Datasets:

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like 0

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Hi @DataNoob0723,\r\n\r\nUnder the hood, we use `pandas` to load CSV/TSV files. Therefore, you should use \"csv\" and pass `sep=\"\\t\"`, as explained in our docs: https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/loading_methods#from-files\r\n```python\r\nds = load_dataset('csv', sep=\"\\t\", data_files=data_files)\r\n``` " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "We are having an unrelated issue that makes several tests fail. We are working on that. Once fixed, you will be able to merge the main branch into this, so that you get the fix and the tests pass..." ]

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[ "Thanks for reporting, it's fixed now (I refreshed it manually). It's a known issue; we hope it will be fixed permanently in a few days.\r\n\r\n<img width=\"1508\" alt=\"Capture d’écran 2022-09-05 à 18 31 22\" src=\"https://user-images.githubusercontent.com/1676121/188489762-0ed86a7e-dfb3-46e8-a125-43b815a2c6f4.png\">\r\n", "Thanks @severo! " ]

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[ "Good idea ! I think this can be improved in `Features.reorder_fields_as()` indeed at\r\n\r\nhttps://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/features/features.py#L1739-L1740\r\n\r\nIs it something you would be interested in contributing ?", "Is this open to work on? I'd love to take on this as my first issue.", "Hi @daspartho I’ve opened a PR #4919 \r\nI don’t think there’s much left to do", "ok : )" ]

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[]

[ "See:\r\n- #4915" ]

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[ "Hi @Shilpac20,\r\n\r\nAs explained in the Wikipedia dataset card: https://huggingface.co/datasets/wikipedia\r\n> You can find the full list of languages and dates [here](https://dumps.wikimedia.org/backup-index.html).\r\n\r\nThis means that, before passing a specific date, you should first make sure it is available online, as Wikimedia only keeps last X months (depending on the size of the corresponding language dump)): e.g. to see which dates \"aa\" Wikipedia is available online, see https://dumps.wikimedia.org/aawiki/ (as of today 2022-08-31, the available dates are from [20220401](https://dumps.wikimedia.org/aawiki/20220401/) to [20220820](https://dumps.wikimedia.org/aawiki/20220820/)).", "Hi, the date that I have specified \"20220401\" is available for the language \"aa\". The error persists for any other available dates as present in https://dumps.wikimedia.org/aawiki/. The error is mainly due to apache beam not able to write the downloaded files. Any help on this?", "I see, sorry, I misread your issue.\r\n\r\nWe are investigating this.", "I am struggling with basically the same issue. I am trying to download the German Wikipedia dump.\r\n\r\nAs per the [documentation](https://huggingface.co/datasets/wikipedia), `\"20220301.de\"` should be available as a pre-processed dataset.\r\n\r\nIssuing the command mentioned in the documentation cited above\r\n\r\n from datasets import load_dataset\r\n load_dataset(\"wikipedia\", \"20220301.de\")\r\n\r\nraises the following `FileNotFound` error\r\n\r\n FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/dewiki/20220301/dumpstatus.json\r\n\r\nUsing the ([undocumented](https://huggingface.co/docs/datasets/v1.2.1/package_reference/loading_methods.html#datasets.load_dataset)?) call to `load_dataset()` with `language` and `date` parameters\r\n\r\n load_dataset(\"wikipedia\", language=\"de\", date=\"20220301\", beam_runner=\"DirectRunner\")\r\n\r\nproduces the same error.\r\n\r\nEDIT: as I am using `datasets` v2.7.1, I should be looking at [that version's documentation](https://huggingface.co/docs/datasets/v2.7.1/en/package_reference/loading_methods#datasets.load_dataset)! It is mentioned there, that additional `kwargs` are \"passed to the [BuilderConfig](https://huggingface.co/docs/datasets/v2.7.1/en/package_reference/builder_classes#datasets.BuilderConfig) and used in the [DatasetBuilder](https://huggingface.co/docs/datasets/v2.7.1/en/package_reference/builder_classes#datasets.DatasetBuilder)\". So I guess that is how `language` and `date` are used.\r\n\r\nAs I can see a folder `20221130` on `https://dumps.wikimedia.org/dewiki/`, I also tried\r\n\r\n from datasets import load_dataset\r\n load_dataset(\"wikipedia\", \"20221130.de\")\r\n\r\nwhich throws another error:\r\n\r\n ValueError: BuilderConfig 20221120.de not found. Available: ['20220301.aa', ... '20220301.de', ...\r\n\r\nbasically telling me that the dataset I originally requested (`'20220301.de'`) is available...\r\n\r\nIt seems that `load_dataset` is not handling the vanishing older dumps for Wikipedia correctly?", "I am able to start downloading the dataset when trying anything with the recent dumps for 20221201. But obviously, those are the big wiki dumps and I need the smaller preloaded version.\r\n\r\nI am now getting some error when the files show up in my cache but it will say FileNotFoundError at the end of the download for some reason. The cache directory to the datasets\\wikipedia\\date.bn\\ had something in it, then when the error came up it disappeared. \r\n\r\nIt is easy to test with the langauge \"bn\" because the amount of files is low.\r\n\r\ndataset = load_dataset('wikipedia', date=\"20221201\", language=\"bn\", split='train', beam_runner='DirectRunner')" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Hi ! Interesting question ;)\r\n\r\n> Which is better? Process in map() or in data-collator\r\n\r\nAs you said, both can be used in practice: map() if you want to preprocess before training, or a data-collator (or the equivalent `dataset.set_transform`) if you want to preprocess on-the-fly during training. Both options are great and really depend on your case.\r\n\r\nTo choose between the two, here are IMO the main caveats of each approach:\r\n- if your preprocessing takes too much CPU for example, using a data-collator may slow down your training and your GPUs may not work at full speed\r\n- on the other hand, map() may take a lot of time and disk space to run if your dataset is too big.\r\n\r\n> Why huggingface advises map() function? There should be some advantages to using map()\r\n\r\nTo get the best throughput when training a model, it is often recommended to preprocess your dataset before training. Note that preprocessing may include other steps before tokenization such as data filtering, cleaning, chunking etc. which are often done before training.", "Thanks for your clear explanation @lhoestq ! \r\n> * if your preprocessing takes too much CPU for example, using a data-collator may slow down your training and your GPUs may not work at full speed\r\n> * on the other hand, map() may take a lot of time and disk space to run if your dataset is too big.\r\n\r\nI really agree with you. There should be some trade-off between processing before and during the train loop.\r\nBesides, I find `map()` function can cache the results once it has been executed. Very useful!", "I'm closing this issue if you don't mind, feel free to reopen if needed ;)" ]

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[ "You could also switch to using `huggingface_hub` more directly, where such a guarantee is already tested =)\r\n\r\ncc @Wauplin " ]

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[ "I am getting similar error - `TypeError: type object got multiple values for keyword argument 'name'` while following this [tutorial](https://huggingface.co/docs/datasets/dataset_script#create-a-dataset-loading-script). I am getting this error with the `dataset-cli test` command.\r\n\r\n`datasets` version: 2.4.0", "In my case, this was happening because I defined multiple `BuilderConfig` for multiple types, but didn't had all the data files that are requierd by those configs. \r\n\r\nI think this is different than the original issue by @bablf .", "Hi ! I think this can be fixed by letting the config_kwargs take over the builder kwargs here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/load.py#L1533-L1534\r\n\r\nmaybe something like this ?\r\n```python\r\n **{**builder_kwargs, **config_kwargs}\r\n```\r\n\r\nLet me know if you'd like to contribute and fix this bug, so I can assign you :)\r\n\r\n> In my case, this was happening because I defined multiple BuilderConfig for multiple types, but didn't had all the data files that are requierd by those configs.\r\n> \r\n> I think this is different than the original issue by @bablf .\r\n\r\nFeel free to to open an new issue, I'd be happy to help\r\n", "@lhoestq Yeah, I want to, please assign.", "Cool thank you ! Let me know if you have questions or if I can help", "@lhoestq On second thoughts, I think this might be expected behavior; although a better error message might help.\r\n\r\nReasoning: Given n configs, if no data file is provided for any config, then it should be an error. Then why it should not be the case if out of n configs, for some data files are provided but not for others. Also, I was using `--all_configs` flag with `dataset-cli test`.", "Ok I see - maybe we should check the values of builder_kwargs raise an error if any key in config_kwargs tries to overwrite it ? The builder kwargs are determined from the builder's type and location (in some cases it forces the base_path, data_files and config name for example)" ]

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[ "Thanks for reporting @hannan72 ! I couldn't reproduce the error on my side, can you share the full stack trace please ?", "Thanks a lot for your response @lhoestq \r\nThe problem is solved accidentally today and I don't know exactly why it was happened yesterday.\r\nThe issue can be closed.", "Ok, let us know if you encounter the issue again ;)" ]

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[ "Thanks for reporting, @OPterminator.\r\n\r\nHowever, we are not able to reproduce this issue.\r\n\r\nThere might be 2 reasons why you get this exception:\r\n- Either the name of your local Python file: if it is called `datasets.py` this could generate a circular import when trying to import the Hugging Face `datasets` library.\r\n - You could try to rename it and run it again.\r\n- Another cause could be the simultaneous use of the packages `nlp` and `datasets`. Please note that we renamed the Hugging Face `nlp` library to `datasets` more than 2 years ago: they are 2 versions of the same library.\r\n - Please try to update your script and use only `datasets` (`nlp` name is no longer in use and is out of date)." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "This PR fixes a bug introduced in:\r\n- #4184" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Seems to be linked to the use of the undocumented `_resolve_features` method in the dataset viewer backend:\r\n\r\n```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"asaxena1990/Dummy_dataset\", name=\"asaxena1990--Dummy_dataset\", split=\"train\", streaming=True)\r\nUsing custom data configuration asaxena1990--Dummy_dataset-4a704ed7e5627563\r\n>>> dataset._resolve_features()\r\nFailed to read file 'https://huggingface.co/datasets/asaxena1990/Dummy_dataset/resolve/06885879a8bdd767d2d27695484fc6c83244617a/dummy_dataset_train.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Column() changed from object to array in row 0\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py\", line 109, in _generate_tables\r\n pa_table = paj.read_json(\r\n File \"pyarrow/_json.pyx\", line 246, in pyarrow._json.read_json\r\n File \"pyarrow/error.pxi\", line 143, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 99, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to array in row 0\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 1261, in _resolve_features\r\n features = _infer_features_from_batch(self._head())\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 686, in _head\r\n return _examples_to_batch([x for key, x in islice(self._iter(), n)])\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 686, in <listcomp>\r\n return _examples_to_batch([x for key, x in islice(self._iter(), n)])\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 708, in _iter\r\n yield from ex_iterable\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 112, in __iter__\r\n yield from self.generate_examples_fn(**self.kwargs)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 651, in wrapper\r\n for key, table in generate_tables_fn(**kwargs):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py\", line 137, in _generate_tables\r\n f\"This JSON file contain the following fields: {str(list(dataset.keys()))}. \"\r\nAttributeError: 'list' object has no attribute 'keys'\r\n```\r\n\r\nPinging @huggingface/datasets", "Hi ! JSON files containing a list of object are not supported yet, you can use JSON Lines files instead in the meantime\r\n```json\r\n{\"text\": \"can I know this?\", \"intent\": \"Know\", \"type\": \"Test\"}\r\n{\"text\": \"can I know this?\", \"intent\": \"Know\", \"type\": \"Test\"}\r\n...\r\n```" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Yes, the dataset viewer is based on `datasets`, and the following does not work:\r\n\r\n```\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names('timit_asr')\r\nDownloading builder script: 7.48kB [00:00, 6.69MB/s]\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 354, in get_dataset_config_info\r\n for split_generator in builder._split_generators(\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/timit_asr/43f9448dd5db58e95ee48a277f466481b151f112ea53e27f8173784da9254fb2/timit_asr.py\", line 117, in _split_generators\r\n data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))\r\n File \"/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/posixpath.py\", line 231, in expanduser\r\n path = os.fspath(path)\r\nTypeError: expected str, bytes or os.PathLike object, not NoneType\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 404, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 359, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```\r\n\r\ncc @huggingface/datasets ", "Due to license restriction, this dataset needs manual downloading of the original data.\r\n\r\nThis information is in the dataset card: https://huggingface.co/datasets/timit_asr\r\n> The dataset needs to be downloaded manually from https://catalog.ldc.upenn.edu/LDC93S1", "Maybe a better error message for datasets that need manual downloading? @severo \r\n\r\nMaybe we can raise a specific excpetion as done from `load_dataset`...", "Yes, ideally something like https://github.com/huggingface/datasets/blob/main/src/datasets/builder.py#L81\r\n", "The preview is now disabled (and a descriptive warning is displayed) for datasets requiring manual download. See:\r\n\r\n\r\n" ]

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[ "Hi ! The cache files are the results of all the transforms you applied to the dataset using `map` for example.\r\nDid you run a transform that could potentially blow up the size of the dataset ?", "@lhoestq,\r\nI don't remember, but I can't imagine what kind of transform may generate data that grow over 200 times in size. \r\nI think maybe it doesn' matter, it's just cache after all." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "I don't know the main problem but it looks like, it is ignoring the last directory in your case. So, create a directory called 'zzz' in the same folder as train, validation and test. if it doesn't work, create a directory called \"aaa\". It worked for me.\r\n", "@SamSamhuns could you please try to load it with the current main-branch version of `datasets`? I suppose the problem is that it tries to get splits names from filenames in this case, ignoring directories names, but `val` wasn't in keywords at that time, but it was fixed recently in this PR https://github.com/huggingface/datasets/pull/4844. ", "I have a similar problem.\r\nWhen I try to create `data_infos.json` using `datasets-cli test Peter.py --save_infos --all_configs` I get an error:\r\n`ValueError: Unknown split \"test\". Should be one of ['train'].`\r\n\r\nThe `data_infos.json` is created perfectly fine when I use only one split - `datasets.Split.TRAIN`\r\n\r\n@polinaeterna Could you help here please?\r\n\r\nYou can find the code here: https://huggingface.co/datasets/sberbank-ai/Peter/tree/add_splits (add_splits branch)", "@skalinin It seems the `dataset_infos.json` of your dataset is missing the info on the test split (and `datasets-cli` doesn't ignore the cached infos at the moment, which is a known bug), so your issue is not related to this one. I think you can fix your issue by deleting all the cached `dataset_infos.json` (in the local repo and in `~/.cache/huggingface/modules`) before running the `datasets-cli test` command. Let us know if that doesn't help, and I can try to generate it myself.", "This code indeed behaves as expected on `main`. But suppose the `val_234.png` is renamed to some other value not containing one of [these](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L31) keywords, in that case, this issue becomes relevant again because the real cause of it is the order in which we check the predefined split patterns to assign data files to each split - first we assign data files based on filenames, and only if this fails meaning not a single split found (`val` is not recognized here in the older versions of `datasets`, which results in an empty `validation` split), do we assign based on directory names.\r\n\r\n@polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if `data_dir` is specified (or if `load_dataset(data_dir)` is called)? ", "> @polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if data_dir is specified (or if load_dataset(data_dir) is called)?\r\n\r\nyes that makes sense !", "Looks like the `val/validation` dir name issue is fixed with the current main-branch version of the `datasets` repository. \r\n\r\n> @polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if data_dir is specified (or if load_dataset(data_dir) is called)?\r\n\r\nI agree with this as well. I would expect higher precedence to the directory name over the file name. Right now if I place a single file named `train_00001.jpg` under the `validation` directory, `load_dataset` cannot find the validation split.", "Thanks for the reply\r\n\r\nI've created a separate [issue](https://github.com/huggingface/datasets/issues/4982#issue-1375604693) for my problem.", "> @polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if data_dir is specified (or if load_dataset(data_dir) is called)?\r\n\r\nSounds good to me! opened a PR: https://github.com/huggingface/datasets/pull/4985", "Hi there @polinaeterna @mariosasko ! I have installed 5.2.3.dev0, which should have this fix. Unfortunately, I am still getting the error:\r\n`ValueError: Unknown split \"validation\". Should be one of ['train'].` When I call `load_dataset(\"csv\", data_files=files, split=split)`\r\n\r\nAny help would be greatly appreciated!", "hi @shaneacton ! could you please show your dataset structure?", "Hi there @polinaeterna . My local CSV files are stored as follows:\r\nbinding:\r\n---------- tune.csv\r\n---------- public_data:\r\n--------------------------- train.csv\r\n\r\n`self.list_shards(split)` sucessfully finds the relevant data files", "@shaneacton do you have `validation.csv`/`val.csv`/`valid.csv`/`dev.csv` file in your data folder? I can't find it in the structure you provided", "@polinaeterna no, does the name of the split need to match the name of the file exactly?\r\n\r\nBut my train file is not actually named 'train.py' its called 'XXXXXXXXX_train_XXXXXXXX.csv'\r\nAnd the code works fine for train, but fails for validation.\r\nDoes the file name need to _contain_ the split name?", "@shaneacton what files do you expect to be included in \"validation\" split? yes, you should somehow indicate that a file belongs to a certain split - either by including split name in a filename or by putting it into a folder with split name, you can also check out [this documentation page](https://huggingface.co/docs/datasets/main/en/repository_structure) :)\r\nby default all the data goes to a single `train` split", "@polinaeterna I have specified my train/test/tune files via the `split_to_filepattern` argument when initialising my `FileDataSource` class. This is how `list_shards` is able to find the right files.\r\nAfter your last message, I have tried renaminig my data files to simply `train.csv` and `validation.csv`, however I am still getting the same error: `Unknown split \"validation\". Should be one of ['train']`", "@polinaeterna I have solved the issue. The solution was to call:\r\n`load_dataset(\"csv\", data_files={split: files}, split=split)`" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Hi @lhoestq,\r\nI plunged into the code and it should be manageable for me to work on it!\r\n#take\r\n\r\nAlso, setting `d1`, `d2` and `d3` as you did raised a `SyntaxError: 'yield' inside list comprehension` for me, on Python 3.8.10.\r\nThe following snippet works for me though:\r\n```\r\nd1 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [0, 1, 2]])), {}))\r\nd2 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [10, 11, 12, 13]])), {}))\r\nd3 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [20, 21, 22, 23, 24]])), {}))\r\n```\r\n\r\n", "Great @ylacombe thanks ! I'm assigning you this issue", "Hi @ylacombe :) Is there anything I can do to help ? Feel free to ping me if you have any question :)", "Hi @lhoestq,\r\n\r\nI actually have already wrote the code last time [on this commit](https://github.com/ylacombe/datasets/commit/84769db97facc78a33ec53f7b1b395951e1804df) but I still have to change the docs and write some tests though. I'm working on it.\r\n\r\nHowever, I still your advice on one matter. \r\nIn #4831, when using a `Dataset` list with probabilities, I had change the original behavior so that it stops as soon as one or all datasets are out of samples. By nature, this behavior can't be applied with an `IterableDataset` because one only knows an iterable dataset is out of sample when receiving a StopIteration error after calling the iterator once again. \r\nTo sum up, as it is right know, the behavior is not consistent with an `IterableDataset` list or a `Dataset` list, when using probabilities.\r\nTo be honest, I think that the current behavior with a `Dataset` list is desirable and avoid having too many samples, so I would recommand keeping that as it is, but I can understand the desire to have the same behavior for both classes. \r\nWhat do you think ? Please let me know if you need more details.\r\n\r\n\r\nEDIT:\r\nHere is an example:\r\n```\r\n>>> from tests.test_iterable_dataset import *\r\n>>> d1 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [0, 1, 2]])), {}))\r\n>>> d2 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [10, 11, 12, 13]])), {}))\r\n>>> d3 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [20, 21, 22, 23, 24]])), {}))\r\n>>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)\r\n>>> [x[\"a\"] for x in dataset]\r\n[10, 0, 11, 1, 2, 20, 12, 13]\r\n>>> from tests.test_arrow_dataset import *\r\n>>> d1 = Dataset.from_dict({\"a\": [0, 1, 2]})\r\n>>> d2 = Dataset.from_dict({\"a\": [10, 11, 12]})\r\n>>> d3 = Dataset.from_dict({\"a\": [20, 21, 22]})\r\n>>> interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)[\"a\"]\r\n[10, 0, 11, 1, 2]\r\n[10, 0, 11, 1, 2]\r\n```\r\n ", "Hi ! Awesome :) \r\n\r\nMaybe you can pre-load the next sample to know if the dataset is empty or not ?\r\nThis way it should be possible to have the same behavior for `IterableDataset`", "Hi @ylacombe let us know if we can help with anything :)", "Hi @lhoestq, I've finally made some advances in the matter. I've modified the `IterableDataset` behavior so that it aligns with the `Dataset` behavior as we have discussed. The documentation has been dealt with too. \r\nIt works as expected on my examples. However I'm having trouble figuring out how to test `interleave_datasets` on `test_iterable_datasets.py` as I have never worked with pytest. Could you help me on that or give me some indications? \r\n", "Thanks @ylacombe :)\r\n\r\nUsing the `pytest` command, you can run all the functions in a python file that start with \"test_*\" and make sure they return not errors:\r\n```\r\npytest tests/test_iterable_dataset.py\r\n```\r\n\r\nIn our case it can be nice to define a `test_interleave_datasets_with_oversampling` function. This function can contain the code example that we mentioned earlier in this github issue to make sure it works as expected.", "Resolved via #5036." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4892). All of your documentation changes will be reflected on that endpoint." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@albertvillanova it seems tests fail on pyarrow 6, perhaps from_pylist is a v7 method? How do you usually handle these version differences?\r\nAdded something that at least works" ]

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[ "Maybe we can just pass this along to torchaudio @lhoestq @albertvillanova ? It be great if you could investigate if the errors lies in datasets or in torchaudio.", "torchaudio did a change in [0.12](https://github.com/pytorch/audio/releases/tag/v0.12.0) on MP3 decoding (which affects common voice):\r\n> MP3 decoding is now handled by FFmpeg in sox_io backend. (https://github.com/pytorch/audio/pull/2419, https://github.com/pytorch/audio/pull/2428)\r\n> - FFmpeg is now used as fallback in sox_io backend, and now MP3 decoding is handled by FFmpeg. To load MP3 audio with torchaudio.load, please install a compatible version of FFmpeg (Version 4 when using an official binary distribution).\r\n> - Note that, whereas the previous MP3 decoding scheme pads the output audio, the new scheme does not. As a consequence, the new version returns shorter audio tensors.", "Do we have a solution for this now? Should we just upgrade to `torchaudio 0.12.0` then? ", "`datasets` supports `torchaudio` 0.12 if you have an environment that supports reading MP3 with `torchaudio`, i.e. if you have `ffmpeg>=4`" ]

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[ "It's a bug in the viewer, thanks for reporting it. We're hoping to update to a new version in the next few days which should fix it.", "Fixed \r\n\r\nhttps://huggingface.co/datasets/subjqa\r\n\r\n<img width=\"1040\" alt=\"Capture d’écran 2022-09-08 à 10 23 26\" src=\"https://user-images.githubusercontent.com/1676121/189073210-2a57ff88-8bb1-44bd-851e-0e75473cea3f.png\">\r\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Sorry for the issue @albertvillanova! I think it's now fixed! :heart: " ]

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[ "Hi! IIRC one of the files in this dataset is corrupted due to https://github.com/huggingface/datasets/pull/4081 (fixed now).\r\n\r\n@NielsRogge Could you please re-generate and re-push this dataset (or I can do it if you share the generation script)?" ]

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[ "Hi @sanderland, thanks for your enhancement proposal.\r\n\r\nI agree with you that this would be useful.\r\n\r\nPlease note that under the hood, we use PyArrow tables as backend:\r\n- The implementation of `Dataset.from_dict` uses the PyArrow `Table.from_pydict`\r\n\r\nTherefore, I would suggest:\r\n- Implementing `Dataset.from_list` using the PyArrow `Table.from_pylist`\r\n\r\nWhat do you think?\r\nLet's see if other people have other suggestions...", "Thanks for the quick and positive reply @albertvillanova! \r\n`from_list` seems sensible. Have opened a PR so we can discuss details there.", "Resolved via #4890." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4884). All of your documentation changes will be reflected on that endpoint." ]

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[ "Are you sure there is a leak? How can I see it? You shared the script but not the output which you believe should indicate a leak.\r\n\r\nI modified your reproduction script to print only once per try as your original was printing too much info and you absolutely must add `gc.collect()` when doing any memory measurements, since python's GC is scheduled so you might be measuring the wrong thing. This gives us:\r\n\r\n```\r\nimport psutil\r\nimport os\r\nimport gc\r\nfrom transformers import BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\n\r\nBATCH_SIZE = 32\r\nNUM_TRIES = 100\r\n\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\ndef transform(x):\r\n x.update(tokenizer(x[\"text\"], return_tensors=\"pt\", max_length=64, padding=\"max_length\", truncation=True))\r\n x.pop(\"text\")\r\n x.pop(\"label\")\r\n return x\r\ndataset = load_dataset(\"imdb\", split=\"train\")\r\ndataset.set_transform(transform)\r\ntrain_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4)\r\n\r\nmem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n\r\ncount = 0\r\nwhile count < NUM_TRIES:\r\n for idx, batch in enumerate(train_loader): pass\r\n gc.collect()\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n print(count, mem_after - mem_before)\r\n count += 1\r\n```\r\n\r\nNow running it:\r\n\r\n```\r\n$ python dl-leak.py \r\nReusing dataset imdb (/home/stas/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1)\r\n0 4.43359375\r\n1 4.4453125\r\n2 4.44921875\r\n3 4.44921875\r\n4 4.4609375\r\n5 4.46484375\r\n6 4.46484375\r\n7 4.46484375\r\n8 4.46484375\r\n9 4.46484375\r\n10 4.46484375\r\n11 4.46484375\r\n12 4.46484375\r\n13 4.46484375\r\n14 4.46484375\r\n15 4.46484375\r\n16 4.46484375\r\n```\r\n\r\nIt's normal that at the beginning there is a small growth in memory usage, but after 5 cycles it gets steady.", "Unless of course you're referring the memory growth during the first try. Is that what you're referring to? And since your ds is small it's hard to see the growth - could it be just because some records are longer and it needs to allocate more memory for those?\r\n\r\nThough while experimenting with this I have observed a peculiar thing, if I concatenate 2 datasets, I don't see any growth at all. But that's probably because the program allocated additional peak RSS memory to concatenate and then is re-using the memory\r\n\r\nI basically tried to see if I make the dataset much longer, I'd expect not to see any memory growth once the 780 records of the imdb ds have been processed once.", "It is hard to say if it is directly reproducible in this setup. Maybe it is specific to the images stored in the CM4 case which cause a memory leak. I am still running your script and seeing if I can reproduce that particular leak in this case.", "I was able to reproduce the leak with:\r\n\r\n```\r\nimport psutil\r\nimport os\r\nimport gc\r\nfrom datasets import load_from_disk\r\nimport time\r\n\r\nDATASET_PATH = \"/hf/m4-master/data/cm4/cm4-10000-v0.1\"\r\n\r\ndataset = load_from_disk(DATASET_PATH)\r\n\r\n# truncate to a tiny dataset\r\ndataset = dataset.select(range(1000))\r\n\r\nprint(f\"dataset: {len(dataset)} records\")\r\n\r\nmem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\nfor idx, rec in enumerate(dataset):\r\n if idx % 100 == 0:\r\n gc.collect()\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n print(f\"{idx:4d} {mem_after - mem_before:12.4f}MB\")\r\n```\r\nYou need to adjust the DATASET_PATH record.\r\n\r\nwhich you get from\r\n\r\n```\r\ngsutil -m cp \"gs://hf-science-m4/cm4/cm4-10000-v0.1/dataset.arrow\" \"gs://hf-science-m4/cm4/cm4-10000-v0.1/dataset_info.json\" \"gs://hf-science-m4/cm4/cm4-10000-v0.1/state.json\" .\r\n```\r\n(I assume the hf folks have the perms) - it's a smallish dataset (10k)\r\n\r\nthen you run:\r\n```\r\n$ python ds.py\r\ndataset: 1000 records\r\n 0 1.0156MB\r\n 100 126.3906MB\r\n 200 142.8906MB\r\n 300 168.5586MB\r\n 400 218.3867MB\r\n 500 230.7070MB\r\n 600 238.9570MB\r\n 700 263.3789MB\r\n 800 288.1289MB\r\n 900 300.5039MB\r\n```\r\n\r\nyou should be able to see the leak ", "This issue has nothing to do with `PIL`'s decoder. I removed it and the problem is still there.\r\n\r\nI then traced this leak to this single call: `pa_table.to_pydict()` here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/08a7b389cdd6fb49264a72aa8ccfc49a233494b6/src/datasets/formatting/formatting.py#L138-L140\r\n\r\nI can make it leak much faster by modifying that code to repeat `pa_table.to_pydict()` many times in a row. It shouldn't have that impact:\r\n\r\n```\r\nclass PythonArrowExtractor(BaseArrowExtractor[dict, list, dict]):\r\n def extract_row(self, pa_table: pa.Table) -> dict:\r\n x = [pa_table.to_pydict() for x in range(200)]\r\n return _unnest(pa_table.to_pydict())\r\n```\r\n\r\n@lhoestq - do you know what might be happening inside `pa_table.to_pydict()`, as this is in the `pyarrow` domain. Perhaps you know someone to tag from that project?\r\n\r\nProbably next need to remove `datasets` from the equation and make a reproducible case with just `pyarrow` directly.\r\n\r\nThe problem already happens with `pyarrow==6.0.0` or later (minimum for current `datasets`)\r\n\r\nI'm also trying to dig in with `objgraph` to see if there are any circular references which prevent objects from being freed, but no luck there so far. And I'm pretty sure `to_pydict` is not a python code, so the problem is likely to happen somewhere outside of python's GC.", "This appears to be the same issue I think: https://github.com/huggingface/datasets/issues/4528\r\nI dug into the repro code there and it's the same behavior with the same leak, but it's a pure nlp dataset and thus much faster to work with. \r\n", "I went all the way back to `pyarrow==1.0.0` and `datasets==1.12.0` and the problem is still there. How is it even possible that it wasn't noticed all this time. \r\n\r\nCould it be that the leak is in some 3rd party component `pyarrow` relies on? as while downgrading I have only downgraded the above 2 packages.\r\n", "Also found this warning \r\n\r\n> Be careful: if you don't pass the ArrowArray struct to a consumer,\r\n> array memory will leak. This is a low-level function intended for\r\n> expert users.\r\n\r\nsee: https://github.com/apache/arrow/blob/99b57e84277f24e8ec1ddadbb11ef8b4f43c8c89/python/pyarrow/table.pxi#L2515-L2517\r\n\r\nperhaps something triggers this condition?\r\n\r\nI have no idea if it's related - this is just something that came up during my research.", "Does it crash with OOM at some point? If it doesn't, it isn't a leak, just agressive caching or a custom allocator that doesn't like to give memory back (not uncommon). #4528 looks like it hits a steady state.\r\n\r\nI believe the underlying arrow libs use a custom C allocator. Some of those are designed not to give back to OS, but keep heap memory for themselves to re-use (hitting up the OS involves more expensive mutex locks, contention, etc). The greedy behaviour can be undesirable though. There are likely flags to change the allocator behaviour, and one could likely build without any custom allocators (or use a different one).", "> Does it crash with OOM at some point?\r\n\r\nIn the original setup where we noticed this problem, it was indeed ending in an OOM", "> https://github.com/huggingface/datasets/issues/4528 looks like it hits a steady state.\r\n\r\n@rwightman in the plot I shared, the steady state comes from the `time.sleep(100)` I added in the end of the script, to showcase that even the garbage collector couldn't free that allocated memory.\r\n", "Could this be related to this discussion about a potential memory leak in pyarrow: https://issues.apache.org/jira/browse/ARROW-11007 ?\r\n\r\n(Note: I've tried `import pyarrow; pyarrow.jemalloc_set_decay_ms(0)` and the memory leak is still happening on your toy example)", "> @lhoestq - do you know what might be happening inside pa_table.to_pydict(), as this is in the pyarrow domain. Perhaps you know someone to tag from that project?\r\n\r\n`to_pydict` calls `to_pylist` on each column (i.e. on each PyArrow Array). Then it iterates on the array and calls `as_py` on each element. The `as_py` implementation depends on the data type. For strings I think it simply gets the buffer that contains the binary string data that is defined in C++\r\n\r\nThe Arrow team is pretty responsive at user@arrow.apache.org if it can help\r\n\r\n> Probably next need to remove datasets from the equation and make a reproducible case with just pyarrow directly.\r\n\r\nThat would be ideal indeed. Would be happy to help on this, can you give me access to the bucket so I can try with your data ?", "> That would be ideal indeed. Would be happy to help on this, can you give me access to the bucket so I can try with your data ?\r\n\r\nI added you to the bucket @lhoestq ", "It looks like an issue with memory mapping:\r\n- the amount of memory used in the end corresponds to the size of the dataset\r\n- setting `keep_in_memory=True` in `load_from_disk` loads the dataset in RAM, and **doesn't cause any memory leak**", "Here is a code to reproduce this issue using only PyArrow and a dummy arrow file:\r\n```python\r\nimport psutil\r\nimport os\r\nimport gc\r\nimport pyarrow as pa\r\nimport time\r\n\r\nARROW_PATH = \"tmp.arrow\"\r\n\r\nif not os.path.exists(ARROW_PATH):\r\n arr = pa.array([b\"a\" * (200 * 1024)] * 1000) # ~200MB\r\n table = pa.table({\"a\": arr})\r\n\r\n with open(ARROW_PATH, \"wb\") as f:\r\n writer = pa.RecordBatchStreamWriter(f, schema=table.schema)\r\n writer.write_table(table)\r\n writer.close()\r\n\r\n\r\ndef memory_mapped_arrow_table_from_file(filename: str) -> pa.Table:\r\n memory_mapped_stream = pa.memory_map(filename)\r\n opened_stream = pa.ipc.open_stream(memory_mapped_stream)\r\n pa_table = opened_stream.read_all()\r\n return pa_table\r\n\r\n\r\ntable = memory_mapped_arrow_table_from_file(ARROW_PATH)\r\narr = table[0]\r\n\r\nmem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\nfor idx, x in enumerate(arr):\r\n if idx % 100 == 0:\r\n gc.collect()\r\n time.sleep(0.1)\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n print(f\"{idx:4d} {mem_after - mem_before:12.4f}MB\")\r\n```\r\nprints\r\n```\r\n 0 0.2500MB\r\n 100 19.8008MB\r\n 200 39.3320MB\r\n 300 58.8633MB\r\n 400 78.3945MB\r\n 500 97.9258MB\r\n 600 117.4570MB\r\n 700 136.9883MB\r\n 800 156.5195MB\r\n 900 176.0508MB\r\n```\r\nNote that this example simply iterates over the `pyarrow.lib.BinaryScalar` objects in the array. Running `.as_py()` is not needed to experience the memory issue.", "@lhoestq that does indeed increase in memory, but if you iterate over array again after the first time, or re-open and remap the same file (repeat `table = memory_mapped_arrow_table_from_file(ARROW_PATH)`) before re-iterating, it doesn't move pas 195MB.... it would appear another step is needed to continue consuming memory past that.. hmmm\r\n\r\nAre the pa_tables held on to anywhere after they are iterated in the real code?\r\n\r\nin my hack, if you do a bunch cut & paste and then change the arr name for each iter \r\n\r\n```\r\ntable = memory_mapped_arrow_table_from_file(ARROW_PATH)\r\narr = table[0]\r\n\r\nfor idx, x in enumerate(arr):\r\n if idx % 100 == 0:\r\n gc.collect()\r\n time.sleep(0.1)\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n print(f\"{idx:4d} {mem_after - mem_before:12.4f}MB\")\r\n\r\ntable = memory_mapped_arrow_table_from_file(ARROW_PATH)\r\narr1 = table[0]\r\n\r\nfor idx, x in enumerate(arr1):\r\n if idx % 100 == 0:\r\n gc.collect()\r\n time.sleep(0.1)\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n print(f\"{idx:4d} {mem_after - mem_before:12.4f}MB\")\r\n\r\ntable = memory_mapped_arrow_table_from_file(ARROW_PATH)\r\narr2 = table[0]\r\n\r\nfor idx, x in enumerate(arr2):\r\n if idx % 100 == 0:\r\n gc.collect()\r\n time.sleep(0.1)\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n print(f\"{idx:4d} {mem_after - mem_before:12.4f}MB\")\r\n```\r\n\r\nit leaks, if all arr are the same name (so prev one gets cleaned up) it does not and goes back to 0, anything that could be holding onto a reference of an intermediary equivalent like arr in the real use case?\r\n\r\n\r\n\r\n", "Yes, we have already established here https://github.com/huggingface/datasets/issues/4883#issuecomment-1232063891 that when one iterates over the whole dataset multiple times, it consumes a bit more memory in the next few repetitions and then remains steady. \r\n\r\nWhich means that when a new iterator is created over the same dataset, all the memory from the previous iterator is re-used.\r\n\r\nSo the leak happens primarily when the iterator is \"drained\" the first time. which tells me that either a circular reference is created somewhere which only gets released when the iterator is destroyed, or there is some global variable that keeps piling up the memory and doesn't release it in time.\r\n\r\nAlso I noticed some `__del__` methods which won't destroy objects automatically and there is usually a warning against using it https://stackoverflow.com/a/1481512/9201239\r\n\r\nThere are also some `weakref`s in the code which too may lead to leaks or weird problems at times.\r\n", "@stas00 my point was, I'm not convinced @lhoestq last example illustrates the leak, but rather the differences between memory mapping and in memory usage patterns. If you destroy arr, memory map impl goes back to 0 each iteration. The amount of memory that 'looks' like it is leaked in first pass differes quite a bit between memory mapped vs in memory, but the underlying issue likely a circular reference, or reference(s) which were not cleaned up that would impact either case, but likely much more visible with mmap.", "Thank you for clarifying, Ross. \r\n\r\nI think we agree that it's almost certain that the `datasets` iterator traps some inner variable that prevents object freeing, since if we create the iterator multiple times (and drain it) after a few runs no new memory is allocated. We could try to dig in more with `objgraph` - my main concern is if the problem happens somewhere outside of python, (i.e. in pyarrow cpp implementation) in which case it'd be much more difficult to trace. \r\n\r\nI wish there was a way on linux to tell the program to free no longer used memory at will.", "FWIW, I revisted some code I had in the works to use HF datasets w/ timm train & val scripts. There is no leak there across multipe epochs. It uses the defaults. \r\n\r\nIt's worth noting that with imagenet `keep_in_memory=True` isn't even an option because the train arrow file is ~140GB and my local memory is less. The virtual address space reflects mmap (> 150GB) and doesn't increase over epochs that I noticed. I have some perf issues to bring up wrt to the current setup, but that's a separate and lower prio discussion to have elsewhere...", "# Notes \r\n\r\nAfter reading many issues and trying many things here is the summary of my learning\r\n\r\nI'm now using @lhoestq repro case as it's pyarrow-isolated: https://github.com/huggingface/datasets/issues/4883#issuecomment-1242034985\r\n\r\n\r\n## 1. pyarrow memory backends\r\n\r\nit has 3 backends, I tried them all with the same results\r\n\r\n```\r\npa.set_memory_pool(pa.jemalloc_memory_pool())\r\npa.set_memory_pool(pa.mimalloc_memory_pool())\r\npa.set_memory_pool(pa.system_memory_pool())\r\n```\r\n\r\n## 2. quick release\r\n\r\nThe `jemalloc` backend supports quick release\r\n\r\n```\r\npa.jemalloc_set_decay_ms(0)\r\n```\r\n\r\nit doesn't make any difference in this case\r\n\r\n## 3. actual memory allocations\r\n\r\nthis is a useful tracer for PA memory allocators\r\n```\r\npa.log_memory_allocations(enable=True)\r\n```\r\n\r\nit nicely reports memory allocations and releases when the arrow file is created the first time.\r\n\r\nbut when we then try to do `enumerate(arr)` this logger reports 0 allocations.\r\n\r\nThis summary also reports no allocations when the script run the second time (post file creation):\r\n```\r\nmem_pool = pa.default_memory_pool()\r\nprint(f\"PyArrow mem pool info: {mem_pool.backend_name} backend, {mem_pool.bytes_allocated()} allocated, \"\r\n f\"{mem_pool.max_memory()} max allocated, \")\r\n\r\nprint(f\"PyArrow total allocated bytes: {pa.total_allocated_bytes()}\")\r\n```\r\n\r\nHowever it's easy to see by using `tracemalloc` which only measures python's memory allocations that it's PA that leaks, since `tracemalloc` shows fixed memory\r\n\r\n(this is bolted on top of the original repro script)\r\n\r\n```\r\nimport tracemalloc\r\ntracemalloc.start()\r\n\r\n[...]\r\nfor idx, x in enumerate(arr):\r\n if idx % 10 == 0:\r\n gc.collect()\r\n time.sleep(0.1)\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / 2**20\r\n mem_use = pa.total_allocated_bytes() - start_use\r\n mem_peak = pool.max_memory() - start_peak_use\r\n\r\n second_size, second_peak = tracemalloc.get_traced_memory()\r\n mem_diff = (second_size - first_size) / 2**20\r\n mem_peak_diff = (second_peak - first_peak) / 2**20\r\n\r\n # pa.jemalloc_memory_pool().release_unused()\r\n # pa.mimalloc_memory_pool().release_unused()\r\n # pa.system_memory_pool().release_unused()\r\n\r\n print(f\"{idx:4d} {mem_after - mem_before:12.4f}MB {mem_diff:12.4f} {mem_peak_diff:12.4f} {memory_mapped_stream.size()/2**20:4.4}MB {mem_use/2**20:4.4}MB {mem_peak/2**20:4.4}MB\")\r\n\r\n```\r\n\r\ngives:\r\n\r\n```\r\n 0 5.4258MB 0.0110 0.0201 195.3MB 0.0MB 0.0MB\r\n 10 25.3672MB 0.0112 0.0202 195.3MB 0.0MB 0.0MB\r\n 20 45.9336MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 30 62.4336MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 40 83.0586MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 50 103.6836MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 60 124.3086MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 70 140.8086MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 80 161.4336MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 90 182.0586MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n```\r\n\r\nthe 3rd and 4th columns are `tracemalloc`'s report.\r\n\r\nthe 5th column is the size of mmaped stream - fixed.\r\n\r\nthe last 2 are the PA's malloc reports - you can see it's totally fixed and 0.\r\n\r\nSo what gives? PA's memory allocator says nothing was allocated and we can see python doesn't allocate any memory either.\r\n\r\nAs someone suggested in one of the PA issues that **IPC/GRPC could be the issue.** Any suggestions on how debug this one?\r\n\r\nThe main issue is that one can't step through with a python debugger as `arr` is an opaque cpp object binded to python.\r\n\r\nPlease see the next comment for a possible answer.\r\n\r\n# ref-count\r\n\r\nI also traced reference counts and they are all fixed using either `sys.getrefcount(x)` or `len(gc.get_referrers(x))`\r\n\r\nso it's not the python object\r\n\r\n# Important related discussions\r\n\r\nhttps://issues.apache.org/jira/browse/ARROW-11007 - looks very similar to our issue\r\nin particular this part of the report:\r\nhttps://issues.apache.org/jira/browse/ARROW-11007?focusedCommentId=17279642&page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#comment-17279642\r\n", "# There is no leak, just badly communicated linux RSS memory usage stats\r\n\r\nNext, lets revisit @rwightman's suggestion that there is actually no leak.\r\n\r\nAfter all - we are using mmap which **will try to map** the file to RAM as much as it can and then page out if there is no memory. i.e. MMAP is only fast if you have a lot of CPU RAM.\r\n\r\nSo let's do it:\r\n\r\n# Memory mapping OOM test\r\n\r\nWe first quickly start a cgroups-controlled shell which will instantly kill any program that consumes more than 1GB of memory:\r\n\r\n```\r\n$ systemd-run --user --scope -p MemoryHigh=1G -p MemoryMax=1G -p MemorySwapMax=1G --setenv=\"MEMLIMIT=1GB\" bash\r\n```\r\n\r\nLet's check that it indeed does so. Let's change @lhoestq's script to allocate a 10GB arrow file:\r\n\r\n```\r\n$ python -c 'import pyarrow as pa; pa.array([b\"a\" * (2000 * 1024)] * 5000)'\r\nKilled\r\n```\r\noops, that didn't work, as we tried to allocate 10GB when only 1GB is allowed. This is what we want!\r\n\r\nLet's do a sanity check - can we allocate 0.1GB?\r\n```\r\npython -c 'import pyarrow as pa; pa.array([b\"a\" * (2000 * 1024)] * 50)'\r\n```\r\n\r\nYes. So the limited shell does the right thing. It let's allocate `< 1GB` of RSS RAM.\r\n\r\nNext let's go back to @lhoestq's script but with 10GB arrow file.\r\n\r\nwe change his repro script https://github.com/huggingface/datasets/issues/4883#issuecomment-1242034985 to 50x larger file\r\n```\r\n arr = pa.array([b\"a\" * (2000 * 1024)] * 5000) # ~10000MB\r\n```\r\nwe first have to run into a normal unlimited shell so that we don't get killed (as the script allocates 10GB)\r\n\r\nlet's run the script now in the 1GB-limited shell while running a monitor:\r\n\r\n```\r\n$ htop -F python -s M_RESIDENT -u `whoami`\r\n```\r\n\r\nso we have 2 sources of RSS info just in case.\r\n\r\n```\r\n$ python pyar.py\r\n 0 4.3516MB 0.0103 0.0194 9.766e+03MB 0.0MB 0.0MB\r\n 10 24.3008MB 0.0104 0.0195 9.766e+03MB 0.0MB 0.0MB\r\n[...]\r\n4980 9730.3672MB 0.0108 0.0199 9.766e+03MB 0.0MB 0.0MB\r\n4990 9750.9922MB 0.0108 0.0199 9.766e+03MB 0.0MB 0.0MB\r\nPyArrow mem pool info: jemalloc backend, 0 allocated, 0 max allocated,\r\nPyArrow total allocated bytes: 0\r\n```\r\n\r\nBut wait, it reported 10GB RSS both in `htop` and in our log!\r\n\r\nSo that means it never allocated 10GB otherwise it'd have been killed.\r\n\r\n**Which tells us that there is no leak whatsoever** and this is just a really difficult situation where MMAPPED memory is reported as part of RSS which it probably shouldn't. As now we have no way how to measure real memory usage.\r\n\r\nI also attached the script with all the different things I have tried in it, so it should be easy to turn them on/off if you want to reproduce any of my findings.\r\n\r\n[pyar.txt](https://github.com/huggingface/datasets/files/9539430/pyar.txt)\r\n\r\njust rename it to `pyra.py` as gh doesn't let attaching scripts...\r\n\r\n(I have to remember to exit that special mem-limited shell or else I won't be able to do anything serious there.)\r\n\r\n", "The original leak in the multi-modal code is very likely something else. But of course now it'd be very difficult to trace it using mmap.\r\n\r\nI think to debug we have to set `keep_in_memory=True` in `load_from_disk` to load the small dataset in RAM, so there will be no mmap misleading reporting component and then continue searching for another source of a leak.", "To add to what @stas00 found, I'm gonna leave some links to where I believe the confusion came from in pyarrow's APIs, for future reference:\r\n* In the section where they talk about [efficiently writing and reading arrow data](https://arrow.apache.org/docs/dev/python/ipc.html?#efficiently-writing-and-reading-arrow-data), they give an example of how \r\n\r\n> Arrow can directly reference the data mapped from disk and avoid having to allocate its own memory. \r\n\r\nAnd where their example shows 0 RSS memory allocation, the way we used to measure RSS shows 39.6719MB allocated. Here's the script to reproduce:\r\n```\r\nimport psutil\r\nimport os\r\nimport gc\r\nimport pyarrow as pa\r\nimport time\r\nimport sys\r\n\r\n# gc.set_debug(gc.DEBUG_LEAK)\r\n# gc.set_threshold(0,0,0)\r\n\r\n#pa.set_memory_pool(pa.mimalloc_memory_pool())\r\n#pa.set_memory_pool(pa.system_memory_pool())\r\n\r\nimport tracemalloc\r\n\r\n#pa.jemalloc_set_decay_ms(0)\r\n# pa.log_memory_allocations(enable=True)\r\n\r\nBATCH_SIZE = 10000\r\nNUM_BATCHES = 1000\r\nschema = pa.schema([pa.field('nums', pa.int32())])\r\nwith pa.OSFile('bigfile.arrow', 'wb') as sink:\r\n with pa.ipc.new_file(sink, schema) as writer:\r\n for row in range(NUM_BATCHES):\r\n batch = pa.record_batch([pa.array(range(BATCH_SIZE), type=pa.int32())], schema)\r\n writer.write(batch)\r\n\r\nstart_use = pa.total_allocated_bytes()\r\npool = pa.default_memory_pool()\r\nstart_peak_use = pool.max_memory()\r\ntracemalloc.start()\r\nfirst_size, first_peak = tracemalloc.get_traced_memory()\r\nmem_before = psutil.Process(os.getpid()).memory_info().rss / 2**20\r\n\r\n\r\n# with pa.OSFile('bigfile.arrow', 'rb') as source:\r\n# loaded_array = pa.ipc.open_file(source).read_all()\r\n\r\nwith pa.memory_map('bigfile.arrow', 'rb') as source:\r\n loaded_array = pa.ipc.open_file(source).read_all()\r\n\r\n\r\nprint(\"LEN:\", len(loaded_array))\r\nprint(\"RSS: {}MB\".format(pa.total_allocated_bytes() >> 20))\r\n\r\ngc.collect()\r\ntime.sleep(0.1)\r\nmem_after = psutil.Process(os.getpid()).memory_info().rss / 2**20\r\nmem_use = pa.total_allocated_bytes() - start_use\r\nmem_peak = pool.max_memory() - start_peak_use\r\nsecond_size, second_peak = tracemalloc.get_traced_memory()\r\nmem_diff = (second_size - first_size) / 2**20\r\nmem_peak_diff = (second_peak - first_peak) / 2**20\r\n\r\nidx = 0\r\nprint(f\"{idx:4d} {mem_after - mem_before:12.4f}MB {mem_diff:12.4f} {mem_peak_diff:12.4f} {mem_use/2**20:4.4}MB {mem_peak/2**20:4.4}MB\")\r\n```\r\ngives:\r\n```\r\n\r\nLEN: 10000000\r\nRSS: 0MB\r\n 0 39.6719MB 0.0132 0.0529 0.0MB 0.0MB\r\n```\r\nWhich again just proves that we uncorrectly measure RSS, in the case of MMAPPED memory\r\n\r\n\r\n* [The recommended way to do memory profiling from Arrow's docs](https://arrow.apache.org/docs/dev/cpp/memory.html#memory-profiling)\r\n", "@lhoestq, I have been working on a detailed article that shows that MMAP doesn't leak and it's mostly ready. I will share when it's ready.\r\n\r\nThe issue is that we still need to be able to debug memory leaks by turning MMAP off.\r\n\r\nBut, once I tried to show the user that using `load_dataset(... keep_in_memory=True)` is the way to debug an actual memory leak - guess I what I discovered? A potential actual leak.\r\n\r\nHere is the repro:\r\n\r\n```\r\n$ cat ds-mmap.py\r\nfrom datasets import load_dataset\r\nimport gc\r\nimport os\r\nimport psutil\r\n\r\nproc = psutil.Process(os.getpid())\r\ndef mem_read():\r\n gc.collect()\r\n return proc.memory_info().rss / 2**20\r\n\r\ndataset = load_dataset(\"wmt19\", 'cs-en', keep_in_memory=True, streaming=False)['train']\r\n\r\nprint(f\"{'idx':>6} {'RSS':>10} {'Δ RSS':>15}\")\r\nstep = 20000\r\nfor i in range(0, 10*step, step):\r\n mem_before = mem_read()\r\n _ = dataset[i:i+step]\r\n mem_after = mem_read()\r\n print(f\"{i:6d} {mem_after:12.4f}MB {mem_after - mem_before:12.4f}MB \")\r\n```\r\n\r\n```\r\npython ds-io.py\r\nReusing dataset wmt19 (/home/stas/.cache/huggingface/datasets/wmt19/cs-en/1.0.0/c3db1bf4240362ed1ef4673b354f468d70aac66d4e67d45f536d493a0840f0d3)\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 5.66it/s]\r\n idx RSS Δ RSS\r\n 0 1398.4609MB 3.5195MB\r\n 20000 1398.5742MB 0.1133MB\r\n 40000 1398.6016MB 0.0273MB\r\n 60000 1398.6016MB 0.0000MB\r\n 80000 1398.6016MB 0.0000MB\r\n100000 1398.6328MB 0.0312MB\r\n120000 1398.6953MB 0.0625MB\r\n140000 1398.6953MB 0.0000MB\r\n160000 1398.7500MB 0.0547MB\r\n180000 1398.7500MB 0.0000MB\r\n```", "as I suggested on slack perhaps it was due to dataset records length variation, so with your help I wrote another repro with synthetic records which are all identical - which should remove my hypothese from the equation and we should expect 0 incremental growth as we iterate over the datasets. But alas this is not the case. There is a tiny but definite leak-like behavior.\r\n\r\nHere is the new repro:\r\n\r\n```\r\n$ cat ds-synthetic-no-mmap.py\r\nfrom datasets import load_from_disk, Dataset\r\nimport gc\r\nimport sys\r\nimport os\r\nimport psutil\r\n\r\nproc = psutil.Process(os.getpid())\r\ndef mem_read():\r\n gc.collect()\r\n return proc.memory_info().rss / 2**20\r\n\r\nDS_PATH = \"synthetic-ds\"\r\nif not os.path.exists(DS_PATH):\r\n records = 1_000_000\r\n print(\"Creating a synthetic dataset\")\r\n row = dict(foo=[dict(a='a'*500, b='b'*1000)])\r\n ds = Dataset.from_dict({k: [v] * records for k, v in row.items()})\r\n ds.save_to_disk(DS_PATH)\r\n print(\"Done. Please restart the program\")\r\n sys.exit()\r\n\r\ndataset = load_from_disk(DS_PATH, keep_in_memory=True)\r\nprint(f\"Dataset len={len(dataset)}\")\r\n\r\nprint(f\"{'idx':>8} {'RSS':>10} {'Δ RSS':>15}\")\r\nmem_start = 0\r\nstep = 25_000\r\nwarmup_iterations = 4\r\nfor idx, i in enumerate(range(0, len(dataset), step)):\r\n if idx == warmup_iterations: # skip the first few iterations while things get set up\r\n mem_start = mem_read()\r\n mem_before = mem_read()\r\n _ = dataset[i:i+step]\r\n mem_after = mem_read()\r\n print(f\"{i:8d} {mem_after:12.4f}MB {mem_after - mem_before:12.4f}MB\")\r\nmem_end = mem_read()\r\n\r\nprint(f\"Total diff: {mem_end - mem_start:12.4f}MB (after {warmup_iterations} warmup iterations)\")\r\n```\r\n\r\nand the run:\r\n\r\n```\r\n$ python ds-synthetic-no-mmap.py\r\nDataset len=1000000\r\n idx RSS Δ RSS\r\n 0 1601.9258MB 47.9688MB\r\n 25000 1641.6289MB 39.7031MB\r\n 50000 1641.8594MB 0.2305MB\r\n 75000 1642.1289MB 0.2695MB\r\n 100000 1642.1289MB 0.0000MB\r\n 125000 1642.3789MB 0.2500MB\r\n 150000 1642.3789MB 0.0000MB\r\n 175000 1642.6289MB 0.2500MB\r\n 200000 1642.6289MB 0.0000MB\r\n 225000 1642.8789MB 0.2500MB\r\n 250000 1642.8828MB 0.0039MB\r\n 275000 1643.1328MB 0.2500MB\r\n 300000 1643.1328MB 0.0000MB\r\n 325000 1643.3828MB 0.2500MB\r\n 350000 1643.3828MB 0.0000MB\r\n 375000 1643.6328MB 0.2500MB\r\n 400000 1643.6328MB 0.0000MB\r\n 425000 1643.8828MB 0.2500MB\r\n 450000 1643.8828MB 0.0000MB\r\n 475000 1644.1328MB 0.2500MB\r\n 500000 1644.1328MB 0.0000MB\r\n 525000 1644.3828MB 0.2500MB\r\n 550000 1644.3828MB 0.0000MB\r\n 575000 1644.6328MB 0.2500MB\r\n 600000 1644.6328MB 0.0000MB\r\n 625000 1644.8828MB 0.2500MB\r\n 650000 1644.8828MB 0.0000MB\r\n 675000 1645.1328MB 0.2500MB\r\n 700000 1645.1328MB 0.0000MB\r\n 725000 1645.3828MB 0.2500MB\r\n 750000 1645.3828MB 0.0000MB\r\n 775000 1645.6328MB 0.2500MB\r\n 800000 1645.6328MB 0.0000MB\r\n 825000 1645.8828MB 0.2500MB\r\n 850000 1645.8828MB 0.0000MB\r\n 875000 1646.1328MB 0.2500MB\r\n 900000 1646.1328MB 0.0000MB\r\n 925000 1646.3828MB 0.2500MB\r\n 950000 1646.3828MB 0.0000MB\r\n 975000 1646.6328MB 0.2500MB\r\nTotal diff: 4.5039MB (after 4 warmup iterations)\r\n```\r\nso I'm still not sure why we get this.\r\n\r\nAs you can see I started skipping the first few iterations where memory isn't stable yet. As the actual diff is much larger if we count all iterations.\r\n\r\nWhat do you think?", "@stas00 my 2 cents from having looked at a LOT of memory leaks over the years, esp in Python, .3% memory increase over that many iterations of something is difficult to say with certainty it is a leak. \r\n\r\nAlso, just looking at RSS makes it hard to analyze leaks. RSS can stay near constant while you are leaking. RSS is paged in mem, if you have a big leak your RSS might not increase much (leaked mem tends not to get used again so often paged out) while your virtual page allocation could be going through the roof...", "yes, that's true, but unless the leak is big, I'm yet to find another measurement tool.\r\n\r\nTo prove your point here is a very simple IO in a loop program that also reads the same line all over again:\r\n\r\n```\r\n$ cat mmap-no-leak-debug.py\r\nimport gc\r\nimport mmap\r\nimport os\r\nimport psutil\r\nimport sys\r\n\r\nproc = psutil.Process(os.getpid())\r\n\r\nPATH = \"./tmp.txt\"\r\n\r\ndef mem_read():\r\n gc.collect()\r\n return proc.memory_info().rss / 2**20\r\n\r\n# create a large data file with a few long lines\r\nif not os.path.exists(PATH):\r\n with open(PATH, \"w\") as fh:\r\n s = 'a'* 2**27 + \"\\n\" # 128MB\r\n # write ~2GB file\r\n for i in range(16):\r\n fh.write(s)\r\n\r\nprint(f\"{'idx':>4} {'RSS':>10} {'Δ RSS':>12} {'Δ accumulated':>10}\")\r\n\r\ntotal_read = 0\r\ncontent = ''\r\nmem_after = mem_before_acc = mem_after_acc = mem_before = proc.memory_info().rss / 2**20\r\nprint(f\"{0:4d} {mem_after:10.2f}MB {mem_after - 0:10.2f}MB {0:10.2f}MB\")\r\n\r\nmmap_mode = True if \"--mmap\" in sys.argv else False\r\n\r\nwith open(PATH, \"r\") as fh:\r\n\r\n if mmap_mode:\r\n mm = mmap.mmap(fh.fileno(), 0, access=mmap.ACCESS_READ)\r\n\r\n idx = 0\r\n while True:\r\n idx += 1\r\n mem_before = mem_read()\r\n line = mm.readline() if mmap_mode else fh.readline()\r\n if not line:\r\n break\r\n\r\n #total_read += len(line)\r\n\r\n if \"--accumulate\" in sys.argv:\r\n mem_before_acc = mem_read()\r\n content += str(line)\r\n mem_after_acc = mem_read()\r\n\r\n mem_after = mem_read()\r\n\r\n print(f\"{idx:4d} {mem_after:10.2f}MB {mem_after - mem_before:10.2f}MB {mem_after_acc - mem_before_acc:10.2f}MB\")\r\n```\r\n\r\nit has some other instrumentations to do mmap and accumulate data, but let's ignore that for now.\r\n\r\nHere it is running in a simple non-mmap IO:\r\n\r\n```\r\n$ python mmap-no-leak-debug.py\r\n idx RSS Δ RSS Δ accumulated\r\n 0 12.43MB 12.43MB 0.00MB\r\n 1 269.72MB 257.29MB 0.00MB\r\n 2 269.73MB 0.02MB 0.00MB\r\n 3 269.73MB 0.00MB 0.00MB\r\n 4 269.74MB 0.01MB 0.00MB\r\n 5 269.74MB 0.00MB 0.00MB\r\n 6 269.75MB 0.01MB 0.00MB\r\n 7 269.75MB 0.00MB 0.00MB\r\n 8 269.76MB 0.01MB 0.00MB\r\n 9 269.76MB 0.00MB 0.00MB\r\n 10 269.77MB 0.01MB 0.00MB\r\n 11 269.77MB 0.00MB 0.00MB\r\n 12 269.77MB 0.00MB 0.00MB\r\n 13 269.77MB 0.00MB 0.00MB\r\n 14 269.77MB 0.00MB 0.00MB\r\n 15 269.77MB 0.00MB 0.00MB\r\n 16 146.02MB -123.75MB 0.00MB\r\n```\r\n\r\nas you can see even this super-simplistic program that just performs `readline()` slightly increases in RSS over iterations.\r\n\r\nIf you have a better tool for measurement other than RSS, I'm all ears.", "@stas00 if you aren't using memory maps, you should be able to clearly see the increase in the virtual mem for the process as well. Even then, it could still be challenging to determine if it's leak vs fragmentation due to problematic allocation patterns (not uncommon with Python). Using a better mem allocator like tcmalloc via LD_PRELOAD hooks could reduce impact of fragmentation across both Python and c libs. Not sure that plays nice with any allocator that arrow might use itself though. " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4882). All of your documentation changes will be reflected on that endpoint." ]

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[ "Thanks for opening this discussion, @alexis-michaud.\r\n\r\nAs the language validation procedure is shared with other Hugging Face projects, I'm tagging them as well.\r\n\r\nCC: @huggingface/moon-landing ", "on the Hub side, there is not fine grained validation we just check that `language:` contains an array of lowercase strings between 2 and 3 characters long =)\r\n\r\nand for `language_bcp47:` we just check it's an array of strings.\r\n\r\nThe only page where we have a hardcoded list of languages is https://huggingface.co/languages and I've been thinking of hooking that page on an external database of languages (so any suggestion is super interesting), but it's not used for validation.\r\n\r\nThat being said, in `datasets` this file https://github.com/huggingface/datasets/blob/main/src/datasets/utils/resources/languages.json is not really used no? Or just in the tagging tool? What about just removing it?\r\n\r\nalso cc'ing @lbourdois who's been active and helpful on those subjects in the past!", "PS @alexis-michaud is there a DB of language codes you would recommend? That would contain all `ISO 639-1, 639-2 or 639-3 codes` and be kept up to date, and ideally that would be accessible as a Node.js npm package?\r\n\r\ncc @albertvillanova too", "> PS @alexis-michaud is there a DB of language codes you would recommend? That would contain all `ISO 639-1, 639-2 or 639-3 codes` and be kept up to date, and ideally that would be accessible as a Node.js npm package?\r\n> \r\n> cc @albertvillanova too\r\n\r\nMany thanks for your answer! \r\n\r\nThe Glottolog database is kept up to date, and has information on the closest ISO code for each Glottocode. So providing a clean table with equivalences sounds (to me) like something perfectly reasonable to expect from their team. \r\nTo what extent would [pyglottolog](https://github.com/glottolog/pyglottolog) fit the bill / do the job? (API documentation [here](https://pyglottolog.readthedocs.io/en/latest/index.html)) I'm reaching my technical limitations here: I can't assess the distance between what they offer and what the HF team needs. \r\nI have opened an Issue in [their repo](https://github.com/glottolog/glottolog-cldf/issues/13). \r\n\r\nVery interested to see where it goes from there.", "I just tried pyglottolog to generate a file with all the current IDs (first column).\r\n\r\n`glottolog languoids` inside the `glottolog` repository.\r\n\r\n[glottolog-languoids-v4.6-10-g5c66eec874.csv](https://github.com/huggingface/datasets/files/9417456/glottolog-languoids-v4.6-10-g5c66eec874.csv)\r\n\r\n", "Greetings @alexis-michaud and others,\r\nI think perhaps a standards-based approach here would help everyone out both at the technical and social layers of technical innovations. \r\n\r\nLet me say a few things: \r\n1. there are multiple kinds of assets in AI that should have associated language codes. \r\n * AI Training Data sets\r\n * AI models\r\n * AI outputs\r\nThese are all distinct components which should be tagged for the language and encoding methods they operate on or enhance. For example, an AI based cross-language tool from French to English (UK variety) still needs to consider if it is operating on oral language speech or written text. This is where [IANA language sub-tags](https://www.iana.org/assignments/language-subtag-registry/language-subtag-registry) come in and are so important. I link to the official source. If one wants to use middleware such as a python package or npm package to manage strings then please make sure those packages are updating codes as they are being revised. I see that @julien-c mentioned BCP-47. BCP-47 is the current standard for language tagging. Following it will make the resources you create more findable and let future users better understand or expect any biases which may have been introduced in the different AI based products.\r\n2. BCP-47 is a technical read. However, you will notice that it identifies when to use an ISO 639-1, ISO 639-2, or ISO 639-3. code. This is important for interoperability with many systems. If you are using library systems then you should likely just stick with ISO 639-3 codes.\r\n3. If you are going to use Glottolog codes use them after an `-x-` tag in the BCP-47 format to maintain BCP-47 validity. \r\n4. You should source ISO 639-3 codes directly from the [ISO 639-3 registrar](https://iso639-3.sil.org/code_tables/639/data) as these codes are updated annually, usually in February or March. ISO 639-3 codes have multiple classes: `Active`, `Deprecated`, and `Unassigned`. This means that string length checking is not a sufficient strategy for validation.\r\n5. The names of smaller languages often change depending on the language used to describe them. The [ISO639-2 documentation](https://www.loc.gov/standards/iso639-2/php/code_list.php) has a list of language names for languages with smaller populations for languages in which descriptions about these languages are often written. For example, ISO 639-2's documentation contains the names of languages as they are used in French, German, and English. ISO 639-2 rarely is updated as it is now tied to ISO 639-3's evolution and modern systems should just use ISO 639-3, but these additional names of languages in other languages may not appear in the ISO 369-3 tables.\r\n6. Glottolog codes are also updated at least annually. Usually sometime after ISO 639-3 updates.\r\n7. Please, if the material is in a written mode, please indicate which script is used unless the IANA field has a `suppress script` value. Please use the script tag that BCP-47 calls for from [ISO 15924](https://unicode.org/iso15924/iso15924-codes.html). This also updates at least annually. \r\n8. Another great place to look for language names is the [Unicode CLDR database for locales](https://cldr.unicode.org/translation/displaynames/languagelocale-names). These ought to be congruent with ISO 639-3 but, sometimes CLDR has additional references to languages (such as the french name for a language) which is not contained in ISO 639-2 or ISO 639-3.\r\n9. Wikidata for language names is not always a great source of authoritative information. Language names are asymmetrical. Many times they are contrived because there is no actual name for the language in the language referring... e.g. French doesn't have a name for every language in the world, often they say something like: the language of 'x' people. — English does the same. When a language name standard does not have the best name for a language the best way to handle that is to make a change request with the standards registrar. Keeping track of the source list and the version of your source list for your language codes is very important. \r\n10. Finally, It would be a great service to technologist, minority language communities, and linguists if for all resources of the three types mentioned in number 1 above you added a record to [OLAC](http://www.language-archives.org/). — I can help you with that. OLAC is a search interface for language resources.\r\n", "Hi everybody!\r\n\r\nAbout the point:\r\n> also cc'ing @lbourdois who's been active and helpful on those subjects in the past!\r\n\r\nDiscussions on the need to improve the Hub's tagging system (applying to both datasets and models) can be found in the following discussion: https://github.com/huggingface/hub-docs/issues/193\r\nOnce this system has been redone and satisfies the identified needs, a redesign of the [Languages page](https://huggingface.co/languages) would also be relevant: https://github.com/huggingface/hub-docs/issues/194. \r\nI invite you to read them. But as a quick summary, the exchanges were oriented towards the ISO standard (the first HF system was based on it and it is generally the standard indicated in AI/DL papers) by favouring ISO 639-1 if it exists, and fallback to ISO 639-2 or ISO 639-3 if it doesn't. In addition, it is possible to add BCP-47 tags to consider existing varieties/regionalisms within a language (https://huggingface.co/datasets/AmazonScience/massive/discussions/1). If a language does not belong to either of these two standards, then a request should be made to the HF team to add it manually.\r\n\r\n\r\nTo return to the present discussion, thank you for the various databases and methodologies you mention. It makes a big difference to have linguists in the loop 🚀.\r\n\r\nI have a couple of questions where I think an expert perspective would be appreciated:\r\n- Do you think it's possible to easily handle tags that have been deprecated potentially for decades?\r\nFor example (I'm taking the case of Hebrew but this has happened for other languages) I tagged Google models with the \"iw\" [tag](https://huggingface.co/models?language=iw&sort=downloads) because I based it on what the authors gave in their [paper](https://arxiv.org/pdf/2010.11934.pdf) see table 6 page 12). It turns out that this ISO tag has in fact been deprecated since 1989 in favour of the \"he\" tag. It would therefore be necessary to have a verification that transforms the old tags into the most recent ones.\r\n\r\n- When you look up a language on Wikipedia, it usually shows, in addition to the ISO standard, the codes in the Glottolog (which you have already mentioned), [ELP](https://www.endangeredlanguages.com/?hl=en) and [Linguasphere](http://www.linguasphere.info/jr/index.php?l1=home&l2=welcome) databases. Would you have any opinion about these two other databases?\r\n\r\n- On the Hub, there is the following dataset where French people speak in English: https://huggingface.co/datasets/Datatang/French_Speaking_English_Speech_Data_by_Mobile_Phone \r\nIs there a database to take this case into account? I have not found any code in the Glottolog database. If based on an IETF BCP-47 standard, I would tend to tag the dataset with \"en-fr\" but would this be something accepted by linguists?\r\nBased on the first post in this thread that there are about 8000 languages, if one considers that a given language can be pronounced by a speaker of the other 7999, that would theoretically make about 64 million BCP-47 language1-language2 codes existing. And even much more if we consider regionalists with language1_regionalism_x-language2_regionalism_y. I guess there is no such database.\r\n\r\n- Are there any databases that take into account all the existing sign languages in the world?\r\nIt would be nice to have them included on the Hub.\r\n\r\n- Is there an international classification of languages?\r\nA bit like the [International Classification of Diseases](https://en.wikipedia.org/wiki/International_Classification_of_Diseases) in medicine, which is established by the WHO and used as a reference throughout the world. The idea would be to have a precise number of languages to which we would then have to assign a unique tag in order to find them later. \r\n\r\n- Finally for the CNRS team, when can we expect to see all the datasets of [Pangloss](https://pangloss.cnrs.fr/) on HF? 👀 And I don't know if you have a way to help to add also the datasets of [CoCoON](https://cocoon.huma-num.fr/exist/crdo/).", "> I invite you to read them. But as a quick summary, the exchanges were oriented towards the ISO standard (the first HF system was based on it and it is generally the standard indicated in AI/DL papers) by favouring ISO 639-1 if it exists, and fallback to ISO 639-2 or ISO 639-3 if it doesn't. In addition, it is possible to add BCP-47 tags to consider existing varieties/regionalisms within a language (https://huggingface.co/datasets/AmazonScience/massive/discussions/1). If a language does not belong to either of these two standards, then a request should be made to the HF team to add it manually.\r\n\r\nOne comment on this fall back system (which generally follows the BCP-47 process). ISO 639-2 has some codes which refer to a language ambiguously. For example, I believe code `ara` is used for arabic. In some contexts arabic is considered a single language, however, Egyptian Arabic is quite different from Moroccan Arabic, which are both considered separate languages. These ambiguous codes are valid ISO 639-3 codes but they have a special status. They are called `macro codes`. They exist inside the ISO 639-3 standard to provide absolute fallback compatibility between ISO 639-2 and ISO 639-3. However, when considering AI and MT applications with language data, the unforeseen potential applications and the potential for bias using macro codes should be avoided for new applications of language tags to resources. For historical cases where it is not clear what resources were used to create the AI tools or datasets then I understand the use of ambiguous tag uses. So for clarity in language tagging I suggest:\r\n\r\n1. Strictly following BCP-47\r\n2. Whenever possible avoid the use of macro tags in the ISO 639-3 standard. These are BCP-47 valid, but could introduce biases in the application of their use in society. (Generally there are more specific tags available to use in the ISO 639-3 standard.)", "> * Are there any databases that take into account all the existing sign languages in the world?\r\n> It would be nice to have them included on the Hub.\r\n\r\nSign Languages present an interesting case. As I understand the situation. The identification of sign languages has been identified as a component of their endangerment. Some sign languages do exist in ISO 639-3. For further discussion on the issue I refer readers to the following publications: \r\n\r\n* https://doi.org/10.3390/languages7010049\r\n* https://www.academia.edu/35870983/The_ethics_of_of_language_identification_and_ISO_639\r\n\r\nOne way to be BCP-47 compliant and identify a sign language which is not identified in any of the BCP-47 referenced standards is to use the ISO 639-3 code for undetermined language `und` and then apply a custom suffix indicator (as explained in BCP-47) `-x-` and a custom code, such as the ones used in https://doi.org/10.3390/languages7010049", "> * Is there an international classification of languages?\r\n> A bit like the [International Classification of Diseases](https://en.wikipedia.org/wiki/International_Classification_of_Diseases) in medicine, which is established by the WHO and used as a reference throughout the world. The idea would be to have a precise number of languages to which we would then have to assign a unique tag in order to find them later.\r\n\r\nYes that would be the function of ISO 639-3. It is the reference standard for languages. It includes a code and its name and the status of the code. Many technical metadata standards for file and computer interoperability reference it, many technical library metadata standards reference it. Some linguists use it. Many governments reference it. \r\n\r\nIndexing diseases are different from indexing languages in several ways, one way is that diseases are the impact of a pathogen not the pathogen itself. If we take COVID-19 as an example, there are many varieties of the pathogen but broadly speaking there is only one disease — with many symptoms.\r\n\r\n", ">* When you look up a language on Wikipedia, it usually shows, in addition to the ISO standard, the codes in the Glottolog (which you have already mentioned), [ELP](https://www.endangeredlanguages.com/?hl=en) and [Linguasphere](http://www.linguasphere.info/jr/index.php?l1=home&l2=welcome) databases. Would you have any opinion about these two other databases?\r\n\r\nWhile these do appear on wikipedia, I don't know of any information system which uses these codes. I do know that glottolog did import ELP data at one time and its database does contain ELP data I'm not sure if Glottolog regularly ingests new versions of ELP data. I suspect that the use of Linguasphere data may be relevant to users of wikidata as a linked data attribute but I haven't heard of any linked data projects using Linguasphere data for analysis or product development. My impression is that it is fairly unused.", "> * Do you think it's possible to easily handle tags that have been deprecated potentially for decades?\r\n>For example (I'm taking the case of Hebrew but this has happened for other languages) I [tag](https://huggingface.co/models?language=iw&sort=downloads)ged Google models with the \"iw\" tag because I based it on what the authors gave in their [paper](https://arxiv.org/pdf/2010.11934.pdf) see table 6 page 12). It turns out that this ISO tag has in fact been deprecated since 1989 in favour of the \"he\" tag. It would therefore be necessary to have a verification that transforms the old tags into the most recent ones.\r\n\r\nYes. You can parse the IANA file linked to above (it is regularly updated). All deprecated tags are marked as such in that file. The new prefered tag if there is one, is indicated. ISO 639-3 also indicates a code's status but their list is relevant only codes within their domain (ISO 639-3).", "> * On the Hub, there is the following dataset where French people speak in English: https://huggingface.co/datasets/Datatang/French_Speaking_English_Speech_Data_by_Mobile_Phone\r\nIs there a database to take this case into account? I have not found any code in the Glottolog database. If based on an IETF BCP-47 standard, I would tend to tag the dataset with \"en-fr\" but would this be something accepted by linguists?\r\n\r\nI would interpret `en-fr` as english as spoken in France. `fr`in this position refers to the geo-political entity not a second language. I see no reason that other linguists should have a different option after having read BCP-47 and understood how it works.\r\n\r\nThe functional goal here is to tag a language resource as being produced by nonnative speakers, while tagging both languages. There are several problems here. The first is that BCP-47 has no way explicit way to do this. One could use the sub code `x-` with a private use code to indicate a second language and infer some meaning as to that language's role. However, there is another problem here which complexifies the situation greatly... how do we know that those english speakers (in France, or from France, or who were native French speakers) were not speaking their third or fourth language rather than their second language. So to conceptualize a sub-tag which indicates the first language of a speech act for speakers in a second (or other) language would need to be carefully crafted. It might then be proposed to the appropriate authorities. For example three sub-tags exist.\r\n\r\nThere are three registered sub-tags out of a BCP-47 allowed 35. These are `x-`, `u-`, and `t-`. `u-` and `t-` are defined in [RFC6067 ](https://www.rfc-editor.org/rfc/rfc6067)and [RFC6497](https://www.rfc-editor.org/rfc/rfc6497) . For more information see the [Unicode CLDR documentation](https://cldr.unicode.org/index/bcp47-extension) where it says: \r\n\r\n\r\n>[IETF BCP 47 ](http://www.google.com/url?q=http%3A%2F%2Ftools.ietf.org%2Fhtml%2Fbcp47&sa=D&sntz=1&usg=AOvVaw1DoMN1IBGg-JHgECBvdW1t)[Tags for Identifying Languages](http://www.google.com/url?q=http%3A%2F%2Ftools.ietf.org%2Fhtml%2Fbcp47&sa=D&sntz=1&usg=AOvVaw1DoMN1IBGg-JHgECBvdW1t) defines the language identifiers (tags) used on the Internet and in many standards. It has an extension mechanism that allows additional information to be included. The Unicode Consortium is the maintainer of the extension ‘u’ for Locale Extensions, as described in [rfc6067](https://www.google.com/url?q=https%3A%2F%2Ftools.ietf.org%2Fhtml%2Frfc6067&sa=D&sntz=1&usg=AOvVaw0gGWi0EjHfy1WId8k8oKAi), and the extension 't' for Transformed Content, as described in [rfc6497](https://www.google.com/url?q=https%3A%2F%2Ftools.ietf.org%2Fhtml%2Frfc6497&sa=D&sntz=1&usg=AOvVaw0w-OUsFX1PtaKYIq31P64I).\r\n>\r\n>The subtags available for use in the 'u' extension provide language tag extensions that provide for additional information needed for identifying locales. The 'u' subtags consist of a set of keys and associated values (types). For example, a locale identifier for British English with numeric collation has the following form: en-GB-u-kn-true\r\n>\r\n>The subtags available for use in the 't' extension provide language tag extensions that provide for additional information needed for identifying transformed content, or a request to transform content in a certain way. For example, the language tag \"ja-Kana-t-it\" can be used as a content tag indicates Japanese Katakana transformed from Italian. It can also be used as a request for a given transformation.\r\n>\r\n>For more details on the valid subtags for these extensions, their syntax, and their meanings, see LDML Section 3.7 [Unicode BCP 47 Extension Data](http://www.google.com/url?q=http%3A%2F%2Fwww.unicode.org%2Freports%2Ftr35%2F%23Locale_Extension_Key_and_Type_Data&sa=D&sntz=1&usg=AOvVaw0lMthb9KbTJtoOd5mvv3Ha).", "Hi @lbourdois ! Many thanks for the detailed information.\r\n\r\n> Discussions on the need to improve the Hub's tagging system (applying to both datasets and models) can be found in the following discussion: [huggingface/hub-docs#193](https://github.com/huggingface/hub-docs/issues/193) \r\nFascinating topic! To me, the following suggestion has a lot of appeal:\r\n\"if consider that it was necessary to create an ISO 639-3 because ISO 639-1 was deficient, it would be to do the reverse and thus convert the tags from ISO 639-1 to ISO 639-2 or 3 (https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes or https://iso639-3.sil.org/code_tables/639/data).\"\r\n\r\nYes, ISO 639-1 is unsuitable because it has so few codes: less than 200. To address linguistic diversity in 'unrestricted mode', a list of all languages is wanted. \r\n\r\nThe idea of letting people use their favourite nomenclature and automatically adding the ISO 639-3 three-letter code as a tag is appealing. Thus all the HF datasets would have three-letter language tags (handy for basic search), alongside the authors' preferred tags and language names (including Glottolog tags as well as ISO 639-{1, 2}, and all other options allowed by BCP-47). \r\n\r\nRetaining the authors' original tags and language names would be best. \r\n* For language names: some people favour one name over another and it is important to respect their choice. In the case of Yongning Na: alternative names include 'Mosuo', 'Narua', 'Eastern Naxi'... and the names carry implications: people have been reported to come to blows about the use of the term 'Mosuo'. \r\n* For language tags: Glottocodes can be more fine-grained than Ethnologue (ISO 639-3), and some colleagues feel strongly about those. \r\n\r\nThus there would be a BCP-47 tag (sounds like a solid technical choice, though not 'passer-by-friendly': requiring some expertise to interpret) **plus** an ISO 639-3 tag that could be grabbed easily, and (last but not least) language names spelled out in full. Searches would be easier. No information would be lost. \r\n\r\nAre industry practices so conservative that many people are happy with two-letter codes, and consider ISO 639-3 three-letter codes an unnecessary complication? That would be a pity, since there are so many advantages to using longer lists. (Somewhat like the transition to Unicode: sooo much better!) But maybe that conservative attitude _is_ widespread, and it would then need to be taken into account. In which case, one could consider offering two-letter codes as a search option. Internally, the search engine would look up the corresponding 3-letter codes, and produce the search results accordingly. \r\n\r\nNow to the other questions:\r\n\r\n> * Do you think it's possible to easily handle tags that have been deprecated potentially for decades?\r\n> For example (I'm taking the case of Hebrew but this has happened for other languages) I tagged Google models with the \"iw\" [tag](https://huggingface.co/models?language=iw&sort=downloads) because I based it on what the authors gave in their [paper](https://arxiv.org/pdf/2010.11934.pdf) see table 6 page 12). It turns out that this ISO tag has in fact been deprecated since 1989 in favour of the \"he\" tag. It would therefore be necessary to have a verification that transforms the old tags into the most recent ones.\r\nI guess that the above suggestion takes care of this case. The original tag (in this example, \"iw\") is retained (facilitating cross-reference with the published paper, and respecting the real: the way the dataset was originally tagged). This old tag goes into the `BCP-47` field of the dataset, which can handle quirks & oddities like this one. And a new tag is added in the `ISO 639-3` field: the 3-letter code \"heb\". \r\n\r\n> * When you look up a language on Wikipedia, it usually shows, in addition to the ISO standard, the codes in the Glottolog (which you have already mentioned), [ELP](https://www.endangeredlanguages.com/?hl=en) and [Linguasphere](http://www.linguasphere.info/jr/index.php?l1=home&l2=welcome) databases. Would you have any opinion about these two other databases?\r\n\r\nI'm afraid I never heard about Linguasphere. The [online register for Linguasphere (PDF)](http://www.linguasphere.info/jr/pdf/index/LS_index_n-n.pdf) seems to be from 1999-2000. It seems that the level of interoperability is not very high right now. (By contrast, Glottolog has [pyglottolog](https://github.com/glottolog/pyglottolog) and in my experience contacts flow well.) \r\n\r\nThe Endangered Languages Project is something Google started but initially did not 'push' very strongly, it seems. Just airing an opinion on the public Internet, it seems that the project is now solidly rooted at University of Hawaiʻi at Mānoa. It seems that they do not generate codes of their own. They refer to ISO 639-3 (Ethnologue) as a code authority when applicable, and otherwise provide comments in so many words, such as that language L currently lacks an Ethnologue code of its own (example [here](https://www.endangeredlanguages.com/lang/10624)). \r\n\r\n> * On the Hub, there is the following dataset where French people speak in English: https://huggingface.co/datasets/Datatang/French_Speaking_English_Speech_Data_by_Mobile_Phone\r\n> Is there a database to take this case into account? I have not found any code in the Glottolog database. If based on an IETF BCP-47 standard, I would tend to tag the dataset with \"en-fr\" but would this be something accepted by linguists?\r\n> Based on the first post in this thread that there are about 8000 languages, if one considers that a given language can be pronounced by a speaker of the other 7999, that would theoretically make about 64 million BCP-47 language1-language2 codes existing. And even much more if we consider regionalists with language1_regionalism_x-language2_regionalism_y. I guess there is no such database.\r\n\r\nYes, you noted the difficulty here: that there are so many possible situations. Eventually, each dataset would required descriptors of its own. @BenjaminGalliot points out that, in addition to specifying the speakers' native languages, the degree of language proficiency would also be relevant. How many years did the speakers spend in which area? Talking which languages? In what chronological order? Etc. The complexity defies encoding. The purpose of language codes is to allow for searches that group resources into sets that make sense. Additional information is very important, but would seem to be a matter for 'comments' fields. \r\n\r\n> * Is there an international classification of languages?\r\n> A bit like the [International Classification of Diseases](https://en.wikipedia.org/wiki/International_Classification_of_Diseases) in medicine, which is established by the WHO and used as a reference throughout the world. The idea would be to have a precise number of languages to which we would then have to assign a unique tag in order to find them later.\r\n\r\nAs I understand, Ethnologue and Glottolog both try to do that, each in its own way. The simile with diseases seems interesting, to some extent: in both cases it's about human classification of phenomena that have complexity (though some diseases are simpler than others, whereas all languages have much complexity, in different ways).\r\n\r\n> * Finally, when can we expect to see all the datasets of [Pangloss](https://pangloss.cnrs.fr/) on HF? eyes And I don't know if you have a way to help to add also the datasets of [CoCoON](https://cocoon.huma-num.fr/exist/crdo/).\r\n\r\nThree concerns: (i) Technical specifications: we have not yet received feedback on the Japhug and Na datasets in HF. There may be technical considerations that we have not yet thought of and that would need to be taken into account before 'bulk upload'. (ii) Would there be a way to automate the process? The way @BenjaminGalliot did it for Japhug and Na, there was a manual component involved, and doing it by hand for all 200 datasets would not be an ideal workflow, given that the metadata are all clearly arranged. (iii) Some datasets are currently under a 'No derivatives' CreativeCommons license. We could go back to the depositors and argue that the 'No derivatives' mention were best omitted (see [here a similar argument about publications](https://creativecommons.org/2020/04/21/academic-publications-under-no-derivatives-licenses-is-misguided/)): again, we'd want to be sure about the way forward before we set the process into motion.\r\n\r\nOur hope would be that some colleagues try out the [OutilsPangloss](https://gitlab.com/lacito/outilspangloss) download tool, assemble datasets from Pangloss/Cocoon as they wish, then deposit them to HF.", "> The idea of letting people use their favourite nomenclature and automatically adding the ISO 639-3 three-letter code as a tag is appealing. Thus all the HF datasets would have three-letter language tags (handy for basic search), alongside the authors' preferred tags and language names (including Glottolog tags as well as ISO 639-{1, 2}, and all other options allowed by BCP-47).\r\n> \r\n> Retaining the authors' original tags and language names would be best.\r\n> \r\n> * For language names: some people favour one name over another and it is important to respect their choice. In the case of Yongning Na: alternative names include 'Mosuo', 'Narua', 'Eastern Naxi'... and the names carry implications: people have been reported to come to blows about the use of the term 'Mosuo'.\r\n> * For language tags: Glottocodes can be more fine-grained than Ethnologue (ISO 639-3), and some colleagues feel strongly about those.\r\n> \r\n> Thus there would be a BCP-47 tag (sounds like a solid technical choice, though not 'passer-by-friendly': requiring some expertise to interpret) **plus** an ISO 639-3 tag that could be grabbed easily, and (last but not least) language names spelled out in full. Searches would be easier. No information would be lost.\r\n\r\n@alexis-michaud raises an excellent point. Language Resource users have varying search habits (or approaches). This includes cases where two or more language names refer to a single language. A search utility/interface needs to be flexible and able to present results from various kinds of input in the search process. This could be like how the terms French/Français/Franzosisch (en/fr/de) are names for the same language or it could be a variety of the following: autoglottonyms (how the speakers of the language refer to their language), or exoglottonyms (how others refer to the language). Additionally, in web based searches I have also needed to implement diacritic sensitive and insensitive logic so that users can type with or without diacritics and not have results unnecessarily excluded. \r\n\r\nDepending on how detailed of a search problem HF seeks to solve. It may be better to off load complex search to search engines like OLAC which aggregate a lot of language resources. — as I mentioned above I can assist with the informatics on creating an OLAC feed.\r\n\r\nAbstracting search logic from actual metadata may prove a useful way to lower the technical debt overhead. Technical tools and library standards use ISO and BCP-47 Standards. So, from a bibliographic metadata perspective this seems to be the way forward with the widest set of use cases. ", "To get a visual idea of these first exchanges, I coded a Streamlit app that I put online on Spaces: https://huggingface.co/spaces/lbourdois/Language-tags-demo. \r\nThe code is in Python so I don't know if it can be used by HF who seems to need something in Node.js but it serves as a proof of concept. The advantage is also that you can directly test ideas by enter things in a search bar and see what comes up. \r\n\r\nThis application is divided into 3 points:\r\n- The first is to enter a language in natural language to get its code which can then be filled in the YAML file of the README.MD files of the HF datasets or models in order to be referenced and found by everyone.\r\nIn practice, enter the language (e.g: `English`) you are interested in to get its associated tag (e.g: `en`). You can enter several languages by separating them with a comma (e.g `French,English,German`). You will be given priority to the ISO 639-3 code if it exists otherwise the Glottocode or the BCP47 code (for varieties in particular). If none of these codes are available, it links to a page where the user can contact HF to request to add this tag. \r\nIf you enter a BCP47 code, it must be entered as follows: `Language(Territory)`, for example `French(Canada)`. Attention! If you enter a BCP-47 language, it must be entered first, otherwise the plant code will be displayed. I have to fix this problem but I am moving to a new place, I don't have an internet connection when I want and I prefer to push this first version so that you can already test things now and not have to wait days or weeks.\r\nThis point is intended to simulate the user's side of the equation, which wonders which tag he should fill in for his language.\r\n\r\n\r\n- The second is to enter a language code to obtain the name of the language in natural language.\r\nIn practice, enter the tag (ISO 639-1/2/3, Glottolog or BCP-47) you are interested in (e.g: `fra`) to get its associated language (e.g: French). You can enter several languages by separating them with a comma (e.g `fra,eng,deu`). Attention! If you enter a BCP-47 code, it must be entered first, otherwise the plant code will be displayed. Same as the other bug above (it's actually the same one).\r\nThis point is intended to simulate the side of HF that for a given tag must return the correct language.\r\n\r\n\r\n\r\nTo code these two points, I tested two approaches. \r\n\r\n1. The first one (internal DB in the app) consists in querying a database that HF would have locally at their place. To create this database, I merged the ISO 639 database (https://iso639-3.sil.org/sites/iso639-3/files/downloads/iso-639-3.tab) and the Glottolog database (https://glottolog.org/meta/downloads). The result of this merge is visible in the 3rd point of the application qui is an overview of the database.\r\nIn the image below, on line 1 of the database, we can see that the Glottocode database gives an ISO 639-3 code (column ISO639P3code) but not the ISO 639 database (column 639-3). Do you have an explanation for this phenomenon?\r\n\r\n\r\n\r\nFor BCP 47 codes of the type `fr-CA`, I have retrieved the ISO-3166 alpha 1 codes of the territories (https://www.iso.org/iso-3166-country-codes.html).\r\nIn practice, what I do is if we enter `fr-CA` is that the letters before the `-` refer to a language in the `Name` column for a `639-1` == `fr` (`639-3` for `fra` or `fre`) in the base of my image above. Then I look at the letters after the `-` which refers to a territory. It comes out `French (Canada)`. I used https://cldr.unicode.org/translation/displaynames/languagelocale-name-patterns for the pattern that came up.\r\n\r\n\r\n2. The second approach (with langcodes lib in the app) consists in using the Python `langcodes` library (https://github.com/rspeer/langcodes) which offers a lot of features in ready-made functions. It manages for example deprecated codes, the validity of an entered code, gives languages from code in the language of your choice (by default in English, but also autoglottonyms), etc. I invite you to read the README of the library. The only negative point is that it hasn't been updated for 10 months so basing your tag system on an external tool that isn't necessarily up to date can cause problems in the long run. But it is certainly an interesting source.\r\n\r\nFinally, I have added some information on the number of people speaking/reading the language(s) searched (figures provided by langcodes which are based on those given by ISO). This is not relevant for our topic but it could be figures that could be added as information on the https://huggingface.co/languages page. \r\n\r\n\r\n\r\nWhat could be done to improve the app if I have time:\r\n- Write the text for the app's homepage to describe what it does. This could serve as a basis for a documentation that I think will be necessary to add somewhere on the HF website to explain how the language tagging system works.\r\n- Deal with the bug mentioned above\r\n- Integrate ISO 3166-1 alpha 2 territories (https://www.iso.org/obp/ui#iso:pub:PUB500001:en)? They offer a finer granularity than ISO 3166-1 alpha 1 which is limited to the country level, but they are very administrative (for French, ISO 3166-1 alpha 2 gives us the \"départements\" for example).\r\n- Add autoglottonyms? (I only handle English language names for the moment)\r\n- For each language indicate to which family it belongs, in practice this could help to make data augmentation, but especially to classify the languages and find them more easily on the page https://huggingface.co/languages.", "Very impressive! Using the prompt 'Japhug' (a language name), the app finds the intended language:\r\n\r\n\r\nA first question: based on the Glottocode, would it be possible to grab the closest ISO639-3 code? In case there is no match for the exact language variety, one needs to explore the higher-level groupings, level by level. For this language (Japhug), the information provided in the extracted CSV file (`glottolog-languoids-v4.6.csv`) is: \r\n`sino1245/burm1265/naqi1236/qian1263/rgya1241/core1262/jiar1240` \r\nOne need not look further than the first higher-level grouping, [`jiar1240`](https://glottolog.org/resource/languoid/id/jiar1240), to get an ISO639-3 code, namely `jya`.\r\n\r\nThus users searching by language names would get ISO639-3 (often less fine-grained than Glottolog) as a bonus.\r\nIt might be possible to ask the Glottolog team to provide this piece of information as part of an export from their database.", "> on line 1 of the database, we can see that the Glottocode database gives an ISO 639-3 code (column ISO639P3code) but not the ISO 639 database (column 639-3). Do you have an explanation for this phenomenon?\r\n\r\nThat is because the language name 'Aewa' is not found in the Ethnologue (ISO 639-3) export that you are using. [This export in table form](https://iso639-3.sil.org/sites/iso639-3/files/downloads/iso-639-3.tab) only has one reference name (`Ref_Name`). For the language at issue, it is not 'Aewa' but ['Awishira'](https://www.ethnologue.com/language/ash).\r\n\r\nBy contrast, the language on line 0 of the database is called 'Abinomn' by both Ethnologue and Glottolog, and accordingly, columns `ISO639P3code` and `639-3` both contain the ISO 639-3 code, `bsa`.\r\n \r\nThe full Ethnologue database records alternate names for each language, and I'd bet that 'Aewa' is recorded among alternate names for the 'Ashiwira' language. I can't check because the full Ethnologue database is paywalled. \r\n\r\n\r\n[Glottolog](https://glottolog.org/resource/languoid/id/abis1238) does provide the corresponding ISO 639-3 code for 'Aewa', `ash`, which is an exact match (it refers to the same variety as Glottolog `abis1238`).\r\nIn this specific case, Glottolog provides all the relevant information. I'd say that Glottolog can be trusted for all the codes they provide, including ISO 639-3 codes: they only include them when the match is good. \r\n\r\nSee previous comment about the cases where there is no exact match between Glottolog and ISO 639-3 (suggested workaround: look at a higher-level grouping to get an ISO 639-3 code).", "I will add these two points to my TODO list.\r\n- Since Glottolog can be trust, I will add a condition to the code that if there is no ISO 639-3 code in the \"official\" database (https://iso639-3.sil.org/sites/iso639-3/files/downloads/iso-639-3.tab), look for it in the \"ISO639P3code\" column of Glottolog.\r\n- For the point of adding the closest ISO 639-3 code for a Glottolog code, what convention should be adopted for the output? Just the ISO 639-3 code, or the ISO 639-3 code - Glottolog code, or the ISO 639-3 code - language name?\r\nTo use the example of `Japhug` , should it be just `jya`, or `jya-japh1234` or `jya-Japhug`?", "> * Integrate ISO 3166-1 alpha 2 territories (https://www.iso.org/obp/ui#iso:pub:PUB500001:en)? They offer a finer granularity than ISO 3166-1 alpha 1 which is limited to the country level, but they are very administrative (for French, ISO 3166-1 alpha 2 gives us the \"départements\" for example).\r\n\r\nI'm concerned with this sort of exploration. Not because I am against innovation. In fact this is an interesting thought exercise. However, to explore this thought further creates cognitive dissidence between BCP-47 authorized codes and other code sets which are not BP-47 compliant. For that reason, I think adding additional codes is a waste of time both for HF devs and for future users who get a confusing idea about language tagging. ", "Good job for the application!\r\n\r\n> On the Hub, there is the following dataset where French people speak in English: https://huggingface.co/datasets/Datatang/French_Speaking_English_Speech_Data_by_Mobile_Phone\r\n Is there a database to take this case into account? I have not found any code in the Glottolog database. If based on an IETF BCP-47 standard, I would tend to tag the dataset with \"en-fr\" but would this be something accepted by linguists?\r\n Based on the first post in this thread that there are about 8000 languages, if one considers that a given language can be pronounced by a speaker of the other 7999, that would theoretically make about 64 million BCP-47 language1-language2 codes existing. And even much more if we consider regionalists with language1_regionalism_x-language2_regionalism_y. I guess there is no such database.\r\n\r\n> Yes, you noted the difficulty here: that there are so many possible situations. Eventually, each dataset would required descriptors of its own. @BenjaminGalliot points out that, in addition to specifying the speakers' native languages, the degree of language proficiency would also be relevant. How many years did the speakers spend in which area? Talking which languages? In what chronological order? Etc. The complexity defies encoding. The purpose of language codes is to allow for searches that group resources into sets that make sense. Additional information is very important, but would seem to be a matter for 'comments' fields.\r\n\r\nTo briefly complete what I said on this subject in a private discussion group, there is a lot of (meta)data associated with each element of a corpus (which language level, according to which criteria, knowing that even among native speakers there are differences, some of which may go beyond what seems obvious to us from a linguistic point of view, such as socio-professional category, life history, environment in the broad sense, etc.), which can be placed in ad-hoc columns, or more freely in a comment/note column. And it is the role of the researcher (in this case a linguist, in all likelihood) to do analyses (statistics...) to determine the relevant data, including criteria that may justify separating different languages (in the broad sense), making separate corpora, etc. Putting this information in the language code is in my opinion doing the job in the opposite and wrong direction, as well as bringing other problems, like where to stop in the list of multidimensional criteria to be integrated, so in my opinion, here, the minimum is the best (the important thing is in my opinion to have well-documented data, globally, by sub-corpus or by line)...\r\n\r\n> If you are going to use Glottolog codes use them after an -x- tag in the BCP-47 format to maintain BCP-47 validity.\r\n\r\nYes, for the current corpora, I have written:\r\n\r\n```\r\nlanguage:\r\n- jya\r\n- nru\r\nlanguage_bcp47:\r\n- x-japh1234\r\n- x-yong1288\r\n```\r\n\r\n> * Add autoglottonyms? (I only handle English language names for the moment)\r\n\r\nAutoglossonyms are useful (I use them prior to other glossonyms), but I'm not sure there is an easy way to retrieve them. We can find some of them in the \"Alternative Names\" panel of Glottolog, but even if we have an API to retrieve them easily, their associated language code will often not be the one we are in (hence the need to do several cycles to find one, which might not be the right one...). Maybe this problem needs more investigation...\r\n\r\n> For the point of adding the closest ISO 639-3 code for a Glottolog code, what convention should be adopted for the output? Just the ISO 639-3 code, or the ISO 639-3 code - Glottolog code, or the ISO 639-3 code - language name?\r\nTo use the example of Japhug , should it be just jya, or jya-japh1234 or jya-Japhug?\r\n\r\nI strongly insist not to add **a** language name after the code, it would restart a spiral of problems, notably the choice of the language in question:\r\n* the autoglossonym: in my opinion the best choice, but you have to know it…\r\n* the English name: iniquitous,\r\n* the name in the administratively/politically dominant language of the target language if it is relevant (strictly localized without overlapping, for example): iniquitous and tendentious (and in a way a special case of the previous one)...\r\n* etc.\r\n", "> To get a visual idea of these first exchanges, I coded a Streamlit app that I put online on Spaces: https://huggingface.co/spaces/lbourdois/Language-tags-demo.\r\n> The code is in Python so I don't know if it can be used by HF who seems to need something in Node.js but it serves as a proof of concept. The advantage is also that you can directly test ideas by enter things in a search bar and see what comes up.\r\n\r\nThis is really great. You're doing a fantastic job. I love watching the creative process evolve. It is exciting. Let me provide some links to some search interfaces for further inspiration. I always find it helpful to know how others have approached a problem when figuring out my approach. I will link to three examples Glottolog, r12a's language sub-tag chooser, and the FLEx project builder wizard. The first two are online, but the last one is in an application which must be downloaded and works only on windows or linux. I have placed some notes on each of the screenshots.\r\n\r\n* **[Glottolog](https://glottolog.org/)** | [Search Query](https://glottolog.org/glottolog?name=en&namequerytype=part&multilingual=on#2/20.9/150.0) \r\n\r\n\r\n\r\n\r\n\r\n* **[r12a language sub-tag chooser](https://r12a.github.io/app-subtags/)** | [Code on github](https://github.com/r12a/app-subtags)\r\n\r\n\r\n\r\n\r\n* **FLEx Language Chooser** | [application page](https://software.sil.org/fieldworks/)\r\n\r\n\r\n", "> In practice, what I do is if we enter `fr-CA` is that the letters before the `-` refer to a language in the `Name` column for a `639-1` == `fr` (`639-3` for `fra` or `fre`) in the base of my image above. Then I look at the letters after the `-` which refers to a territory. It comes out `French (Canada)`. I used https://cldr.unicode.org/translation/displaynames/languagelocale-name-patterns for the pattern that came up.\r\n\r\nWhat you are doing is looking at the algorithm for Locale generation rather than BCP-47's original documentation. I'm not sure there are difference, there might be. I know that locale IDs generally follow BCP-47 But I think there are some differences such as the use of `_` vs. `-`. ", "> A first question: based on the Glottocode, would it be possible to grab the closest ISO639-3 code? In case there is no match for the exact language variety, one needs to explore the higher-level groupings, level by level. For this language (Japhug), the information provided in the extracted CSV file (`glottolog-languoids-v4.6.csv`) is: `sino1245/burm1265/naqi1236/qian1263/rgya1241/core1262/jiar1240` One need not look further than the first higher-level grouping, [`jiar1240`](https://glottolog.org/resource/languoid/id/jiar1240), to get an ISO639-3 code, namely `jya`.\r\n> \r\n> Thus users searching by language names would get ISO639-3 (often less fine-grained than Glottolog) as a bonus. It might be possible to ask the Glottolog team to provide this piece of information as part of an export from their database.\r\n\r\nThis is logical, but the fine grained assertions are not the same. That is just because they are in a hierarchical structure today doesn't mean they will be tomorrow. In some cases the glottolog is clearly referring to sub-language variants which will never receive full language status, whereas in other cases glottolog is referencing to unequal entities one or more of which should be a language. Many of the codes in glottolog have no associated documentation indicating what sort of speech variety they are. ", "@lbourdois \r\n> * Since Glottolog can be trust, I will add a condition to the code that if there is no ISO 639-3 code in the \"official\" database (https://iso639-3.sil.org/sites/iso639-3/files/downloads/iso-639-3.tab), look for it in the \"ISO639P3code\" column of Glottolog.\r\n\r\nI'm confused here... if there is no ISO639-3 code in the official database from the registrar, why would you look for an \"unofficial\" code from someone else? What is the use case here?", "> For the point of adding the closest ISO 639-3 code for a Glottolog code, what convention should be adopted for the output? Just the ISO 639-3 code, or the ISO 639-3 code - Glottolog code, or the ISO 639-3 code - language name?\r\nTo use the example of Japhug , should it be just jya, or jya-japh1234 or jya-Japhug?\r\n\r\n(answer edited in view of [Benjamin Galliot's comment](https://github.com/huggingface/datasets/issues/4881#issuecomment-1237420600) \r\nEasy part of the answer first: jya-Japhug is out, because, as @BenjaminGalliot pointed out above, mixing language names with language codes will make trouble. For Japhug, `jya-Japhug` looks rather good: the pair looks nice, the one (`jya`) packed together, the other (`Japhug`) good and complete while still pretty compact. But think about languages like 'Yongning Na' or 'Yucatán Maya': a code with a space in the middle, like `nru-Yongning Na`, is really unsightly and unwieldy, not?\r\n\r\nSome [principles for language naming in English](http://hdl.handle.net/10125/24725) have been put forward, with some linguistic arguments, but always supposing that such standardization is desirable, actual standardization of language names in English may well never happen.\r\n\r\nAs for `jya-japh1234`: again, at first sight it seems cute, combining two fierce competitors (Ethnologue and Glottolog) into something that gets the best of both worlds. \r\nBut @HughP has a point: _adding additional codes is a waste of time both for HF devs and for future users who get a confusing idea about language tagging_ Strong wording, for an important comment: better stick with BCP 47. \r\n\r\nSo the solution pointed out by Benjamin, from Frances Gillis-Webber and Sabine Tittel, looks attractive: \r\njya-x-japh1234\r\n\r\nOn the other hand, if the idea for HF Datasets is simply to add the closest ISO 639-3 code for a Glottolog code, maybe it could be provided simply in three letters: providing the 'raw' ISO 639-3 code `jya`. Availability of 'straight' ISO 639-3 codes could save trouble for some users, and those who want more detail could look at the rest of the metadata and general information associated with datasets.", "The problem seems to have already been raised here: https://drops.dagstuhl.de/opus/volltexte/2019/10368/pdf/OASIcs-LDK-2019-4.pdf\r\n\r\nAn example can be seen here :\r\n\r\n> 3.1.2 The use of privateuse sub-tag\r\nIn light of unambiguous language codes being available for the two Khoisan varieties, we\r\npropose to combine the ISO 639-3 code for the parent language N‖ng, i.e., ‘ngh’, with the\r\nprivateuse sub-tag ‘x-’ and the respective Glottocodes stated above.\r\nThe language tags for N|uu and ‖’Au can then be defined accordingly:\r\nN|uu: ngh-x-nuuu1242\r\n‖’Au: ngh-x-auni1243\r\n\r\nBy the way, while searching for this, I came across this application: https://huggingface.co/spaces/cdleong/langcode-search", "> > * Since Glottolog can be trust, I will add a condition to the code that if there is no ISO 639-3 code in the \"official\" database (https://iso639-3.sil.org/sites/iso639-3/files/downloads/iso-639-3.tab), look for it in the \"ISO639P3code\" column of Glottolog.\r\n> \r\n> I'm confused here... if there is no ISO639-3 code in the official database from the registrar, why would you look for an \"unofficial\" code from someone else? What is the use case here?\r\n\r\nHi @HughP, I'm happy to clear what confusion may exist here :innocent: Here is the use case. \r\nGuillaume Jacques (@rgyalrong) put together a sizeable corpus of the Japhug language. It is up on HF Datasets ([here](https://huggingface.co/datasets/Lacito/pangloss/viewer/japh1234)) as well as on Zenodo. \r\n\r\nZenodo is an all-purpose repository without adequate domain-specific metadata (\"[métadonnées métier](https://www.cines.fr/archivage/des-expertises/les-metadonnees/metadonnees-metier/)\"), and the deposits in there are not easy to locate. The Zenodo deposit is intended for a highly specific user case: someone reads about the dataset in a paper, goes to the address on Zenodo and grabs the dataset at one go. \r\n\r\nHF Datasets, on the other hand, allows users to look around among corpora. The Japhug corpus needs proper tagging so that HF Datasets users can find out about it. \r\nJaphug has an entry of its own in Glottolog, whereas it lacks an entry of its own in Ethnologue. Hence the practical usefulness of Glottolog. Ethnologue pools together, under the code `jya`, three different languages (Japhug, Tshobdun `tsho1240` and Zbu `zbua1234`). \r\n\r\nI hope that this helps.", "> By the way, while searching for this, I came across this application: https://huggingface.co/spaces/cdleong/langcode-search\r\n\r\nReally relevant Space, so tagging its author @cdleong, just in case!", "@cdleong A one-stop shop for language codes: terrific!\r\nHow do you feel about the use of Glottocodes? When searching the language names 'Japhug' and 'Yongning Na' (real examples, related to a HF Datasets deposit & various research projects), the relevant Glottocodes are retrieved, and that is great (and not that easy, notably with the space in the middle of 'Yongning Na'). But this positive result is 'hidden' in the results page. Specifically: \r\n\r\n- for Japhug: when searching by language name ('Japhug'), the result in big print is 'Failure', even though there is an available Glottocode (at bottom).\r\n\r\nWhen searching by Glottocode (japh1234), same outcome: 'Result: failure!' (even though this _is_ the right Glottocode\r\nWhen searching by x-japh1234 (Glottocode encapsulated in BCP 47 syntax), one gets the message \r\n\r\n> ''x-japh1234' parses meaningfully as a language tag according to IANA\"\r\n\r\nbut there is paradoxically no link provided to Glottolog: the 'Glottolog' part of the results page is empty\r\n\r\n\r\n- Yongning Na: the correct code is identified (yong1288) but instead of foregrounding this exact match, the first result that comes up is a completely different language, called 'Yong'. \r\n\r\nTrying to formulate a conclusion (admittedly, this note is not based on intensive testing, it is just feedback on initial contact): from a user perspective, it seems that the tool could make more extensive use of Glottolog. `langcode-search` does a great job querying Glottolog, why not make more extensive use of that information? (including: to arrive at the nearest ISO 639-3 code)" ]

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[ "OK, I removed Glottolog codes and only added ISO 639-3 ones. The former are for the moment in corpus card description, language details, and in subcorpora names.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4880). All of your documentation changes will be reflected on that endpoint." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4879). All of your documentation changes will be reflected on that endpoint." ]

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[ "Resolved via https://github.com/huggingface/datasets/pull/4937." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4877). All of your documentation changes will be reflected on that endpoint." ]

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[ "also @osanseviero @Pierrci @SBrandeis potentially", "Love this in principle 🚀 \r\n\r\nLet's keep in mind users might rely on `dataset_infos.json` already.\r\n\r\nI'm not convinced by the two-syntax solution, wouldn't it be simpler to have only one syntax with a `default` config for datasets with only one config? ie, always having the `configs` field. This makes parsing the metadata easier IMO.\r\n\r\nMight also be good to wrap the tags under a `datasets_info` tag as follows:\r\n\r\n```yaml\r\ndescription: ...\r\ncitation: ...\r\ndataset_infos:\r\n download_size: 35142551\r\n dataset_size: 89789763\r\n version: 1.0.0\r\n configs:\r\n - ...\r\n[...]\r\n```\r\n\r\nLet's also keep in mind that extracting YAML metadata from a markdown readme is a bit more fastidious for users than just parsing a JSON file.", "> Let's keep in mind users might rely on dataset_infos.json already.\r\n\r\nYea we'll full full backward compatibility\r\n\r\n> Let's also keep in mind that extracting YAML metadata from a markdown readme is a bit more fastidious for users than just parsing a JSON file.\r\n\r\nThe main things that may use or ingest these data IMO are:\r\n- users in the UI or IDE\r\n- `datasets` to populate `DatasetInfo` python object\r\n- moon landing which is already parsing YAML\r\n\r\nAm I missing something ? If not I think it's ok to use YAML\r\n\r\n> Might also be good to wrap the tags under a datasets_info tag as follows:\r\n\r\nMaybe one single syntax like this then ?\r\n```yaml\r\ndataset_infos:\r\n- config: unlabeled\r\n download_size: 35142551\r\n dataset_size: 89789763\r\n version: 1.0.0\r\n splits:\r\n - name: train\r\n num_examples: 10000\r\n features:\r\n - name: text\r\n dtype: string\r\n- config: labeled\r\n download_size: 35142551\r\n dataset_size: 89789763\r\n version: 1.0.0\r\n splits:\r\n - name: train\r\n num_examples: 100\r\n features:\r\n - name: text\r\n dtype: string\r\n - name: label\r\n dtype: ClassLabel\r\n names:\r\n - negative\r\n - positive\r\n```\r\nand when you have only one config\r\n```yaml\r\ndataset_infos:\r\n- config: default\r\n splits:\r\n - name: train\r\n num_examples: 10000\r\n features:\r\n - name: text\r\n dtype: string\r\n```", "love the idea, and the trend in general to move more things (like tasks) to a single place (YAML).\r\n\r\nalso, if you browse files on a dataset's page (in \"Files and versions\"), raw `README.md` files looks nice and readable, while `.json` files are just one long line that users need to scroll. \r\n\r\n> Let's also keep in mind that extracting YAML metadata from a markdown readme is a bit more fastidious for users than just parsing a JSON file.\r\n\r\ndo users often parse `datasets_infos.json` file themselves? ", "> do users often parse datasets_infos.json file themselves?\r\n\r\nNot AFAIK, but I'm sure there should be a few users.\r\nUsers that access these info via the `DatasetInfo` from `datasets` won't see the change though e.g.\r\n```python\r\n>> from datasets import get_datasets_infos\r\n>>> get_datasets_infos(\"squad\")\r\n{'plain_text': DatasetInfo(description='Stanford Question Answering Dataset...\r\n```", "> Maybe one single syntax like this then ?\r\n\r\nLGTM!\r\n\r\n> The main things that may use or ingest these data IMO are:\r\n> - users in the UI or IDE\r\n> - datasets to populate DatasetInfo python object\r\n> - moon landing which is already parsing YAML\r\n\r\nFair point!\r\n\r\nHaving dataset info in the README's YAML is great for API / `huggingface_hub` consumers as well as it will be inserted in the `cardData` field out of the box 🔥 \r\n", "Very supportive of this!\r\n\r\nNesting an array of configs inside `dataset_infos: ` sounds good to me. One small tweak is that `config: default` can be optional for the default config (which can be the first one by convention)\r\n\r\nWe'll be able to implement metadata validation on the Hub side so we ensure that those metadata are always in the right format (maybe for @coyotte508 ? cc @Pierrci). From a quick glance the `features` might be the harder part to validate here, any doc will be welcome.\r\n\r\n### Other high-level points:\r\n- as we move from mostly academic datasets to *all* datasets (which include the data inside the repos), my intuition is that more and more datasets (Hub-stored) are going to be **single-config**\r\n- similarly, less and less datasets will have a loading script, **just the data + some metadata**\r\n- to lower the barrier to entry to contribution, in the long term users shouldn't need to compute/update this data via a command line. It could be filled automatically on the Hub through a \"bot\" inside Discussions & Pull requests for instance.", "re: `config: default`\r\n\r\nNote also that the default config is not named `default`, afaiu, but create from the repo name, eg: https://huggingface.co/datasets/nbtpj/bionlp2021SAS default config is `nbtpj--bionlp2021SAS` (which is awful)", "> Note also that the default config is not named default, afaiu, but create from the repo name, eg: https://huggingface.co/datasets/nbtpj/bionlp2021SAS default config is nbtpj--bionlp2021SAS (which is awful)\r\n\r\nWe can change this to `default` I think or something else", "> From a quick glance the features might be the harder part to validate here, any doc will be welcome.\r\n\r\nI dug into features validation, see:\r\n\r\n- the OpenAPI spec: https://github.com/huggingface/datasets-server/blob/main/chart/static-files/openapi.json#L460-L697\r\n- the node.js code: https://github.com/huggingface/moon-landing/blob/upgrade-datasets-server-client/server/lib/datasets/FeatureType.ts", "> We can change this to default I think or something else\r\n\r\nI created https://github.com/huggingface/datasets/issues/4902 to discuss that", "> Note also that the default config is not named `default`, afaiu, but create from the repo name\r\n\r\nin case of single-config you can even hide the config name from the UI IMO\r\n\r\n> I dug into features validation, see: the OpenAPI spec\r\n\r\nin moon-landing we use [Joi](https://joi.dev/api/) to validate metadata so we would need to generate from Joi code from the OpenAPI spec (or from somewhere else) but I guess that's doable – or just rewrite it manually, as it won't change often", "I remember there was an ongoing discussion on this topic:\r\n- #3507\r\n\r\nI recall some of the concerns raised on that discussion:\r\n- @lhoestq: Tensorflow Datasets catalog includes a community catalog where you can find and use HF datasets. They are using the exported dataset_infos.json files from github to get the metadata: [#3507 (comment)](https://github.com/huggingface/datasets/issues/3507#issuecomment-1056997627)\r\n- @severo: [#3507 (comment)](https://github.com/huggingface/datasets/issues/3507#issuecomment-1042779776)\r\n - the metadata header might be very long, before reaching the start of the README/dataset card. \r\n - It also somewhat prevents including large strings like the checksums\r\n - two concepts are mixed in the same file (metadata and documentation). This means that if you're interested only in one of them, you still have to know how to parse the whole file. \r\n- @severo: the future \"datasets server\" could be in charge of generating the dataset-info.json file: [#3507 (comment)](https://github.com/huggingface/datasets/issues/3507#issuecomment-1033752157)", "Thanks for bringing these points up !\r\n\r\n> @lhoestq: Tensorflow Datasets catalog includes a community catalog where you can find and use HF datasets. They are using the exported dataset_infos.json files from github to get the metadata: https://github.com/huggingface/datasets/issues/3507#issuecomment-1056997627\r\n\r\nThe TFDS implementation is not super advanced, so it's ok IMO as long as we don't break all the dataset scripts. Note that users can still use `to_tf_dataset`.\r\n\r\nWe had a chance to discuss the two nexts points with @julien-c as well:\r\n\r\n> @severo: https://github.com/huggingface/datasets/issues/3507#issuecomment-1042779776\r\nthe metadata header might be very long, before reaching the start of the README/dataset card.\r\n\r\nIf we don't add the checksums we should be fine. We can also set a maximum number of supported configs in the README to keep it readable.\r\n\r\n> @severo: the future \"datasets server\" could be in charge of generating the dataset-info.json file: https://github.com/huggingface/datasets/issues/3507#issuecomment-1033752157\r\n\r\nI guess the \"HF Hub actions\" could open PRs to do the same in the YAML directly\r\n", "Thanks for linking that similar discussion for context, @albertvillanova!" ]

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[ "Hi ! Your HF_ENDPOINT seems wrong because of the extra \"/\"\r\n```diff\r\n- os.environ[\"HF_ENDPOINT\"] = \"https://hub-ci.huggingface.co/\"\r\n+ os.environ[\"HF_ENDPOINT\"] = \"https://hub-ci.huggingface.co\"\r\n```\r\n\r\ncan you try again without the extra \"/\" ?", "Oh, yes, sorry, but it's not the issue.\r\n\r\nIn my code, I set `HF_ENDPOINT=https://hub-ci.huggingface.co`. I added `os.environ[\"HF_ENDPOINT\"] = \"https://hub-ci.huggingface.co/\"` afterward just to indicate that we had to have this env var and made a mistake there", "I can't reproduce on my side. I tried using a private dataset repo with a CSV file on hub-ci\r\n\r\nWhat's your version of `huggingface_hub` ?", "I can't reproduce either... Not sure what has occurred, very sorry to have made you lost your time on that ", "I got something similar in https://github.com/huggingface/datasets-server/pull/608. Look how changing the order of the tests (https://github.com/huggingface/datasets-server/pull/608/commits/2c50fe833323de3dfdc76c5cd68639279e0887f8) change the result, which means that something has a side-effect:\r\n- https://github.com/huggingface/datasets-server/actions/runs/3264636253/jobs/5365612918 works\r\n- https://github.com/huggingface/datasets-server/actions/runs/3264651839/jobs/5365654924 does not work\r\n\r\nI still couldn't reproduce it with a simpler script... ", "The issue happens because `extend_module_for_streaming` can't be used several times on packaged builders like `csv` to apply a new auth token. Indeed `extend_module_for_streaming` only applies authentication once, and on subsequent calls does nothing:\r\n\r\nhttps://github.com/huggingface/datasets/blob/07b7c38d9e9c72c74b02524c432ca64d0d3738f4/src/datasets/streaming.py#L62-L64\r\n\r\nThis behavior exists because the authenticatoin wrapper only supports one token. This is an issue for packaged builders which can be used to load several datasets, so it may require several tokens.\r\n\r\nThis can be fixed by storing a dict `token_per_repo_id` instead of `use_auth_token` in the authentication wrapper, and by making it possible to update the authentication wrapper with a new token", "I fixed the datasets-server CI with: https://github.com/huggingface/datasets-server/pull/608\r\n\r\nSee https://github.com/huggingface/datasets-server/actions/runs/3265359326/jobs/5367445018\r\n\r\nThanks for the help @lhoestq !", "> This can be fixed by storing a dict token_per_repo_id instead of use_auth_token in the authentication wrapper, and by making it possible to update the authentication wrapper with a new token\r\n\r\nIf I call the module on the same repo twice: first with authentication, then without authentication, would the second call use authentication anyway? It sounds like a bug: the argument passed to the function would be silently ignored.", "Yes exactly, this is a known bug", "And do you think this bug could be solved as well when fixing this issue?", "yes definitely !" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4874). All of your documentation changes will be reflected on that endpoint." ]

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[ "Hi!\r\n\r\n200+ data loaders is a lot. Have you tried to reduce the number of datasets by concatenating/interleaving the ones with the same structure/task (the API is `{concatenate_datasets/interleave_datasets}([dset1, ..., dset_N])`)?", "Hi @mariosasko, thank you for your reply. I tried pre-concatenating different datasets into one, but one key need is to keep each batch the same data type. Considering that the concatenate-then-segment operation for prefetched samples may span across different data types after concatenating/interleaving (cuz different data sources are mixed), any solution to remain the same data source for each batch?" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Awesome thanks ! I think we can also encourage TAR archives as for image dataset scripts (feel free to copy paste some parts from there lol)", "Thanks for all the great feedback @polinaeterna and @lhoestq! 🥰\r\n\r\nI added all the other feedback, and I'll look into the `librivox-indonesia` script now!", "If you don't mind, I'm taking over this PR since we'll do a release pretty soon", "@lhoestq no, I do :D ", "haha sorry ^^" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4871). All of your documentation changes will be reflected on that endpoint." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Hi @dadelani, thanks for your awesome contribution!!! :heart: \r\n\r\nHowever, now we are using the Hub to add new datasets, instead of this GitHub repo. \r\n\r\nYou could share this dataset under your Hub organization namespace: [Masakhane NLP](https://huggingface.co/masakhane). This way the dataset will be accessible using:\r\n```python\r\nds = load_dataset(\"masakhane/mafand\")\r\n```\r\n\r\nYou have the procedure documented in our online docs: \r\n- [Create a dataset loading script](https://huggingface.co/docs/datasets/dataset_script)\r\n- [Share](https://huggingface.co/docs/datasets/share)\r\n\r\nMoreover, datasets shared on the Hub no longer need the dummy data files.\r\n\r\nPlease, feel free to ping me if you need any further guidance/support.", "thank you for the comment. I have moved it to the Hub https://huggingface.co/datasets/masakhane/mafand", "Great job, @dadelani!!\r\n\r\nPlease, note that in the README.md file, the YAML tags should be preceded and followed by three dashes `---`, so that they are properly parsed. See, e.g.: https://raw.githubusercontent.com/huggingface/datasets/main/templates/README.md", "Also you could replace the line:\r\n```\r\n# Dataset Card for [Needs More Information]\r\n```\r\nwith\r\n```\r\n# Dataset Card for MAFAND-MT\r\n```", "Great, thank you for the feedback. I have fixed both issues." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4866). All of your documentation changes will be reflected on that endpoint." ]

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[ "Thanks for reporting @MoritzLaurer.\r\n\r\nCurrently, the dataset preview is working properly: https://huggingface.co/datasets/MoritzLaurer/multilingual_nli\r\n\r\nPlease note that when a dataset is modified, it might take some time until the preview is completely updated.\r\n\r\n@severo might it be worth adding a clearer error message, something like \"The preview is updating, please retry later\"?", "Thanks for your response. You are right, its now working well. I had waited for 30 min or so and refreshed several times and thought there was some other error. Yeah, a different error message sounds like a good idea to avoid confusion. ", "I'm closing this issue then.", "> @severo might it be worth adding a clearer error message, something like \"The preview is updating, please retry later\"?\r\n\r\nYes, it's a known issue, and we're about to ship a better version" ]

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[ "@albertvillanova any help ? The linked dataset is in beam format which is similar to wikipedia dataset in huggingface that you scripted..", "Nvm, I was able to port it to huggingface datasets, will upload to the hub soon", "https://huggingface.co/datasets/djaym7/wiki_dialog", "Thanks for the addition, @djaym7." ]

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[ "What's more, the downloaded data is actually a folder instead of an excel file.", "Hi hi, instead of using `download_and_extract` function, I only use `download` function: `base_dir = Path(dl_manager.download(urls))`. It turns out that the code works for `datasets==2.2.2`, however, it doesn't work with `datasets==2.4.0`. ", "Hi @yana-xuyan, thanks for reporting.\r\n\r\nIndeed you already found the answer: an Excel file should be just downloaded and not downloaded-and-extracted.\r\n\r\nThe reason why is that if you call also extract, our library will try to infer the compression format (and extract it). And Excel files are viewed as ZIP files and extracted as so (into a directory). This is because the Office Open XML is indeed a zipped file under the hood): https://en.wikipedia.org/wiki/Office_Open_XML\r\n> Office Open XML (also informally known as OOXML) is a **zipped**, XML-based file format\r\n```python\r\nimport zipfile\r\n\r\nzipfile.is_zipfile(\"filename.xlsx\")\r\n```\r\nreturns `True`.", "Hi @albertvillanova, thank you for your reply! Do you have any clue on why the same error still exists with `datasets==2.4.0` even after I don't extract the downloaded file? FYI, if I downgrade to `datasets==2.2.2`, the code works fine.", "I guess this has to do with the cache: you should remove the previously-wrongly generated directory from the cache; otherwise `datasets` tries to re-use it." ]

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[ "Hi @pefimov. Thanks for you awesome work on this dataset contribution.\r\n\r\nHowever, now we are using the Hub to add new datasets, instead of this GitHub repo. \r\n\r\nYou could share this dataset under the appropriate Hub organization namespace. This way the dataset will be accessible using:\r\n```python\r\nds = load_dataset(\"<org_namespace>/collection3\")\r\n```\r\n\r\nYou have the procedure documented in our online docs: \r\n- [Create a dataset loading script](https://huggingface.co/docs/datasets/dataset_script)\r\n- [Share](https://huggingface.co/docs/datasets/share)\r\n\r\nMoreover, datasets shared on the Hub no longer need the dummy data files.\r\n\r\nPlease, feel free to ping me if you need any further guidance/support. ", "> However, now we are using the Hub to add new datasets, instead of this GitHub repo.\r\n> \r\n> You could share this dataset under the appropriate Hub organization namespace. This way the dataset will be accessible using:\r\n> \r\n> ```python\r\n> ds = load_dataset(\"<org_namespace>/collection3\")\r\n> ```\r\n> \r\nHi @albertvillanova . Thank you for your response.\r\n\r\nI thought that Collection3 is large and important dataset in Russian presented in 2016 but not represented in huggingface.\r\n\r\nAlso I am not related to authors or organisation of dataset", "The current policy of sharing datasets on the Hub instead of in this GitHub repo has no relation with the importance of the dataset: https://huggingface.co/docs/datasets/share#datasets-on-github-legacy \r\n> The distinction between a Hub dataset and a dataset from GitHub only comes from the legacy sharing workflow. It does not involve any ranking, decisioning, or opinion regarding the contents of the dataset itself.\r\n\r\nIt is not required to be an author/owner (or belong to the organization that is owner) of the dataset in order to share it on the Hub (as it was not the case when sharing them on this GitHub repo). \r\n\r\nIt is recommended to share it under an organization namespace that makes sense though. For this specific dataset, do you know of a clear organization under which it could be shared on the Hub? Maybe \"labinform\", or \"Information Research Laboratory\" or \"Lomonosov Moscow State University\"?\r\n\r\nIn cases like this, where the org is not evident, one possibility could be to contact the dataset owners/creators and ask them. According the publication paper, the authors are:\r\n- V.A. Mozharova\r\n- N.V. Loukachevitch\r\n\r\nI think maybe it would be worth contacting them.", "@pefimov I have contacted the authors (and put you in CC).", "Reply from the authors:\r\n> It is better to use name: Research Computing Center of Lomonosov Moscow State University (short name RCC-MSU)\r\n> https://rcc.msu.ru/en", "I have created the corresponding org namespace and dataset empty repository: https://huggingface.co/datasets/RCC-MSU/collection3\r\n\r\n@pefimov feel free to open a PR on the Hub if you are willing to do so: \r\n- Go to the *Community* tab on the repo: https://huggingface.co/datasets/RCC-MSU/collection3/discussions\r\n- And click: *New pull request* button\r\n\r\nDocs: [Pull requests and Discussions](https://huggingface.co/docs/hub/repositories-pull-requests-discussions) on the Hub", "Thanks" ]

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[ "Hi! Thanks for reporting! This looks like a bug. I've just opened a PR with the fix.", "Awesome! Thank you. I'll close the issue once the PR gets merged. :-)", "I guess we should reopen after the revert by:\r\n- #5006" ]

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[ "Thanks for reporting @aninrusimha.\r\n\r\nPlease, note that the preprocessed datasets are still available, as described in the dataset card, e.g.: https://huggingface.co/datasets/wikipedia\r\n```python\r\nds = load_dataset(\"wikipedia\", \"20220301.en\")\r\n``` ", "This is working now, but I was getting an error a few days ago when running an existing script. Unfortunately I did not do a proper bug report, but for some reason I was unable to load the dataset due to a request being made to the wikimedia website. However, its working now. Thanks for the reply!" ]

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[ "I have tried to extract ```0015896-b1054262f7da52a0518521e29c8e352c.txt``` from ```17ecf461bfccd469a1fbc264ccb03731f8606eea7b3e2e8b86e13d18040bf5b3/urlsf_subset00-16_data.xz``` with 7-zip\r\nand put the file into cache_path ```F://huggingface/datasets/downloads/extracted/0901d27f43b7e9ac0577da0d0061c8c632ba0b70ecd1b4bfb21562d9b7486faa```\r\nthere is still raise the same error and I find the file was removed from cache_path after I run the run_mlm.py with ```python run_mlm.py --model_type roberta --tokenizer_name roberta-base --dataset_name openwebtext --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --do_train --do_eval --output_dir F:/model/roberta-base```." ]

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[ "Thanks for reporting @wzsxxa.\r\n\r\nHowever the \"super_glue\" dataset is rendered properly by the Dataset preview: https://huggingface.co/datasets/super_glue" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Hi @albertvillanova. Unfortunately I still get this error. Is this because the merge has yet to be released? Is there a way to track the release?", "Hi @thesofakillers, yes you are right: the fix will be available after next release (it was planned for today; Monday at the latest).\r\n\r\nIn the meantime, you can use the version of the `exams` on our main branch by passing `revision` to `load_dataset`:\r\n```python\r\nds = load_dataset(\"exams\", revision=\"main\")\r\n```" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "thanks @albertvillanova done thank you!" ]

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