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[ "Hi ! It looks like your \"isDif\" column is a Sequence of Value(\"string\"), not a Sequence of ClassLabel.\r\n\r\nYou can convert your Value(\"string\") feature type to a ClassLabel feature type this way:\r\n```python\r\nfrom datasets import ClassLabel, Sequence\r\n\r\n# provide the label_names yourself\r\nlabel_names = [...]\r\n# OR get them from the dataset\r\nlabel_names = sorted(set(label for labels in raw_datasets[\"train\"][\"isDif\"] for label in labels))\r\n\r\n# Cast to ClassLabel\r\nraw_datasets = raw_datasets.cast_column(\"isDif\", Sequence(ClassLabel(names=label_names)))\r\n```\r\n", "thank you \r\nit works 💯 " ]

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[ "cc @albertvillanova the author would like to add this dataset to the hub: https://github.com/microsoft/table-transformer/issues/68#issuecomment-1319114621. Could you help him out?" ]

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[ "Thanks for reporting, @adiprasad.\r\n\r\nWe are having a look at it.", "I have opened an issue in that dataset Community tab on the Hub: https://huggingface.co/datasets/consumer-finance-complaints/discussions/1\r\n\r\nPlease note that in the meantime, you can load the dataset by passing `ignore_verifications=True`:\r\n```python\r\n>>> ds = load_dataset(\"consumer-finance-complaints\", ignore_verifications=True)\r\n>>> ds\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['Date Received', 'Product', 'Sub Product', 'Issue', 'Sub Issue', 'Complaint Text', 'Company Public Response', 'Company', 'State', 'Zip Code', 'Tags', 'Consumer Consent Provided', 'Submitted via', 'Date Sent To Company', 'Company Response To Consumer', 'Timely Response', 'Consumer Disputed', 'Complaint ID'],\r\n num_rows: 3079747\r\n })\r\n})\r\n```", "PR fixing this issue: https://huggingface.co/datasets/consumer-finance-complaints/discussions/2" ]

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[ "I notice a comment in the code says:\r\n`Having lists of different sizes makes sharding ambigious, raise an error in this case until we decide how to define sharding without ambiguity for users` \r\n \r\n ... which suggests this update was pushed knowing that it might break some things. But, it didn't seem to have a useful error message of an argument that could be passed to avoid the error.", "Sorry for the inconvenience, I opened a PR in your repo to fix this: https://huggingface.co/datasets/sil-ai/bloom-speech/discussions/2\r\n\r\nBasically we've always considered lists in `gen_kwargs` to be a shard list that we can split and pass into different workers to generate the dataset (e.g. if you pass `num_proc=` in `load_dataset()` to generate the dataset in parallel), but it was documented only recently", "@lhoestq Thanks for the help. It looks like that took care of it." ]

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[ "The bulk edit is running...\r\n\r\nSee for example: \r\n- A single config: https://huggingface.co/datasets/acronym_identification/discussions/2\r\n- Multiple configs: https://huggingface.co/datasets/babi_qa/discussions/1", "TODO: Add \"dataset_info\" YAML metadata to:\r\n- [x] \"chr_en\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card\r\n - Fixing PR: https://huggingface.co/datasets/chr_en/discussions/1 \r\n- [x] \"conll2000\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [x] \"crime_and_punish\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [x] \"dart\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [x] \"iwslt2017\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [ ] \"mc4\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card\r\n- [ ] \"the_pile\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card\r\n- [ ] \"timit_asr\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card", "The bulk edit is finished." ]

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[ "Also cc @mariosasko and @lhoestq ", "Cool ! Let us know if you have questions or if we can help :)\r\n\r\nI guess we'll also have to create the NYU CS Department on the Hub ?", "> I guess we'll also have to create the NYU CS Department on the Hub ?\r\n\r\nYes, you're right! Let me add it to my profile first, and then we can transfer. Meanwhile, if it's recommended to loop the dataset author in here, let me know. \r\n\r\nAlso, the NYU Depth dataset seems big. Any example scripts for creating image datasets that I could refer? ", "You can check the imagenet-1k one.\r\n\r\nPS: If the licenses allows it, it'b be nice to host the dataset as sharded TAR archives (like imagenet-1k) instead of the ZIP format they use:\r\n- it will make streaming much faster\r\n- ZIP compression is not well suited for images\r\n- it will allow parallel processing of the dataset (you can pass a subset of shards to each worker)\r\n\r\n> if it's recommended to loop the dataset author in here, let me know.\r\n\r\nIt's recommended indeed, you can send them an email once you have the dataset ready and invite them to the org on the Hub", "> You can check the imagenet-1k one.\r\n\r\nWhere can I find the script? Are you referring to https://huggingface.co/docs/datasets/image_process ? Or is there anything more specific? ", "You can find it here: https://huggingface.co/datasets/imagenet-1k/blob/main/imagenet-1k.py", "Update: started working on it here: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2. \r\n\r\nI am facing an issue and I have detailed it here: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2/discussions/1\r\n\r\nEdit: The issue is gone. \r\n\r\nHowever, since the dataset is distributed as a single TAR archive (following the [URL used in TensorFlow Datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py)) the loading is taking longer. How would suggest to shard the single TAR archive? \r\n\r\n@lhoestq \r\n\r\n", "A Colab Notebook demonstrating the dataset loading part: \r\n\r\nhttps://colab.research.google.com/gist/sayakpaul/aa0958c8d4ad8518d52a78f28044d871/scratchpad.ipynb\r\n\r\n@osanseviero @lhoestq \r\n\r\nI will work on a notebook to work with the dataset including data visualization.", "@osanseviero @lhoestq things seem to work fine with the current version of the dataset [here](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2). Here's a notebook I developed to help with visualization: https://colab.research.google.com/drive/1K3ZU8XUPRDOYD38MQS9nreQXJYitlKSW?usp=sharing. \r\n\r\n@lhoestq I need your help with the following:\r\n\r\n> However, since the dataset is distributed as a single TAR archive (following the [URL used in TensorFlow Datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py)) the loading is taking longer. How would suggest to shard the single TAR archive?\r\n\r\n@osanseviero @lhoestq question for you:\r\n\r\nWhere should we host the dataset? I think hosting it under hf.co/datasets (that is HF is the org) is fine as we have ImageNet-1k hosted similarly. We could then reach out to Diana Wofk (author of [Fast Depth](https://github.com/dwofk/fast-depth) and the owner of the repo on which TFDS NYU Depth V2 is based) for a review. WDYT? ", "> However, since the dataset is distributed as a single TAR archive (following the [URL used in TensorFlow Datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py)) the loading is taking longer. How would suggest to shard the single TAR archive?\r\n\r\nFirst you can separate the train data and the validation data.\r\n\r\nThen since the dataset is quite big, you can even shard the train split and the validation split in multiple TAR archives. Something around 16 archives for train and 4 for validation would be fine for example.\r\n\r\nAlso no need to gzip the TAR archives, the images are already compressed in png or jpeg.", "> Then since the dataset is quite big, you can even shard the train split and the validation split in multiple TAR archives. Something around 16 archives for train and 4 for validation would be fine for example.\r\n\r\nYes, I got you. But this process seems to be manual and should be tailored for the given dataset. Do you have any script that you used to create the ImageNet-1k shards? \r\n\r\n> Also no need to gzip the TAR archives, the images are already compressed in png or jpeg.\r\n\r\nI was not going to do that. Not sure what brought it up. ", "> Yes, I got you. But this process seems to be manual and should be tailored for the given dataset. Do you have any script that you used to create the ImageNet-1k shards?\r\n\r\nI don't, but I agree it'd be nice to have a script for that !\r\n\r\n> I was not going to do that. Not sure what brought it up.\r\n\r\nThe original dataset is gzipped for some reason", "Oh, I am using this URL for the download: https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py#L24. ", "> Where should we host the dataset? I think hosting it under hf.co/datasets (that is HF is the org) is fine as we have ImageNet-1k hosted similarly.\r\n\r\nMaybe you can create an org for NYU Courant (this is the institute of the lab of the main author of the dataset if I'm not mistaken), and invite the authors to join.\r\n\r\nWe don't add datasets without namespace anymore", "Updates: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2/discussions/5\r\n\r\nThe entire process (preparing multiple archives, preparing data loading script, etc.) was fun and engaging, thanks to the documentation. I believe we could work on a small blog post that would work as a reference for the future contributors following this path. What say? \r\n\r\nCc: @lhoestq @osanseviero ", "> I believe we could work on a small blog post that would work as a reference for the future contributors following this path. What say?\r\n\r\n@polinaeterna already mentioned it would be nice to present this process for audio (it's exactly the same), I believe it can be useful to many people", "Cool. Let's work on that after the NYU Depth Dataset is fully in on Hub (under the appropriate org). 🤗", "@lhoestq need to discuss something while I am adding the dataset card to https://huggingface.co/datasets/sayakpaul/nyu_depth_v2/. \r\n\r\nAs per [Papers With Code](https://paperswithcode.com/dataset/nyuv2), NYU Depth v2 is used for many different tasks:\r\n\r\n* Monocular depth estimation\r\n* Depth estimation \r\n* Semantic segmentation\r\n* Plane instance segmentation \r\n* ...\r\n\r\nSo, while writing the supported task part of the dataset card, should we focus on all these? IMO, we could focus on just depth estimation and semantic segmentation for now since we have supported models for these two. WDYT?\r\n\r\nAlso, I am getting: \r\n\r\n\r\n```\r\nremote: Your push was accepted, but with warnings:\r\nremote: - Warning: The task_ids \"depth-estimation\" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-generation, dialogue-modeling, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering\r\nremote: ----------------------------------------------------------\r\nremote: Please find the documentation at:\r\nremote: https://huggingface.co/docs/hub/model-cards#model-card-metadata\r\n```\r\n\r\nWhat should be the plan of action for this?\r\n\r\nCc: @osanseviero \r\n\r\n", "> What should be the plan of action for this?\r\n\r\nWhen you merged https://github.com/huggingface/hub-docs/pull/488, there is a JS Interfaces GitHub Actions workflow that runs https://github.com/huggingface/hub-docs/actions/workflows/js-interfaces-tests.yml. It has a step called [export-task scripts](https://github.com/huggingface/hub-docs/actions/runs/3622479064/jobs/6107238948) which exports an interface you can use in `dataset`. If you look at the logs, it prints out a map. This map can replace https://github.com/huggingface/datasets/blob/main/src/datasets/utils/resources/tasks.json (tasks.json was generated with this script), which should add depth estimation\r\n", "Thanks @osanseviero. \r\n\r\nhttps://github.com/huggingface/datasets/pull/5335", "Closing the issue as the dataset has been successfully added: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5252). All of your documentation changes will be reflected on that endpoint.", "Yes, if the image column is the first in the batch keys, it will decode the images because it reads the actual values. We could avoid this by checking the batch type, and if it's `LazyDict`, `num_examples` is equal to `len(batch.pa_table)`, which doesn't lead to decoding.", "Good idea. This can be done in a subsequent PR btw, since it's out of scope of the original goal of this PR", "Just fixed a small bug where it would show the pyarrow 10 warning about None -> empty lists conversions even with an Array2D with no nulls", "Fixed another bug when your map function returns a mix of LazyDict or regular dict and added some tests" ]

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[ "After a discussion with @mishig25:\r\n- He said that this action should be triggered if we call our release branch according to the regex `v*-release`, as transformers does\r\n- I said that our procedure is different: our release branch is *temporary* and it is deleted just after the release PR is merged to main\r\n - Indeed the release tag is not yet created when we make the release PR (not event when this is merged to main), but when we make the Release itself.\r\n\r\nI was thinking that maybe we could change the triggering event: use `release` instead of `push`.\r\n\r\nWhat do you think, @huggingface/datasets?", "Why is it an issue if our branch is temporary ?", "He says not; but the branch has no tag yet; does the doc building require the tag? Or just the version number in `__init__.py` or setup.py?", "It uses `module.__version__` (i.e. the one defined in `__init__.py`) - no need to have a tag\r\n\r\nhttps://github.com/huggingface/doc-builder/blob/81575cf081964c30ea5fd39450f4820db963f18e/src/doc_builder/commands/build.py#L69", "Thanks, @lhoestq.\r\n\r\n@mishig25 has manually forced the generation of the docs, that are live for 2.7.0 version: https://huggingface.co/docs/datasets/v2.7.0/en/index ", "Cool ! this can be closed then ?", "I was waiting for #5250 to be merged to close this.", "just to confirm, is there anything I need to do from my side ? Or is everything good here ?" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5250). All of your documentation changes will be reflected on that endpoint.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5250). All of your documentation changes will be reflected on that endpoint.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5250). All of your documentation changes will be reflected on that endpoint.", "Little recap:\r\n- The release-conda GH action was properly triggered by push-tag event: therefore I guess this event is also created when we publish a release and create a tag within it (as it is the case in the new procedure)\r\n - However, the package was only uploaded to huggingface channel and not to conda-forge channel\r\n - [x] Why? Need to address this.\r\n - Reply by @lhoestq: https://github.com/huggingface/datasets/pull/5250#discussion_r1025047531\r\n - We only maintain the huggingface channel\r\n - The conda-forge channel is maintained by the community; the 2.7.0 has been finally added as well to this channel \r\n- The generate-documentation GH action will be triggered by the push-to-branch event if we align the name of the release branch with the expected regex `v*-release`\r\n - [x] The naming has been aligned in the new procedure\r\n - [ ] Question: why do we have different triggering events for generate-doc and release-conda? Maybe we could set the same for both: either push-tag (when publishing the release), or push-to-branch\r\n - I think it will be better to use the push-tag event because in the new release procedure this happens later (when we publish the release), once we have already tested that everything works using the test-PyPI; on the contrary, the push-to-branch event happens before, even before opening the release PR: we could see afterwards that there is an issue, and cancel the Pull Request, but the docs and conda-package will already be published.\r\n- For the naming of the dev-version branch/PR, instead of having a complicated version naming, I'm proposing:\r\n - Using always the same branch name `dev-version`\r\n - Just include a step to delete this branch locally if it exists: `git branch -D dev-version`\r\n - The remote version will not exist because it is deleted once the PR is merged\r\n - This approach is approved by @lhoestq: https://github.com/huggingface/datasets/pull/5250#discussion_r1025048300", "Just one question to be addressed: why do we have different triggering events for generate-doc and release-conda? Maybe we could set the same for both: either push-tag (when publishing the release), or push-to-branch\r\n\r\nI think it will be better to use the push-tag event because in the new release procedure this happens later (when we publish the release), once we have already tested that everything works using the test-PyPI; on the contrary, the push-to-branch event happens before, even before opening the release PR: we could see afterwards that there is an issue, and cancel the Pull Request, but the docs and conda-package will already be published.\r\n\r\nWe could even use the release-published event instead: [8694901](https://github.com/huggingface/datasets/pull/5250/commits/86949013c9dc59a07b55fad5b78104b8a03f60cd)\r\n", "@lhoestq now that we have push-tag event for both build_documentation and release-conda, we have no constraint on the naming of the release branch:\r\n- we could name it simpler: maybe as you suggested above: https://github.com/huggingface/datasets/pull/5250#discussion_r1024119018\r\n `release-VERSION` instead of `vVERSION-release` (we do not use the prefix \"v\" anywhere in our repo)" ]

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[ "It seems all the tasks have been addressed, meaning this issue can be closed, no?" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5248). All of your documentation changes will be reflected on that endpoint.", "Thanks for the fix @mishig25.\r\n\r\nI guess this is the reason why the docs are not generated for the latest release version 2.7.0? https://huggingface.co/docs/datasets/index " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5247). 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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[ "Hi @peregilk this bug is directly related to https://github.com/huggingface/datasets/issues/3888, and still not fixed... But I'll try to have a look!", "Thanks @alvarobartt. It is great if you are able to fix it, but when reading the explanation it seems like it is possible to work around it.\r\n\r\nWe also tried keeping the 'info.features' and then adding a modified version back after the remove/rename. Unforutunately that leads to a dataset that is not possible to iterate over.", "So if you iterate over the `IterableDataset` as `next(iter(ds))` and then run `rename_columns` when checking that data it will work, but in the end, it's just renaming the column one example/batch at a time, not renaming the column name for all the entries in the dataset, which is the ideal.", "@alvarobartt Thanks. My use case was that I wanted to do multiple things, ie removing all unnecessary columns, renaming some valid columns, and then using cast (in my case checking if the audio is not 16K and casting it). It is just convenient to look into the info.features between each of these operations. Alternatively, I will just plan ahead...;) To me it seems like all the operations are working.\r\n\r\nThanks for the advice. It was very useful.", "If we know the features before renaming, then we know the features after renaming, so we can pass the new features to the returned dataset in `rename_column` indeed ! If anyone is interested in contributing, feel free to open a PR and I'd be happy to help / give some pointers :)", "Sure @lhoestq thanks! I’ll try to work on that", "#self-assign" ]

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[ "Hi ! What kind of private source ? We're exploring adding support for cloud storage and URIs like s3://, gs:// etc. with authentication in the download manager", "Hello! It's a google cloud storage, so gs://, but I'm using it with https.\r\nBeing able to provide a file system like [here](https://huggingface.co/docs/datasets/main/filesystems#load-serialized-datasets) would be even more practical indeed.\r\nI've found a quite complicated workaround which consists of monkey patching all of the functions in streaming_download_manager.py to use my own _get_authentication_headers_for_url_ . \r\n\r\nA support for this use case would be greatly appreciated!\r\n\r\nFor reference my _get_authentication_headers_for_url_ looks like this:\r\n```\r\nimport os\r\nfrom typing import Optional, Union\r\n\r\nfrom datasets import config\r\nfrom huggingface_hub import HfFolder\r\nfrom gcsfs.credentials import GoogleCredentials\r\n\r\nDEFAULT_PROJECT = os.environ.get(\"GCSFS_DEFAULT_PROJECT\", \"\")\r\naccess = \"full_control\"\r\ngcs_token = os.environ.get(\"GCS_TOKEN\")\r\n\r\n\r\ndef get_authentication_headers_for_url(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> dict:\r\n \"\"\"Handle the HF authentication\"\"\"\r\n headers = {}\r\n if url.startswith(config.HF_ENDPOINT):\r\n if use_auth_token is False:\r\n token = None\r\n elif isinstance(use_auth_token, str):\r\n token = use_auth_token\r\n else:\r\n token = HfFolder.get_token()\r\n elif url.startswith(\"https://storage.googleapis.com\"):\r\n credentials = GoogleCredentials(DEFAULT_PROJECT, access, gcs_token)\r\n credentials.maybe_refresh()\r\n token = credentials.credentials.token\r\n else:\r\n token = None\r\n if token:\r\n headers[\"authorization\"] = f\"Bearer {token}\"\r\n return headers\r\n```", "I would be a big fan of this feature! @Hubert-Bonisseur if this doesn't become a supported feature, would you mind sharing your code? Thanks!", "> I would be a big fan of this feature! @Hubert-Bonisseur if this doesn't become a supported feature, would you mind sharing your code? Thanks!\r\n\r\nI published it here:\r\nhttps://github.com/Hubert-Bonisseur/private-dataset-hub\r\n\r\nI modified the names of a lot of functions for privacy and I don't have time to test it again so you may get import errors, but you have the code. The custom_load_dataset is the function you are interested in I think.\r\n\r\nIt relies a lot on patching, if you find a better way to do this, I'd be interested.", "Given the amount of patching it does, this is likely to break at one point. I'd encourage you to wait for a proper support in `datasets` directly if you can wait." ]

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[ "Hi @capsabogdan! Unfortunately, it's hard to implement because quite often datasets data is being hosted in a single archive for all splits :( So we have to download the whole archive to split it into splits. This is the case for CommonVoice too. \r\n\r\nHowever, for cases when data is distributed in separate archives ащк different splits I suppose it can (and will) be implemented someday. \r\n\r\n\r\nBtw for quick check of the dataset you can use [streaming](https://huggingface.co/docs/datasets/stream):\r\n```python\r\ncv = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"en\", split=\"test\", streaming=True)\r\ncv = iter(cv)\r\nprint(next(cv))\r\n\r\n>> {'client_id': 'a07b17f8234ded5e847443ea6f423cef745cbbc7537fb637d58326000aa751e829a21c4fd0a35fc17fb833aa7e95ebafce5efd19beeb8d843887b85e4eb35f5b',\r\n>> 'path': None,\r\n>> 'audio': {'path': 'cv-corpus-11.0-2022-09-21/en/clips/common_voice_en_100363.mp3',\r\n>> 'array': array([ 0.0000000e+00, 1.1748125e-14, 1.5450088e-14, ...,\r\n>> 1.3011958e-06, -6.3548953e-08, -9.9098514e-08], dtype=float32),\r\n>> ...}\r\n\r\n```", "thank you for the answer but am not sure if this will not be helpful, as we\nneed maybe just 10% of the datasets for some experiment\n\ncan we get just a portion of the dataset with stream?\n\n\nis there really no solution? :(\n\nAm Di., 15. Nov. 2022 um 16:55 Uhr schrieb Polina Kazakova <\n***@***.***>:\n\n> Hi @capsabogdan <https://github.com/capsabogdan>! Unfortunately, it's\n> hard to implement because quite often datasets data is being hosted in a\n> single archive for all splits :( So we have to download the whole archive\n> to split it into splits. This is the case for CommonVoice too.\n>\n> However, for cases when data is distributed in separate archives in\n> different splits I suppose it can be implemented someday.\n>\n> Btw for quick check of the dataset you can use streaming\n> <https://huggingface.co/docs/datasets/stream>:\n>\n> cv = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"en\", split=\"test\", streaming=True)cv = iter(cv)print(next(cv))\n> >> {'client_id': 'a07b17f8234ded5e847443ea6f423cef745cbbc7537fb637d58326000aa751e829a21c4fd0a35fc17fb833aa7e95ebafce5efd19beeb8d843887b85e4eb35f5b',>> 'path': None,>> 'audio': {'path': 'cv-corpus-11.0-2022-09-21/en/clips/common_voice_en_100363.mp3',>> 'array': array([ 0.0000000e+00, 1.1748125e-14, 1.5450088e-14, ...,>> 1.3011958e-06, -6.3548953e-08, -9.9098514e-08], dtype=float32),>> ...}\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5243#issuecomment-1315512887>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ALSIFOC3JYRCTH54OBRUJULWIOW6PANCNFSM6AAAAAASAYO2LY>\n> .\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n", "maybe it would be nice if you guys ould do some sort of shard before\nloading the dataset, so users can download just chunks of data :)\n\nI think this would be very helpful\n\nAm Di., 15. Nov. 2022 um 19:24 Uhr schrieb Bogdan Capsa <\n***@***.***>:\n\n> thank you for the answer but am not sure if this will not be helpful, as\n> we need maybe just 10% of the datasets for some experiment\n>\n> can we get just a portion of the dataset with stream?\n>\n>\n> is there really no solution? :(\n>\n> Am Di., 15. Nov. 2022 um 16:55 Uhr schrieb Polina Kazakova <\n> ***@***.***>:\n>\n>> Hi @capsabogdan <https://github.com/capsabogdan>! Unfortunately, it's\n>> hard to implement because quite often datasets data is being hosted in a\n>> single archive for all splits :( So we have to download the whole archive\n>> to split it into splits. This is the case for CommonVoice too.\n>>\n>> However, for cases when data is distributed in separate archives in\n>> different splits I suppose it can be implemented someday.\n>>\n>> Btw for quick check of the dataset you can use streaming\n>> <https://huggingface.co/docs/datasets/stream>:\n>>\n>> cv = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"en\", split=\"test\", streaming=True)cv = iter(cv)print(next(cv))\n>> >> {'client_id': 'a07b17f8234ded5e847443ea6f423cef745cbbc7537fb637d58326000aa751e829a21c4fd0a35fc17fb833aa7e95ebafce5efd19beeb8d843887b85e4eb35f5b',>> 'path': None,>> 'audio': {'path': 'cv-corpus-11.0-2022-09-21/en/clips/common_voice_en_100363.mp3',>> 'array': array([ 0.0000000e+00, 1.1748125e-14, 1.5450088e-14, ...,>> 1.3011958e-06, -6.3548953e-08, -9.9098514e-08], dtype=float32),>> ...}\n>>\n>> —\n>> Reply to this email directly, view it on GitHub\n>> <https://github.com/huggingface/datasets/issues/5243#issuecomment-1315512887>,\n>> or unsubscribe\n>> <https://github.com/notifications/unsubscribe-auth/ALSIFOC3JYRCTH54OBRUJULWIOW6PANCNFSM6AAAAAASAYO2LY>\n>> .\n>> You are receiving this because you were mentioned.Message ID:\n>> ***@***.***>\n>>\n>\n", "+1 on this feature request - I am running into the same problem, where I only need the test set for a dataset that has a huge training set", "Hey, I'm also interested in that as a feature. I'm having the same problem with Common Voice 13.0. The dataset is super big but I only want the test data to benchmark multilingual models, but I don't have much Terabytes to store all the dataset..." ]

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[ "cc @abhishekkrthakur @SBrandeis " ]

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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._", "@lhoestq Good catch! This currently leads to an AttributeError (due to `writer` being None) on this line:\r\nhttps://github.com/huggingface/datasets/blob/fed1628d49a91f9ae259ddf6edbb252c7972d9a3/src/datasets/builder.py#L1552\r\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5239). All of your documentation changes will be reflected on that endpoint.", "I ended up moving `num_proc` to `AbstractDatasetReader.__init__` :)\r\n\r\nLet me know if it sounds good to you now" ]

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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._", "> Not sure how we can have a test that is relevant for this though - feel free to add one if you have ideas\r\n\r\nYes, this was my reasoning for not adding a test. This change is pretty simple, so I think it's OK not to have a test for it." ]

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[ "Good catch thanks ! Have you tried to use the absolue path in `MemoryMappedTable.__init__` in `table.py`?\r\n\r\nI think it can fix issues with relative paths at more levels than just fixing it `load_from_disk`. If it works I think it would be a more robust fix to this issue", "@lhoestq right, that actually fixed it indeed. I've pushed the changes (one-liner). lemme know if there's anything else you need for this fix", "_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, @vinaykakade.\r\n\r\nWe are discussing about making a release early this week.\r\n\r\nPlease note that in the meantime, in your specific case (as we also pointed out here: https://github.com/huggingface/datasets/issues/5162#issuecomment-1291720293), you can circumvent the issue by pinning `multiprocess` to 0.70.13 version (instead of using latest 0.70.14).\r\n\r\nDuplicate of:\r\n- https://github.com/huggingface/datasets/issues/5162", "You can also make `pip-compile` work by using the backtracking resolver (instead of the legacy one): https://pip-tools.readthedocs.io/en/latest/#a-note-on-resolvers\r\n```\r\npip-compile --resolver=backtracking requirements.in\r\n```\r\nThis resolver will automatically use `multiprocess` 0.70.13 version.\r\n" ]

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[ "In case others find this, the problem was not with set_format, but my usages of `to_pandas()` and `from_pandas()` which I was using during dataset splitting; somewhere in the chain of converting to and from pandas the `Array2D/Array3D` types get converted to series of `Sequence()` types" ]

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[ "I opened [this issue](https://github.com/python/cpython/issues/99401).\r\nPython's maintainers say that the issue is caused by [this change](https://docs.python.org/3.11/whatsnew/3.11.html#dataclasses).\r\nI believe adding a `__hash__` method to `datasets.utils.version.Version` should solve (at least partially) this issue.", "Has this been fixed? I am running into this issue now. \r\n\r\nIf this has been fixed, could have a new release with this?\r\n", "Hi, I am getting error while training \r\n\r\n(tensorflow) C:\\tensorflow\\models\\research\\object_detection>python train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/faster_rcnn_inception_v2_pets.config\r\nTraceback (most recent call last):\r\n File \"C:\\tensorflow\\models\\research\\object_detection\\train.py\", line 54, in <module>\r\n from object_detection.legacy import trainer\r\n File \"C:\\tensorflow\\models\\research\\object_detection\\legacy\\trainer.py\", line 27, in <module>\r\n from object_detection.builders import optimizer_builder\r\n File \"C:\\tensorflow\\models\\research\\object_detection\\builders\\optimizer_builder.py\", line 25, in <module>\r\n from official.modeling.optimization import ema_optimizer\r\n File \"C:\\tensorflow\\models\\official\\modeling\\optimization\\__init__.py\", line 19, in <module>\r\n from official.modeling.optimization.configs.optimization_config import *\r\n File \"C:\\tensorflow\\models\\official\\modeling\\optimization\\configs\\optimization_config.py\", line 31, in <module>\r\n @dataclasses.dataclass\r\n ^^^^^^^^^^^^^^^^^^^^^\r\n File \"C:\\Users\\x0133252\\AppData\\Local\\anaconda3\\envs\\tensorflow\\Lib\\dataclasses.py\", line 1223, in dataclass\r\n return wrap(cls)\r\n ^^^^^^^^^\r\n File \"C:\\Users\\x0133252\\AppData\\Local\\anaconda3\\envs\\tensorflow\\Lib\\dataclasses.py\", line 1213, in wrap\r\n return _process_class(cls, init, repr, eq, order, unsafe_hash,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"C:\\Users\\x0133252\\AppData\\Local\\anaconda3\\envs\\tensorflow\\Lib\\dataclasses.py\", line 958, in _process_class\r\n cls_fields.append(_get_field(cls, name, type, kw_only))\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"C:\\Users\\x0133252\\AppData\\Local\\anaconda3\\envs\\tensorflow\\Lib\\dataclasses.py\", line 815, in _get_field\r\n raise ValueError(f'mutable default {type(f.default)} for field '\r\nValueError: mutable default <class 'official.modeling.optimization.configs.optimizer_config.SGDConfig'> for field sgd is not allowed: use default_factory", "@Jayanth1812 and anyone else receiving a similar issue, it most likely has to do with your Python version. Downgrading to Python 3.9 works for me, but doing a downgrade might impact a lot of things. So to be safe and what worked for me was creating a new conda environment and following the installations here: https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/install.html\r\n\r\nAnd for Tensorflow GPU compatibility, after installing TensorFlow follow the instructions in section 4 'GPU Setup' in this document: https://www.tensorflow.org/install/pip", "@Jayanth1812, you can see in your error stack trace, that the error is caused by the `tensorflow` library, not by the `datasets` library. See:\r\n```\r\nFile \"C:\\Users\\x0133252\\AppData\\Local\\anaconda3\\envs\\tensorflow\\Lib\\dataclasses.py\"\r\n```\r\n\r\nYou should open an issue in their repository instead: https://github.com/tensorflow/tensorflow " ]

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[ "Hi! \r\n\r\nWe could address this by calling `.item()` on such tensors to extract the value, but this would lose us the type, which could lead to storing the generated dataset in a suboptimal format. Considering this, I think the only proper fix would be implementing support for 0-D tensors on Apache Arrow's side (Arrow is the underlying format we use to store datasets on disk/in memory). WDYT @lhoestq?", "I think we can just convert the item to a numpy typed scalar using `.numpy()` ?\r\n\r\nFor example this works:\r\n```python\r\nimport numpy as np\r\nimport pyarrow as pa\r\n\r\nassert pa.array([np.float64(1.0)]).type == pa.float64()\r\nassert pa.array([np.float32(1.0)]).type == pa.float32()\r\nassert pa.array([np.int32(1)]).type == pa.int32()\r\nassert pa.array([np.int64(1)]).type == pa.int64()\r\n```\r\n\r\nAnd therefore it would work the same as for PyTorch N-D Tensors: convert to Numpy Array to keep the type in `_cast_to_python_objects`, then convert to Arrow" ]

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[ "`load_dataset` first checks for a local directory before checking for the Hub.\r\n\r\nTo make it explicit that it has to fetch the Hub, we could support the `hffs` syntax:\r\n```python\r\nload_dataset(\"hf://datasets/glue\")\r\n```\r\n\r\nwould that work for you ? Also cc @mariosasko who's leading the `hffs` project", "yeah, that would be a fine solution.", "This still has no proper solution in 2.11\r\n\r\nperhaps have a `download_config=\"force_remote\"` or just backtrack once you reach `EmptyDatasetError` locally and then try to load it from the hub (or a local cache, as that only gets checked if there is no local folder...?)" ]

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[ "Fixed. Please close." ]

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[ "Hi ! Datasets are memory mapped from your disk, i.e. they're not loaded in RAM. This is possible thanks to the Arrow data format.\r\n\r\nTherefore the column you remove is not in RAM, so removing it doesn't cause the RAM to decrease.", "Thanks for the explanation! @lhoestq \r\nI wonder since it is memory mapped, can we reduce or remove this memory map?", "Yes you can `del common_voice` for example or wait for it to be garbage collected" ]

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[ "@NielsRogge @rwightman may have additional requirements regarding this feature.\r\n\r\nWhen adding a new (decodable) type, the hardest part is choosing the right decoding library. What I mean by \"right\" here is that it has all the features we need and is easy to install (with GPU support?).\r\n\r\nSome candidates/options:\r\n* [`decord`](https://github.com/dmlc/decord): no longer [maintained](https://github.com/dmlc/decord/issues/214), not trivial to install with GPU support\r\n* [`pyAV`](https://github.com/PyAV-Org/PyAV): used for CPU decoding in `torchvision`, GPU decoding not supported if I'm not mistaken, otherwise the best candidate probably\r\n* [`video_reader`](https://github.com/pytorch/vision/blob/de350bc01ad2193ea2888f0ce8a6a346d3cba5a9/torchvision/csrc/io/video_reader/video_reader.cpp): used for GPU decoding in `torchvision`, depends on `torch'\r\n* OpenCV: uses `ffmpeg` for video decoding under the hood\r\n* ...\r\n\r\nAnd the last resort is building our own library, which is the most flexible solution but also requires the most work.\r\n\r\nPS: I'm adding a link to an article that compares various video decoding libraries: https://towardsdatascience.com/lightning-fast-video-reading-in-python-c1438771c4e6", "@mariosasko is GPU decoding a hard requirement here? Do we really need it? (I don't know)\r\n\r\nSomething to consider with `decord` is that it doesn't (AFAIK) support writing videos, so you'd still need something else for that. also I've noticed [issues](https://github.com/dmlc/decord/issues/242) with decord's ability to decode stereo audio streams along side the video (which you don't run into with PyAV).\r\n\r\n---\r\n\r\nI think PyAV should be able to do the job just fine to start. If we write the video io utilities as their own functions, we can hot swap them later if we find/write a different solution that's faster/better.", "Video is still a bit of a mess, but I'd say pyAV is likely the best approach (or supporting all three via pytorchvideo, but that adds a middle man dependency).\r\n\r\nBeing able to decode on the GPU, into memory that could be passed off to a Tensor in whatever framework is being used would be the dream, I don't think there is any interop of that nature working right now. Number of decoder instances per GPU is limited so it's not clear if balancing load btw GPU decoders and CPUs would be needed in say large scale video training.\r\n\r\nAny of these solutions is less than ideal due to the nature of video, having a simple Python interface video / start -> end results in lots of extra memory (you need to decode whole range of the clips into a buffer before using anything). Any scalable video system would be streaming on the fly (issuing frames via callbacks as soon as the stream is far enough along to have re-ordered the frames and synced audio+video+other metadata (sensors, CC, etc).\r\n\r\n", "For standalone usage, decoding on GPU could be ideal but isn't async processing of inputs on CPUs while letting the accelerator busy for training the de-facto? Of course, I am aware of other advanced mechanisms such as CPU offloading, but I think my point is conveyed. ", "Here's a minimal implementation of the helper functions we'd need from PyAV, a lot of which I borrowed from `pytorchvideo`, stripping out the `torch` specific stuff:\r\n\r\n[](https://colab.research.google.com/gist/nateraw/c327cb6ff6b074e6ddc8068d19c0367d/pyav-io.ipynb)\r\n \r\nIt's not too much code...@mariosasko we could probably just maintain these helper fns within the `datasets` library, right? ", "Also wanted to note I added a PR for video classification in `transformers` here, which uses `decord`. It's still open...should we make a decision now to align the libraries we are using between `datasets` and `transformers`? (CC @Narsil )\r\n\r\nhttps://github.com/huggingface/transformers/pull/20151", "Fully agree on at least trying to unite things.\r\n\r\nMaking clear function boundaries to help us change dependency if needed seems like a good idea since there doesn't seem to be a clear winner.\r\n\r\nI also happen to like directly calling ffmpeg. For some reason it was a lot faster than pyav. " ]

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[ "I just tried to do the same but changing the `.wav` files to `.mp3` files and that doesn't fix it.", "I don't know if anyone will ever read this but I've tried to upload the same dataset with google colab and the output seems more clarifying. I didn't specify the train/test split so the dataset wasn't fully uploaded (or that is what I understood, might be wrong!!).\r\n\r\nNow, including the `drop_metadata` flag I can load the dataset normally (at least with colab notebook):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"audiofolder\", data_dir=\"../archive/Dataset\", , drop_metadata=True)\r\n```\r\n\r\nI'll close the issue.", "@uriii3 Hello, I understand correctly that you converted your wav files to mp3?", "Yes but it didn't matter. I don't remember which of them I ended up working with." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5223). All of your documentation changes will be reflected on that endpoint.", "I think we may want more content on this page that's not SQL related. Some of that content probably already lives in the main `load` docs page, but might be bad to remove major things like csv/pandas from there...WDYT we should do @lhoestq ?", "Maybe the main load page can only show one example and redirect to this page for more details ?\r\n\r\nWe can do the same for pandas stuff: have one example in load, and redirect to this page for more details", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5223). All of your documentation changes will be reflected on that endpoint." ]

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[ "cc @McPatate maybe you know what's happening ?", "Yes I think I know what is happening. We check in zips for pickles, and the UI must display the pickle jar when a scan has an associated list of imports, even when empty.\r\n~I'll fix ASAP !~", "> I'll fix ASAP !\r\n\r\nActually I'd rather leave it like that for now, as it indicates that we checked for pickles and nothing dangerous appeared :)", "Closing the issue with the typical \"feature not a bug\" " ]

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[ "Did you run `huggingface-cli lfs-enable-largefiles` before committing or before adding ? Maybe you can try before adding\r\n\r\nAnyway I'd encourage you to split your data into several TAR archives if possible, this way the dataset can loaded faster using multiprocessing (by giving each process a subset of shards to process)", "@lhoestq \r\nThanks for the help!\r\n> Maybe you can try before adding\r\n\r\nIt did not help\r\n\r\nBut I totally got your point about split into multiple TAR archives. It really helped!" ]

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[ "I think this behavior comes from PyArrow:\r\n```python\r\nimport pyarrow as pa\r\nt = pa.table({\"a\": [[0]]})\r\nt.to_pandas().a.values[0]\r\n# array([0])\r\n```\r\n\r\nI believe this has to do with zero-copy: you can get a pandas DataFrame without copying the buffers from arrow, and therefore end up with numpy arrays.", "That's interesting, I guess not much to do here then." ]

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[ "Hi ! Interesting :) Can you provide concrete examples of cases where it can be useful ?", "Few example blogs and posts that might help on this - \r\n\r\n1. https://hevodata.com/learn/databricks-delta-tables/\r\n2. https://docs.databricks.com/delta/index.html\r\n\r\nBasically, we are looking at utility of Datasets library with Delta Lake Tables.\r\n", "`datasets` can already read/write from parquet from/to a cloud storage using fsspec, if I understand correctly it's should be possible to load parquet files as delat lake tables no ? :) Or is there someting missing ?", "@lhoestq Per my understanding, delta lake table is a bunch of paruqet files together with the meta to support ACID. For example file 1 contains v0.1 of record A while file 2 contains v0.2 of record A. I am assuming the Hugging face dataset would delegate the read/write delta table to 3rd party lib, maybe pyarrow. Correct me if I was wrong @reichenbch \r\n\r\nAnd I am assuming, people are asking the versioning of Hugging face datasets. But I am assuming Hugging face delegate this function to github and it is not the key requirement for Public Data set. It actually the key function of ML Ops, I am not sure whether hugging face would like expand to that area." ]

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

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[ "Hi ! I think there exist tools to dump and reload an index in your elastic search but I'm not super familiar with it.\r\n\r\nAnyway after reloading an index in elastic search you can call `ds.load_elasticsearch_index` which will connect the index to the dataset without re-indexing" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5214). 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._", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5213). All of your documentation changes will be reflected on that endpoint.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5213). All of your documentation changes will be reflected on that endpoint.", "Nice thanks !\r\n\r\nWould it be possible to have the new folders at the same level as \"data\" ? This way they're all separated\r\n```\r\n├─ config-v1/\r\n│ ├── train-00000-00002-...-.parquet\r\n│ └── train-00001-00002-...-.parquet\r\n└ config-v2/\r\n ├── train-00000-00002-...-.parquet\r\n └── train-00001-00002-...-.parquet\r\n```\r\nand if you don't provide a config name, it goes in a folder named \"default\" instead, that would be loaded by default.\r\n\r\nWe could also write in the YAML something like\r\n```yaml\r\nconfigs:\r\n- name: config-v1\r\n data_dir: config-v1\r\n- name: config-v2\r\n data_dir: config-v2\r\n```\r\nand loading `config-v1` would be equivalent to run `load_dataset(ds_name, \"config-v1\", data_dir=\"config-v1\")`\r\n\r\nDo you think it would make sense ?\r\n\r\nFor backward compatibility we can just keep the \"data/*\" pattern. It's ok to expect users to have an updated version of `datasets` to be able to load datasets with configurations.", "@lhoestq thank you for the feedback! i'll reflect on this on Moday, my mind just melted because of the fever.\r\n\r\n@mariosasko @albertvillanova what do you think?", "Thanks for addressing this, @polinaeterna. It is good:\r\n- we support configs for datasets without scripts\r\n- we align the behavior to datasets with scripts as much as possible\r\n\r\nMaybe adding some tests will help clarify what is the expected behavior...", "After some discussion with @lhoestq we decided that it's better to rely on metadata file than on data files patterns. \r\n\r\nSo we decided to introduce a new field to yaml (like `configs` or smth like that) that would contain arbitrary configs kwargs to be passed to loader, including `data_dir` and `data_files`. \r\nThis is more aligned with datasets with custom scripts where we explicitly write all the supported configs and config parameters in the code and is extendable to all packaged modules.\r\nThis would solve https://github.com/huggingface/datasets/issues/5209\r\n\r\n(@lhoestq was right 21 days ago, this is a more general solution idk why i ignored this...)", "closed in favor of https://github.com/huggingface/datasets/pull/5331" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5212). 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._", "WDYT @albertvillanova ?", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5211). 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._", "Nit: We should also update the `Disclaimers` section to let the dataset owners know they should use Hub discussions rather than GH issues for removal requests/updates", "Updated the disclaimers section, thanks !\r\n\r\nDoes it sound good to you @albertvillanova ?" ]

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[ "@merveenoyan Do you want different files to be splits or configurations?\r\n\r\nFrom [what you specified in `Readme.md`](https://huggingface.co/datasets/inria-soda/tabular-benchmark/commit/fb4575853772c62a20203bdd6cc0202f5db4ce4e) I hypothesize that you want to have 4 **configs** corresponding to directories: `\"clf_cat\", \"clf_num\", \"reg_cat\", \"reg_num\"`. And inside each config you require to have as many splits as there are `csv` files\r\nso if you run \r\n```python\r\nload_dataset(\"inria-soda/tabular-benchmark\", \"clf_cat\", split=\"compass\")\r\n```\r\nyou will generate the data only from `compass.csv` file.\r\nIn this case, running `load_dataset(\"inria-soda/tabular-benchmark\", \"clf_cat\"`) without split parameter will return `DatasetDict` object with `\"KDDCup09_upselling\", \"cat_compass\", \"cat_covertype\", ... \"road_safety\"` keys (which values are splits - `Dataset` objects)\r\n\r\n**or**\r\ndo you want each file to be a separate config? Like:\r\n```python\r\nload_dataset(\"inria-soda/tabular-benchmark\", \"clf_cat_compass\") # returns DatasetDict with a single \"train\" split\r\n```\r\n**or**\r\nmaybe smth completely different? :smile: \r\n\r\nAnyway, now I have an impression that this is probably rather a matter of automatically inferring configs from repository structure rather than providing parameters in metadata yaml.\r\n", "@polinaeterna I want the latter where you can think of every CSV file as a config, like MNLI from GLUE.", "@merveenoyan @lhoestq I see two solutions to this case. \r\n1. Parse configurations automatically from directories names. That is, if you have data structure like:\r\n```\r\ntabular-benchmark\r\n └─clf_cat_compass\r\n └─compass.csv\r\n └─clf_cat_cat_covertype\r\n └─covertype.csv\r\n ...\r\n └─reg_cat_house_sales\r\n └─house_sales.csv\r\n```\r\nyou'll get \"clf_cat_compass\", \"clf_cat_cat_covertype\", ... \"reg_cat_house_sales\" configurations that would contain **only files from corresponding directories**. \r\n**\\+** this is a requested change and needed in general and would solve other problems, see https://github.com/huggingface/datasets/issues/4578, would also help with https://github.com/huggingface/datasets/pull/5213 which I'm working on currently\r\n**\\+** would allow users to do just `load_dataset(“inria-soda/tabular-benchmark”, “clf_cat_compass”)`, no `data_files` param required\r\n**\\-** in this specific case it would require restructuring of the data - putting each file in a directory named as a config name (to me personally it doesn't seem to be a big deal) \r\n\r\n2. More or less what we discussed before - add support for manually specifying parameters in the metadata. We can add new metadata yaml field (say, `\"custom_configs_info\"`), so that we can provide smth like:\r\n```yaml\r\n---\r\n...\r\ndataset_info:\r\n ... \r\ncustom_configs_info:\r\n- config_name: reg_cat_house_sales\r\n data_files:\r\n - reg_cat/house_sales.csv\r\n- config_name: clf_cat_compass\r\n data_files:\r\n - clf_cat/compass.csv\r\n...\r\n---\r\n```\r\n**\\+** Would be useful not only for tabular data and not only for `data_files` parameter - any packaged dataset’s viewer can be customized to use specific, non-default parameters. @merveenoyan do you maybe have any other examples/use cases in mind where you want to provide any specific parameters to the viewer? \r\n**\\-** I'm not sure here but assume that it might require changes in interaction with the viewer on the hub side - to parse these configurations, as they not default configurations (not in `BUILDER_CONFIGS` list). cc @severo But probably this can be solved on the `datasets` side too.\r\n\r\nOverall, I would start from implementing the first solution since it's related to what I'm doing now and is super useful for `datasets` in general. And then if we agree that having more flexibility in providing parameters to the viewer is required, I can implement the second one. Let me know what you think :) ", "> We can add new metadata yaml field (say, \"custom_configs_info\"), so that we can provide smth like:\r\n\r\nLove it ! Some other ideas to name the \"custom_configs_info\" field: \"configs\", \"parameters\", \"config_args\", \"configurations\"\r\n\r\n> it might require changes in interaction with the viewer on the hub side - to parse these configurations, as they not default configurations (not in BUILDER_CONFIGS list)\r\n\r\nIf we update the `get_dataset_config_names()` function in `datasets` in inspect.py we should be fine - that's what the viewer is using\r\n\r\n> Overall, I would start from implementing the first solution since it's related to what I'm doing now and is super useful for datasets in general. And then if we agree that having more flexibility in providing parameters to the viewer is required, I can implement the second one. Let me know what you think :)\r\n\r\nActually I feel like the second solution includes the first use case you mentioned. If you implement the second solution, then users would just have to add a few lines of YAML and their directories would be considered configurations no ? Maybe there's no need to implement two different logics to do the same thing", "is there any update on this? 🕵🏻", "@merveenoyan I haven't started working on this yet, working on adding configs to packaged datasets instead: https://github.com/huggingface/datasets/pull/5213 because this both would allow you to solve your issue and is a frequently requested feature.\r\n\r\nadding arbitrary parameters to yaml would be my next task i think!", "@merveenoyan ignore my comment above, I'm switching to this task now :D", "I want to be able to create folders in a model.", "Addressed in #5331 " ]

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

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[ "Hi ! It looks like an issue with your python environment, can you make sure you're able to run GET requests to https://huggingface.co using `requests` in python ?", "Thanks for your reply. Does this mean that I have to use the `do_dataset `function and the `requests `function to download the dataset from the company's proxy environment?\r\n\r\n\r\n* Reference: \r\n```bash\r\n### How to load this dataset directly with the [datasets](https://github.com/huggingface/datasets) library\r\n\r\n\r\n* https://huggingface.co/datasets/moyix/debian_csrc\r\n\r\n* from datasets import load_dataset\r\ndataset = load_dataset(\"moyix/debian_csrc\")\r\n\r\n\r\n\r\n### Or just clone the dataset repo\r\n\r\n\r\ngit lfs install\r\ngit clone https://huggingface.co/datasets/moyix/debian_csrc\r\n# if you want to clone without large files – just their pointers\r\n# prepend your git clone with the following env var:\r\nGIT_LFS_SKIP_SMUDGE=1\r\n```", "You can use `requests` to see if downloading a file from the Hugging Face Hub works. If so, then `datasets` should work as well. If not, then you have to find another way using an internet connection that works", "I resolved this issue by applying to \"unblock websites\" at https://huggingface.com in a corporate network environment with a firewall. \r\n" ]

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[ "Actually upon closer inspection, it is documented in the code that this behavior is intentional, so I'll close this." ]

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

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[ "#self-assign", "@lhoestq can you close this issue as part of the recent #5205 merge? Thanks 🤗 ", "Thank you :)" ]

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

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[ "Fixed by: https://huggingface.co/datasets/paws/discussions/1" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "It would be coherent with https://github.com/huggingface/datasets-server/issues/614#issuecomment-1290534153", "I think we started working on this issue nearly at the same time... :sweat_smile: \r\n- CI was fixed with this: https://huggingface.co/datasets/paws/discussions/1\r\n\r\nRelated issue:\r\n- #5202", "@albertvillanova yeah I noticed it right after the PR :smile: thank you! the fix of the dataset info yaml fixes tests on CI, but in general order of splits in yaml influences the order in which they are displayed in the viewer, if I understand it correctly. So I suggest not to sort splits in yaml initially to avoid this for other datasets in the future. I think [this change](https://github.com/huggingface/datasets/pull/5201/files#diff-198ba4fdf2f94cb3e1aba8a0170a43b08d4ab5636d682374321c5a383a8be24dR571) should work for it. \r\n\r\nChanges to tests here maybe can be reverted considering that order in yaml now corresponds to the one in tests, thanks to your change in the dataset info.", "Hehe, @polinaeterna, we make comments nearly at the same time as well... :laughing: " ]

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[ "Thanks for catching this, I can go through the docs and replace the links to their corresponding datasets on the Hub!" ]

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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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[ "@albertvillanova Please take a review.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5197). 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_5196). All of your documentation changes will be reflected on that endpoint.", "@lhoestq I think we should first agree if `datasets` can introduce the breaking change of ignoring `config.HUB_DATASETS_URL`: some users may have override this.\r\n\r\nIf so, I then would suggest to initiate a deprecation cycle.", "After a discussion with the rest of the datasets team, we agreed we can introduce the breaking change of ignoring `config.HUB_DATASETS_URL`: this will have minimal impact, only for **private Hubs**. We will address eventual possible impacts in the future.\r\n\r\nAdditionally, we also ignore `config.HUB_DEFAULT_VERSION`.\r\n\r\nSee explanation in this PR description: https://github.com/huggingface/datasets/pull/5196#issue-1434401646", "I'm trying to upgrade datasets to 2.7.0 in https://github.com/huggingface/datasets-server, and the tests fail due to this change. I think it's a breaking change (that was not listed in https://github.com/huggingface/datasets/releases/tag/2.7.0) since code that previously worked (by setting `datasets.config.HUB_DATASETS_URL = CI_HUB_DATASETS_URL` for example) does not work anymore.\r\n\r\nI'm not sure what is the correct way to set up the tests; besides setting the env var \"HF_ENDPOINT\" before launching the tests (which, I think, is not a good way to do: the tests should not depend on the environment).", "OK, I re-read this thread, and https://github.com/huggingface/datasets/pull/5196#issuecomment-1307430175 explicitely states that `config.HUB_DATASETS_URL` (as well as `config.HUB_DEFAULT_VERSION`) is now ignored. I was expecting the breaking changes to be listed in the release notes: https://github.com/huggingface/datasets/releases/tag/2.7.0.", "> I'm not sure what is the correct way to set up the tests; besides setting the env var \"HF_ENDPOINT\" before launching the tests (which, I think, is not a good way to do: the tests should not depend on the environment).\r\n\r\nI think the current workaround of settings an env variable before launching the tests is \"not so bad\" when considering the fact that env variables are evaluated at import time in `huggingface_hub` (and most probable `datasets` as well). I think that when refactoring this in huggingface_hub (https://github.com/huggingface/huggingface_hub/issues/1172) I'll opt for instantiating a `Settings` object (or `Constants`) that contains all the settings variables. This way it will not be possible to import attributes individually + tests would be easier. As I see it, it would be similar to [what `Pydantic` does](https://pydantic-docs.helpmanual.io/usage/settings/) even though we most probably don't want Pydantic as a root dependency just for that. ", "You can use fixtures in your tests:\r\n```python\r\nCI_HUB_ENDPOINT = \"https://hub-ci.huggingface.co\"\r\nCI_HUB_DATASETS_URL = CI_HUB_ENDPOINT + \"/datasets/{repo_id}/resolve/{revision}/{path}\"\r\nCI_HFH_HUGGINGFACE_CO_URL_TEMPLATE = CI_HUB_ENDPOINT + \"/{repo_id}/resolve/{revision}/{filename}\"\r\n\r\n@pytest.fixture\r\ndef ci_hfh_hf_hub_url(monkeypatch):\r\n monkeypatch.setattr(\r\n \"huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE\", CI_HFH_HUGGINGFACE_CO_URL_TEMPLATE\r\n )\r\n\r\n@pytest.fixture\r\ndef ci_hub_config(monkeypatch):\r\n monkeypatch.setattr(\"datasets.config.HF_ENDPOINT\", CI_HUB_ENDPOINT)\r\n monkeypatch.setattr(\"datasets.config.HUB_DATASETS_URL\", CI_HUB_DATASETS_URL)\r\n```\r\n\r\nand use `@pytest.fixture(autouse=True)` if you want to always use the CI endpoints.\r\n\r\nAnd when `huggingface-hub` and `datasets` change the way we can set the endpoint, we'll just need to update the fixtures.\r\nI think ultimately you'll only have to change the `huggingface-hub` endpoint settings\r\n", "OK.\r\n\r\nIn fact, in datasets-server we set `config.HUB_DATASETS_URL` (https://github.com/huggingface/datasets-server/blob/35a30dbcd687b26db1f02502ea8305f70c064473/workers/splits/src/splits/config.py#L26) at config time, before starting the workers. It's not an issue with how to launch the tests, but with the app in itself.\r\n\r\nI understand that for now, the only way to fix this is to setup `HF_ENDPOINT` in the env when launching the app (currently, we set the endpoint with `COMMON_HF_ENDPOINT`, a custom env var I set to be sure not to have side-effects)", "> You can use fixtures in your tests:\r\n\r\nThanks, used in https://github.com/huggingface/datasets-server/pull/644." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5195). 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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[ "Also unrelated but still: https://huggingface.co/docs/datasets/image_dataset#generate-the-dataset\r\n```If your loading script passed the test, you should now have a dataset_infos.json file in your dataset folder.```\r\nIt's not the case anymore as it's now in the readme.md, it was confusing to me", "And here is my data loader script: https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data/blob/main/SDH_16k.py\r\nI have one file archive to download that contains the images for all splits and one `metadata.jsonl` to download that contains the informations about what image goes into what split.", "Hi @lambda-science! It seems that your repo is recognized as a packaged module [ImageFolder](https://huggingface.co/docs/datasets/main/en/image_dataset#imagefolder), not as a dataset with the custom loading script, because loader looks for a script that has the same name as the dataset repo. So please try to rename your script to `MyoQuant-SDH-Data.py`, this should help.", "> Hi @lambda-science! It seems that your repo is recognized as a packaged module [ImageFolder](https://huggingface.co/docs/datasets/main/en/image_dataset#imagefolder), not as a dataset with the custom loading script, because loader looks for a script that has the same name as the dataset repo. So please try to rename your script to `MyoQuant-SDH-Data.py`, this should help.\r\n\r\nHi !\r\n\r\nThank you for your answer. That was... embarrassingly easy, sorry for this issue, everything is fixed now ! \r\n\r\nHave a nice day ! :)", "@lambda-science that's not embarrassing at all! it's actually not clear from the documentation that the script should have the same name, so thank you for the issue, we'll add this information to the docs :) " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5192). All of your documentation changes will be reflected on that endpoint.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5192). All of your documentation changes will be reflected on that endpoint.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5192). All of your documentation changes will be reflected on that endpoint.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5192). All of your documentation changes will be reflected on that endpoint.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5192). All of your documentation changes will be reflected on that endpoint.", "> Nit: maybe we can use the count_path_segments function from this file for counting (updated with your logic to make it faster).\r\n\r\n@mariosasko just to make sure I understood you correctly - are you okay with this change? (actually `os.path.normpath` is redundant here as paths from `data_files` should be already normalized but just in case)\r\nhttps://github.com/huggingface/datasets/pull/5192/files#diff-1f09f7a178211f7539b1499b64b69793bd53b30c8b7b34cfcc5835e25d31929fR33\r\nIf you are, we can merge.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5192). All of your documentation changes will be reflected on that endpoint.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5192). All of your documentation changes will be reflected on that endpoint.", "awesome ! :D" ]

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

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