Datasets:

gusornu

/

github-issues

like 0

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[ "Yes, I need to use the latest version of fsspec and datasets for my usecase. \r\nhttps://github.com/fsspec/s3fs/pull/888#issuecomment-2404204606\r\nhttps://github.com/apache/arrow/issues/34363#issuecomment-2403553473\r\n\r\nlast version where things install without conflict is: 2.14.4\r\n\r\nSo this issue starts from:\r\nhttps://github.com/huggingface/datasets/releases/tag/2.14.5" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7188). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[]

[ "Fixed by #7205" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7184). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7182). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "The CI issue is unrelated:\r\n- #7183" ]

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[ "Linked Issue: #7037\r\nIdeas: #7039 ", "@albertvillanova / @lhoestq any early feedback?\r\n\r\nAFAIK there is no param `orient` in `load_dataset()`. So for orientations other than \"records\", the loading isn't very accurate. Any thoughts?", "`orient = \"split\"` can also be handled. I will add the changes soon", "Thanks for diving into this ! I don't think we want the JSON export to be that complex though, especially if people can do `ds.to_pandas().to_json(orient=...)`. Maybe we can just raise an error and suggest users to use pandas ? And also note that it loads the full dataset in memory so it's mainly for small scale datasets. The only acceptable option for large scale datasets is probably just JSON Lines anyway since it enables streaming.", "@lhoestq Simply doing `ds.to_pandas().to_json(orient=...)` is not going to give any batching or multiprocessing benefits right? Also, which function are you referring to - when you say that its meant for small scale datasets only?", "Yes indeed. Though I think it's fine since using something else than orient=\"lines\" is only suitable/useful for small datasets. Or you know a case where a big dataset need to be in a format that is not orient=\"lines\" ?", "@lhoestq Let me close this PR and open another one where I will add an error message, as suggested here.\r\n\r\n> Thanks for diving into this ! I don't think we want the JSON export to be that complex though, especially if people can do `ds.to_pandas().to_json(orient=...)`. Maybe we can just raise an error and suggest users to use pandas ? And also note that it loads the full dataset in memory so it's mainly for small scale datasets. The only acceptable option for large scale datasets is probably just JSON Lines anyway since it enables streaming.\r\n\r\n", "Addressed here: #7273 \r\n@lhoestq " ]

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[ "> I've encountered a memory leak when wrapping the HuggingFace dataset into a PyTorch Dataset. The RAM usage constantly increases during iteration if items are not explicitly deleted after use.\r\n\r\nDatasets are memory mapped so they work like SWAP memory. In particular as long as you have RAM available the data will stay in RAM, and get paged out once your system needs RAM for something else (no OOM).\r\n\r\nrelated: https://github.com/huggingface/datasets/issues/4883" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7179). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7177). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "Is this `FSTimeoutError` due to download network issue from remote resource (from where it is being accessed)?", "It seems to happen for all datasets, not just a specific one, and especially for versions after 3.0. (3.0.0, 3.0.1 have this problem)\r\n\r\nI had the same error on a different dataset, but after downgrading to datasets==2.21.0, the problem was solved.", "Same as https://github.com/huggingface/datasets/issues/7164\r\n\r\nThis dataset is made of a python script that downloads data from elsewhere than HF, so availability depends on the original host. Ultimately it would be nice to host the files of this dataset on HF\r\n\r\nin `datasets` <3.0 there were lots of mechanisms that got removed after the decision to make datasets with python loading scripts legacy for security and maintenance reasons (we only do very basic support now)", "@lhoestq Thank you for the clarification! Closing the issue.", "I'm getting this too, and also at 5 minutes. But for `CSTR-Edinburgh/vctk`, so it's not just this dataset, it seems to be a timeout that was introduced and needs to be raised. The progress bar was moving along just fine before the timeout, and I get more or less of it depending on how fast the network is.", "You can change the `aiohttp` timeout from 5min to 1h like this:\r\n\r\n```python\r\nimport datasets, aiohttp\r\ndataset = datasets.load_dataset(\r\n dataset_name,\r\n storage_options={'client_kwargs': {'timeout': aiohttp.ClientTimeout(total=3600)}}\r\n)\r\n```" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7174). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7173). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7172). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7170). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "not sure why the issue is formatting oddly", "I guess this is a dupe of\r\n\r\nhttps://github.com/huggingface/datasets/issues/7071", "this turned out to be because of a bad image in dataset" ]

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[ "this is happening on large datasets, if anyone happens upon this i was able to fix by changing\r\n\r\n```\r\ntrain_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)\r\n```\r\n\r\nto\r\n\r\n```\r\ntrain_dataset = train_dataset.map(compute_embeddings_fn, batched=True, batch_size=16, new_fingerprint=new_fingerprint)\r\n```" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7166). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7165). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I tested a few load_dataset and they do show up in download stats now", "Thanks for having noticed and fixed." ]

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[ "Hi ! If you check the dataset loading script [here](https://huggingface.co/datasets/openslr/librispeech_asr/blob/main/librispeech_asr.py) you'll see that it downloads the data from OpenSLR, and apparently their storage has timeout issues. It would be great to ultimately host the dataset on Hugging Face instead.\r\n\r\nIn the meantime I can only recommend to try again later :/", "Ok, still many thanks!", "I'm also getting this same error but for `CSTR-Edinburgh/vctk`, so I don't think it's the remote host that's timing out, since I also time out at exactly 5 minutes. It seems there is a universal fsspec timeout that's getting hit starting in v3.", "in v3 we cleaned the download parts of the library to make it more robust for HF downloads and to simplify support of script-based datasets. As a side effect it's not the same code that is used for other hosts, maybe time out handling changed. Anyway it should be possible to tweak fsspec to use retries\r\n\r\nFor example using [aiohttp_retry](https://github.com/inyutin/aiohttp_retry) maybe (haven't tried) ?\r\n\r\n```python\r\nimport fsspec\r\nfrom aiohttp_retry import RetryClient\r\n\r\nfsspec.filesystem(\"http\")._session = RetryClient()\r\n```\r\n\r\nrelated topic : https://github.com/huggingface/datasets/issues/7175", "Adding a timeout argument to the `fs.get_file` call in `fsspec_get` in `datasets/utils/file_utils.py` might fix this ([source code](https://github.com/huggingface/datasets/blob/65f6eb54aa0e8bb44cea35deea28e0e8fecc25b9/src/datasets/utils/file_utils.py#L330)):\r\n\r\n```python\r\nfs.get_file(path, temp_file.name, callback=callback, timeout=3600)\r\n```\r\n\r\nSetting `timeout=1` fails after about one second, so setting it to 3600 should give us 1h. Havn't really tested this though. I'm also not sure what implications this has and if it causes errors for other `fs` implementations/configurations.\r\n\r\nThis is using `datasets==3.0.1` and Python 3.11.6.\r\n\r\n---\r\n\r\nEdit: This doesn't seem to change the timeout time, but add a second timeout counter (probably in `fsspec/asyn.py/sync`). So one can reduce the time for downloading like this, but not expand.\r\n\r\n---\r\n\r\nEdit 2: `fs` is of type `fsspec.implementations.http.HTTPFileSystem` which initializes a `aiohttp.ClientSession` using `client_kwargs`. We can pass these when calling `load_dataset`.\r\n\r\n**TLDR; This fixes it:**\r\n\r\n```python\r\nimport datasets, aiohttp\r\ndataset = datasets.load_dataset(\r\n dataset_name,\r\n storage_options={'client_kwargs': {'timeout': aiohttp.ClientTimeout(total=3600)}}\r\n)\r\n```" ]

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[ "thanks for reporting !" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7162). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7160). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "Hello,\r\n\r\nI have still the same issue when loading the dataset with the new version:\r\n[https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/5](https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/5)\r\n\r\nI have downloaded and unzipped the wikimedia/structured-wikipedia dataset locally but when loading I have the same issue.\r\n\r\n```\r\nimport datasets\r\n\r\ndataset = datasets.load_dataset(\"/gpfsdsdir/dataset/HuggingFace/wikimedia/structured-wikipedia/20240916.fr\")\r\n```\r\n```\r\nTypeError: Couldn't cast array of type\r\nstruct<content_url: string, width: int64, height: int64, alternative_text: string>\r\nto\r\n{'content_url': Value(dtype='string', id=None), 'width': Value(dtype='int64', id=None), 'height': Value(dtype='int64', id=None)}\r\n\r\nThe above exception was the direct cause of the following exception:\r\n```\r\nMy version of datasets is 3.0.1" ]

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[ "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." ]

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[ "I have fixed it! But I would appreciate a new feature wheere I could iterate over and see what each file looks like. " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7154). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Thanks for your review, @severo.\r\n\r\nYes, I was aware of this. From internal conversation:\r\n> Please note that although NDJSON was planned to be submitted as an RFC standard spec, it is no longer maintained:\r\n> - See note from the author: https://github.com/ndjson/ndjson-spec/issues/35#issuecomment-1285673417\r\n> - See that their official website domain has expired: https://ndjson.org/ \r\n\r\nThe purpose of this PR is just supporting datasets with ndjson data files (e.g. Wikimedia Enterprise data files), but it should not imply any recommendation or endorsement of this format from our part." ]

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[ "Thanks, for reporting.\r\n\r\nWe have been fixing most Hub datasets to remove the deprecated (and now non-supported) task templates, but we missed the \"facebook/winoground\".\r\n\r\nIt is fixed now: https://huggingface.co/datasets/facebook/winoground/discussions/8\r\n\r\n", "Hello @albertvillanova \r\n\r\nI got the same error while loading this dataset: https://huggingface.co/datasets/alaleye/aloresb...\r\n\r\nHow can I fix it ? \r\nThanks" ]

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[ "Could you please try with `force_redownload` instead?\r\nEDIT:\r\n```python\r\ndata = load_dataset(\"mteb/mtop_domain\", \"en\", download_mode=\"force_redownload\")\r\n```", "Seems the error is still there", "I am not able to reproduce the issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: data = load_dataset(\"mteb/mtop_domain\", \"en\")\r\n\r\nIn [3]: data\r\nOut[3]: DatasetDict({\r\n train: Dataset({\r\n features: ['id', 'text', 'label', 'label_text'],\r\n num_rows: 15667\r\n })\r\n validation: Dataset({\r\n features: ['id', 'text', 'label', 'label_text'],\r\n num_rows: 2235\r\n })\r\n test: Dataset({\r\n features: ['id', 'text', 'label', 'label_text'],\r\n num_rows: 4386\r\n })\r\n})\r\n```", "Just solved this by reinstall Huggingface Hub and datasets. Thanks for your help!" ]

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[ "Yes `interleave_datasets` seems to have an issue with shuffling, could you open a new issue on this ?\r\n\r\nThen regarding the deadlock, it has to do with interleave_dataset with probabilities=[1, 0] with workers that may contain an empty dataset in first position (it can be empty since you distribute 1024 shard to 8 workers, so some workers may not have an example that satisfies your condition `if shard < 25`). It creates an infinite loop, trying to get samples from empty datasets with probability 1.", "Opened https://github.com/huggingface/datasets/issues/7156\r\n\r\nCan the deadlock be fixed somehow? The point of IterableDataset is so we don't need to preload the entire dataset, which loses some meaning if we need to see how many examples are in the dataset in order to set shards correctly.", "~~And it is kinda strange that `Commenting out the final shuffle avoids the issue` since if the infinite loop is inside interleave_datasets you'd expect that to happen regardless of the additional shuffle call?~~\r\n\r\nEdit: oh I guess without the shuffle it's guaranteed every worker gets something, but the shuffle makes it so some workers could have nothing\r\n\r\n~~Edit2: maybe the shuffle can be changed so initially it gives one example to each worker, and only starts the random shuffle after that~~ wait it's not about the workers not getting any shards, it's about a worker getting shards but all of the shards it gets are empty shards\r\n\r\nEdit3: If it's trying to get samples from empty datasets, it should be getting back a StopIteration -- and \"all_exhausted\" should mean it eventually discovers all its datasets are empty, and then it should just raise a StopIteration itself. So it seems like there is a reasonable behavior result for this?", "well the second dataset passed to interleave_datasets is never exhausted, since it's never sampled. But we could also state that the stream of examples from the second dataset is empty if it has probability 0, so I opened https://github.com/huggingface/datasets/pull/7157 to fix the infinite loop issue by ignoring datasets with probability 0, let me know what you think !", "Thanks for taking a look!\r\n\r\nI think you're right that this is ultimately an issue that the user opts into by specifying a dataset with probability 0, because the user is basically saying \"I want to force this `interleave_datasets` call to run forever\" and yet one of the workers can end up having only empty shards to mix...\r\n\r\nThat said it's probably not a good idea to randomly change the behavior of `interleave_datasets` with probability 0, I can't be the only one that uses it to repeat many different datasets (since there is no `datasets.repeat()` function). https://xkcd.com/1172/\r\n\r\nI think just the knowledge that filtering out probability 0 datasets fixes the deadlock is good enough for me. I can filter it out on my side and add a restart loop around the dataloader instead.\r\n\r\nThanks again for investigating.", "Ok I see ! We can also add .repeat() as well" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7146). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7145). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "hi ! What version of `datasets` are you using ? Is this issue also happening with `datasets==3.0.0` ?\r\nAsking because we made sure to replicate the official webdataset logic, which is to use the latest dot as separator between the sample base name and the key", "Hi, yes this is still a problem on `datasets==3.0.0`.\r\n\r\nI was using `datasets=2.20.0` and in that version you get the key error.\r\n\r\nI just upgraded to `datasets==3.0.0` and in this version, you do not get a key error because it sets all keys to none by default in `_generate_examples` function:\r\n\r\n```python\r\nif field_name not in example:\r\n example[field_name] = None\r\n```\r\n\r\nHowever, the behaviour is still incorrect. This `if` condition is triggered because the filename is not split properly and it returns the data as `None` when it shouldn't.\r\n\r\n> we made sure to replicate the official webdataset logic, which is to use the latest dot as separator\r\n \r\nAh, but that's not what `split(, 1)` does though. This is exactly why I'm suggesting to use `rsplit` instead. In general, using `rsplit` should not be a breaking change I believe.", "Hi @ragavsachdeva,\r\n\r\nWe already had this discussion in the issue you have linked:\r\n- #6880\r\n- I even opened a PR with your proposed fix:\r\n - #6888\r\n\r\nHowever, we decided not to implement this feature because it is NOT aligned with the behavior of the `webdataset` library:\r\n> The prefix of a file is all directory components of the file plus the file name component up to the *first* “.” in the file name.\r\n```python\r\nIn [1]: import webdataset as wds\r\n\r\nIn [2]: wds.tariterators.base_plus_ext(\"22.05.png\")\r\nOut[2]: ('22', '05.png')\r\n```\r\n\r\n", "Ah, my apologies I missed https://github.com/huggingface/datasets/pull/6888 (clearly didn't do my due diligence). It's such a weird convention to have though. My keys are `/some/path/22.0/1.1.png` and it splits them at `/some/path/22` and `.0/1.1.png`(!) I'm okay with this PR not being merged though. Thanks for your time.", "Actually `datasets` is not behaving correctly in this case and should not split as `.0/1.1.png` - even webdataset handles this correctly via their regex `^((?:.*/|)[^.]+)[.]([^/]*)$` in `wds.tariterators.base_plus_ext` here:\r\n\r\nhttps://github.com/webdataset/webdataset/blob/87bd5aa41602d57f070f65a670893ee625702f2f/webdataset/tariterators.py#L36", "Oh.. the intention with that regex is to capture \"multi-part\" extensions e.g. `.tar.gz`. Makes sense. So `rsplit` isn't the solution then and neither is `split`. This expression makes so much more sense. Nice find! I'm assuming you'll add a patch?", "Issue addressed by:\r\n- #7151" ]

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[ "Requesting review @lhoestq \r\nI will also update the docs if this looks good.", "Cool ! maybe you can rename the argument `feature` and with type `FeatureType` ? This way it would work the same way as `.cast_column()` ?", "@lhoestq Since there is no way to get a `pyarrow.Schema` from a `FeatureType`, I had to go via `Features`. How does this look?", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7143). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@lhoestq done!", "@lhoestq anything pending on this?" ]

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[ "#self-assign" ]

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[ "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!" ]

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[ "Imagenet-1k is a gated dataset which means you’ll have to agree to share your contact info to access it. Have you tried this yet? Once you have, you can sign in with your user token (you can find this in your Hugging Face account settings) when prompted by running.\r\n\r\n```\r\nhuggingface-cli login\r\ntrain_set = load_dataset('imagenet-1k', split='train', use_auth_token=True)\r\n``` ", "Thanks a lot! It helps me" ]

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[ "so I guess a workaround to this is to simply remove all columns except the ones to cache and then add them back with `concatenate_datasets(..., axis=1)`.", "yes this is the right workaround. We're keeping the cache like this to make it easier for people to delete intermediate cache files" ]

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[ "The non-sequence case works well (`dict[str, str]` instead of `list[dict[str, str]]`), which makes me believe it shall be a bug for `sequence` and my proposed behavior shall be expected.\r\n```\r\ndataset_info:\r\n- config_name: default\r\n features:\r\n - name: answers\r\n dtype:\r\n - name: text\r\n dtype: string\r\n - name: label\r\n dtype: string\r\n\r\n\r\n# data\r\n{\"answers\": {\"text\": \"ADDRESS\", \"label\": \"abc\"}}\r\n```" ]

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[ "By the way, following code is working. This show the inconsistentcy.\r\n```python\r\nfrom datasets import Dataset\r\n\r\n# Original data\r\ndata = {\r\n 'text': ['Hello', 'world', 'this', 'is', 'a', 'test'],\r\n 'label': [0, 1, 0, 1, 1, 0]\r\n}\r\n\r\n# Creating a Dataset object\r\ndataset = Dataset.from_dict(data)\r\n\r\n# Mapping function to convert label to string\r\ndef add_one(example):\r\n example['label'] += 1\r\n return example\r\n\r\n# Applying the mapping function\r\ndataset = dataset.map(add_one)\r\n\r\n# Iterating over the dataset to show results\r\nfor item in dataset:\r\n print(item)\r\n print(type(item['label']))\r\n```", "Hello, thanks for submitting an issue.\r\n\r\nFWIU, the issue is that `datasets` tries to limit casting [ref](https://github.com/huggingface/datasets/blob/ca58154bba185c1916ca5eea4e33b27258642044/src/datasets/arrow_writer.py#L526) and as such will try to convert your strings back to int to preserve the `Features`. \r\n\r\nA quick solution would be to use `dataset.cast` or to supply `features` when calling `dataset.map`.\r\n\r\n\r\n```python\r\n# using Dataset.cast\r\ndataset = dataset.cast_column('label', Value('string'))\r\n\r\n# Alternative, supply features\r\ndataset = dataset.map(add_one, features=Features({**dataset.features, 'label': Value('string')}))\r\n```", "LGTM! Thanks for the review.\r\n\r\nJust to clarify, is this intended behavior, or is it something that might be addressed in a future update?\r\nI'll leave this issue open until it's fixed if this is not the intended behavior." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7133). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "The CI is failing, looks like it breaks imagefolder loading.\r\n\r\nI just checked fsspec internals and maybe instead we can detect symlink by checking `islink` and `size` to make sure it's a file\r\n```python\r\nif info[\"type\"] == \"file\" or (info.get(\"islink\") and info[\"size\"])\r\n```\r\n", "hmm actually `size` doesn't seem to filter symlinked directories, we need another way", "Does fsspec perhaps allow resolving symlinks? Something like https://docs.python.org/3/library/pathlib.html#pathlib.Path.resolve", "there is `info[\"destination\"]` in case of a symlink, so maybe\r\n\r\n\r\n```python\r\nif info[\"type\"] == \"file\" or (info.get(\"islink\") and info.get(\"destination\") and os.path.isfile(info[\"destination\"]))\r\n```" ]

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[ "Hi ! datasets saved using `save_to_disk` should be loaded with `load_from_disk` ;)", "It is convienient to just pass in a path to a local dataset or one from the hub and use the same function to load it. Is it not possible to get this fix merged in to allow this? ", "We can modify `save_to_disk` to write the dataset in a structure supported by the Hub in this case, it's kind of a legacy function anyway" ]

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[ "Hi ! you can do\r\n\r\n```python\r\nfiltered_dataset = dataset.filter(filter_function)\r\n```\r\n\r\non a subset:\r\n\r\n```python\r\nfiltered_subset = dataset.select(range(10_000)).filter(filter_function)\r\n```\r\n", "Jumping on this as it seems relevant - when I use the `filter` method, it often results in an OOM (or at least unacceptably high memory usage).\r\n\r\nFor example in the [this notebook](https://colab.research.google.com/drive/1N_rWko6jzGji3j_ayDR7ngT5lf4P8at_), we load an object detection dataset from HF and imagine I want to filter such that I only have images which contain a single annotation class. Each row has a JSON field that contains MS-COCO annotations for the image, so we could load that field and filter on it.\r\n\r\nThe test dataset is only about 440 images, probably less than 1GB, but running the following filter crashes the VM (over 12 GB RAM):\r\n\r\n```python\r\nimport json\r\ndef filter_single_class(example, target_class_id):\r\n \"\"\"Filters examples based on whether they contain annotations from a single class.\r\n\r\n Args:\r\n example: A dictionary representing a single example from the dataset.\r\n target_class_id: The target class ID to filter for.\r\n\r\n Returns:\r\n True if the example contains only annotations from the target class, False otherwise.\r\n \"\"\"\r\n if not example['coco_annotations']:\r\n return False\r\n\r\n annotation_category_ids = set([annotation['category_id'] for annotation in json.loads(example['coco_annotations'])])\r\n\r\n return len(annotation_category_ids) == 1 and target_class_id in annotation_category_ids\r\n\r\ntarget_class_id = 1 \r\nfiltered_dataset = dataset['test'].filter(lambda example: filter_single_class(example, target_class_id))\r\n```\r\n\r\n<img width=\"255\" alt=\"image\" src=\"https://github.com/user-attachments/assets/be475f15-5b6b-4df2-b5b5-a1f60ae2b05c\">\r\n\r\nIterating over the dataset works fine:\r\n\r\n```python\r\nfiltered_dataset = []\r\nfor example in dataset['test']:\r\n if filter_single_class(example, target_class_id):\r\n filtered_dataset.append(example)\r\n```\r\n\r\n<img width=\"129\" alt=\"image\" src=\"https://github.com/user-attachments/assets/34fa5612-0394-4c46-9f34-e94650f05d65\">\r\n\r\nIt would be great if there was guidance in the documentation on how to use filters efficiently, or if this is some performance bug that could be addressed. At the very least I would expect a filter operation to use at most 2x the footprint of the database plus some overhead for the lambda (i.e. worst case would be a duplicate copy with all entries retained). Even if the operation is parallelised, each thread/worker should only take a subset of the dataset - so I'm not sure where this ballooning in memory usage comes from.\r\n\r\nFrom some other comments there seems to be a workaround with `writer_batch_size` or caching to file, but in the [docs](https://huggingface.co/docs/datasets/v3.0.0/en/package_reference/main_classes#datasets.Dataset.filter) at least, `keep_in_memory` defaults to `False`.", "You can try passing input_columns=[\"coco_annotations\"] to only load this column instead of all the columns. In that case your function should take coco_annotations as input instead of example", "If your filter_function is large and computationally intensive, consider using multi-processing or multi-threading with concurrent.futures to filter the dataset. This approach allows you to process multiple tables concurrently, reducing overall processing time, especially for CPU-bound tasks. Use ThreadPoolExecutor for I/O-bound operations and ProcessPoolExecutor for CPU-bound operations.\r\n" ]

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[ "I first thought this was a mistake of mine, and also posted on stack overflow. https://stackoverflow.com/questions/78913797/iterating-a-huggingface-dataset-from-disk-using-generator-seems-broken-how-to-d \r\n\r\nIt seems to me the issue is the caching step in \r\n\r\nhttps://github.com/huggingface/datasets/blob/be5cff059a2a5b89d7a97bc04739c4919ab8089f/src/datasets/arrow_dataset.py#L4306-L4316\r\n\r\nbecause the shuffle happens after checking the cache, the rng state won't advance if the cache is used. This is VERY confusing. Also not documented.\r\n\r\nMy proposal is that you remove the API for using a Generator, and only keep the seed-based API since that is functional and cache-compatible." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7126). 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) | 0.005232 / 0.011353 (-0.006121) | 0.003428 / 0.011008 (-0.007580) | 0.062673 / 0.038508 (0.024164) | 0.030111 / 0.023109 (0.007002) | 0.238017 / 0.275898 (-0.037881) | 0.262655 / 0.323480 (-0.060825) | 0.003015 / 0.007986 (-0.004971) | 0.002664 / 0.004328 (-0.001665) | 0.050010 / 0.004250 (0.045759) | 0.045620 / 0.037052 (0.008567) | 0.251800 / 0.258489 (-0.006689) | 0.278829 / 0.293841 (-0.015011) | 0.029838 / 0.128546 (-0.098709) | 0.011703 / 0.075646 (-0.063943) | 0.204503 / 0.419271 (-0.214768) | 0.036173 / 0.043533 (-0.007359) | 0.242850 / 0.255139 (-0.012289) | 0.263811 / 0.283200 (-0.019389) | 0.019027 / 0.141683 (-0.122656) | 1.168028 / 1.452155 (-0.284126) | 1.208975 / 1.492716 (-0.283742) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091309 / 0.018006 (0.073303) | 0.299583 / 0.000490 (0.299093) | 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) | 0.018451 / 0.037411 (-0.018960) | 0.062516 / 0.014526 (0.047991) | 0.073983 / 0.176557 (-0.102573) | 0.120952 / 0.737135 (-0.616184) | 0.075275 / 0.296338 (-0.221063) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286870 / 0.215209 (0.071661) | 2.810498 / 2.077655 (0.732843) | 1.490028 / 1.504120 (-0.014092) | 1.362249 / 1.541195 (-0.178946) | 1.368939 / 1.468490 (-0.099551) | 0.736643 / 4.584777 (-3.848134) | 2.414237 / 3.745712 (-1.331475) | 2.898911 / 5.269862 (-2.370951) | 1.840630 / 4.565676 (-2.725047) | 0.077872 / 0.424275 (-0.346403) | 0.005087 / 0.007607 (-0.002520) | 0.337054 / 0.226044 (0.111009) | 3.390734 / 2.268929 (1.121806) | 1.844174 / 55.444624 (-53.600451) | 1.532741 / 6.876477 (-5.343736) | 1.551650 / 2.142072 (-0.590422) | 0.778642 / 4.805227 (-4.026585) | 0.131899 / 6.500664 (-6.368765) | 0.041801 / 0.075469 (-0.033668) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.958362 / 1.841788 (-0.883425) | 11.323330 / 8.074308 (3.249022) | 9.396199 / 10.191392 (-0.795193) | 0.131154 / 0.680424 (-0.549270) | 0.014705 / 0.534201 (-0.519496) | 0.302424 / 0.579283 (-0.276859) | 0.261870 / 0.434364 (-0.172494) | 0.340788 / 0.540337 (-0.199550) | 0.433360 / 1.386936 (-0.953576) |\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) | 0.005571 / 0.011353 (-0.005782) | 0.003388 / 0.011008 (-0.007621) | 0.050366 / 0.038508 (0.011858) | 0.032633 / 0.023109 (0.009524) | 0.261847 / 0.275898 (-0.014051) | 0.292197 / 0.323480 (-0.031283) | 0.005070 / 0.007986 (-0.002916) | 0.002753 / 0.004328 (-0.001575) | 0.048613 / 0.004250 (0.044363) | 0.040272 / 0.037052 (0.003219) | 0.275441 / 0.258489 (0.016952) | 0.309175 / 0.293841 (0.015334) | 0.032403 / 0.128546 (-0.096143) | 0.011734 / 0.075646 (-0.063912) | 0.059532 / 0.419271 (-0.359740) | 0.033886 / 0.043533 (-0.009647) | 0.263453 / 0.255139 (0.008314) | 0.281997 / 0.283200 (-0.001203) | 0.018522 / 0.141683 (-0.123161) | 1.150364 / 1.452155 (-0.301791) | 1.204090 / 1.492716 (-0.288627) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093129 / 0.018006 (0.075123) | 0.303691 / 0.000490 (0.303201) | 0.000231 / 0.000200 (0.000031) | 0.000062 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022084 / 0.037411 (-0.015327) | 0.076354 / 0.014526 (0.061828) | 0.087710 / 0.176557 (-0.088847) | 0.128907 / 0.737135 (-0.608228) | 0.088603 / 0.296338 (-0.207735) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.301161 / 0.215209 (0.085952) | 2.954780 / 2.077655 (0.877125) | 1.601366 / 1.504120 (0.097246) | 1.477225 / 1.541195 (-0.063970) | 1.482355 / 1.468490 (0.013865) | 0.722461 / 4.584777 (-3.862315) | 0.981439 / 3.745712 (-2.764273) | 2.927006 / 5.269862 (-2.342856) | 1.884444 / 4.565676 (-2.681233) | 0.079044 / 0.424275 (-0.345231) | 0.005530 / 0.007607 (-0.002077) | 0.347082 / 0.226044 (0.121037) | 3.491984 / 2.268929 (1.223056) | 1.944317 / 55.444624 (-53.500307) | 1.645792 / 6.876477 (-5.230685) | 1.649506 / 2.142072 (-0.492567) | 0.800822 / 4.805227 (-4.004405) | 0.133936 / 6.500664 (-6.366729) | 0.041198 / 0.075469 (-0.034271) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.029764 / 1.841788 (-0.812024) | 11.928840 / 8.074308 (3.854532) | 10.021390 / 10.191392 (-0.170002) | 0.141608 / 0.680424 (-0.538816) | 0.014921 / 0.534201 (-0.519280) | 0.302050 / 0.579283 (-0.277233) | 0.124151 / 0.434364 (-0.310213) | 0.347143 / 0.540337 (-0.193195) | 0.467649 / 1.386936 (-0.919287) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e4c87a6bf57b3aa094c28895c5b89b91b3509c58 \"CML watermark\")\n" ]

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[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7125). 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) | 0.005741 / 0.011353 (-0.005612) | 0.004011 / 0.011008 (-0.006998) | 0.063962 / 0.038508 (0.025454) | 0.031512 / 0.023109 (0.008403) | 0.242249 / 0.275898 (-0.033649) | 0.269601 / 0.323480 (-0.053879) | 0.004502 / 0.007986 (-0.003483) | 0.002835 / 0.004328 (-0.001494) | 0.049878 / 0.004250 (0.045628) | 0.048012 / 0.037052 (0.010959) | 0.250454 / 0.258489 (-0.008035) | 0.283266 / 0.293841 (-0.010575) | 0.030752 / 0.128546 (-0.097794) | 0.012655 / 0.075646 (-0.062991) | 0.211043 / 0.419271 (-0.208229) | 0.037165 / 0.043533 (-0.006367) | 0.246815 / 0.255139 (-0.008324) | 0.264306 / 0.283200 (-0.018893) | 0.018343 / 0.141683 (-0.123340) | 1.140452 / 1.452155 (-0.311702) | 1.214849 / 1.492716 (-0.277867) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098048 / 0.018006 (0.080042) | 0.292201 / 0.000490 (0.291712) | 0.000217 / 0.000200 (0.000017) | 0.000056 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018732 / 0.037411 (-0.018679) | 0.062887 / 0.014526 (0.048361) | 0.074353 / 0.176557 (-0.102204) | 0.120794 / 0.737135 (-0.616341) | 0.077066 / 0.296338 (-0.219272) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276335 / 0.215209 (0.061126) | 2.722905 / 2.077655 (0.645250) | 1.423080 / 1.504120 (-0.081040) | 1.305443 / 1.541195 (-0.235752) | 1.342142 / 1.468490 (-0.126348) | 0.741899 / 4.584777 (-3.842878) | 2.407567 / 3.745712 (-1.338145) | 3.070263 / 5.269862 (-2.199599) | 1.935732 / 4.565676 (-2.629944) | 0.081371 / 0.424275 (-0.342904) | 0.005207 / 0.007607 (-0.002401) | 0.328988 / 0.226044 (0.102943) | 3.240771 / 2.268929 (0.971842) | 1.801028 / 55.444624 (-53.643597) | 1.490593 / 6.876477 (-5.385884) | 1.521317 / 2.142072 (-0.620756) | 0.794051 / 4.805227 (-4.011176) | 0.136398 / 6.500664 (-6.364266) | 0.042902 / 0.075469 (-0.032567) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974186 / 1.841788 (-0.867602) | 12.280011 / 8.074308 (4.205703) | 9.453389 / 10.191392 (-0.738003) | 0.132627 / 0.680424 (-0.547797) | 0.014608 / 0.534201 (-0.519593) | 0.309298 / 0.579283 (-0.269985) | 0.275911 / 0.434364 (-0.158452) | 0.348261 / 0.540337 (-0.192077) | 0.439031 / 1.386936 (-0.947905) |\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) | 0.006248 / 0.011353 (-0.005105) | 0.004369 / 0.011008 (-0.006639) | 0.050588 / 0.038508 (0.012080) | 0.032880 / 0.023109 (0.009771) | 0.268979 / 0.275898 (-0.006919) | 0.294714 / 0.323480 (-0.028766) | 0.004518 / 0.007986 (-0.003467) | 0.002995 / 0.004328 (-0.001333) | 0.048776 / 0.004250 (0.044525) | 0.041696 / 0.037052 (0.004644) | 0.283413 / 0.258489 (0.024924) | 0.322137 / 0.293841 (0.028296) | 0.032809 / 0.128546 (-0.095737) | 0.012559 / 0.075646 (-0.063087) | 0.060456 / 0.419271 (-0.358815) | 0.034564 / 0.043533 (-0.008968) | 0.267263 / 0.255139 (0.012124) | 0.292633 / 0.283200 (0.009434) | 0.019011 / 0.141683 (-0.122672) | 1.199820 / 1.452155 (-0.252335) | 1.251829 / 1.492716 (-0.240887) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097615 / 0.018006 (0.079609) | 0.313764 / 0.000490 (0.313274) | 0.000220 / 0.000200 (0.000020) | 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) | 0.024365 / 0.037411 (-0.013046) | 0.089301 / 0.014526 (0.074775) | 0.092964 / 0.176557 (-0.083592) | 0.131724 / 0.737135 (-0.605412) | 0.094792 / 0.296338 (-0.201546) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.305119 / 0.215209 (0.089910) | 2.932192 / 2.077655 (0.854537) | 1.610573 / 1.504120 (0.106453) | 1.487502 / 1.541195 (-0.053693) | 1.533300 / 1.468490 (0.064810) | 0.717223 / 4.584777 (-3.867554) | 0.964402 / 3.745712 (-2.781310) | 3.111398 / 5.269862 (-2.158464) | 1.957942 / 4.565676 (-2.607734) | 0.079160 / 0.424275 (-0.345116) | 0.005639 / 0.007607 (-0.001968) | 0.358971 / 0.226044 (0.132927) | 3.564401 / 2.268929 (1.295472) | 2.043079 / 55.444624 (-53.401546) | 1.742681 / 6.876477 (-5.133795) | 1.784758 / 2.142072 (-0.357314) | 0.798508 / 4.805227 (-4.006719) | 0.133905 / 6.500664 (-6.366759) | 0.043008 / 0.075469 (-0.032461) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.031715 / 1.841788 (-0.810073) | 13.374312 / 8.074308 (5.300004) | 10.789098 / 10.191392 (0.597706) | 0.133663 / 0.680424 (-0.546761) | 0.016692 / 0.534201 (-0.517509) | 0.304716 / 0.579283 (-0.274567) | 0.129074 / 0.434364 (-0.305290) | 0.346440 / 0.540337 (-0.193897) | 0.464593 / 1.386936 (-0.922343) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#880a52cea337032d39e90e6f0dcc55198a75a285 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7124). 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) | 0.005339 / 0.011353 (-0.006014) | 0.003640 / 0.011008 (-0.007368) | 0.064012 / 0.038508 (0.025504) | 0.030424 / 0.023109 (0.007314) | 0.239966 / 0.275898 (-0.035932) | 0.264361 / 0.323480 (-0.059119) | 0.004247 / 0.007986 (-0.003739) | 0.002847 / 0.004328 (-0.001481) | 0.049640 / 0.004250 (0.045390) | 0.044903 / 0.037052 (0.007851) | 0.250174 / 0.258489 (-0.008315) | 0.281423 / 0.293841 (-0.012418) | 0.029419 / 0.128546 (-0.099127) | 0.012221 / 0.075646 (-0.063426) | 0.205907 / 0.419271 (-0.213365) | 0.036654 / 0.043533 (-0.006878) | 0.245805 / 0.255139 (-0.009334) | 0.265029 / 0.283200 (-0.018170) | 0.018081 / 0.141683 (-0.123602) | 1.113831 / 1.452155 (-0.338324) | 1.156443 / 1.492716 (-0.336274) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.134389 / 0.018006 (0.116383) | 0.300637 / 0.000490 (0.300147) | 0.000240 / 0.000200 (0.000040) | 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) | 0.019111 / 0.037411 (-0.018300) | 0.062585 / 0.014526 (0.048059) | 0.075909 / 0.176557 (-0.100647) | 0.121382 / 0.737135 (-0.615753) | 0.074980 / 0.296338 (-0.221359) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285062 / 0.215209 (0.069853) | 2.850130 / 2.077655 (0.772476) | 1.519877 / 1.504120 (0.015757) | 1.388711 / 1.541195 (-0.152484) | 1.397284 / 1.468490 (-0.071206) | 0.723100 / 4.584777 (-3.861677) | 2.393184 / 3.745712 (-1.352529) | 2.908418 / 5.269862 (-2.361443) | 1.871024 / 4.565676 (-2.694653) | 0.078230 / 0.424275 (-0.346045) | 0.005158 / 0.007607 (-0.002449) | 0.345622 / 0.226044 (0.119577) | 3.357611 / 2.268929 (1.088683) | 1.844492 / 55.444624 (-53.600132) | 1.584237 / 6.876477 (-5.292240) | 1.577158 / 2.142072 (-0.564915) | 0.789702 / 4.805227 (-4.015525) | 0.132045 / 6.500664 (-6.368619) | 0.042304 / 0.075469 (-0.033165) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977166 / 1.841788 (-0.864622) | 11.306118 / 8.074308 (3.231810) | 9.490778 / 10.191392 (-0.700614) | 0.143536 / 0.680424 (-0.536888) | 0.015304 / 0.534201 (-0.518897) | 0.313892 / 0.579283 (-0.265391) | 0.267009 / 0.434364 (-0.167355) | 0.345560 / 0.540337 (-0.194778) | 0.435649 / 1.386936 (-0.951287) |\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) | 0.005700 / 0.011353 (-0.005653) | 0.003490 / 0.011008 (-0.007519) | 0.049990 / 0.038508 (0.011482) | 0.032070 / 0.023109 (0.008961) | 0.272622 / 0.275898 (-0.003276) | 0.298265 / 0.323480 (-0.025215) | 0.004379 / 0.007986 (-0.003606) | 0.002786 / 0.004328 (-0.001543) | 0.048271 / 0.004250 (0.044020) | 0.040102 / 0.037052 (0.003050) | 0.286433 / 0.258489 (0.027944) | 0.319306 / 0.293841 (0.025465) | 0.032872 / 0.128546 (-0.095675) | 0.011870 / 0.075646 (-0.063776) | 0.059886 / 0.419271 (-0.359385) | 0.034281 / 0.043533 (-0.009252) | 0.275588 / 0.255139 (0.020450) | 0.292951 / 0.283200 (0.009751) | 0.018095 / 0.141683 (-0.123588) | 1.130870 / 1.452155 (-0.321285) | 1.190761 / 1.492716 (-0.301955) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093346 / 0.018006 (0.075340) | 0.307506 / 0.000490 (0.307016) | 0.000214 / 0.000200 (0.000014) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022873 / 0.037411 (-0.014538) | 0.077070 / 0.014526 (0.062544) | 0.089152 / 0.176557 (-0.087404) | 0.130186 / 0.737135 (-0.606949) | 0.090244 / 0.296338 (-0.206095) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297950 / 0.215209 (0.082740) | 2.942360 / 2.077655 (0.864705) | 1.614324 / 1.504120 (0.110204) | 1.495795 / 1.541195 (-0.045400) | 1.506155 / 1.468490 (0.037665) | 0.730307 / 4.584777 (-3.854470) | 0.966312 / 3.745712 (-2.779400) | 2.928955 / 5.269862 (-2.340906) | 1.940049 / 4.565676 (-2.625627) | 0.079589 / 0.424275 (-0.344686) | 0.006004 / 0.007607 (-0.001604) | 0.356630 / 0.226044 (0.130585) | 3.516652 / 2.268929 (1.247724) | 1.963196 / 55.444624 (-53.481429) | 1.674489 / 6.876477 (-5.201988) | 1.677558 / 2.142072 (-0.464514) | 0.806447 / 4.805227 (-3.998780) | 0.133819 / 6.500664 (-6.366845) | 0.040762 / 0.075469 (-0.034707) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.038495 / 1.841788 (-0.803293) | 11.829186 / 8.074308 (3.754878) | 10.214158 / 10.191392 (0.022766) | 0.140590 / 0.680424 (-0.539834) | 0.014729 / 0.534201 (-0.519472) | 0.300557 / 0.579283 (-0.278726) | 0.122772 / 0.434364 (-0.311592) | 0.344618 / 0.540337 (-0.195720) | 0.460064 / 1.386936 (-0.926872) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#be5cff059a2a5b89d7a97bc04739c4919ab8089f \"CML watermark\")\n" ]

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[ "Note that you can already have one directory per subset just for the metadata, e.g.\r\n\r\n```\r\nconfigs:\r\n - config_name: subset0\r\n data_files:\r\n - subset0/metadata.csv\r\n - images/*.jpg\r\n - config_name: subset1\r\n data_files:\r\n - subset1/metadata.csv\r\n - images/*.jpg\r\n```\r\n\r\nEDIT: ah maybe it doesn't work because you'd have to provide relative paths from the metadata files to the images", "Yes, that's part of the issue. Also, `metadata.csv` is a very ambiguous name and we generally try to avoid using the same name for different files within a dataset, as this can quickly lead to confusion.", "I think supporting `**/*-metadata.csv` or `**/*_metadata.csv` makes sense to me. If it sounds good to you feel free to open a PR to update the patterns here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/d4422cc24a56dc7132ddc3fd6b285c5edbd60b8c/src/datasets/data_files.py#L104-L115" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7121). 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) | 0.005273 / 0.011353 (-0.006079) | 0.003789 / 0.011008 (-0.007219) | 0.062811 / 0.038508 (0.024303) | 0.031055 / 0.023109 (0.007946) | 0.238663 / 0.275898 (-0.037235) | 0.269706 / 0.323480 (-0.053774) | 0.004105 / 0.007986 (-0.003881) | 0.002781 / 0.004328 (-0.001547) | 0.048800 / 0.004250 (0.044549) | 0.045759 / 0.037052 (0.008707) | 0.260467 / 0.258489 (0.001978) | 0.288800 / 0.293841 (-0.005041) | 0.029341 / 0.128546 (-0.099205) | 0.012413 / 0.075646 (-0.063233) | 0.203493 / 0.419271 (-0.215778) | 0.037270 / 0.043533 (-0.006263) | 0.246130 / 0.255139 (-0.009009) | 0.269046 / 0.283200 (-0.014154) | 0.017788 / 0.141683 (-0.123895) | 1.175537 / 1.452155 (-0.276617) | 1.197909 / 1.492716 (-0.294808) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098258 / 0.018006 (0.080251) | 0.305283 / 0.000490 (0.304794) | 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) | 0.019066 / 0.037411 (-0.018345) | 0.062723 / 0.014526 (0.048197) | 0.075827 / 0.176557 (-0.100730) | 0.121371 / 0.737135 (-0.615764) | 0.075167 / 0.296338 (-0.221171) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296650 / 0.215209 (0.081441) | 2.910593 / 2.077655 (0.832939) | 1.510798 / 1.504120 (0.006678) | 1.375461 / 1.541195 (-0.165733) | 1.386423 / 1.468490 (-0.082067) | 0.743818 / 4.584777 (-3.840959) | 2.437848 / 3.745712 (-1.307864) | 2.943661 / 5.269862 (-2.326201) | 1.888977 / 4.565676 (-2.676699) | 0.080126 / 0.424275 (-0.344149) | 0.005168 / 0.007607 (-0.002439) | 0.348699 / 0.226044 (0.122654) | 3.477686 / 2.268929 (1.208758) | 1.901282 / 55.444624 (-53.543343) | 1.574847 / 6.876477 (-5.301629) | 1.594359 / 2.142072 (-0.547714) | 0.793415 / 4.805227 (-4.011812) | 0.133982 / 6.500664 (-6.366682) | 0.042435 / 0.075469 (-0.033034) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963057 / 1.841788 (-0.878731) | 11.597217 / 8.074308 (3.522909) | 9.285172 / 10.191392 (-0.906220) | 0.130510 / 0.680424 (-0.549914) | 0.013964 / 0.534201 (-0.520237) | 0.299334 / 0.579283 (-0.279949) | 0.267775 / 0.434364 (-0.166589) | 0.336922 / 0.540337 (-0.203416) | 0.430493 / 1.386936 (-0.956443) |\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) | 0.005701 / 0.011353 (-0.005652) | 0.003941 / 0.011008 (-0.007067) | 0.050204 / 0.038508 (0.011696) | 0.032275 / 0.023109 (0.009166) | 0.271076 / 0.275898 (-0.004822) | 0.295565 / 0.323480 (-0.027914) | 0.004393 / 0.007986 (-0.003592) | 0.002881 / 0.004328 (-0.001447) | 0.048032 / 0.004250 (0.043782) | 0.040430 / 0.037052 (0.003378) | 0.281631 / 0.258489 (0.023142) | 0.317964 / 0.293841 (0.024124) | 0.032318 / 0.128546 (-0.096228) | 0.012348 / 0.075646 (-0.063298) | 0.060336 / 0.419271 (-0.358936) | 0.034148 / 0.043533 (-0.009385) | 0.273803 / 0.255139 (0.018664) | 0.292068 / 0.283200 (0.008868) | 0.018693 / 0.141683 (-0.122990) | 1.155704 / 1.452155 (-0.296451) | 1.192245 / 1.492716 (-0.300472) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097588 / 0.018006 (0.079582) | 0.311760 / 0.000490 (0.311270) | 0.000232 / 0.000200 (0.000032) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022825 / 0.037411 (-0.014586) | 0.077698 / 0.014526 (0.063172) | 0.088567 / 0.176557 (-0.087989) | 0.129689 / 0.737135 (-0.607446) | 0.090626 / 0.296338 (-0.205712) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299791 / 0.215209 (0.084582) | 2.978558 / 2.077655 (0.900903) | 1.594095 / 1.504120 (0.089975) | 1.468476 / 1.541195 (-0.072719) | 1.482880 / 1.468490 (0.014390) | 0.717553 / 4.584777 (-3.867224) | 0.977501 / 3.745712 (-2.768211) | 2.954289 / 5.269862 (-2.315572) | 1.895473 / 4.565676 (-2.670203) | 0.078452 / 0.424275 (-0.345824) | 0.005508 / 0.007607 (-0.002099) | 0.350882 / 0.226044 (0.124837) | 3.480878 / 2.268929 (1.211949) | 1.965240 / 55.444624 (-53.479385) | 1.672448 / 6.876477 (-5.204029) | 1.674319 / 2.142072 (-0.467753) | 0.789049 / 4.805227 (-4.016178) | 0.132715 / 6.500664 (-6.367949) | 0.041081 / 0.075469 (-0.034388) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.022953 / 1.841788 (-0.818834) | 12.123349 / 8.074308 (4.049041) | 10.336115 / 10.191392 (0.144723) | 0.142233 / 0.680424 (-0.538191) | 0.015416 / 0.534201 (-0.518785) | 0.303088 / 0.579283 (-0.276195) | 0.124942 / 0.434364 (-0.309422) | 0.338454 / 0.540337 (-0.201883) | 0.460039 / 1.386936 (-0.926897) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3813ce846e52824b38e53895810682f0a496a2e3 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7120). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Note that in this case, we could even expect this kind of message:\r\n\r\n```\r\nDataFilesNotFoundError: Unable to find 'hf://datasets/Omega02gdfdd/bioclip-demo-zero-shot-mistakes@12b0313ba4c3189ee5a24cb76200959e9bf7492e/data.csv'\r\n```\r\n\r\nWe generally return `DataFilesNotFoundError` for this case (data files passed as an argument), not sure why it does not occur with this dataset.", "<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) | 0.005484 / 0.011353 (-0.005869) | 0.003932 / 0.011008 (-0.007077) | 0.063177 / 0.038508 (0.024669) | 0.031311 / 0.023109 (0.008202) | 0.254881 / 0.275898 (-0.021017) | 0.273818 / 0.323480 (-0.049662) | 0.003312 / 0.007986 (-0.004674) | 0.003251 / 0.004328 (-0.001078) | 0.049307 / 0.004250 (0.045057) | 0.046189 / 0.037052 (0.009137) | 0.268182 / 0.258489 (0.009693) | 0.303659 / 0.293841 (0.009818) | 0.029312 / 0.128546 (-0.099234) | 0.013649 / 0.075646 (-0.061997) | 0.204240 / 0.419271 (-0.215032) | 0.036607 / 0.043533 (-0.006926) | 0.252232 / 0.255139 (-0.002907) | 0.271960 / 0.283200 (-0.011239) | 0.018043 / 0.141683 (-0.123640) | 1.148601 / 1.452155 (-0.303553) | 1.212313 / 1.492716 (-0.280403) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096354 / 0.018006 (0.078348) | 0.302575 / 0.000490 (0.302085) | 0.000246 / 0.000200 (0.000046) | 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) | 0.019023 / 0.037411 (-0.018389) | 0.064821 / 0.014526 (0.050295) | 0.077046 / 0.176557 (-0.099510) | 0.122896 / 0.737135 (-0.614239) | 0.078300 / 0.296338 (-0.218038) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283681 / 0.215209 (0.068472) | 2.801473 / 2.077655 (0.723818) | 1.505611 / 1.504120 (0.001491) | 1.385832 / 1.541195 (-0.155363) | 1.430284 / 1.468490 (-0.038206) | 0.752041 / 4.584777 (-3.832736) | 2.406138 / 3.745712 (-1.339574) | 2.941370 / 5.269862 (-2.328492) | 1.887681 / 4.565676 (-2.677996) | 0.078693 / 0.424275 (-0.345582) | 0.005266 / 0.007607 (-0.002341) | 0.336484 / 0.226044 (0.110440) | 3.372262 / 2.268929 (1.103334) | 1.861541 / 55.444624 (-53.583084) | 1.572782 / 6.876477 (-5.303694) | 1.592387 / 2.142072 (-0.549685) | 0.796557 / 4.805227 (-4.008670) | 0.134923 / 6.500664 (-6.365741) | 0.043007 / 0.075469 (-0.032462) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.982690 / 1.841788 (-0.859097) | 11.700213 / 8.074308 (3.625905) | 9.122642 / 10.191392 (-1.068750) | 0.141430 / 0.680424 (-0.538994) | 0.014971 / 0.534201 (-0.519230) | 0.300938 / 0.579283 (-0.278345) | 0.268315 / 0.434364 (-0.166049) | 0.339891 / 0.540337 (-0.200447) | 0.428302 / 1.386936 (-0.958634) |\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) | 0.005732 / 0.011353 (-0.005621) | 0.003905 / 0.011008 (-0.007103) | 0.049900 / 0.038508 (0.011392) | 0.032255 / 0.023109 (0.009145) | 0.267929 / 0.275898 (-0.007969) | 0.295595 / 0.323480 (-0.027885) | 0.004437 / 0.007986 (-0.003549) | 0.003008 / 0.004328 (-0.001321) | 0.048357 / 0.004250 (0.044107) | 0.040118 / 0.037052 (0.003066) | 0.282859 / 0.258489 (0.024370) | 0.319243 / 0.293841 (0.025402) | 0.032793 / 0.128546 (-0.095754) | 0.012091 / 0.075646 (-0.063555) | 0.060082 / 0.419271 (-0.359189) | 0.034426 / 0.043533 (-0.009107) | 0.273668 / 0.255139 (0.018529) | 0.292110 / 0.283200 (0.008910) | 0.019002 / 0.141683 (-0.122680) | 1.165850 / 1.452155 (-0.286304) | 1.209195 / 1.492716 (-0.283521) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099267 / 0.018006 (0.081261) | 0.316746 / 0.000490 (0.316256) | 0.000267 / 0.000200 (0.000067) | 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) | 0.023117 / 0.037411 (-0.014294) | 0.076691 / 0.014526 (0.062165) | 0.092190 / 0.176557 (-0.084367) | 0.130620 / 0.737135 (-0.606515) | 0.091068 / 0.296338 (-0.205271) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296419 / 0.215209 (0.081210) | 2.933964 / 2.077655 (0.856309) | 1.595015 / 1.504120 (0.090895) | 1.467610 / 1.541195 (-0.073585) | 1.487386 / 1.468490 (0.018896) | 0.730927 / 4.584777 (-3.853850) | 0.971276 / 3.745712 (-2.774436) | 2.969735 / 5.269862 (-2.300127) | 1.916126 / 4.565676 (-2.649550) | 0.078863 / 0.424275 (-0.345412) | 0.005506 / 0.007607 (-0.002101) | 0.345191 / 0.226044 (0.119147) | 3.407481 / 2.268929 (1.138553) | 1.955966 / 55.444624 (-53.488659) | 1.677365 / 6.876477 (-5.199112) | 1.716052 / 2.142072 (-0.426020) | 0.797208 / 4.805227 (-4.008020) | 0.132853 / 6.500664 (-6.367811) | 0.041691 / 0.075469 (-0.033778) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.042331 / 1.841788 (-0.799456) | 12.186080 / 8.074308 (4.111772) | 10.288961 / 10.191392 (0.097569) | 0.141897 / 0.680424 (-0.538526) | 0.015321 / 0.534201 (-0.518880) | 0.308302 / 0.579283 (-0.270981) | 0.123292 / 0.434364 (-0.311072) | 0.348515 / 0.540337 (-0.191823) | 0.473045 / 1.386936 (-0.913891) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cedffa52879ebc5e4df43f0bcf8660ee7229f0dc \"CML watermark\")\n" ]

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[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7119). 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) | 0.005156 / 0.011353 (-0.006197) | 0.003365 / 0.011008 (-0.007643) | 0.063451 / 0.038508 (0.024943) | 0.029510 / 0.023109 (0.006401) | 0.244825 / 0.275898 (-0.031074) | 0.265157 / 0.323480 (-0.058323) | 0.004239 / 0.007986 (-0.003747) | 0.002732 / 0.004328 (-0.001596) | 0.050412 / 0.004250 (0.046162) | 0.043608 / 0.037052 (0.006556) | 0.256635 / 0.258489 (-0.001854) | 0.277472 / 0.293841 (-0.016369) | 0.029329 / 0.128546 (-0.099217) | 0.012318 / 0.075646 (-0.063329) | 0.204751 / 0.419271 (-0.214520) | 0.036468 / 0.043533 (-0.007065) | 0.246773 / 0.255139 (-0.008366) | 0.263932 / 0.283200 (-0.019268) | 0.017053 / 0.141683 (-0.124629) | 1.173249 / 1.452155 (-0.278905) | 1.234186 / 1.492716 (-0.258531) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092398 / 0.018006 (0.074391) | 0.309473 / 0.000490 (0.308983) | 0.000220 / 0.000200 (0.000020) | 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) | 0.018553 / 0.037411 (-0.018858) | 0.062546 / 0.014526 (0.048020) | 0.073943 / 0.176557 (-0.102613) | 0.120498 / 0.737135 (-0.616638) | 0.075185 / 0.296338 (-0.221153) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296899 / 0.215209 (0.081690) | 2.919088 / 2.077655 (0.841433) | 1.533146 / 1.504120 (0.029026) | 1.395441 / 1.541195 (-0.145754) | 1.399089 / 1.468490 (-0.069401) | 0.742750 / 4.584777 (-3.842027) | 2.390317 / 3.745712 (-1.355395) | 2.883166 / 5.269862 (-2.386695) | 1.854003 / 4.565676 (-2.711674) | 0.077140 / 0.424275 (-0.347136) | 0.005176 / 0.007607 (-0.002432) | 0.349391 / 0.226044 (0.123347) | 3.466043 / 2.268929 (1.197114) | 1.870619 / 55.444624 (-53.574005) | 1.559173 / 6.876477 (-5.317303) | 1.605480 / 2.142072 (-0.536592) | 0.786753 / 4.805227 (-4.018474) | 0.134869 / 6.500664 (-6.365795) | 0.042176 / 0.075469 (-0.033293) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.954256 / 1.841788 (-0.887532) | 11.194758 / 8.074308 (3.120449) | 9.129670 / 10.191392 (-1.061722) | 0.138318 / 0.680424 (-0.542106) | 0.014299 / 0.534201 (-0.519902) | 0.303704 / 0.579283 (-0.275579) | 0.262513 / 0.434364 (-0.171851) | 0.346539 / 0.540337 (-0.193798) | 0.429524 / 1.386936 (-0.957412) |\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) | 0.005692 / 0.011353 (-0.005661) | 0.003423 / 0.011008 (-0.007586) | 0.050618 / 0.038508 (0.012110) | 0.031053 / 0.023109 (0.007944) | 0.275901 / 0.275898 (0.000003) | 0.294404 / 0.323480 (-0.029076) | 0.004303 / 0.007986 (-0.003682) | 0.002728 / 0.004328 (-0.001600) | 0.049757 / 0.004250 (0.045507) | 0.039997 / 0.037052 (0.002945) | 0.287291 / 0.258489 (0.028802) | 0.319186 / 0.293841 (0.025345) | 0.032558 / 0.128546 (-0.095988) | 0.012088 / 0.075646 (-0.063558) | 0.060746 / 0.419271 (-0.358525) | 0.034046 / 0.043533 (-0.009486) | 0.276170 / 0.255139 (0.021031) | 0.293673 / 0.283200 (0.010474) | 0.018018 / 0.141683 (-0.123665) | 1.158453 / 1.452155 (-0.293701) | 1.198599 / 1.492716 (-0.294118) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093134 / 0.018006 (0.075127) | 0.304511 / 0.000490 (0.304021) | 0.000216 / 0.000200 (0.000016) | 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) | 0.022991 / 0.037411 (-0.014421) | 0.077548 / 0.014526 (0.063022) | 0.087887 / 0.176557 (-0.088670) | 0.131786 / 0.737135 (-0.605349) | 0.088747 / 0.296338 (-0.207591) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302811 / 0.215209 (0.087602) | 2.959276 / 2.077655 (0.881621) | 1.591348 / 1.504120 (0.087229) | 1.464731 / 1.541195 (-0.076464) | 1.474112 / 1.468490 (0.005622) | 0.741573 / 4.584777 (-3.843204) | 0.959229 / 3.745712 (-2.786483) | 2.895750 / 5.269862 (-2.374111) | 1.896051 / 4.565676 (-2.669625) | 0.079012 / 0.424275 (-0.345264) | 0.005494 / 0.007607 (-0.002113) | 0.355699 / 0.226044 (0.129655) | 3.524833 / 2.268929 (1.255905) | 1.972358 / 55.444624 (-53.472266) | 1.667249 / 6.876477 (-5.209228) | 1.658635 / 2.142072 (-0.483438) | 0.813184 / 4.805227 (-3.992044) | 0.134226 / 6.500664 (-6.366438) | 0.041087 / 0.075469 (-0.034382) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.038963 / 1.841788 (-0.802824) | 11.785835 / 8.074308 (3.711526) | 10.397027 / 10.191392 (0.205635) | 0.141748 / 0.680424 (-0.538676) | 0.014738 / 0.534201 (-0.519463) | 0.300056 / 0.579283 (-0.279227) | 0.127442 / 0.434364 (-0.306922) | 0.345013 / 0.540337 (-0.195324) | 0.449598 / 1.386936 (-0.937338) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#70bac27ef861b2b11f581a291a6b76adeee24f98 \"CML watermark\")\n" ]

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