts", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5602). All of your documentation changes will be reflected on that endpoint.", "This is a great PR! Thinking about the UX though, maybe we could do it without the extra argument? Before this PR, the logic in `to_tf_dataset` was that if the user passed a single column name in either `columns` or `label_cols`, we converted it to a length-1 list. Then, later in the code, we convert output dicts with only one key to naked Tensors.\r\n\r\nWould it be easier if we removed the argument, but instead treated the cases differently? Passing a column name as a string could yield a single naked Tensor in the output as before, but passing a list of length 1 would yield a full dict? That way if you wanted dict output with a single key you could just say `columns=[col_name]`.\r\n\r\n(I'm not totally convinced this is a good idea yet, it just seems like it might be more intuitive)", "@Rocketknight1 Happy to implement it that way - it's certainly cleaner to not have another arg. In this case, am I right in saying we'd effectively set `return_dict` [here](https://github.com/huggingface/datasets/blob/6569014a9948eab7d031a3587405e64ba92d6c59/src/datasets/arrow_dataset.py#L410) - where columns are made into a list if they were a string? \r\n\r\nThere only concern I have is this changes the default behaviour, which might break things for people who were happily using `columns=[\"my_col_str\"]` before. \r\n\r\n\r\n", "@amyeroberts That's correct! Probably the simplest way to implement it would be to just add the flag there.\r\n\r\nAnd yeah, I'm aware this might be a slightly breaking change, but we've mostly tried to move users to `prepare_tf_dataset` in `transformers` at this point, so hopefully as long as that method doesn't break then most users won't be negatively affected by the change.", "@lhoestq @Rocketknight1 - I've remove the `return_dict` argument and implemented @Rocketknight1 's suggestion. LMK what you think :) ", "@lhoestq Of course :) I've opened a draft PR here for the updates needed in transformers examples and docs to keep the returned data structure consistent: https://github.com/huggingface/transformers/pull/21935. Note: even with the different structure, `model.fit` can still successfully be called. \r\n\r\nFor the [link you shared](https://github.com/huggingface/datasets/pull/url) - for me it returns a 404 error. Is there another link I could follow to see how to run the transformers CI with this branch? \r\n\r\nCurrently looking into the failing tests 😭 ", "Oh sorry - I fixed the URL: https://github.com/huggingface/transformers/commit/4eb55bbd593adf2e49362613ee32a11ddc4a854d", "The error shows `There appear to be 80 leaked shared_memory objects to clean up at shutdown`. IIRC to_tf_dataset does some shared memory stuff for multiprocessing - maybe @Rocketknight1 you know what's going on ?", "@lhoestq That warning appears anytime you interrupt a process using Python `SharedMemory` objects - it's only a problem if you still get the error when the process finishes normally! Our implementation of `to_tf_dataset` should clean things up properly.", "Ok, not sure why it fails then :/", "Hmm, will investigate! Sorry, I misread - I thought that warning was coming up in the context of another error", "IMO outputing different types based on nuances in the input could confuse users.\r\n\r\nAlso, in the ideal scenario,`to_tf_function` should return a `tf.data.Dataset` that iterates over the underlying Arrow data and yields (unprocessed) dicts of TF tensors, and all the model-specific code should live in Transformers (e.g., in `prepare_tf_dataset`). So the goal would be to make `to_tf_dataset` more user-friendly, not more complex :).", "I think we agree @mariosasko :) \r\n\r\n> Also, in the ideal scenario,to_tf_function should return a tf.data.Dataset that iterates over the underlying Arrow data and yields (unprocessed) dicts of TF tensors\r\n\r\nThis I'll leave for another PR as it's outside the scope of this one and @Rocketknight1 will have far more knowledge and ideas about what is possible\r\n\r\n> all the model-specific code should live in Transformers (e.g., in prepare_tf_dataset\r\n\r\nAgreed! This PR isn't really a model specific change - although it was highlighted when trying to train a model. We definitely want to move model specific things out of datasets as much as possible. \r\n\r\n> IMO outputing different types based on nuances in the input could confuse users.\r\n> So the goal would be to make to_tf_dataset more user-friendly, not more complex :).\r\n\r\nThe aim was to move more towards being able to return the dict of TF tensors you suggest, whilst maintaining backwards compatibility. Personally, I found it surprising to be returned a tuple structure when I was using `to_tf_dataset`. The aim was to make `to_tf_dataset` more user friendly, but I agree that it has the potential to be confusing. \r\n\r\nFor context, the thought process behind this design was to: \r\n* Not add even more arguments to `to_tf_dataset`. \r\n* Have a feature selection -> return type logic in keeping with `datasets` e.g. `dataset['train'][:10]['feat1']` returns a list of values, whereas `dataset['train'][:10]['feat1', 'feat2']` returns a dictionary. \r\n\r\nVery happy to add any suggestions or changes you might have about how to make this design better! :) \r\n", "Hi ! Anything blocking here ? I'b be happy to help", "Hi @lhoestq - sorry this hasn't been very active for the past ~1.5 weeks. There's nothing specific blocking, other than not being able to replicate without running on CI, and still need to test a bit more to narrow down the issue. I should have time tomorrow to pick it up again :) ", "@lhoestq @Rocketknight1 Friendly ping for a review :) ", "Awesome ! What about showing a warning that this change is about to happen in the next version of `datasets`, and then apply this change in a subsequent major release ? This way folks at twitter won't hate us: https://github.com/twitter/the-algorithm/blob/138bb519975407d4ea0dc1478d897d451ef05dab/trust_and_safety_models/toxicity/data/mb_generator.py#L142-L148", "@lhoestq Sounds good! How would you like this warning to happen? I could open a PR to add a warning message within `to_tf_dataset`?", "Yup sounds good :)" ]	2023-03-02T15:51:12Z	2023-04-12T15:54:53Z	null	CONTRIBUTOR	null	null	null	This PR introduces new logic to `to_tf_dataset` affecting the returned data structure, enabling a dictionary structure to be returned, even if only one feature column is selected. If the passed in `columns` or `label_cols` to `to_tf_dataset` are a list, they are returned as a dictionary, respectively. If they are a string, the tensor is returned. An outline of the behaviour: ``` dataset,to_tf_dataset(columns=["col_1"], label_cols="col_2") # ({'col_1': col_1}, col_2} dataset,to_tf_dataset(columns="col1", label_cols="col_2") # (col1, col2) dataset,to_tf_dataset(columns="col1") # col1 dataset,to_tf_dataset(columns=["col_1"], labels=["col_2"]) # ({'col1': tensor}, {'col2': tensor}} dataset,to_tf_dataset(columns="col_1", labels=["col_2"]) # (col1, {'col2': tensor}} ``` ## Motivation Currently, when calling `to_tf_dataset`, the returned dataset will always return a tuple structure if a single feature column is used. This can cause issues when calling `model.fit` on models which train without labels e.g. [TFVitMAEForPreTraining](https://github.com/huggingface/transformers/blob/b6f47b539377ac1fd845c7adb4ccaa5eb514e126/src/transformers/models/vit_mae/modeling_vit_mae.py#L849). Specifically, [this line](https://github.com/huggingface/transformers/blob/d9e28d91a8b2d09b51a33155d3a03ad9fcfcbd1f/src/transformers/modeling_tf_utils.py#L1521) where it's assumed the input `x` is a dictionary if there is no label. ## Example Previous behaviour ```python In [1]: import tensorflow as tf ...: from datasets import load_dataset ...: ...: ...: def transform(batch): ...: def _transform_img(img): ...: img = img.convert("RGB") ...: img = tf.keras.utils.img_to_array(img) ...: img = tf.image.resize(img, (224, 224)) ...: img /= 255.0 ...: img = tf.transpose(img, perm=[2, 0, 1]) ...: return img ...: batch['pixel_values'] = [_transform_img(pil_img) for pil_img in batch['img']] ...: return batch ...: ...: ...: def collate_fn(examples): ...: pixel_values = tf.stack([example["pixel_values"] for example in examples]) ...: return {"pixel_values": pixel_values} ...: ...: ...: dataset = load_dataset('cifar10')['train'] ...: dataset = dataset.with_transform(transform) ...: dataset.to_tf_dataset(batch_size=8, columns=['pixel_values'], collate_fn=collate_fn) Out[1]: <PrefetchDataset element_spec=TensorSpec(shape=(None, 3, 224, 224), dtype=tf.float32, name=None)> ``` New behaviour ```python In [1]: import tensorflow as tf ...: from datasets import load_dataset ...: ...: ...: def transform(batch): ...: def _transform_img(img): ...: img = img.convert("RGB") ...: img = tf.keras.utils.img_to_array(img) ...: img = tf.image.resize(img, (224, 224)) ...: img /= 255.0 ...: img = tf.transpose(img, perm=[2, 0, 1]) ...: return img ...: batch['pixel_values'] = [_transform_img(pil_img) for pil_img in batch['img']] ...: return batch ...: ...: ...: def collate_fn(examples): ...: pixel_values = tf.stack([example["pixel_values"] for example in examples]) ...: return {"pixel_values": pixel_values} ...: ...: ...: dataset = load_dataset('cifar10')['train'] ...: dataset = dataset.with_transform(transform) ...: dataset.to_tf_dataset(batch_size=8, columns=['pixel_values'], collate_fn=collate_fn) Out[1]: <PrefetchDataset element_spec={'pixel_values': TensorSpec(shape=(None, 3, 224, 224), dtype=tf.float32, name=None)}> ```	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/5602/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5602/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5602.diff", "html_url": "https://github.com/huggingface/datasets/pull/5602", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5602.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5602" }
https://api.github.com/repos/huggingface/datasets/issues/6704	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6704/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6704/comments	https://api.github.com/repos/huggingface/datasets/issues/6704/events	https://github.com/huggingface/datasets/pull/6704	2,163,752,391	PR_kwDODunzps5obZyj	6,704	Improve default patterns resolution	{ "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_6704). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Awesome !\r\n\r\nNote that it can still create duplicates if a path matches several dir patterns, e.g.\r\n\r\n```\r\ndata/train-train/data/txt\r\n```\r\nmatches two dir patterns:\r\n```\r\n*/{keyword}[{sep}]/\r\n/[{sep}]{keyword}/\r\n```\r\n\r\nPS: feel free to update your branch, I just updated ruff on `main`", "Yes, I didn't mention that case on purpose 🙂. One solution would be deprecating the `/[{sep}]{keyword}/**` pattern (and eventually removing it). This way, the directory patterns would align more with the filename ones. Or do you think this is too big of a breaking change?", "I think it's too big of a breaking change yes :/ (and would make the docs / logic more complex for users to get imo) Though I think your approach is already a nice step in the right direction", "These changes to the `resolve_pattern` function lead to 20-30x faster local file resolution in my benchmarks.", "Nice ! Though since `fsspec` caches the filesystem, is there a risk when adding new files and reloading a dataset ?\r\n\r\n\r\n```python\r\nwith open(\"my/local/dir/0000.txt\", \"w\") as f:\r\n f.write(\"Hello there\")\r\nd1 = load_dataset(\"my/local/dir\")\r\nwith open(\"my/local/dir/0001.txt\", \"w\") as f:\r\n f.write(\"General Kenobi\")\r\nd2 = load_dataset(\"my/local/dir\")\r\nassert list(d1) != list(d2)\r\n```", "Yes. But I think I have a solution for this.", "I'm not satisfied with the context manager approach...\r\n\r\nA clean solution would require a bigger rewrite of the resolution logic (e.g., merging `get_data_patterns` and `DataFilesDict.from_patterns` into a `get_data_files` function that would build the `DataFilesDict` by matching the paths using `fs.find` and `fsspec.utils.glob_translate` (available in `fsspec>=2023.12.0`))\r\n\r\nThe current changes make the local resolution 2-3x faster, which is good enough for now, I think.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004888 / 0.011353 (-0.006465) \| 0.003267 / 0.011008 (-0.007742) \| 0.065117 / 0.038508 (0.026609) \| 0.029416 / 0.023109 (0.006306) \| 0.232021 / 0.275898 (-0.043877) \| 0.258053 / 0.323480 (-0.065427) \| 0.003971 / 0.007986 (-0.004014) \| 0.002550 / 0.004328 (-0.001779) \| 0.049126 / 0.004250 (0.044876) \| 0.040620 / 0.037052 (0.003568) \| 0.253437 / 0.258489 (-0.005052) \| 0.273583 / 0.293841 (-0.020258) \| 0.026775 / 0.128546 (-0.101771) \| 0.010073 / 0.075646 (-0.065573) \| 0.219089 / 0.419271 (-0.200183) \| 0.035047 / 0.043533 (-0.008486) \| 0.247661 / 0.255139 (-0.007478) \| 0.258674 / 0.283200 (-0.024525) \| 0.018428 / 0.141683 (-0.123255) \| 1.130394 / 1.452155 (-0.321761) \| 1.173167 / 1.492716 (-0.319549) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092581 / 0.018006 (0.074574) \| 0.303657 / 0.000490 (0.303167) \| 0.000215 / 0.000200 (0.000015) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018640 / 0.037411 (-0.018771) \| 0.062032 / 0.014526 (0.047506) \| 0.073982 / 0.176557 (-0.102575) \| 0.121499 / 0.737135 (-0.615636) \| 0.076780 / 0.296338 (-0.219559) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279411 / 0.215209 (0.064202) \| 2.737977 / 2.077655 (0.660322) \| 1.454135 / 1.504120 (-0.049985) \| 1.343144 / 1.541195 (-0.198051) \| 1.339876 / 1.468490 (-0.128614) \| 0.567306 / 4.584777 (-4.017471) \| 2.372569 / 3.745712 (-1.373143) \| 2.716810 / 5.269862 (-2.553052) \| 1.697895 / 4.565676 (-2.867782) \| 0.061804 / 0.424275 (-0.362471) \| 0.004986 / 0.007607 (-0.002622) \| 0.332721 / 0.226044 (0.106676) \| 3.274572 / 2.268929 (1.005644) \| 1.789900 / 55.444624 (-53.654725) \| 1.536346 / 6.876477 (-5.340131) \| 1.551940 / 2.142072 (-0.590132) \| 0.634539 / 4.805227 (-4.170688) \| 0.115860 / 6.500664 (-6.384805) \| 0.041737 / 0.075469 (-0.033732) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.024469 / 1.841788 (-0.817319) \| 11.327496 / 8.074308 (3.253188) \| 9.265855 / 10.191392 (-0.925537) \| 0.142200 / 0.680424 (-0.538224) \| 0.013945 / 0.534201 (-0.520256) \| 0.289670 / 0.579283 (-0.289614) \| 0.269240 / 0.434364 (-0.165124) \| 0.324748 / 0.540337 (-0.215590) \| 0.421393 / 1.386936 (-0.965543) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005284 / 0.011353 (-0.006069) \| 0.003351 / 0.011008 (-0.007658) \| 0.049973 / 0.038508 (0.011465) \| 0.030257 / 0.023109 (0.007148) \| 0.273660 / 0.275898 (-0.002238) \| 0.300328 / 0.323480 (-0.023152) \| 0.004133 / 0.007986 (-0.003852) \| 0.002614 / 0.004328 (-0.001715) \| 0.048055 / 0.004250 (0.043804) \| 0.044731 / 0.037052 (0.007678) \| 0.290257 / 0.258489 (0.031768) \| 0.321243 / 0.293841 (0.027402) \| 0.029542 / 0.128546 (-0.099004) \| 0.010074 / 0.075646 (-0.065573) \| 0.057944 / 0.419271 (-0.361327) \| 0.051267 / 0.043533 (0.007734) \| 0.276278 / 0.255139 (0.021139) \| 0.302464 / 0.283200 (0.019264) \| 0.018231 / 0.141683 (-0.123452) \| 1.140782 / 1.452155 (-0.311373) \| 1.182991 / 1.492716 (-0.309725) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092325 / 0.018006 (0.074319) \| 0.302610 / 0.000490 (0.302121) \| 0.000202 / 0.000200 (0.000002) \| 0.000049 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021458 / 0.037411 (-0.015954) \| 0.074883 / 0.014526 (0.060357) \| 0.085747 / 0.176557 (-0.090809) \| 0.125506 / 0.737135 (-0.611629) \| 0.086921 / 0.296338 (-0.209417) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290485 / 0.215209 (0.075276) \| 2.853898 / 2.077655 (0.776243) \| 1.615606 / 1.504120 (0.111486) \| 1.491797 / 1.541195 (-0.049397) \| 1.515981 / 1.468490 (0.047491) \| 0.566760 / 4.584777 (-4.018017) \| 2.462593 / 3.745712 (-1.283119) \| 2.765516 / 5.269862 (-2.504345) \| 1.755078 / 4.565676 (-2.810598) \| 0.063614 / 0.424275 (-0.360661) \| 0.005040 / 0.007607 (-0.002567) \| 0.347957 / 0.226044 (0.121912) \| 3.464258 / 2.268929 (1.195330) \| 1.992273 / 55.444624 (-53.452351) \| 1.699147 / 6.876477 (-5.177330) \| 1.868438 / 2.142072 (-0.273635) \| 0.660756 / 4.805227 (-4.144471) \| 0.118142 / 6.500664 (-6.382522) \| 0.041974 / 0.075469 (-0.033495) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.012206 / 1.841788 (-0.829581) \| 12.343735 / 8.074308 (4.269427) \| 10.321975 / 10.191392 (0.130583) \| 0.140007 / 0.680424 (-0.540417) \| 0.015755 / 0.534201 (-0.518446) \| 0.291978 / 0.579283 (-0.287305) \| 0.278792 / 0.434364 (-0.155572) \| 0.325366 / 0.540337 (-0.214972) \| 0.439403 / 1.386936 (-0.947533) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d1d3c06a651c6ad5142f331cb5dc0008ddcade33 \"CML watermark\")\n", "This change is breaking in \r\n\r\nhttps://github.com/huggingface/datasets/blob/f96e74d5c633cd5435dd526adb4a74631eb05c43/src/datasets/arrow_dataset.py#L1515\r\n\r\nwhen the input is `pathlib.Path`. The issue is that `url_to_fs` expects a `str` and cannot deal with `Path`. `get_fs_token_paths` converts to `str` so it is not a problem", "I opened https://github.com/huggingface/datasets/pull/6828 to add proper Path support to save_to_disk / load_from_disk" ]	2024-03-01T16:31:25Z	2024-04-23T09:43:09Z	2024-03-15T15:22:03Z	COLLABORATOR	null	null	null	Separate the default patterns that match directories from the ones matching files and ensure directories are checked first (reverts the change from https://github.com/huggingface/datasets/pull/6244, which merged these patterns). Also, ensure that the glob patterns do not overlap to avoid duplicates in the result. Additionally, replace `get_fs_token_paths` with `url_to_fs` to avoid [unnecessary glob calls](https://github.com/fsspec/filesystem_spec/blob/14dce8ca78f7aa509a20edb263bff83a7760c24d/fsspec/core.py#L655-L656). fix https://github.com/huggingface/datasets/issues/6259 fix https://github.com/huggingface/datasets/issues/6272	{ "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": 1, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6704/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6704/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6704.diff", "html_url": "https://github.com/huggingface/datasets/pull/6704", "merged_at": "2024-03-15T15:22:03Z", "patch_url": "https://github.com/huggingface/datasets/pull/6704.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6704" }
https://api.github.com/repos/huggingface/datasets/issues/6425	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6425/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6425/comments	https://api.github.com/repos/huggingface/datasets/issues/6425/events	https://github.com/huggingface/datasets/pull/6425	1,995,269,382	PR_kwDODunzps5fi5ye	6,425	Fix deprecation warning when building conda package	{ "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	[ "<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) \| 0.004811 / 0.011353 (-0.006542) \| 0.002478 / 0.011008 (-0.008530) \| 0.062241 / 0.038508 (0.023733) \| 0.031153 / 0.023109 (0.008044) \| 0.248896 / 0.275898 (-0.027002) \| 0.276860 / 0.323480 (-0.046620) \| 0.002934 / 0.007986 (-0.005052) \| 0.002428 / 0.004328 (-0.001901) \| 0.048507 / 0.004250 (0.044257) \| 0.044567 / 0.037052 (0.007515) \| 0.253570 / 0.258489 (-0.004919) \| 0.280762 / 0.293841 (-0.013079) \| 0.023549 / 0.128546 (-0.104997) \| 0.006985 / 0.075646 (-0.068661) \| 0.206227 / 0.419271 (-0.213044) \| 0.054027 / 0.043533 (0.010494) \| 0.257655 / 0.255139 (0.002516) \| 0.273498 / 0.283200 (-0.009702) \| 0.018997 / 0.141683 (-0.122685) \| 1.111732 / 1.452155 (-0.340422) \| 1.162078 / 1.492716 (-0.330639) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091816 / 0.018006 (0.073810) \| 0.299428 / 0.000490 (0.298938) \| 0.000211 / 0.000200 (0.000012) \| 0.000048 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018503 / 0.037411 (-0.018908) \| 0.062933 / 0.014526 (0.048407) \| 0.076349 / 0.176557 (-0.100208) \| 0.123291 / 0.737135 (-0.613844) \| 0.077491 / 0.296338 (-0.218847) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280770 / 0.215209 (0.065561) \| 2.762185 / 2.077655 (0.684530) \| 1.429124 / 1.504120 (-0.074996) \| 1.303162 / 1.541195 (-0.238033) \| 1.307523 / 1.468490 (-0.160967) \| 0.405593 / 4.584777 (-4.179184) \| 2.396992 / 3.745712 (-1.348721) \| 2.550968 / 5.269862 (-2.718894) \| 1.557358 / 4.565676 (-3.008318) \| 0.046149 / 0.424275 (-0.378126) \| 0.004808 / 0.007607 (-0.002799) \| 0.341870 / 0.226044 (0.115825) \| 3.362478 / 2.268929 (1.093550) \| 1.786360 / 55.444624 (-53.658264) \| 1.483419 / 6.876477 (-5.393058) \| 1.493463 / 2.142072 (-0.648609) \| 0.470605 / 4.805227 (-4.334623) \| 0.098372 / 6.500664 (-6.402292) \| 0.041722 / 0.075469 (-0.033748) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.938148 / 1.841788 (-0.903640) \| 11.219184 / 8.074308 (3.144876) \| 10.454439 / 10.191392 (0.263047) \| 0.139645 / 0.680424 (-0.540778) \| 0.014453 / 0.534201 (-0.519748) \| 0.268975 / 0.579283 (-0.310308) \| 0.262060 / 0.434364 (-0.172304) \| 0.313652 / 0.540337 (-0.226686) \| 0.423992 / 1.386936 (-0.962944) \|\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) \| 0.004829 / 0.011353 (-0.006524) \| 0.002426 / 0.011008 (-0.008582) \| 0.049064 / 0.038508 (0.010555) \| 0.049728 / 0.023109 (0.026619) \| 0.273263 / 0.275898 (-0.002635) \| 0.295645 / 0.323480 (-0.027835) \| 0.004156 / 0.007986 (-0.003830) \| 0.002397 / 0.004328 (-0.001932) \| 0.048902 / 0.004250 (0.044652) \| 0.038414 / 0.037052 (0.001362) \| 0.276176 / 0.258489 (0.017687) \| 0.306844 / 0.293841 (0.013003) \| 0.024546 / 0.128546 (-0.104000) \| 0.006946 / 0.075646 (-0.068701) \| 0.054024 / 0.419271 (-0.365247) \| 0.032444 / 0.043533 (-0.011089) \| 0.274125 / 0.255139 (0.018986) \| 0.293226 / 0.283200 (0.010027) \| 0.018003 / 0.141683 (-0.123680) \| 1.130402 / 1.452155 (-0.321752) \| 1.195969 / 1.492716 (-0.296748) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090043 / 0.018006 (0.072037) \| 0.298699 / 0.000490 (0.298209) \| 0.000214 / 0.000200 (0.000014) \| 0.000047 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021284 / 0.037411 (-0.016127) \| 0.069954 / 0.014526 (0.055428) \| 0.080445 / 0.176557 (-0.096111) \| 0.119461 / 0.737135 (-0.617674) \| 0.080632 / 0.296338 (-0.215706) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302246 / 0.215209 (0.087037) \| 2.991936 / 2.077655 (0.914281) \| 1.662969 / 1.504120 (0.158850) \| 1.533141 / 1.541195 (-0.008054) \| 1.583183 / 1.468490 (0.114693) \| 0.402864 / 4.584777 (-4.181913) \| 2.424119 / 3.745712 (-1.321593) \| 2.489558 / 5.269862 (-2.780303) \| 1.502196 / 4.565676 (-3.063481) \| 0.045980 / 0.424275 (-0.378295) \| 0.004768 / 0.007607 (-0.002839) \| 0.356089 / 0.226044 (0.130044) \| 3.481333 / 2.268929 (1.212404) \| 2.009713 / 55.444624 (-53.434912) \| 1.730021 / 6.876477 (-5.146455) \| 1.704656 / 2.142072 (-0.437416) \| 0.470832 / 4.805227 (-4.334395) \| 0.097473 / 6.500664 (-6.403191) \| 0.040437 / 0.075469 (-0.035032) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981497 / 1.841788 (-0.860291) \| 11.827242 / 8.074308 (3.752933) \| 10.888324 / 10.191392 (0.696932) \| 0.129249 / 0.680424 (-0.551174) \| 0.015812 / 0.534201 (-0.518389) \| 0.269657 / 0.579283 (-0.309626) \| 0.275585 / 0.434364 (-0.158779) \| 0.305698 / 0.540337 (-0.234639) \| 0.411497 / 1.386936 (-0.975439) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bcde318293af04fd5044b42ddfcb650f9b092d45 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6425). 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) \| 0.005402 / 0.011353 (-0.005951) \| 0.003955 / 0.011008 (-0.007053) \| 0.064096 / 0.038508 (0.025588) \| 0.062330 / 0.023109 (0.039221) \| 0.254729 / 0.275898 (-0.021169) \| 0.276259 / 0.323480 (-0.047221) \| 0.003052 / 0.007986 (-0.004934) \| 0.003474 / 0.004328 (-0.000854) \| 0.048938 / 0.004250 (0.044687) \| 0.038635 / 0.037052 (0.001583) \| 0.267953 / 0.258489 (0.009464) \| 0.293725 / 0.293841 (-0.000116) \| 0.028266 / 0.128546 (-0.100280) \| 0.011188 / 0.075646 (-0.064458) \| 0.221204 / 0.419271 (-0.198067) \| 0.036549 / 0.043533 (-0.006984) \| 0.252484 / 0.255139 (-0.002655) \| 0.273855 / 0.283200 (-0.009345) \| 0.017975 / 0.141683 (-0.123708) \| 1.112265 / 1.452155 (-0.339890) \| 1.185647 / 1.492716 (-0.307069) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096223 / 0.018006 (0.078217) \| 0.305010 / 0.000490 (0.304520) \| 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) \| 0.018924 / 0.037411 (-0.018488) \| 0.061910 / 0.014526 (0.047384) \| 0.073751 / 0.176557 (-0.102806) \| 0.120956 / 0.737135 (-0.616179) \| 0.075090 / 0.296338 (-0.221249) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293277 / 0.215209 (0.078068) \| 2.867468 / 2.077655 (0.789813) \| 1.518218 / 1.504120 (0.014098) \| 1.393741 / 1.541195 (-0.147454) \| 1.424979 / 1.468490 (-0.043511) \| 0.579766 / 4.584777 (-4.005011) \| 2.434951 / 3.745712 (-1.310761) \| 2.909924 / 5.269862 (-2.359937) \| 1.838123 / 4.565676 (-2.727554) \| 0.064260 / 0.424275 (-0.360015) \| 0.005169 / 0.007607 (-0.002438) \| 0.348228 / 0.226044 (0.122184) \| 3.447558 / 2.268929 (1.178629) \| 1.884988 / 55.444624 (-53.559636) \| 1.570921 / 6.876477 (-5.305556) \| 1.646341 / 2.142072 (-0.495732) \| 0.660189 / 4.805227 (-4.145038) \| 0.120026 / 6.500664 (-6.380638) \| 0.043715 / 0.075469 (-0.031754) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.953253 / 1.841788 (-0.888535) \| 12.576112 / 8.074308 (4.501804) \| 11.132637 / 10.191392 (0.941245) \| 0.132870 / 0.680424 (-0.547553) \| 0.014720 / 0.534201 (-0.519481) \| 0.291866 / 0.579283 (-0.287417) \| 0.265456 / 0.434364 (-0.168908) \| 0.338629 / 0.540337 (-0.201709) \| 0.456323 / 1.386936 (-0.930613) \|\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) \| 0.005644 / 0.011353 (-0.005709) \| 0.003624 / 0.011008 (-0.007384) \| 0.049043 / 0.038508 (0.010535) \| 0.059572 / 0.023109 (0.036463) \| 0.277159 / 0.275898 (0.001261) \| 0.303933 / 0.323480 (-0.019547) \| 0.004294 / 0.007986 (-0.003692) \| 0.002744 / 0.004328 (-0.001584) \| 0.048187 / 0.004250 (0.043937) \| 0.043655 / 0.037052 (0.006603) \| 0.282441 / 0.258489 (0.023952) \| 0.317130 / 0.293841 (0.023289) \| 0.030159 / 0.128546 (-0.098387) \| 0.011300 / 0.075646 (-0.064346) \| 0.057451 / 0.419271 (-0.361821) \| 0.033666 / 0.043533 (-0.009866) \| 0.274554 / 0.255139 (0.019415) \| 0.292470 / 0.283200 (0.009270) \| 0.018757 / 0.141683 (-0.122926) \| 1.170094 / 1.452155 (-0.282060) \| 1.244626 / 1.492716 (-0.248090) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094920 / 0.018006 (0.076914) \| 0.304156 / 0.000490 (0.303666) \| 0.000226 / 0.000200 (0.000026) \| 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) \| 0.022297 / 0.037411 (-0.015115) \| 0.068908 / 0.014526 (0.054383) \| 0.081520 / 0.176557 (-0.095037) \| 0.122422 / 0.737135 (-0.614714) \| 0.082533 / 0.296338 (-0.213806) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296080 / 0.215209 (0.080871) \| 2.883120 / 2.077655 (0.805465) \| 1.607950 / 1.504120 (0.103830) \| 1.496191 / 1.541195 (-0.045004) \| 1.520549 / 1.468490 (0.052059) \| 0.562081 / 4.584777 (-4.022696) \| 2.453447 / 3.745712 (-1.292265) \| 2.943676 / 5.269862 (-2.326186) \| 1.820581 / 4.565676 (-2.745096) \| 0.064518 / 0.424275 (-0.359757) \| 0.005406 / 0.007607 (-0.002201) \| 0.349022 / 0.226044 (0.122978) \| 3.472117 / 2.268929 (1.203188) \| 2.006928 / 55.444624 (-53.437696) \| 1.704800 / 6.876477 (-5.171677) \| 1.719025 / 2.142072 (-0.423048) \| 0.643719 / 4.805227 (-4.161508) \| 0.117723 / 6.500664 (-6.382941) \| 0.043158 / 0.075469 (-0.032311) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981229 / 1.841788 (-0.860559) \| 12.637620 / 8.074308 (4.563312) \| 10.848775 / 10.191392 (0.657383) \| 0.143981 / 0.680424 (-0.536443) \| 0.015950 / 0.534201 (-0.518251) \| 0.287542 / 0.579283 (-0.291741) \| 0.278989 / 0.434364 (-0.155375) \| 0.331786 / 0.540337 (-0.208552) \| 0.607238 / 1.386936 (-0.779698) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#06fb2f9973962ee97d1af7888209819b8ba7de37 \"CML watermark\")\n" ]	2023-11-15T18:00:11Z	2023-12-13T14:22:30Z	2023-12-13T14:16:00Z	MEMBER	null	null	null	When building/releasing conda package, we get this deprecation warning: ``` /usr/share/miniconda/envs/build-datasets/bin/conda-build:11: DeprecationWarning: conda_build.cli.main_build.main is deprecated and will be removed in 4.0.0. Use `conda build` instead. ``` This PR fixes the deprecation warning by using `conda build` instead.	{ "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/6425/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6425/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6425.diff", "html_url": "https://github.com/huggingface/datasets/pull/6425", "merged_at": "2023-12-13T14:16:00Z", "patch_url": "https://github.com/huggingface/datasets/pull/6425.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6425" }
https://api.github.com/repos/huggingface/datasets/issues/5818	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5818/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5818/comments	https://api.github.com/repos/huggingface/datasets/issues/5818/events	https://github.com/huggingface/datasets/issues/5818	1,695,052,555	I_kwDODunzps5lCHML	5,818	Ability to update a dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/4443482?v=4", "events_url": "https://api.github.com/users/davidgilbertson/events{/privacy}", "followers_url": "https://api.github.com/users/davidgilbertson/followers", "following_url": "https://api.github.com/users/davidgilbertson/following{/other_user}", "gists_url": "https://api.github.com/users/davidgilbertson/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/davidgilbertson", "id": 4443482, "login": "davidgilbertson", "node_id": "MDQ6VXNlcjQ0NDM0ODI=", "organizations_url": "https://api.github.com/users/davidgilbertson/orgs", "received_events_url": "https://api.github.com/users/davidgilbertson/received_events", "repos_url": "https://api.github.com/users/davidgilbertson/repos", "site_admin": false, "starred_url": "https://api.github.com/users/davidgilbertson/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/davidgilbertson/subscriptions", "type": "User", "url": "https://api.github.com/users/davidgilbertson", "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	[ "This [reply](https://discuss.huggingface.co/t/how-do-i-add-things-rows-to-an-already-saved-dataset/27423) from @mariosasko on the forums may be useful :)", "In this case, I think we can avoid the `PermissionError` by unpacking the underlying `ConcatenationTable` and saving only the newly added data blocks (in new files).", "Thanks @stevhliu and @mariosasko , so saving to individual files then loading them later, concatenating again and saving again is the recommended way. Good to know.\r\n\r\nQuestion that I hope doesn't sound rude: is this sort of thing (processing a dataset that doesn't fit in memory) outside of `datasets`'s core area of focus? Are there other tools you would recommend to do this sort of thing that play nice with `datasets`? Or is it just that I've found myself in a niche situation that hasn't specifically been catered for?" ]	2023-05-04T01:08:13Z	2023-05-04T20:43:39Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request The ability to load a dataset, add or change something, and save it back to disk. Maybe it's possible, but I can't work out how to do it, e.g. this fails: ```py import datasets dataset = datasets.load_from_disk("data/test1") dataset = dataset.add_item({"text": "A new item"}) dataset.save_to_disk("data/test1") ``` With the error: ``` PermissionError: Tried to overwrite /mnt/c/Users/david/py/learning/mini_projects/data_sorting_and_filtering/data/test1 but a dataset can't overwrite itself. ``` ### Motivation My use case is that I want to process a dataset in a particular way but it doesn't fit in memory if I do it in one go. So I want to perform a loop and at each step in the loop, process one shard and append it to an ever-growing dataset. The code in the loop will load a dataset, add some rows, then save it again. Maybe I'm just thinking about things incorrectly and there's a better approach. FWIW I can't use `dataset.map()` to do the task because that doesn't work with `num_proc` when adding rows, so is confined to a single process which is too slow. The only other way I can think of is to create a new file each time, but surely that's not how people do this sort of thing. ### Your contribution na	null	{ "+1": 2, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/5818/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5818/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5662	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5662/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5662/comments	https://api.github.com/repos/huggingface/datasets/issues/5662/events	https://github.com/huggingface/datasets/pull/5662	1,637,140,813	PR_kwDODunzps5MtvsM	5,662	Fix unnecessary dict comprehension	{ "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 am merging because the CI error is unrelated.", "<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) \| 0.009448 / 0.011353 (-0.001905) \| 0.006156 / 0.011008 (-0.004852) \| 0.123656 / 0.038508 (0.085147) \| 0.034998 / 0.023109 (0.011889) \| 0.374722 / 0.275898 (0.098824) \| 0.418912 / 0.323480 (0.095432) \| 0.007348 / 0.007986 (-0.000637) \| 0.004779 / 0.004328 (0.000450) \| 0.097541 / 0.004250 (0.093291) \| 0.052523 / 0.037052 (0.015471) \| 0.380118 / 0.258489 (0.121628) \| 0.429448 / 0.293841 (0.135607) \| 0.055156 / 0.128546 (-0.073390) \| 0.019884 / 0.075646 (-0.055763) \| 0.429613 / 0.419271 (0.010341) \| 0.067554 / 0.043533 (0.024021) \| 0.373940 / 0.255139 (0.118801) \| 0.408115 / 0.283200 (0.124916) \| 0.111353 / 0.141683 (-0.030329) \| 1.821013 / 1.452155 (0.368858) \| 1.972882 / 1.492716 (0.480165) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236686 / 0.018006 (0.218679) \| 0.516519 / 0.000490 (0.516029) \| 0.009582 / 0.000200 (0.009383) \| 0.000404 / 0.000054 (0.000349) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029425 / 0.037411 (-0.007986) \| 0.123972 / 0.014526 (0.109446) \| 0.133768 / 0.176557 (-0.042789) \| 0.207562 / 0.737135 (-0.529573) \| 0.142841 / 0.296338 (-0.153497) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.618531 / 0.215209 (0.403322) \| 6.216854 / 2.077655 (4.139199) \| 2.480138 / 1.504120 (0.976018) \| 2.139884 / 1.541195 (0.598689) \| 2.122992 / 1.468490 (0.654502) \| 1.233824 / 4.584777 (-3.350953) \| 5.426142 / 3.745712 (1.680430) \| 4.891039 / 5.269862 (-0.378822) \| 2.767033 / 4.565676 (-1.798643) \| 0.142224 / 0.424275 (-0.282051) \| 0.015754 / 0.007607 (0.008147) \| 0.772210 / 0.226044 (0.546166) \| 7.620484 / 2.268929 (5.351556) \| 3.141617 / 55.444624 (-52.303007) \| 2.471406 / 6.876477 (-4.405070) \| 2.648008 / 2.142072 (0.505935) \| 1.429281 / 4.805227 (-3.375946) \| 0.255981 / 6.500664 (-6.244683) \| 0.077710 / 0.075469 (0.002241) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.547714 / 1.841788 (-0.294073) \| 17.859985 / 8.074308 (9.785677) \| 21.791878 / 10.191392 (11.600486) \| 0.238569 / 0.680424 (-0.441854) \| 0.027520 / 0.534201 (-0.506681) \| 0.553960 / 0.579283 (-0.025324) \| 0.616165 / 0.434364 (0.181801) \| 0.622492 / 0.540337 (0.082154) \| 0.716345 / 1.386936 (-0.670591) \|\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) \| 0.009624 / 0.011353 (-0.001729) \| 0.006091 / 0.011008 (-0.004917) \| 0.096623 / 0.038508 (0.058115) \| 0.034903 / 0.023109 (0.011793) \| 0.421009 / 0.275898 (0.145111) \| 0.459236 / 0.323480 (0.135756) \| 0.007778 / 0.007986 (-0.000207) \| 0.004726 / 0.004328 (0.000398) \| 0.099603 / 0.004250 (0.095353) \| 0.051426 / 0.037052 (0.014373) \| 0.420461 / 0.258489 (0.161972) \| 0.469747 / 0.293841 (0.175906) \| 0.053769 / 0.128546 (-0.074777) \| 0.020636 / 0.075646 (-0.055011) \| 0.115785 / 0.419271 (-0.303486) \| 0.062692 / 0.043533 (0.019160) \| 0.419388 / 0.255139 (0.164249) \| 0.448675 / 0.283200 (0.165475) \| 0.112099 / 0.141683 (-0.029584) \| 1.787982 / 1.452155 (0.335827) \| 1.884581 / 1.492716 (0.391864) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208837 / 0.018006 (0.190831) \| 0.515593 / 0.000490 (0.515103) \| 0.000447 / 0.000200 (0.000247) \| 0.000086 / 0.000054 (0.000032) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031025 / 0.037411 (-0.006386) \| 0.125179 / 0.014526 (0.110653) \| 0.137050 / 0.176557 (-0.039506) \| 0.203582 / 0.737135 (-0.533553) \| 0.139209 / 0.296338 (-0.157130) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.601507 / 0.215209 (0.386298) \| 6.034778 / 2.077655 (3.957123) \| 2.550277 / 1.504120 (1.046157) \| 2.242277 / 1.541195 (0.701082) \| 2.306378 / 1.468490 (0.837888) \| 1.251219 / 4.584777 (-3.333558) \| 5.448698 / 3.745712 (1.702986) \| 3.044666 / 5.269862 (-2.225196) \| 2.000684 / 4.565676 (-2.564992) \| 0.148385 / 0.424275 (-0.275890) \| 0.015175 / 0.007607 (0.007567) \| 0.800839 / 0.226044 (0.574795) \| 8.062099 / 2.268929 (5.793171) \| 3.400980 / 55.444624 (-52.043644) \| 2.639583 / 6.876477 (-4.236894) \| 2.660691 / 2.142072 (0.518618) \| 1.467715 / 4.805227 (-3.337512) \| 0.266429 / 6.500664 (-6.234235) \| 0.076981 / 0.075469 (0.001512) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.621128 / 1.841788 (-0.220659) \| 17.949989 / 8.074308 (9.875680) \| 20.946426 / 10.191392 (10.755034) \| 0.259357 / 0.680424 (-0.421067) \| 0.026094 / 0.534201 (-0.508107) \| 0.527840 / 0.579283 (-0.051443) \| 0.629027 / 0.434364 (0.194663) \| 0.603931 / 0.540337 (0.063594) \| 0.711370 / 1.386936 (-0.675566) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2ccf01db81bb7b70f3ea97b185e345c2b1df0274 \"CML watermark\")\n" ]	2023-03-23T09:18:58Z	2023-03-23T09:46:59Z	2023-03-23T09:37:49Z	MEMBER	null	null	null	After ruff-0.0.258 release, the C416 rule was updated with unnecessary dict comprehensions. See: - https://github.com/charliermarsh/ruff/releases/tag/v0.0.258 - https://github.com/charliermarsh/ruff/pull/3605 This PR fixes one unnecessary dict comprehension in our code: no need to unpack and re-pack the tuple values. Fix #5661	{ "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/5662/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5662/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5662.diff", "html_url": "https://github.com/huggingface/datasets/pull/5662", "merged_at": "2023-03-23T09:37:49Z", "patch_url": "https://github.com/huggingface/datasets/pull/5662.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5662" }
https://api.github.com/repos/huggingface/datasets/issues/7535	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7535/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7535/comments	https://api.github.com/repos/huggingface/datasets/issues/7535/events	https://github.com/huggingface/datasets/pull/7535	3,018,289,872	PR_kwDODunzps6T0lm3	7,535	Change dill version in requirements	{ "avatar_url": "https://avatars.githubusercontent.com/u/98061329?v=4", "events_url": "https://api.github.com/users/JGrel/events{/privacy}", "followers_url": "https://api.github.com/users/JGrel/followers", "following_url": "https://api.github.com/users/JGrel/following{/other_user}", "gists_url": "https://api.github.com/users/JGrel/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/JGrel", "id": 98061329, "login": "JGrel", "node_id": "U_kgDOBdhMEQ", "organizations_url": "https://api.github.com/users/JGrel/orgs", "received_events_url": "https://api.github.com/users/JGrel/received_events", "repos_url": "https://api.github.com/users/JGrel/repos", "site_admin": false, "starred_url": "https://api.github.com/users/JGrel/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/JGrel/subscriptions", "type": "User", "url": "https://api.github.com/users/JGrel", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7535). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]	2025-04-24T19:44:28Z	2025-04-25T09:31:44Z	null	NONE	null	null	null	Change dill version to >=0.3.9,<0.4.5 and check for errors	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/7535/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7535/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7535.diff", "html_url": "https://github.com/huggingface/datasets/pull/7535", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/7535.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7535" }
https://api.github.com/repos/huggingface/datasets/issues/5636	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5636/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5636/comments	https://api.github.com/repos/huggingface/datasets/issues/5636/events	https://github.com/huggingface/datasets/pull/5636	1,623,721,577	PR_kwDODunzps5MAunR	5,636	Fix CI: ignore C901 ("some_func" is to complex) in `ruff`	{ "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) \| 0.006529 / 0.011353 (-0.004824) \| 0.004527 / 0.011008 (-0.006481) \| 0.098051 / 0.038508 (0.059543) \| 0.028058 / 0.023109 (0.004949) \| 0.368543 / 0.275898 (0.092645) \| 0.397126 / 0.323480 (0.073646) \| 0.005072 / 0.007986 (-0.002913) \| 0.003377 / 0.004328 (-0.000952) \| 0.076867 / 0.004250 (0.072617) \| 0.040121 / 0.037052 (0.003069) \| 0.373422 / 0.258489 (0.114933) \| 0.403969 / 0.293841 (0.110128) \| 0.031485 / 0.128546 (-0.097061) \| 0.011673 / 0.075646 (-0.063973) \| 0.321837 / 0.419271 (-0.097434) \| 0.042828 / 0.043533 (-0.000704) \| 0.370391 / 0.255139 (0.115252) \| 0.391737 / 0.283200 (0.108538) \| 0.084764 / 0.141683 (-0.056919) \| 1.463114 / 1.452155 (0.010959) \| 1.527042 / 1.492716 (0.034325) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.200964 / 0.018006 (0.182958) \| 0.403967 / 0.000490 (0.403477) \| 0.002439 / 0.000200 (0.002239) \| 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) \| 0.023531 / 0.037411 (-0.013880) \| 0.097424 / 0.014526 (0.082899) \| 0.104854 / 0.176557 (-0.071703) \| 0.165682 / 0.737135 (-0.571453) \| 0.109416 / 0.296338 (-0.186922) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.431041 / 0.215209 (0.215832) \| 4.326039 / 2.077655 (2.248384) \| 2.085123 / 1.504120 (0.581003) \| 1.922720 / 1.541195 (0.381525) \| 2.006608 / 1.468490 (0.538118) \| 0.703348 / 4.584777 (-3.881428) \| 3.441516 / 3.745712 (-0.304196) \| 1.875244 / 5.269862 (-3.394618) \| 1.181341 / 4.565676 (-3.384336) \| 0.083442 / 0.424275 (-0.340833) \| 0.012966 / 0.007607 (0.005359) \| 0.536047 / 0.226044 (0.310002) \| 5.354856 / 2.268929 (3.085927) \| 2.451064 / 55.444624 (-52.993560) \| 2.076110 / 6.876477 (-4.800367) \| 2.196507 / 2.142072 (0.054435) \| 0.811196 / 4.805227 (-3.994032) \| 0.152547 / 6.500664 (-6.348118) \| 0.067978 / 0.075469 (-0.007491) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.196169 / 1.841788 (-0.645618) \| 13.697234 / 8.074308 (5.622926) \| 13.966652 / 10.191392 (3.775260) \| 0.143735 / 0.680424 (-0.536688) \| 0.016484 / 0.534201 (-0.517717) \| 0.382349 / 0.579283 (-0.196934) \| 0.401507 / 0.434364 (-0.032857) \| 0.447297 / 0.540337 (-0.093041) \| 0.529779 / 1.386936 (-0.857157) \|\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) \| 0.006698 / 0.011353 (-0.004655) \| 0.004608 / 0.011008 (-0.006400) \| 0.076220 / 0.038508 (0.037712) \| 0.027340 / 0.023109 (0.004231) \| 0.344095 / 0.275898 (0.068197) \| 0.374715 / 0.323480 (0.051235) \| 0.004883 / 0.007986 (-0.003102) \| 0.004658 / 0.004328 (0.000330) \| 0.075381 / 0.004250 (0.071130) \| 0.036099 / 0.037052 (-0.000953) \| 0.340382 / 0.258489 (0.081893) \| 0.383488 / 0.293841 (0.089647) \| 0.031534 / 0.128546 (-0.097012) \| 0.011735 / 0.075646 (-0.063912) \| 0.085895 / 0.419271 (-0.333377) \| 0.042226 / 0.043533 (-0.001306) \| 0.340301 / 0.255139 (0.085162) \| 0.366079 / 0.283200 (0.082879) \| 0.088828 / 0.141683 (-0.052854) \| 1.487880 / 1.452155 (0.035725) \| 1.561318 / 1.492716 (0.068601) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226366 / 0.018006 (0.208360) \| 0.408934 / 0.000490 (0.408444) \| 0.000396 / 0.000200 (0.000196) \| 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) \| 0.024521 / 0.037411 (-0.012891) \| 0.100167 / 0.014526 (0.085641) \| 0.106480 / 0.176557 (-0.070077) \| 0.156377 / 0.737135 (-0.580758) \| 0.111709 / 0.296338 (-0.184630) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436138 / 0.215209 (0.220928) \| 4.370919 / 2.077655 (2.293265) \| 2.066402 / 1.504120 (0.562282) \| 1.862157 / 1.541195 (0.320962) \| 1.920701 / 1.468490 (0.452211) \| 0.695517 / 4.584777 (-3.889260) \| 3.435558 / 3.745712 (-0.310154) \| 1.864000 / 5.269862 (-3.405861) \| 1.164134 / 4.565676 (-3.401543) \| 0.083006 / 0.424275 (-0.341269) \| 0.012751 / 0.007607 (0.005144) \| 0.535405 / 0.226044 (0.309360) \| 5.368530 / 2.268929 (3.099602) \| 2.494197 / 55.444624 (-52.950427) \| 2.161370 / 6.876477 (-4.715107) \| 2.180345 / 2.142072 (0.038272) \| 0.808076 / 4.805227 (-3.997151) \| 0.151891 / 6.500664 (-6.348773) \| 0.067643 / 0.075469 (-0.007826) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.334245 / 1.841788 (-0.507543) \| 14.112805 / 8.074308 (6.038497) \| 14.152303 / 10.191392 (3.960911) \| 0.153492 / 0.680424 (-0.526932) \| 0.016542 / 0.534201 (-0.517659) \| 0.376013 / 0.579283 (-0.203270) \| 0.386528 / 0.434364 (-0.047836) \| 0.436461 / 0.540337 (-0.103876) \| 0.519278 / 1.386936 (-0.867658) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ce1d1076fc55ac49277398304e551f0b56c3c9e2 \"CML watermark\")\n" ]	2023-03-14T15:29:11Z	2023-03-14T16:37:06Z	2023-03-14T16:29:52Z	CONTRIBUTOR	null	null	null	idk if I should have added this ignore to `ruff` too, but I added :)	{ "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/5636/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5636/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5636.diff", "html_url": "https://github.com/huggingface/datasets/pull/5636", "merged_at": "2023-03-14T16:29:52Z", "patch_url": "https://github.com/huggingface/datasets/pull/5636.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5636" }
https://api.github.com/repos/huggingface/datasets/issues/6135	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6135/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6135/comments	https://api.github.com/repos/huggingface/datasets/issues/6135/events	https://github.com/huggingface/datasets/pull/6135	1,844,870,943	PR_kwDODunzps5Xn2AT	6,135	Remove unused allowed_extensions param	{ "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._", "<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) \| 0.009055 / 0.011353 (-0.002298) \| 0.008835 / 0.011008 (-0.002173) \| 0.117048 / 0.038508 (0.078540) \| 0.096268 / 0.023109 (0.073159) \| 0.474678 / 0.275898 (0.198780) \| 0.550509 / 0.323480 (0.227029) \| 0.005552 / 0.007986 (-0.002434) \| 0.004315 / 0.004328 (-0.000013) \| 0.094336 / 0.004250 (0.090086) \| 0.061945 / 0.037052 (0.024892) \| 0.461422 / 0.258489 (0.202933) \| 0.521271 / 0.293841 (0.227430) \| 0.049116 / 0.128546 (-0.079430) \| 0.015007 / 0.075646 (-0.060639) \| 0.414351 / 0.419271 (-0.004920) \| 0.137520 / 0.043533 (0.093987) \| 0.465627 / 0.255139 (0.210488) \| 0.537244 / 0.283200 (0.254044) \| 0.068577 / 0.141683 (-0.073106) \| 1.921373 / 1.452155 (0.469219) \| 2.506653 / 1.492716 (1.013937) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.273970 / 0.018006 (0.255963) \| 0.750295 / 0.000490 (0.749805) \| 0.004241 / 0.000200 (0.004041) \| 0.000128 / 0.000054 (0.000073) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033793 / 0.037411 (-0.003618) \| 0.105562 / 0.014526 (0.091037) \| 0.131771 / 0.176557 (-0.044786) \| 0.196890 / 0.737135 (-0.540245) \| 0.119842 / 0.296338 (-0.176496) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.634881 / 0.215209 (0.419672) \| 6.069221 / 2.077655 (3.991566) \| 2.678765 / 1.504120 (1.174646) \| 2.460309 / 1.541195 (0.919114) \| 2.517579 / 1.468490 (1.049089) \| 0.869558 / 4.584777 (-3.715219) \| 5.407686 / 3.745712 (1.661974) \| 4.920687 / 5.269862 (-0.349175) \| 3.130066 / 4.565676 (-1.435611) \| 0.100337 / 0.424275 (-0.323938) \| 0.009615 / 0.007607 (0.002008) \| 0.745275 / 0.226044 (0.519231) \| 7.577890 / 2.268929 (5.308962) \| 3.607887 / 55.444624 (-51.836738) \| 2.922211 / 6.876477 (-3.954266) \| 3.205592 / 2.142072 (1.063519) \| 1.052298 / 4.805227 (-3.752929) \| 0.218798 / 6.500664 (-6.281866) \| 0.082137 / 0.075469 (0.006667) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.696551 / 1.841788 (-0.145237) \| 24.946074 / 8.074308 (16.871766) \| 23.114202 / 10.191392 (12.922810) \| 0.220498 / 0.680424 (-0.459925) \| 0.029388 / 0.534201 (-0.504813) \| 0.494721 / 0.579283 (-0.084562) \| 0.603085 / 0.434364 (0.168722) \| 0.573093 / 0.540337 (0.032756) \| 0.784937 / 1.386936 (-0.601999) \|\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) \| 0.009642 / 0.011353 (-0.001711) \| 0.007551 / 0.011008 (-0.003457) \| 0.085224 / 0.038508 (0.046716) \| 0.099493 / 0.023109 (0.076384) \| 0.503824 / 0.275898 (0.227926) \| 0.546583 / 0.323480 (0.223103) \| 0.006385 / 0.007986 (-0.001601) \| 0.004751 / 0.004328 (0.000423) \| 0.084699 / 0.004250 (0.080449) \| 0.067875 / 0.037052 (0.030823) \| 0.485313 / 0.258489 (0.226824) \| 0.535808 / 0.293841 (0.241967) \| 0.049935 / 0.128546 (-0.078611) \| 0.014427 / 0.075646 (-0.061219) \| 0.095531 / 0.419271 (-0.323741) \| 0.068487 / 0.043533 (0.024954) \| 0.502204 / 0.255139 (0.247065) \| 0.514393 / 0.283200 (0.231193) \| 0.037350 / 0.141683 (-0.104333) \| 1.849380 / 1.452155 (0.397226) \| 1.920151 / 1.492716 (0.427434) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298363 / 0.018006 (0.280357) \| 0.651555 / 0.000490 (0.651065) \| 0.005910 / 0.000200 (0.005710) \| 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) \| 0.039170 / 0.037411 (0.001758) \| 0.106436 / 0.014526 (0.091910) \| 0.129880 / 0.176557 (-0.046677) \| 0.185401 / 0.737135 (-0.551734) \| 0.125732 / 0.296338 (-0.170607) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.643248 / 0.215209 (0.428039) \| 6.374807 / 2.077655 (4.297152) \| 3.057296 / 1.504120 (1.553176) \| 2.779534 / 1.541195 (1.238340) \| 2.790165 / 1.468490 (1.321675) \| 0.841580 / 4.584777 (-3.743197) \| 5.371478 / 3.745712 (1.625766) \| 4.973251 / 5.269862 (-0.296610) \| 3.235817 / 4.565676 (-1.329860) \| 0.097276 / 0.424275 (-0.326999) \| 0.008840 / 0.007607 (0.001233) \| 0.728678 / 0.226044 (0.502634) \| 7.526382 / 2.268929 (5.257454) \| 3.792550 / 55.444624 (-51.652074) \| 3.439134 / 6.876477 (-3.437342) \| 3.466626 / 2.142072 (1.324553) \| 1.035894 / 4.805227 (-3.769333) \| 0.211670 / 6.500664 (-6.288994) \| 0.087596 / 0.075469 (0.012127) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.782755 / 1.841788 (-0.059033) \| 25.704407 / 8.074308 (17.630099) \| 23.799672 / 10.191392 (13.608280) \| 0.233952 / 0.680424 (-0.446472) \| 0.030810 / 0.534201 (-0.503391) \| 0.505857 / 0.579283 (-0.073426) \| 0.629331 / 0.434364 (0.194967) \| 0.608530 / 0.540337 (0.068192) \| 0.813688 / 1.386936 (-0.573248) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ed4d6bb5f1331576c41b04acd9872a5349a0915c \"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) \| 0.006401 / 0.011353 (-0.004952) \| 0.003916 / 0.011008 (-0.007092) \| 0.083976 / 0.038508 (0.045468) \| 0.072583 / 0.023109 (0.049474) \| 0.322747 / 0.275898 (0.046849) \| 0.345159 / 0.323480 (0.021679) \| 0.005366 / 0.007986 (-0.002620) \| 0.003399 / 0.004328 (-0.000930) \| 0.064232 / 0.004250 (0.059982) \| 0.053313 / 0.037052 (0.016261) \| 0.353127 / 0.258489 (0.094638) \| 0.361398 / 0.293841 (0.067557) \| 0.030604 / 0.128546 (-0.097942) \| 0.008615 / 0.075646 (-0.067031) \| 0.285806 / 0.419271 (-0.133466) \| 0.050887 / 0.043533 (0.007354) \| 0.312293 / 0.255139 (0.057154) \| 0.349716 / 0.283200 (0.066516) \| 0.024546 / 0.141683 (-0.117137) \| 1.472318 / 1.452155 (0.020163) \| 1.536063 / 1.492716 (0.043347) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280012 / 0.018006 (0.262006) \| 0.593574 / 0.000490 (0.593085) \| 0.004083 / 0.000200 (0.003883) \| 0.000195 / 0.000054 (0.000141) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027715 / 0.037411 (-0.009696) \| 0.081392 / 0.014526 (0.066866) \| 0.096445 / 0.176557 (-0.080112) \| 0.152131 / 0.737135 (-0.585004) \| 0.094825 / 0.296338 (-0.201514) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.380749 / 0.215209 (0.165540) \| 3.806994 / 2.077655 (1.729339) \| 1.842544 / 1.504120 (0.338424) \| 1.682829 / 1.541195 (0.141635) \| 1.701679 / 1.468490 (0.233189) \| 0.484830 / 4.584777 (-4.099947) \| 3.517359 / 3.745712 (-0.228353) \| 3.231211 / 5.269862 (-2.038651) \| 2.029371 / 4.565676 (-2.536306) \| 0.057199 / 0.424275 (-0.367077) \| 0.007653 / 0.007607 (0.000046) \| 0.458572 / 0.226044 (0.232528) \| 4.579835 / 2.268929 (2.310907) \| 2.326467 / 55.444624 (-53.118157) \| 1.939646 / 6.876477 (-4.936831) \| 2.133150 / 2.142072 (-0.008922) \| 0.596251 / 4.805227 (-4.208976) \| 0.131979 / 6.500664 (-6.368686) \| 0.059226 / 0.075469 (-0.016243) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.234833 / 1.841788 (-0.606955) \| 19.475522 / 8.074308 (11.401214) \| 14.102760 / 10.191392 (3.911368) \| 0.159657 / 0.680424 (-0.520767) \| 0.018292 / 0.534201 (-0.515909) \| 0.391079 / 0.579283 (-0.188204) \| 0.406736 / 0.434364 (-0.027628) \| 0.459159 / 0.540337 (-0.081178) \| 0.618159 / 1.386936 (-0.768777) \|\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) \| 0.006592 / 0.011353 (-0.004761) \| 0.004052 / 0.011008 (-0.006957) \| 0.064536 / 0.038508 (0.026028) \| 0.075051 / 0.023109 (0.051942) \| 0.379596 / 0.275898 (0.103698) \| 0.412413 / 0.323480 (0.088933) \| 0.005377 / 0.007986 (-0.002608) \| 0.003466 / 0.004328 (-0.000863) \| 0.064958 / 0.004250 (0.060708) \| 0.055265 / 0.037052 (0.018213) \| 0.391505 / 0.258489 (0.133016) \| 0.425345 / 0.293841 (0.131504) \| 0.030750 / 0.128546 (-0.097796) \| 0.008652 / 0.075646 (-0.066994) \| 0.072107 / 0.419271 (-0.347165) \| 0.048340 / 0.043533 (0.004807) \| 0.387714 / 0.255139 (0.132575) \| 0.402602 / 0.283200 (0.119402) \| 0.023492 / 0.141683 (-0.118191) \| 1.528377 / 1.452155 (0.076222) \| 1.574827 / 1.492716 (0.082110) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.316999 / 0.018006 (0.298993) \| 0.528391 / 0.000490 (0.527901) \| 0.005183 / 0.000200 (0.004983) \| 0.000085 / 0.000054 (0.000031) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029670 / 0.037411 (-0.007741) \| 0.087130 / 0.014526 (0.072604) \| 0.099897 / 0.176557 (-0.076660) \| 0.154074 / 0.737135 (-0.583062) \| 0.104309 / 0.296338 (-0.192030) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.408804 / 0.215209 (0.193595) \| 4.072248 / 2.077655 (1.994593) \| 2.103333 / 1.504120 (0.599213) \| 1.931972 / 1.541195 (0.390777) \| 1.980132 / 1.468490 (0.511642) \| 0.482623 / 4.584777 (-4.102154) \| 3.532789 / 3.745712 (-0.212923) \| 3.304962 / 5.269862 (-1.964899) \| 2.036672 / 4.565676 (-2.529004) \| 0.056944 / 0.424275 (-0.367331) \| 0.007190 / 0.007607 (-0.000417) \| 0.490650 / 0.226044 (0.264606) \| 4.903604 / 2.268929 (2.634675) \| 2.586247 / 55.444624 (-52.858377) \| 2.227631 / 6.876477 (-4.648846) \| 2.397286 / 2.142072 (0.255214) \| 0.579167 / 4.805227 (-4.226060) \| 0.132037 / 6.500664 (-6.368627) \| 0.059971 / 0.075469 (-0.015498) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.336430 / 1.841788 (-0.505358) \| 19.915846 / 8.074308 (11.841538) \| 14.102781 / 10.191392 (3.911389) \| 0.147956 / 0.680424 (-0.532468) \| 0.018192 / 0.534201 (-0.516009) \| 0.397949 / 0.579283 (-0.181334) \| 0.408529 / 0.434364 (-0.025835) \| 0.479382 / 0.540337 (-0.060955) \| 0.659735 / 1.386936 (-0.727201) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98074122449bc031f7269f298f1c55f20e39b975 \"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) \| 0.005880 / 0.011353 (-0.005473) \| 0.003677 / 0.011008 (-0.007332) \| 0.080022 / 0.038508 (0.041514) \| 0.055554 / 0.023109 (0.032445) \| 0.397449 / 0.275898 (0.121551) \| 0.428346 / 0.323480 (0.104867) \| 0.004613 / 0.007986 (-0.003373) \| 0.002873 / 0.004328 (-0.001455) \| 0.062226 / 0.004250 (0.057976) \| 0.044721 / 0.037052 (0.007669) \| 0.404792 / 0.258489 (0.146303) \| 0.437467 / 0.293841 (0.143626) \| 0.027166 / 0.128546 (-0.101381) \| 0.008077 / 0.075646 (-0.067569) \| 0.260469 / 0.419271 (-0.158803) \| 0.043551 / 0.043533 (0.000018) \| 0.401712 / 0.255139 (0.146573) \| 0.427294 / 0.283200 (0.144094) \| 0.021243 / 0.141683 (-0.120440) \| 1.464553 / 1.452155 (0.012398) \| 1.507112 / 1.492716 (0.014396) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.198415 / 0.018006 (0.180408) \| 0.427940 / 0.000490 (0.427450) \| 0.004236 / 0.000200 (0.004036) \| 0.000067 / 0.000054 (0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023759 / 0.037411 (-0.013652) \| 0.073262 / 0.014526 (0.058736) \| 0.677113 / 0.176557 (0.500557) \| 0.194964 / 0.737135 (-0.542172) \| 0.086121 / 0.296338 (-0.210217) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401176 / 0.215209 (0.185967) \| 4.028688 / 2.077655 (1.951034) \| 2.026804 / 1.504120 (0.522685) \| 1.887964 / 1.541195 (0.346770) \| 2.008991 / 1.468490 (0.540501) \| 0.498847 / 4.584777 (-4.085930) \| 3.015920 / 3.745712 (-0.729792) \| 2.837019 / 5.269862 (-2.432843) \| 1.849976 / 4.565676 (-2.715701) \| 0.057545 / 0.424275 (-0.366730) \| 0.006645 / 0.007607 (-0.000962) \| 0.470225 / 0.226044 (0.244180) \| 4.720910 / 2.268929 (2.451982) \| 2.473693 / 55.444624 (-52.970931) \| 2.177525 / 6.876477 (-4.698952) \| 2.374702 / 2.142072 (0.232630) \| 0.588253 / 4.805227 (-4.216974) \| 0.125512 / 6.500664 (-6.375152) \| 0.061247 / 0.075469 (-0.014222) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.255829 / 1.841788 (-0.585959) \| 18.251689 / 8.074308 (10.177381) \| 13.690373 / 10.191392 (3.498981) \| 0.146928 / 0.680424 (-0.533496) \| 0.016534 / 0.534201 (-0.517667) \| 0.335249 / 0.579283 (-0.244034) \| 0.338940 / 0.434364 (-0.095424) \| 0.382170 / 0.540337 (-0.158168) \| 0.529570 / 1.386936 (-0.857366) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005920 / 0.011353 (-0.005433) \| 0.003557 / 0.011008 (-0.007451) \| 0.062776 / 0.038508 (0.024267) \| 0.058473 / 0.023109 (0.035364) \| 0.358780 / 0.275898 (0.082882) \| 0.394161 / 0.323480 (0.070682) \| 0.004636 / 0.007986 (-0.003349) \| 0.002865 / 0.004328 (-0.001463) \| 0.062033 / 0.004250 (0.057782) \| 0.047154 / 0.037052 (0.010101) \| 0.367718 / 0.258489 (0.109229) \| 0.400814 / 0.293841 (0.106973) \| 0.026919 / 0.128546 (-0.101628) \| 0.008071 / 0.075646 (-0.067575) \| 0.067802 / 0.419271 (-0.351469) \| 0.040894 / 0.043533 (-0.002638) \| 0.358757 / 0.255139 (0.103618) \| 0.384971 / 0.283200 (0.101771) \| 0.020019 / 0.141683 (-0.121664) \| 1.458578 / 1.452155 (0.006423) \| 1.525059 / 1.492716 (0.032342) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207795 / 0.018006 (0.189789) \| 0.413201 / 0.000490 (0.412712) \| 0.005199 / 0.000200 (0.004999) \| 0.000085 / 0.000054 (0.000031) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025716 / 0.037411 (-0.011696) \| 0.078434 / 0.014526 (0.063908) \| 0.086920 / 0.176557 (-0.089637) \| 0.138327 / 0.737135 (-0.598808) \| 0.088120 / 0.296338 (-0.208219) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434344 / 0.215209 (0.219135) \| 4.343114 / 2.077655 (2.265459) \| 2.384439 / 1.504120 (0.880319) \| 2.253929 / 1.541195 (0.712735) \| 2.306811 / 1.468490 (0.838321) \| 0.497572 / 4.584777 (-4.087205) \| 3.028794 / 3.745712 (-0.716919) \| 2.833484 / 5.269862 (-2.436377) \| 1.878918 / 4.565676 (-2.686759) \| 0.057133 / 0.424275 (-0.367143) \| 0.006357 / 0.007607 (-0.001251) \| 0.508019 / 0.226044 (0.281975) \| 5.076935 / 2.268929 (2.808007) \| 2.745784 / 55.444624 (-52.698841) \| 2.476291 / 6.876477 (-4.400186) \| 2.677264 / 2.142072 (0.535191) \| 0.587173 / 4.805227 (-4.218054) \| 0.126373 / 6.500664 (-6.374291) \| 0.062815 / 0.075469 (-0.012654) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.355482 / 1.841788 (-0.486305) \| 18.818227 / 8.074308 (10.743919) \| 13.954289 / 10.191392 (3.762896) \| 0.143413 / 0.680424 (-0.537011) \| 0.016844 / 0.534201 (-0.517357) \| 0.338334 / 0.579283 (-0.240949) \| 0.344559 / 0.434364 (-0.089805) \| 0.400669 / 0.540337 (-0.139669) \| 0.563835 / 1.386936 (-0.823101) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c02a44715c036b5261686669727394b1308a3a4b \"CML watermark\")\n" ]	2023-08-10T10:09:54Z	2023-08-10T12:08:38Z	2023-08-10T12:00:02Z	MEMBER	null	null	null	This PR removes unused `allowed_extensions` parameter from `create_builder_configs_from_metadata_configs`.	{ "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/6135/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6135/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6135.diff", "html_url": "https://github.com/huggingface/datasets/pull/6135", "merged_at": "2023-08-10T12:00:01Z", "patch_url": "https://github.com/huggingface/datasets/pull/6135.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6135" }
https://api.github.com/repos/huggingface/datasets/issues/5915	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5915/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5915/comments	https://api.github.com/repos/huggingface/datasets/issues/5915/events	https://github.com/huggingface/datasets/pull/5915	1,732,389,984	PR_kwDODunzps5RsVzj	5,915	Raise error in `DatasetBuilder.as_dataset` when `file_format` is not `"arrow"`	{ "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) \| 0.006416 / 0.011353 (-0.004937) \| 0.004278 / 0.011008 (-0.006731) \| 0.097562 / 0.038508 (0.059054) \| 0.029488 / 0.023109 (0.006379) \| 0.308648 / 0.275898 (0.032750) \| 0.339879 / 0.323480 (0.016399) \| 0.005288 / 0.007986 (-0.002697) \| 0.005033 / 0.004328 (0.000704) \| 0.074666 / 0.004250 (0.070416) \| 0.034888 / 0.037052 (-0.002164) \| 0.309960 / 0.258489 (0.051471) \| 0.344276 / 0.293841 (0.050435) \| 0.025564 / 0.128546 (-0.102982) \| 0.008579 / 0.075646 (-0.067067) \| 0.319796 / 0.419271 (-0.099476) \| 0.044786 / 0.043533 (0.001253) \| 0.308888 / 0.255139 (0.053749) \| 0.334001 / 0.283200 (0.050802) \| 0.089917 / 0.141683 (-0.051766) \| 1.456696 / 1.452155 (0.004541) \| 1.542273 / 1.492716 (0.049557) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.213236 / 0.018006 (0.195230) \| 0.425139 / 0.000490 (0.424650) \| 0.008831 / 0.000200 (0.008631) \| 0.000209 / 0.000054 (0.000155) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023990 / 0.037411 (-0.013421) \| 0.096787 / 0.014526 (0.082261) \| 0.105783 / 0.176557 (-0.070774) \| 0.167182 / 0.737135 (-0.569954) \| 0.108896 / 0.296338 (-0.187442) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419844 / 0.215209 (0.204635) \| 4.201909 / 2.077655 (2.124254) \| 1.910784 / 1.504120 (0.406664) \| 1.685183 / 1.541195 (0.143988) \| 1.716927 / 1.468490 (0.248437) \| 0.548261 / 4.584777 (-4.036516) \| 3.414168 / 3.745712 (-0.331544) \| 1.695446 / 5.269862 (-3.574415) \| 0.989668 / 4.565676 (-3.576008) \| 0.067328 / 0.424275 (-0.356948) \| 0.012084 / 0.007607 (0.004477) \| 0.523799 / 0.226044 (0.297754) \| 5.240589 / 2.268929 (2.971661) \| 2.331618 / 55.444624 (-53.113007) \| 1.996094 / 6.876477 (-4.880383) \| 2.105450 / 2.142072 (-0.036623) \| 0.654614 / 4.805227 (-4.150613) \| 0.134721 / 6.500664 (-6.365943) \| 0.066227 / 0.075469 (-0.009242) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.196266 / 1.841788 (-0.645521) \| 13.990045 / 8.074308 (5.915737) \| 13.928126 / 10.191392 (3.736734) \| 0.142600 / 0.680424 (-0.537824) \| 0.016462 / 0.534201 (-0.517739) \| 0.363113 / 0.579283 (-0.216170) \| 0.428590 / 0.434364 (-0.005773) \| 0.452594 / 0.540337 (-0.087743) \| 0.551678 / 1.386936 (-0.835258) \|\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) \| 0.005992 / 0.011353 (-0.005361) \| 0.004161 / 0.011008 (-0.006847) \| 0.076098 / 0.038508 (0.037589) \| 0.028559 / 0.023109 (0.005450) \| 0.411696 / 0.275898 (0.135798) \| 0.444519 / 0.323480 (0.121040) \| 0.004965 / 0.007986 (-0.003021) \| 0.003452 / 0.004328 (-0.000876) \| 0.075107 / 0.004250 (0.070857) \| 0.037305 / 0.037052 (0.000252) \| 0.429728 / 0.258489 (0.171239) \| 0.444313 / 0.293841 (0.150472) \| 0.025278 / 0.128546 (-0.103268) \| 0.008527 / 0.075646 (-0.067120) \| 0.081502 / 0.419271 (-0.337770) \| 0.041237 / 0.043533 (-0.002296) \| 0.417848 / 0.255139 (0.162709) \| 0.426615 / 0.283200 (0.143415) \| 0.094641 / 0.141683 (-0.047041) \| 1.525141 / 1.452155 (0.072987) \| 1.615608 / 1.492716 (0.122892) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.192867 / 0.018006 (0.174861) \| 0.414979 / 0.000490 (0.414490) \| 0.000815 / 0.000200 (0.000615) \| 0.000068 / 0.000054 (0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025354 / 0.037411 (-0.012058) \| 0.102085 / 0.014526 (0.087559) \| 0.107930 / 0.176557 (-0.068626) \| 0.160483 / 0.737135 (-0.576652) \| 0.112341 / 0.296338 (-0.183997) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.446938 / 0.215209 (0.231728) \| 4.480057 / 2.077655 (2.402402) \| 2.154825 / 1.504120 (0.650705) \| 1.942774 / 1.541195 (0.401580) \| 1.996418 / 1.468490 (0.527928) \| 0.556728 / 4.584777 (-4.028049) \| 3.441228 / 3.745712 (-0.304484) \| 3.004179 / 5.269862 (-2.265683) \| 1.314104 / 4.565676 (-3.251573) \| 0.068670 / 0.424275 (-0.355606) \| 0.011972 / 0.007607 (0.004365) \| 0.556604 / 0.226044 (0.330560) \| 5.561783 / 2.268929 (3.292855) \| 2.631262 / 55.444624 (-52.813363) \| 2.262143 / 6.876477 (-4.614333) \| 2.364243 / 2.142072 (0.222170) \| 0.660621 / 4.805227 (-4.144607) \| 0.137371 / 6.500664 (-6.363293) \| 0.069104 / 0.075469 (-0.006365) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.305706 / 1.841788 (-0.536081) \| 14.015932 / 8.074308 (5.941624) \| 14.353580 / 10.191392 (4.162187) \| 0.146172 / 0.680424 (-0.534251) \| 0.016699 / 0.534201 (-0.517502) \| 0.357970 / 0.579283 (-0.221313) \| 0.389067 / 0.434364 (-0.045297) \| 0.415470 / 0.540337 (-0.124867) \| 0.501359 / 1.386936 (-0.885577) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2b837b4e7267db9e32d2613d8bf8d70d2ce0b47 \"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) \| 0.006800 / 0.011353 (-0.004552) \| 0.004721 / 0.011008 (-0.006287) \| 0.097760 / 0.038508 (0.059252) \| 0.034192 / 0.023109 (0.011083) \| 0.298240 / 0.275898 (0.022342) \| 0.331119 / 0.323480 (0.007639) \| 0.005826 / 0.007986 (-0.002160) \| 0.003968 / 0.004328 (-0.000360) \| 0.073833 / 0.004250 (0.069582) \| 0.046288 / 0.037052 (0.009236) \| 0.303018 / 0.258489 (0.044529) \| 0.342163 / 0.293841 (0.048322) \| 0.028504 / 0.128546 (-0.100042) \| 0.009031 / 0.075646 (-0.066615) \| 0.331617 / 0.419271 (-0.087655) \| 0.060911 / 0.043533 (0.017379) \| 0.304044 / 0.255139 (0.048905) \| 0.328959 / 0.283200 (0.045759) \| 0.113174 / 0.141683 (-0.028509) \| 1.424652 / 1.452155 (-0.027502) \| 1.531392 / 1.492716 (0.038676) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.206175 / 0.018006 (0.188169) \| 0.435916 / 0.000490 (0.435426) \| 0.002587 / 0.000200 (0.002387) \| 0.000083 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026996 / 0.037411 (-0.010415) \| 0.106722 / 0.014526 (0.092196) \| 0.117655 / 0.176557 (-0.058902) \| 0.176969 / 0.737135 (-0.560166) \| 0.122577 / 0.296338 (-0.173762) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.396086 / 0.215209 (0.180877) \| 3.972465 / 2.077655 (1.894811) \| 1.800798 / 1.504120 (0.296678) \| 1.616747 / 1.541195 (0.075552) \| 1.680711 / 1.468490 (0.212221) \| 0.526479 / 4.584777 (-4.058298) \| 3.791528 / 3.745712 (0.045816) \| 2.989518 / 5.269862 (-2.280344) \| 1.463221 / 4.565676 (-3.102455) \| 0.065649 / 0.424275 (-0.358626) \| 0.012155 / 0.007607 (0.004548) \| 0.500241 / 0.226044 (0.274197) \| 5.008895 / 2.268929 (2.739966) \| 2.315288 / 55.444624 (-53.129336) \| 1.959409 / 6.876477 (-4.917067) \| 2.102371 / 2.142072 (-0.039701) \| 0.639611 / 4.805227 (-4.165617) \| 0.140101 / 6.500664 (-6.360563) \| 0.063599 / 0.075469 (-0.011870) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.206729 / 1.841788 (-0.635059) \| 15.127250 / 8.074308 (7.052942) \| 14.397228 / 10.191392 (4.205836) \| 0.148802 / 0.680424 (-0.531622) \| 0.017628 / 0.534201 (-0.516573) \| 0.396150 / 0.579283 (-0.183133) \| 0.435826 / 0.434364 (0.001462) \| 0.471215 / 0.540337 (-0.069122) \| 0.559413 / 1.386936 (-0.827523) \|\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) \| 0.006479 / 0.011353 (-0.004874) \| 0.004520 / 0.011008 (-0.006488) \| 0.074395 / 0.038508 (0.035887) \| 0.033400 / 0.023109 (0.010291) \| 0.388411 / 0.275898 (0.112513) \| 0.396714 / 0.323480 (0.073234) \| 0.005736 / 0.007986 (-0.002250) \| 0.004038 / 0.004328 (-0.000291) \| 0.073595 / 0.004250 (0.069345) \| 0.045207 / 0.037052 (0.008155) \| 0.378096 / 0.258489 (0.119607) \| 0.417830 / 0.293841 (0.123989) \| 0.028365 / 0.128546 (-0.100181) \| 0.008887 / 0.075646 (-0.066760) \| 0.080766 / 0.419271 (-0.338505) \| 0.046923 / 0.043533 (0.003390) \| 0.376190 / 0.255139 (0.121051) \| 0.385875 / 0.283200 (0.102675) \| 0.107542 / 0.141683 (-0.034141) \| 1.409257 / 1.452155 (-0.042898) \| 1.518475 / 1.492716 (0.025759) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223299 / 0.018006 (0.205292) \| 0.440640 / 0.000490 (0.440150) \| 0.000397 / 0.000200 (0.000197) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031388 / 0.037411 (-0.006024) \| 0.113078 / 0.014526 (0.098552) \| 0.124398 / 0.176557 (-0.052159) \| 0.173802 / 0.737135 (-0.563333) \| 0.129555 / 0.296338 (-0.166783) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440220 / 0.215209 (0.225011) \| 4.398052 / 2.077655 (2.320398) \| 2.188396 / 1.504120 (0.684276) \| 1.997811 / 1.541195 (0.456616) \| 2.093338 / 1.468490 (0.624847) \| 0.519597 / 4.584777 (-4.065180) \| 3.885795 / 3.745712 (0.140083) \| 2.896327 / 5.269862 (-2.373534) \| 1.245785 / 4.565676 (-3.319891) \| 0.065675 / 0.424275 (-0.358600) \| 0.011729 / 0.007607 (0.004121) \| 0.541526 / 0.226044 (0.315482) \| 5.406763 / 2.268929 (3.137834) \| 2.722914 / 55.444624 (-52.721711) \| 2.471111 / 6.876477 (-4.405366) \| 2.541488 / 2.142072 (0.399415) \| 0.633566 / 4.805227 (-4.171661) \| 0.139622 / 6.500664 (-6.361042) \| 0.064220 / 0.075469 (-0.011249) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.296097 / 1.841788 (-0.545690) \| 15.095320 / 8.074308 (7.021012) \| 14.300821 / 10.191392 (4.109429) \| 0.145470 / 0.680424 (-0.534954) \| 0.017496 / 0.534201 (-0.516705) \| 0.400589 / 0.579283 (-0.178694) \| 0.423091 / 0.434364 (-0.011273) \| 0.468258 / 0.540337 (-0.072079) \| 0.570873 / 1.386936 (-0.816063) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aee6c67034d6ff298b2153a2fcdab97f14ee6d66 \"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) \| 0.005918 / 0.011353 (-0.005435) \| 0.004393 / 0.011008 (-0.006615) \| 0.091677 / 0.038508 (0.053169) \| 0.033546 / 0.023109 (0.010437) \| 0.344682 / 0.275898 (0.068784) \| 0.388906 / 0.323480 (0.065426) \| 0.005412 / 0.007986 (-0.002574) \| 0.004909 / 0.004328 (0.000580) \| 0.082589 / 0.004250 (0.078339) \| 0.045242 / 0.037052 (0.008190) \| 0.339191 / 0.258489 (0.080702) \| 0.349673 / 0.293841 (0.055832) \| 0.026805 / 0.128546 (-0.101742) \| 0.007529 / 0.075646 (-0.068117) \| 0.319108 / 0.419271 (-0.100164) \| 0.049482 / 0.043533 (0.005949) \| 0.320013 / 0.255139 (0.064874) \| 0.342059 / 0.283200 (0.058859) \| 0.096623 / 0.141683 (-0.045060) \| 1.458204 / 1.452155 (0.006049) \| 1.571172 / 1.492716 (0.078455) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235171 / 0.018006 (0.217165) \| 0.479678 / 0.000490 (0.479188) \| 0.006627 / 0.000200 (0.006427) \| 0.000257 / 0.000054 (0.000202) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025716 / 0.037411 (-0.011696) \| 0.107730 / 0.014526 (0.093204) \| 0.111595 / 0.176557 (-0.064962) \| 0.171316 / 0.737135 (-0.565819) \| 0.118962 / 0.296338 (-0.177377) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.376318 / 0.215209 (0.161109) \| 4.039484 / 2.077655 (1.961829) \| 1.811548 / 1.504120 (0.307428) \| 1.646728 / 1.541195 (0.105533) \| 1.688071 / 1.468490 (0.219581) \| 0.551256 / 4.584777 (-4.033520) \| 4.153931 / 3.745712 (0.408218) \| 3.424154 / 5.269862 (-1.845707) \| 1.734860 / 4.565676 (-2.830816) \| 0.067753 / 0.424275 (-0.356522) \| 0.012699 / 0.007607 (0.005092) \| 0.505722 / 0.226044 (0.279677) \| 4.997321 / 2.268929 (2.728392) \| 2.258755 / 55.444624 (-53.185869) \| 1.954382 / 6.876477 (-4.922095) \| 1.967545 / 2.142072 (-0.174527) \| 0.630489 / 4.805227 (-4.174738) \| 0.138738 / 6.500664 (-6.361926) \| 0.064907 / 0.075469 (-0.010562) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.209634 / 1.841788 (-0.632154) \| 15.055062 / 8.074308 (6.980754) \| 12.721606 / 10.191392 (2.530214) \| 0.164908 / 0.680424 (-0.515516) \| 0.019528 / 0.534201 (-0.514673) \| 0.400136 / 0.579283 (-0.179147) \| 0.451640 / 0.434364 (0.017276) \| 0.466272 / 0.540337 (-0.074065) \| 0.553258 / 1.386936 (-0.833679) \|\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) \| 0.006341 / 0.011353 (-0.005011) \| 0.004617 / 0.011008 (-0.006391) \| 0.077953 / 0.038508 (0.039445) \| 0.031104 / 0.023109 (0.007995) \| 0.360328 / 0.275898 (0.084430) \| 0.408403 / 0.323480 (0.084923) \| 0.005704 / 0.007986 (-0.002282) \| 0.003588 / 0.004328 (-0.000741) \| 0.071441 / 0.004250 (0.067190) \| 0.043520 / 0.037052 (0.006468) \| 0.375798 / 0.258489 (0.117309) \| 0.400955 / 0.293841 (0.107114) \| 0.028166 / 0.128546 (-0.100381) \| 0.008578 / 0.075646 (-0.067068) \| 0.086673 / 0.419271 (-0.332598) \| 0.046424 / 0.043533 (0.002891) \| 0.367276 / 0.255139 (0.112137) \| 0.414550 / 0.283200 (0.131351) \| 0.097355 / 0.141683 (-0.044328) \| 1.465191 / 1.452155 (0.013036) \| 1.555028 / 1.492716 (0.062312) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.196642 / 0.018006 (0.178636) \| 0.464221 / 0.000490 (0.463731) \| 0.002726 / 0.000200 (0.002526) \| 0.000110 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028078 / 0.037411 (-0.009333) \| 0.110762 / 0.014526 (0.096236) \| 0.122212 / 0.176557 (-0.054344) \| 0.164758 / 0.737135 (-0.572377) \| 0.133969 / 0.296338 (-0.162370) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.448134 / 0.215209 (0.232925) \| 4.339335 / 2.077655 (2.261680) \| 2.129209 / 1.504120 (0.625089) \| 1.957805 / 1.541195 (0.416611) \| 1.994038 / 1.468490 (0.525548) \| 0.497101 / 4.584777 (-4.087676) \| 4.114432 / 3.745712 (0.368720) \| 3.437305 / 5.269862 (-1.832556) \| 1.692810 / 4.565676 (-2.872866) \| 0.071077 / 0.424275 (-0.353198) \| 0.012735 / 0.007607 (0.005128) \| 0.534393 / 0.226044 (0.308348) \| 5.217445 / 2.268929 (2.948517) \| 2.594858 / 55.444624 (-52.849766) \| 2.317464 / 6.876477 (-4.559012) \| 2.337974 / 2.142072 (0.195902) \| 0.622291 / 4.805227 (-4.182936) \| 0.144934 / 6.500664 (-6.355730) \| 0.068524 / 0.075469 (-0.006945) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.310601 / 1.841788 (-0.531187) \| 15.771527 / 8.074308 (7.697219) \| 13.952032 / 10.191392 (3.760640) \| 0.212473 / 0.680424 (-0.467951) \| 0.017963 / 0.534201 (-0.516238) \| 0.400755 / 0.579283 (-0.178528) \| 0.439817 / 0.434364 (0.005453) \| 0.472614 / 0.540337 (-0.067724) \| 0.558410 / 1.386936 (-0.828526) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b51429d02a0da1ff798873afe655309136c5689 \"CML watermark\")\n" ]	2023-05-30T14:27:55Z	2023-05-31T13:31:21Z	2023-05-31T13:23:54Z	COLLABORATOR	null	null	null	Raise an error in `DatasetBuilder.as_dataset` when `file_format != "arrow"` (and fix the docstring) Fix #5874	{ "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/5915/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5915/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5915.diff", "html_url": "https://github.com/huggingface/datasets/pull/5915", "merged_at": "2023-05-31T13:23:54Z", "patch_url": "https://github.com/huggingface/datasets/pull/5915.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5915" }
https://api.github.com/repos/huggingface/datasets/issues/4842	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4842/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4842/comments	https://api.github.com/repos/huggingface/datasets/issues/4842/events	https://github.com/huggingface/datasets/pull/4842	1,337,527,764	PR_kwDODunzps49G8CC	4,842	Update stackexchange license	{ "avatar_url": "https://avatars.githubusercontent.com/u/3664563?v=4", "events_url": "https://api.github.com/users/cakiki/events{/privacy}", "followers_url": "https://api.github.com/users/cakiki/followers", "following_url": "https://api.github.com/users/cakiki/following{/other_user}", "gists_url": "https://api.github.com/users/cakiki/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/cakiki", "id": 3664563, "login": "cakiki", "node_id": "MDQ6VXNlcjM2NjQ1NjM=", "organizations_url": "https://api.github.com/users/cakiki/orgs", "received_events_url": "https://api.github.com/users/cakiki/received_events", "repos_url": "https://api.github.com/users/cakiki/repos", "site_admin": false, "starred_url": "https://api.github.com/users/cakiki/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/cakiki/subscriptions", "type": "User", "url": "https://api.github.com/users/cakiki", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-08-12T17:39:06Z	2022-08-14T10:43:18Z	2022-08-14T10:28:49Z	CONTRIBUTOR	null	null	null	The correct license of the stackexchange subset of the Pile is `cc-by-sa-4.0`, as can for example be seen here: https://stackoverflow.com/help/licensing	{ "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/4842/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4842/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4842.diff", "html_url": "https://github.com/huggingface/datasets/pull/4842", "merged_at": "2022-08-14T10:28:49Z", "patch_url": "https://github.com/huggingface/datasets/pull/4842.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4842" }
https://api.github.com/repos/huggingface/datasets/issues/5322	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5322/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5322/comments	https://api.github.com/repos/huggingface/datasets/issues/5322/events	https://github.com/huggingface/datasets/pull/5322	1,471,502,162	PR_kwDODunzps5EEeQP	5,322	Raise error for `.tar` archives in the same way as for `.tar.gz` and `.tgz` in `_get_extraction_protocol`	{ "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._" ]	2022-12-01T15:19:28Z	2022-12-14T16:37:16Z	2022-12-14T16:33:30Z	CONTRIBUTOR	null	null	null	Currently `download_and_extract` doesn't throw an error when it is used with files with `.tar` extension in streaming mode because `_get_extraction_protocol` doesn't do it (like it does for `tar.gz` and `tgz`). `_get_extraction_protocol` returns formatted url as if we support tar protocol but we don't. That means that in dataset scripts `.tar` files would be attempted to load and fail during examples generation (after `download_and_extract` execution). So this PR raises error for `tar` files too.	{ "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/5322/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5322/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5322.diff", "html_url": "https://github.com/huggingface/datasets/pull/5322", "merged_at": "2022-12-14T16:33:30Z", "patch_url": "https://github.com/huggingface/datasets/pull/5322.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5322" }
https://api.github.com/repos/huggingface/datasets/issues/6176	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6176/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6176/comments	https://api.github.com/repos/huggingface/datasets/issues/6176/events	https://github.com/huggingface/datasets/issues/6176	1,864,436,408	I_kwDODunzps5vIQq4	6,176	how to limit the size of memory mapped file?	{ "avatar_url": "https://avatars.githubusercontent.com/u/47763855?v=4", "events_url": "https://api.github.com/users/williamium3000/events{/privacy}", "followers_url": "https://api.github.com/users/williamium3000/followers", "following_url": "https://api.github.com/users/williamium3000/following{/other_user}", "gists_url": "https://api.github.com/users/williamium3000/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/williamium3000", "id": 47763855, "login": "williamium3000", "node_id": "MDQ6VXNlcjQ3NzYzODU1", "organizations_url": "https://api.github.com/users/williamium3000/orgs", "received_events_url": "https://api.github.com/users/williamium3000/received_events", "repos_url": "https://api.github.com/users/williamium3000/repos", "site_admin": false, "starred_url": "https://api.github.com/users/williamium3000/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/williamium3000/subscriptions", "type": "User", "url": "https://api.github.com/users/williamium3000", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Hi! Can you share the error this reproducer throws in your environment? `streaming=True` streams the dataset as it's iterated over without creating a memory-map file.", "The trace of the error. Streaming works but is slower.\r\n```\r\nRoot Cause (first observed failure):\r\n[0]:\r\n time : 2023-08-24_06:06:01\r\n host : compute-126.cm.cluster\r\n rank : 0 (local_rank: 0)\r\n exitcode : 1 (pid: 48442)\r\n error_file: /tmp/torchelastic_4fqzcuuz/none_rx2470jl/attempt_0/0/error.json\r\n traceback : Traceback (most recent call last):\r\n File \"/users/yli7/.conda/envs/pytorch2.0/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py\", line 346, in wrapper\r\n return f(args, *kwargs)\r\n File \"Pretrain.py\", line 214, in main\r\n pair_dataset, c4_dataset = create_dataset('pretrain', config)\r\n File \"/dcs05/qiao/data/william/project/DaVinci/dataset/__init__.py\", line 109, in create_dataset\r\n c4_dataset = load_dataset(\"c4\", \"en\", split=\"train\").to_iterable_dataset(num_shards=1024).map(pre_caption_huggingface)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/load.py\", line 1810, in load_dataset\r\n ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1145, in as_dataset\r\n datasets = map_nested(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 436, in map_nested\r\n return function(data_struct)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1175, in _build_single_dataset\r\n ds = self._as_dataset(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1246, in _as_dataset\r\n dataset_kwargs = ArrowReader(cache_dir, self.info).read(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 244, in read\r\n return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 265, in read_files\r\n pa_table = self._read_files(files, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 200, in _read_files\r\n pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 336, in _get_table_from_filename\r\n table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 357, in read_table\r\n return table_cls.from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 1059, in from_file\r\n table = _memory_mapped_arrow_table_from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 65, in _memory_mapped_arrow_table_from_file\r\n opened_stream = _memory_mapped_record_batch_reader_from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 50, in _memory_mapped_record_batch_reader_from_file\r\n memory_mapped_stream = pa.memory_map(filename)\r\n File \"pyarrow/io.pxi\", line 1009, in pyarrow.lib.memory_map\r\n File \"pyarrow/io.pxi\", line 956, in pyarrow.lib.MemoryMappedFile._open\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 115, in pyarrow.lib.check_status\r\n OSError: Memory mapping file failed: Cannot allocate memory\r\n```", "This issue has previously been reported here: https://github.com/huggingface/datasets/issues/5710. Reporting it in the Arrow repo makes more sense as they have control over memory mapping.\r\n\r\nPS: this is the API to reduce the size of the generated Arrow file:\r\n```python\r\nfrom datasets import load_dataset\r\nbuilder = load_dataset_builder(\"c4\", \"en\")\r\nbuilder.download_and_prepare(max_shard_size=\"5GB\")\r\ndataset = builder.as_dataset()\r\n```\r\n\r\nIf this resolves the issue, we can consider exposing `max_shard_size` in `load_dataset`.", "Thanks for the response. The problem seems not resolved. The memory I allocated to the environment is 64G and the following error still occurs\r\n`Python 3.8.16 (default, Jun 12 2023, 18:09:05) \r\n[GCC 11.2.0] :: Anaconda, Inc. on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_dataset_builder\r\n>>> builder = load_dataset_builder(\"c4\", \"en\")\r\n>>> builder.download_and_prepare(max_shard_size=\"5GB\")\r\nFound cached dataset c4 (/users/yli7/.cache/huggingface/datasets/c4/en/0.0.0/df532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01)\r\n>>> dataset = builder.as_dataset()\r\n 0%\| \| 0/2 [00:00<?, ?it/s]Traceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1145, in as_dataset\r\n datasets = map_nested(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 444, in map_nested\r\n mapped = [\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 445, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True, None))\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 347, in _single_map_nested\r\n return function(data_struct)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1175, in _build_single_dataset\r\n ds = self._as_dataset(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1246, in _as_dataset\r\n dataset_kwargs = ArrowReader(cache_dir, self.info).read(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 244, in read\r\n return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 265, in read_files\r\n pa_table = self._read_files(files, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 200, in _read_files\r\n pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 336, in _get_table_from_filename\r\n table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 357, in read_table\r\n return table_cls.from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 1059, in from_file\r\n table = _memory_mapped_arrow_table_from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 65, in _memory_mapped_arrow_table_from_file\r\n opened_stream = _memory_mapped_record_batch_reader_from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 50, in _memory_mapped_record_batch_reader_from_file\r\n memory_mapped_stream = pa.memory_map(filename)\r\n File \"pyarrow/io.pxi\", line 1009, in pyarrow.lib.memory_map\r\n File \"pyarrow/io.pxi\", line 956, in pyarrow.lib.MemoryMappedFile._open\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 115, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory`", "Have you solved the problem?", "Nope. Streaming works but is slower." ]	2023-08-24T05:33:45Z	2023-10-11T06:00:10Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Huggingface datasets use memory-mapped file to map large datasets in memory for fast access. However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. So is there a way to explicitly limit the size of memory mapped file? ### Steps to reproduce the bug python >>> from datasets import load_dataset >>> dataset = load_dataset("c4", "en", streaming=True) ### Expected behavior In a normal environment, this will not have any problem. However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. ### Environment info linux cluster with SGE（Sun Grid Engine）	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/6176/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6176/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5077	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5077/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5077/comments	https://api.github.com/repos/huggingface/datasets/issues/5077/events	https://github.com/huggingface/datasets/pull/5077	1,398,080,859	PR_kwDODunzps5AOs9L	5,077	Fix passed download_config in HubDatasetModuleFactoryWithoutScript	{ "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._" ]	2022-10-05T16:42:36Z	2022-10-06T05:31:22Z	2022-10-06T05:29:06Z	MEMBER	null	null	null	Fix passed `download_config` in `HubDatasetModuleFactoryWithoutScript`.	{ "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/5077/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5077/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5077.diff", "html_url": "https://github.com/huggingface/datasets/pull/5077", "merged_at": "2022-10-06T05:29:06Z", "patch_url": "https://github.com/huggingface/datasets/pull/5077.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5077" }
https://api.github.com/repos/huggingface/datasets/issues/7261	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7261/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7261/comments	https://api.github.com/repos/huggingface/datasets/issues/7261/events	https://github.com/huggingface/datasets/issues/7261	2,620,510,840	I_kwDODunzps6cMdJ4	7,261	Cannot load the cache when mapping the dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/43033959?v=4", "events_url": "https://api.github.com/users/zhangn77/events{/privacy}", "followers_url": "https://api.github.com/users/zhangn77/followers", "following_url": "https://api.github.com/users/zhangn77/following{/other_user}", "gists_url": "https://api.github.com/users/zhangn77/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/zhangn77", "id": 43033959, "login": "zhangn77", "node_id": "MDQ6VXNlcjQzMDMzOTU5", "organizations_url": "https://api.github.com/users/zhangn77/orgs", "received_events_url": "https://api.github.com/users/zhangn77/received_events", "repos_url": "https://api.github.com/users/zhangn77/repos", "site_admin": false, "starred_url": "https://api.github.com/users/zhangn77/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/zhangn77/subscriptions", "type": "User", "url": "https://api.github.com/users/zhangn77", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "@zhangn77 Hi ，have you solved this problem? I encountered the same issue during training. Could we discuss it?", "I also encountered the same problem, why is that？" ]	2024-10-29T08:29:40Z	2025-03-24T13:27:55Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I'm training the flux controlnet. The train_dataset.map() takes long time to finish. However, when I killed one training process and want to restart a new training with the same dataset. I can't reuse the mapped result even I defined the cache dir for the dataset. with accelerator.main_process_first(): from datasets.fingerprint import Hasher # fingerprint used by the cache for the other processes to load the result # details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401 new_fingerprint = Hasher.hash(args) train_dataset = train_dataset.map( compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint, batch_size=10, ) ### Steps to reproduce the bug train flux controlnet and start again ### Expected behavior will not map again ### Environment info latest 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/7261/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7261/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7336	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7336/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7336/comments	https://api.github.com/repos/huggingface/datasets/issues/7336/events	https://github.com/huggingface/datasets/issues/7336	2,744,746,456	I_kwDODunzps6jmYHY	7,336	Clarify documentation or Create DatasetCard	{ "avatar_url": "https://avatars.githubusercontent.com/u/145011209?v=4", "events_url": "https://api.github.com/users/August-murr/events{/privacy}", "followers_url": "https://api.github.com/users/August-murr/followers", "following_url": "https://api.github.com/users/August-murr/following{/other_user}", "gists_url": "https://api.github.com/users/August-murr/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/August-murr", "id": 145011209, "login": "August-murr", "node_id": "U_kgDOCKSyCQ", "organizations_url": "https://api.github.com/users/August-murr/orgs", "received_events_url": "https://api.github.com/users/August-murr/received_events", "repos_url": "https://api.github.com/users/August-murr/repos", "site_admin": false, "starred_url": "https://api.github.com/users/August-murr/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/August-murr/subscriptions", "type": "User", "url": "https://api.github.com/users/August-murr", "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	[]	2024-12-17T12:01:00Z	2024-12-17T12:01:00Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request I noticed that you can use a Model Card instead of a Dataset Card when pushing a dataset to the Hub, but this isn’t clearly mentioned in [the docs.](https://huggingface.co/docs/datasets/dataset_card) - Update the docs to clarify that a Model Card can work for datasets too. - It might be worth creating a dedicated DatasetCard module, similar to the ModelCard module, for consistency and better support. Not sure if this belongs here or on the [Hub repo](https://github.com/huggingface/huggingface_hub), but thought I’d bring it up! ### Motivation I just spent an hour like on [this issue](https://github.com/huggingface/trl/pull/2491) trying to create a `DatasetCard` for a script. ### Your contribution might later	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/7336/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7336/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/4536	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4536/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4536/comments	https://api.github.com/repos/huggingface/datasets/issues/4536/events	https://github.com/huggingface/datasets/pull/4536	1,278,734,727	PR_kwDODunzps46C2z6	4,536	Properly raise FileNotFound even if the dataset is private	{ "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._" ]	2022-06-21T17:05:50Z	2022-06-28T10:46:51Z	2022-06-28T10:36:10Z	MEMBER	null	null	null	`tests/test_load.py::test_load_streaming_private_dataset` was failing because the hub now returns 401 when getting the HfApi.dataset_info of a dataset without authentication. `load_dataset` was raising ConnectionError, while it should be FileNoteFoundError since it first checks for local files before checking the Hub. Moreover when use_auth_token is not set (default is False), we should not pass `token=None` to HfApi.dataset_info, or it will use the local token by default - instead it should use no token. It's currently not possible to ask for no token to be used, so as a workaround I simply set token="no-token"	{ "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/4536/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4536/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4536.diff", "html_url": "https://github.com/huggingface/datasets/pull/4536", "merged_at": "2022-06-28T10:36:10Z", "patch_url": "https://github.com/huggingface/datasets/pull/4536.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4536" }
https://api.github.com/repos/huggingface/datasets/issues/6133	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6133/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6133/comments	https://api.github.com/repos/huggingface/datasets/issues/6133/events	https://github.com/huggingface/datasets/issues/6133	1,844,511,519	I_kwDODunzps5t8QMf	6,133	Dataset is slower after calling `to_iterable_dataset`	{ "avatar_url": "https://avatars.githubusercontent.com/u/11533479?v=4", "events_url": "https://api.github.com/users/npuichigo/events{/privacy}", "followers_url": "https://api.github.com/users/npuichigo/followers", "following_url": "https://api.github.com/users/npuichigo/following{/other_user}", "gists_url": "https://api.github.com/users/npuichigo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/npuichigo", "id": 11533479, "login": "npuichigo", "node_id": "MDQ6VXNlcjExNTMzNDc5", "organizations_url": "https://api.github.com/users/npuichigo/orgs", "received_events_url": "https://api.github.com/users/npuichigo/received_events", "repos_url": "https://api.github.com/users/npuichigo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/npuichigo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/npuichigo/subscriptions", "type": "User", "url": "https://api.github.com/users/npuichigo", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "@lhoestq ", "It's roughly the same code between the two so we can expected roughly the same speed, could you share a benchmark ?" ]	2023-08-10T06:36:23Z	2023-08-16T09:18:54Z	null	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Can anyone explain why looping over a dataset becomes slower after calling `to_iterable_dataset` to convert to `IterableDataset` ### Steps to reproduce the bug Any dataset after converting to `IterableDataset` ### Expected behavior Maybe it should be faster on big dataset? I only test on small dataset ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3	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/6133/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6133/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7201	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7201/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7201/comments	https://api.github.com/repos/huggingface/datasets/issues/7201/events	https://github.com/huggingface/datasets/issues/7201	2,569,837,015	I_kwDODunzps6ZLJnX	7,201	`load_dataset()` of images from a single directory where `train.png` image exists	{ "avatar_url": "https://avatars.githubusercontent.com/u/56922146?v=4", "events_url": "https://api.github.com/users/SagiPolaczek/events{/privacy}", "followers_url": "https://api.github.com/users/SagiPolaczek/followers", "following_url": "https://api.github.com/users/SagiPolaczek/following{/other_user}", "gists_url": "https://api.github.com/users/SagiPolaczek/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/SagiPolaczek", "id": 56922146, "login": "SagiPolaczek", "node_id": "MDQ6VXNlcjU2OTIyMTQ2", "organizations_url": "https://api.github.com/users/SagiPolaczek/orgs", "received_events_url": "https://api.github.com/users/SagiPolaczek/received_events", "repos_url": "https://api.github.com/users/SagiPolaczek/repos", "site_admin": false, "starred_url": "https://api.github.com/users/SagiPolaczek/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/SagiPolaczek/subscriptions", "type": "User", "url": "https://api.github.com/users/SagiPolaczek", "user_view_type": "public" }	[]	open	false	null	[]	null	[]	2024-10-07T09:14:17Z	2024-10-07T09:14:17Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Hey! Firstly, thanks for maintaining such framework! I had a small issue, where I wanted to load a custom dataset of image+text captioning. I had all of my images in a single directory, and one of the images had the name `train.png`. Then, the loaded dataset had only this image. I guess it's related to "train" as a split name, but it's definitely an unexpected behavior :) Unfortunately I don't have time to submit a proper PR. I'm attaching a toy example to reproduce the issue. Thanks, Sagi ### Steps to reproduce the bug All of the steps I'm attaching are in a fresh env :) ``` (base) sagipolaczek@Sagis-MacBook-Pro ~ % conda activate hf_issue_env (hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % python --version Python 3.10.15 (hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % pip list \| grep datasets datasets 3.0.1 (hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % ls -la Documents/hf_datasets_issue total 352 drwxr-xr-x 6 sagipolaczek staff 192 Oct 7 11:59 . drwx------@ 23 sagipolaczek staff 736 Oct 7 11:46 .. -rw-r--r--@ 1 sagipolaczek staff 72 Oct 7 11:59 metadata.csv -rw-r--r--@ 1 sagipolaczek staff 160154 Oct 6 18:00 pika.png -rw-r--r--@ 1 sagipolaczek staff 5495 Oct 6 12:02 pika_pika.png -rw-r--r--@ 1 sagipolaczek staff 1753 Oct 6 11:50 train.png (hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % cat Documents/hf_datasets_issue/metadata.csv file_name,text train.png,A train pika.png,Pika pika_pika.png,Pika Pika! (hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % python Python 3.10.15 (main, Oct 3 2024, 02:33:33) [Clang 14.0.6 ] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> dataset = load_dataset("imagefolder", data_dir="Documents/hf_datasets_issue/") >>> dataset DatasetDict({ train: Dataset({ features: ['image', 'text'], num_rows: 1 }) }) >>> dataset["train"][0] {'image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=354x84 at 0x10B50FD90>, 'text': 'A train'} ### DELETING `train.png` sample ### (hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % vim Documents/hf_datasets_issue/metadata.csv (hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % rm Documents/hf_datasets_issue/train.png (hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % python Python 3.10.15 (main, Oct 3 2024, 02:33:33) [Clang 14.0.6 ] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> dataset = load_dataset("imagefolder", data_dir="Documents/hf_datasets_issue/") Generating train split: 2 examples [00:00, 65.99 examples/s] >>> dataset DatasetDict({ train: Dataset({ features: ['image', 'text'], num_rows: 2 }) }) >>> dataset["train"] Dataset({ features: ['image', 'text'], num_rows: 2 }) >>> dataset["train"][0],dataset["train"][1] ({'image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=2356x1054 at 0x10DD11E70>, 'text': 'Pika'}, {'image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=343x154 at 0x10E258C70>, 'text': 'Pika Pika!'}) ``` ### Expected behavior My expected behavior would be to get a dataset with the sample `train.png` in it (along with the others data points). ### Environment info I've attached it in the example: Python 3.10.15 datasets 3.0.1	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7201/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7201/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/4743	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4743/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4743/comments	https://api.github.com/repos/huggingface/datasets/issues/4743/events	https://github.com/huggingface/datasets/pull/4743	1,317,362,561	PR_kwDODunzps48EUFs	4,743	Update map 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._" ]	2022-07-25T20:59:35Z	2022-07-27T16:22:04Z	2022-07-27T16:10:04Z	MEMBER	null	null	null	This PR updates the `map` docs for processing text to include `return_tensors="np"` to make it run faster (see #4676).	{ "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/4743/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4743/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4743.diff", "html_url": "https://github.com/huggingface/datasets/pull/4743", "merged_at": "2022-07-27T16:10:04Z", "patch_url": "https://github.com/huggingface/datasets/pull/4743.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4743" }
https://api.github.com/repos/huggingface/datasets/issues/4781	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4781/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4781/comments	https://api.github.com/repos/huggingface/datasets/issues/4781/events	https://github.com/huggingface/datasets/pull/4781	1,326,114,161	PR_kwDODunzps48hOie	4,781	Fix label renaming and add a battery of tests	{ "avatar_url": "https://avatars.githubusercontent.com/u/12866554?v=4", "events_url": "https://api.github.com/users/Rocketknight1/events{/privacy}", "followers_url": "https://api.github.com/users/Rocketknight1/followers", "following_url": "https://api.github.com/users/Rocketknight1/following{/other_user}", "gists_url": "https://api.github.com/users/Rocketknight1/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Rocketknight1", "id": 12866554, "login": "Rocketknight1", "node_id": "MDQ6VXNlcjEyODY2NTU0", "organizations_url": "https://api.github.com/users/Rocketknight1/orgs", "received_events_url": "https://api.github.com/users/Rocketknight1/received_events", "repos_url": "https://api.github.com/users/Rocketknight1/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Rocketknight1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Rocketknight1/subscriptions", "type": "User", "url": "https://api.github.com/users/Rocketknight1", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "Why don't we deprecate label renaming already instead ?", "I think it'll break a lot of workflows if we deprecate it now! There isn't really a non-deprecated workflow yet - once we've added the `auto_rename_labels` option, then we can have `prepare_tf_dataset` on the `transformers` side use that, and then we can consider setting the default option to `False`, or beginning to deprecate it somehow.", "I'm worried it's a bit of a waste of time to continue working on this behavior that shouldn't be here in the first place. Do you have a plan in mind ?", "@lhoestq Broadly! The plan is:\r\n\r\n1) Create the `auto_rename_labels` flag with this PR and skip label renaming if it isn't set. Leave it as `True` for backward compatibility.\r\n2) Add the label renaming logic to `model.prepare_tf_dataset` in `transformers`. That method calls `to_tf_dataset()` right now. Once the label renaming logic is moved there, `model.prepare_tf_dataset` will set `auto_rename_labels=False` when calling `to_tf_dataset()`, and do label renaming itself.\r\n\r\nAfter step 2, `auto_rename_labels` is now only necessary for backward compatibility when users use `to_tf_dataset` directly. I want to leave it alone for a while because the `model.prepare_tf_dataset` workflow is very new. However, once it is established, we can deprecate `auto_rename_labels` and then finally remove it from the `datasets` code and keep it in `transformers` where it belongs.", "I see ! Could it be possible to not add `auto_rename_labels` at all, since you want to remove it at the end ? Something roughly like this:\r\n1. show a warning in `to_tf_dataset` whevener a label is renamed automatically, saying that in the next major release this will be removed\r\n1. add the label renaming logic in `transformers` (to not have the warning)\r\n1. after some time, do a major release 3.0.0 and remove label renaming completely in `to_tf_dataset`\r\n\r\nWhat do you think ? cc @LysandreJik in case you have an opinion on this process.", "@lhoestq I think that plan is mostly good, but if we make the change to `datasets` first then all users will keep getting deprecation warnings until we update the method in `transformers` and release a new version. \r\n\r\nI think we can follow your plan, but make the change to `transformers` first and wait for a new release before changing `datasets` - that way there are no visible warnings or API changes for users using `prepare_tf_dataset`. It also gives us more time to update the docs and try to move people to `prepare_tf_dataset` so they aren't confused by this!", "Sounds good to me ! To summarize:\r\n1. add the label renaming logic in `transformers` + release\r\n1. show a warning in `to_tf_dataset` whevener a label is renamed automatically, saying that in the next major release this will be removed + minor release\r\n1. after some time, do a major release 3.0.0 and remove label renaming completely in `to_tf_dataset`", "Yep, that's the plan! ", "@lhoestq Are you okay with me merging this for now? ", "Can you remove `auto_rename_labels` ? I don't think it's a good idea to add it if the plan is to remove it later", "Right now, the `auto_rename_labels` behaviour happens in all cases! Making it an option is the first step in the process of disabling it (and moving the functionality to `transformers`) and then finally deprecating it." ]	2022-08-02T16:42:07Z	2022-09-12T11:27:06Z	2022-09-12T11:24:45Z	MEMBER	null	null	null	This PR makes some changes to label renaming in `to_tf_dataset()`, both to fix some issues when users input something we weren't expecting, and also to make it easier to deprecate label renaming in future, if/when we want to move this special-casing logic to a function in `transformers`. The main changes are: - Label renaming now only happens when the `auto_rename_labels` argument is set. For backward compatibility, this defaults to `True` for now. - If the user requests "label" but the data collator renames that column to "labels", the label renaming logic will now handle that case correctly. - Added a battery of tests to make this more reliable in future. - Adds an optimization to loading in `to_tf_dataset()` for unshuffled datasets (uses slicing instead of a list of indices) Fixes #4772	{ "avatar_url": "https://avatars.githubusercontent.com/u/12866554?v=4", "events_url": "https://api.github.com/users/Rocketknight1/events{/privacy}", "followers_url": "https://api.github.com/users/Rocketknight1/followers", "following_url": "https://api.github.com/users/Rocketknight1/following{/other_user}", "gists_url": "https://api.github.com/users/Rocketknight1/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Rocketknight1", "id": 12866554, "login": "Rocketknight1", "node_id": "MDQ6VXNlcjEyODY2NTU0", "organizations_url": "https://api.github.com/users/Rocketknight1/orgs", "received_events_url": "https://api.github.com/users/Rocketknight1/received_events", "repos_url": "https://api.github.com/users/Rocketknight1/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Rocketknight1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Rocketknight1/subscriptions", "type": "User", "url": "https://api.github.com/users/Rocketknight1", "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/4781/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4781/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4781.diff", "html_url": "https://github.com/huggingface/datasets/pull/4781", "merged_at": "2022-09-12T11:24:45Z", "patch_url": "https://github.com/huggingface/datasets/pull/4781.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4781" }
https://api.github.com/repos/huggingface/datasets/issues/5108	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5108/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5108/comments	https://api.github.com/repos/huggingface/datasets/issues/5108/events	https://github.com/huggingface/datasets/pull/5108	1,407,044,107	PR_kwDODunzps5AskeK	5,108	Fix a typo in arrow_dataset.py	{ "avatar_url": "https://avatars.githubusercontent.com/u/5431913?v=4", "events_url": "https://api.github.com/users/yangky11/events{/privacy}", "followers_url": "https://api.github.com/users/yangky11/followers", "following_url": "https://api.github.com/users/yangky11/following{/other_user}", "gists_url": "https://api.github.com/users/yangky11/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/yangky11", "id": 5431913, "login": "yangky11", "node_id": "MDQ6VXNlcjU0MzE5MTM=", "organizations_url": "https://api.github.com/users/yangky11/orgs", "received_events_url": "https://api.github.com/users/yangky11/received_events", "repos_url": "https://api.github.com/users/yangky11/repos", "site_admin": false, "starred_url": "https://api.github.com/users/yangky11/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/yangky11/subscriptions", "type": "User", "url": "https://api.github.com/users/yangky11", "user_view_type": "public" }	[]	closed	false	null	[]	null	[]	2022-10-13T02:33:55Z	2022-10-14T09:47:28Z	2022-10-14T09:47:27Z	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/5108/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5108/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5108.diff", "html_url": "https://github.com/huggingface/datasets/pull/5108", "merged_at": "2022-10-14T09:47:27Z", "patch_url": "https://github.com/huggingface/datasets/pull/5108.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5108" }
https://api.github.com/repos/huggingface/datasets/issues/6960	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6960/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6960/comments	https://api.github.com/repos/huggingface/datasets/issues/6960/events	https://github.com/huggingface/datasets/pull/6960	2,340,791,685	PR_kwDODunzps5x0R3T	6,960	feat(ci): add trufflehog secrets detection	{ "avatar_url": "https://avatars.githubusercontent.com/u/9112841?v=4", "events_url": "https://api.github.com/users/McPatate/events{/privacy}", "followers_url": "https://api.github.com/users/McPatate/followers", "following_url": "https://api.github.com/users/McPatate/following{/other_user}", "gists_url": "https://api.github.com/users/McPatate/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/McPatate", "id": 9112841, "login": "McPatate", "node_id": "MDQ6VXNlcjkxMTI4NDE=", "organizations_url": "https://api.github.com/users/McPatate/orgs", "received_events_url": "https://api.github.com/users/McPatate/received_events", "repos_url": "https://api.github.com/users/McPatate/repos", "site_admin": false, "starred_url": "https://api.github.com/users/McPatate/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/McPatate/subscriptions", "type": "User", "url": "https://api.github.com/users/McPatate", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6960). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Yes!", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005007 / 0.011353 (-0.006346) \| 0.003603 / 0.011008 (-0.007405) \| 0.062719 / 0.038508 (0.024211) \| 0.029327 / 0.023109 (0.006217) \| 0.250360 / 0.275898 (-0.025538) \| 0.265095 / 0.323480 (-0.058385) \| 0.004205 / 0.007986 (-0.003781) \| 0.002713 / 0.004328 (-0.001616) \| 0.049209 / 0.004250 (0.044958) \| 0.045162 / 0.037052 (0.008110) \| 0.260439 / 0.258489 (0.001950) \| 0.287778 / 0.293841 (-0.006063) \| 0.027458 / 0.128546 (-0.101088) \| 0.010169 / 0.075646 (-0.065477) \| 0.199487 / 0.419271 (-0.219784) \| 0.036584 / 0.043533 (-0.006949) \| 0.254523 / 0.255139 (-0.000616) \| 0.269902 / 0.283200 (-0.013298) \| 0.017138 / 0.141683 (-0.124545) \| 1.099285 / 1.452155 (-0.352869) \| 1.150878 / 1.492716 (-0.341839) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092868 / 0.018006 (0.074862) \| 0.300421 / 0.000490 (0.299932) \| 0.000213 / 0.000200 (0.000013) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018810 / 0.037411 (-0.018601) \| 0.062341 / 0.014526 (0.047815) \| 0.074779 / 0.176557 (-0.101777) \| 0.120641 / 0.737135 (-0.616494) \| 0.075020 / 0.296338 (-0.221318) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277782 / 0.215209 (0.062573) \| 2.716427 / 2.077655 (0.638772) \| 1.434204 / 1.504120 (-0.069916) \| 1.335990 / 1.541195 (-0.205205) \| 1.336636 / 1.468490 (-0.131854) \| 0.557562 / 4.584777 (-4.027215) \| 2.323517 / 3.745712 (-1.422196) \| 2.647937 / 5.269862 (-2.621925) \| 1.728735 / 4.565676 (-2.836941) \| 0.061888 / 0.424275 (-0.362387) \| 0.004981 / 0.007607 (-0.002627) \| 0.329429 / 0.226044 (0.103385) \| 3.324708 / 2.268929 (1.055779) \| 1.832641 / 55.444624 (-53.611983) \| 1.514386 / 6.876477 (-5.362091) \| 1.656912 / 2.142072 (-0.485160) \| 0.630706 / 4.805227 (-4.174521) \| 0.116250 / 6.500664 (-6.384414) \| 0.042598 / 0.075469 (-0.032871) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969217 / 1.841788 (-0.872570) \| 11.232580 / 8.074308 (3.158272) \| 9.541306 / 10.191392 (-0.650086) \| 0.139544 / 0.680424 (-0.540880) \| 0.014441 / 0.534201 (-0.519760) \| 0.285834 / 0.579283 (-0.293449) \| 0.261950 / 0.434364 (-0.172414) \| 0.325449 / 0.540337 (-0.214889) \| 0.415501 / 1.386936 (-0.971435) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005422 / 0.011353 (-0.005931) \| 0.003528 / 0.011008 (-0.007480) \| 0.049582 / 0.038508 (0.011074) \| 0.032683 / 0.023109 (0.009574) \| 0.277309 / 0.275898 (0.001411) \| 0.298598 / 0.323480 (-0.024882) \| 0.004325 / 0.007986 (-0.003661) \| 0.002741 / 0.004328 (-0.001588) \| 0.047933 / 0.004250 (0.043683) \| 0.040778 / 0.037052 (0.003726) \| 0.287492 / 0.258489 (0.029003) \| 0.311408 / 0.293841 (0.017567) \| 0.029482 / 0.128546 (-0.099064) \| 0.010630 / 0.075646 (-0.065016) \| 0.057745 / 0.419271 (-0.361526) \| 0.033501 / 0.043533 (-0.010031) \| 0.279880 / 0.255139 (0.024741) \| 0.297421 / 0.283200 (0.014221) \| 0.017907 / 0.141683 (-0.123776) \| 1.152221 / 1.452155 (-0.299934) \| 1.189332 / 1.492716 (-0.303385) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094464 / 0.018006 (0.076457) \| 0.300769 / 0.000490 (0.300279) \| 0.000196 / 0.000200 (-0.000004) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022232 / 0.037411 (-0.015179) \| 0.076626 / 0.014526 (0.062100) \| 0.087807 / 0.176557 (-0.088750) \| 0.128847 / 0.737135 (-0.608288) \| 0.092135 / 0.296338 (-0.204203) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299013 / 0.215209 (0.083804) \| 2.929788 / 2.077655 (0.852133) \| 1.614185 / 1.504120 (0.110065) \| 1.486720 / 1.541195 (-0.054475) \| 1.492473 / 1.468490 (0.023983) \| 0.563699 / 4.584777 (-4.021078) \| 0.928820 / 3.745712 (-2.816892) \| 2.597271 / 5.269862 (-2.672590) \| 1.716534 / 4.565676 (-2.849142) \| 0.062568 / 0.424275 (-0.361707) \| 0.005168 / 0.007607 (-0.002439) \| 0.353781 / 0.226044 (0.127737) \| 3.493732 / 2.268929 (1.224803) \| 2.018343 / 55.444624 (-53.426282) \| 1.694516 / 6.876477 (-5.181961) \| 1.796950 / 2.142072 (-0.345123) \| 0.634846 / 4.805227 (-4.170382) \| 0.115230 / 6.500664 (-6.385434) \| 0.040816 / 0.075469 (-0.034654) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.986212 / 1.841788 (-0.855575) \| 11.954392 / 8.074308 (3.880084) \| 10.299670 / 10.191392 (0.108278) \| 0.128358 / 0.680424 (-0.552066) \| 0.016313 / 0.534201 (-0.517888) \| 0.289621 / 0.579283 (-0.289662) \| 0.124708 / 0.434364 (-0.309656) \| 0.325269 / 0.540337 (-0.215068) \| 0.415133 / 1.386936 (-0.971803) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#97513be330114a8aa07e5199ec252ac662aeb76d \"CML watermark\")\n" ]	2024-06-07T16:18:23Z	2024-06-08T14:58:27Z	2024-06-08T14:52:18Z	MEMBER	null	null	null	### What does this PR do? Adding a GH action to scan for leaked secrets on each commit.	{ "avatar_url": "https://avatars.githubusercontent.com/u/9112841?v=4", "events_url": "https://api.github.com/users/McPatate/events{/privacy}", "followers_url": "https://api.github.com/users/McPatate/followers", "following_url": "https://api.github.com/users/McPatate/following{/other_user}", "gists_url": "https://api.github.com/users/McPatate/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/McPatate", "id": 9112841, "login": "McPatate", "node_id": "MDQ6VXNlcjkxMTI4NDE=", "organizations_url": "https://api.github.com/users/McPatate/orgs", "received_events_url": "https://api.github.com/users/McPatate/received_events", "repos_url": "https://api.github.com/users/McPatate/repos", "site_admin": false, "starred_url": "https://api.github.com/users/McPatate/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/McPatate/subscriptions", "type": "User", "url": "https://api.github.com/users/McPatate", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6960/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6960/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6960.diff", "html_url": "https://github.com/huggingface/datasets/pull/6960", "merged_at": "2024-06-08T14:52:18Z", "patch_url": "https://github.com/huggingface/datasets/pull/6960.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6960" }
https://api.github.com/repos/huggingface/datasets/issues/5237	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5237/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5237/comments	https://api.github.com/repos/huggingface/datasets/issues/5237/events	https://github.com/huggingface/datasets/pull/5237	1,448,202,491	PR_kwDODunzps5C2KGz	5,237	Encode path only for old versions of hfh	{ "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._" ]	2022-11-14T14:46:57Z	2022-11-14T17:38:18Z	2022-11-14T17:35:59Z	MEMBER	null	null	null	Next version of `huggingface-hub` 0.11 does encode the `path`, and we don't want to encode twice	{ "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/5237/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5237/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5237.diff", "html_url": "https://github.com/huggingface/datasets/pull/5237", "merged_at": "2022-11-14T17:35:59Z", "patch_url": "https://github.com/huggingface/datasets/pull/5237.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5237" }
https://api.github.com/repos/huggingface/datasets/issues/4599	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4599/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4599/comments	https://api.github.com/repos/huggingface/datasets/issues/4599/events	https://github.com/huggingface/datasets/pull/4599	1,288,849,933	PR_kwDODunzps46kvfC	4,599	Smooth-BLEU bug fixed	{ "avatar_url": "https://avatars.githubusercontent.com/u/36672861?v=4", "events_url": "https://api.github.com/users/Aktsvigun/events{/privacy}", "followers_url": "https://api.github.com/users/Aktsvigun/followers", "following_url": "https://api.github.com/users/Aktsvigun/following{/other_user}", "gists_url": "https://api.github.com/users/Aktsvigun/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Aktsvigun", "id": 36672861, "login": "Aktsvigun", "node_id": "MDQ6VXNlcjM2NjcyODYx", "organizations_url": "https://api.github.com/users/Aktsvigun/orgs", "received_events_url": "https://api.github.com/users/Aktsvigun/received_events", "repos_url": "https://api.github.com/users/Aktsvigun/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Aktsvigun/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Aktsvigun/subscriptions", "type": "User", "url": "https://api.github.com/users/Aktsvigun", "user_view_type": "public" }	[ { "color": "E3165C", "default": false, "description": "", "id": 4190228726, "name": "transfer-to-evaluate", "node_id": "LA_kwDODunzps75wdD2", "url": "https://api.github.com/repos/huggingface/datasets/labels/transfer-to-evaluate" } ]	closed	false	null	[]	null	[ "Thanks @Aktsvigun for your fix.\r\n\r\nHowever, metrics in `datasets` are in deprecation mode:\r\n- #4739\r\n\r\nYou should transfer this PR to the `evaluate` library: https://github.com/huggingface/evaluate\r\n\r\nJust for context, here the link to the PR by @Aktsvigun on tensorflow/nmt:\r\n- https://github.com/tensorflow/nmt/pull/488" ]	2022-06-29T14:51:42Z	2022-09-23T07:42:40Z	2022-09-23T07:42:40Z	NONE	null	null	null	Hi, the current implementation of smooth-BLEU contains a bug: it smoothes unigrams as well. Consequently, when both the reference and translation consist of totally different tokens, it anyway returns a non-zero value (please see the attached image). This however contradicts the source paper suggesting the smooth-BLEU _(Chin-Yew Lin, Franz Josef Och. ORANGE: a method for evaluating automatic evaluation metrics for machine translation. COLING 2004.)_ : > Add one count to the n-gram hit and total ngram count for n > 1. Therefore, for candidate translations with less than n words, they can still get a positive smoothed BLEU score from shorter n-gram matches; however if nothing matches then they will get zero scores. This pull request aims at fixing this bug. I made a pull request in the target repository `tensorflow/nmt`, which implements this script, yet the last commit there is dating 19.02.2019 and I doubt whether this will be fixed promptly. Yet, this bug is critical, for instance for summarization datasets with short summaries (e.g. AESLC), since smoothing needs to be applied there. Therefore, the easiest solution that I found is to fork the repo and download this script directly from the forked fixed repo. Kind, Akim Tsvigun <img width="516" alt="Снимок экрана 2022-06-29 в 17 49 27" src="https://user-images.githubusercontent.com/36672861/176466935-ac579e6d-6a93-4111-ab41-9b33056e7d47.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/4599/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4599/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4599.diff", "html_url": "https://github.com/huggingface/datasets/pull/4599", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/4599.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4599" }
https://api.github.com/repos/huggingface/datasets/issues/5844	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5844/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5844/comments	https://api.github.com/repos/huggingface/datasets/issues/5844/events	https://github.com/huggingface/datasets/issues/5844	1,705,907,812	I_kwDODunzps5lrhZk	5,844	TypeError: Couldn't cast array of type struct<answer: struct<unanswerable: bool, answerType: string, free_form_answer: string, evidence: list<item: string>, evidenceAnnotate: list<item: string>, highlighted_evidence: list<item: string>>> to ...	{ "avatar_url": "https://avatars.githubusercontent.com/u/54010030?v=4", "events_url": "https://api.github.com/users/chen-coding/events{/privacy}", "followers_url": "https://api.github.com/users/chen-coding/followers", "following_url": "https://api.github.com/users/chen-coding/following{/other_user}", "gists_url": "https://api.github.com/users/chen-coding/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/chen-coding", "id": 54010030, "login": "chen-coding", "node_id": "MDQ6VXNlcjU0MDEwMDMw", "organizations_url": "https://api.github.com/users/chen-coding/orgs", "received_events_url": "https://api.github.com/users/chen-coding/received_events", "repos_url": "https://api.github.com/users/chen-coding/repos", "site_admin": false, "starred_url": "https://api.github.com/users/chen-coding/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/chen-coding/subscriptions", "type": "User", "url": "https://api.github.com/users/chen-coding", "user_view_type": "public" }	[]	open	false	null	[]	null	[]	2023-05-11T14:15:01Z	2023-05-11T14:15:01Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug TypeError: Couldn't cast array of type struct<answer: struct<unanswerable: bool, answerType: string, free_form_answer: string, evidence: list<item: string>, evidenceAnnotate: list<item: string>, highlighted_evidence: list<item: string>>> to {'answer': {'unanswerable': Value(dtype='bool', id=None), 'answerType': Value(dtype='string', id=None), 'free_form_answer': Value(dtype='string', id=None), 'evidence': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'evidenceAnnotate': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'highlighted_evidence': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'unanswerable': Value(dtype='bool', id=None), 'answerType': Value(dtype='string', id=None), 'free_form_answer': Value(dtype='string', id=None), 'evidence': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'evidenceAnnotate': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'highlighted_evidence': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)} When I use _load_dataset()_ I get the error `from datasets import load_dataset datafiles = {'train': './data/train.json', 'validation': './data/validation.json', 'test': './data/test.json'} raw_data = load_dataset("json", data_files=datafiles, cache_dir="./cache") ` Detailed error information is as follows： Traceback (most recent call last): File "C:/Users/CHENJIALEI/Desktop/NLPCC2023/NLPCC23_SciMRC-main/test2.py", line 9, in <module> raw_data = load_dataset("json", data_files=datafiles, cache_dir="./cache") File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\load.py", line 1747, in load_dataset builder_instance.download_and_prepare( File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\builder.py", line 814, in download_and_prepare self._download_and_prepare( File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\builder.py", line 905, in _download_and_prepare self._prepare_split(split_generator, *prepare_split_kwargs) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\builder.py", line 1521, in _prepare_split writer.write_table(table) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\arrow_writer.py", line 540, in write_table pa_table = table_cast(pa_table, self._schema) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 2069, in table_cast return cast_table_to_schema(table, schema) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 2031, in cast_table_to_schema arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 2031, in <listcomp> arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1740, in wrapper return pa.chunked_array([func(chunk, args, *kwargs) for chunk in array.chunks]) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1740, in <listcomp> return pa.chunked_array([func(chunk, args, *kwargs) for chunk in array.chunks]) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1867, in cast_array_to_feature casted_values = _c(array.values, feature[0]) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1742, in wrapper return func(array, args, *kwargs) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1862, in cast_array_to_feature arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1862, in <listcomp> arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1742, in wrapper return func(array, args, *kwargs) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1867, in cast_array_to_feature casted_values = _c(array.values, feature[0]) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1742, in wrapper return func(array, args, **kwargs) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1913, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") It is successful when I load the data separately `raw_data = load_dataset("json", data_files="./data/train.json", cache_dir="./cache")` ### Steps to reproduce the bug 1.from datasets import load_dataset 2.datafiles = {'train': './data/train.json', 'validation': './data/validation.json', 'test': './data/test.json'} 3.raw_data = load_dataset("json", data_files=datafiles, cache_dir="./cache") ### Expected behavior Successfully load dataset ### Environment info datasets == 2.6.1 pyarrow == 8.0.0 python == 3.8 platform:windows11	null	{ "+1": 2, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/5844/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5844/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6549	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6549/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6549/comments	https://api.github.com/repos/huggingface/datasets/issues/6549/events	https://github.com/huggingface/datasets/issues/6549	2,062,420,259	I_kwDODunzps567gkj	6,549	Loading from hf hub with clearer error message	{ "avatar_url": "https://avatars.githubusercontent.com/u/7353373?v=4", "events_url": "https://api.github.com/users/thomwolf/events{/privacy}", "followers_url": "https://api.github.com/users/thomwolf/followers", "following_url": "https://api.github.com/users/thomwolf/following{/other_user}", "gists_url": "https://api.github.com/users/thomwolf/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/thomwolf", "id": 7353373, "login": "thomwolf", "node_id": "MDQ6VXNlcjczNTMzNzM=", "organizations_url": "https://api.github.com/users/thomwolf/orgs", "received_events_url": "https://api.github.com/users/thomwolf/received_events", "repos_url": "https://api.github.com/users/thomwolf/repos", "site_admin": false, "starred_url": "https://api.github.com/users/thomwolf/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/thomwolf/subscriptions", "type": "User", "url": "https://api.github.com/users/thomwolf", "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	[ "Maybe we can add a helper message like `Maybe try again using \"hf://path/without/resolve\"` if the path contains `/resolve/` ?\r\n\r\ne.g.\r\n\r\n```\r\nFileNotFoundError: Unable to find 'hf://datasets/HuggingFaceTB/eval_data/resolve/main/eval_data_context_and_answers.json'\r\nIt looks like you used parts of the URL of the file from the Hugging Face website, but you should remove the \"/resolve/<revision>\" part to have a valid `hf://` path.\r\nPlease try again using this path instead:\r\n hf://datasets/HuggingFaceTB/eval_data/eval_data_context_and_answers.json\r\n```\r\n\r\nand suggest `f\"hf://datasets/HuggingFaceTB/eval_data@{revision}/eval_data_context_and_answers.json\"` if revision != \"main\"\r\n\r\nEDIT: I think this message should also be raised from the `huggingface_hub`'s `HfFileSystem` implementation" ]	2024-01-02T13:26:34Z	2024-01-02T14:06:49Z	null	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request Shouldn't this kinda work ? ``` Dataset.from_json("hf://datasets/HuggingFaceTB/eval_data/resolve/main/eval_data_context_and_answers.json") ``` I got an error ``` File ~/miniconda3/envs/datatrove/lib/python3.10/site-packages/datasets/data_files.py:380, in resolve_pattern(pattern, base_path, allowed_extensions, download_config) 378 if allowed_extensions is not None: 379 error_msg += f" with any supported extension {list(allowed_extensions)}" --> 380 raise FileNotFoundError(error_msg) 381 return out FileNotFoundError: Unable to find 'hf://datasets/HuggingFaceTB/eval_data/resolve/main/eval_data_context_and_answers.json' (I'm logged in) ``` Fix: the correct path is ``` hf://datasets/HuggingFaceTB/eval_data/eval_data_context_and_answers.json ``` Proposal: raise a clearer error ### Motivation Clearer error message ### Your contribution Can open 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/6549/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6549/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5619	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5619/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5619/comments	https://api.github.com/repos/huggingface/datasets/issues/5619/events	https://github.com/huggingface/datasets/pull/5619	1,613,439,709	PR_kwDODunzps5LeaYP	5,619	unpin fsspec	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009954 / 0.011353 (-0.001398) \| 0.005468 / 0.011008 (-0.005541) \| 0.101228 / 0.038508 (0.062720) \| 0.037878 / 0.023109 (0.014769) \| 0.305635 / 0.275898 (0.029737) \| 0.391672 / 0.323480 (0.068192) \| 0.008893 / 0.007986 (0.000908) \| 0.005861 / 0.004328 (0.001533) \| 0.076940 / 0.004250 (0.072689) \| 0.046242 / 0.037052 (0.009190) \| 0.324033 / 0.258489 (0.065544) \| 0.383306 / 0.293841 (0.089465) \| 0.039298 / 0.128546 (-0.089249) \| 0.012187 / 0.075646 (-0.063459) \| 0.336774 / 0.419271 (-0.082498) \| 0.053493 / 0.043533 (0.009960) \| 0.303381 / 0.255139 (0.048242) \| 0.323494 / 0.283200 (0.040295) \| 0.118613 / 0.141683 (-0.023070) \| 1.463430 / 1.452155 (0.011275) \| 1.549856 / 1.492716 (0.057139) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289264 / 0.018006 (0.271258) \| 0.520348 / 0.000490 (0.519858) \| 0.004543 / 0.000200 (0.004343) \| 0.000090 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028183 / 0.037411 (-0.009229) \| 0.107869 / 0.014526 (0.093343) \| 0.124019 / 0.176557 (-0.052537) \| 0.167769 / 0.737135 (-0.569367) \| 0.130304 / 0.296338 (-0.166034) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.402296 / 0.215209 (0.187087) \| 4.018884 / 2.077655 (1.941229) \| 1.834050 / 1.504120 (0.329930) \| 1.649974 / 1.541195 (0.108779) \| 1.741697 / 1.468490 (0.273207) \| 0.684354 / 4.584777 (-3.900423) \| 3.778213 / 3.745712 (0.032501) \| 2.158086 / 5.269862 (-3.111775) \| 1.472671 / 4.565676 (-3.093006) \| 0.083912 / 0.424275 (-0.340363) \| 0.012285 / 0.007607 (0.004678) \| 0.501689 / 0.226044 (0.275645) \| 5.014722 / 2.268929 (2.745794) \| 2.310722 / 55.444624 (-53.133902) \| 1.983214 / 6.876477 (-4.893262) \| 2.154518 / 2.142072 (0.012446) \| 0.821277 / 4.805227 (-3.983950) \| 0.164434 / 6.500664 (-6.336231) \| 0.062568 / 0.075469 (-0.012901) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.224338 / 1.841788 (-0.617450) \| 14.981623 / 8.074308 (6.907315) \| 14.296356 / 10.191392 (4.104964) \| 0.193554 / 0.680424 (-0.486870) \| 0.028511 / 0.534201 (-0.505690) \| 0.437649 / 0.579283 (-0.141634) \| 0.448934 / 0.434364 (0.014570) \| 0.552624 / 0.540337 (0.012287) \| 0.654268 / 1.386936 (-0.732668) \|\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) \| 0.007772 / 0.011353 (-0.003581) \| 0.005534 / 0.011008 (-0.005474) \| 0.074347 / 0.038508 (0.035839) \| 0.034486 / 0.023109 (0.011376) \| 0.343430 / 0.275898 (0.067532) \| 0.385778 / 0.323480 (0.062298) \| 0.006424 / 0.007986 (-0.001562) \| 0.004241 / 0.004328 (-0.000087) \| 0.072839 / 0.004250 (0.068589) \| 0.055523 / 0.037052 (0.018471) \| 0.342778 / 0.258489 (0.084289) \| 0.389961 / 0.293841 (0.096120) \| 0.037238 / 0.128546 (-0.091308) \| 0.012450 / 0.075646 (-0.063197) \| 0.085282 / 0.419271 (-0.333990) \| 0.049678 / 0.043533 (0.006146) \| 0.345300 / 0.255139 (0.090161) \| 0.365220 / 0.283200 (0.082020) \| 0.109257 / 0.141683 (-0.032426) \| 1.480284 / 1.452155 (0.028129) \| 1.627881 / 1.492716 (0.135165) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.323330 / 0.018006 (0.305324) \| 0.530824 / 0.000490 (0.530334) \| 0.000463 / 0.000200 (0.000263) \| 0.000063 / 0.000054 (0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032398 / 0.037411 (-0.005013) \| 0.115889 / 0.014526 (0.101363) \| 0.131093 / 0.176557 (-0.045464) \| 0.180757 / 0.737135 (-0.556379) \| 0.134395 / 0.296338 (-0.161943) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.423931 / 0.215209 (0.208722) \| 4.238207 / 2.077655 (2.160553) \| 2.075721 / 1.504120 (0.571602) \| 1.887752 / 1.541195 (0.346557) \| 2.055054 / 1.468490 (0.586564) \| 0.703145 / 4.584777 (-3.881632) \| 3.937120 / 3.745712 (0.191408) \| 3.748550 / 5.269862 (-1.521311) \| 1.562849 / 4.565676 (-3.002827) \| 0.087695 / 0.424275 (-0.336580) \| 0.012614 / 0.007607 (0.005007) \| 0.523901 / 0.226044 (0.297856) \| 5.230210 / 2.268929 (2.961282) \| 2.592667 / 55.444624 (-52.851958) \| 2.345662 / 6.876477 (-4.530815) \| 2.475388 / 2.142072 (0.333316) \| 0.836443 / 4.805227 (-3.968784) \| 0.170304 / 6.500664 (-6.330360) \| 0.067741 / 0.075469 (-0.007729) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.255171 / 1.841788 (-0.586617) \| 16.312856 / 8.074308 (8.238548) \| 13.184770 / 10.191392 (2.993378) \| 0.145557 / 0.680424 (-0.534867) \| 0.017723 / 0.534201 (-0.516478) \| 0.423447 / 0.579283 (-0.155836) \| 0.423063 / 0.434364 (-0.011301) \| 0.494159 / 0.540337 (-0.046179) \| 0.589590 / 1.386936 (-0.797346) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4ea6f1db3f80eb3bb7ac6f252c2cd5bd97537c01 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012068 / 0.011353 (0.000715) \| 0.006127 / 0.011008 (-0.004881) \| 0.112550 / 0.038508 (0.074042) \| 0.043201 / 0.023109 (0.020092) \| 0.346666 / 0.275898 (0.070768) \| 0.413852 / 0.323480 (0.090372) \| 0.009342 / 0.007986 (0.001356) \| 0.006302 / 0.004328 (0.001974) \| 0.086901 / 0.004250 (0.082650) \| 0.053992 / 0.037052 (0.016940) \| 0.362192 / 0.258489 (0.103703) \| 0.409867 / 0.293841 (0.116026) \| 0.046124 / 0.128546 (-0.082422) \| 0.014139 / 0.075646 (-0.061507) \| 0.386386 / 0.419271 (-0.032886) \| 0.058465 / 0.043533 (0.014932) \| 0.344832 / 0.255139 (0.089693) \| 0.370684 / 0.283200 (0.087485) \| 0.122886 / 0.141683 (-0.018796) \| 1.724013 / 1.452155 (0.271858) \| 1.775756 / 1.492716 (0.283039) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.220289 / 0.018006 (0.202283) \| 0.493585 / 0.000490 (0.493096) \| 0.001970 / 0.000200 (0.001770) \| 0.000099 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030763 / 0.037411 (-0.006649) \| 0.128237 / 0.014526 (0.113711) \| 0.138364 / 0.176557 (-0.038192) \| 0.188115 / 0.737135 (-0.549021) \| 0.145367 / 0.296338 (-0.150972) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.452487 / 0.215209 (0.237277) \| 4.592728 / 2.077655 (2.515074) \| 2.075712 / 1.504120 (0.571592) \| 1.845424 / 1.541195 (0.304229) \| 1.956400 / 1.468490 (0.487910) \| 0.808387 / 4.584777 (-3.776390) \| 4.483678 / 3.745712 (0.737966) \| 3.870287 / 5.269862 (-1.399574) \| 2.151205 / 4.565676 (-2.414471) \| 0.098123 / 0.424275 (-0.326152) \| 0.014139 / 0.007607 (0.006531) \| 0.577775 / 0.226044 (0.351730) \| 5.785545 / 2.268929 (3.516616) \| 2.614418 / 55.444624 (-52.830206) \| 2.312136 / 6.876477 (-4.564341) \| 2.364189 / 2.142072 (0.222117) \| 0.970028 / 4.805227 (-3.835199) \| 0.189592 / 6.500664 (-6.311072) \| 0.072883 / 0.075469 (-0.002586) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.414252 / 1.841788 (-0.427535) \| 17.518307 / 8.074308 (9.443999) \| 16.053748 / 10.191392 (5.862356) \| 0.215297 / 0.680424 (-0.465127) \| 0.033947 / 0.534201 (-0.500253) \| 0.525794 / 0.579283 (-0.053489) \| 0.514676 / 0.434364 (0.080312) \| 0.595066 / 0.540337 (0.054728) \| 0.689404 / 1.386936 (-0.697532) \|\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) \| 0.008185 / 0.011353 (-0.003168) \| 0.005776 / 0.011008 (-0.005232) \| 0.084919 / 0.038508 (0.046411) \| 0.037575 / 0.023109 (0.014466) \| 0.401192 / 0.275898 (0.125294) \| 0.443920 / 0.323480 (0.120440) \| 0.006446 / 0.007986 (-0.001540) \| 0.004428 / 0.004328 (0.000099) \| 0.084013 / 0.004250 (0.079763) \| 0.052013 / 0.037052 (0.014961) \| 0.398429 / 0.258489 (0.139940) \| 0.455676 / 0.293841 (0.161836) \| 0.041568 / 0.128546 (-0.086978) \| 0.013631 / 0.075646 (-0.062015) \| 0.098709 / 0.419271 (-0.320563) \| 0.055889 / 0.043533 (0.012356) \| 0.402002 / 0.255139 (0.146863) \| 0.424248 / 0.283200 (0.141049) \| 0.113288 / 0.141683 (-0.028395) \| 1.672214 / 1.452155 (0.220059) \| 1.792940 / 1.492716 (0.300223) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211847 / 0.018006 (0.193841) \| 0.486711 / 0.000490 (0.486221) \| 0.002907 / 0.000200 (0.002707) \| 0.000118 / 0.000054 (0.000063) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032931 / 0.037411 (-0.004480) \| 0.142073 / 0.014526 (0.127547) \| 0.142872 / 0.176557 (-0.033685) \| 0.202612 / 0.737135 (-0.534523) \| 0.154390 / 0.296338 (-0.141949) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.488682 / 0.215209 (0.273473) \| 4.755805 / 2.077655 (2.678150) \| 2.348778 / 1.504120 (0.844658) \| 2.144992 / 1.541195 (0.603797) \| 2.245654 / 1.468490 (0.777164) \| 0.792690 / 4.584777 (-3.792087) \| 4.569190 / 3.745712 (0.823478) \| 3.919317 / 5.269862 (-1.350545) \| 2.140302 / 4.565676 (-2.425374) \| 0.096430 / 0.424275 (-0.327845) \| 0.014551 / 0.007607 (0.006944) \| 0.605138 / 0.226044 (0.379094) \| 5.989470 / 2.268929 (3.720542) \| 2.915525 / 55.444624 (-52.529099) \| 2.516243 / 6.876477 (-4.360234) \| 2.673114 / 2.142072 (0.531041) \| 0.932330 / 4.805227 (-3.872897) \| 0.191456 / 6.500664 (-6.309209) \| 0.073887 / 0.075469 (-0.001582) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.455552 / 1.841788 (-0.386236) \| 17.824864 / 8.074308 (9.750556) \| 15.764150 / 10.191392 (5.572758) \| 0.184935 / 0.680424 (-0.495489) \| 0.020552 / 0.534201 (-0.513649) \| 0.486816 / 0.579283 (-0.092467) \| 0.489006 / 0.434364 (0.054642) \| 0.609826 / 0.540337 (0.069488) \| 0.721313 / 1.386936 (-0.665623) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a0a35c5fa84a8a7df656c1f5b0a7266126fa9b75 \"CML watermark\")\n" ]	2023-03-07T13:22:41Z	2023-03-07T13:47:01Z	2023-03-07T13:39:02Z	MEMBER	null	null	null	close https://github.com/huggingface/datasets/issues/5618	{ "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/5619/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5619/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5619.diff", "html_url": "https://github.com/huggingface/datasets/pull/5619", "merged_at": "2023-03-07T13:39:02Z", "patch_url": "https://github.com/huggingface/datasets/pull/5619.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5619" }
https://api.github.com/repos/huggingface/datasets/issues/6028	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6028/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6028/comments	https://api.github.com/repos/huggingface/datasets/issues/6028/events	https://github.com/huggingface/datasets/pull/6028	1,803,294,981	PR_kwDODunzps5Vb3LJ	6,028	Use new hffs	{ "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) \| 0.006665 / 0.011353 (-0.004688) \| 0.004376 / 0.011008 (-0.006633) \| 0.085529 / 0.038508 (0.047021) \| 0.076372 / 0.023109 (0.053263) \| 0.310019 / 0.275898 (0.034121) \| 0.341404 / 0.323480 (0.017924) \| 0.005666 / 0.007986 (-0.002320) \| 0.003763 / 0.004328 (-0.000566) \| 0.064678 / 0.004250 (0.060427) \| 0.059283 / 0.037052 (0.022231) \| 0.316194 / 0.258489 (0.057704) \| 0.349397 / 0.293841 (0.055557) \| 0.031199 / 0.128546 (-0.097347) \| 0.008724 / 0.075646 (-0.066923) \| 0.300236 / 0.419271 (-0.119035) \| 0.068872 / 0.043533 (0.025339) \| 0.308521 / 0.255139 (0.053382) \| 0.331292 / 0.283200 (0.048092) \| 0.028236 / 0.141683 (-0.113447) \| 1.501365 / 1.452155 (0.049211) \| 1.554334 / 1.492716 (0.061618) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.238291 / 0.018006 (0.220285) \| 0.565069 / 0.000490 (0.564580) \| 0.001626 / 0.000200 (0.001426) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029777 / 0.037411 (-0.007634) \| 0.082873 / 0.014526 (0.068347) \| 0.099619 / 0.176557 (-0.076937) \| 0.156572 / 0.737135 (-0.580563) \| 0.099887 / 0.296338 (-0.196452) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401017 / 0.215209 (0.185808) \| 3.827192 / 2.077655 (1.749537) \| 1.861554 / 1.504120 (0.357434) \| 1.699869 / 1.541195 (0.158674) \| 1.720043 / 1.468490 (0.251553) \| 0.486757 / 4.584777 (-4.098020) \| 3.638125 / 3.745712 (-0.107587) \| 5.844959 / 5.269862 (0.575097) \| 3.454901 / 4.565676 (-1.110775) \| 0.057650 / 0.424275 (-0.366625) \| 0.007341 / 0.007607 (-0.000266) \| 0.462698 / 0.226044 (0.236654) \| 4.633472 / 2.268929 (2.364544) \| 2.287607 / 55.444624 (-53.157017) \| 2.057318 / 6.876477 (-4.819159) \| 2.203657 / 2.142072 (0.061584) \| 0.598136 / 4.805227 (-4.207091) \| 0.134012 / 6.500664 (-6.366653) \| 0.060824 / 0.075469 (-0.014645) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.277752 / 1.841788 (-0.564036) \| 20.013398 / 8.074308 (11.939089) \| 14.372993 / 10.191392 (4.181601) \| 0.169991 / 0.680424 (-0.510433) \| 0.018344 / 0.534201 (-0.515857) \| 0.396985 / 0.579283 (-0.182299) \| 0.416289 / 0.434364 (-0.018075) \| 0.458658 / 0.540337 (-0.081680) \| 0.692980 / 1.386936 (-0.693956) \|\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) \| 0.006689 / 0.011353 (-0.004664) \| 0.004393 / 0.011008 (-0.006615) \| 0.064069 / 0.038508 (0.025561) \| 0.080717 / 0.023109 (0.057607) \| 0.370090 / 0.275898 (0.094191) \| 0.400432 / 0.323480 (0.076952) \| 0.005613 / 0.007986 (-0.002372) \| 0.003641 / 0.004328 (-0.000687) \| 0.064771 / 0.004250 (0.060520) \| 0.057555 / 0.037052 (0.020502) \| 0.392156 / 0.258489 (0.133667) \| 0.409842 / 0.293841 (0.116001) \| 0.031500 / 0.128546 (-0.097047) \| 0.008786 / 0.075646 (-0.066860) \| 0.070342 / 0.419271 (-0.348929) \| 0.048646 / 0.043533 (0.005113) \| 0.360914 / 0.255139 (0.105775) \| 0.387626 / 0.283200 (0.104426) \| 0.022787 / 0.141683 (-0.118896) \| 1.508915 / 1.452155 (0.056761) \| 1.539719 / 1.492716 (0.047002) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.257985 / 0.018006 (0.239979) \| 0.550990 / 0.000490 (0.550501) \| 0.000407 / 0.000200 (0.000207) \| 0.000057 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030183 / 0.037411 (-0.007228) \| 0.086882 / 0.014526 (0.072356) \| 0.102382 / 0.176557 (-0.074175) \| 0.154745 / 0.737135 (-0.582390) \| 0.104008 / 0.296338 (-0.192331) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426284 / 0.215209 (0.211075) \| 4.240812 / 2.077655 (2.163158) \| 2.261240 / 1.504120 (0.757120) \| 2.085905 / 1.541195 (0.544710) \| 2.160374 / 1.468490 (0.691883) \| 0.481126 / 4.584777 (-4.103651) \| 3.516234 / 3.745712 (-0.229478) \| 3.325322 / 5.269862 (-1.944539) \| 2.043307 / 4.565676 (-2.522369) \| 0.056663 / 0.424275 (-0.367612) \| 0.007786 / 0.007607 (0.000179) \| 0.497614 / 0.226044 (0.271570) \| 4.974529 / 2.268929 (2.705600) \| 2.700018 / 55.444624 (-52.744606) \| 2.393778 / 6.876477 (-4.482699) \| 2.628202 / 2.142072 (0.486130) \| 0.594316 / 4.805227 (-4.210911) \| 0.147092 / 6.500664 (-6.353572) \| 0.062207 / 0.075469 (-0.013262) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.315676 / 1.841788 (-0.526112) \| 20.749251 / 8.074308 (12.674943) \| 14.371553 / 10.191392 (4.180160) \| 0.170249 / 0.680424 (-0.510175) \| 0.018478 / 0.534201 (-0.515722) \| 0.395710 / 0.579283 (-0.183573) \| 0.409706 / 0.434364 (-0.024658) \| 0.463454 / 0.540337 (-0.076884) \| 0.615657 / 1.386936 (-0.771279) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5a752d8e8ca0a6ed118b024ba03c1b4a2881177 \"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) \| 0.007224 / 0.011353 (-0.004129) \| 0.004506 / 0.011008 (-0.006503) \| 0.096729 / 0.038508 (0.058221) \| 0.082394 / 0.023109 (0.059284) \| 0.390954 / 0.275898 (0.115056) \| 0.416647 / 0.323480 (0.093167) \| 0.005894 / 0.007986 (-0.002092) \| 0.003756 / 0.004328 (-0.000572) \| 0.075800 / 0.004250 (0.071549) \| 0.062683 / 0.037052 (0.025631) \| 0.398959 / 0.258489 (0.140470) \| 0.436624 / 0.293841 (0.142783) \| 0.034650 / 0.128546 (-0.093896) \| 0.009655 / 0.075646 (-0.065991) \| 0.315761 / 0.419271 (-0.103511) \| 0.060957 / 0.043533 (0.017424) \| 0.385649 / 0.255139 (0.130510) \| 0.394022 / 0.283200 (0.110822) \| 0.024601 / 0.141683 (-0.117082) \| 1.729586 / 1.452155 (0.277431) \| 1.724153 / 1.492716 (0.231437) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207070 / 0.018006 (0.189063) \| 0.466502 / 0.000490 (0.466012) \| 0.010739 / 0.000200 (0.010540) \| 0.000214 / 0.000054 (0.000160) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031633 / 0.037411 (-0.005779) \| 0.095345 / 0.014526 (0.080819) \| 0.105399 / 0.176557 (-0.071157) \| 0.174173 / 0.737135 (-0.562962) \| 0.104207 / 0.296338 (-0.192132) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.435312 / 0.215209 (0.220103) \| 4.265600 / 2.077655 (2.187946) \| 2.056500 / 1.504120 (0.552380) \| 1.848023 / 1.541195 (0.306828) \| 1.946156 / 1.468490 (0.477666) \| 0.557788 / 4.584777 (-4.026989) \| 4.070289 / 3.745712 (0.324577) \| 3.608027 / 5.269862 (-1.661835) \| 2.214556 / 4.565676 (-2.351121) \| 0.062623 / 0.424275 (-0.361652) \| 0.008083 / 0.007607 (0.000476) \| 0.491782 / 0.226044 (0.265738) \| 4.989963 / 2.268929 (2.721035) \| 2.575867 / 55.444624 (-52.868757) \| 2.208045 / 6.876477 (-4.668431) \| 2.364184 / 2.142072 (0.222112) \| 0.633925 / 4.805227 (-4.171302) \| 0.144323 / 6.500664 (-6.356341) \| 0.067505 / 0.075469 (-0.007965) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.467219 / 1.841788 (-0.374569) \| 22.334967 / 8.074308 (14.260659) \| 15.715747 / 10.191392 (5.524355) \| 0.175443 / 0.680424 (-0.504980) \| 0.026165 / 0.534201 (-0.508036) \| 0.490675 / 0.579283 (-0.088608) \| 0.509211 / 0.434364 (0.074847) \| 0.586303 / 0.540337 (0.045965) \| 0.785052 / 1.386936 (-0.601884) \|\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) \| 0.007893 / 0.011353 (-0.003460) \| 0.004577 / 0.011008 (-0.006431) \| 0.075781 / 0.038508 (0.037273) \| 0.095492 / 0.023109 (0.072382) \| 0.433259 / 0.275898 (0.157361) \| 0.469386 / 0.323480 (0.145906) \| 0.006317 / 0.007986 (-0.001669) \| 0.003708 / 0.004328 (-0.000621) \| 0.074417 / 0.004250 (0.070167) \| 0.068605 / 0.037052 (0.031552) \| 0.448701 / 0.258489 (0.190212) \| 0.469131 / 0.293841 (0.175290) \| 0.036647 / 0.128546 (-0.091899) \| 0.010077 / 0.075646 (-0.065570) \| 0.082457 / 0.419271 (-0.336815) \| 0.063255 / 0.043533 (0.019722) \| 0.428144 / 0.255139 (0.173005) \| 0.451872 / 0.283200 (0.168672) \| 0.033953 / 0.141683 (-0.107730) \| 1.781752 / 1.452155 (0.329597) \| 1.869014 / 1.492716 (0.376297) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223596 / 0.018006 (0.205590) \| 0.470307 / 0.000490 (0.469818) \| 0.005059 / 0.000200 (0.004859) \| 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) \| 0.038804 / 0.037411 (0.001393) \| 0.117879 / 0.014526 (0.103353) \| 0.140701 / 0.176557 (-0.035855) \| 0.194672 / 0.737135 (-0.542463) \| 0.132806 / 0.296338 (-0.163533) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.510109 / 0.215209 (0.294900) \| 4.729457 / 2.077655 (2.651803) \| 2.512113 / 1.504120 (1.007993) \| 2.302553 / 1.541195 (0.761358) \| 2.420462 / 1.468490 (0.951972) \| 0.531682 / 4.584777 (-4.053095) \| 4.061208 / 3.745712 (0.315496) \| 3.588542 / 5.269862 (-1.681320) \| 2.203187 / 4.565676 (-2.362489) \| 0.065791 / 0.424275 (-0.358484) \| 0.008839 / 0.007607 (0.001232) \| 0.562041 / 0.226044 (0.335997) \| 5.702340 / 2.268929 (3.433412) \| 3.127609 / 55.444624 (-52.317015) \| 2.823060 / 6.876477 (-4.053417) \| 2.898675 / 2.142072 (0.756603) \| 0.659589 / 4.805227 (-4.145638) \| 0.148798 / 6.500664 (-6.351866) \| 0.070787 / 0.075469 (-0.004682) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.478317 / 1.841788 (-0.363471) \| 21.995400 / 8.074308 (13.921092) \| 16.770729 / 10.191392 (6.579337) \| 0.226333 / 0.680424 (-0.454091) \| 0.021835 / 0.534201 (-0.512366) \| 0.460373 / 0.579283 (-0.118910) \| 0.479494 / 0.434364 (0.045130) \| 0.529470 / 0.540337 (-0.010868) \| 0.718066 / 1.386936 (-0.668870) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9a717b8eb80b0e50b25818127f79a35e0866fb14 \"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) \| 0.007824 / 0.011353 (-0.003529) \| 0.004601 / 0.011008 (-0.006407) \| 0.100025 / 0.038508 (0.061517) \| 0.096046 / 0.023109 (0.072936) \| 0.376226 / 0.275898 (0.100328) \| 0.410905 / 0.323480 (0.087425) \| 0.006048 / 0.007986 (-0.001938) \| 0.003817 / 0.004328 (-0.000511) \| 0.076624 / 0.004250 (0.072374) \| 0.066390 / 0.037052 (0.029338) \| 0.380098 / 0.258489 (0.121609) \| 0.413603 / 0.293841 (0.119762) \| 0.036546 / 0.128546 (-0.092001) \| 0.009881 / 0.075646 (-0.065765) \| 0.344338 / 0.419271 (-0.074934) \| 0.061882 / 0.043533 (0.018350) \| 0.368568 / 0.255139 (0.113429) \| 0.397133 / 0.283200 (0.113934) \| 0.027255 / 0.141683 (-0.114428) \| 1.795099 / 1.452155 (0.342945) \| 1.852443 / 1.492716 (0.359727) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.247436 / 0.018006 (0.229430) \| 0.494119 / 0.000490 (0.493629) \| 0.004359 / 0.000200 (0.004159) \| 0.000089 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034765 / 0.037411 (-0.002647) \| 0.104541 / 0.014526 (0.090015) \| 0.113898 / 0.176557 (-0.062659) \| 0.183634 / 0.737135 (-0.553501) \| 0.116423 / 0.296338 (-0.179916) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.458747 / 0.215209 (0.243538) \| 4.555740 / 2.077655 (2.478085) \| 2.217240 / 1.504120 (0.713121) \| 2.039879 / 1.541195 (0.498684) \| 2.088581 / 1.468490 (0.620091) \| 0.588063 / 4.584777 (-3.996714) \| 4.238226 / 3.745712 (0.492514) \| 4.768060 / 5.269862 (-0.501802) \| 2.857117 / 4.565676 (-1.708560) \| 0.068742 / 0.424275 (-0.355533) \| 0.008667 / 0.007607 (0.001059) \| 0.549294 / 0.226044 (0.323249) \| 5.464635 / 2.268929 (3.195706) \| 2.744435 / 55.444624 (-52.700189) \| 2.347660 / 6.876477 (-4.528816) \| 2.616816 / 2.142072 (0.474743) \| 0.703701 / 4.805227 (-4.101526) \| 0.159749 / 6.500664 (-6.340915) \| 0.071990 / 0.075469 (-0.003479) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.486599 / 1.841788 (-0.355188) \| 22.745438 / 8.074308 (14.671130) \| 16.822332 / 10.191392 (6.630940) \| 0.184730 / 0.680424 (-0.495694) \| 0.021267 / 0.534201 (-0.512934) \| 0.467108 / 0.579283 (-0.112176) \| 0.472674 / 0.434364 (0.038311) \| 0.548094 / 0.540337 (0.007756) \| 0.735885 / 1.386936 (-0.651051) \|\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) \| 0.007746 / 0.011353 (-0.003607) \| 0.004585 / 0.011008 (-0.006423) \| 0.076943 / 0.038508 (0.038435) \| 0.087473 / 0.023109 (0.064363) \| 0.480099 / 0.275898 (0.204201) \| 0.495271 / 0.323480 (0.171791) \| 0.006348 / 0.007986 (-0.001638) \| 0.003902 / 0.004328 (-0.000426) \| 0.077586 / 0.004250 (0.073335) \| 0.066467 / 0.037052 (0.029415) \| 0.468741 / 0.258489 (0.210252) \| 0.506778 / 0.293841 (0.212937) \| 0.036877 / 0.128546 (-0.091669) \| 0.010102 / 0.075646 (-0.065545) \| 0.084419 / 0.419271 (-0.334852) \| 0.058721 / 0.043533 (0.015188) \| 0.453633 / 0.255139 (0.198494) \| 0.481171 / 0.283200 (0.197971) \| 0.028716 / 0.141683 (-0.112967) \| 1.853048 / 1.452155 (0.400893) \| 1.885847 / 1.492716 (0.393130) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.192136 / 0.018006 (0.174130) \| 0.484481 / 0.000490 (0.483991) \| 0.002951 / 0.000200 (0.002751) \| 0.000098 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037949 / 0.037411 (0.000538) \| 0.108364 / 0.014526 (0.093838) \| 0.119542 / 0.176557 (-0.057014) \| 0.188542 / 0.737135 (-0.548593) \| 0.122011 / 0.296338 (-0.174327) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.483135 / 0.215209 (0.267926) \| 4.849715 / 2.077655 (2.772060) \| 2.497736 / 1.504120 (0.993616) \| 2.314243 / 1.541195 (0.773048) \| 2.412739 / 1.468490 (0.944249) \| 0.564137 / 4.584777 (-4.020639) \| 4.242273 / 3.745712 (0.496561) \| 6.337843 / 5.269862 (1.067982) \| 3.923250 / 4.565676 (-0.642426) \| 0.066464 / 0.424275 (-0.357811) \| 0.009217 / 0.007607 (0.001610) \| 0.575667 / 0.226044 (0.349623) \| 5.746187 / 2.268929 (3.477258) \| 3.069655 / 55.444624 (-52.374969) \| 2.674798 / 6.876477 (-4.201679) \| 2.956535 / 2.142072 (0.814463) \| 0.701043 / 4.805227 (-4.104185) \| 0.157241 / 6.500664 (-6.343423) \| 0.073175 / 0.075469 (-0.002294) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.609943 / 1.841788 (-0.231844) \| 23.478594 / 8.074308 (15.404286) \| 17.454437 / 10.191392 (7.263045) \| 0.186422 / 0.680424 (-0.494002) \| 0.021703 / 0.534201 (-0.512498) \| 0.471704 / 0.579283 (-0.107579) \| 0.480553 / 0.434364 (0.046189) \| 0.552881 / 0.540337 (0.012544) \| 0.722515 / 1.386936 (-0.664421) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#84645f80049cd00d9e0d4908faf3c3203fdcf21d \"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) \| 0.007542 / 0.011353 (-0.003811) \| 0.004692 / 0.011008 (-0.006316) \| 0.099155 / 0.038508 (0.060647) \| 0.089365 / 0.023109 (0.066256) \| 0.370870 / 0.275898 (0.094972) \| 0.422152 / 0.323480 (0.098673) \| 0.006223 / 0.007986 (-0.001763) \| 0.003852 / 0.004328 (-0.000476) \| 0.075438 / 0.004250 (0.071188) \| 0.065973 / 0.037052 (0.028921) \| 0.381513 / 0.258489 (0.123024) \| 0.416196 / 0.293841 (0.122355) \| 0.035483 / 0.128546 (-0.093063) \| 0.009884 / 0.075646 (-0.065762) \| 0.341290 / 0.419271 (-0.077982) \| 0.060546 / 0.043533 (0.017014) \| 0.365101 / 0.255139 (0.109962) \| 0.391058 / 0.283200 (0.107859) \| 0.026325 / 0.141683 (-0.115358) \| 1.815168 / 1.452155 (0.363013) \| 1.834711 / 1.492716 (0.341994) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222177 / 0.018006 (0.204171) \| 0.501151 / 0.000490 (0.500662) \| 0.010202 / 0.000200 (0.010002) \| 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) \| 0.034043 / 0.037411 (-0.003368) \| 0.097884 / 0.014526 (0.083358) \| 0.114022 / 0.176557 (-0.062534) \| 0.186200 / 0.737135 (-0.550935) \| 0.115555 / 0.296338 (-0.180783) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.485857 / 0.215209 (0.270648) \| 4.959263 / 2.077655 (2.881608) \| 2.501085 / 1.504120 (0.996965) \| 2.234660 / 1.541195 (0.693465) \| 2.238585 / 1.468490 (0.770095) \| 0.645431 / 4.584777 (-3.939345) \| 4.434311 / 3.745712 (0.688599) \| 4.771491 / 5.269862 (-0.498371) \| 2.778963 / 4.565676 (-1.786714) \| 0.075615 / 0.424275 (-0.348660) \| 0.009502 / 0.007607 (0.001895) \| 0.546539 / 0.226044 (0.320495) \| 5.464242 / 2.268929 (3.195314) \| 2.894101 / 55.444624 (-52.550524) \| 2.513761 / 6.876477 (-4.362715) \| 2.719843 / 2.142072 (0.577770) \| 0.678828 / 4.805227 (-4.126399) \| 0.157839 / 6.500664 (-6.342825) \| 0.071305 / 0.075469 (-0.004164) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.496879 / 1.841788 (-0.344909) \| 22.214452 / 8.074308 (14.140144) \| 17.707541 / 10.191392 (7.516149) \| 0.197008 / 0.680424 (-0.483416) \| 0.024883 / 0.534201 (-0.509318) \| 0.493611 / 0.579283 (-0.085672) \| 0.500677 / 0.434364 (0.066313) \| 0.569381 / 0.540337 (0.029044) \| 0.773950 / 1.386936 (-0.612986) \|\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) \| 0.007337 / 0.011353 (-0.004015) \| 0.004572 / 0.011008 (-0.006436) \| 0.091123 / 0.038508 (0.052615) \| 0.079762 / 0.023109 (0.056652) \| 0.450527 / 0.275898 (0.174629) \| 0.525097 / 0.323480 (0.201617) \| 0.005873 / 0.007986 (-0.002112) \| 0.003797 / 0.004328 (-0.000532) \| 0.076259 / 0.004250 (0.072009) \| 0.062745 / 0.037052 (0.025692) \| 0.465553 / 0.258489 (0.207064) \| 0.546026 / 0.293841 (0.252186) \| 0.035638 / 0.128546 (-0.092909) \| 0.010086 / 0.075646 (-0.065560) \| 0.109269 / 0.419271 (-0.310002) \| 0.056765 / 0.043533 (0.013233) \| 0.440887 / 0.255139 (0.185748) \| 0.513325 / 0.283200 (0.230125) \| 0.027206 / 0.141683 (-0.114476) \| 1.863564 / 1.452155 (0.411409) \| 1.918206 / 1.492716 (0.425490) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.266479 / 0.018006 (0.248473) \| 0.487971 / 0.000490 (0.487481) \| 0.012246 / 0.000200 (0.012046) \| 0.000119 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035281 / 0.037411 (-0.002130) \| 0.102991 / 0.014526 (0.088465) \| 0.114638 / 0.176557 (-0.061919) \| 0.184117 / 0.737135 (-0.553018) \| 0.117943 / 0.296338 (-0.178396) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.497897 / 0.215209 (0.282688) \| 4.973806 / 2.077655 (2.896151) \| 2.596146 / 1.504120 (1.092026) \| 2.419694 / 1.541195 (0.878499) \| 2.525784 / 1.468490 (1.057294) \| 0.568021 / 4.584777 (-4.016756) \| 4.296431 / 3.745712 (0.550719) \| 3.690682 / 5.269862 (-1.579179) \| 2.345965 / 4.565676 (-2.219712) \| 0.066859 / 0.424275 (-0.357416) \| 0.009093 / 0.007607 (0.001486) \| 0.582616 / 0.226044 (0.356571) \| 5.826528 / 2.268929 (3.557600) \| 3.253222 / 55.444624 (-52.191403) \| 2.798447 / 6.876477 (-4.078030) \| 3.054609 / 2.142072 (0.912537) \| 0.678816 / 4.805227 (-4.126411) \| 0.157966 / 6.500664 (-6.342698) \| 0.073797 / 0.075469 (-0.001672) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.599480 / 1.841788 (-0.242308) \| 23.249738 / 8.074308 (15.175430) \| 16.965406 / 10.191392 (6.774014) \| 0.171390 / 0.680424 (-0.509034) \| 0.021810 / 0.534201 (-0.512391) \| 0.483339 / 0.579283 (-0.095944) \| 0.496615 / 0.434364 (0.062251) \| 0.583786 / 0.540337 (0.043448) \| 0.741699 / 1.386936 (-0.645237) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7935cd2e564f5d1c66ed1acf731703724ba7a287 \"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) \| 0.006054 / 0.011353 (-0.005299) \| 0.003706 / 0.011008 (-0.007302) \| 0.080060 / 0.038508 (0.041552) \| 0.061479 / 0.023109 (0.038370) \| 0.327981 / 0.275898 (0.052083) \| 0.356930 / 0.323480 (0.033450) \| 0.004671 / 0.007986 (-0.003315) \| 0.002901 / 0.004328 (-0.001428) \| 0.062425 / 0.004250 (0.058174) \| 0.046310 / 0.037052 (0.009258) \| 0.323657 / 0.258489 (0.065168) \| 0.370130 / 0.293841 (0.076289) \| 0.027151 / 0.128546 (-0.101395) \| 0.007850 / 0.075646 (-0.067797) \| 0.262300 / 0.419271 (-0.156971) \| 0.045456 / 0.043533 (0.001923) \| 0.325569 / 0.255139 (0.070430) \| 0.352962 / 0.283200 (0.069762) \| 0.020156 / 0.141683 (-0.121527) \| 1.429404 / 1.452155 (-0.022750) \| 1.615032 / 1.492716 (0.122316) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.187309 / 0.018006 (0.169303) \| 0.428848 / 0.000490 (0.428358) \| 0.003599 / 0.000200 (0.003399) \| 0.000069 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023260 / 0.037411 (-0.014151) \| 0.072467 / 0.014526 (0.057941) \| 0.082398 / 0.176557 (-0.094159) \| 0.142573 / 0.737135 (-0.594562) \| 0.082570 / 0.296338 (-0.213768) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426503 / 0.215209 (0.211294) \| 4.267875 / 2.077655 (2.190220) \| 2.189762 / 1.504120 (0.685642) \| 2.027992 / 1.541195 (0.486798) \| 2.053211 / 1.468490 (0.584721) \| 0.503850 / 4.584777 (-4.080927) \| 3.086444 / 3.745712 (-0.659268) \| 3.319492 / 5.269862 (-1.950370) \| 2.070714 / 4.565676 (-2.494962) \| 0.057591 / 0.424275 (-0.366684) \| 0.006407 / 0.007607 (-0.001200) \| 0.501145 / 0.226044 (0.275100) \| 5.017753 / 2.268929 (2.748825) \| 2.643145 / 55.444624 (-52.801479) \| 2.327440 / 6.876477 (-4.549037) \| 2.460250 / 2.142072 (0.318178) \| 0.589397 / 4.805227 (-4.215830) \| 0.124948 / 6.500664 (-6.375716) \| 0.060450 / 0.075469 (-0.015020) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.279870 / 1.841788 (-0.561918) \| 18.115908 / 8.074308 (10.041600) \| 13.570032 / 10.191392 (3.378640) \| 0.132981 / 0.680424 (-0.547442) \| 0.016942 / 0.534201 (-0.517259) \| 0.333591 / 0.579283 (-0.245692) \| 0.358844 / 0.434364 (-0.075520) \| 0.395748 / 0.540337 (-0.144590) \| 0.546213 / 1.386936 (-0.840723) \|\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) \| 0.006062 / 0.011353 (-0.005291) \| 0.003673 / 0.011008 (-0.007336) \| 0.064726 / 0.038508 (0.026218) \| 0.061854 / 0.023109 (0.038745) \| 0.385343 / 0.275898 (0.109445) \| 0.441284 / 0.323480 (0.117805) \| 0.004830 / 0.007986 (-0.003156) \| 0.002909 / 0.004328 (-0.001420) \| 0.063874 / 0.004250 (0.059624) \| 0.049331 / 0.037052 (0.012278) \| 0.418484 / 0.258489 (0.159995) \| 0.451397 / 0.293841 (0.157556) \| 0.027665 / 0.128546 (-0.100881) \| 0.008088 / 0.075646 (-0.067558) \| 0.069625 / 0.419271 (-0.349646) \| 0.043437 / 0.043533 (-0.000095) \| 0.359789 / 0.255139 (0.104650) \| 0.430206 / 0.283200 (0.147007) \| 0.022308 / 0.141683 (-0.119375) \| 1.461030 / 1.452155 (0.008875) \| 1.513683 / 1.492716 (0.020966) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230958 / 0.018006 (0.212952) \| 0.417553 / 0.000490 (0.417063) \| 0.000802 / 0.000200 (0.000602) \| 0.000066 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025421 / 0.037411 (-0.011991) \| 0.077156 / 0.014526 (0.062630) \| 0.087533 / 0.176557 (-0.089024) \| 0.138048 / 0.737135 (-0.599087) \| 0.089358 / 0.296338 (-0.206981) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.439172 / 0.215209 (0.223963) \| 4.409509 / 2.077655 (2.331854) \| 2.491270 / 1.504120 (0.987150) \| 2.308446 / 1.541195 (0.767252) \| 2.378440 / 1.468490 (0.909950) \| 0.499834 / 4.584777 (-4.084943) \| 3.083168 / 3.745712 (-0.662544) \| 2.867543 / 5.269862 (-2.402318) \| 1.876354 / 4.565676 (-2.689323) \| 0.057092 / 0.424275 (-0.367183) \| 0.006955 / 0.007607 (-0.000653) \| 0.513799 / 0.226044 (0.287754) \| 5.126660 / 2.268929 (2.857731) \| 2.917348 / 55.444624 (-52.527277) \| 2.508035 / 6.876477 (-4.368441) \| 2.698089 / 2.142072 (0.556016) \| 0.586828 / 4.805227 (-4.218399) \| 0.124740 / 6.500664 (-6.375924) \| 0.062276 / 0.075469 (-0.013193) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.291624 / 1.841788 (-0.550164) \| 18.199968 / 8.074308 (10.125660) \| 13.888139 / 10.191392 (3.696747) \| 0.162955 / 0.680424 (-0.517469) \| 0.017343 / 0.534201 (-0.516858) \| 0.334683 / 0.579283 (-0.244600) \| 0.352708 / 0.434364 (-0.081656) \| 0.400629 / 0.540337 (-0.139708) \| 0.539497 / 1.386936 (-0.847439) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e7976db7fe22c6b93a869488d07b8137ea6a0db4 \"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) \| 0.007500 / 0.011353 (-0.003853) \| 0.004498 / 0.011008 (-0.006510) \| 0.100239 / 0.038508 (0.061731) \| 0.083424 / 0.023109 (0.060315) \| 0.366664 / 0.275898 (0.090766) \| 0.406641 / 0.323480 (0.083161) \| 0.004577 / 0.007986 (-0.003409) \| 0.004809 / 0.004328 (0.000480) \| 0.076898 / 0.004250 (0.072647) \| 0.064021 / 0.037052 (0.026969) \| 0.375836 / 0.258489 (0.117347) \| 0.413008 / 0.293841 (0.119167) \| 0.036010 / 0.128546 (-0.092537) \| 0.009655 / 0.075646 (-0.065991) \| 0.342595 / 0.419271 (-0.076677) \| 0.061846 / 0.043533 (0.018313) \| 0.376543 / 0.255139 (0.121404) \| 0.395858 / 0.283200 (0.112659) \| 0.026792 / 0.141683 (-0.114891) \| 1.775569 / 1.452155 (0.323414) \| 1.865077 / 1.492716 (0.372360) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.221521 / 0.018006 (0.203514) \| 0.474604 / 0.000490 (0.474114) \| 0.004354 / 0.000200 (0.004154) \| 0.000090 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032947 / 0.037411 (-0.004464) \| 0.100454 / 0.014526 (0.085928) \| 0.111955 / 0.176557 (-0.064602) \| 0.179752 / 0.737135 (-0.557383) \| 0.114282 / 0.296338 (-0.182056) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.458261 / 0.215209 (0.243052) \| 4.563536 / 2.077655 (2.485881) \| 2.231928 / 1.504120 (0.727808) \| 2.036751 / 1.541195 (0.495556) \| 2.170413 / 1.468490 (0.701923) \| 0.570825 / 4.584777 (-4.013952) \| 4.505762 / 3.745712 (0.760050) \| 5.033461 / 5.269862 (-0.236401) \| 2.704989 / 4.565676 (-1.860687) \| 0.067011 / 0.424275 (-0.357264) \| 0.008568 / 0.007607 (0.000961) \| 0.545151 / 0.226044 (0.319106) \| 5.438984 / 2.268929 (3.170055) \| 2.771818 / 55.444624 (-52.672806) \| 2.393082 / 6.876477 (-4.483395) \| 2.467173 / 2.142072 (0.325101) \| 0.678849 / 4.805227 (-4.126379) \| 0.160480 / 6.500664 (-6.340184) \| 0.073681 / 0.075469 (-0.001788) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.532272 / 1.841788 (-0.309516) \| 22.548741 / 8.074308 (14.474433) \| 17.091044 / 10.191392 (6.899652) \| 0.172100 / 0.680424 (-0.508324) \| 0.022220 / 0.534201 (-0.511981) \| 0.467871 / 0.579283 (-0.111412) \| 0.491135 / 0.434364 (0.056771) \| 0.548433 / 0.540337 (0.008096) \| 0.733340 / 1.386936 (-0.653596) \|\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) \| 0.007593 / 0.011353 (-0.003760) \| 0.004656 / 0.011008 (-0.006352) \| 0.076940 / 0.038508 (0.038431) \| 0.085183 / 0.023109 (0.062073) \| 0.447178 / 0.275898 (0.171280) \| 0.469545 / 0.323480 (0.146065) \| 0.006023 / 0.007986 (-0.001962) \| 0.003808 / 0.004328 (-0.000520) \| 0.076767 / 0.004250 (0.072517) \| 0.065713 / 0.037052 (0.028661) \| 0.445573 / 0.258489 (0.187084) \| 0.481689 / 0.293841 (0.187848) \| 0.036893 / 0.128546 (-0.091654) \| 0.009976 / 0.075646 (-0.065670) \| 0.084443 / 0.419271 (-0.334829) \| 0.058829 / 0.043533 (0.015297) \| 0.429291 / 0.255139 (0.174152) \| 0.454016 / 0.283200 (0.170816) \| 0.027289 / 0.141683 (-0.114394) \| 1.806786 / 1.452155 (0.354632) \| 1.887680 / 1.492716 (0.394964) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.241012 / 0.018006 (0.223006) \| 0.470629 / 0.000490 (0.470139) \| 0.003213 / 0.000200 (0.003013) \| 0.000107 / 0.000054 (0.000052) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036896 / 0.037411 (-0.000515) \| 0.106932 / 0.014526 (0.092406) \| 0.120333 / 0.176557 (-0.056223) \| 0.186271 / 0.737135 (-0.550865) \| 0.121581 / 0.296338 (-0.174758) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.507782 / 0.215209 (0.292573) \| 5.062932 / 2.077655 (2.985278) \| 2.689539 / 1.504120 (1.185419) \| 2.482978 / 1.541195 (0.941784) \| 2.561320 / 1.468490 (1.092830) \| 0.570664 / 4.584777 (-4.014113) \| 4.346051 / 3.745712 (0.600339) \| 6.479374 / 5.269862 (1.209513) \| 4.096483 / 4.565676 (-0.469194) \| 0.067564 / 0.424275 (-0.356711) \| 0.009147 / 0.007607 (0.001540) \| 0.596059 / 0.226044 (0.370015) \| 5.963223 / 2.268929 (3.694295) \| 3.201039 / 55.444624 (-52.243585) \| 2.816581 / 6.876477 (-4.059896) \| 3.047821 / 2.142072 (0.905748) \| 0.687749 / 4.805227 (-4.117478) \| 0.158174 / 6.500664 (-6.342490) \| 0.073329 / 0.075469 (-0.002140) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.601346 / 1.841788 (-0.240441) \| 23.712210 / 8.074308 (15.637902) \| 16.567272 / 10.191392 (6.375880) \| 0.224745 / 0.680424 (-0.455679) \| 0.021662 / 0.534201 (-0.512539) \| 0.471427 / 0.579283 (-0.107856) \| 0.498751 / 0.434364 (0.064387) \| 0.572047 / 0.540337 (0.031710) \| 0.821868 / 1.386936 (-0.565068) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#34d0c9027c750adc89f3d04a6bf2e9cb95915da4 \"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) \| 0.006371 / 0.011353 (-0.004981) \| 0.003749 / 0.011008 (-0.007259) \| 0.084155 / 0.038508 (0.045647) \| 0.072450 / 0.023109 (0.049340) \| 0.308002 / 0.275898 (0.032104) \| 0.340471 / 0.323480 (0.016991) \| 0.005054 / 0.007986 (-0.002931) \| 0.003176 / 0.004328 (-0.001152) \| 0.064867 / 0.004250 (0.060616) \| 0.054305 / 0.037052 (0.017252) \| 0.321047 / 0.258489 (0.062558) \| 0.345999 / 0.293841 (0.052158) \| 0.030507 / 0.128546 (-0.098039) \| 0.008299 / 0.075646 (-0.067347) \| 0.287682 / 0.419271 (-0.131590) \| 0.052048 / 0.043533 (0.008515) \| 0.308322 / 0.255139 (0.053183) \| 0.333220 / 0.283200 (0.050020) \| 0.022698 / 0.141683 (-0.118985) \| 1.474033 / 1.452155 (0.021879) \| 1.544790 / 1.492716 (0.052074) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.200612 / 0.018006 (0.182606) \| 0.450934 / 0.000490 (0.450445) \| 0.005383 / 0.000200 (0.005183) \| 0.000200 / 0.000054 (0.000145) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027759 / 0.037411 (-0.009652) \| 0.080935 / 0.014526 (0.066409) \| 0.093041 / 0.176557 (-0.083516) \| 0.148643 / 0.737135 (-0.588492) \| 0.093463 / 0.296338 (-0.202876) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.381653 / 0.215209 (0.166444) \| 3.810699 / 2.077655 (1.733044) \| 1.866858 / 1.504120 (0.362738) \| 1.716985 / 1.541195 (0.175790) \| 1.788071 / 1.468490 (0.319581) \| 0.481130 / 4.584777 (-4.103647) \| 3.529798 / 3.745712 (-0.215914) \| 3.982037 / 5.269862 (-1.287824) \| 2.324866 / 4.565676 (-2.240811) \| 0.056767 / 0.424275 (-0.367508) \| 0.007306 / 0.007607 (-0.000301) \| 0.459472 / 0.226044 (0.233428) \| 4.602808 / 2.268929 (2.333879) \| 2.332014 / 55.444624 (-53.112610) \| 2.044858 / 6.876477 (-4.831619) \| 2.204165 / 2.142072 (0.062093) \| 0.577946 / 4.805227 (-4.227281) \| 0.130900 / 6.500664 (-6.369764) \| 0.059054 / 0.075469 (-0.016415) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.245211 / 1.841788 (-0.596576) \| 19.176397 / 8.074308 (11.102089) \| 13.995280 / 10.191392 (3.803888) \| 0.171743 / 0.680424 (-0.508681) \| 0.018038 / 0.534201 (-0.516163) \| 0.392338 / 0.579283 (-0.186945) \| 0.419370 / 0.434364 (-0.014994) \| 0.477829 / 0.540337 (-0.062508) \| 0.677409 / 1.386936 (-0.709527) \|\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 (-0.004840) \| 0.003984 / 0.011008 (-0.007024) \| 0.064516 / 0.038508 (0.026008) \| 0.070504 / 0.023109 (0.047395) \| 0.384509 / 0.275898 (0.108611) \| 0.410564 / 0.323480 (0.087084) \| 0.005310 / 0.007986 (-0.002675) \| 0.003268 / 0.004328 (-0.001061) \| 0.064684 / 0.004250 (0.060433) \| 0.055367 / 0.037052 (0.018315) \| 0.399108 / 0.258489 (0.140619) \| 0.422740 / 0.293841 (0.128900) \| 0.031624 / 0.128546 (-0.096922) \| 0.008617 / 0.075646 (-0.067030) \| 0.070929 / 0.419271 (-0.348342) \| 0.049146 / 0.043533 (0.005613) \| 0.385492 / 0.255139 (0.130353) \| 0.407434 / 0.283200 (0.124234) \| 0.021972 / 0.141683 (-0.119711) \| 1.496135 / 1.452155 (0.043980) \| 1.533739 / 1.492716 (0.041023) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226218 / 0.018006 (0.208211) \| 0.443176 / 0.000490 (0.442686) \| 0.000376 / 0.000200 (0.000176) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030315 / 0.037411 (-0.007097) \| 0.086416 / 0.014526 (0.071890) \| 0.097725 / 0.176557 (-0.078831) \| 0.150407 / 0.737135 (-0.586728) \| 0.099914 / 0.296338 (-0.196424) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.409807 / 0.215209 (0.194598) \| 4.099086 / 2.077655 (2.021431) \| 2.103160 / 1.504120 (0.599040) \| 1.927927 / 1.541195 (0.386733) \| 1.977751 / 1.468490 (0.509261) \| 0.476995 / 4.584777 (-4.107781) \| 3.521835 / 3.745712 (-0.223877) \| 3.237695 / 5.269862 (-2.032167) \| 1.995953 / 4.565676 (-2.569724) \| 0.056208 / 0.424275 (-0.368068) \| 0.007660 / 0.007607 (0.000053) \| 0.483537 / 0.226044 (0.257492) \| 4.833974 / 2.268929 (2.565046) \| 2.589115 / 55.444624 (-52.855510) \| 2.228076 / 6.876477 (-4.648401) \| 2.395271 / 2.142072 (0.253198) \| 0.577534 / 4.805227 (-4.227694) \| 0.131432 / 6.500664 (-6.369232) \| 0.060999 / 0.075469 (-0.014471) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.356043 / 1.841788 (-0.485745) \| 19.470401 / 8.074308 (11.396093) \| 14.091266 / 10.191392 (3.899874) \| 0.166809 / 0.680424 (-0.513615) \| 0.018782 / 0.534201 (-0.515419) \| 0.394916 / 0.579283 (-0.184367) \| 0.411378 / 0.434364 (-0.022986) \| 0.466886 / 0.540337 (-0.073451) \| 0.617369 / 1.386936 (-0.769567) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#601ae6c7baff33a600fd10b12940966024fd2221 \"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) \| 0.007590 / 0.011353 (-0.003762) \| 0.004068 / 0.011008 (-0.006941) \| 0.105479 / 0.038508 (0.066971) \| 0.085614 / 0.023109 (0.062505) \| 0.384325 / 0.275898 (0.108427) \| 0.467867 / 0.323480 (0.144387) \| 0.004652 / 0.007986 (-0.003333) \| 0.005445 / 0.004328 (0.001117) \| 0.079604 / 0.004250 (0.075353) \| 0.066031 / 0.037052 (0.028978) \| 0.426184 / 0.258489 (0.167695) \| 0.480712 / 0.293841 (0.186871) \| 0.037837 / 0.128546 (-0.090709) \| 0.009765 / 0.075646 (-0.065882) \| 0.351316 / 0.419271 (-0.067955) \| 0.063634 / 0.043533 (0.020101) \| 0.420297 / 0.255139 (0.165158) \| 0.449169 / 0.283200 (0.165969) \| 0.030947 / 0.141683 (-0.110736) \| 1.840184 / 1.452155 (0.388029) \| 1.934074 / 1.492716 (0.441357) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223483 / 0.018006 (0.205477) \| 0.521086 / 0.000490 (0.520596) \| 0.000379 / 0.000200 (0.000179) \| 0.000065 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032011 / 0.037411 (-0.005400) \| 0.101474 / 0.014526 (0.086948) \| 0.108652 / 0.176557 (-0.067904) \| 0.173340 / 0.737135 (-0.563796) \| 0.114186 / 0.296338 (-0.182153) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.478020 / 0.215209 (0.262811) \| 4.645400 / 2.077655 (2.567746) \| 2.590763 / 1.504120 (1.086643) \| 2.383002 / 1.541195 (0.841807) \| 2.482550 / 1.468490 (1.014060) \| 0.572417 / 4.584777 (-4.012360) \| 4.233436 / 3.745712 (0.487724) \| 4.858823 / 5.269862 (-0.411038) \| 2.838913 / 4.565676 (-1.726764) \| 0.070010 / 0.424275 (-0.354265) \| 0.009602 / 0.007607 (0.001995) \| 0.538735 / 0.226044 (0.312691) \| 5.534340 / 2.268929 (3.265411) \| 2.915006 / 55.444624 (-52.529619) \| 2.625132 / 6.876477 (-4.251345) \| 2.537838 / 2.142072 (0.395766) \| 0.667870 / 4.805227 (-4.137357) \| 0.146330 / 6.500664 (-6.354334) \| 0.071631 / 0.075469 (-0.003838) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.594686 / 1.841788 (-0.247101) \| 22.311113 / 8.074308 (14.236804) \| 17.603983 / 10.191392 (7.412591) \| 0.195995 / 0.680424 (-0.484428) \| 0.022254 / 0.534201 (-0.511947) \| 0.479661 / 0.579283 (-0.099622) \| 0.463626 / 0.434364 (0.029262) \| 0.483465 / 0.540337 (-0.056873) \| 0.676141 / 1.386936 (-0.710795) \|\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) \| 0.006146 / 0.011353 (-0.005207) \| 0.004856 / 0.011008 (-0.006152) \| 0.067506 / 0.038508 (0.028998) \| 0.073968 / 0.023109 (0.050859) \| 0.470013 / 0.275898 (0.194115) \| 0.479022 / 0.323480 (0.155542) \| 0.005972 / 0.007986 (-0.002014) \| 0.003846 / 0.004328 (-0.000483) \| 0.075141 / 0.004250 (0.070890) \| 0.058597 / 0.037052 (0.021544) \| 0.481454 / 0.258489 (0.222965) \| 0.515634 / 0.293841 (0.221793) \| 0.034979 / 0.128546 (-0.093567) \| 0.010385 / 0.075646 (-0.065261) \| 0.072649 / 0.419271 (-0.346622) \| 0.058183 / 0.043533 (0.014650) \| 0.462138 / 0.255139 (0.206999) \| 0.476093 / 0.283200 (0.192893) \| 0.032918 / 0.141683 (-0.108765) \| 1.820530 / 1.452155 (0.368375) \| 1.626360 / 1.492716 (0.133644) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208970 / 0.018006 (0.190964) \| 0.492478 / 0.000490 (0.491988) \| 0.005487 / 0.000200 (0.005287) \| 0.000140 / 0.000054 (0.000086) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037896 / 0.037411 (0.000484) \| 0.089752 / 0.014526 (0.075227) \| 0.107445 / 0.176557 (-0.069111) \| 0.181260 / 0.737135 (-0.555876) \| 0.105700 / 0.296338 (-0.190639) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.495031 / 0.215209 (0.279821) \| 4.806939 / 2.077655 (2.729284) \| 2.227928 / 1.504120 (0.723808) \| 2.067117 / 1.541195 (0.525922) \| 2.348982 / 1.468490 (0.880492) \| 0.567201 / 4.584777 (-4.017576) \| 4.166592 / 3.745712 (0.420880) \| 3.654329 / 5.269862 (-1.615533) \| 2.331092 / 4.565676 (-2.234584) \| 0.062212 / 0.424275 (-0.362063) \| 0.008775 / 0.007607 (0.001168) \| 0.515413 / 0.226044 (0.289369) \| 5.449300 / 2.268929 (3.180371) \| 3.206574 / 55.444624 (-52.238050) \| 2.600455 / 6.876477 (-4.276022) \| 3.041162 / 2.142072 (0.899089) \| 0.681899 / 4.805227 (-4.123328) \| 0.155400 / 6.500664 (-6.345265) \| 0.073933 / 0.075469 (-0.001537) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.572329 / 1.841788 (-0.269459) \| 23.638519 / 8.074308 (15.564211) \| 17.145663 / 10.191392 (6.954271) \| 0.232690 / 0.680424 (-0.447734) \| 0.028620 / 0.534201 (-0.505581) \| 0.488105 / 0.579283 (-0.091178) \| 0.490365 / 0.434364 (0.056001) \| 0.599501 / 0.540337 (0.059164) \| 0.708101 / 1.386936 (-0.678835) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4a761315900880a25b347ad19b78bd567cfce1f0 \"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) \| 0.005947 / 0.011353 (-0.005406) \| 0.003577 / 0.011008 (-0.007431) \| 0.081631 / 0.038508 (0.043122) \| 0.058651 / 0.023109 (0.035541) \| 0.342742 / 0.275898 (0.066843) \| 0.384130 / 0.323480 (0.060650) \| 0.004620 / 0.007986 (-0.003366) \| 0.002885 / 0.004328 (-0.001444) \| 0.063698 / 0.004250 (0.059448) \| 0.048953 / 0.037052 (0.011901) \| 0.367880 / 0.258489 (0.109391) \| 0.407050 / 0.293841 (0.113209) \| 0.027242 / 0.128546 (-0.101305) \| 0.007914 / 0.075646 (-0.067733) \| 0.262156 / 0.419271 (-0.157116) \| 0.044750 / 0.043533 (0.001218) \| 0.351613 / 0.255139 (0.096474) \| 0.380284 / 0.283200 (0.097084) \| 0.020080 / 0.141683 (-0.121603) \| 1.498101 / 1.452155 (0.045946) \| 1.543608 / 1.492716 (0.050892) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.180014 / 0.018006 (0.162008) \| 0.436172 / 0.000490 (0.435682) \| 0.003694 / 0.000200 (0.003494) \| 0.000071 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024389 / 0.037411 (-0.013022) \| 0.072874 / 0.014526 (0.058348) \| 0.083469 / 0.176557 (-0.093088) \| 0.144600 / 0.737135 (-0.592536) \| 0.084229 / 0.296338 (-0.212110) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.391636 / 0.215209 (0.176427) \| 3.906941 / 2.077655 (1.829286) \| 1.901944 / 1.504120 (0.397825) \| 1.762702 / 1.541195 (0.221507) \| 1.817970 / 1.468490 (0.349480) \| 0.500345 / 4.584777 (-4.084432) \| 3.011351 / 3.745712 (-0.734361) \| 4.417763 / 5.269862 (-0.852098) \| 2.689744 / 4.565676 (-1.875933) \| 0.057765 / 0.424275 (-0.366511) \| 0.006412 / 0.007607 (-0.001195) \| 0.468156 / 0.226044 (0.242112) \| 4.664975 / 2.268929 (2.396047) \| 2.323355 / 55.444624 (-53.121270) \| 1.984280 / 6.876477 (-4.892197) \| 2.165215 / 2.142072 (0.023142) \| 0.586950 / 4.805227 (-4.218278) \| 0.124363 / 6.500664 (-6.376301) \| 0.060702 / 0.075469 (-0.014767) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.238870 / 1.841788 (-0.602917) \| 18.587360 / 8.074308 (10.513052) \| 13.831674 / 10.191392 (3.640282) \| 0.143542 / 0.680424 (-0.536882) \| 0.016913 / 0.534201 (-0.517288) \| 0.332314 / 0.579283 (-0.246969) \| 0.345419 / 0.434364 (-0.088945) \| 0.381257 / 0.540337 (-0.159081) \| 0.537844 / 1.386936 (-0.849092) \|\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) \| 0.006294 / 0.011353 (-0.005059) \| 0.003714 / 0.011008 (-0.007294) \| 0.062684 / 0.038508 (0.024176) \| 0.063520 / 0.023109 (0.040411) \| 0.389591 / 0.275898 (0.113693) \| 0.444278 / 0.323480 (0.120798) \| 0.004825 / 0.007986 (-0.003160) \| 0.003010 / 0.004328 (-0.001318) \| 0.062767 / 0.004250 (0.058517) \| 0.051739 / 0.037052 (0.014686) \| 0.434299 / 0.258489 (0.175810) \| 0.452003 / 0.293841 (0.158162) \| 0.027375 / 0.128546 (-0.101171) \| 0.008135 / 0.075646 (-0.067511) \| 0.067401 / 0.419271 (-0.351871) \| 0.042752 / 0.043533 (-0.000780) \| 0.367633 / 0.255139 (0.112494) \| 0.433039 / 0.283200 (0.149840) \| 0.021086 / 0.141683 (-0.120597) \| 1.488024 / 1.452155 (0.035870) \| 1.507767 / 1.492716 (0.015050) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230046 / 0.018006 (0.212040) \| 0.428085 / 0.000490 (0.427595) \| 0.002188 / 0.000200 (0.001988) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026705 / 0.037411 (-0.010706) \| 0.082466 / 0.014526 (0.067940) \| 0.089378 / 0.176557 (-0.087179) \| 0.147287 / 0.737135 (-0.589849) \| 0.090426 / 0.296338 (-0.205913) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430882 / 0.215209 (0.215672) \| 4.296224 / 2.077655 (2.218569) \| 2.229982 / 1.504120 (0.725862) \| 2.048506 / 1.541195 (0.507311) \| 2.129514 / 1.468490 (0.661024) \| 0.502964 / 4.584777 (-4.081813) \| 3.048125 / 3.745712 (-0.697587) \| 4.208636 / 5.269862 (-1.061226) \| 2.594015 / 4.565676 (-1.971661) \| 0.057967 / 0.424275 (-0.366308) \| 0.006875 / 0.007607 (-0.000732) \| 0.513872 / 0.226044 (0.287828) \| 5.126435 / 2.268929 (2.857506) \| 2.691278 / 55.444624 (-52.753346) \| 2.361723 / 6.876477 (-4.514754) \| 2.511213 / 2.142072 (0.369141) \| 0.593558 / 4.805227 (-4.211670) \| 0.129332 / 6.500664 (-6.371332) \| 0.064051 / 0.075469 (-0.011418) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.289049 / 1.841788 (-0.552739) \| 18.912363 / 8.074308 (10.838055) \| 14.226500 / 10.191392 (4.035108) \| 0.131392 / 0.680424 (-0.549032) \| 0.016750 / 0.534201 (-0.517451) \| 0.330078 / 0.579283 (-0.249205) \| 0.347588 / 0.434364 (-0.086776) \| 0.383234 / 0.540337 (-0.157103) \| 0.510967 / 1.386936 (-0.875969) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d7892beb30bab0633b84398c5ea43d7e69fe38cc \"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) \| 0.005974 / 0.011353 (-0.005379) \| 0.003691 / 0.011008 (-0.007317) \| 0.079410 / 0.038508 (0.040902) \| 0.061769 / 0.023109 (0.038660) \| 0.323310 / 0.275898 (0.047412) \| 0.354325 / 0.323480 (0.030845) \| 0.004794 / 0.007986 (-0.003191) \| 0.002899 / 0.004328 (-0.001430) \| 0.062104 / 0.004250 (0.057854) \| 0.048973 / 0.037052 (0.011921) \| 0.326497 / 0.258489 (0.068008) \| 0.361347 / 0.293841 (0.067506) \| 0.026741 / 0.128546 (-0.101805) \| 0.007936 / 0.075646 (-0.067710) \| 0.259168 / 0.419271 (-0.160104) \| 0.044859 / 0.043533 (0.001327) \| 0.319342 / 0.255139 (0.064203) \| 0.343711 / 0.283200 (0.060511) \| 0.022298 / 0.141683 (-0.119384) \| 1.451595 / 1.452155 (-0.000560) \| 1.573730 / 1.492716 (0.081014) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.173086 / 0.018006 (0.155080) \| 0.432400 / 0.000490 (0.431910) \| 0.003739 / 0.000200 (0.003539) \| 0.000073 / 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) \| 0.024477 / 0.037411 (-0.012934) \| 0.073463 / 0.014526 (0.058937) \| 0.083410 / 0.176557 (-0.093146) \| 0.144760 / 0.737135 (-0.592376) \| 0.084199 / 0.296338 (-0.212140) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.388251 / 0.215209 (0.173042) \| 3.875375 / 2.077655 (1.797720) \| 1.875515 / 1.504120 (0.371395) \| 1.729282 / 1.541195 (0.188087) \| 1.784732 / 1.468490 (0.316242) \| 0.496985 / 4.584777 (-4.087792) \| 3.030276 / 3.745712 (-0.715436) \| 2.813192 / 5.269862 (-2.456669) \| 1.868647 / 4.565676 (-2.697030) \| 0.057376 / 0.424275 (-0.366899) \| 0.006463 / 0.007607 (-0.001144) \| 0.462153 / 0.226044 (0.236108) \| 4.586583 / 2.268929 (2.317654) \| 2.287730 / 55.444624 (-53.156894) \| 1.972177 / 6.876477 (-4.904299) \| 2.151592 / 2.142072 (0.009520) \| 0.587169 / 4.805227 (-4.218058) \| 0.127063 / 6.500664 (-6.373601) \| 0.060297 / 0.075469 (-0.015172) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.267651 / 1.841788 (-0.574136) \| 18.426011 / 8.074308 (10.351703) \| 14.050470 / 10.191392 (3.859078) \| 0.148063 / 0.680424 (-0.532361) \| 0.017112 / 0.534201 (-0.517089) \| 0.330051 / 0.579283 (-0.249232) \| 0.358730 / 0.434364 (-0.075634) \| 0.392365 / 0.540337 (-0.147972) \| 0.534650 / 1.386936 (-0.852286) \|\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) \| 0.005936 / 0.011353 (-0.005417) \| 0.003652 / 0.011008 (-0.007356) \| 0.063066 / 0.038508 (0.024558) \| 0.060617 / 0.023109 (0.037507) \| 0.388293 / 0.275898 (0.112395) \| 0.411422 / 0.323480 (0.087942) \| 0.004691 / 0.007986 (-0.003295) \| 0.002857 / 0.004328 (-0.001472) \| 0.064198 / 0.004250 (0.059947) \| 0.049124 / 0.037052 (0.012071) \| 0.403601 / 0.258489 (0.145112) \| 0.413619 / 0.293841 (0.119778) \| 0.027279 / 0.128546 (-0.101267) \| 0.008072 / 0.075646 (-0.067575) \| 0.067890 / 0.419271 (-0.351381) \| 0.041866 / 0.043533 (-0.001667) \| 0.393438 / 0.255139 (0.138299) \| 0.402865 / 0.283200 (0.119666) \| 0.023381 / 0.141683 (-0.118302) \| 1.496324 / 1.452155 (0.044170) \| 1.538080 / 1.492716 (0.045364) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212065 / 0.018006 (0.194059) \| 0.410511 / 0.000490 (0.410021) \| 0.001236 / 0.000200 (0.001036) \| 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) \| 0.026012 / 0.037411 (-0.011399) \| 0.076592 / 0.014526 (0.062066) \| 0.085963 / 0.176557 (-0.090594) \| 0.137803 / 0.737135 (-0.599332) \| 0.087594 / 0.296338 (-0.208745) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434283 / 0.215209 (0.219074) \| 4.345478 / 2.077655 (2.267824) \| 2.400954 / 1.504120 (0.896834) \| 2.282024 / 1.541195 (0.740829) \| 2.414247 / 1.468490 (0.945757) \| 0.501855 / 4.584777 (-4.082922) \| 3.059433 / 3.745712 (-0.686279) \| 2.811288 / 5.269862 (-2.458574) \| 1.856839 / 4.565676 (-2.708838) \| 0.058017 / 0.424275 (-0.366258) \| 0.006844 / 0.007607 (-0.000763) \| 0.515376 / 0.226044 (0.289332) \| 5.148775 / 2.268929 (2.879847) \| 2.930807 / 55.444624 (-52.513817) \| 2.520532 / 6.876477 (-4.355944) \| 2.746299 / 2.142072 (0.604227) \| 0.590102 / 4.805227 (-4.215125) \| 0.125747 / 6.500664 (-6.374917) \| 0.061873 / 0.075469 (-0.013597) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.306247 / 1.841788 (-0.535541) \| 18.366048 / 8.074308 (10.291740) \| 13.855617 / 10.191392 (3.664225) \| 0.150124 / 0.680424 (-0.530300) \| 0.017189 / 0.534201 (-0.517012) \| 0.336285 / 0.579283 (-0.242998) \| 0.344985 / 0.434364 (-0.089379) \| 0.397973 / 0.540337 (-0.142364) \| 0.536142 / 1.386936 (-0.850794) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ae24cf12054b4a512f198979b1ca7707bb99d56 \"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) \| 0.006401 / 0.011353 (-0.004952) \| 0.003789 / 0.011008 (-0.007219) \| 0.079516 / 0.038508 (0.041008) \| 0.068279 / 0.023109 (0.045170) \| 0.295691 / 0.275898 (0.019793) \| 0.327208 / 0.323480 (0.003728) \| 0.005070 / 0.007986 (-0.002915) \| 0.003044 / 0.004328 (-0.001285) \| 0.061411 / 0.004250 (0.057161) \| 0.053227 / 0.037052 (0.016175) \| 0.297368 / 0.258489 (0.038879) \| 0.334740 / 0.293841 (0.040899) \| 0.029459 / 0.128546 (-0.099087) \| 0.008080 / 0.075646 (-0.067566) \| 0.267344 / 0.419271 (-0.151927) \| 0.049877 / 0.043533 (0.006344) \| 0.293853 / 0.255139 (0.038714) \| 0.319819 / 0.283200 (0.036620) \| 0.022593 / 0.141683 (-0.119089) \| 1.459054 / 1.452155 (0.006900) \| 1.471250 / 1.492716 (-0.021466) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.194326 / 0.018006 (0.176320) \| 0.443565 / 0.000490 (0.443075) \| 0.003745 / 0.000200 (0.003545) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026640 / 0.037411 (-0.010772) \| 0.077630 / 0.014526 (0.063104) \| 0.089364 / 0.176557 (-0.087192) \| 0.147327 / 0.737135 (-0.589809) \| 0.089603 / 0.296338 (-0.206735) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.373758 / 0.215209 (0.158549) \| 3.746778 / 2.077655 (1.669123) \| 1.814991 / 1.504120 (0.310871) \| 1.645650 / 1.541195 (0.104455) \| 1.690752 / 1.468490 (0.222262) \| 0.472117 / 4.584777 (-4.112660) \| 3.457346 / 3.745712 (-0.288367) \| 3.138869 / 5.269862 (-2.130993) \| 1.934924 / 4.565676 (-2.630753) \| 0.055709 / 0.424275 (-0.368566) \| 0.006680 / 0.007607 (-0.000927) \| 0.446874 / 0.226044 (0.220829) \| 4.458409 / 2.268929 (2.189480) \| 2.253932 / 55.444624 (-53.190693) \| 2.007240 / 6.876477 (-4.869237) \| 2.081687 / 2.142072 (-0.060386) \| 0.563379 / 4.805227 (-4.241848) \| 0.128694 / 6.500664 (-6.371970) \| 0.057409 / 0.075469 (-0.018060) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.212231 / 1.841788 (-0.629556) \| 18.519121 / 8.074308 (10.444813) \| 13.582243 / 10.191392 (3.390851) \| 0.142488 / 0.680424 (-0.537936) \| 0.017421 / 0.534201 (-0.516780) \| 0.366864 / 0.579283 (-0.212419) \| 0.401467 / 0.434364 (-0.032897) \| 0.443659 / 0.540337 (-0.096679) \| 0.618854 / 1.386936 (-0.768082) \|\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) \| 0.006121 / 0.011353 (-0.005232) \| 0.003690 / 0.011008 (-0.007318) \| 0.060340 / 0.038508 (0.021832) \| 0.067215 / 0.023109 (0.044106) \| 0.382846 / 0.275898 (0.106948) \| 0.415774 / 0.323480 (0.092294) \| 0.004868 / 0.007986 (-0.003118) \| 0.003108 / 0.004328 (-0.001221) \| 0.060572 / 0.004250 (0.056321) \| 0.050453 / 0.037052 (0.013401) \| 0.400494 / 0.258489 (0.142005) \| 0.424368 / 0.293841 (0.130527) \| 0.030279 / 0.128546 (-0.098267) \| 0.008151 / 0.075646 (-0.067495) \| 0.066707 / 0.419271 (-0.352564) \| 0.046118 / 0.043533 (0.002585) \| 0.386697 / 0.255139 (0.131558) \| 0.410156 / 0.283200 (0.126957) \| 0.020688 / 0.141683 (-0.120995) \| 1.418162 / 1.452155 (-0.033993) \| 1.463057 / 1.492716 (-0.029659) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216081 / 0.018006 (0.198075) \| 0.440541 / 0.000490 (0.440051) \| 0.000371 / 0.000200 (0.000171) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027763 / 0.037411 (-0.009648) \| 0.082316 / 0.014526 (0.067791) \| 0.094086 / 0.176557 (-0.082471) \| 0.144738 / 0.737135 (-0.592398) \| 0.094837 / 0.296338 (-0.201501) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.396277 / 0.215209 (0.181068) \| 3.958791 / 2.077655 (1.881136) \| 2.021367 / 1.504120 (0.517247) \| 1.860112 / 1.541195 (0.318917) \| 1.886032 / 1.468490 (0.417541) \| 0.468536 / 4.584777 (-4.116241) \| 3.417950 / 3.745712 (-0.327762) \| 4.849991 / 5.269862 (-0.419871) \| 2.773935 / 4.565676 (-1.791742) \| 0.055813 / 0.424275 (-0.368462) \| 0.007053 / 0.007607 (-0.000554) \| 0.470167 / 0.226044 (0.244122) \| 4.702969 / 2.268929 (2.434041) \| 2.474161 / 55.444624 (-52.970464) \| 2.171256 / 6.876477 (-4.705220) \| 2.315373 / 2.142072 (0.173301) \| 0.589195 / 4.805227 (-4.216032) \| 0.128237 / 6.500664 (-6.372427) \| 0.058641 / 0.075469 (-0.016828) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.292947 / 1.841788 (-0.548841) \| 18.851300 / 8.074308 (10.776992) \| 14.089764 / 10.191392 (3.898372) \| 0.164853 / 0.680424 (-0.515571) \| 0.017281 / 0.534201 (-0.516920) \| 0.359112 / 0.579283 (-0.220171) \| 0.386696 / 0.434364 (-0.047668) \| 0.428222 / 0.540337 (-0.112115) \| 0.568659 / 1.386936 (-0.818277) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#563864ded894b468e2ba3f677ef79c5ab3fe65df \"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) \| 0.006051 / 0.011353 (-0.005301) \| 0.003654 / 0.011008 (-0.007355) \| 0.080081 / 0.038508 (0.041572) \| 0.062925 / 0.023109 (0.039815) \| 0.358097 / 0.275898 (0.082199) \| 0.405728 / 0.323480 (0.082248) \| 0.005359 / 0.007986 (-0.002627) \| 0.002820 / 0.004328 (-0.001508) \| 0.063108 / 0.004250 (0.058858) \| 0.049627 / 0.037052 (0.012575) \| 0.397870 / 0.258489 (0.139381) \| 0.437157 / 0.293841 (0.143316) \| 0.027707 / 0.128546 (-0.100839) \| 0.007911 / 0.075646 (-0.067735) \| 0.260991 / 0.419271 (-0.158280) \| 0.044771 / 0.043533 (0.001238) \| 0.340230 / 0.255139 (0.085091) \| 0.384925 / 0.283200 (0.101725) \| 0.021369 / 0.141683 (-0.120314) \| 1.431439 / 1.452155 (-0.020715) \| 1.478794 / 1.492716 (-0.013922) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.182626 / 0.018006 (0.164620) \| 0.435551 / 0.000490 (0.435061) \| 0.003015 / 0.000200 (0.002815) \| 0.000064 / 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) \| 0.024703 / 0.037411 (-0.012708) \| 0.073640 / 0.014526 (0.059114) \| 0.084598 / 0.176557 (-0.091959) \| 0.145810 / 0.737135 (-0.591325) \| 0.085125 / 0.296338 (-0.211213) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.394539 / 0.215209 (0.179330) \| 3.945882 / 2.077655 (1.868227) \| 1.947166 / 1.504120 (0.443046) \| 1.763305 / 1.541195 (0.222111) \| 1.816208 / 1.468490 (0.347718) \| 0.498880 / 4.584777 (-4.085897) \| 3.098283 / 3.745712 (-0.647429) \| 2.823474 / 5.269862 (-2.446388) \| 1.873993 / 4.565676 (-2.691684) \| 0.058097 / 0.424275 (-0.366179) \| 0.006488 / 0.007607 (-0.001119) \| 0.466711 / 0.226044 (0.240667) \| 4.671520 / 2.268929 (2.402592) \| 2.363381 / 55.444624 (-53.081243) \| 2.052092 / 6.876477 (-4.824385) \| 2.209212 / 2.142072 (0.067140) \| 0.594650 / 4.805227 (-4.210577) \| 0.125604 / 6.500664 (-6.375060) \| 0.061511 / 0.075469 (-0.013958) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.226564 / 1.841788 (-0.615224) \| 18.583605 / 8.074308 (10.509297) \| 13.993091 / 10.191392 (3.801699) \| 0.146185 / 0.680424 (-0.534239) \| 0.016839 / 0.534201 (-0.517362) \| 0.334116 / 0.579283 (-0.245167) \| 0.360780 / 0.434364 (-0.073584) \| 0.386008 / 0.540337 (-0.154329) \| 0.643278 / 1.386936 (-0.743658) \|\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) \| 0.006174 / 0.011353 (-0.005179) \| 0.003658 / 0.011008 (-0.007350) \| 0.063250 / 0.038508 (0.024742) \| 0.063542 / 0.023109 (0.040433) \| 0.366845 / 0.275898 (0.090947) \| 0.409794 / 0.323480 (0.086314) \| 0.005678 / 0.007986 (-0.002308) \| 0.003061 / 0.004328 (-0.001268) \| 0.063561 / 0.004250 (0.059311) \| 0.052648 / 0.037052 (0.015596) \| 0.378096 / 0.258489 (0.119607) \| 0.410706 / 0.293841 (0.116865) \| 0.027668 / 0.128546 (-0.100878) \| 0.008045 / 0.075646 (-0.067601) \| 0.068290 / 0.419271 (-0.350981) \| 0.042602 / 0.043533 (-0.000930) \| 0.364976 / 0.255139 (0.109837) \| 0.395599 / 0.283200 (0.112400) \| 0.022733 / 0.141683 (-0.118950) \| 1.522473 / 1.452155 (0.070319) \| 1.515891 / 1.492716 (0.023175) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.232554 / 0.018006 (0.214547) \| 0.420702 / 0.000490 (0.420213) \| 0.002161 / 0.000200 (0.001961) \| 0.000064 / 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) \| 0.026276 / 0.037411 (-0.011135) \| 0.078504 / 0.014526 (0.063978) \| 0.088989 / 0.176557 (-0.087567) \| 0.144044 / 0.737135 (-0.593091) \| 0.091074 / 0.296338 (-0.205265) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420189 / 0.215209 (0.204980) \| 4.189596 / 2.077655 (2.111941) \| 2.316425 / 1.504120 (0.812305) \| 2.186877 / 1.541195 (0.645682) \| 2.259065 / 1.468490 (0.790575) \| 0.502827 / 4.584777 (-4.081950) \| 3.135266 / 3.745712 (-0.610446) \| 2.838808 / 5.269862 (-2.431053) \| 1.876519 / 4.565676 (-2.689158) \| 0.057802 / 0.424275 (-0.366473) \| 0.006824 / 0.007607 (-0.000784) \| 0.500213 / 0.226044 (0.274168) \| 4.999798 / 2.268929 (2.730869) \| 2.627713 / 55.444624 (-52.816911) \| 2.344263 / 6.876477 (-4.532214) \| 2.415449 / 2.142072 (0.273376) \| 0.593082 / 4.805227 (-4.212145) \| 0.125787 / 6.500664 (-6.374877) \| 0.062699 / 0.075469 (-0.012770) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.308219 / 1.841788 (-0.533569) \| 18.703099 / 8.074308 (10.628791) \| 13.976234 / 10.191392 (3.784842) \| 0.144037 / 0.680424 (-0.536387) \| 0.016592 / 0.534201 (-0.517609) \| 0.333078 / 0.579283 (-0.246206) \| 0.342317 / 0.434364 (-0.092047) \| 0.396837 / 0.540337 (-0.143500) \| 0.532641 / 1.386936 (-0.854295) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#14f6edd9222e577dccb962ed5338b79b73502fa5 \"CML watermark\")\n" ]	2023-07-13T15:41:44Z	2023-07-17T17:09:39Z	2023-07-17T17:01:00Z	MEMBER	null	null	null	Thanks to @janineguo 's work in https://github.com/huggingface/datasets/pull/5919 which was needed to support HfFileSystem. Switching to `HfFileSystem` will help implementing optimization in data files resolution ## Implementation details I replaced all the from_hf_repo and from_local_or_remote in data_files.py to only use a new `from_patterns` which works for any fsspec path, including hf:// paths, https:// URLs and local paths. This simplifies the codebase since there is no logic duplication anymore when it comes to data files resolution. I added `_prepare_path_and_storage_options` which returns the right storage_options to use given a path and a `DownloadConfig`. This is the only place where the logic depends on the filesystem type that must be used. I also removed the `get_metadata_data_files_list ` and `get_patterns_and_data_files` functions added recently, since data files resolution is now handled using a common interface. ## New features hf:// paths are now supported in data_files ## Breaking changes DataFilesList and DataFilesDict: - use `str` paths instead of `Union[Path, Url]` - require posix paths for windows paths close https://github.com/huggingface/datasets/issues/6017	{ "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/6028/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6028/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6028.diff", "html_url": "https://github.com/huggingface/datasets/pull/6028", "merged_at": "2023-07-17T17:01:00Z", "patch_url": "https://github.com/huggingface/datasets/pull/6028.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6028" }
https://api.github.com/repos/huggingface/datasets/issues/6793	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6793/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6793/comments	https://api.github.com/repos/huggingface/datasets/issues/6793/events	https://github.com/huggingface/datasets/issues/6793	2,231,400,200	I_kwDODunzps6FAHcI	6,793	Loading just one particular split is not possible for imagenet-1k	{ "avatar_url": "https://avatars.githubusercontent.com/u/165930106?v=4", "events_url": "https://api.github.com/users/PaulPSta/events{/privacy}", "followers_url": "https://api.github.com/users/PaulPSta/followers", "following_url": "https://api.github.com/users/PaulPSta/following{/other_user}", "gists_url": "https://api.github.com/users/PaulPSta/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/PaulPSta", "id": 165930106, "login": "PaulPSta", "node_id": "U_kgDOCePkeg", "organizations_url": "https://api.github.com/users/PaulPSta/orgs", "received_events_url": "https://api.github.com/users/PaulPSta/received_events", "repos_url": "https://api.github.com/users/PaulPSta/repos", "site_admin": false, "starred_url": "https://api.github.com/users/PaulPSta/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/PaulPSta/subscriptions", "type": "User", "url": "https://api.github.com/users/PaulPSta", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "+1" ]	2024-04-08T14:39:14Z	2024-09-12T16:24:48Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I'd expect the following code to download just the validation split but instead I get all data on my disk (train, test and validation splits) ` from datasets import load_dataset dataset = load_dataset("imagenet-1k", split="validation", trust_remote_code=True) ` Is it expected to work like that? ### Steps to reproduce the bug 1. Install the required libraries (python, datasets, huggingface_hub) 2. Login using huggingface cli 2. Run the code in the description ### Expected behavior Just a single (validation) split should be downloaded. ### Environment info python: 3.12.2 datasets: 2.18.0 huggingface_hub: 0.22.2	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/6793/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6793/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5718	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5718/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5718/comments	https://api.github.com/repos/huggingface/datasets/issues/5718/events	https://github.com/huggingface/datasets/pull/5718	1,658,958,406	PR_kwDODunzps5N2IZC	5,718	Reorder default data splits to have validation before test	{ "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._", "After this CI error: https://github.com/huggingface/datasets/actions/runs/4639528358/jobs/8210492953?pr=5718\r\n```\r\nFAILED tests/test_data_files.py::test_get_data_files_patterns[data_file_per_split4] - AssertionError: assert ['random', 'train'] == ['train', 'random']\r\n At index 0 diff: 'random' != 'train'\r\n Full diff:\r\n - ['train', 'random']\r\n + ['random', 'train']\r\n```\r\nI have checked locally and found out that the data split order is nondeterministic. I am addressing this in a separate issue.\r\n\r\nWe should first address:\r\n- #5728 \r\n- #5729", "<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) \| 0.007728 / 0.011353 (-0.003624) \| 0.005275 / 0.011008 (-0.005734) \| 0.097708 / 0.038508 (0.059199) \| 0.039851 / 0.023109 (0.016741) \| 0.333360 / 0.275898 (0.057462) \| 0.376135 / 0.323480 (0.052655) \| 0.006355 / 0.007986 (-0.001630) \| 0.004193 / 0.004328 (-0.000135) \| 0.072882 / 0.004250 (0.068631) \| 0.052668 / 0.037052 (0.015615) \| 0.347359 / 0.258489 (0.088870) \| 0.382280 / 0.293841 (0.088440) \| 0.035996 / 0.128546 (-0.092550) \| 0.012517 / 0.075646 (-0.063129) \| 0.334520 / 0.419271 (-0.084751) \| 0.051969 / 0.043533 (0.008436) \| 0.335735 / 0.255139 (0.080596) \| 0.359921 / 0.283200 (0.076722) \| 0.113971 / 0.141683 (-0.027712) \| 1.465636 / 1.452155 (0.013481) \| 1.559824 / 1.492716 (0.067108) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223997 / 0.018006 (0.205991) \| 0.499041 / 0.000490 (0.498551) \| 0.009697 / 0.000200 (0.009497) \| 0.000245 / 0.000054 (0.000190) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027031 / 0.037411 (-0.010381) \| 0.110271 / 0.014526 (0.095745) \| 0.115848 / 0.176557 (-0.060709) \| 0.174253 / 0.737135 (-0.562883) \| 0.122616 / 0.296338 (-0.173723) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417275 / 0.215209 (0.202066) \| 4.158678 / 2.077655 (2.081023) \| 1.917585 / 1.504120 (0.413465) \| 1.722219 / 1.541195 (0.181025) \| 1.813284 / 1.468490 (0.344793) \| 0.707193 / 4.584777 (-3.877584) \| 3.853545 / 3.745712 (0.107833) \| 3.369240 / 5.269862 (-1.900621) \| 1.820264 / 4.565676 (-2.745412) \| 0.087340 / 0.424275 (-0.336936) \| 0.012305 / 0.007607 (0.004698) \| 0.520326 / 0.226044 (0.294281) \| 5.107383 / 2.268929 (2.838455) \| 2.413977 / 55.444624 (-53.030647) \| 2.074356 / 6.876477 (-4.802121) \| 2.255959 / 2.142072 (0.113887) \| 0.849850 / 4.805227 (-3.955377) \| 0.170116 / 6.500664 (-6.330548) \| 0.067203 / 0.075469 (-0.008267) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.168158 / 1.841788 (-0.673629) \| 15.046312 / 8.074308 (6.972004) \| 15.113924 / 10.191392 (4.922532) \| 0.145288 / 0.680424 (-0.535136) \| 0.017959 / 0.534201 (-0.516242) \| 0.424666 / 0.579283 (-0.154617) \| 0.422560 / 0.434364 (-0.011804) \| 0.526386 / 0.540337 (-0.013952) \| 0.623755 / 1.386936 (-0.763181) \|\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) \| 0.007676 / 0.011353 (-0.003677) \| 0.005240 / 0.011008 (-0.005769) \| 0.074668 / 0.038508 (0.036160) \| 0.035570 / 0.023109 (0.012461) \| 0.348524 / 0.275898 (0.072626) \| 0.378157 / 0.323480 (0.054677) \| 0.006112 / 0.007986 (-0.001873) \| 0.005641 / 0.004328 (0.001312) \| 0.073536 / 0.004250 (0.069286) \| 0.048651 / 0.037052 (0.011599) \| 0.359282 / 0.258489 (0.100793) \| 0.385961 / 0.293841 (0.092120) \| 0.035417 / 0.128546 (-0.093129) \| 0.012227 / 0.075646 (-0.063419) \| 0.085725 / 0.419271 (-0.333546) \| 0.049651 / 0.043533 (0.006118) \| 0.344122 / 0.255139 (0.088983) \| 0.364795 / 0.283200 (0.081595) \| 0.112711 / 0.141683 (-0.028972) \| 1.426823 / 1.452155 (-0.025332) \| 1.534745 / 1.492716 (0.042029) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.201728 / 0.018006 (0.183721) \| 0.448533 / 0.000490 (0.448043) \| 0.003554 / 0.000200 (0.003354) \| 0.000092 / 0.000054 (0.000038) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030917 / 0.037411 (-0.006494) \| 0.117966 / 0.014526 (0.103440) \| 0.125954 / 0.176557 (-0.050602) \| 0.176382 / 0.737135 (-0.560753) \| 0.130757 / 0.296338 (-0.165582) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422167 / 0.215209 (0.206958) \| 4.213948 / 2.077655 (2.136294) \| 2.040049 / 1.504120 (0.535929) \| 1.858317 / 1.541195 (0.317122) \| 1.937108 / 1.468490 (0.468618) \| 0.707797 / 4.584777 (-3.876979) \| 3.831061 / 3.745712 (0.085349) \| 3.373711 / 5.269862 (-1.896151) \| 1.590343 / 4.565676 (-2.975333) \| 0.086672 / 0.424275 (-0.337603) \| 0.012429 / 0.007607 (0.004821) \| 0.520269 / 0.226044 (0.294225) \| 5.207285 / 2.268929 (2.938357) \| 2.518107 / 55.444624 (-52.926517) \| 2.230696 / 6.876477 (-4.645781) \| 2.363164 / 2.142072 (0.221091) \| 0.836749 / 4.805227 (-3.968479) \| 0.169676 / 6.500664 (-6.330988) \| 0.065766 / 0.075469 (-0.009703) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.251195 / 1.841788 (-0.590592) \| 15.196091 / 8.074308 (7.121782) \| 14.991600 / 10.191392 (4.800208) \| 0.165335 / 0.680424 (-0.515089) \| 0.017789 / 0.534201 (-0.516412) \| 0.433863 / 0.579283 (-0.145420) \| 0.428660 / 0.434364 (-0.005704) \| 0.527385 / 0.540337 (-0.012952) \| 0.628067 / 1.386936 (-0.758869) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d06b8c21ba98ae85971a2b1d135ac2ef035b59c9 \"CML watermark\")\n" ]	2023-04-07T16:01:26Z	2023-04-27T14:43:13Z	2023-04-27T14:35:52Z	MEMBER	null	null	null	This PR reorders data splits, so that by default validation appears before test. The default order becomes: [train, validation, test] instead of [train, test, validation].	{ "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/5718/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5718/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5718.diff", "html_url": "https://github.com/huggingface/datasets/pull/5718", "merged_at": "2023-04-27T14:35:52Z", "patch_url": "https://github.com/huggingface/datasets/pull/5718.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5718" }
https://api.github.com/repos/huggingface/datasets/issues/6147	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6147/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6147/comments	https://api.github.com/repos/huggingface/datasets/issues/6147/events	https://github.com/huggingface/datasets/issues/6147	1,848,914,830	I_kwDODunzps5uNDOO	6,147	ValueError when running BeamBasedBuilder with GCS path in cache_dir	{ "avatar_url": "https://avatars.githubusercontent.com/u/13844767?v=4", "events_url": "https://api.github.com/users/ktrk115/events{/privacy}", "followers_url": "https://api.github.com/users/ktrk115/followers", "following_url": "https://api.github.com/users/ktrk115/following{/other_user}", "gists_url": "https://api.github.com/users/ktrk115/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ktrk115", "id": 13844767, "login": "ktrk115", "node_id": "MDQ6VXNlcjEzODQ0NzY3", "organizations_url": "https://api.github.com/users/ktrk115/orgs", "received_events_url": "https://api.github.com/users/ktrk115/received_events", "repos_url": "https://api.github.com/users/ktrk115/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ktrk115/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ktrk115/subscriptions", "type": "User", "url": "https://api.github.com/users/ktrk115", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The cause of the error seems to be that `datasets` adds \"gcs://\" as a schema, while `beam` checks only \"gs://\".\r\n\r\ndatasets: https://github.com/huggingface/datasets/blob/c02a44715c036b5261686669727394b1308a3a4b/src/datasets/builder.py#L822\r\n\r\nbeam: [link](https://github.com/apache/beam/blob/25e1a64641b1c8a3c0a6c75c6e86031b87307f22/sdks/python/apache_beam/io/filesystems.py#L98-L101)\r\n```\r\n systems = [\r\n fs for fs in FileSystem.get_all_subclasses()\r\n if fs.scheme() == path_scheme\r\n ]\r\n```", "Hi! We've deprecated the Beam API, as we don't have the bandwidth to support it properly..." ]	2023-08-14T03:11:34Z	2024-03-18T16:59:15Z	2024-03-18T16:59:14Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug When running the BeamBasedBuilder with a GCS path specified in the cache_dir, the following ValueError occurs: ``` ValueError: Unable to get filesystem from specified path, please use the correct path or ensure the required dependency is installed, e.g., pip install apache-beam[gcp]. Path specified: gcs://my-bucket/huggingface_datasets/my_beam_dataset/default/0.0.0/my_beam_dataset-train [while running 'train/Save to parquet/Write/WriteImpl/InitializeWrite'] ``` Same error occurs after running `pip install apache-beam[gcp]` as instructed. ### Steps to reproduce the bug Put `my_beam_dataset.py`: ```python import datasets class MyBeamDataset(datasets.BeamBasedBuilder): def _info(self): features = datasets.Features({"value": datasets.Value("int64")}) return datasets.DatasetInfo(features=features) def _split_generators(self, dl_manager, pipeline): return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={})] def _build_pcollection(self, pipeline): import apache_beam as beam return pipeline \| beam.Create([{"value": i} for i in range(10)]) ``` Run: ```bash datasets-cli run_beam my_beam_dataset.py --cache_dir=gs://my-bucket/huggingface_datasets/ --beam_pipeline_options="runner=DirectRunner" ``` ### Expected behavior Running the BeamBasedBuilder with a GCS cache path without any errors. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 2.0.3	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6147/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6147/timeline	null	not_planned	null	null
https://api.github.com/repos/huggingface/datasets/issues/6754	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6754/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6754/comments	https://api.github.com/repos/huggingface/datasets/issues/6754/events	https://github.com/huggingface/datasets/pull/6754	2,204,214,595	PR_kwDODunzps5qk-nr	6,754	Fix cache path to snakecase for `CachedDatasetModuleFactory` and `Cache`	{ "avatar_url": "https://avatars.githubusercontent.com/u/26690193?v=4", "events_url": "https://api.github.com/users/izhx/events{/privacy}", "followers_url": "https://api.github.com/users/izhx/followers", "following_url": "https://api.github.com/users/izhx/following{/other_user}", "gists_url": "https://api.github.com/users/izhx/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/izhx", "id": 26690193, "login": "izhx", "node_id": "MDQ6VXNlcjI2NjkwMTkz", "organizations_url": "https://api.github.com/users/izhx/orgs", "received_events_url": "https://api.github.com/users/izhx/received_events", "repos_url": "https://api.github.com/users/izhx/repos", "site_admin": false, "starred_url": "https://api.github.com/users/izhx/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/izhx/subscriptions", "type": "User", "url": "https://api.github.com/users/izhx", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "@lhoestq hi 😃, is there something else I need to do to check this change?", "I added two tests and passed them on my server.\r\n\r\n```\r\npytest tests/packaged_modules/test_cache.py \r\n========================================================================== test session starts ==========================================================================\r\nplatform linux -- Python 3.11.5, pytest-8.1.1, pluggy-1.4.0\r\nrootdir: /mnt/nas/datasets\r\nconfigfile: pyproject.toml\r\nplugins: xdist-3.5.0, datadir-1.5.0\r\ncollected 8 items \r\n\r\ntests/packaged_modules/test_cache.py ........ [100%]\r\n\r\n========================================================================== 8 passed in 50.71s ===========================================================================\r\n\r\n```\r\n\r\n```\r\npytest tests/test_load.py\r\n========================================================================== test session starts ==========================================================================\r\nplatform linux -- Python 3.11.5, pytest-8.1.1, pluggy-1.4.0\r\nrootdir: /mnt/nas/datasets\r\nconfigfile: pyproject.toml\r\nplugins: xdist-3.5.0, datadir-1.5.0\r\ncollected 151 items \r\n\r\ntests/test_load.py .............................................................................................................................................. [ 94%]\r\n......... [100%]\r\n\r\n...\r\n\r\n============================================================= 151 passed, 29 warnings in 578.36s (0:09:38) ==============================================================\r\n```\r\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6754). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Hi @izhx! I have also faced this issue, happy to see it already addressed, looking forward for PR merge :)", "@lhoestq What do you think of these tests? 😀", "<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) \| 0.005060 / 0.011353 (-0.006293) \| 0.003251 / 0.011008 (-0.007757) \| 0.063538 / 0.038508 (0.025030) \| 0.031178 / 0.023109 (0.008069) \| 0.249971 / 0.275898 (-0.025927) \| 0.284828 / 0.323480 (-0.038652) \| 0.004183 / 0.007986 (-0.003802) \| 0.002656 / 0.004328 (-0.001673) \| 0.049585 / 0.004250 (0.045335) \| 0.042656 / 0.037052 (0.005604) \| 0.270962 / 0.258489 (0.012473) \| 0.296091 / 0.293841 (0.002250) \| 0.028065 / 0.128546 (-0.100482) \| 0.010545 / 0.075646 (-0.065102) \| 0.207323 / 0.419271 (-0.211948) \| 0.035977 / 0.043533 (-0.007556) \| 0.257315 / 0.255139 (0.002176) \| 0.272238 / 0.283200 (-0.010962) \| 0.017984 / 0.141683 (-0.123699) \| 1.131314 / 1.452155 (-0.320840) \| 1.180259 / 1.492716 (-0.312457) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090977 / 0.018006 (0.072971) \| 0.284021 / 0.000490 (0.283531) \| 0.000264 / 0.000200 (0.000065) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.017852 / 0.037411 (-0.019559) \| 0.061288 / 0.014526 (0.046762) \| 0.073844 / 0.176557 (-0.102713) \| 0.121371 / 0.737135 (-0.615764) \| 0.075036 / 0.296338 (-0.221303) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287599 / 0.215209 (0.072390) \| 2.821172 / 2.077655 (0.743517) \| 1.459904 / 1.504120 (-0.044216) \| 1.340224 / 1.541195 (-0.200970) \| 1.357350 / 1.468490 (-0.111140) \| 0.557344 / 4.584777 (-4.027433) \| 2.412177 / 3.745712 (-1.333535) \| 2.745126 / 5.269862 (-2.524735) \| 1.754600 / 4.565676 (-2.811077) \| 0.062487 / 0.424275 (-0.361788) \| 0.005306 / 0.007607 (-0.002301) \| 0.338856 / 0.226044 (0.112811) \| 3.354953 / 2.268929 (1.086024) \| 1.803208 / 55.444624 (-53.641417) \| 1.553051 / 6.876477 (-5.323426) \| 1.554790 / 2.142072 (-0.587282) \| 0.651380 / 4.805227 (-4.153847) \| 0.117777 / 6.500664 (-6.382887) \| 0.041992 / 0.075469 (-0.033477) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.977588 / 1.841788 (-0.864200) \| 11.363058 / 8.074308 (3.288750) \| 9.791770 / 10.191392 (-0.399622) \| 0.130708 / 0.680424 (-0.549716) \| 0.013798 / 0.534201 (-0.520403) \| 0.288313 / 0.579283 (-0.290970) \| 0.268170 / 0.434364 (-0.166194) \| 0.324815 / 0.540337 (-0.215522) \| 0.419260 / 1.386936 (-0.967676) \|\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) \| 0.005187 / 0.011353 (-0.006166) \| 0.003348 / 0.011008 (-0.007660) \| 0.050309 / 0.038508 (0.011801) \| 0.031334 / 0.023109 (0.008225) \| 0.279542 / 0.275898 (0.003644) \| 0.299608 / 0.323480 (-0.023872) \| 0.004202 / 0.007986 (-0.003784) \| 0.002735 / 0.004328 (-0.001593) \| 0.050321 / 0.004250 (0.046070) \| 0.039793 / 0.037052 (0.002740) \| 0.289972 / 0.258489 (0.031483) \| 0.313887 / 0.293841 (0.020046) \| 0.028797 / 0.128546 (-0.099750) \| 0.010166 / 0.075646 (-0.065480) \| 0.059228 / 0.419271 (-0.360044) \| 0.032667 / 0.043533 (-0.010866) \| 0.278409 / 0.255139 (0.023270) \| 0.292208 / 0.283200 (0.009008) \| 0.017577 / 0.141683 (-0.124106) \| 1.175046 / 1.452155 (-0.277109) \| 1.200766 / 1.492716 (-0.291950) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092236 / 0.018006 (0.074230) \| 0.298860 / 0.000490 (0.298370) \| 0.000211 / 0.000200 (0.000011) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021475 / 0.037411 (-0.015936) \| 0.074414 / 0.014526 (0.059888) \| 0.087746 / 0.176557 (-0.088811) \| 0.124757 / 0.737135 (-0.612378) \| 0.088513 / 0.296338 (-0.207826) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296583 / 0.215209 (0.081374) \| 2.894978 / 2.077655 (0.817323) \| 1.590806 / 1.504120 (0.086686) \| 1.463251 / 1.541195 (-0.077944) \| 1.478751 / 1.468490 (0.010261) \| 0.571724 / 4.584777 (-4.013053) \| 2.454356 / 3.745712 (-1.291356) \| 2.789275 / 5.269862 (-2.480586) \| 1.753866 / 4.565676 (-2.811811) \| 0.064787 / 0.424275 (-0.359488) \| 0.005321 / 0.007607 (-0.002287) \| 0.348454 / 0.226044 (0.122410) \| 3.453052 / 2.268929 (1.184124) \| 1.972237 / 55.444624 (-53.472388) \| 1.677822 / 6.876477 (-5.198655) \| 1.674750 / 2.142072 (-0.467322) \| 0.649353 / 4.805227 (-4.155874) \| 0.117135 / 6.500664 (-6.383529) \| 0.040018 / 0.075469 (-0.035451) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.029812 / 1.841788 (-0.811976) \| 11.945063 / 8.074308 (3.870755) \| 10.238380 / 10.191392 (0.046988) \| 0.146225 / 0.680424 (-0.534199) \| 0.015262 / 0.534201 (-0.518939) \| 0.286632 / 0.579283 (-0.292651) \| 0.272952 / 0.434364 (-0.161412) \| 0.323098 / 0.540337 (-0.217239) \| 0.423549 / 1.386936 (-0.963387) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#91b07b90915d7f7313d44ca3ff67673b9ad26bf4 \"CML watermark\")\n" ]	2024-03-24T06:59:15Z	2024-04-15T15:45:44Z	2024-04-15T15:38:51Z	CONTRIBUTOR	null	null	null	Fix https://github.com/huggingface/datasets/issues/6750#issuecomment-2016678729 I didn't find a guideline on how to run the tests, so i just run the following steps to make sure that this bug is fixed. 1. `python test.py`, 2. then `HF_DATASETS_OFFLINE=1 python test.py` The `test.py` is ``` import datasets datasets.utils.logging.set_verbosity_info() ds = datasets.load_dataset('izhx/STS17-debug') print(ds) ds = datasets.load_dataset('C-MTEB/AFQMC', revision='b44c3b011063adb25877c13823db83bb193913c4') print(ds) ```	{ "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": 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}	https://api.github.com/repos/huggingface/datasets/issues/6754/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6754.diff", "html_url": "https://github.com/huggingface/datasets/pull/6754", "merged_at": "2024-04-15T15:38:51Z", "patch_url": "https://github.com/huggingface/datasets/pull/6754.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6754" }
https://api.github.com/repos/huggingface/datasets/issues/4714	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4714/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4714/comments	https://api.github.com/repos/huggingface/datasets/issues/4714/events	https://github.com/huggingface/datasets/pull/4714	1,309,265,682	PR_kwDODunzps47o0YG	4,714	Fix named split sorting and remove unnecessary casting	{ "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._", "hahaha what a timing, I added my comment right after you merged x)\r\n\r\nyou can ignore my (nit), it's fine", "Sorry, just too sync... :sweat_smile: " ]	2022-07-19T09:48:28Z	2022-07-22T09:39:45Z	2022-07-22T09:10:57Z	MEMBER	null	null	null	This PR: - makes `NamedSplit` sortable: so that `sorted()` can be called on them - removes unnecessary `sorted()` on `dict.keys()`: `dict_keys` view is already like a `set` - removes unnecessary casting of `NamedSplit` to `str`	{ "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/4714/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4714/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4714.diff", "html_url": "https://github.com/huggingface/datasets/pull/4714", "merged_at": "2022-07-22T09:10:57Z", "patch_url": "https://github.com/huggingface/datasets/pull/4714.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4714" }
https://api.github.com/repos/huggingface/datasets/issues/4962	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4962/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4962/comments	https://api.github.com/repos/huggingface/datasets/issues/4962/events	https://github.com/huggingface/datasets/pull/4962	1,368,155,365	PR_kwDODunzps4-sh-o	4,962	Update setup.py	{ "avatar_url": "https://avatars.githubusercontent.com/u/3616964?v=4", "events_url": "https://api.github.com/users/DCNemesis/events{/privacy}", "followers_url": "https://api.github.com/users/DCNemesis/followers", "following_url": "https://api.github.com/users/DCNemesis/following{/other_user}", "gists_url": "https://api.github.com/users/DCNemesis/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/DCNemesis", "id": 3616964, "login": "DCNemesis", "node_id": "MDQ6VXNlcjM2MTY5NjQ=", "organizations_url": "https://api.github.com/users/DCNemesis/orgs", "received_events_url": "https://api.github.com/users/DCNemesis/received_events", "repos_url": "https://api.github.com/users/DCNemesis/repos", "site_admin": false, "starred_url": "https://api.github.com/users/DCNemesis/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/DCNemesis/subscriptions", "type": "User", "url": "https://api.github.com/users/DCNemesis", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Before addressing this PR, we should be sure about the issue. See my comment in:\r\n- https://github.com/huggingface/datasets/issues/4961#issuecomment-1243376247", "Once we know 2022.8.2 works, I'm closing this PR, as the corresponding issue." ]	2022-09-09T17:57:56Z	2022-09-12T14:33:04Z	2022-09-12T14:33:04Z	NONE	null	null	null	exclude broken version of fsspec. See the [related issue](https://github.com/huggingface/datasets/issues/4961)	{ "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/4962/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4962/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4962.diff", "html_url": "https://github.com/huggingface/datasets/pull/4962", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/4962.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4962" }
https://api.github.com/repos/huggingface/datasets/issues/6102	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6102/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6102/comments	https://api.github.com/repos/huggingface/datasets/issues/6102/events	https://github.com/huggingface/datasets/pull/6102	1,828,494,896	PR_kwDODunzps5WwyGy	6,102	Release 2.14.2	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "<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) \| 0.006517 / 0.011353 (-0.004836) \| 0.004217 / 0.011008 (-0.006792) \| 0.083162 / 0.038508 (0.044654) \| 0.074476 / 0.023109 (0.051367) \| 0.321193 / 0.275898 (0.045295) \| 0.358348 / 0.323480 (0.034868) \| 0.005531 / 0.007986 (-0.002455) \| 0.003621 / 0.004328 (-0.000707) \| 0.063819 / 0.004250 (0.059568) \| 0.056524 / 0.037052 (0.019471) \| 0.322145 / 0.258489 (0.063656) \| 0.371415 / 0.293841 (0.077574) \| 0.030612 / 0.128546 (-0.097934) \| 0.008907 / 0.075646 (-0.066739) \| 0.289451 / 0.419271 (-0.129821) \| 0.051959 / 0.043533 (0.008426) \| 0.317729 / 0.255139 (0.062590) \| 0.339750 / 0.283200 (0.056550) \| 0.022430 / 0.141683 (-0.119253) \| 1.487661 / 1.452155 (0.035506) \| 1.554916 / 1.492716 (0.062199) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296673 / 0.018006 (0.278667) \| 0.599183 / 0.000490 (0.598694) \| 0.002524 / 0.000200 (0.002324) \| 0.000076 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027898 / 0.037411 (-0.009514) \| 0.080870 / 0.014526 (0.066344) \| 0.094894 / 0.176557 (-0.081662) \| 0.152350 / 0.737135 (-0.584785) \| 0.095765 / 0.296338 (-0.200573) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415442 / 0.215209 (0.200233) \| 4.161155 / 2.077655 (2.083500) \| 2.117061 / 1.504120 (0.612941) \| 1.937846 / 1.541195 (0.396651) \| 1.979635 / 1.468490 (0.511145) \| 0.488381 / 4.584777 (-4.096396) \| 3.509836 / 3.745712 (-0.235876) \| 3.833074 / 5.269862 (-1.436788) \| 2.307536 / 4.565676 (-2.258141) \| 0.057059 / 0.424275 (-0.367216) \| 0.007366 / 0.007607 (-0.000241) \| 0.487752 / 0.226044 (0.261708) \| 4.869406 / 2.268929 (2.600478) \| 2.594775 / 55.444624 (-52.849849) \| 2.191712 / 6.876477 (-4.684765) \| 2.413220 / 2.142072 (0.271147) \| 0.584513 / 4.805227 (-4.220714) \| 0.132162 / 6.500664 (-6.368502) \| 0.061059 / 0.075469 (-0.014410) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.245178 / 1.841788 (-0.596610) \| 20.624563 / 8.074308 (12.550255) \| 14.675545 / 10.191392 (4.484153) \| 0.165838 / 0.680424 (-0.514586) \| 0.018700 / 0.534201 (-0.515501) \| 0.392475 / 0.579283 (-0.186808) \| 0.399884 / 0.434364 (-0.034480) \| 0.457478 / 0.540337 (-0.082859) \| 0.624553 / 1.386936 (-0.762383) \|\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) \| 0.006716 / 0.011353 (-0.004637) \| 0.004308 / 0.011008 (-0.006700) \| 0.064495 / 0.038508 (0.025987) \| 0.083194 / 0.023109 (0.060085) \| 0.371994 / 0.275898 (0.096096) \| 0.433045 / 0.323480 (0.109566) \| 0.005535 / 0.007986 (-0.002450) \| 0.003469 / 0.004328 (-0.000859) \| 0.064342 / 0.004250 (0.060092) \| 0.059362 / 0.037052 (0.022309) \| 0.393819 / 0.258489 (0.135330) \| 0.442591 / 0.293841 (0.148750) \| 0.031594 / 0.128546 (-0.096952) \| 0.008943 / 0.075646 (-0.066703) \| 0.070689 / 0.419271 (-0.348582) \| 0.049219 / 0.043533 (0.005686) \| 0.361568 / 0.255139 (0.106429) \| 0.417085 / 0.283200 (0.133886) \| 0.025112 / 0.141683 (-0.116571) \| 1.497204 / 1.452155 (0.045049) \| 1.552781 / 1.492716 (0.060064) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.325254 / 0.018006 (0.307248) \| 0.528399 / 0.000490 (0.527909) \| 0.007429 / 0.000200 (0.007229) \| 0.000101 / 0.000054 (0.000047) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029908 / 0.037411 (-0.007504) \| 0.087114 / 0.014526 (0.072588) \| 0.103366 / 0.176557 (-0.073191) \| 0.155145 / 0.737135 (-0.581990) \| 0.103458 / 0.296338 (-0.192880) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.409432 / 0.215209 (0.194223) \| 4.093327 / 2.077655 (2.015673) \| 2.154115 / 1.504120 (0.649995) \| 1.953492 / 1.541195 (0.412297) \| 2.021532 / 1.468490 (0.553042) \| 0.478928 / 4.584777 (-4.105849) \| 3.515287 / 3.745712 (-0.230426) \| 4.976239 / 5.269862 (-0.293623) \| 2.832803 / 4.565676 (-1.732873) \| 0.057239 / 0.424275 (-0.367036) \| 0.007718 / 0.007607 (0.000111) \| 0.484102 / 0.226044 (0.258057) \| 4.833020 / 2.268929 (2.564092) \| 2.564550 / 55.444624 (-52.880074) \| 2.268969 / 6.876477 (-4.607508) \| 2.513308 / 2.142072 (0.371235) \| 0.582822 / 4.805227 (-4.222406) \| 0.133989 / 6.500664 (-6.366675) \| 0.062078 / 0.075469 (-0.013391) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.393766 / 1.841788 (-0.448021) \| 20.224546 / 8.074308 (12.150238) \| 14.359438 / 10.191392 (4.168046) \| 0.166358 / 0.680424 (-0.514066) \| 0.018840 / 0.534201 (-0.515361) \| 0.393206 / 0.579283 (-0.186077) \| 0.404220 / 0.434364 (-0.030144) \| 0.462346 / 0.540337 (-0.077992) \| 0.603078 / 1.386936 (-0.783858) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53e8007baeff133aaad8cbb366196be18a5e57fd \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006835 / 0.011353 (-0.004518) \| 0.004530 / 0.011008 (-0.006478) \| 0.087506 / 0.038508 (0.048997) \| 0.088289 / 0.023109 (0.065180) \| 0.351575 / 0.275898 (0.075677) \| 0.391873 / 0.323480 (0.068393) \| 0.005627 / 0.007986 (-0.002359) \| 0.003735 / 0.004328 (-0.000594) \| 0.065747 / 0.004250 (0.061497) \| 0.058779 / 0.037052 (0.021726) \| 0.358076 / 0.258489 (0.099587) \| 0.408466 / 0.293841 (0.114626) \| 0.031369 / 0.128546 (-0.097178) \| 0.008807 / 0.075646 (-0.066839) \| 0.293253 / 0.419271 (-0.126019) \| 0.052950 / 0.043533 (0.009417) \| 0.350411 / 0.255139 (0.095272) \| 0.384827 / 0.283200 (0.101627) \| 0.026219 / 0.141683 (-0.115464) \| 1.464290 / 1.452155 (0.012136) \| 1.549688 / 1.492716 (0.056972) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.270354 / 0.018006 (0.252348) \| 0.593436 / 0.000490 (0.592946) \| 0.003872 / 0.000200 (0.003673) \| 0.000091 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031625 / 0.037411 (-0.005787) \| 0.092599 / 0.014526 (0.078073) \| 0.104619 / 0.176557 (-0.071938) \| 0.163183 / 0.737135 (-0.573952) \| 0.103245 / 0.296338 (-0.193094) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.390213 / 0.215209 (0.175004) \| 3.894519 / 2.077655 (1.816864) \| 1.905739 / 1.504120 (0.401619) \| 1.728873 / 1.541195 (0.187678) \| 1.838692 / 1.468490 (0.370202) \| 0.484730 / 4.584777 (-4.100047) \| 3.706749 / 3.745712 (-0.038963) \| 5.572311 / 5.269862 (0.302449) \| 3.389949 / 4.565676 (-1.175727) \| 0.057315 / 0.424275 (-0.366960) \| 0.007475 / 0.007607 (-0.000132) \| 0.464690 / 0.226044 (0.238645) \| 4.622242 / 2.268929 (2.353314) \| 2.380957 / 55.444624 (-53.063667) \| 2.038225 / 6.876477 (-4.838251) \| 2.358881 / 2.142072 (0.216809) \| 0.606358 / 4.805227 (-4.198869) \| 0.133584 / 6.500664 (-6.367080) \| 0.061894 / 0.075469 (-0.013575) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.259575 / 1.841788 (-0.582213) \| 20.915216 / 8.074308 (12.840908) \| 14.971952 / 10.191392 (4.780560) \| 0.160206 / 0.680424 (-0.520218) \| 0.018675 / 0.534201 (-0.515526) \| 0.396821 / 0.579283 (-0.182462) \| 0.430982 / 0.434364 (-0.003382) \| 0.452895 / 0.540337 (-0.087443) \| 0.647869 / 1.386936 (-0.739067) \|\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) \| 0.007194 / 0.011353 (-0.004158) \| 0.004340 / 0.011008 (-0.006669) \| 0.065125 / 0.038508 (0.026617) \| 0.096243 / 0.023109 (0.073134) \| 0.374361 / 0.275898 (0.098463) \| 0.411863 / 0.323480 (0.088383) \| 0.005813 / 0.007986 (-0.002172) \| 0.003615 / 0.004328 (-0.000713) \| 0.064953 / 0.004250 (0.060703) \| 0.063171 / 0.037052 (0.026119) \| 0.376238 / 0.258489 (0.117749) \| 0.415826 / 0.293841 (0.121985) \| 0.031926 / 0.128546 (-0.096620) \| 0.008821 / 0.075646 (-0.066825) \| 0.072150 / 0.419271 (-0.347122) \| 0.049484 / 0.043533 (0.005951) \| 0.369691 / 0.255139 (0.114552) \| 0.390669 / 0.283200 (0.107470) \| 0.025732 / 0.141683 (-0.115950) \| 1.493833 / 1.452155 (0.041679) \| 1.601786 / 1.492716 (0.109070) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284279 / 0.018006 (0.266272) \| 0.585909 / 0.000490 (0.585419) \| 0.000411 / 0.000200 (0.000211) \| 0.000057 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033642 / 0.037411 (-0.003769) \| 0.095328 / 0.014526 (0.080802) \| 0.105810 / 0.176557 (-0.070746) \| 0.159779 / 0.737135 (-0.577357) \| 0.108938 / 0.296338 (-0.187400) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.408112 / 0.215209 (0.192902) \| 4.067035 / 2.077655 (1.989380) \| 2.114504 / 1.504120 (0.610384) \| 1.944027 / 1.541195 (0.402832) \| 2.066117 / 1.468490 (0.597627) \| 0.486441 / 4.584777 (-4.098336) \| 3.622659 / 3.745712 (-0.123053) \| 3.399310 / 5.269862 (-1.870552) \| 2.183151 / 4.565676 (-2.382525) \| 0.057490 / 0.424275 (-0.366785) \| 0.007955 / 0.007607 (0.000347) \| 0.490221 / 0.226044 (0.264177) \| 4.887301 / 2.268929 (2.618373) \| 2.679806 / 55.444624 (-52.764819) \| 2.258992 / 6.876477 (-4.617484) \| 2.592493 / 2.142072 (0.450420) \| 0.606515 / 4.805227 (-4.198712) \| 0.135645 / 6.500664 (-6.365019) \| 0.063956 / 0.075469 (-0.011513) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.331304 / 1.841788 (-0.510483) \| 21.458611 / 8.074308 (13.384303) \| 14.898964 / 10.191392 (4.707572) \| 0.172110 / 0.680424 (-0.508314) \| 0.018791 / 0.534201 (-0.515409) \| 0.395944 / 0.579283 (-0.183339) \| 0.424526 / 0.434364 (-0.009838) \| 0.462517 / 0.540337 (-0.077821) \| 0.610139 / 1.386936 (-0.776797) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#09492ba523518289a84175ddb7ab3bc555e742ee \"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) \| 0.005957 / 0.011353 (-0.005396) \| 0.003581 / 0.011008 (-0.007427) \| 0.079624 / 0.038508 (0.041116) \| 0.058004 / 0.023109 (0.034895) \| 0.309345 / 0.275898 (0.033447) \| 0.346653 / 0.323480 (0.023173) \| 0.005420 / 0.007986 (-0.002566) \| 0.002906 / 0.004328 (-0.001423) \| 0.061970 / 0.004250 (0.057720) \| 0.047627 / 0.037052 (0.010575) \| 0.314096 / 0.258489 (0.055607) \| 0.361368 / 0.293841 (0.067527) \| 0.027211 / 0.128546 (-0.101335) \| 0.007853 / 0.075646 (-0.067793) \| 0.260202 / 0.419271 (-0.159070) \| 0.045308 / 0.043533 (0.001775) \| 0.312150 / 0.255139 (0.057011) \| 0.341085 / 0.283200 (0.057886) \| 0.021302 / 0.141683 (-0.120381) \| 1.430315 / 1.452155 (-0.021840) \| 1.608989 / 1.492716 (0.116273) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.185289 / 0.018006 (0.167283) \| 0.423318 / 0.000490 (0.422828) \| 0.005741 / 0.000200 (0.005541) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023777 / 0.037411 (-0.013634) \| 0.071937 / 0.014526 (0.057412) \| 0.079406 / 0.176557 (-0.097151) \| 0.143815 / 0.737135 (-0.593320) \| 0.081648 / 0.296338 (-0.214690) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.431514 / 0.215209 (0.216305) \| 4.314471 / 2.077655 (2.236817) \| 2.305167 / 1.504120 (0.801047) \| 2.137894 / 1.541195 (0.596699) \| 2.161034 / 1.468490 (0.692544) \| 0.511701 / 4.584777 (-4.073076) \| 3.098213 / 3.745712 (-0.647499) \| 4.086837 / 5.269862 (-1.183024) \| 2.517184 / 4.565676 (-2.048492) \| 0.058272 / 0.424275 (-0.366003) \| 0.006415 / 0.007607 (-0.001192) \| 0.504792 / 0.226044 (0.278747) \| 5.046758 / 2.268929 (2.777829) \| 2.752049 / 55.444624 (-52.692576) \| 2.407707 / 6.876477 (-4.468770) \| 2.532162 / 2.142072 (0.390090) \| 0.597562 / 4.805227 (-4.207666) \| 0.125935 / 6.500664 (-6.374729) \| 0.060837 / 0.075469 (-0.014632) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.257048 / 1.841788 (-0.584740) \| 17.877849 / 8.074308 (9.803541) \| 13.904805 / 10.191392 (3.713413) \| 0.131647 / 0.680424 (-0.548776) \| 0.016975 / 0.534201 (-0.517226) \| 0.329651 / 0.579283 (-0.249633) \| 0.354358 / 0.434364 (-0.080006) \| 0.377545 / 0.540337 (-0.162792) \| 0.545593 / 1.386936 (-0.841343) \|\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) \| 0.005839 / 0.011353 (-0.005514) \| 0.003580 / 0.011008 (-0.007428) \| 0.062204 / 0.038508 (0.023696) \| 0.057943 / 0.023109 (0.034834) \| 0.400165 / 0.275898 (0.124267) \| 0.427911 / 0.323480 (0.104431) \| 0.004412 / 0.007986 (-0.003574) \| 0.002794 / 0.004328 (-0.001534) \| 0.062933 / 0.004250 (0.058683) \| 0.046243 / 0.037052 (0.009191) \| 0.413640 / 0.258489 (0.155151) \| 0.418592 / 0.293841 (0.124751) \| 0.027020 / 0.128546 (-0.101526) \| 0.007927 / 0.075646 (-0.067720) \| 0.067581 / 0.419271 (-0.351691) \| 0.041927 / 0.043533 (-0.001606) \| 0.381863 / 0.255139 (0.126724) \| 0.415711 / 0.283200 (0.132511) \| 0.019827 / 0.141683 (-0.121856) \| 1.464049 / 1.452155 (0.011894) \| 1.528387 / 1.492716 (0.035671) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224999 / 0.018006 (0.206993) \| 0.419167 / 0.000490 (0.418678) \| 0.000363 / 0.000200 (0.000163) \| 0.000054 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024827 / 0.037411 (-0.012585) \| 0.077134 / 0.014526 (0.062608) \| 0.085142 / 0.176557 (-0.091414) \| 0.137400 / 0.737135 (-0.599735) \| 0.086434 / 0.296338 (-0.209905) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.452716 / 0.215209 (0.237507) \| 4.530610 / 2.077655 (2.452955) \| 2.467309 / 1.504120 (0.963189) \| 2.300441 / 1.541195 (0.759246) \| 2.323475 / 1.468490 (0.854985) \| 0.501847 / 4.584777 (-4.082930) \| 3.079432 / 3.745712 (-0.666280) \| 2.793107 / 5.269862 (-2.476755) \| 1.835010 / 4.565676 (-2.730666) \| 0.057698 / 0.424275 (-0.366577) \| 0.006756 / 0.007607 (-0.000851) \| 0.529062 / 0.226044 (0.303017) \| 5.287822 / 2.268929 (3.018894) \| 2.908411 / 55.444624 (-52.536214) \| 2.571627 / 6.876477 (-4.304850) \| 2.691188 / 2.142072 (0.549116) \| 0.592289 / 4.805227 (-4.212938) \| 0.126091 / 6.500664 (-6.374573) \| 0.062312 / 0.075469 (-0.013157) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.328854 / 1.841788 (-0.512933) \| 18.185628 / 8.074308 (10.111320) \| 13.858781 / 10.191392 (3.667389) \| 0.142421 / 0.680424 (-0.538003) \| 0.016535 / 0.534201 (-0.517666) \| 0.330839 / 0.579283 (-0.248444) \| 0.346559 / 0.434364 (-0.087805) \| 0.389153 / 0.540337 (-0.151185) \| 0.516897 / 1.386936 (-0.870039) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#09492ba523518289a84175ddb7ab3bc555e742ee \"CML watermark\")\n" ]	2023-07-31T06:27:47Z	2023-07-31T06:48:09Z	2023-07-31T06:32:58Z	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/6102/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6102/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6102.diff", "html_url": "https://github.com/huggingface/datasets/pull/6102", "merged_at": "2023-07-31T06:32:58Z", "patch_url": "https://github.com/huggingface/datasets/pull/6102.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6102" }
https://api.github.com/repos/huggingface/datasets/issues/4584	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4584/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4584/comments	https://api.github.com/repos/huggingface/datasets/issues/4584/events	https://github.com/huggingface/datasets/pull/4584	1,286,911,993	PR_kwDODunzps46eVF7	4,584	Add binary classification task IDs	{ "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" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4584). All of your documentation changes will be reflected on that endpoint.", "> Awesome thanks ! Can you add it to https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts first please ? This is where we define the cross libraries tasks taxonomy ;)\r\n\r\nThanks for the tip! Done in https://github.com/huggingface/hub-docs/pull/217", "I don't think we need to update this file anymore. We should remove it IMO, and simply update the dataset [tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging)", "I'm closing this PR." ]	2022-06-28T07:30:39Z	2023-09-24T10:04:04Z	2023-01-26T09:27:52Z	MEMBER	null	null	null	As a precursor to aligning the task IDs in `datasets` and AutoTrain, we need a way to distinguish binary vs multiclass vs multilabel classification. This PR adds binary classification to the task IDs to enable this. Related AutoTrain issue: https://github.com/huggingface/autonlp-backend/issues/597 cc @abhishekkrthakur @SBrandeis	{ "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/4584/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4584/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4584.diff", "html_url": "https://github.com/huggingface/datasets/pull/4584", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/4584.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4584" }
https://api.github.com/repos/huggingface/datasets/issues/7478	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7478/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7478/comments	https://api.github.com/repos/huggingface/datasets/issues/7478/events	https://github.com/huggingface/datasets/pull/7478	2,948,993,461	PR_kwDODunzps6QLPe3	7,478	update fsspec 2025.3.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/487783?v=4", "events_url": "https://api.github.com/users/peteski22/events{/privacy}", "followers_url": "https://api.github.com/users/peteski22/followers", "following_url": "https://api.github.com/users/peteski22/following{/other_user}", "gists_url": "https://api.github.com/users/peteski22/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/peteski22", "id": 487783, "login": "peteski22", "node_id": "MDQ6VXNlcjQ4Nzc4Mw==", "organizations_url": "https://api.github.com/users/peteski22/orgs", "received_events_url": "https://api.github.com/users/peteski22/received_events", "repos_url": "https://api.github.com/users/peteski22/repos", "site_admin": false, "starred_url": "https://api.github.com/users/peteski22/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/peteski22/subscriptions", "type": "User", "url": "https://api.github.com/users/peteski22", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Sorry for tagging you @lhoestq but since you merged the linked PR, I wondered if you might be able to help me get this triaged so it can be reviewed/rejected etc. 🙏🏼 ", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7478). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]	2025-03-26T09:53:05Z	2025-03-28T19:15:54Z	2025-03-28T15:51:55Z	CONTRIBUTOR	null	null	null	It appears there have been two releases of fsspec since this dependency was last updated, it would be great if Datasets could be updated so that it didn't hold back the usage of newer fsspec versions in consuming projects. PR based on https://github.com/huggingface/datasets/pull/7352	{ "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/7478/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7478/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7478.diff", "html_url": "https://github.com/huggingface/datasets/pull/7478", "merged_at": "2025-03-28T15:51:54Z", "patch_url": "https://github.com/huggingface/datasets/pull/7478.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7478" }
https://api.github.com/repos/huggingface/datasets/issues/7301	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7301/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7301/comments	https://api.github.com/repos/huggingface/datasets/issues/7301/events	https://github.com/huggingface/datasets/pull/7301	2,701,813,922	PR_kwDODunzps6DdYLZ	7,301	update load_dataset doctring	{ "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_7301). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]	2024-11-28T11:19:20Z	2024-11-29T10:31:43Z	2024-11-29T10:31:40Z	MEMBER	null	null	null	- remove canonical dataset name - remove dataset script logic - add streaming info - clearer download and prepare steps	{ "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/7301/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7301/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7301.diff", "html_url": "https://github.com/huggingface/datasets/pull/7301", "merged_at": "2024-11-29T10:31:40Z", "patch_url": "https://github.com/huggingface/datasets/pull/7301.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7301" }
https://api.github.com/repos/huggingface/datasets/issues/6347	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6347/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6347/comments	https://api.github.com/repos/huggingface/datasets/issues/6347/events	https://github.com/huggingface/datasets/issues/6347	1,959,004,835	I_kwDODunzps50xAqj	6,347	Incorrect example code in 'Create a dataset' docs	{ "avatar_url": "https://avatars.githubusercontent.com/u/72076688?v=4", "events_url": "https://api.github.com/users/rwood-97/events{/privacy}", "followers_url": "https://api.github.com/users/rwood-97/followers", "following_url": "https://api.github.com/users/rwood-97/following{/other_user}", "gists_url": "https://api.github.com/users/rwood-97/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/rwood-97", "id": 72076688, "login": "rwood-97", "node_id": "MDQ6VXNlcjcyMDc2Njg4", "organizations_url": "https://api.github.com/users/rwood-97/orgs", "received_events_url": "https://api.github.com/users/rwood-97/received_events", "repos_url": "https://api.github.com/users/rwood-97/repos", "site_admin": false, "starred_url": "https://api.github.com/users/rwood-97/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/rwood-97/subscriptions", "type": "User", "url": "https://api.github.com/users/rwood-97", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "This was fixed in https://github.com/huggingface/datasets/pull/6247. You can find the fix in the `main` version of the docs", "Ah great, thanks :)" ]	2023-10-24T11:01:21Z	2023-10-25T13:05:21Z	2023-10-25T13:05:21Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug On [this](https://huggingface.co/docs/datasets/create_dataset) page, the example code for loading in images and audio is incorrect. Currently, examples are: ``` python from datasets import ImageFolder dataset = load_dataset("imagefolder", data_dir="/path/to/pokemon") ``` and ``` python from datasets import AudioFolder dataset = load_dataset("audiofolder", data_dir="/path/to/folder") ``` I'm pretty sure the imports are wrong and should be: ``` python from datasets import load_dataset dataset = load_dataset("audiofolder", data_dir="/path/to/folder") ``` I am happy to update this if this is right but just wanted to check before making any changes. ### Steps to reproduce the bug Go to https://huggingface.co/docs/datasets/create_dataset ### Expected behavior N/A ### Environment info N/A	{ "avatar_url": "https://avatars.githubusercontent.com/u/72076688?v=4", "events_url": "https://api.github.com/users/rwood-97/events{/privacy}", "followers_url": "https://api.github.com/users/rwood-97/followers", "following_url": "https://api.github.com/users/rwood-97/following{/other_user}", "gists_url": "https://api.github.com/users/rwood-97/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/rwood-97", "id": 72076688, "login": "rwood-97", "node_id": "MDQ6VXNlcjcyMDc2Njg4", "organizations_url": "https://api.github.com/users/rwood-97/orgs", "received_events_url": "https://api.github.com/users/rwood-97/received_events", "repos_url": "https://api.github.com/users/rwood-97/repos", "site_admin": false, "starred_url": "https://api.github.com/users/rwood-97/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/rwood-97/subscriptions", "type": "User", "url": "https://api.github.com/users/rwood-97", "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/6347/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6347/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6891	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6891/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6891/comments	https://api.github.com/repos/huggingface/datasets/issues/6891/events	https://github.com/huggingface/datasets/issues/6891	2,291,118,869	I_kwDODunzps6Ij7MV	6,891	Unable to load JSON saved using `to_json`	{ "avatar_url": "https://avatars.githubusercontent.com/u/39432636?v=4", "events_url": "https://api.github.com/users/DarshanDeshpande/events{/privacy}", "followers_url": "https://api.github.com/users/DarshanDeshpande/followers", "following_url": "https://api.github.com/users/DarshanDeshpande/following{/other_user}", "gists_url": "https://api.github.com/users/DarshanDeshpande/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/DarshanDeshpande", "id": 39432636, "login": "DarshanDeshpande", "node_id": "MDQ6VXNlcjM5NDMyNjM2", "organizations_url": "https://api.github.com/users/DarshanDeshpande/orgs", "received_events_url": "https://api.github.com/users/DarshanDeshpande/received_events", "repos_url": "https://api.github.com/users/DarshanDeshpande/repos", "site_admin": false, "starred_url": "https://api.github.com/users/DarshanDeshpande/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/DarshanDeshpande/subscriptions", "type": "User", "url": "https://api.github.com/users/DarshanDeshpande", "user_view_type": "public" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[ "Hi @DarshanDeshpande,\r\n\r\nPlease note that the default format of the method `Dataset.to_json` is [JSON-Lines](https://jsonlines.org/): it passes `orient=\"records\", lines=True` to `pandas.DataFrame.to_json`. This format is specially useful for large datasets, since unlike regular JSON files, it does not require loading all the data into memory at once, but can be done iteratively by batches.\r\n\r\nIn order to read this file using the `json` library, you should parse line by line:\r\n```python\r\nwith open(\"full_dataset.json\", \"r\") as f:\r\n data = [json.loads(line) for line in f]\r\nlen(data)\r\n```\r\nMaybe we should explain this better in our docs.", "Now we explain this better in out docs:\r\n- #6895" ]	2024-05-12T01:02:51Z	2024-05-16T14:32:55Z	2024-05-12T07:02:02Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Datasets stored in the JSON format cannot be loaded using `json.load()` ### Steps to reproduce the bug ``` import json from datasets import load_dataset dataset = load_dataset("squad") train_dataset, test_dataset = dataset["train"], dataset["validation"] test_dataset.to_json("full_dataset.json") # This works loaded_test = load_dataset("json", data_files="full_dataset.json") # This fails loaded_test = json.load(open("full_dataset.json", "r")) ``` ### Expected behavior The JSON should be correctly formatted when writing so that it can be loaded using `json.load()`. ### Environment info Colab: https://colab.research.google.com/drive/1st1iStFUVgu9ZPvnzSzL4vDeYWDwYpUm?usp=sharing	{ "avatar_url": "https://avatars.githubusercontent.com/u/39432636?v=4", "events_url": "https://api.github.com/users/DarshanDeshpande/events{/privacy}", "followers_url": "https://api.github.com/users/DarshanDeshpande/followers", "following_url": "https://api.github.com/users/DarshanDeshpande/following{/other_user}", "gists_url": "https://api.github.com/users/DarshanDeshpande/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/DarshanDeshpande", "id": 39432636, "login": "DarshanDeshpande", "node_id": "MDQ6VXNlcjM5NDMyNjM2", "organizations_url": "https://api.github.com/users/DarshanDeshpande/orgs", "received_events_url": "https://api.github.com/users/DarshanDeshpande/received_events", "repos_url": "https://api.github.com/users/DarshanDeshpande/repos", "site_admin": false, "starred_url": "https://api.github.com/users/DarshanDeshpande/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/DarshanDeshpande/subscriptions", "type": "User", "url": "https://api.github.com/users/DarshanDeshpande", "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/6891/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6891/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5868	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5868/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5868/comments	https://api.github.com/repos/huggingface/datasets/issues/5868/events	https://github.com/huggingface/datasets/issues/5868	1,711,173,098	I_kwDODunzps5l_m3q	5,868	Is it possible to change a cached file and 're-cache' it instead of re-generating?	{ "avatar_url": "https://avatars.githubusercontent.com/u/31238754?v=4", "events_url": "https://api.github.com/users/zyh3826/events{/privacy}", "followers_url": "https://api.github.com/users/zyh3826/followers", "following_url": "https://api.github.com/users/zyh3826/following{/other_user}", "gists_url": "https://api.github.com/users/zyh3826/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/zyh3826", "id": 31238754, "login": "zyh3826", "node_id": "MDQ6VXNlcjMxMjM4NzU0", "organizations_url": "https://api.github.com/users/zyh3826/orgs", "received_events_url": "https://api.github.com/users/zyh3826/received_events", "repos_url": "https://api.github.com/users/zyh3826/repos", "site_admin": false, "starred_url": "https://api.github.com/users/zyh3826/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/zyh3826/subscriptions", "type": "User", "url": "https://api.github.com/users/zyh3826", "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	[ "Arrow files/primitives (tables and arrays) are immutable, so re-generating them is the only option, I'm afraid.", "> \r\n\r\nGot it, thanks for your reply" ]	2023-05-16T03:45:42Z	2023-05-17T11:21:36Z	2023-05-17T11:21:36Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request Hi, I have a huge cached file using `map`(over 500GB), and I want to change an attribution of each element, is there possible to do it using some method instead of re-generating, because `map` takes over 24 hours ### Motivation For large datasets, I think it is very important because we always face the problem which is changing something in the original cache without re-generating it. ### Your contribution For now, I can't help, sorry.	{ "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/5868/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5868/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5466	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5466/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5466/comments	https://api.github.com/repos/huggingface/datasets/issues/5466/events	https://github.com/huggingface/datasets/pull/5466	1,557,584,845	PR_kwDODunzps5Ij-z1	5,466	remove pathlib.Path with URIs	{ "avatar_url": "https://avatars.githubusercontent.com/u/121845112?v=4", "events_url": "https://api.github.com/users/jonny-cyberhaven/events{/privacy}", "followers_url": "https://api.github.com/users/jonny-cyberhaven/followers", "following_url": "https://api.github.com/users/jonny-cyberhaven/following{/other_user}", "gists_url": "https://api.github.com/users/jonny-cyberhaven/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jonny-cyberhaven", "id": 121845112, "login": "jonny-cyberhaven", "node_id": "U_kgDOB0M1eA", "organizations_url": "https://api.github.com/users/jonny-cyberhaven/orgs", "received_events_url": "https://api.github.com/users/jonny-cyberhaven/received_events", "repos_url": "https://api.github.com/users/jonny-cyberhaven/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jonny-cyberhaven/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jonny-cyberhaven/subscriptions", "type": "User", "url": "https://api.github.com/users/jonny-cyberhaven", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Thanks !\r\n`os.path.join` will use a backslash `\\` on windows which will also fail. You can use this instead in `load_from_disk`:\r\n```python\r\nfrom .filesystems import is_remote_filesystem\r\n\r\nis_local = not is_remote_filesystem(fs)\r\npath_join = os.path.join if is_local else posixpath.join\r\n```", "Thank you ! I did a minor change to not have to define a new function and I ran the CI. If it's green we can merge :)", "_The documentation is not available anymore as the PR was closed or merged._", "> \r\n\r\n\r\n\r\n> Thank you ! I did a minor change to not have to define a new function and I ran the CI. If it's green we can merge :)\r\n\r\nlol it's a battle of +1 imports or +1 functions. LGTM, I was editing fast and swapped which branch gets os vs Path. Should be ok now 🤙", "<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) \| 0.012043 / 0.011353 (0.000690) \| 0.006585 / 0.011008 (-0.004423) \| 0.149007 / 0.038508 (0.110499) \| 0.039514 / 0.023109 (0.016405) \| 0.403893 / 0.275898 (0.127995) \| 0.431252 / 0.323480 (0.107772) \| 0.009218 / 0.007986 (0.001233) \| 0.006108 / 0.004328 (0.001779) \| 0.114666 / 0.004250 (0.110416) \| 0.044962 / 0.037052 (0.007910) \| 0.411592 / 0.258489 (0.153103) \| 0.461561 / 0.293841 (0.167721) \| 0.059958 / 0.128546 (-0.068589) \| 0.029047 / 0.075646 (-0.046599) \| 0.456000 / 0.419271 (0.036728) \| 0.060744 / 0.043533 (0.017211) \| 0.415816 / 0.255139 (0.160677) \| 0.430488 / 0.283200 (0.147289) \| 0.122477 / 0.141683 (-0.019205) \| 1.862910 / 1.452155 (0.410755) \| 1.974698 / 1.492716 (0.481981) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.257230 / 0.018006 (0.239224) \| 0.606854 / 0.000490 (0.606364) \| 0.006175 / 0.000200 (0.005975) \| 0.000099 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030533 / 0.037411 (-0.006879) \| 0.130702 / 0.014526 (0.116177) \| 0.143781 / 0.176557 (-0.032775) \| 0.183272 / 0.737135 (-0.553863) \| 0.151267 / 0.296338 (-0.145071) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.637422 / 0.215209 (0.422213) \| 6.503535 / 2.077655 (4.425880) \| 2.630387 / 1.504120 (1.126267) \| 2.281180 / 1.541195 (0.739985) \| 2.354341 / 1.468490 (0.885851) \| 1.306497 / 4.584777 (-3.278280) \| 5.837184 / 3.745712 (2.091472) \| 3.257198 / 5.269862 (-2.012663) \| 2.050681 / 4.565676 (-2.514995) \| 0.146415 / 0.424275 (-0.277860) \| 0.015386 / 0.007607 (0.007779) \| 0.790146 / 0.226044 (0.564102) \| 8.056137 / 2.268929 (5.787209) \| 3.383566 / 55.444624 (-52.061059) \| 2.707620 / 6.876477 (-4.168856) \| 2.714857 / 2.142072 (0.572785) \| 1.520847 / 4.805227 (-3.284380) \| 0.266028 / 6.500664 (-6.234636) \| 0.091422 / 0.075469 (0.015953) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.656148 / 1.841788 (-0.185640) \| 18.833393 / 8.074308 (10.759085) \| 21.360824 / 10.191392 (11.169432) \| 0.227608 / 0.680424 (-0.452816) \| 0.049018 / 0.534201 (-0.485183) \| 0.593418 / 0.579283 (0.014135) \| 0.656690 / 0.434364 (0.222326) \| 0.709171 / 0.540337 (0.168833) \| 0.828226 / 1.386936 (-0.558710) \|\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) \| 0.010112 / 0.011353 (-0.001241) \| 0.006761 / 0.011008 (-0.004247) \| 0.146723 / 0.038508 (0.108215) \| 0.038451 / 0.023109 (0.015342) \| 0.524267 / 0.275898 (0.248369) \| 0.609484 / 0.323480 (0.286004) \| 0.008502 / 0.007986 (0.000516) \| 0.006964 / 0.004328 (0.002635) \| 0.111396 / 0.004250 (0.107146) \| 0.056839 / 0.037052 (0.019787) \| 0.514649 / 0.258489 (0.256160) \| 0.604212 / 0.293841 (0.310372) \| 0.061410 / 0.128546 (-0.067137) \| 0.020396 / 0.075646 (-0.055250) \| 0.505026 / 0.419271 (0.085754) \| 0.067280 / 0.043533 (0.023747) \| 0.522249 / 0.255139 (0.267110) \| 0.559484 / 0.283200 (0.276284) \| 0.120943 / 0.141683 (-0.020740) \| 2.124323 / 1.452155 (0.672169) \| 2.153397 / 1.492716 (0.660681) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216614 / 0.018006 (0.198608) \| 0.594181 / 0.000490 (0.593692) \| 0.004079 / 0.000200 (0.003879) \| 0.000117 / 0.000054 (0.000063) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036925 / 0.037411 (-0.000486) \| 0.131322 / 0.014526 (0.116797) \| 0.148542 / 0.176557 (-0.028015) \| 0.196045 / 0.737135 (-0.541090) \| 0.156867 / 0.296338 (-0.139472) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.669722 / 0.215209 (0.454513) \| 6.858856 / 2.077655 (4.781202) \| 3.093969 / 1.504120 (1.589849) \| 2.667385 / 1.541195 (1.126190) \| 2.797192 / 1.468490 (1.328702) \| 1.334759 / 4.584777 (-3.250018) \| 6.024861 / 3.745712 (2.279149) \| 3.257779 / 5.269862 (-2.012083) \| 2.202816 / 4.565676 (-2.362860) \| 0.147617 / 0.424275 (-0.276658) \| 0.015451 / 0.007607 (0.007844) \| 0.887015 / 0.226044 (0.660970) \| 8.371288 / 2.268929 (6.102360) \| 3.807451 / 55.444624 (-51.637173) \| 3.079483 / 6.876477 (-3.796994) \| 3.103321 / 2.142072 (0.961249) \| 1.520272 / 4.805227 (-3.284955) \| 0.273079 / 6.500664 (-6.227585) \| 0.088613 / 0.075469 (0.013143) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.818913 / 1.841788 (-0.022875) \| 19.274269 / 8.074308 (11.199960) \| 19.871784 / 10.191392 (9.680392) \| 0.250388 / 0.680424 (-0.430036) \| 0.030562 / 0.534201 (-0.503638) \| 0.560566 / 0.579283 (-0.018717) \| 0.664701 / 0.434364 (0.230337) \| 0.714513 / 0.540337 (0.174176) \| 0.827227 / 1.386936 (-0.559710) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7a9bf823ea41b85313c0392388ec68b3033ef29 \"CML watermark\")\n" ]	2023-01-26T03:25:45Z	2023-01-26T17:08:57Z	2023-01-26T16:59:11Z	CONTRIBUTOR	null	null	null	Pathlib will convert "//" to "/" which causes retry errors when downloading from cloud storage	{ "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/5466/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5466/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5466.diff", "html_url": "https://github.com/huggingface/datasets/pull/5466", "merged_at": "2023-01-26T16:59:11Z", "patch_url": "https://github.com/huggingface/datasets/pull/5466.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5466" }
https://api.github.com/repos/huggingface/datasets/issues/4895	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4895/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4895/comments	https://api.github.com/repos/huggingface/datasets/issues/4895/events	https://github.com/huggingface/datasets/issues/4895	1,350,798,527	I_kwDODunzps5Qg4y_	4,895	load_dataset method returns Unknown split "validation" even if this dir exists	{ "avatar_url": "https://avatars.githubusercontent.com/u/13418507?v=4", "events_url": "https://api.github.com/users/SamSamhuns/events{/privacy}", "followers_url": "https://api.github.com/users/SamSamhuns/followers", "following_url": "https://api.github.com/users/SamSamhuns/following{/other_user}", "gists_url": "https://api.github.com/users/SamSamhuns/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/SamSamhuns", "id": 13418507, "login": "SamSamhuns", "node_id": "MDQ6VXNlcjEzNDE4NTA3", "organizations_url": "https://api.github.com/users/SamSamhuns/orgs", "received_events_url": "https://api.github.com/users/SamSamhuns/received_events", "repos_url": "https://api.github.com/users/SamSamhuns/repos", "site_admin": false, "starred_url": "https://api.github.com/users/SamSamhuns/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/SamSamhuns/subscriptions", "type": "User", "url": "https://api.github.com/users/SamSamhuns", "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 don't know the main problem but it looks like, it is ignoring the last directory in your case. So, create a directory called 'zzz' in the same folder as train, validation and test. if it doesn't work, create a directory called \"aaa\". It worked for me.\r\n", "@SamSamhuns could you please try to load it with the current main-branch version of `datasets`? I suppose the problem is that it tries to get splits names from filenames in this case, ignoring directories names, but `val` wasn't in keywords at that time, but it was fixed recently in this PR https://github.com/huggingface/datasets/pull/4844. ", "I have a similar problem.\r\nWhen I try to create `data_infos.json` using `datasets-cli test Peter.py --save_infos --all_configs` I get an error:\r\n`ValueError: Unknown split \"test\". Should be one of ['train'].`\r\n\r\nThe `data_infos.json` is created perfectly fine when I use only one split - `datasets.Split.TRAIN`\r\n\r\n@polinaeterna Could you help here please?\r\n\r\nYou can find the code here: https://huggingface.co/datasets/sberbank-ai/Peter/tree/add_splits (add_splits branch)", "@skalinin It seems the `dataset_infos.json` of your dataset is missing the info on the test split (and `datasets-cli` doesn't ignore the cached infos at the moment, which is a known bug), so your issue is not related to this one. I think you can fix your issue by deleting all the cached `dataset_infos.json` (in the local repo and in `~/.cache/huggingface/modules`) before running the `datasets-cli test` command. Let us know if that doesn't help, and I can try to generate it myself.", "This code indeed behaves as expected on `main`. But suppose the `val_234.png` is renamed to some other value not containing one of [these](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L31) keywords, in that case, this issue becomes relevant again because the real cause of it is the order in which we check the predefined split patterns to assign data files to each split - first we assign data files based on filenames, and only if this fails meaning not a single split found (`val` is not recognized here in the older versions of `datasets`, which results in an empty `validation` split), do we assign based on directory names.\r\n\r\n@polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if `data_dir` is specified (or if `load_dataset(data_dir)` is called)? ", "> @polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if data_dir is specified (or if load_dataset(data_dir) is called)?\r\n\r\nyes that makes sense !", "Looks like the `val/validation` dir name issue is fixed with the current main-branch version of the `datasets` repository. \r\n\r\n> @polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if data_dir is specified (or if load_dataset(data_dir) is called)?\r\n\r\nI agree with this as well. I would expect higher precedence to the directory name over the file name. Right now if I place a single file named `train_00001.jpg` under the `validation` directory, `load_dataset` cannot find the validation split.", "Thanks for the reply\r\n\r\nI've created a separate [issue](https://github.com/huggingface/datasets/issues/4982#issue-1375604693) for my problem.", "> @polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if data_dir is specified (or if load_dataset(data_dir) is called)?\r\n\r\nSounds good to me! opened a PR: https://github.com/huggingface/datasets/pull/4985", "Hi there @polinaeterna @mariosasko ! I have installed 5.2.3.dev0, which should have this fix. Unfortunately, I am still getting the error:\r\n`ValueError: Unknown split \"validation\". Should be one of ['train'].` When I call `load_dataset(\"csv\", data_files=files, split=split)`\r\n\r\nAny help would be greatly appreciated!", "hi @shaneacton ! could you please show your dataset structure?", "Hi there @polinaeterna . My local CSV files are stored as follows:\r\nbinding:\r\n---------- tune.csv\r\n---------- public_data:\r\n--------------------------- train.csv\r\n\r\n`self.list_shards(split)` sucessfully finds the relevant data files", "@shaneacton do you have `validation.csv`/`val.csv`/`valid.csv`/`dev.csv` file in your data folder? I can't find it in the structure you provided", "@polinaeterna no, does the name of the split need to match the name of the file exactly?\r\n\r\nBut my train file is not actually named 'train.py' its called 'XXXXXXXXX_train_XXXXXXXX.csv'\r\nAnd the code works fine for train, but fails for validation.\r\nDoes the file name need to _contain_ the split name?", "@shaneacton what files do you expect to be included in \"validation\" split? yes, you should somehow indicate that a file belongs to a certain split - either by including split name in a filename or by putting it into a folder with split name, you can also check out [this documentation page](https://huggingface.co/docs/datasets/main/en/repository_structure) :)\r\nby default all the data goes to a single `train` split", "@polinaeterna I have specified my train/test/tune files via the `split_to_filepattern` argument when initialising my `FileDataSource` class. This is how `list_shards` is able to find the right files.\r\nAfter your last message, I have tried renaminig my data files to simply `train.csv` and `validation.csv`, however I am still getting the same error: `Unknown split \"validation\". Should be one of ['train']`", "@polinaeterna I have solved the issue. The solution was to call:\r\n`load_dataset(\"csv\", data_files={split: files}, split=split)`", "For me it resolved by adding the verification_mode param:\r\n```\r\n imdb_ds = load_dataset(\r\n \"imdb\", verification_mode=\"no_checks\"\r\n )\r\n imdb_ds\r\n```" ]	2022-08-25T12:11:00Z	2024-03-26T16:47:48Z	2022-09-29T08:07:50Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	## Describe the bug The `datasets.load_dataset` returns a `ValueError: Unknown split "validation". Should be one of ['train', 'test'].` when running `load_dataset(local_data_dir_path, split="validation")` even if the `validation` sub-directory exists in the local data path. The data directories are as follows and attached to this issue: ``` test_data1 \|_ train \|_ 1012.png \|_ metadata.jsonl ... \|_ test ... \|_ validation \|_ 234.png \|_ metadata.jsonl ... test_data2 \|_ train \|_ train_1012.png \|_ metadata.jsonl ... \|_ test ... \|_ validation \|_ val_234.png \|_ metadata.jsonl ... ``` They contain the same image files and `metadata.jsonl` but the images in `test_data2` have the split names prepended i.e. `train_1012.png, val_234.png` and the images in `test_data1` do not have the split names prepended to the image names i.e. `1012.png, 234.png` I actually saw in another issue `val` was not recognized as a split name but here I would expect the files to take the split from the parent directory name i.e. val should become part of the validation split? ## Steps to reproduce the bug ```python import datasets datasets.logging.set_verbosity_error() from datasets import load_dataset, get_dataset_split_names # the following only finds train, validation and test splits correctly path = "./test_data1" print("######################", get_dataset_split_names(path), "######################") dataset_list = [] for spt in ["train", "test", "validation"]: dataset = load_dataset(path, split=spt) dataset_list.append(dataset) # the following only finds train and test splits path = "./test_data2" print("######################", get_dataset_split_names(path), "######################") dataset_list = [] for spt in ["train", "test", "validation"]: dataset = load_dataset(path, split=spt) dataset_list.append(dataset) ``` ## Expected results ``` ###################### ['train', 'test', 'validation'] ###################### ###################### ['train', 'test', 'validation'] ###################### ``` ## Actual results ``` Traceback (most recent call last): File "test_data_loader.py", line 11, in <module> dataset = load_dataset(path, split=spt) File "/home/venv/lib/python3.8/site-packages/datasets/load.py", line 1758, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/home/venv/lib/python3.8/site-packages/datasets/builder.py", line 893, in as_dataset datasets = map_nested( File "/home/venv/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 385, in map_nested return function(data_struct) File "/home/venv/lib/python3.8/site-packages/datasets/builder.py", line 924, in _build_single_dataset ds = self._as_dataset( File "/home/venv/lib/python3.8/site-packages/datasets/builder.py", line 993, in _as_dataset dataset_kwargs = ArrowReader(self._cache_dir, self.info).read( File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 211, in read files = self.get_file_instructions(name, instructions, split_infos) File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 184, in get_file_instructions file_instructions = make_file_instructions( File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 107, in make_file_instructions absolute_instructions = instruction.to_absolute(name2len) File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 616, in to_absolute return [_rel_to_abs_instr(rel_instr, name2len) for rel_instr in self._relative_instructions] File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 616, in <listcomp> return [_rel_to_abs_instr(rel_instr, name2len) for rel_instr in self._relative_instructions] File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 433, in _rel_to_abs_instr raise ValueError(f'Unknown split "{split}". Should be one of {list(name2len)}.') ValueError: Unknown split "validation". Should be one of ['train', 'test']. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: Linux Ubuntu 18.04 - Python version: 3.8.12 - PyArrow version: 9.0.0 Data files [test_data1.zip](https://github.com/huggingface/datasets/files/9424463/test_data1.zip) [test_data2.zip](https://github.com/huggingface/datasets/files/9424468/test_data2.zip)	{ "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/4895/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4895/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6922	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6922/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6922/comments	https://api.github.com/repos/huggingface/datasets/issues/6922/events	https://github.com/huggingface/datasets/pull/6922	2,318,602,059	PR_kwDODunzps5wolTm	6,922	Remove torchaudio remnants from code	{ "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_6922). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005525 / 0.011353 (-0.005828) \| 0.004013 / 0.011008 (-0.006996) \| 0.063931 / 0.038508 (0.025423) \| 0.033857 / 0.023109 (0.010748) \| 0.250910 / 0.275898 (-0.024988) \| 0.278289 / 0.323480 (-0.045191) \| 0.004289 / 0.007986 (-0.003697) \| 0.002800 / 0.004328 (-0.001529) \| 0.050127 / 0.004250 (0.045877) \| 0.048901 / 0.037052 (0.011848) \| 0.260628 / 0.258489 (0.002139) \| 0.293904 / 0.293841 (0.000063) \| 0.028339 / 0.128546 (-0.100207) \| 0.010879 / 0.075646 (-0.064767) \| 0.203618 / 0.419271 (-0.215654) \| 0.036241 / 0.043533 (-0.007292) \| 0.250481 / 0.255139 (-0.004657) \| 0.274274 / 0.283200 (-0.008926) \| 0.018912 / 0.141683 (-0.122771) \| 1.146785 / 1.452155 (-0.305370) \| 1.199795 / 1.492716 (-0.292921) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095571 / 0.018006 (0.077564) \| 0.302961 / 0.000490 (0.302471) \| 0.000217 / 0.000200 (0.000017) \| 0.000109 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020121 / 0.037411 (-0.017290) \| 0.063231 / 0.014526 (0.048705) \| 0.075434 / 0.176557 (-0.101122) \| 0.123994 / 0.737135 (-0.613141) \| 0.076479 / 0.296338 (-0.219860) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277816 / 0.215209 (0.062607) \| 2.775481 / 2.077655 (0.697826) \| 1.454881 / 1.504120 (-0.049239) \| 1.339055 / 1.541195 (-0.202140) \| 1.347810 / 1.468490 (-0.120681) \| 0.572802 / 4.584777 (-4.011975) \| 2.357490 / 3.745712 (-1.388222) \| 2.822548 / 5.269862 (-2.447313) \| 1.746538 / 4.565676 (-2.819138) \| 0.066159 / 0.424275 (-0.358116) \| 0.005037 / 0.007607 (-0.002570) \| 0.329256 / 0.226044 (0.103212) \| 3.277511 / 2.268929 (1.008582) \| 1.807855 / 55.444624 (-53.636769) \| 1.505507 / 6.876477 (-5.370970) \| 1.634237 / 2.142072 (-0.507835) \| 0.643999 / 4.805227 (-4.161229) \| 0.117494 / 6.500664 (-6.383170) \| 0.042634 / 0.075469 (-0.032835) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.977689 / 1.841788 (-0.864098) \| 12.261836 / 8.074308 (4.187528) \| 9.871541 / 10.191392 (-0.319851) \| 0.147293 / 0.680424 (-0.533130) \| 0.015134 / 0.534201 (-0.519067) \| 0.287677 / 0.579283 (-0.291606) \| 0.264622 / 0.434364 (-0.169742) \| 0.330511 / 0.540337 (-0.209826) \| 0.467618 / 1.386936 (-0.919318) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005690 / 0.011353 (-0.005663) \| 0.003801 / 0.011008 (-0.007207) \| 0.051817 / 0.038508 (0.013309) \| 0.033355 / 0.023109 (0.010246) \| 0.264416 / 0.275898 (-0.011482) \| 0.288494 / 0.323480 (-0.034986) \| 0.004246 / 0.007986 (-0.003740) \| 0.002814 / 0.004328 (-0.001515) \| 0.050547 / 0.004250 (0.046297) \| 0.042977 / 0.037052 (0.005925) \| 0.276884 / 0.258489 (0.018395) \| 0.303758 / 0.293841 (0.009917) \| 0.029412 / 0.128546 (-0.099134) \| 0.010697 / 0.075646 (-0.064949) \| 0.059497 / 0.419271 (-0.359775) \| 0.033670 / 0.043533 (-0.009862) \| 0.261311 / 0.255139 (0.006172) \| 0.286478 / 0.283200 (0.003278) \| 0.019386 / 0.141683 (-0.122297) \| 1.155943 / 1.452155 (-0.296211) \| 1.198512 / 1.492716 (-0.294205) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092954 / 0.018006 (0.074948) \| 0.294144 / 0.000490 (0.293655) \| 0.000213 / 0.000200 (0.000013) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023013 / 0.037411 (-0.014398) \| 0.077161 / 0.014526 (0.062635) \| 0.089957 / 0.176557 (-0.086600) \| 0.129305 / 0.737135 (-0.607831) \| 0.091006 / 0.296338 (-0.205333) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294091 / 0.215209 (0.078882) \| 2.885395 / 2.077655 (0.807741) \| 1.555658 / 1.504120 (0.051538) \| 1.423276 / 1.541195 (-0.117919) \| 1.476485 / 1.468490 (0.007995) \| 0.569507 / 4.584777 (-4.015270) \| 0.979221 / 3.745712 (-2.766491) \| 2.818503 / 5.269862 (-2.451358) \| 1.871938 / 4.565676 (-2.693739) \| 0.064342 / 0.424275 (-0.359933) \| 0.005495 / 0.007607 (-0.002112) \| 0.351451 / 0.226044 (0.125407) \| 3.516078 / 2.268929 (1.247149) \| 1.928351 / 55.444624 (-53.516273) \| 1.625362 / 6.876477 (-5.251115) \| 1.813756 / 2.142072 (-0.328317) \| 0.657642 / 4.805227 (-4.147585) \| 0.117893 / 6.500664 (-6.382771) \| 0.042009 / 0.075469 (-0.033460) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.032893 / 1.841788 (-0.808894) \| 12.983400 / 8.074308 (4.909092) \| 10.747204 / 10.191392 (0.555812) \| 0.133163 / 0.680424 (-0.547261) \| 0.015875 / 0.534201 (-0.518326) \| 0.312592 / 0.579283 (-0.266691) \| 0.124780 / 0.434364 (-0.309584) \| 0.350735 / 0.540337 (-0.189603) \| 0.447130 / 1.386936 (-0.939806) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#048c789607af0370c1f2337248897956f7a91617 \"CML watermark\")\n" ]	2024-05-27T08:45:07Z	2024-05-27T09:08:19Z	2024-05-27T08:59:21Z	MEMBER	null	null	null	Remove torchaudio remnants from code. Follow-up on: - #5573	{ "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/6922/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6922/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6922.diff", "html_url": "https://github.com/huggingface/datasets/pull/6922", "merged_at": "2024-05-27T08:59:21Z", "patch_url": "https://github.com/huggingface/datasets/pull/6922.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6922" }
https://api.github.com/repos/huggingface/datasets/issues/5070	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5070/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5070/comments	https://api.github.com/repos/huggingface/datasets/issues/5070/events	https://github.com/huggingface/datasets/issues/5070	1,396,765,647	I_kwDODunzps5TQPPP	5,070	Support default config name when no builder configs	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/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	[ "Thank you for creating this feature request, Albert.\r\n\r\nFor context this is the datatest where Albert has been helping me to switch to on-the-fly split config https://huggingface.co/datasets/HuggingFaceM4/cm4-synthetic-testing\r\n\r\nand the attempt to switch on-the-fly splits was here: https://huggingface.co/datasets/HuggingFaceM4/cm4-synthetic-testing/discussions/2/files\r\n\r\nbut which I had to revert since providing no split breaks at run time.\r\n" ]	2022-10-04T19:49:35Z	2022-10-06T14:40:26Z	2022-10-06T14:40:26Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	Is your feature request related to a problem? Please describe. As discussed with @stas00, we could support defining a default config name, even if no predefined allowed config names are set. That is, support `DEFAULT_CONFIG_NAME`, even when `BUILDER_CONFIGS` is not defined. Additional context In order to support creating configs on the fly by name (not using kwargs), the list of allowed builder configs `BUILDER_CONFIGS` must not be set. However, if so, then `DEFAULT_CONFIG_NAME` is not supported.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 1, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/5070/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5070/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6363	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6363/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6363/comments	https://api.github.com/repos/huggingface/datasets/issues/6363/events	https://github.com/huggingface/datasets/issues/6363	1,968,891,277	I_kwDODunzps51WuWN	6,363	dataset.transform() hangs indefinitely while finetuning the stable diffusion XL	{ "avatar_url": "https://avatars.githubusercontent.com/u/10846405?v=4", "events_url": "https://api.github.com/users/bhosalems/events{/privacy}", "followers_url": "https://api.github.com/users/bhosalems/followers", "following_url": "https://api.github.com/users/bhosalems/following{/other_user}", "gists_url": "https://api.github.com/users/bhosalems/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/bhosalems", "id": 10846405, "login": "bhosalems", "node_id": "MDQ6VXNlcjEwODQ2NDA1", "organizations_url": "https://api.github.com/users/bhosalems/orgs", "received_events_url": "https://api.github.com/users/bhosalems/received_events", "repos_url": "https://api.github.com/users/bhosalems/repos", "site_admin": false, "starred_url": "https://api.github.com/users/bhosalems/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/bhosalems/subscriptions", "type": "User", "url": "https://api.github.com/users/bhosalems", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "I think the code hangs on the `accelerator.main_process_first()` context manager exit. To verify this, you can append a print statement to the end of the `accelerator.main_process_first()` block. \r\n\r\n\r\nIf the problem is in `with_transform`, it would help if you could share the error stack trace printed when you interrupt the process (while it hangs)", "@bhosalems Were you able to fix that ? I face this issue as well", "@matankley No I am not able to resolve this issue yet.", "@mariosasko yes the problem seems to be to exit from accelerator.main_process_first(). Is there any known problem?", "NCCL debug info I get below output, if it helps.\r\n```\r\n11/09/2023 13:36:44 - INFO - __main__ - Distributed environment: MULTI_GPU Backend: nccl\r\nNum processes: 2\r\nProcess index: 1\r\nLocal process index: 1\r\nDevice: cuda:1\r\n\r\nMixed precision type: fp16\r\n\r\nDetected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\r\n11/09/2023 13:36:44 - INFO - __main__ - Distributed environment: MULTI_GPU Backend: nccl\r\nNum processes: 2\r\nProcess index: 0\r\nLocal process index: 0\r\nDevice: cuda:0\r\n\r\nMixed precision type: fp16\r\n\r\n{'timestep_spacing', 'thresholding', 'variance_type', 'clip_sample_range', 'prediction_type', 'dynamic_thresholding_ratio', 'sample_max_value'} was not found in config. Values will be initialized to default values.\r\n{'norm_num_groups', 'force_upcast'} was not found in config. Values will be initialized to default values.\r\n{'num_attention_heads', 'projection_class_embeddings_input_dim', 'addition_embed_type_num_heads', 'mid_block_only_cross_attention', 'addition_embed_type', 'num_class_embeds', 'upcast_attention', 'cross_attention_norm', 'addition_time_embed_dim', 'time_embedding_dim', 'class_embeddings_concat', 'encoder_hid_dim', 'encoder_hid_dim_type', 'resnet_out_scale_factor', 'attention_type', 'conv_out_kernel', 'only_cross_attention', 'resnet_time_scale_shift', 'resnet_skip_time_act', 'reverse_transformer_layers_per_block', 'conv_in_kernel', 'time_cond_proj_dim', 'use_linear_projection', 'mid_block_type', 'time_embedding_act_fn', 'dropout', 'timestep_post_act', 'dual_cross_attention', 'class_embed_type', 'transformer_layers_per_block', 'time_embedding_type'} was not found in config. Values will be initialized to default values.\r\n{'num_attention_heads', 'projection_class_embeddings_input_dim', 'addition_embed_type_num_heads', 'mid_block_only_cross_attention', 'addition_embed_type', 'num_class_embeds', 'upcast_attention', 'cross_attention_norm', 'addition_time_embed_dim', 'time_embedding_dim', 'class_embeddings_concat', 'encoder_hid_dim', 'encoder_hid_dim_type', 'resnet_out_scale_factor', 'attention_type', 'conv_out_kernel', 'only_cross_attention', 'resnet_time_scale_shift', 'resnet_skip_time_act', 'reverse_transformer_layers_per_block', 'conv_in_kernel', 'time_cond_proj_dim', 'use_linear_projection', 'mid_block_type', 'time_embedding_act_fn', 'dropout', 'timestep_post_act', 'dual_cross_attention', 'class_embed_type', 'transformer_layers_per_block', 'time_embedding_type'} was not found in config. Values will be initialized to default values.\r\ndeepbull5:1311249:1311249 [0] NCCL INFO Bootstrap : Using enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311249:1311249 [0] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation\r\ndeepbull5:1311249:1311249 [0] NCCL INFO cudaDriverVersion 11070\r\nNCCL version 2.14.3+cuda11.7\r\ndeepbull5:1311250:1311250 [1] NCCL INFO cudaDriverVersion 11070\r\ndeepbull5:1311249:1311365 [0] NCCL INFO NET/IB : No device found.\r\ndeepbull5:1311249:1311365 [0] NCCL INFO NET/Socket : Using [0]enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Using network Socket\r\ndeepbull5:1311250:1311250 [1] NCCL INFO Bootstrap : Using enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311250:1311250 [1] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation\r\ndeepbull5:1311250:1311366 [1] NCCL INFO NET/IB : No device found.\r\ndeepbull5:1311250:1311366 [1] NCCL INFO NET/Socket : Using [0]enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Using network Socket\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Setting affinity for GPU 1 to ff,ffff0000,00ffffff\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Setting affinity for GPU 0 to ff,ffff0000,00ffffff\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 00/04 : 0 1\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Trees [0] -1/-1/-1->1->0 [1] 0/-1/-1->1->-1 [2] -1/-1/-1->1->0 [3] 0/-1/-1->1->-1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 01/04 : 0 1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 02/04 : 0 1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 03/04 : 0 1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] -1/-1/-1->0->1 [2] 1/-1/-1->0->-1 [3] -1/-1/-1->0->1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 00/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 00/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 01/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 01/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 02/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 02/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 03/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 03/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Connected all rings\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Connected all trees\r\ndeepbull5:1311249:1311365 [0] NCCL INFO threadThresholds 8/8/64 \| 16/8/64 \| 512 \| 512\r\ndeepbull5:1311249:1311365 [0] NCCL INFO 4 coll channels, 4 p2p channels, 2 p2p channels per peer\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Connected all rings\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Connected all trees\r\ndeepbull5:1311250:1311366 [1] NCCL INFO threadThresholds 8/8/64 \| 16/8/64 \| 512 \| 512\r\ndeepbull5:1311250:1311366 [1] NCCL INFO 4 coll channels, 4 p2p channels, 2 p2p channels per peer\r\ndeepbull5:1311249:1311365 [0] NCCL INFO comm 0x88a84ee0 rank 0 nranks 2 cudaDev 0 busId 1000 - Init COMPLETE\r\ndeepbull5:1311250:1311366 [1] NCCL INFO comm 0x89a42f60 rank 1 nranks 2 cudaDev 1 busId 24000 - Init COMPLETE\r\n\r\n```", "Maybe @muellerzr can help as an `accelerate` maintainer.", "I don't know what the issue was, but after going through the thread here I loved the issue with https://github.com/huggingface/accelerate/issues/314#issuecomment-1565259831" ]	2023-10-30T17:34:05Z	2023-11-22T00:29:21Z	2023-11-22T00:29:21Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Multi-GPU fine-tuning the stable diffusion X by following https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/README_sdxl.md hangs indefinitely. ### Steps to reproduce the bug accelerate launch train_text_to_image_sdxl.py --pretrained_model_name_or_path=$MODEL_NAME --pretrained_vae_model_name_or_path=$VAE_NAME --dataset_name=$DATASET_NAME --enable_xformers_memory_efficient_attention --resolution=512 --center_crop --random_flip --proportion_empty_prompts=0.2 --train_batch_size=1 --gradient_accumulation_steps=4 --gradient_checkpointing --max_train_steps=10000 --use_8bit_adam --learning_rate=1e-06 --lr_scheduler="constant" --lr_warmup_steps=0 --mixed_precision="fp16" --report_to="wandb" --validation_prompt="a cute Sundar Pichai creature" --validation_epochs 5 --checkpointing_steps=5000 --output_dir="sdxl-pokemon-model" ### Expected behavior It should start the training as it does for the single GPU training. I opened the issue in diffusers **https://github.com/huggingface/diffusers/issues/5534 but it does seem to be an issue with the Pokemon dataset. I added some debug prints ``` print("==========HERE3=============") with accelerator.main_process_first(): print(accelerator.is_main_process) print("===========Here3.1===========") if args.max_train_samples is not None: dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples)) print("===========Here3.2===========") # Set the training transforms train_dataset = dataset["train"].with_transform(preprocess_train) print("==========HERE4=============") Corresponding Output Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. 10/25/2023 21:18:04 - INFO - main - Distributed environment: MULTI_GPU Backend: nccl Num processes: 3 Process index: 1 Local process index: 1 Device: cuda:1 Mixed precision type: fp16 10/25/2023 21:18:04 - INFO - main - Distributed environment: MULTI_GPU Backend: nccl Num processes: 3 Process index: 2 Local process index: 2 Device: cuda:2 Mixed precision type: fp16 10/25/2023 21:18:04 - INFO - main - Distributed environment: MULTI_GPU Backend: nccl Num processes: 3 Process index: 0 Local process index: 0 Device: cuda:0 Mixed precision type: fp16 You are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors. You are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors. {‘variance_type’, ‘clip_sample_range’, ‘thresholding’, ‘dynamic_thresholding_ratio’} was not found in config. Values will be initialized to default values. {‘attention_type’, ‘reverse_transformer_layers_per_block’, ‘dropout’} was not found in config. Values will be initialized to default values. ==========HERE1============= ==========HERE1============= ==========HERE1============= ==========HERE2============= ==========HERE2============= ==========HERE2============= ==========HERE3============= True ===========Here3.1=========== ===========Here3.2=========== ==========HERE3============= ==========HERE3========= ``` ### Environment info _libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_kmp_llvm conda-forge absl-py 2.0.0 pypi_0 pypi accelerate 0.24.0 pypi_0 pypi aiohttp 3.8.6 pypi_0 pypi aiosignal 1.3.1 pypi_0 pypi appdirs 1.4.4 pyh9f0ad1d_0 conda-forge async-timeout 4.0.3 pypi_0 pypi attrs 23.1.0 pypi_0 pypi bitsandbytes 0.41.1 pypi_0 pypi blas 1.0 mkl blessings 1.7 py39h06a4308_1002 brotli-python 1.0.9 py39h6a678d5_7 bzip2 1.0.8 h7b6447c_0 ca-certificates 2023.08.22 h06a4308_0 cachetools 5.3.2 pypi_0 pypi certifi 2023.7.22 py39h06a4308_0 cffi 1.15.1 py39h5eee18b_3 charset-normalizer 2.0.4 pyhd3eb1b0_0 click 8.1.7 unix_pyh707e725_0 conda-forge cryptography 41.0.3 py39hdda0065_0 cuda-cudart 11.7.99 0 nvidia cuda-cupti 11.7.101 0 nvidia cuda-libraries 11.7.1 0 nvidia cuda-nvrtc 11.7.99 0 nvidia cuda-nvtx 11.7.91 0 nvidia cuda-runtime 11.7.1 0 nvidia datasets 2.14.6 pypi_0 pypi diffusers 0.22.0.dev0 pypi_0 pypi dill 0.3.7 pypi_0 pypi docker-pycreds 0.4.0 py_0 conda-forge ffmpeg 4.3 hf484d3e_0 pytorch filelock 3.12.4 pypi_0 pypi freetype 2.12.1 h4a9f257_0 frozenlist 1.4.0 pypi_0 pypi fsspec 2023.10.0 pypi_0 pypi ftfy 6.1.1 pypi_0 pypi giflib 5.2.1 h5eee18b_3 gitdb 4.0.11 pyhd8ed1ab_0 conda-forge gitpython 3.1.40 pyhd8ed1ab_0 conda-forge gmp 6.2.1 h295c915_3 gnutls 3.6.15 he1e5248_0 google-auth 2.23.3 pypi_0 pypi google-auth-oauthlib 1.1.0 pypi_0 pypi gpustat 0.6.0 pyhd3eb1b0_1 grpcio 1.59.0 pypi_0 pypi huggingface-hub 0.17.3 pypi_0 pypi idna 3.4 py39h06a4308_0 importlib-metadata 6.8.0 pypi_0 pypi intel-openmp 2023.1.0 hdb19cb5_46305 jinja2 3.1.2 pypi_0 pypi jpeg 9e h5eee18b_1 lame 3.100 h7b6447c_0 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py39hdb19cb5_0 multidict 6.0.4 pypi_0 pypi multiprocess 0.70.15 pypi_0 pypi ncurses 6.4 h6a678d5_0 nettle 3.7.3 hbbd107a_1 numpy 1.26.0 py39h5f9d8c6_0 numpy-base 1.26.0 py39hb5e798b_0 nvidia-ml 7.352.0 pyhd3eb1b0_0 oauthlib 3.2.2 pypi_0 pypi openh264 2.1.1 h4ff587b_0 openjpeg 2.4.0 h3ad879b_0 openssl 3.0.11 h7f8727e_2 packaging 23.2 pypi_0 pypi pandas 2.1.1 pypi_0 pypi pathtools 0.1.2 py_1 conda-forge pillow 10.0.1 py39ha6cbd5a_0 pip 23.3 py39h06a4308_0 protobuf 4.23.4 pypi_0 pypi psutil 5.9.6 pypi_0 pypi pyarrow 13.0.0 pypi_0 pypi pyasn1 0.5.0 pypi_0 pypi pyasn1-modules 0.3.0 pypi_0 pypi pycparser 2.21 pyhd3eb1b0_0 pyopenssl 23.2.0 py39h06a4308_0 pysocks 1.7.1 py39h06a4308_0 python 3.9.18 h955ad1f_0 python-dateutil 2.8.2 pypi_0 pypi python_abi 3.9 2_cp39 conda-forge pytorch 1.13.1 py3.9_cuda11.7_cudnn8.5.0_0 pytorch pytorch-cuda 11.7 h778d358_5 pytorch pytorch-mutex 1.0 cuda pytorch pytz 2023.3.post1 pypi_0 pypi pyyaml 6.0.1 pypi_0 pypi readline 8.2 h5eee18b_0 regex 2023.10.3 pypi_0 pypi requests 2.31.0 py39h06a4308_0 requests-oauthlib 1.3.1 pypi_0 pypi rsa 4.9 pypi_0 pypi safetensors 0.4.0 pypi_0 pypi scipy 1.11.3 py39h5f9d8c6_0 sentry-sdk 1.32.0 pyhd8ed1ab_0 conda-forge setproctitle 1.1.10 py39h3811e60_1004 conda-forge setuptools 68.0.0 py39h06a4308_0 six 1.16.0 pyh6c4a22f_0 conda-forge smmap 5.0.0 pyhd8ed1ab_0 conda-forge sqlite 3.41.2 h5eee18b_0 tbb 2021.8.0 hdb19cb5_0 tensorboard 2.15.0 pypi_0 pypi tensorboard-data-server 0.7.2 pypi_0 pypi tk 8.6.12 h1ccaba5_0 tokenizers 0.14.1 pypi_0 pypi torchaudio 0.13.1 py39_cu117 pytorch torchtriton 2.1.0 py39 pytorch torchvision 0.14.1 py39_cu117 pytorch tqdm 4.66.1 pypi_0 pypi transformers 4.34.1 pypi_0 pypi typing_extensions 4.7.1 py39h06a4308_0 tzdata 2023.3 pypi_0 pypi urllib3 1.26.18 py39h06a4308_0 wandb 0.15.12 pyhd8ed1ab_0 conda-forge wcwidth 0.2.8 pypi_0 pypi werkzeug 3.0.1 pypi_0 pypi wheel 0.41.2 py39h06a4308_0 xformers 0.0.22.post7 py39_cu11.7.1_pyt1.13.1 xformers xxhash 3.4.1 pypi_0 pypi xz 5.4.2 h5eee18b_0 yaml 0.2.5 h7f98852_2 conda-forge yarl 1.9.2 pypi_0 pypi zipp 3.17.0 pypi_0 pypi zlib 1.2.13 h5eee18b_0 zstd 1.5.5 hc292b87_0	{ "avatar_url": "https://avatars.githubusercontent.com/u/10846405?v=4", "events_url": "https://api.github.com/users/bhosalems/events{/privacy}", "followers_url": "https://api.github.com/users/bhosalems/followers", "following_url": "https://api.github.com/users/bhosalems/following{/other_user}", "gists_url": "https://api.github.com/users/bhosalems/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/bhosalems", "id": 10846405, "login": "bhosalems", "node_id": "MDQ6VXNlcjEwODQ2NDA1", "organizations_url": "https://api.github.com/users/bhosalems/orgs", "received_events_url": "https://api.github.com/users/bhosalems/received_events", "repos_url": "https://api.github.com/users/bhosalems/repos", "site_admin": false, "starred_url": "https://api.github.com/users/bhosalems/starred{/owner}{/repo}", "subscriptions_url": 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https://api.github.com/repos/huggingface/datasets/issues/5279	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5279/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5279/comments	https://api.github.com/repos/huggingface/datasets/issues/5279/events	https://github.com/huggingface/datasets/pull/5279	1,459,635,002	PR_kwDODunzps5Dcoue	5,279	Warn about checksums	{ "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._", "I'm also in favor of disabling this by default - it's kinda impractical", "Great, thanks for the quick turnaround on this!" ]	2022-11-22T10:58:48Z	2022-11-23T11:43:50Z	2022-11-23T09:47:02Z	MEMBER	null	null	null	It takes a lot of time on big datasets to compute the checksums, we should at least add a warning to notify the user about this step. I also mentioned how to disable it, and added a tqdm bar (delay=5 seconds) cc @ola13	{ "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": 1, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/5279/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5279/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5279.diff", "html_url": "https://github.com/huggingface/datasets/pull/5279", "merged_at": "2022-11-23T09:47:01Z", "patch_url": "https://github.com/huggingface/datasets/pull/5279.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5279" }
https://api.github.com/repos/huggingface/datasets/issues/6039	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6039/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6039/comments	https://api.github.com/repos/huggingface/datasets/issues/6039/events	https://github.com/huggingface/datasets/issues/6039	1,806,508,451	I_kwDODunzps5rrSGj	6,039	Loading column subset from parquet file produces error since version 2.13	{ "avatar_url": "https://avatars.githubusercontent.com/u/1430243?v=4", "events_url": "https://api.github.com/users/kklemon/events{/privacy}", "followers_url": "https://api.github.com/users/kklemon/followers", "following_url": "https://api.github.com/users/kklemon/following{/other_user}", "gists_url": "https://api.github.com/users/kklemon/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/kklemon", "id": 1430243, "login": "kklemon", "node_id": "MDQ6VXNlcjE0MzAyNDM=", "organizations_url": "https://api.github.com/users/kklemon/orgs", "received_events_url": "https://api.github.com/users/kklemon/received_events", "repos_url": "https://api.github.com/users/kklemon/repos", "site_admin": false, "starred_url": "https://api.github.com/users/kklemon/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kklemon/subscriptions", "type": "User", "url": "https://api.github.com/users/kklemon", "user_view_type": "public" }	[]	closed	false	null	[]	null	[]	2023-07-16T09:13:07Z	2023-07-24T14:35:04Z	2023-07-24T14:35:04Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug `load_dataset` allows loading a subset of columns from a parquet file with the `columns` argument. Since version 2.13, this produces the following error: ``` Traceback (most recent call last): File "/usr/lib/python3.10/site-packages/datasets/builder.py", line 1879, in _prepare_split_single for _, table in generator: File "/usr/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 68, in _generate_tables raise ValueError( ValueError: Tried to load parquet data with columns '['sepal_length']' with mismatching features '{'sepal_length': Value(dtype='float64', id=None), 'sepal_width': Value(dtype='float64', id=None), 'petal_length': Value(dtype='float64', id=None), 'petal_width': Value(dtype='float64', id=None), 'species': Value(dtype='string', id=None)}' ``` This seems to occur because `datasets` is checking whether the columns in the schema exactly match the provided list of columns, instead of whether they are a subset. ### Steps to reproduce the bug ```python # Prepare some sample data import pandas as pd iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv') iris.to_parquet('iris.parquet') # ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species'] print(iris.columns) # Load data with datasets from datasets import load_dataset # Load full parquet file dataset = load_dataset('parquet', data_files='iris.parquet') # Load column subset; throws error for datasets>=2.13 dataset = load_dataset('parquet', data_files='iris.parquet', columns=['sepal_length']) ``` ### Expected behavior No error should be thrown and the given column subset should be loaded. ### Environment info - `datasets` version: 2.13.0 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35 - Python version: 3.10.9 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6039/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6039/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6212	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6212/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6212/comments	https://api.github.com/repos/huggingface/datasets/issues/6212/events	https://github.com/huggingface/datasets/issues/6212	1,880,399,516	I_kwDODunzps5wFJ6c	6,212	Tilde (~) is not supported for data_files	{ "avatar_url": "https://avatars.githubusercontent.com/u/128361578?v=4", "events_url": "https://api.github.com/users/exs-avianello/events{/privacy}", "followers_url": "https://api.github.com/users/exs-avianello/followers", "following_url": "https://api.github.com/users/exs-avianello/following{/other_user}", "gists_url": "https://api.github.com/users/exs-avianello/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/exs-avianello", "id": 128361578, "login": "exs-avianello", "node_id": "U_kgDOB6akag", "organizations_url": "https://api.github.com/users/exs-avianello/orgs", "received_events_url": "https://api.github.com/users/exs-avianello/received_events", "repos_url": "https://api.github.com/users/exs-avianello/repos", "site_admin": false, "starred_url": "https://api.github.com/users/exs-avianello/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/exs-avianello/subscriptions", "type": "User", "url": "https://api.github.com/users/exs-avianello", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Hi @exs-avianello, is it really needed? Note you can alternatively use `pathlib.Path` among others as it follows:\r\n\r\n```python\r\nimport datasets\r\nfrom pathlib import Path\r\n\r\n# save a parquet file at ~/path/to/data.parquet\r\n\r\ndata_files = Path.home() / \"path/to/data.parquet\"\r\ndataset = datasets.load_dataset(\"parquet\", data_files=data_files)\r\n```", "Hi @alvarobartt ! \r\n\r\nThis is definitely just a \"nice to have\" and I am personally more than happy to just use absolute paths client-side. I just wanted to flag it up in case it can help improve the package even more 🙌 It might not be immediately obvious from the stack trace that the error is triggered by the `~` in the path" ]	2023-09-04T14:23:49Z	2023-09-05T08:28:39Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Attempting to `load_dataset` from a path starting with `~` (as a shorthand for the user's home directory) seems not to be fully working - at least as far as the `parquet` dataset builder is concerned. (the same file can be loaded correctly if providing its absolute path instead) I think that this is very similar to https://github.com/huggingface/datasets/issues/5757, but for `data_files` rather than `data_dir` ### Steps to reproduce the bug ```python import datasets # save a parquet file at ~/path/to/data.parquet data_files = "~/path/to/data.parquet" dataset = datasets.load_dataset("parquet", data_files=data_files) ``` ``` Downloading data files: 100%\|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:00<00:00, 12671.61it/s] Extracting data files: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:00<00:00, 22671.91it/s] Generating train split: 0 examples [00:00, ? examples/s] Traceback (most recent call last): File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1949, in _prepare_split_single num_examples, num_bytes = writer.finalize() ^^^^^^^^^^^^^^^^^ File ".venv/lib/python3.11/site-packages/datasets/arrow_writer.py", line 598, in finalize raise SchemaInferenceError("Please pass `features` or at least one example when writing data") datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data The above exception was the direct cause of the following exception: Traceback (most recent call last): File ".venv/lib/python3.11/site-packages/datasets/load.py", line 2133, in load_dataset builder_instance.download_and_prepare( File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1813, in _prepare_split for job_id, done, content in self._prepare_split_single( File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1958, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior Can use `~` shorthand in paths when loading local (parquet) datasets. ### Environment info `datasets 2.14.3`	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/6212/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6212/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7520	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7520/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7520/comments	https://api.github.com/repos/huggingface/datasets/issues/7520/events	https://github.com/huggingface/datasets/issues/7520	2,997,422,044	I_kwDODunzps6yqQfc	7,520	Update items in the dataset without `map`	{ "avatar_url": "https://avatars.githubusercontent.com/u/122402293?v=4", "events_url": "https://api.github.com/users/mashdragon/events{/privacy}", "followers_url": "https://api.github.com/users/mashdragon/followers", "following_url": "https://api.github.com/users/mashdragon/following{/other_user}", "gists_url": "https://api.github.com/users/mashdragon/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mashdragon", "id": 122402293, "login": "mashdragon", "node_id": "U_kgDOB0u19Q", "organizations_url": "https://api.github.com/users/mashdragon/orgs", "received_events_url": "https://api.github.com/users/mashdragon/received_events", "repos_url": "https://api.github.com/users/mashdragon/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mashdragon/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mashdragon/subscriptions", "type": "User", "url": "https://api.github.com/users/mashdragon", "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	[ "Hello!\n\nHave you looked at `Dataset.shard`? [Docs](https://huggingface.co/docs/datasets/en/process#shard)\n\nUsing this method you could break your dataset in N shards. Apply `map` on each shard and concatenate them back." ]	2025-04-15T19:39:01Z	2025-04-19T18:47:46Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request I would like to be able to update items in my dataset without affecting all rows. At least if there was a range option, I would be able to process those items, save the dataset, and then continue. If I am supposed to split the dataset first, that is not clear, since the docs suggest that any of those functions returns a new object, so I don't think I can do that. ### Motivation I am applying an extremely time-consuming function to each item in my `Dataset`. Unfortunately, datasets only supports updating values via `map`, so if my computer dies in the middle of this long-running process, I lose all progress. This is far from ideal. I would like to use `datasets` throughout this processing, but this limitation is now forcing me to write my own dataset format just to do this intermediary operation. It would be less intuitive but I suppose I could split and then concatenate the dataset before saving? But this feels very inefficient. ### Your contribution I can test the 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/7520/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7520/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6825	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6825/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6825/comments	https://api.github.com/repos/huggingface/datasets/issues/6825/events	https://github.com/huggingface/datasets/pull/6825	2,252,404,599	PR_kwDODunzps5tJEMw	6,825	Release: 2.19.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6825). 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) \| 0.004945 / 0.011353 (-0.006407) \| 0.003290 / 0.011008 (-0.007718) \| 0.062404 / 0.038508 (0.023896) \| 0.040056 / 0.023109 (0.016946) \| 0.246574 / 0.275898 (-0.029324) \| 0.275074 / 0.323480 (-0.048406) \| 0.004118 / 0.007986 (-0.003867) \| 0.002604 / 0.004328 (-0.001724) \| 0.048618 / 0.004250 (0.044367) \| 0.044088 / 0.037052 (0.007035) \| 0.263059 / 0.258489 (0.004570) \| 0.294602 / 0.293841 (0.000761) \| 0.027425 / 0.128546 (-0.101121) \| 0.010263 / 0.075646 (-0.065383) \| 0.205925 / 0.419271 (-0.213346) \| 0.048917 / 0.043533 (0.005384) \| 0.264227 / 0.255139 (0.009088) \| 0.273339 / 0.283200 (-0.009860) \| 0.017783 / 0.141683 (-0.123900) \| 1.137526 / 1.452155 (-0.314629) \| 1.179551 / 1.492716 (-0.313165) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096809 / 0.018006 (0.078802) \| 0.303854 / 0.000490 (0.303364) \| 0.000207 / 0.000200 (0.000007) \| 0.000042 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.017756 / 0.037411 (-0.019655) \| 0.061005 / 0.014526 (0.046479) \| 0.072986 / 0.176557 (-0.103571) \| 0.119851 / 0.737135 (-0.617284) \| 0.074733 / 0.296338 (-0.221605) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278270 / 0.215209 (0.063061) \| 2.737874 / 2.077655 (0.660219) \| 1.460658 / 1.504120 (-0.043462) \| 1.337695 / 1.541195 (-0.203499) \| 1.364376 / 1.468490 (-0.104114) \| 0.565622 / 4.584777 (-4.019155) \| 2.365167 / 3.745712 (-1.380546) \| 2.694544 / 5.269862 (-2.575317) \| 1.699689 / 4.565676 (-2.865987) \| 0.062564 / 0.424275 (-0.361712) \| 0.005296 / 0.007607 (-0.002311) \| 0.340122 / 0.226044 (0.114077) \| 3.382133 / 2.268929 (1.113204) \| 1.816907 / 55.444624 (-53.627718) \| 1.530825 / 6.876477 (-5.345652) \| 1.533266 / 2.142072 (-0.608807) \| 0.638215 / 4.805227 (-4.167012) \| 0.116227 / 6.500664 (-6.384437) \| 0.041548 / 0.075469 (-0.033921) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971031 / 1.841788 (-0.870757) \| 11.117905 / 8.074308 (3.043597) \| 9.358159 / 10.191392 (-0.833233) \| 0.127954 / 0.680424 (-0.552470) \| 0.013634 / 0.534201 (-0.520567) \| 0.285399 / 0.579283 (-0.293885) \| 0.267980 / 0.434364 (-0.166383) \| 0.320219 / 0.540337 (-0.220119) \| 0.416035 / 1.386936 (-0.970901) \|\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) \| 0.005177 / 0.011353 (-0.006176) \| 0.003078 / 0.011008 (-0.007930) \| 0.049650 / 0.038508 (0.011142) \| 0.030897 / 0.023109 (0.007787) \| 0.271186 / 0.275898 (-0.004712) \| 0.296050 / 0.323480 (-0.027430) \| 0.004204 / 0.007986 (-0.003781) \| 0.002755 / 0.004328 (-0.001574) \| 0.049550 / 0.004250 (0.045300) \| 0.039801 / 0.037052 (0.002749) \| 0.283243 / 0.258489 (0.024753) \| 0.310932 / 0.293841 (0.017091) \| 0.029136 / 0.128546 (-0.099410) \| 0.010278 / 0.075646 (-0.065368) \| 0.059300 / 0.419271 (-0.359971) \| 0.032965 / 0.043533 (-0.010568) \| 0.272646 / 0.255139 (0.017507) \| 0.293697 / 0.283200 (0.010497) \| 0.018330 / 0.141683 (-0.123353) \| 1.144251 / 1.452155 (-0.307904) \| 1.209660 / 1.492716 (-0.283056) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091020 / 0.018006 (0.073014) \| 0.298294 / 0.000490 (0.297804) \| 0.000214 / 0.000200 (0.000014) \| 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) \| 0.021879 / 0.037411 (-0.015532) \| 0.074728 / 0.014526 (0.060202) \| 0.085499 / 0.176557 (-0.091057) \| 0.125743 / 0.737135 (-0.611392) \| 0.086130 / 0.296338 (-0.210208) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292311 / 0.215209 (0.077102) \| 2.861240 / 2.077655 (0.783585) \| 1.590426 / 1.504120 (0.086306) \| 1.472288 / 1.541195 (-0.068907) \| 1.472901 / 1.468490 (0.004411) \| 0.574924 / 4.584777 (-4.009853) \| 2.450817 / 3.745712 (-1.294895) \| 2.781903 / 5.269862 (-2.487959) \| 1.747110 / 4.565676 (-2.818566) \| 0.064680 / 0.424275 (-0.359595) \| 0.005376 / 0.007607 (-0.002231) \| 0.356846 / 0.226044 (0.130802) \| 3.457851 / 2.268929 (1.188922) \| 1.952678 / 55.444624 (-53.491946) \| 1.670824 / 6.876477 (-5.205653) \| 1.655872 / 2.142072 (-0.486200) \| 0.655874 / 4.805227 (-4.149353) \| 0.117098 / 6.500664 (-6.383566) \| 0.040230 / 0.075469 (-0.035239) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.007423 / 1.841788 (-0.834365) \| 11.818228 / 8.074308 (3.743920) \| 10.153699 / 10.191392 (-0.037693) \| 0.132073 / 0.680424 (-0.548351) \| 0.015101 / 0.534201 (-0.519100) \| 0.286555 / 0.579283 (-0.292728) \| 0.281953 / 0.434364 (-0.152411) \| 0.323647 / 0.540337 (-0.216691) \| 0.418698 / 1.386936 (-0.968238) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0d3c7462bc67407c42d3ad102b7f9d5914219d9d \"CML watermark\")\n" ]	2024-04-19T08:29:02Z	2024-05-04T12:23:26Z	2024-04-19T08:44:57Z	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": 1, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6825/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6825/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6825.diff", "html_url": "https://github.com/huggingface/datasets/pull/6825", "merged_at": "2024-04-19T08:44:57Z", "patch_url": "https://github.com/huggingface/datasets/pull/6825.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6825" }
https://api.github.com/repos/huggingface/datasets/issues/7105	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7105/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7105/comments	https://api.github.com/repos/huggingface/datasets/issues/7105/events	https://github.com/huggingface/datasets/pull/7105	2,468,207,039	PR_kwDODunzps54eZ0D	7,105	Use `huggingface_hub` cache	{ "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_7105). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Nice\r\n\r\n<img width=\"141\" alt=\"Capture d’écran 2024-08-19 à 15 25 00\" src=\"https://github.com/user-attachments/assets/18c7b3ec-a57e-45d7-9b19-0b12df9feccd\">\r\n", "fyi the CI failure on test_py310_numpy2 is unrelated to this PR (it's a dependency install failure)", "<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) \| 0.005677 / 0.011353 (-0.005676) \| 0.004054 / 0.011008 (-0.006954) \| 0.063101 / 0.038508 (0.024592) \| 0.031665 / 0.023109 (0.008556) \| 0.243332 / 0.275898 (-0.032566) \| 0.271067 / 0.323480 (-0.052413) \| 0.004283 / 0.007986 (-0.003703) \| 0.002889 / 0.004328 (-0.001440) \| 0.049269 / 0.004250 (0.045018) \| 0.048707 / 0.037052 (0.011654) \| 0.258599 / 0.258489 (0.000110) \| 0.307715 / 0.293841 (0.013874) \| 0.029850 / 0.128546 (-0.098696) \| 0.012299 / 0.075646 (-0.063347) \| 0.207616 / 0.419271 (-0.211656) \| 0.037655 / 0.043533 (-0.005878) \| 0.246602 / 0.255139 (-0.008537) \| 0.268518 / 0.283200 (-0.014682) \| 0.018128 / 0.141683 (-0.123555) \| 1.181569 / 1.452155 (-0.270586) \| 1.250641 / 1.492716 (-0.242075) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.143911 / 0.018006 (0.125905) \| 0.305608 / 0.000490 (0.305118) \| 0.000250 / 0.000200 (0.000050) \| 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) \| 0.019208 / 0.037411 (-0.018204) \| 0.062502 / 0.014526 (0.047976) \| 0.075896 / 0.176557 (-0.100661) \| 0.123422 / 0.737135 (-0.613713) \| 0.077311 / 0.296338 (-0.219028) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283108 / 0.215209 (0.067899) \| 2.783509 / 2.077655 (0.705855) \| 1.466358 / 1.504120 (-0.037762) \| 1.350989 / 1.541195 (-0.190206) \| 1.370517 / 1.468490 (-0.097973) \| 0.732706 / 4.584777 (-3.852071) \| 2.366710 / 3.745712 (-1.379002) \| 2.988913 / 5.269862 (-2.280949) \| 1.892204 / 4.565676 (-2.673473) \| 0.079077 / 0.424275 (-0.345198) \| 0.005158 / 0.007607 (-0.002449) \| 0.336620 / 0.226044 (0.110576) \| 3.423556 / 2.268929 (1.154628) \| 1.848732 / 55.444624 (-53.595892) \| 1.544996 / 6.876477 (-5.331480) \| 1.550051 / 2.142072 (-0.592022) \| 0.798235 / 4.805227 (-4.006993) \| 0.132945 / 6.500664 (-6.367719) \| 0.041785 / 0.075469 (-0.033684) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963359 / 1.841788 (-0.878429) \| 11.699994 / 8.074308 (3.625686) \| 9.311998 / 10.191392 (-0.879394) \| 0.140493 / 0.680424 (-0.539931) \| 0.013834 / 0.534201 (-0.520367) \| 0.302569 / 0.579283 (-0.276714) \| 0.267377 / 0.434364 (-0.166987) \| 0.341093 / 0.540337 (-0.199244) \| 0.431941 / 1.386936 (-0.954995) \|\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) \| 0.005744 / 0.011353 (-0.005608) \| 0.003668 / 0.011008 (-0.007340) \| 0.049837 / 0.038508 (0.011329) \| 0.032051 / 0.023109 (0.008941) \| 0.271725 / 0.275898 (-0.004173) \| 0.302612 / 0.323480 (-0.020867) \| 0.004455 / 0.007986 (-0.003531) \| 0.002816 / 0.004328 (-0.001512) \| 0.049036 / 0.004250 (0.044785) \| 0.041233 / 0.037052 (0.004181) \| 0.287900 / 0.258489 (0.029411) \| 0.326204 / 0.293841 (0.032363) \| 0.032027 / 0.128546 (-0.096519) \| 0.012033 / 0.075646 (-0.063613) \| 0.060822 / 0.419271 (-0.358449) \| 0.033830 / 0.043533 (-0.009703) \| 0.274855 / 0.255139 (0.019716) \| 0.294191 / 0.283200 (0.010992) \| 0.017979 / 0.141683 (-0.123704) \| 1.151353 / 1.452155 (-0.300801) \| 1.215384 / 1.492716 (-0.277333) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.102552 / 0.018006 (0.084546) \| 0.314148 / 0.000490 (0.313658) \| 0.000217 / 0.000200 (0.000017) \| 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) \| 0.024565 / 0.037411 (-0.012846) \| 0.076968 / 0.014526 (0.062442) \| 0.087982 / 0.176557 (-0.088574) \| 0.129844 / 0.737135 (-0.607292) \| 0.091370 / 0.296338 (-0.204968) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296767 / 0.215209 (0.081558) \| 2.910716 / 2.077655 (0.833062) \| 1.579526 / 1.504120 (0.075406) \| 1.453457 / 1.541195 (-0.087737) \| 1.466296 / 1.468490 (-0.002194) \| 0.728372 / 4.584777 (-3.856405) \| 0.963852 / 3.745712 (-2.781861) \| 2.946582 / 5.269862 (-2.323280) \| 1.936199 / 4.565676 (-2.629478) \| 0.078886 / 0.424275 (-0.345389) \| 0.005537 / 0.007607 (-0.002071) \| 0.346315 / 0.226044 (0.120270) \| 3.440774 / 2.268929 (1.171845) \| 1.937549 / 55.444624 (-53.507076) \| 1.649507 / 6.876477 (-5.226970) \| 1.653386 / 2.142072 (-0.488686) \| 0.806598 / 4.805227 (-3.998629) \| 0.133384 / 6.500664 (-6.367280) \| 0.040552 / 0.075469 (-0.034917) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.030515 / 1.841788 (-0.811272) \| 12.129888 / 8.074308 (4.055580) \| 10.287069 / 10.191392 (0.095677) \| 0.141512 / 0.680424 (-0.538912) \| 0.015483 / 0.534201 (-0.518718) \| 0.300053 / 0.579283 (-0.279230) \| 0.120825 / 0.434364 (-0.313539) \| 0.342681 / 0.540337 (-0.197656) \| 0.470616 / 1.386936 (-0.916320) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#28780197dd3e4c125defae29ac8ef5346c41350a \"CML watermark\")\n", "yay! is this in a shipped release?", "we can do one in the coming days once @albertvillanova is back", "We have made a release and this feature is now included." ]	2024-08-15T14:45:22Z	2024-09-12T04:36:08Z	2024-08-21T15:47:16Z	MEMBER	null	null	null	- use `hf_hub_download()` from `huggingface_hub` for HF files - `datasets` cache_dir is still used for: - caching datasets as Arrow files (that back `Dataset` objects) - extracted archives, uncompressed files - files downloaded via http (datasets with scripts) - I removed code that were made for http files (and also the dummy_data / mock_download_manager stuff that happened to rely on them and have been legacy for a while now)	{ "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": 2, "heart": 0, "hooray": 2, "laugh": 0, "rocket": 0, "total_count": 4, "url": "https://api.github.com/repos/huggingface/datasets/issues/7105/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7105/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7105.diff", "html_url": "https://github.com/huggingface/datasets/pull/7105", "merged_at": "2024-08-21T15:47:15Z", "patch_url": "https://github.com/huggingface/datasets/pull/7105.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7105" }
https://api.github.com/repos/huggingface/datasets/issues/7183	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7183/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7183/comments	https://api.github.com/repos/huggingface/datasets/issues/7183/events	https://github.com/huggingface/datasets/issues/7183	2,556,789,055	I_kwDODunzps6YZYE_	7,183	CI is broken for deps-latest	{ "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	[]	2024-09-30T14:02:07Z	2024-09-30T14:38:58Z	2024-09-30T14:38:58Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	See: https://github.com/huggingface/datasets/actions/runs/11106149906/job/30853879890 ``` =========================== short test summary info ============================ FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_filter_caching_on_disk - AssertionError: Lists differ: [{'fi[44 chars] {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}] != [{'fi[44 chars] {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}] First differing element 1: {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'} {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'} [{'filename': '/tmp/tmp6xcyyjs4/dataset0.arrow'}, - {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}] ? ^^^^^ -------- + {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}] ? ++++++++++ ^^ + FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_on_disk - AssertionError: Lists differ: [{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}] != [{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}] First differing element 0: {'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'} {'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'} - [{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}] ? ^^ ----------- + [{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}] ? +++++++++++ ^^ FAILED tests/test_fingerprint.py::TokenizersHashTest::test_hash_regex - NameError: name 'log' is not defined FAILED tests/test_fingerprint.py::RecurseHashTest::test_hash_ignores_line_definition_of_function - AssertionError: '52e56ee04ad92499' != '0a4f75cec280f634' - 52e56ee04ad92499 + 0a4f75cec280f634 FAILED tests/test_fingerprint.py::RecurseHashTest::test_hash_ipython_function - AssertionError: 'a6bd2041ca63d6c0' != '517bf36b7eecdef5' - a6bd2041ca63d6c0 + 517bf36b7eecdef5 FAILED tests/test_fingerprint.py::HashingTest::test_hash_tiktoken_encoding - NameError: name 'log' is not defined FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_compiled_module - NameError: name 'log' is not defined FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_generator - NameError: name 'log' is not defined FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_tensor - NameError: name 'log' is not defined FAILED tests/test_fingerprint.py::HashingTest::test_set_doesnt_depend_on_order - NameError: name 'log' is not defined FAILED tests/test_fingerprint.py::HashingTest::test_set_stable - NameError: name 'log' is not defined ERROR tests/test_iterable_dataset.py::test_iterable_dataset_from_file - NameError: name 'log' is not defined = 11 failed, 2850 passed, 3 skipped, 23 warnings, 1 error in 191.06s (0:03:11) = ```	{ "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/7183/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7183/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6652	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6652/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6652/comments	https://api.github.com/repos/huggingface/datasets/issues/6652/events	https://github.com/huggingface/datasets/pull/6652	2,126,760,798	PR_kwDODunzps5mdgcv	6,652	Release: 2.17.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6652). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005207 / 0.011353 (-0.006145) \| 0.003785 / 0.011008 (-0.007223) \| 0.064221 / 0.038508 (0.025713) \| 0.028981 / 0.023109 (0.005872) \| 0.246215 / 0.275898 (-0.029683) \| 0.268058 / 0.323480 (-0.055422) \| 0.004028 / 0.007986 (-0.003958) \| 0.002804 / 0.004328 (-0.001525) \| 0.048878 / 0.004250 (0.044627) \| 0.042641 / 0.037052 (0.005589) \| 0.255590 / 0.258489 (-0.002899) \| 0.287377 / 0.293841 (-0.006464) \| 0.027772 / 0.128546 (-0.100774) \| 0.010637 / 0.075646 (-0.065009) \| 0.211526 / 0.419271 (-0.207746) \| 0.035789 / 0.043533 (-0.007744) \| 0.243042 / 0.255139 (-0.012097) \| 0.268369 / 0.283200 (-0.014830) \| 0.017907 / 0.141683 (-0.123776) \| 1.138829 / 1.452155 (-0.313326) \| 1.175732 / 1.492716 (-0.316984) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094205 / 0.018006 (0.076199) \| 0.304317 / 0.000490 (0.303827) \| 0.000206 / 0.000200 (0.000006) \| 0.000049 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018424 / 0.037411 (-0.018987) \| 0.061719 / 0.014526 (0.047193) \| 0.073471 / 0.176557 (-0.103085) \| 0.121577 / 0.737135 (-0.615558) \| 0.075134 / 0.296338 (-0.221204) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275178 / 0.215209 (0.059969) \| 2.689222 / 2.077655 (0.611568) \| 1.396680 / 1.504120 (-0.107439) \| 1.278782 / 1.541195 (-0.262413) \| 1.326632 / 1.468490 (-0.141858) \| 0.566915 / 4.584777 (-4.017862) \| 2.365928 / 3.745712 (-1.379784) \| 2.785435 / 5.269862 (-2.484427) \| 1.745131 / 4.565676 (-2.820546) \| 0.062798 / 0.424275 (-0.361477) \| 0.005107 / 0.007607 (-0.002500) \| 0.330441 / 0.226044 (0.104396) \| 3.266265 / 2.268929 (0.997337) \| 1.792588 / 55.444624 (-53.652036) \| 1.516021 / 6.876477 (-5.360455) \| 1.562750 / 2.142072 (-0.579323) \| 0.652964 / 4.805227 (-4.152264) \| 0.117813 / 6.500664 (-6.382852) \| 0.042372 / 0.075469 (-0.033097) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.010107 / 1.841788 (-0.831680) \| 11.819910 / 8.074308 (3.745602) \| 9.701673 / 10.191392 (-0.489719) \| 0.178165 / 0.680424 (-0.502259) \| 0.014438 / 0.534201 (-0.519763) \| 0.297733 / 0.579283 (-0.281550) \| 0.264914 / 0.434364 (-0.169450) \| 0.324531 / 0.540337 (-0.215806) \| 0.430207 / 1.386936 (-0.956729) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005848 / 0.011353 (-0.005505) \| 0.003870 / 0.011008 (-0.007138) \| 0.050379 / 0.038508 (0.011871) \| 0.031238 / 0.023109 (0.008129) \| 0.276839 / 0.275898 (0.000941) \| 0.299488 / 0.323480 (-0.023992) \| 0.005143 / 0.007986 (-0.002842) \| 0.002725 / 0.004328 (-0.001604) \| 0.048184 / 0.004250 (0.043934) \| 0.046232 / 0.037052 (0.009180) \| 0.287058 / 0.258489 (0.028569) \| 0.322659 / 0.293841 (0.028818) \| 0.047598 / 0.128546 (-0.080949) \| 0.011116 / 0.075646 (-0.064530) \| 0.058252 / 0.419271 (-0.361019) \| 0.033404 / 0.043533 (-0.010128) \| 0.277650 / 0.255139 (0.022511) \| 0.295610 / 0.283200 (0.012410) \| 0.018124 / 0.141683 (-0.123559) \| 1.135052 / 1.452155 (-0.317103) \| 1.194261 / 1.492716 (-0.298456) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095595 / 0.018006 (0.077588) \| 0.306408 / 0.000490 (0.305918) \| 0.000216 / 0.000200 (0.000016) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022027 / 0.037411 (-0.015385) \| 0.076224 / 0.014526 (0.061698) \| 0.087441 / 0.176557 (-0.089116) \| 0.126636 / 0.737135 (-0.610499) \| 0.089442 / 0.296338 (-0.206896) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291315 / 0.215209 (0.076106) \| 2.835304 / 2.077655 (0.757650) \| 1.581102 / 1.504120 (0.076982) \| 1.463046 / 1.541195 (-0.078149) \| 1.481982 / 1.468490 (0.013492) \| 0.559989 / 4.584777 (-4.024788) \| 2.385262 / 3.745712 (-1.360450) \| 2.773478 / 5.269862 (-2.496383) \| 1.744427 / 4.565676 (-2.821249) \| 0.062687 / 0.424275 (-0.361589) \| 0.005149 / 0.007607 (-0.002458) \| 0.374600 / 0.226044 (0.148555) \| 3.376507 / 2.268929 (1.107579) \| 1.935290 / 55.444624 (-53.509334) \| 1.663227 / 6.876477 (-5.213250) \| 1.678987 / 2.142072 (-0.463085) \| 0.638970 / 4.805227 (-4.166258) \| 0.120000 / 6.500664 (-6.380664) \| 0.040862 / 0.075469 (-0.034608) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.008795 / 1.841788 (-0.832993) \| 12.275084 / 8.074308 (4.200776) \| 10.340088 / 10.191392 (0.148696) \| 0.136454 / 0.680424 (-0.543970) \| 0.014404 / 0.534201 (-0.519797) \| 0.289478 / 0.579283 (-0.289805) \| 0.279243 / 0.434364 (-0.155121) \| 0.330992 / 0.540337 (-0.209346) \| 0.422043 / 1.386936 (-0.964893) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#70633576ecf1f3f5e5cdfd8c9189246b3604f4b6 \"CML watermark\")\n" ]	2024-02-09T09:25:01Z	2024-02-09T10:11:48Z	2024-02-09T10:05:35Z	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/6652/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6652/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6652.diff", "html_url": "https://github.com/huggingface/datasets/pull/6652", "merged_at": "2024-02-09T10:05:35Z", "patch_url": "https://github.com/huggingface/datasets/pull/6652.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6652" }
https://api.github.com/repos/huggingface/datasets/issues/7380	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7380/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7380/comments	https://api.github.com/repos/huggingface/datasets/issues/7380/events	https://github.com/huggingface/datasets/pull/7380	2,811,566,116	PR_kwDODunzps6JAkj5	7,380	fix: dill default for version bigger 0.3.8	{ "avatar_url": "https://avatars.githubusercontent.com/u/40773225?v=4", "events_url": "https://api.github.com/users/sam-hey/events{/privacy}", "followers_url": "https://api.github.com/users/sam-hey/followers", "following_url": "https://api.github.com/users/sam-hey/following{/other_user}", "gists_url": "https://api.github.com/users/sam-hey/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sam-hey", "id": 40773225, "login": "sam-hey", "node_id": "MDQ6VXNlcjQwNzczMjI1", "organizations_url": "https://api.github.com/users/sam-hey/orgs", "received_events_url": "https://api.github.com/users/sam-hey/received_events", "repos_url": "https://api.github.com/users/sam-hey/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sam-hey/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sam-hey/subscriptions", "type": "User", "url": "https://api.github.com/users/sam-hey", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "`datasets` doesn't support `dill` 0.3.9 yet afaik since `dill` made some changes related to the determinism of dumps\r\n\r\nIt would be cool to investigate (maybe run the `datasets` test) with recent `dill` to see excactly what breaks and if we can make `dill` 0.3.9 work with `datasets`" ]	2025-01-26T13:37:16Z	2025-03-13T20:40:19Z	2025-03-13T20:40:19Z	NONE	null	null	null	Fixes def log for dill version >= 0.3.9 https://pypi.org/project/dill/ This project uses dill with the release of version 0.3.9 the datasets lib.	{ "avatar_url": "https://avatars.githubusercontent.com/u/40773225?v=4", "events_url": "https://api.github.com/users/sam-hey/events{/privacy}", "followers_url": "https://api.github.com/users/sam-hey/followers", "following_url": "https://api.github.com/users/sam-hey/following{/other_user}", "gists_url": "https://api.github.com/users/sam-hey/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sam-hey", "id": 40773225, "login": "sam-hey", "node_id": "MDQ6VXNlcjQwNzczMjI1", "organizations_url": "https://api.github.com/users/sam-hey/orgs", "received_events_url": "https://api.github.com/users/sam-hey/received_events", "repos_url": "https://api.github.com/users/sam-hey/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sam-hey/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sam-hey/subscriptions", "type": "User", "url": "https://api.github.com/users/sam-hey", "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/7380/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7380/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7380.diff", "html_url": "https://github.com/huggingface/datasets/pull/7380", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/7380.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7380" }
https://api.github.com/repos/huggingface/datasets/issues/6151	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6151/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6151/comments	https://api.github.com/repos/huggingface/datasets/issues/6151/events	https://github.com/huggingface/datasets/issues/6151	1,851,497,818	I_kwDODunzps5uW51a	6,151	Faster sorting for single key items	{ "avatar_url": "https://avatars.githubusercontent.com/u/47942453?v=4", "events_url": "https://api.github.com/users/jackapbutler/events{/privacy}", "followers_url": "https://api.github.com/users/jackapbutler/followers", "following_url": "https://api.github.com/users/jackapbutler/following{/other_user}", "gists_url": "https://api.github.com/users/jackapbutler/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jackapbutler", "id": 47942453, "login": "jackapbutler", "node_id": "MDQ6VXNlcjQ3OTQyNDUz", "organizations_url": "https://api.github.com/users/jackapbutler/orgs", "received_events_url": "https://api.github.com/users/jackapbutler/received_events", "repos_url": "https://api.github.com/users/jackapbutler/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jackapbutler/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jackapbutler/subscriptions", "type": "User", "url": "https://api.github.com/users/jackapbutler", "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	[ "`Dataset.sort` essentially does the same thing except it uses `pyarrow.compute.sort_indices` which doesn't involve copying the data into python objects (saving memory)\r\n\r\n```python\r\nsort_keys = [(col, \"ascending\") for col in column_names]\r\nindices = pc.sort_indices(self.data, sort_keys=sort_keys)\r\nreturn self.select(indices)\r\n```", "Ok interesting, I'll continue debugging to see what is going wrong on my end." ]	2023-08-15T14:02:31Z	2023-08-21T14:38:26Z	2023-08-21T14:38:25Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request A faster way to sort a dataset which contains a large number of rows. ### Motivation The current sorting implementations took significantly longer than expected when I was running on a dataset trying to sort by timestamps. Code snippet: ```python ds = datasets.load_dataset( "json", **{"data_files": {"train": "path-to-jsonlines"}, "split": "train"}, num_proc=os.cpu_count(), keep_in_memory=True) sorted_ds = ds.sort("pubDate", keep_in_memory=True) ``` However, once I switched to a different method which 1. unpacked to a list of tuples 2. sorted tuples by key 3. run `.select` with the sorted list of indices It was significantly faster (orders of magnitude, especially with M's of rows) ### Your contribution I'd be happy to implement a crude single key sorting algorithm so that other users can benefit from this trick. Broadly, this would take a `Dataset` and perform; ```python # ds is a Dataset object # key_name is the sorting key class Dataset: ... def _sort(key_name: str) -> Dataset: index_keys = [(i,x) for i,x in enumerate(self[key_name])] sorted_rows = sorted(row_pubdate, key=lambda x: x[1]) sorted_indicies = [x[0] for x in sorted_rows] return self.select(sorted_indicies) ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/47942453?v=4", "events_url": "https://api.github.com/users/jackapbutler/events{/privacy}", "followers_url": "https://api.github.com/users/jackapbutler/followers", "following_url": "https://api.github.com/users/jackapbutler/following{/other_user}", "gists_url": "https://api.github.com/users/jackapbutler/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jackapbutler", "id": 47942453, "login": "jackapbutler", "node_id": "MDQ6VXNlcjQ3OTQyNDUz", "organizations_url": "https://api.github.com/users/jackapbutler/orgs", "received_events_url": "https://api.github.com/users/jackapbutler/received_events", "repos_url": "https://api.github.com/users/jackapbutler/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jackapbutler/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jackapbutler/subscriptions", "type": "User", "url": "https://api.github.com/users/jackapbutler", "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/6151/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6151/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/4561	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4561/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4561/comments	https://api.github.com/repos/huggingface/datasets/issues/4561/events	https://github.com/huggingface/datasets/pull/4561	1,283,624,242	PR_kwDODunzps46TnVe	4,561	Add evaluation data to acronym_identification	{ "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" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-06-24T11:17:33Z	2022-06-27T09:37:55Z	2022-06-27T08:49:22Z	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/4561/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4561/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4561.diff", "html_url": "https://github.com/huggingface/datasets/pull/4561", "merged_at": "2022-06-27T08:49:22Z", "patch_url": "https://github.com/huggingface/datasets/pull/4561.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4561" }
https://api.github.com/repos/huggingface/datasets/issues/5355	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5355/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5355/comments	https://api.github.com/repos/huggingface/datasets/issues/5355/events	https://github.com/huggingface/datasets/pull/5355	1,493,076,860	PR_kwDODunzps5FQcYG	5,355	Clean up Table class docstrings	{ "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._" ]	2022-12-13T00:29:47Z	2022-12-13T18:17:56Z	2022-12-13T18:14:42Z	MEMBER	null	null	null	This PR cleans up the `Table` class docstrings :)	{ "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/5355/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5355/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5355.diff", "html_url": "https://github.com/huggingface/datasets/pull/5355", "merged_at": "2022-12-13T18:14:42Z", "patch_url": "https://github.com/huggingface/datasets/pull/5355.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5355" }
https://api.github.com/repos/huggingface/datasets/issues/7468	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7468/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7468/comments	https://api.github.com/repos/huggingface/datasets/issues/7468/events	https://github.com/huggingface/datasets/issues/7468	2,934,094,103	I_kwDODunzps6u4rkX	7,468	function `load_dataset` can't solve folder path with regex characters like "[]"	{ "avatar_url": "https://avatars.githubusercontent.com/u/89294013?v=4", "events_url": "https://api.github.com/users/Hpeox/events{/privacy}", "followers_url": "https://api.github.com/users/Hpeox/followers", "following_url": "https://api.github.com/users/Hpeox/following{/other_user}", "gists_url": "https://api.github.com/users/Hpeox/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Hpeox", "id": 89294013, "login": "Hpeox", "node_id": "MDQ6VXNlcjg5Mjk0MDEz", "organizations_url": "https://api.github.com/users/Hpeox/orgs", "received_events_url": "https://api.github.com/users/Hpeox/received_events", "repos_url": "https://api.github.com/users/Hpeox/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Hpeox/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Hpeox/subscriptions", "type": "User", "url": "https://api.github.com/users/Hpeox", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Hi ! Have you tried escaping the glob special characters `[` and `]` ?\n\nbtw note that`AbstractFileSystem.glob` doesn't support regex, instead it supports glob patterns as in the python library [glob](https://docs.python.org/3/library/glob.html)\n" ]	2025-03-20T05:21:59Z	2025-03-25T10:18:12Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug When using the `load_dataset` function with a folder path containing regex special characters (such as "[]"), the issue occurs due to how the path is handled in the `resolve_pattern` function. This function passes the unprocessed path directly to `AbstractFileSystem.glob`, which supports regular expressions. As a result, the globbing mechanism interprets these characters as regex patterns, leading to a traversal of the entire disk partition instead of confining the search to the intended directory. ### Steps to reproduce the bug just create a folder like `E:\[D_DATA]\koch_test`, then `load_dataset("parquet", data_dir="E:\[D_DATA]\\test", split="train")` it will keep searching the whole disk. I add two `print` in `glob` and `resolve_pattern` to see the path ### Expected behavior it should load the dataset as in normal folders ### Environment info - `datasets` version: 3.3.2 - Platform: Windows-10-10.0.22631-SP0 - Python version: 3.10.16 - `huggingface_hub` version: 0.29.1 - PyArrow version: 19.0.1 - Pandas version: 2.2.3 - `fsspec` version: 2024.12.0	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/7468/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7468/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/4799	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4799/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4799/comments	https://api.github.com/repos/huggingface/datasets/issues/4799/events	https://github.com/huggingface/datasets/issues/4799	1,330,889,854	I_kwDODunzps5PU8R-	4,799	video dataset loader/parser	{ "avatar_url": "https://avatars.githubusercontent.com/u/26421036?v=4", "events_url": "https://api.github.com/users/verbiix/events{/privacy}", "followers_url": "https://api.github.com/users/verbiix/followers", "following_url": "https://api.github.com/users/verbiix/following{/other_user}", "gists_url": "https://api.github.com/users/verbiix/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/verbiix", "id": 26421036, "login": "verbiix", "node_id": "MDQ6VXNlcjI2NDIxMDM2", "organizations_url": "https://api.github.com/users/verbiix/orgs", "received_events_url": "https://api.github.com/users/verbiix/received_events", "repos_url": "https://api.github.com/users/verbiix/repos", "site_admin": false, "starred_url": "https://api.github.com/users/verbiix/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/verbiix/subscriptions", "type": "User", "url": "https://api.github.com/users/verbiix", "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	[ "Hi! We've just started discussing the video support in `datasets` (decoding backends, video feature type, etc.), so I believe we should have something tangible by the end of this year.\r\n\r\nAlso, if you have additional video features in mind that you would like to see, feel free to let us know", "Coool thanks @mariosasko ", "Hey @mariosasko, I was wondering if there's a way to load video data currently in the library? \r\nAlternatively is there a way I could hack it through the dataset.from_dict() method? I tried to hack it, but the issue I run into is that earlier I was doing a `cast_column()` call for the `Image` feature, but now I'm not sure about to do if I want the dataset to have the following keys when I call from_dict on it:\r\n`{\"caption\":[list of text captions], \"video_frames\": [list of image lists with one image list corresponding to one video]}`\r\n\r\nMaybe something like `cast_column(\"video_frames\", List(Image))` ..\r\n(This is assuming I have already extracted frames from video)" ]	2022-08-07T01:54:12Z	2023-10-01T00:08:31Z	2022-08-09T16:42:51Z	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	you know how you can [use `load_dataset` with any arbitrary csv file](https://huggingface.co/docs/datasets/loading#csv)? and you can also [use it to load a local image dataset](https://huggingface.co/docs/datasets/image_load#local-files)? could you please add functionality to load a video dataset? it would be really cool if i could point it to a bunch of video files and use pytorch to start looping through batches of videos. like if my batch size is 16, each sample in the batch is a frame from a video. i'm competing in the [minerl challenge](https://www.aicrowd.com/challenges/neurips-2022-minerl-basalt-competition), and it would be awesome to use the HF ecosystem.	{ "avatar_url": "https://avatars.githubusercontent.com/u/26421036?v=4", "events_url": "https://api.github.com/users/verbiix/events{/privacy}", "followers_url": "https://api.github.com/users/verbiix/followers", "following_url": "https://api.github.com/users/verbiix/following{/other_user}", "gists_url": "https://api.github.com/users/verbiix/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/verbiix", "id": 26421036, "login": "verbiix", "node_id": "MDQ6VXNlcjI2NDIxMDM2", "organizations_url": "https://api.github.com/users/verbiix/orgs", "received_events_url": "https://api.github.com/users/verbiix/received_events", "repos_url": "https://api.github.com/users/verbiix/repos", "site_admin": false, "starred_url": "https://api.github.com/users/verbiix/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/verbiix/subscriptions", "type": "User", "url": "https://api.github.com/users/verbiix", "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/4799/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4799/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7082	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7082/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7082/comments	https://api.github.com/repos/huggingface/datasets/issues/7082/events	https://github.com/huggingface/datasets/pull/7082	2,437,354,975	PR_kwDODunzps522dTJ	7,082	Support HTTP authentication in non-streaming mode	{ "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_7082). 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) \| 0.005280 / 0.011353 (-0.006073) \| 0.003726 / 0.011008 (-0.007282) \| 0.067028 / 0.038508 (0.028520) \| 0.030833 / 0.023109 (0.007724) \| 0.256888 / 0.275898 (-0.019010) \| 0.271252 / 0.323480 (-0.052228) \| 0.003149 / 0.007986 (-0.004836) \| 0.004031 / 0.004328 (-0.000298) \| 0.051178 / 0.004250 (0.046927) \| 0.042751 / 0.037052 (0.005699) \| 0.268385 / 0.258489 (0.009896) \| 0.295547 / 0.293841 (0.001706) \| 0.030218 / 0.128546 (-0.098328) \| 0.012033 / 0.075646 (-0.063613) \| 0.206389 / 0.419271 (-0.212882) \| 0.036227 / 0.043533 (-0.007306) \| 0.258778 / 0.255139 (0.003639) \| 0.276027 / 0.283200 (-0.007172) \| 0.020309 / 0.141683 (-0.121374) \| 1.109689 / 1.452155 (-0.342466) \| 1.139979 / 1.492716 (-0.352738) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093615 / 0.018006 (0.075609) \| 0.301279 / 0.000490 (0.300789) \| 0.000207 / 0.000200 (0.000007) \| 0.000048 / 0.000054 (-0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018697 / 0.037411 (-0.018715) \| 0.062627 / 0.014526 (0.048101) \| 0.075119 / 0.176557 (-0.101438) \| 0.119960 / 0.737135 (-0.617175) \| 0.074606 / 0.296338 (-0.221732) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281042 / 0.215209 (0.065833) \| 2.746232 / 2.077655 (0.668578) \| 1.422351 / 1.504120 (-0.081769) \| 1.290087 / 1.541195 (-0.251108) \| 1.321067 / 1.468490 (-0.147423) \| 0.727514 / 4.584777 (-3.857263) \| 2.407086 / 3.745712 (-1.338626) \| 2.914191 / 5.269862 (-2.355670) \| 1.872206 / 4.565676 (-2.693471) \| 0.079538 / 0.424275 (-0.344738) \| 0.005250 / 0.007607 (-0.002357) \| 0.335536 / 0.226044 (0.109491) \| 3.324922 / 2.268929 (1.055994) \| 1.790688 / 55.444624 (-53.653936) \| 1.475738 / 6.876477 (-5.400739) \| 1.492465 / 2.142072 (-0.649607) \| 0.812342 / 4.805227 (-3.992885) \| 0.135036 / 6.500664 (-6.365628) \| 0.041484 / 0.075469 (-0.033985) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948425 / 1.841788 (-0.893363) \| 11.321564 / 8.074308 (3.247256) \| 9.635661 / 10.191392 (-0.555731) \| 0.142793 / 0.680424 (-0.537631) \| 0.014988 / 0.534201 (-0.519213) \| 0.300209 / 0.579283 (-0.279074) \| 0.262303 / 0.434364 (-0.172061) \| 0.337927 / 0.540337 (-0.202411) \| 0.427962 / 1.386936 (-0.958975) \|\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) \| 0.005664 / 0.011353 (-0.005689) \| 0.003946 / 0.011008 (-0.007062) \| 0.050034 / 0.038508 (0.011526) \| 0.031652 / 0.023109 (0.008543) \| 0.281139 / 0.275898 (0.005241) \| 0.299203 / 0.323480 (-0.024277) \| 0.004332 / 0.007986 (-0.003653) \| 0.002769 / 0.004328 (-0.001560) \| 0.048336 / 0.004250 (0.044086) \| 0.039744 / 0.037052 (0.002692) \| 0.289344 / 0.258489 (0.030855) \| 0.320470 / 0.293841 (0.026629) \| 0.032372 / 0.128546 (-0.096174) \| 0.012090 / 0.075646 (-0.063557) \| 0.060838 / 0.419271 (-0.358433) \| 0.034227 / 0.043533 (-0.009306) \| 0.275007 / 0.255139 (0.019868) \| 0.293455 / 0.283200 (0.010256) \| 0.017203 / 0.141683 (-0.124480) \| 1.141577 / 1.452155 (-0.310578) \| 1.176761 / 1.492716 (-0.315955) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093562 / 0.018006 (0.075556) \| 0.302695 / 0.000490 (0.302205) \| 0.000215 / 0.000200 (0.000015) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022638 / 0.037411 (-0.014774) \| 0.078788 / 0.014526 (0.064262) \| 0.088474 / 0.176557 (-0.088082) \| 0.128421 / 0.737135 (-0.608714) \| 0.089297 / 0.296338 (-0.207041) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302669 / 0.215209 (0.087459) \| 2.963855 / 2.077655 (0.886200) \| 1.600053 / 1.504120 (0.095933) \| 1.461456 / 1.541195 (-0.079739) \| 1.469877 / 1.468490 (0.001387) \| 0.725752 / 4.584777 (-3.859025) \| 0.968970 / 3.745712 (-2.776742) \| 2.910502 / 5.269862 (-2.359359) \| 1.902762 / 4.565676 (-2.662914) \| 0.079977 / 0.424275 (-0.344298) \| 0.005582 / 0.007607 (-0.002025) \| 0.351626 / 0.226044 (0.125581) \| 3.520593 / 2.268929 (1.251664) \| 1.968950 / 55.444624 (-53.475675) \| 1.662190 / 6.876477 (-5.214286) \| 1.677909 / 2.142072 (-0.464163) \| 0.791541 / 4.805227 (-4.013687) \| 0.134647 / 6.500664 (-6.366017) \| 0.040687 / 0.075469 (-0.034782) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.028885 / 1.841788 (-0.812903) \| 11.928358 / 8.074308 (3.854050) \| 10.199165 / 10.191392 (0.007773) \| 0.142930 / 0.680424 (-0.537493) \| 0.016479 / 0.534201 (-0.517722) \| 0.302993 / 0.579283 (-0.276290) \| 0.128878 / 0.434364 (-0.305486) \| 0.342591 / 0.540337 (-0.197747) \| 0.456735 / 1.386936 (-0.930201) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d298f5549893228c03e9e3a42727327cb83f3dff \"CML watermark\")\n" ]	2024-07-30T09:25:49Z	2024-08-08T08:29:55Z	2024-08-08T08:24:06Z	MEMBER	null	null	null	Support HTTP authentication in non-streaming mode, by support passing HTTP storage_options in non-streaming mode. - Note that currently, HTTP authentication is supported only in streaming mode. For example, this is necessary if a remote HTTP host requires authentication to download the data.	{ "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/7082/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7082/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7082.diff", "html_url": "https://github.com/huggingface/datasets/pull/7082", "merged_at": "2024-08-08T08:24:06Z", "patch_url": "https://github.com/huggingface/datasets/pull/7082.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7082" }
https://api.github.com/repos/huggingface/datasets/issues/6329	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6329/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6329/comments	https://api.github.com/repos/huggingface/datasets/issues/6329/events	https://github.com/huggingface/datasets/issues/6329	1,955,858,020	I_kwDODunzps50lAZk	6,329	شبکه های متن به گفتار ابتدا متن داده شده را به بازنمایی میانی	{ "avatar_url": "https://avatars.githubusercontent.com/u/147399213?v=4", "events_url": "https://api.github.com/users/shabnam706/events{/privacy}", "followers_url": "https://api.github.com/users/shabnam706/followers", "following_url": "https://api.github.com/users/shabnam706/following{/other_user}", "gists_url": "https://api.github.com/users/shabnam706/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/shabnam706", "id": 147399213, "login": "shabnam706", "node_id": "U_kgDOCMkiLQ", "organizations_url": "https://api.github.com/users/shabnam706/orgs", "received_events_url": "https://api.github.com/users/shabnam706/received_events", "repos_url": "https://api.github.com/users/shabnam706/repos", "site_admin": false, "starred_url": "https://api.github.com/users/shabnam706/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/shabnam706/subscriptions", "type": "User", "url": "https://api.github.com/users/shabnam706", "user_view_type": "public" }	[]	closed	false	null	[]	null	[]	2023-10-22T11:07:46Z	2023-10-23T09:22:58Z	2023-10-23T09:22:58Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	شبکه های متن به گفتار ابتدا متن داده شده را به بازنمایی میانی	{ "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/6329/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6329/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5402	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5402/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5402/comments	https://api.github.com/repos/huggingface/datasets/issues/5402/events	https://github.com/huggingface/datasets/issues/5402	1,517,409,429	I_kwDODunzps5acdSV	5,402	Missing state.json when creating a cloud dataset using a dataset_builder	{ "avatar_url": "https://avatars.githubusercontent.com/u/22022514?v=4", "events_url": "https://api.github.com/users/danielfleischer/events{/privacy}", "followers_url": "https://api.github.com/users/danielfleischer/followers", "following_url": "https://api.github.com/users/danielfleischer/following{/other_user}", "gists_url": "https://api.github.com/users/danielfleischer/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/danielfleischer", "id": 22022514, "login": "danielfleischer", "node_id": "MDQ6VXNlcjIyMDIyNTE0", "organizations_url": "https://api.github.com/users/danielfleischer/orgs", "received_events_url": "https://api.github.com/users/danielfleischer/received_events", "repos_url": "https://api.github.com/users/danielfleischer/repos", "site_admin": false, "starred_url": "https://api.github.com/users/danielfleischer/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/danielfleischer/subscriptions", "type": "User", "url": "https://api.github.com/users/danielfleischer", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "`load_from_disk` must be used on datasets saved using `save_to_disk`: they correspond to fully serialized datasets including their state.\r\n\r\nOn the other hand, `download_and_prepare` just downloads the raw data and convert them to arrow (or parquet if you want). We are working on allowing you to reload a dataset saved on S3 with `download_and_prepare` using `load_dataset` in #5281 \r\n\r\nFor now I'd encourage you to keep using `save_to_disk`", "Thanks, I'll follow that issue. \r\n\r\nI was following the [cloud storage](https://huggingface.co/docs/datasets/filesystems) docs section and perhaps I'm missing some part of the flow; start with `load_dataset_builder` + `download_and_prepare`. You say I need an explicit `save_to_disk` but what object needs to be saved? the builder? is that related to the other issue?", "Right now `load_dataset_builder` + `download_and_prepare` is to be used with tools like dask or spark, but `load_dataset` will support private cloud storage soon as well so you'll be able to reload the dataset with `datasets`.\r\n\r\nRight now the only function that can load a dataset from a cloud storage is `load_from_disk`, that must be used with a dataset serialized with `save_to_disk`." ]	2023-01-03T13:39:59Z	2023-01-04T17:23:57Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Using `load_dataset_builder` to create a builder, run `download_and_prepare` do upload it to S3. However when trying to load it, there are missing `state.json` files. Complete example: ```python from aiobotocore.session import AioSession as Session from datasets import load_from_disk, load_datase, load_dataset_builder import s3fs storage_options = {"session": Session()} fs = s3fs.S3FileSystem(storage_options) output_dir = "s3://bucket/imdb" builder = load_dataset_builder("imdb") builder.download_and_prepare(output_dir, storage_options=storage_options) load_from_disk(output_dir, fs=fs) # ERROR # [Errno 2] No such file or directory: '/tmp/tmpy22yys8o/bucket/imdb/state.json' ``` As a comparison, if you use the non lazy `load_dataset`, it works and the S3 folder has different structure + state.json files. Example: ```python from aiobotocore.session import AioSession as Session from datasets import load_from_disk, load_dataset, load_dataset_builder import s3fs storage_options = {"session": Session()} fs = s3fs.S3FileSystem(storage_options) output_dir = "s3://bucket/imdb" dataset = load_dataset("imdb",) dataset.save_to_disk(output_dir, fs=fs) load_from_disk(output_dir, fs=fs) # WORKS ``` You still want the 1st option for the laziness and the parquet conversion. Thanks! ### Steps to reproduce the bug ```python from aiobotocore.session import AioSession as Session from datasets import load_from_disk, load_datase, load_dataset_builder import s3fs storage_options = {"session": Session()} fs = s3fs.S3FileSystem(**storage_options) output_dir = "s3://bucket/imdb" builder = load_dataset_builder("imdb") builder.download_and_prepare(output_dir, storage_options=storage_options) load_from_disk(output_dir, fs=fs) # ERROR # [Errno 2] No such file or directory: '/tmp/tmpy22yys8o/bucket/imdb/state.json' ``` BTW, you need the AioSession as s3fs is now based on aiobotocore, see https://github.com/fsspec/s3fs/issues/385. ### Expected behavior Expected to be able to load the dataset from S3. ### Environment info ``` s3fs 2022.11.0 s3transfer 0.6.0 datasets 2.8.0 aiobotocore 2.4.2 boto3 1.24.59 botocore 1.27.59 ``` python 3.7.15.	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/5402/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5402/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/4975	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4975/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4975/comments	https://api.github.com/repos/huggingface/datasets/issues/4975/events	https://github.com/huggingface/datasets/pull/4975	1,371,703,691	PR_kwDODunzps4-4NXX	4,975	Add `fn_kwargs` param to `IterableDataset.map`	{ "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._", "Thank you for adding this fix! \r\n\r\nWould it be possible to get `fn_kwargs` added to `IterableDatasetDict.map` as well? It looks like a very similar problem, and hopefully shouldn't be a huge change. \r\n", "Hi @brianhill11! https://github.com/huggingface/datasets/pull/5810 adds this (opened a couple of days ago). It should be merged soon.", "That's fantastic news, thanks @mariosasko ! I'll give it a shot once the changes are merged in. " ]	2022-09-13T16:19:05Z	2023-05-05T16:53:43Z	2022-09-13T16:45:34Z	COLLABORATOR	null	null	null	Add the `fn_kwargs` parameter to `IterableDataset.map`. ("Resolves" https://discuss.huggingface.co/t/how-to-use-large-image-text-datasets-in-hugging-face-hub-without-downloading-for-free/22780/3)	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4975/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4975/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4975.diff", "html_url": "https://github.com/huggingface/datasets/pull/4975", "merged_at": "2022-09-13T16:45:34Z", "patch_url": "https://github.com/huggingface/datasets/pull/4975.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4975" }

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