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[ "_The documentation is not available anymore as the PR was closed or merged._", "I opened a PR before I noticed yours ^^' You can find it here: https://github.com/huggingface/datasets/pull/4484\r\n\r\nThe only comment I have regarding your message is that it possibly shows several `pip install` commands, whereas one can run one single `pip install` command with the list of missing dependencies, which is maybe simpler.\r\n\r\nLet me know which one your prefer", "Closing in favor of #4484. " ]

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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._", "on the hub this dataset is referenced as `sst-2` not `sst2` – is there a canonical orthography? If not, could we name it `sst-2`?", "@julien-c, we normally do not use hyphens for dataset names: whenever the original dataset name contains a hyphen, we usually:\r\n- either suppress it: CoNLL-2000 (`conll2000`), CORD-19 (`cord19`)\r\n- or replace it with underscore: CC-News (`cc_news`), SQuAD-es (`squad_es`)\r\n\r\nThere are some exceptions though... (I wonder why)\r\n\r\nI think, the reason is there was a 1-to-1 relation with the corresponding Python module name.\r\n\r\nI personally find confusing not having a rule and using both hyphens and underscores indistinctly: you never know which is the right orthography.\r\n\r\nWhichever the decision we make, I would prefer to be applied consistently.\r\n\r\nAlso note that we already implemented this dataset as part of GLUE: https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py#L163\r\n- dataset name: `glue`\r\n- config name: `sst2`\r\n\r\nOn the other hand, let's see how other libraries name it:\r\n- torchtext: `SST2` https://pytorch.org/text/stable/datasets.html#sst2\r\n- OpenAI CLIP: `rendered-sst2` https://github.com/openai/CLIP/blob/main/data/rendered-sst2.md\r\n- Kaggle: `SST2` https://www.kaggle.com/datasets/atulanandjha/stanford-sentiment-treebank-v2-sst2/version/22\r\n- TensorFlow Datasets: `glue/sst2` https://www.tensorflow.org/datasets/catalog/glue#gluesst2", "Ok, another option is to open PRs against the models in https://huggingface.co/models?datasets=sst-2 to change their dataset reference to `sst2`\r\n\r\n(BTW some models refer to `sst2` already – but they're less popular: https://huggingface.co/models?datasets=sst2)", "OK, I'm taking care of the subsequent PRs on models to align with this dataset name." ]

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

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[ "fixed by https://github.com/huggingface/datasets/pull/4472" ]

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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._" ]

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[ "Hi @mbarnig, thanks for reporting.\r\n\r\nPlease note that is an expected behavior by `pandas` (we use the `pandas` library to parse CSV files): https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html\r\n```\r\nBy default the following values are interpreted as NaN: \r\n‘’, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1.#IND’, ‘-1.#QNAN’, ‘-NaN’, ‘-nan’, ‘1.#IND’, ‘1.#QNAN’, ‘<NA>’, ‘N/A’, ‘NA’, ‘NULL’, ‘NaN’, ‘n/a’, ‘nan’, ‘null’.\r\n```\r\n(see \"null\" in the last position in the above list).\r\n\r\nIn order to prevent `pandas` from performing that automatic conversion from the string \"null\" to a NaN value, you should pass the `pandas` parameter `keep_default_na=False`:\r\n```python\r\nIn [2]: dataset = load_dataset('csv', data_files={'train': 'null-test.csv'}, keep_default_na=False)\r\nIn [3]: dataset[\"train\"][0][\"transcript\"]\r\nOut[3]: 'null'\r\n```", "Thanks for the quick answer.", "@albertvillanova I also ran into this issue, it had me scratching my head for a while! In my case it was tripped by a literal \"NA\" comment collected from a user-facing form (e.g., this question does not apply to me). Thankfully this answer was here, but I feel it is such a common trap that it deserves to be noted in the official docs, maybe [here](https://huggingface.co/docs/datasets/loading#csv)? \r\n\r\nI'm happy to submit a PR if you agree!" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I thought of just mentioning the benefits I got. Here's the code that @lhoestq provided:\r\n\r\n```py\r\nimport os\r\nfrom datasets import load_dataset\r\nfrom tqdm.auto import tqdm\r\n\r\nds = load_dataset(\"squad\", split=\"train\")\r\nos.makedirs(\"tmp\")\r\n\r\nnum_shards = 5\r\nfor index in tqdm(range(num_shards)):\r\n size = len(ds) // num_shards\r\n shard = Dataset(ds.data.slice(size * index, size), fingerprint=f\"{ds._fingerprint}_{index}\")\r\n shard.to_json(f\"tmp/data_{index}.jsonl\")\r\n```\r\n\r\nIt is 1.64s. Previously the code was:\r\n\r\n```py\r\nnum_shards = 5\r\nfor index in tqdm(range(num_shards)):\r\n shard = ds.shard(num_shards=num_shards, index=index, contiguous=True)\r\n shard.to_json(f\"tmp/data_{index}.jsonl\")\r\n # upload_to_gcs(f\"tmp/data_{index}.jsonl\")\r\n```\r\n\r\nIt was 2min31s. \r\n\r\nI ran it on my humble MacBook Pro:\r\n\r\n<img width=\"574\" alt=\"image\" src=\"https://user-images.githubusercontent.com/22957388/172864881-f1db489a-2305-47f2-a07f-7d3df610b1b8.png\">\r\n", "I addressed your comments @albertvillanova , let me know what you think :)" ]

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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._", "closing in favor of https://github.com/huggingface/datasets/pull/4465" ]

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[ "Why not adding `max_examples` as part of the config name?", "Yup it can also work, and maybe it's simpler this way. Opening a PR to fix bigbench instead of https://github.com/huggingface/datasets/pull/4463", "Hi @lhoestq,\r\n\r\nThank you for taking a look at this issue, and proposing a solution. \r\nUnfortunately, after trying the fix in #4465 I still see the same issue.\r\n\r\nI think there is some subtlety where the config name gets overwritten somewhere when `BUILDER_CONFIGS`[(link)](https://github.com/huggingface/datasets/blob/master/datasets/bigbench/bigbench.py#L126) is defined. \r\n\r\nIf I print out the `self.config.name` in the current version (with the fix in #4465), I see just the task name, but if I comment out `BUILDER_CONFIGS`, the `num_shots` and `max_examples` gets appended as was meant by #4465.\r\n\r\nI haven't managed to track down where this happens, but I thought you might know? \r\n\r\n(Another comment on your fix: the `name` variable is used to fetch the task from the bigbench API, so modifying it causes an error if it's actually called. This can easily be fixed by having `config_name` variable in addition to the `task_name`)\r\n\r\n\r\n" ]

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[ "I'm having the same issue，Can you tell me how to solve it？", "I have the same issue, can you tell me how to solve it? Thanks" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I've disabled the `test_dummy_dataset_serialize_s3` tests in the Linux CI to avoid the failures (these tests only fail on Windows in 3.6). These failures are unrelated to this PR's changes, and I would like to address this in a new PR.", "[This comment](https://github.com/pytorch/audio/issues/2363#issuecomment-1179089175) explains the issue with MP3 decoding in `torchaudio` in the latest release (supports Python 3.7+). I fixed CI by pinning `torchaudio` to `<0.12.0`. Another way to fix this issue is by installing `ffmpeg` with conda or using the unofficial GH action. But I don't think it's worth making CI more complex, considering we can wait for the soundfile release, which should bring MP3 decoding, and drop the `torchaudio` dependency then.", "Yay for dropping Python 3.6!", "I think we can merge in this state. Also, if an env has Python version < 3.7 installed, we raise a warning, so I don't think we even need to create (and pin) an issue to notify the contributors of this change." ]

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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._", "Some links are still missing I think :)", "This is probably quite close to being ready, so cc some TF people @gante @amyeroberts @merveenoyan just so they see it! No need for a full review, but if you have any comments or suggestions feel free.", "Thanks ! We plan to make a new release later today for `to_tf_dataset` FYI, so I think we can merge it soon and include this documentation in the new release" ]

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[ "I use below to load a dataset:\r\n```\r\ndataset = datasets.load_dataset(\"scikit-learn/auto-mpg\")\r\ndf = pd.DataFrame(dataset[\"train\"])\r\n```\r\nTBH as said, tabular folk split their own dataset, they sometimes have two splits, sometimes three. Maybe somehow avoiding it for tabular datasets might be good for later. (it's just UX improvement) ", "is very slow batch access of a dataset (tabular, csv) with many columns to be expected?", "Define \"many\" ? x)", "~20k! I was surprised batch loading with as few as 32 samples was really slow. I was speculating the columnar format was the cause -- or do you see good performance with this approx size of tabular data?", "20k can be a lot for a columnar format but maybe we can optimize a few things.\r\n\r\nIt would be cool to profile the code to see if there's an unoptimized part of the code that slows everything down.\r\n\r\n(it's also possible to kill the job when it accesses the batch, it often gives you the traceback at the location where the code was running)", "FWIW I've worked with tabular data with 540k columns.", "thats awesome, whats your secret? would love to see an example!", "@wconnell I'm not sure what you mean by my secret, I load them into a numpy array 😁 \r\n\r\nAn example dataset is [here](https://portal.gdc.cancer.gov/repository?facetTab=files&filters=%7B%22content%22%3A%5B%7B%22content%22%3A%7B%22field%22%3A%22cases.project.project_id%22%2C%22value%22%3A%5B%22TCGA-CESC%22%5D%7D%2C%22op%22%3A%22in%22%7D%2C%7B%22content%22%3A%7B%22field%22%3A%22files.data_category%22%2C%22value%22%3A%5B%22DNA%20Methylation%22%5D%7D%2C%22op%22%3A%22in%22%7D%5D%2C%22op%22%3A%22and%22%7D&searchTableTab=files) which is a dataset of DNA methylation reads. This dataset is about 950 rows and 450k columns. " ]

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

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[ "Closing since it's a duplicate of https://github.com/huggingface/datasets/issues/4453" ]

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[ "I understand that the issue is that a remote file (URL) is being loaded as a local file. Right @albertvillanova @lhoestq?\r\n\r\n```\r\nMessage: [Errno 2] No such file or directory: 'https://raw.githubusercontent.com/YaxinCui/ABSADataset/main/SemEval2015Task12Corrected/train/restaurants_train.xml'\r\n```", "`xml.dom.minidom.parse` is not supported in streaming mode. I opened a PR here to fix it:\r\nhttps://huggingface.co/datasets/Yaxin/SemEval2015/discussions/1\r\n\r\nPlease review the PR @WithYouTo and let me know if it works !", "Additionally, I'm also patching our library, so that we support streaming datasets that use `xml.dom.minidom.parse`." ]

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[ "Thanks for reporting @santhnm2. We are fixing it.\r\n\r\nData owners updated their URLs recently. We have to align with them, otherwise you do not download anything (that is why ignore_verifications does not work).", "Hello! Is there any update on this? I am having the same issue 6 months later." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Awesome thanks @mariosasko!" ]

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

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[]

[ "Hi! The [datasets_sql](https://github.com/mariosasko/datasets_sql) package lets you easily find distinct rows in a dataset (an example with `SELECT DISTINCT` is in the readme). Deduplication is (still) not part of the official API because it's hard to implement for datasets bigger than RAM while only using the native PyArrow ops.\r\n\r\n(Btw, this is a duplicate of https://github.com/huggingface/datasets/issues/2514)", "Here is an example using the [datasets_sql](https://github.com/mariosasko/datasets_sql) mentioned \r\n\r\n```python \r\nfrom datasets_sql import query\r\n\r\ndataset = load_dataset(\"imdb\", split=\"train\")\r\n\r\n# If you dont have an id column just add one by enumerating\r\ndataset=dataset.map(lambda x,i: {\"id\":i}, with_indices=True)\r\n\r\nid_column='id'\r\nunique_column='text'\r\n\r\n# always selects min id\r\nunique_dataset = query(f\"SELECT dataset.* FROM dataset JOIN (SELECT MIN({id_column}) as unique_id FROM dataset group by {unique_column}) ON unique_id=dataset.{id_column}\")\r\n```\r\nNot ideal for large datasets but good enough for basic cases.\r\nSure would be nice to have in the library 🤗 " ]

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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._" ]

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

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[ "If I understand correctly, this is due to the key `split` missing in the line https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km/blob/main/nimrod-uk-1km.py#L41 of the script.\r\nMaybe @albertvillanova could confirm.", "I'm having a look.", "Indeed there are several issues in this dataset loading script.\r\n\r\nThe one pointed out by @severo: for the default configuration \"crops\": https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km/blob/main/nimrod-uk-1km.py#L244\r\n- The download manager downloads `_URL`\r\n- But `_URL` is not defined: https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km/blob/main/nimrod-uk-1km.py#L41\r\n ```python\r\n _URL = {'train': []}\r\n ```\r\n- Afterwards, for each split, a different key in `_ULR` is used, but it only contains one key: \"train\"\r\n - \"valid\" key: https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km/blob/main/nimrod-uk-1km.py#L260\r\n - \"test key: https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km/blob/main/nimrod-uk-1km.py#L269\r\n \r\nThese keys do not exist inside `_URL`, thus the error message reported in the viewer: \r\n```\r\nException: KeyError\r\nMessage: 'valid'\r\n```", "Would anyone want to submit a Hub PR (or open a Discussion for the authors to be aware) to this dataset? https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km", "Hi, I'm the main author for that dataset, so I'll work on updating it! I was working on debugging some stuff awhile ago, which is what broke it. ", "I've opened a Discussion page, so that we can ask/answer and propose fixes until the script works properly: https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km/discussions/1\r\n\r\nCC: @julien-c @jacobbieker " ]

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[ "Thanks, looking at it", "Not sure what happened 😬, but it's fixed" ]

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[ "Not sure what happened 😬, but it's fixed" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "> Let me know what you think, especially if we should include some code samples for training a model in the audio/vision sections. I left this out since we already showed it in the NLP section. I want to keep the focus on using Datasets to load and process a dataset, and not so much the training part. Maybe we can add links to the Transformers docs instead?\r\n\r\nWe plan to address this with end-to-end examples (for each modality) more focused on preprocessing than the ones in the Transformers docs." ]

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[ "To have some context, please see:\r\n- #4145\r\n\r\nPlease, also note that we have recently made some fixes to the script, which are in our GitHub master branch but not yet released:\r\n- #4422\r\n- #4425 \r\n- #4436", "Thanks Albert! I'll try pulling `datasets` from the git repo instead of PyPI, and/or just wait for the next release.\r\n", "I'm closing this issue then. Please, feel free to reopen it again if the problem persists." ]

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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._" ]

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[ "Hi! `datasets` caches every modification/loading, so you can either rerun the pipeline up to the `map` call or use `Dataset.from_file(modified_dataset)` to load the dataset directly from the cache file.", "Awesome, hvala Mario! This works. " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Added back the `[:]` and a comment to explain why this is needed. " ]

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

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[ "These test errors are related to issue #4428 \r\n", "_The documentation is not available anymore as the PR was closed or merged._", "I only made a trivial modification in my commit https://github.com/huggingface/datasets/pull/4431/commits/402c893d35224d7828176717233909ac5f1e7b3e\r\n\r\nI have submitted a PR #4434 for the about issue.", "> Awesome thanks for adding this dataset :)\r\n> \r\n> I just have one comment about the licensing.\r\n> \r\n> Also it seems that you already have the dataset in https://huggingface.co/datasets/silver/personal_dialog, so it's unnecessary to add it here\r\n\r\nThank you very much for your comment.\r\n\r\nSo, should I close this PR?", "Thanks for fixing the licensing section :)\r\n\r\n> So, should I close this PR?\r\n\r\nYes you can close this PR, it's better if your dataset is under your namespace at https://huggingface.co/datasets/silver/personal_dialog :)\r\n\r\nDon't forget to update the licensing section on https://huggingface.co/datasets/silver/personal_dialog as well" ]

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[ "Hi! Our versioning is based on Git revisions (the `revision` param in `load_dataset`), so you can just replace the old URL with the new one and open a PR :). I can also give you some pointers if needed.", "@mariosasko Awesome thanks! I will do that. Looks like this new version of the data is not available as a zip but as three files (train/dev/test). How is this usually handled in HF Datasets, should `_URL` be a dict with keys `train`, `val`, `test` perhaps?", "Yes! Let me help you with more detailed instructions.\r\n\r\nIn the first step, we need to update the URLs. One of the possible dictionary structures is as follows:\r\n```python\r\n_URLs = {\r\n \"train\": {\"src\": \"https://drive.google.com/uc?export=download&id=1wHAWDOwOoQWSj7HYpyJ3Aeud8WhhaJ7P\", \"tgt\": \"https://drive.google.com/uc?export=download&id=1QVgswwhVTkd3VLCzajK6eVkcrSWEK6kq\"}\r\n \"val\": ...\r\n \"test\": ...\r\n}\r\n```\r\n\r\n(You can use this page to generate direct download links: https://sites.google.com/site/gdocs2direct/)\r\n\r\nThen we move to the `split_generators` method:\r\n```python\r\ndef _split_generators(self, dl_manager):\r\n \"\"\"Returns SplitGenerators.\"\"\"\r\n files = dl_manager.download(_URLs)\r\n return [\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TRAIN,\r\n gen_kwargs={\"src_file\": files[\"train\"][\"src\"], \"tgt_file\": files[\"train\"][\"tgt\"]},\r\n ),\r\n ... # same for val and test\r\n ]\r\n```\r\nFinally, we adjust the signature of `_generate_examples`:\r\n```python\r\ndef _generate_examples(self, src_file, tgt_file):\r\n \"\"\"Yields examples.\"\"\"\r\n with open(src_file, encoding=\"utf-8\") as src_f, open(\r\n tgt_file, encoding=\"utf-8\"\r\n ) as tgt_f:\r\n ... # the rest is the same\r\n```\r\n\r\nAnd that's it!\r\n\r\nPS: Let me know if you need help updating the dummy data and regenerating the metadata file.", "Awesome! Thanks for the detailed help, that was straightforward with your instruction. However, I think I am being blocked by this issue: https://github.com/huggingface/datasets/issues/4428", "Feel free to open a PR, and I can fix this manually.", "Awsome, done in #4451!" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@mishig25 is investigating why deprecated/added do not affect the enclosed text format when used in args docstring: no special formatting appears: \r\n- https://moon-ci-docs.huggingface.co/docs/datasets/pr_4429/en/package_reference/builder_classes#datasets.DatasetBuilder", "@albertvillanova please check now 👍 \r\nhttps://moon-ci-docs.huggingface.co/docs/datasets/pr_4429/en/package_reference/builder_classes#datasets.DatasetBuilder\r\n\r\n<img width=\"500\" alt=\"Screenshot 2022-06-06 at 10 20 34\" src=\"https://user-images.githubusercontent.com/11827707/172123471-fab97138-c903-4a71-ab7f-c90e5e43c58f.png\">\r\n", "Thanks @mishig25.\r\n\r\nJust one question: is it expected to have the deprecated box right edge not filling all the page width (contrary to the added box)?", "> Just one question: is it expected to have the deprecated box right edge not filling all the page width (contrary to the added box)?\r\n\r\nYes, that is expected 😊 because the depreacted box is being bounded by its parent box (the box for `name` argument in the screenshot above)" ]

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

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[ "Hi! Indeed the column is missing, but you shouldn't get an error? Have you made some modifications (locally) to the loading script? I've opened a PR to add the missing columns to the script. " ]

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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._", "Thanks ! As for https://github.com/huggingface/datasets/pull/4431 please also update the licensing section in https://huggingface.co/datasets/silver/mmchat ;)\r\n\r\nThen if it's fine for you feel free to close this PR" ]

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[ "Thanks for reporting, @bhaddow.\r\n\r\nI'm fixing it.", "Thanks for the quick fix!", "@bhaddow we have also made a fix so that you don't have to convert to uppercase the file extensions of the LDC data.\r\n\r\nWould you mind checking if it works OK now for you and reporting if there are any issues? Thanks. ", "Hi @albertvillanova -It loads fine on a copy of the data from deepai - although I have to remove the copies of the .WAV files (with extension .WAV,wav). On a copy of the data that was obtained from the LDC, the glob still fails to find the files. The LDC copy looks like it was copied from CD, in 2004, so the structure may be different to a current download.", "Ah, if I change the train/ and test/ directories to TRAIN/ and TEST/ then it works!", "Thanks for your investigation and report, @bhaddow. I'm adding another fix for the TRAIN/train and TEST/test directory names." ]

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[ "If you call `metric.compute` in a distributed setup like yours, then `metric.compute` is called in each process. `metric.compute` first calls `metric.add_batch`, and it looks like your error appears at that stage.\r\n\r\nTo make sure that all the processes have started writing their predictions/references at the same time, each process waits for process 0 to lock `slurm-{world_size}-0.arrow.lock`. Process 0 locks this file when `metric.add_batch` is called, so here when `metric.compute` is called.\r\n\r\nTherefore your error can happen when process 0 takes too much time to call `metric.compute` compared to process 3 (>100 seconds by default). I haven't tried running your code but could it be the case ?\r\n\r\nI guess it could also happen if you run multiple times the same distributed job at the same time with the same `experiment_id` because they would collide.\r\n", "We've finally been able to isolate the problem, it wasn't a timing problem, but rather a file locking one. \r\nThe locks produced by calling `flock` where not visible between nodes (so the master node couldn't check other node's locks nor the other way around). \r\n\r\nWe are now having issues with the pre-processing in our runner script, but are not related with the rendezvous process during the evaluation phase. We will let you know about it once we address it. \r\n\r\nOur solution to the rendezvous is as follows:\r\n- We solved the problem by calling `lockf` instead of `flock`.\r\n- We had to change slightly the `_check_all_processes_locks` method so that the main process (i.e. process 0) didn't check it's own lock (because `lockf` permits recursive locks and thus checking it only replaced the current lock with a new one). \r\n\r\nWe use a shared file system between nodes using GPFS in our cluster setup. Maybe the difference between the behavior we see with respect to your usage in multi-node executions comes from that fact. Which file system scheme do you use for the multi-node executions? \r\n\r\n`lockf` seems to work in more settings than `flock`, so maybe we could write a PR so you could test it in your environment. ", "Cool, I'm glad you managed to make evaluation work :)\r\n\r\nI'm not completely aware of the differences between lockf and flock, but I've read somewhere that flock is preferable over lockf in multithreading and multiprocessing situations. Here we definitely are in such a situation so unless it is super important I don't think we will switch to lockf", "> * We had to change slightly the `_check_all_processes_locks` method so that the main process (i.e. process 0) didn't check it's own lock (because `lockf` permits recursive locks and thus checking it only replaced the current lock with a new one).\r\n\r\nHi @panserbjorn , Can you share your `_check_all_processes_locks` function? thanks!", "```\r\ndef _check_all_processes_locks(self):\r\n expected_lock_file_names = [\r\n os.path.join(self.data_dir, f\"{self.experiment_id}-{self.num_process}-{process_id}.arrow.lock\")\r\n for process_id in range(self.num_process)\r\n ]\r\n #for expected_lock_file_name in expected_lock_file_names: # OUR CHANGE process 0 shouldn't check its own lock\r\n for expected_lock_file_name in expected_lock_file_names[1:]:\r\n nofilelock = FileFreeLock(expected_lock_file_name)\r\n try:\r\n nofilelock.acquire(timeout=self.timeout)\r\n except Timeout:\r\n raise ValueError(\r\n f\"Expected to find locked file {expected_lock_file_name} from process {self.process_id} but it doesn't exist.\"\r\n )\r\n else:\r\n nofilelock.release()\r\n```\r\n\r\n### Changed files:\r\n- metric.py file in the datasets library \r\n- filelock.py file in the datasets/utils library. \r\n\r\n\r\nChanges we made:\r\n\r\n1. We changed the flock for lockf \r\n flock and lockf both perform a lock over a file (like the lock for writing). \r\n The difference is that flock only works in local file systems, but if you have a shared file system (like what we have in the clusters) the flock fails to “see” the lock of another node. The only disadvantage we had was that a single process couldn’t detect it’s own lock so we did the second change.\r\n2. We prevented the process 0 (which is the one that coordinates the rendezvous) from checking its own lock on its arrow because it didn't work with lockf (as stated in the previous change). \r\n3. We made a second rendezvous so that all the process had the results of the metrics (other than the loss) and not only the process 0.\r\n What happened was that only process 0 computed the metric and that didn’t present any problem if you are using the loss. However, if you are using another metric, the only process which had the information to choose the best checkpoint at evaluation time was the process 0. But since the evaluation was performed over all processes, every process except the process 0 chose a bad check point (bad meaning it wasn’t the best one) because they didn’t have the information of the metric of the best checkpoint. \r\n The consequence was that the evaluation was different from what would result if using only the best checkpoint, because each process chose a different checkpoint to run the evaluation and thus the numbers were often worse than the numbers that would be obtained if all processes choose the best checkpoint (correct one) to perform the evaluation of their samples. \r\n We performed a second rendezvous so that all processes had the same best_metric and best_model as process 0 after the evaluation cycle. \r\n", "Metrics are deprecated in `datasets` and `evaluate` should be used instead: https://github.com/huggingface/evaluate" ]

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[ "Hi! If the only goal is to improve readability, it's better to use `assertTupleEqual` than `assertSequenceEqual` for Python tuples. Also, note that this function is called internally by `assertEqual`, but I guess we can accept a PR to be more verbose.", "Hi @mariosasko, right! I'll update the issue title/desc with `assertTupleEqual` even though as you said it seems to be internally using `assertEqual` so I'm not sure whether it's worth it or not...\r\n\r\nhttps://docs.python.org/3/library/unittest.html#unittest.TestCase.assertTupleEqual", "I thought we were supposed to move gradually from `unittest` to `pytest`..." ]

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[ "@albertvillanova @lhoestq , could you please help me on this issue. ", "Hi ! As mentioned on the [forum](https://discuss.huggingface.co/t/how-to-wrap-a-generator-with-hf-dataset/18464), the simplest for now would be to define a [dataset script](https://huggingface.co/docs/datasets/dataset_script) which can contain your generator. But we can also explore adding something like `ds = Dataset.from_iterable(seqio_dataset)`", "@lhoestq , hey i did as you instructed, but sadly i cannot get pass through the download_manager, as i dont have anything to download. i was skipping the ` def _split_generators(self, dl_manager):` function. but i cannot get around it. I get a `NotImplementedError: `\r\n\r\nthe following is my code for the same: \r\n\r\n\r\n\r\n```\r\nimport datasets \r\nimport functools\r\nimport glob \r\nfrom datasets import load_from_disk\r\nimport seqio\r\nimport tensorflow as tf\r\nimport t5.data\r\nfrom datasets import load_dataset\r\nfrom t5.data import postprocessors\r\nfrom t5.data import preprocessors\r\nfrom t5.evaluation import metrics\r\nfrom seqio import FunctionDataSource, utils\r\n\r\nTaskRegistry = seqio.TaskRegistry\r\n\r\ndata_path = glob.glob(\"/home/stephen/Desktop/MEGA_CORPUS/COMBINED_CORPUS/*\", recursive=False)\r\n\r\n\r\ndef gen_dataset(split, shuffle=False, seed=None, column=\"text\", dataset_path=None):\r\n dataset = load_from_disk(dataset_path)\r\n if shuffle:\r\n if seed:\r\n dataset = dataset.shuffle(seed=seed)\r\n else:\r\n dataset = dataset.shuffle()\r\n while True:\r\n for item in dataset[str(split)]:\r\n yield item[column]\r\n\r\n\r\ndef dataset_fn(split, shuffle_files, seed=None, dataset_path=None):\r\n return tf.data.Dataset.from_generator(\r\n functools.partial(gen_dataset, split, shuffle_files, seed, dataset_path=dataset_path),\r\n output_signature=tf.TensorSpec(shape=(), dtype=tf.string, name=dataset_path)\r\n )\r\n\r\[email protected]_over_dataset\r\ndef target_to_key(x, key_map, target_key):\r\n \"\"\"Assign the value from the dataset to target_key in key_map\"\"\"\r\n return {**key_map, target_key: x}\r\n\r\n\r\n_CITATION = \"Not ready yet\"\r\n_DESCRIPTION = \"a custom seqio based mixed samples on a given temperature value, that again returns a dataset in HF dataset format well samples on the Mixture temperature\"\r\n_HOMEPAGE = \"ldcil.org\"\r\n\r\nclass CustomSeqio(datasets.GeneratorBasedBuilder):\r\n\r\n def _info(self):\r\n return datasets.DatasetInfo(\r\n description=_DESCRIPTION,\r\n features=datasets.Features(\r\n {\r\n \"text\": datasets.Value(\"string\"),\r\n }\r\n ),\r\n homepage=\"https://ldcil.org\",\r\n citation=_CITATION,)\r\n\r\ndef generate_examples(self):\r\n seqio_train_list = []\r\n for lang in data_path:\r\n dataset_name = lang.split(\"/\")[-1]\r\n dataset_shapes = None \r\n\r\n TaskRegistry.add(\r\n str(dataset_name),\r\n source=seqio.FunctionDataSource(\r\n dataset_fn=functools.partial(dataset_fn, dataset_path=lang),\r\n splits=(\"train\", \"test\"),\r\n caching_permitted=False,\r\n num_input_examples=dataset_shapes,\r\n ),\r\n preprocessors=[\r\n functools.partial(\r\n target_to_key, key_map={\r\n \"targets\": None,\r\n }, target_key=\"targets\")],\r\n output_features={\"targets\": seqio.Feature(vocabulary=seqio.PassThroughVocabulary, add_eos=False, dtype=tf.string, rank=0)},\r\n metric_fns=[]\r\n )\r\n\r\n seqio_train_dataset = seqio.get_mixture_or_task(dataset_name).get_dataset(\r\n sequence_length=None,\r\n split=\"train\",\r\n shuffle=True,\r\n num_epochs=1,\r\n shard_info=seqio.ShardInfo(index=0, num_shards=10),\r\n use_cached=False,\r\n seed=42)\r\n seqio_train_list.append(seqio_train_dataset)\r\n \r\n lang_name_list = []\r\n for lang in data_path:\r\n lang_name = lang.split(\"/\")[-1]\r\n lang_name_list.append(lang_name)\r\n\r\n seqio_mixture = seqio.MixtureRegistry.add(\r\n \"seqio_mixture\",\r\n lang_name_list,\r\n default_rate=0.7)\r\n \r\n seqio_mixture_dataset = seqio.get_mixture_or_task(\"seqio_mixture\").get_dataset(\r\n sequence_length=None,\r\n split=\"train\",\r\n shuffle=True,\r\n num_epochs=1,\r\n shard_info=seqio.ShardInfo(index=0, num_shards=10),\r\n use_cached=False,\r\n seed=42)\r\n\r\n for id, ex in enumerate(seqio_mixture_dataset):\r\n yield id, {\"text\": ex[\"targets\"].numpy().decode()}\r\n```\r\n\r\nand i load it by:\r\n\r\n`seqio_mixture = load_dataset(\"seqio_loader\")`", "@lhoestq , just to make things clear ... \r\n\r\nthe following is my original code, thats not in the HF dataset loading script: \r\n\r\n```\r\nimport functools\r\nimport seqio\r\nimport tensorflow as tf\r\nimport t5.data\r\nfrom datasets import load_from_disk\r\nfrom t5.data import postprocessors\r\nfrom t5.data import preprocessors\r\nfrom t5.evaluation import metrics\r\nfrom seqio import FunctionDataSource, utils\r\nimport glob \r\n\r\nTaskRegistry = seqio.TaskRegistry\r\n\r\n\r\n\r\ndef gen_dataset(split, shuffle=False, seed=None, column=\"text\", dataset_path=None):\r\n dataset = load_from_disk(dataset_path)\r\n if shuffle:\r\n if seed:\r\n dataset = dataset.shuffle(seed=seed)\r\n else:\r\n dataset = dataset.shuffle()\r\n while True:\r\n for item in dataset[str(split)]:\r\n yield item[column]\r\n\r\n\r\ndef dataset_fn(split, shuffle_files, seed=None, dataset_path=None):\r\n return tf.data.Dataset.from_generator(\r\n functools.partial(gen_dataset, split, shuffle_files, seed, dataset_path=dataset_path),\r\n output_signature=tf.TensorSpec(shape=(), dtype=tf.string, name=dataset_path)\r\n )\r\n\r\n\r\[email protected]_over_dataset\r\ndef target_to_key(x, key_map, target_key):\r\n \"\"\"Assign the value from the dataset to target_key in key_map\"\"\"\r\n return {**key_map, target_key: x}\r\n\r\ndata_path = glob.glob(\"/home/stephen/Desktop/MEGA_CORPUS/COMBINED_CORPUS/*\", recursive=False)\r\n\r\nseqio_train_list = []\r\n\r\nfor lang in data_path:\r\n dataset_name = lang.split(\"/\")[-1]\r\n dataset_shapes = None \r\n\r\n TaskRegistry.add(\r\n str(dataset_name),\r\n source=seqio.FunctionDataSource(\r\n dataset_fn=functools.partial(dataset_fn, dataset_path=lang),\r\n splits=(\"train\", \"test\"),\r\n caching_permitted=False,\r\n num_input_examples=dataset_shapes,\r\n ),\r\n preprocessors=[\r\n functools.partial(\r\n target_to_key, key_map={\r\n \"targets\": None,\r\n }, target_key=\"targets\")],\r\n output_features={\"targets\": seqio.Feature(vocabulary=seqio.PassThroughVocabulary, add_eos=False, dtype=tf.string, rank=0)},\r\n metric_fns=[]\r\n )\r\n\r\n seqio_train_dataset = seqio.get_mixture_or_task(dataset_name).get_dataset(\r\n sequence_length=None,\r\n split=\"train\",\r\n shuffle=True,\r\n num_epochs=1,\r\n shard_info=seqio.ShardInfo(index=0, num_shards=10),\r\n use_cached=False,\r\n seed=42)\r\n seqio_train_list.append(seqio_train_dataset)\r\n\r\nlang_name_list = []\r\nfor lang in data_path:\r\n lang_name = lang.split(\"/\")[-1]\r\n lang_name_list.append(lang_name)\r\n\r\nseqio_mixture = seqio.MixtureRegistry.add(\r\n \"seqio_mixture\",\r\n lang_name_list,\r\n default_rate=0.7\r\n)\r\n\r\nseqio_mixture_dataset = seqio.get_mixture_or_task(\"seqio_mixture\").get_dataset(\r\n sequence_length=None,\r\n split=\"train\",\r\n shuffle=True,\r\n num_epochs=1,\r\n shard_info=seqio.ShardInfo(index=0, num_shards=10),\r\n use_cached=False,\r\n seed=42)\r\n\r\nfor _, ex in zip(range(15), seqio_mixture_dataset):\r\n print(ex[\"targets\"].numpy().decode())\r\n```\r\n\r\nwhere the seqio_mixture_dataset is the generator that i wanted to be wrapped in HF dataset. \r\n\r\nalso additionally, could you please tell me how do i set the `default_rate=0.7` args where `seqio_mixture` is defined to be made as a custom option in the HF load_dataset() method,\r\n\r\nmaybe like this: \r\n`seqio_mixture_dataset = datasets.load_dataset(\"seqio_loader\",temperature=0.5)`", "I like the idea of having `Dataset.from_iterable(iterable)` in the API. The only problem is that we also want to make this part cachable, which is tricky if `iterable` is a generator. \r\n\r\nSome resources on this issue:\r\n* https://github.com/uqfoundation/dill/issues/311\r\n* https://stackoverflow.com/questions/7180212/why-cant-generators-be-pickled\r\n* https://github.com/tonyroberts/generator_tools - python package for pickling generators; pickles bytecode, so it creates version-specific dumps", "For the caching maybe we can have `Dataset.from_generator` as TF and pickle+hash the generator function (not the generator object itself) ?\r\n\r\nAnd then keep `Dataset.from_iterable` fo pickable objects like lists", "@lhoestq, @mariosasko do you too have any examples where the dataset is a generator and needs to be wrapped into hf dataset ? ", "@lhoestq, following to my previous question ... what possibly could be done in this [link1](https://github.com/huggingface/datasets/issues/4417#issuecomment-1146627404) [link2](https://github.com/huggingface/datasets/issues/4417#issuecomment-1146627593) case? do you have any ideas? ", "@lhoestq +1 for the `Dataset.from_generator` idea.\r\n\r\nHaving thought about it, let's avoid adding `Dataset.from_iterable` to the API since dictionaries are technically iteralbles (\"iterable\" is a broad term in Python), and we already provide `Dataset.from_dict`. And for lists maybe we can add `Dataset.from_list` similar to `pa.Table.from_pylist`. WDYT?\r\n", "Hi @StephennFernandes!\r\n\r\nTo fix the issues in the copied code, rename `generate_examples` to` _generate_examples` and add one level of indentation as this is a method of `GeneratorBasedBuilder` and define `_split_generators` as follows (again as a method of `GeneratorBasedBuilder):\r\n```python\r\n def _split_generators(self, dl_manager):\r\n return [\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TRAIN,\r\n gen_kwargs={},\r\n ),\r\n ]\r\n```\r\n\r\nAnd if you are feeling extra adventurous, you can try to use ArrowWriter to directly create a cache file:\r\n```python\r\nfrom datasets import Dataset\r\nfrom datasets.arrow_writer import ArrowWriter\r\n\r\nwriter = ArrowWriter(path=\"path/to/cache_file.arrow\", writer_batch_size=1000)\r\n\r\nwith writer:\r\n for ex in generator:\r\n writer.write(ex) \r\n writer.finalize()\r\n\r\ndset = Dataset.from_file(\"path/to/cache_file.arrow\")\r\n```\r\n\r\n", "I have a problem which I think is very similar: I would like to \"stream\" data to a HF Array (memory-mapped) Dataset, where the final size of the dataset is unknown, but could be much larger than what fits into memory.\r\nWhat I want to end up with is an Array Dataset which I can open using `Dataset.load_from_disk(dataset_path=\"somename\")` and use e.g. as the training set. \r\n\r\nFor this I would have thought there should be an API which allows me to open/create the dataset (and define the features etc), then write examples to the dataset, but I could not find a way to do this. \r\n\r\nI tried doing this and it looks like it works, but it feels very hacky and I am not sure if this might fail to update some of the fields in the json files which may turn out to be important:\r\n```\r\nfrom datasets import Dataset, Features, ClassLabel, Sequence, Value\r\nfrom datasets.arrow_writer import ArrowWriter \r\n# 1) define the features\r\nfeatures = Features(dict(\r\n id=Value(dtype=\"string\"),\r\n tokens=Sequence(feature=Value(dtype=\"string\")),\r\n ner_tags=Sequence(feature=ClassLabel(names=['O', 'B-corporation', 'I-corporation', 'B-creative-work', 'I-creative-work', 'B-group', 'I-group', 'B-location', 'I-location', 'B-person', 'I-person', 'B-product', 'I-product'])),\r\n))\r\n# 2) create empty dataset for examples with these features and store to disk\r\nempty = dict(\r\n id = [],\r\n tokens = [],\r\n ner_tags = [],\r\n)\r\nds = Dataset.from_dict(empty, features=features)\r\nds.save_to_disk(dataset_path=\"debug_ds1\")\r\n\r\n# 3) directly write all the examples to the arrow dataset \r\nwith ArrowWriter(path=\"debug_ds1/dataset.arrow\") as writer: \r\n writer.write(dict(id=0, tokens=[\"a\", \"b\"], ner_tags=[0, 0])) \r\n writer.write(dict(id=1, tokens=[\"x\", \"y\"], ner_tags=[1, 0])) \r\n writer.finalize() \r\n \r\nds2 = Dataset.load_from_disk(dataset_path=\"debug_ds1\")\r\nlen(ds2)\r\n```\r\nIs there a cleaner/proper way to do this?\r\n\r\nI like the sound of `Dataset.from_iterable` or `Dataset.from_generator` (should not from iterable be able to handle from generator too as all generators are iterables?) but how would I define the features for me examples there? ", "Hi @johann-petrak! You can pass the features directly to ArrowWriter's initializer like so `ArrowWriter(..., features=features)`.\r\n\r\nAnd the reason why I prefer `Dataset.from_generator` over `Dataset.from_iterable` is mentioned in one of my previous comments.", "@mariosasko so at the moment we still have to create a fake `Dataset` first and then use `ArrowWriter` to write an actual dataset? I'm using the latest version of `datasets` on pypi but my final file is always empty. Is there anything wrong with the code below?\r\n\r\n```python\r\n total = 0\r\n with ArrowWriter(path=str(final_data_path), features=features) as writer:\r\n for batch in loader:\r\n for traj in batch:\r\n for generator in question_generators:\r\n for xi in generator(traj):\r\n # print(f\"Question: {xi.question}, answer: {xi.answer}\")\r\n total += 1\r\n writer.write(\r\n {\r\n \"id\": f\"qa_{total}\",\r\n \"question\": xi.question,\r\n \"answer\": xi.answer,\r\n }\r\n )\r\n writer.finalize()\r\n print(f\"Total #questions = {total}\") # this prints 402\r\n```", "This works for me if I then (actually I also close the writer: `writer.close()`) open the Arrow file as a dataset using `ds=Dataset.from_file(final_data_path)` then `ds.save_to_disk(somedir)`. The Dataset created that way contains the expected examples.", "Oh thanks. That did the trick I believe. Shouldn't ArrowWriter have a context manager that does these operations?", "You can just use `Dataset.from_file` to get your dataset, no need to do an extra `save_to_disk` somewhere else ;)", "I was thinking that `save_to_disk` is necessary when one wants to re-use that dataset as a proper HF dataset later, no?\r\nAt least what I wanted to achieve is create a dataset that can be opened like any other local or remote dataset. ", "`save_to_disk`/`load_from_disk` is indeed more general, e.g. it supports datasets that consist in several files, and saves some extra info in a dataset_info.json file (description, citation, split sizes, etc.)\r\n\r\nIf you have one single file it's fine to simply do `.from_file()`" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Thank you very much for your help @albertvillanova .\r\n\r\nI think I have fixed all the comments.\r\n\r\nPlease let me know if this PR need further modification ;)", "@albertvillanova Thank you very much for your kind help.\r\nThese suggestions make the code looks more pythonic.\r\n\r\nI have commited these changes", "Hi ! The dataset seems to be a duplicate of https://huggingface.co/datasets/silver/lccc - next time no need to add it on github if it's already available on huggingface.co ;)", "> Hi ! The dataset seems to be a duplicate of https://huggingface.co/datasets/silver/lccc - next time no need to add it on github if it's already available on huggingface.co ;)\r\n\r\nOK, sorry for the inconvenience. I have closed another two PRs since these datasets are already available on huggingface.co", "It's fine, thanks @silverriver for adding these datasets !" ]

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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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[ "Thanks for reporting @dgcnz.\r\n\r\nI have checked that the dataset works fine in streaming mode.\r\n\r\nAdditionally, other datasets containing timestamps are properly rendered by the viewer: https://huggingface.co/datasets/blbooks\r\n\r\nI have tried to force the refresh of the preview, but the endpoint is not responsive: Connection timed out\r\n\r\nCC: @severo ", "I've just resent the refresh of the preview to the new endpoint, without success.\r\n\r\nCC: @severo ", "Fixed!\r\n\r\nhttps://huggingface.co/datasets/ett/viewer/h1/test\r\n\r\n<img width=\"982\" alt=\"Capture d’écran 2022-06-15 à 09 30 22\" src=\"https://user-images.githubusercontent.com/1676121/173769035-a075d753-ecfc-4a43-b54b-973105d464d3.png\">\r\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "This PR (via new release) broke many transformers tests.\r\n\r\nI will try to post a summary shortly.\r\n\r\ncc: @ydshieh ", "So now it can't handle a local path via: `--train_file tests/deepspeed/../fixtures/tests_samples/wmt_en_ro/train.json` even though it's there. it works just fine if I change the path to not have `..`\r\n\r\nYou can reproduce the original problem with:\r\n\r\n```\r\n$ cd transformers \r\n$ python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --train_file tests/fixtures/tests_samples/wmt_en_ro/train.json --validation_file tests/deepspeed/../fixtures/tests_samples/wmt_en_ro/val.json --output_dir /tmp/tmp5o5to4k0 --overwrite_output_dir --max_source_length 32 --max_target_length 32 --val_max_target_length 32 --warmup_steps 8 --predict_with_generate --save_steps 0 --eval_steps 1 --group_by_length --label_smoothing_factor 0.1 --source_lang en --target_lang ro --report_to none --source_prefix \"translate English to Romanian: \" --fp16 --do_train --num_train_epochs 1 --max_train_samples 16 --per_device_train_batch_size 2 --learning_rate 3e-3\r\n[...]\r\nTraceback (most recent call last):\r\n File \"examples/pytorch/translation/run_translation.py\", line 656, in <module>\r\n main()\r\n File \"examples/pytorch/translation/run_translation.py\", line 346, in main\r\n raw_datasets = load_dataset(\r\n File \"/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/load.py\", line 1656, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/load.py\", line 1439, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n File \"/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/load.py\", line 1097, in dataset_module_factory\r\n return PackagedDatasetModuleFactory(\r\n File \"/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/load.py\", line 743, in get_module\r\n data_files = DataFilesDict.from_local_or_remote(\r\n File \"/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/data_files.py\", line 588, in from_local_or_remote\r\n DataFilesList.from_local_or_remote(\r\n File \"/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/data_files.py\", line 556, in from_local_or_remote\r\n data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n File \"/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/data_files.py\", line 194, in resolve_patterns_locally_or_by_urls\r\n for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):\r\n File \"/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/data_files.py\", line 144, in _resolve_single_pattern_locally\r\n raise FileNotFoundError(error_msg)\r\nFileNotFoundError: Unable to find '/mnt/nvme0/code/huggingface/transformers-master/tests/deepspeed/../fixtures/tests_samples/wmt_en_ro/val.json' at /mnt/nvme0/code/huggingface/transformers-master\r\n```", "will apply a workaround to `transformers` tests here https://github.com/huggingface/transformers/pull/17721\r\n", "This has been fixed with https://github.com/huggingface/datasets/pull/4505, will do a patch release tomorrow for `datasets` ;)", "Thank you for the quick fix, @lhoestq " ]

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[ "🤗 This PR closes https://github.com/huggingface/datasets/issues/4398", "_The documentation is not available anymore as the PR was closed or merged._", "Hi! Thanks for reporting and providing a fix. I made a small change to make the fix easier to understand.", "Hi, @mariosasko thanks! It makes sense, sorry I'm not that familiar with `datasets` code 😩 ", "Sure @albertvillanova I'll do that later today and ping you once done, thanks! :hugs:", "Hi again @albertvillanova! Let me know if those tests are fine 🤗 ", "Hi @alvarobartt,\r\n\r\nI think your tests are failing. I don't know why previously, after your last commit, the CI tests were not triggered. \r\n\r\nIn order to force the re-running of the CI tests, I had to edit your file using the GitHub UI.\r\n\r\nFirst I tried to do it using my terminal, but I don't have push right to your PR branch: I would ask you next time you open a PR, please mark the checkbox \"Allow edits from maintainers\": https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/allowing-changes-to-a-pull-request-branch-created-from-a-fork#enabling-repository-maintainer-permissions-on-existing-pull-requests", "Hi @albertvillanova, let me check those again! And regarding that checkbox I thought it was already checked so my bad there 😩 ", "@albertvillanova again it seems that the tests were not automatically triggered, but I tested those locally and now they work, as previously those were failing as I created an assertion as `self.assertEqual` over an empty list that was compared as `None` while the value was `[]` so I updated it to be `self.assertListEqual` and changed the comparison value to `[]`.", "@lhoestq any idea why the CI is not triggered?", "@alvarobartt I have tested locally and the tests continue failing.\r\n\r\nI think there is a basis error: `new_dset._format_columns` is always `None` in those cases.\r\n", "You're right @albertvillanova I was indeed running the tests with `datasets==2.2.0` rather than with the branch version, I'll check it again! Sorry for the inconvenience...", "> @alvarobartt I have tested locally and the tests continue failing.\r\n> \r\n> I think there is a basis error: `new_dset._format_columns` is always `None` in those cases.\r\n\r\nIn order to have some regressions tests for the fixed scenario, I've manually updated the value of `_format_columns` in the `ArrowDataset` so as to check whether it's updated or not right after calling `remove_columns`, and it does behave as expected, so with the latest version of this branch the reported issue doesn't occur anymore.", "Hi again @albertvillanova sorry I was on leave! I'll do that ASAP :hugs:", "@albertvillanova, does it make sense to add regression tests for `DatasetDict`? As `DatasetDict` doesn't have the attribute `_format_columns`, when we call `remove_columns` over a `DatasetDict` it removes the columns and updates the attributes of each split which is an `ArrowDataset`.\r\n\r\nSo on, we can either:\r\n- Update first the `_format_columns` attribute of each split and then remove the columns over the `DatasetDict`\r\n- Loop over the splits of `DatasetDict` and remove the columns right after updating `_format_columns` of each `ArrowDataset`.\r\n\r\nI assume that the best regression test is the one implemented (mentioned first above), let me know if there's a better way to do that 👍🏻 ", "I think there's already a decorator to support transmitting the right `_format_columns`: `@transmit_format`, have you tried adding this decorator to `remove_columns` ?", "> I think there's already a decorator to support transmitting the right `_format_columns`: `@transmit_format`, have you tried adding this decorator to `remove_columns` ?\r\n\r\nHi @lhoestq I can check now!", "It worked indeed @lhoestq, thanks for the proposal and the review! 🤗 ", "Oops, I forgot about `@transmit_format`'s existence. From what I see, we should also use this decorator in `flatten`, `rename_column` and `rename_columns`. \r\n\r\n@alvarobartt Let me know if you'd like to work on this (in a subsequent PR).", "Sure @mariosasko I can prepare another PR to add those too, thanks! " ]

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

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[ "@lhoestq Maybe I'm missing something, but what's the reason to read and encode PCM files to WAV in `Audio.encode_example`. Isn't the whole purpose of the decodable types to operate on raw files whenever possible? IMO this PR should only modify `Audio.decode_example` to support PCM files/bytes decoding.", "Because the PCM file is not enough, we also need the `sampling_rate` associated to it. Therefore the two alternatives are either:\r\n- convert to WAV\r\n- add a `sampling_rate` field to the Audio arrow storage (not sure how it would behave for backward compatibility though)", "But [`scipy.io.wavfile.read`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.wavfile.read.html), which is used for reading such files, returns a file's sampling rate. The only tricky part is [resampling](https://stackoverflow.com/questions/33682490/how-to-read-a-wav-file-using-scipy-at-a-different-sampling-rate) to a different sampling rate than the default one.", "How does it get the sampling rate of a PCM file then ? According to [SO](https://stackoverflow.com/a/57027667/17517845) it's not possible to infer it from the file alone", "> Awesome thanks ! Could you also add tests in `tests/features/test_audio.py` ?\r\n> \r\n> Maybe add a small pcm file in `tests/features/data` and check that everything works as expected in tests cases like `test_audio_encode_example_pcm` and `test_audio_decode_example_pcm` for example.\r\n\r\n@lhoestq how can i test test_audio.py? where is \"__main__\" func?\r\ndo you have some example or guideline?", "> But [`scipy.io.wavfile.read`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.wavfile.read.html), which is used for reading such files, returns a file's sampling rate. The only tricky part is [resampling](https://stackoverflow.com/questions/33682490/how-to-read-a-wav-file-using-scipy-at-a-different-sampling-rate) to a different sampling rate than the default one.\r\n\r\n@mariosasko @lhoestq \r\nthanks for comment!\r\n\r\nFirst of all, \"PCM file\" can not read alone to any audio library.\r\n\"PCM file\" has not any audio META information header. (it just purely audio byte data. therefore, we don't have to encoding and decoding)\r\nbut, \"PCM file\" is audio extension, so we can use `datasets.Audio`\r\n\r\nif you want to read \"PCM file\" to audio file likely, it have to needs additional parameter. (channel, sampling_rate, else....)\r\nbut, in many situation, we only know sampling_rate for PCM\r\n\r\nand, if we want to use `datasets.Audio` for \"PCM file\", we must process encode_example.\r\ntherefore, i have to use sampling_rate for encoding for making wav-style byte. (we only know sampling_rate)\r\n\r\nIn my source code, I don't compare sampling rate(`datasets.Audio's self.sampling_rate` and `read pcm sampling_rate(value[\"sampling_rate\"])`) and checking mono\r\n@mariosasko ! do you want to process resampling and making mono? then i can modify my source\r\n", "There is no \"main\" function in test scripts :) To run a test script you must use the `pytest` command:\r\n```\r\npytest tests/features/test_audio.py\r\n```\r\n\r\nto run only one function you can also do\r\n```\r\npytest tests/features/test_audio.py::test_audio_feature_type_to_arrow\r\n```\r\nfor example", "@lhoestq\r\nmaybe, if i write test code, i have to commit test_audio.py and send pr?\r\nbecause, we need to keep `test_audio_encode_example_pcm` and `test_audio_decode_example_pcm` method after my pr merged?", "You can add your tests in this PR with the other changes you did", "@lhoestq \r\ntest complete & commit my test_audio.py\r\n\r\nAND, some change in my code.\r\n\r\naudio.py\r\ni think \"sampling_rate\" is already Audio object initial variable. so, we don`t have to use input parameter.\r\n\r\ntest_audio.py\r\nwe can check \"PCM\" file to path (exactly, extenstion)\r\nso, test case has to know `path`. if only have `bytes`, we don`t know that is \"PCM\" or not", "@lhoestq\r\nand, why circleci raised exception?\r\nmaybe, [repo](https://huggingface.co/api/datasets/lhoestq/_dummy?full=true) url is not found!\r\nPLZ, CHK!", "@lhoestq\r\nhello????", "@lhoestq \r\ntest_audio.py\r\nif we don`t use path in pcm, test-case need to be changed\r\nso, we check path just None", "i'm merge branch already and `multiprocess` in `setup.py` but circleci error only win version\r\n\r\nhow can i fixed it?", "@lhoestq thx for comment!\r\ntest_audio.py test complete. it runs sucessfully\r\nand, self.get(\"sampling_rate\") -> value.get(\"sampling_rate\") changed\r\n\r\nand, some comment is not agreed to me, plz check my sub comment!", "_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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[ "Thanks for reporting, @jiangwy99.\r\n\r\nI guess this could be addressed only once we fix our issue with irresponsive backend endpoint.\r\n\r\nCC: @severo ", "I've just sent the forcing of the refresh of the preview to the new endpoint.", "Fixed, thanks for the patience. The issue was the amount of RAM allowed to extract the first rows of the dataset was not sufficient." ]

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[ "And if the problem is that the way I am to construct the {Entity Type: list of spans} makes entity types without any spans hard to handle, is there a better way to meet the demand? Although I have verified that to make entity types without any spans to behave like `entity_chunk[label] = [[\"\"]]` can perform normally, I still wonder if there is a more elegant way?" ]

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[ "Hi! You can already use `dset.builder_name` and `dset.config_name` for that purpose. And when it comes to versioning, it's better to use `dset._fingerprint` than the `version` attribute as the former represents a deterministic hash that encodes all the mutable ops executed on a dataset, and the latter stays the same unless it's manually updated after each op.", "@mariosasko Can we make ._fingerprint not private? seems a critical component for tracking how a model was generated to ensure reproducibility." ]

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