Datasets:

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Francesco-A

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github-issues_huggingface-datasets

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[ "Thanks for the report, can reproduce. Will fix", "Fixed now @ogabrielluiz " ]

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[ "Looks good to me as well but we'll want to add a test indeed.\r\nYou can add one if you have time @TevenLeScao.\r\nOtherwise, we'll do it when we are back with Quentin. ", "I fixed it in #494 " ]

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[ "For this to be possible, we would have to switch from the \"Streaming\" Arrow format to the \"Random Access\" (IPC/Feather) format, which allows reading arbitrary record batches (explained [here](https://arrow.apache.org/docs/python/ipc.html)). We could then use these batches to construct shards.\r\n\r\n@lhoestq @albertvillanova Do you think this use case is worth the switch? Also, we currently shard files, not inner row groups/chunks. Should we also support sharding row groups (e.g. if the number of input files is 1)?\r\n\r\nPS: I don't expect significant speed-up for local, uncompressed Arrow files.", "Alternatively we could support multiprocessing map for iterable datasets and let the user do the CPU intensive task there ?\r\n\r\nThis way it would work on arrow data but also on any iterable dataset", "> For this to be possible, we would have to switch from the \"Streaming\" Arrow format to the \"Random Access\" (IPC/Feather) format, which allows reading arbitrary record batches (explained [here](https://arrow.apache.org/docs/python/ipc.html)). We could then use these batches to construct shards.\r\n> \r\n> @lhoestq @albertvillanova Do you think this use case is worth the switch? Also, we currently shard files, not inner row groups/chunks. Should we also support sharding row groups (e.g. if the number of input files is 1)?\r\n> \r\n> PS: I don't expect significant speed-up for local, uncompressed Arrow files.\r\n\r\nCould you explain why you'd need to change the arrow format?\r\n\r\nWhen we use streaming datasets we simply determine the number of worker shards and then add some modulo logic at the appropriate place. Worst case scenario, you'd skip streaming entries according to the number of shards.\r\n\r\nFor PyTorch, I'd be happy to provide an implementation or a sketch thereof, if you point me toward what the testing requirements would be for such a PR.", "> Could you explain why you'd need to change the arrow format?\r\n\r\nThis way workers have random access to the location of the file where its dataset subset starts. Currently we're using the Arrow streaming format which doesn't include the metadata of the record batches offsets. This is needed here to efficiently split a dataset made of one single file.", "> > Could you explain why you'd need to change the arrow format?\r\n> \r\n> This way workers have random access to the location of the file where its dataset subset starts. Currently we're using the Arrow streaming format which doesn't include the metadata of the record batches offsets. This is needed here to efficiently split a dataset made of one single file.\r\n\r\nI guess I don't understand why you'd need to subset the dataset in the first place. \r\nIt seems sufficient to figure out how to offset or skip rows.\r\n\r\nFor instance, using pyArrow, you could use RecordBatchStreamReader to zero-copy iterate over records with read_next_batch and then only initiate the next step for records modulo worker shard.\r\nThat's one way to do it, where of course you'd need to account for gpu sharding as well.\r\n\r\n\r\nOtherwise, how did you implement worker/node/GPU sharding for iterable/streaming data where you do not have index information or prior splits (e.g. files)?", "> For instance, using pyArrow, you could use RecordBatchStreamReader to zero-copy iterate over records with read_next_batch and then only initiate the next step for records modulo worker shard.\r\n\r\nThat works indeed ! And what we meant is that you can make it even faster to instantiate. Indeed using RecordBatchStreamReader you need to get the list of all the record batches in each worker, whereas you could just get the list of record batches per worker if you use the record batches locations in the Arrow IPC file footer. This would be especially appreciated to have a fast instantiation in case you have tens of thousands of Arrow files for example." ]

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[ "Hi! \r\n\r\nIt's better to report this kind of issue in the `huggingface_hub` repo, so if you still haven't resolved it, I suggest you open an issue there.", "Yeah, I solved it. Problem was in osxkeychain. When I do `hugginface-cli login` it's add token with default account (username)`hg_user` but my repo contain other username. When I changed username in keychain - it works now." ]

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[ "This looks like an issue with PyArrow. Did you try reinstalling it ?", "@lhoestq I did, and then let pip handle the installation in `pip import datasets`. I also tried using conda but it gives the same error.\r\n\r\nEdit: pyarrow version on kaggle is 4.0.0, it gets replaced with 4.0.1. So, I don't think uninstalling will change anything.\r\n```\r\nInstall Trace of datasets:\r\n\r\nCollecting datasets\r\n Downloading datasets-1.9.0-py3-none-any.whl (262 kB)\r\n |████████████████████████████████| 262 kB 834 kB/s eta 0:00:01\r\nRequirement already satisfied: dill in /opt/conda/lib/python3.7/site-packages (from datasets) (0.3.4)\r\nCollecting pyarrow!=4.0.0,>=1.0.0\r\n Downloading pyarrow-4.0.1-cp37-cp37m-manylinux2014_x86_64.whl (21.8 MB)\r\n |████████████████████████████████| 21.8 MB 6.2 MB/s eta 0:00:01\r\nRequirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from datasets) (3.4.0)\r\nRequirement already satisfied: huggingface-hub<0.1.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (0.0.8)\r\nRequirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from datasets) (1.2.4)\r\nRequirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (2.25.1)\r\nRequirement already satisfied: fsspec>=2021.05.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (2021.6.1)\r\nRequirement already satisfied: multiprocess in /opt/conda/lib/python3.7/site-packages (from datasets) (0.70.12.2)\r\nRequirement already satisfied: packaging in /opt/conda/lib/python3.7/site-packages (from datasets) (20.9)\r\nCollecting xxhash\r\n Downloading xxhash-2.0.2-cp37-cp37m-manylinux2010_x86_64.whl (243 kB)\r\n |████████████████████████████████| 243 kB 23.7 MB/s eta 0:00:01\r\nRequirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.7/site-packages (from datasets) (1.19.5)\r\nRequirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.7/site-packages (from datasets) (4.61.1)\r\nRequirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from huggingface-hub<0.1.0->datasets) (3.0.12)\r\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (1.26.5)\r\nRequirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (2.10)\r\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (2021.5.30)\r\nRequirement already satisfied: chardet<5,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (4.0.0)\r\nRequirement already satisfied: typing-extensions>=3.6.4 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->datasets) (3.7.4.3)\r\nRequirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->datasets) (3.4.1)\r\nRequirement already satisfied: pyparsing>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging->datasets) (2.4.7)\r\nRequirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas->datasets) (2.8.1)\r\nRequirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas->datasets) (2021.1)\r\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\r\nInstalling collected packages: xxhash, pyarrow, datasets\r\n Attempting uninstall: pyarrow\r\n Found existing installation: pyarrow 4.0.0\r\n Uninstalling pyarrow-4.0.0:\r\n Successfully uninstalled pyarrow-4.0.0\r\nSuccessfully installed datasets-1.9.0 pyarrow-4.0.1 xxhash-2.0.2\r\nWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\r\n```", "You may need to restart your kaggle notebook after installing a newer version of `pyarrow`.\r\n\r\nIf it doesn't work we'll probably have to create an issue on [arrow's JIRA](https://issues.apache.org/jira/projects/ARROW/issues/), and maybe ask kaggle why it could fail", "> You may need to restart your kaggle notebook before after installing a newer version of `pyarrow`.\r\n> \r\n> If it doesn't work we'll probably have to create an issue on [arrow's JIRA](https://issues.apache.org/jira/projects/ARROW/issues/), and maybe ask kaggle why it could fail\r\n\r\nIt works after restarting.\r\nMy bad, I forgot to restart the notebook. Sorry for the trouble!" ]

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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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[ "Thanks @lhoestq for your remarks. Yes, I agree there are still many issues to be tackled... This PR is just a starting point, so that we can discuss how Dataset should be generalized.", "Sure ! I opened an issue #1877 so we can discuss this specific aspect :)", "I am going to implement this consolidation step in #2151.", "Sounds good !", "I retake this PR once the consolidation step is already implemented by #2151." ]

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[ "I ping @krandiash to keep him up to date.", "TODO:\r\n- [x] Add a section in the docs about this.\r\n- ~Add a warning if someone tries to specify `cache_file_name=` in `map`, `filter` etc. on a dataset that is in memory, since the computation is not going to be cached in this case.~", "@lhoestq I have a question, regarding:\r\n> Also maybe we should add a warning if someone tries to specify cache_file_name= in map, filter etc. on a dataset that is in memory, since the computation is not going to be cached in this case.\r\n\r\n- It might be the case that the user has an in-memory dataset and might want to use `map` and cache it, by passing `cache_file_name=`\r\n- This is indeed allowed by the library and works as expected: the dataset is cached.\r\n\r\nWhy adding a warning?", "Yes right, I meant if `load_from_cache_file` is set to True and `cache_file_name ` is None. my bad :p" ]

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[ "The problem appears to stem from the omission of the lines that you mentioned. If you add them back and try examples from [this](https://huggingface.co/docs/datasets/using_metrics.html) tutorial (sacrebleu metric example) the code you implemented works fine.\r\n\r\nI think the purpose of these lines is follows:\r\n\r\n1. Sacrebleu metrics confusingly expect a nested list of strings when you have just one reference for each hypothesis (i.e. `[[\"example1\", \"example2\", \"example3]]`), while for cases with more than one reference a _nested list of lists of strings_ (i.e. `[[\"ref1a\", \"ref1b\"], [\"ref2a\", \"ref2b\"], [\"ref3a\", \"ref3b\"]]`) is expected instead. So `transformed_references` line outputs the required single reference format for sacrebleu's ter implementation which you can't pass directly to `compute`.\r\n2. I'm assuming that an additional check is also related to that confusing format with one/many references, because it's really difficult to tell what exactly you're doing wrong if you're not aware of that issue." ]

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[ "I renamed the variable, added a test for dataset._indices and fixed an issue with class_encode_column" ]

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[ "I am trying to use `load_dataset` with the French dataset(common voice corpus 1) which is downloaded from a common voice site and the target language is English (using colab)\r\n\r\nSteps I have followed:\r\n\r\n**1. untar:**\r\n`!tar xvzf fr.tar -C data_dir`\r\n\r\n**2. load data:**\r\n`load_dataset('covost2', 'fr_en', data_dir=\"/content/data_dir\")`\r\n\r\n0 rows are loading as shown below:\r\n```\r\nUsing custom data configuration fr_en-data_dir=%2Fcontent%2Fdata_dir\r\nReusing dataset covost2 (/root/.cache/huggingface/datasets/covost2/fr_en-data_dir=%2Fcontent%2Fdata_dir/1.0.0/bba950aae1ffa5a14b876b7e09c17b44de2c3cf60e7bd5d459640beffc78e35b)\r\n100%\r\n3/3 [00:00<00:00, 54.98it/s]\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['client_id', 'file', 'audio', 'sentence', 'translation', 'id'],\r\n num_rows: 0\r\n })\r\n validation: Dataset({\r\n features: ['client_id', 'file', 'audio', 'sentence', 'translation', 'id'],\r\n num_rows: 0\r\n })\r\n test: Dataset({\r\n features: ['client_id', 'file', 'audio', 'sentence', 'translation', 'id'],\r\n num_rows: 0\r\n })\r\n})\r\n```\r\n\r\nCan you please provide a sample working example code to load the dataset?", "Hi ! I think it only works with the subsets of Common Voice Corpus 4, not Common Voice Corpus 1" ]

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[ "Could you share the error you got please ? Maybe the full stack trace if you have it ?\r\n\r\nMaybe `push_to_hub` be implemented as a single commit @Wauplin ? This way if it fails, the repo is still at the previous (valid) state instead of ending-up in an invalid/incimplete state.", "> Maybe push_to_hub be implemented as a single commit ? \r\n\r\nI think that would definitely be the way to go. Do you know the reasons why not implementing it like this in the first place ? I guess it is because of not been able to upload all at once with `huggingface_hub` but if there was another reason, please let me know.\r\nAbout pushing all at once, it seems to be a more and more requested feature. I have created this issue https://github.com/huggingface/huggingface_hub/issues/1085 recently but other discussions already happened in the past. The `moon-landing` team is working on it (cc @coyotte508). The `huggingface_hub` integration will come afterwards.\r\n\r\nFor now, maybe it's best to wait for a proper implementation instead of creating a temporary workaround :)\r\n", "> I think that would definitely be the way to go. Do you know the reasons why not implementing it like this in the first place ? I guess it is because of not been able to upload all at once with huggingface_hub but if there was another reason, please let me know.\r\n\r\nIdeally we would want to upload the files iteratively - and then once everything is uploaded we proceed to commit. When we implemented `push_to_hub`, using `upload_file` for each shard was the only option.\r\n\r\nFor more context: for each shard to upload we do:\r\n1. load the arrow shard in memory\r\n2. convert to parquet\r\n3. upload\r\n\r\nSo to avoid OOM we need to upload the files iteratively.\r\n\r\n> For now, maybe it's best to wait for a proper implementation instead of creating a temporary workaround :)\r\n\r\nLet us know if we can help !", "> Ideally we would want to upload the files iteratively - and then once everything is uploaded we proceed to commit. \r\n\r\nOh I see. So maybe this has to be done in an implementation specific to `datasets/` as it is not a very common case (upload a bunch of files on the fly).\r\n\r\nYou can maybe have a look at how `huggingface_hub` is implemented for LFS files (arrow shards are LFS anyway, right?).\r\nIn [`upload_lfs_files`](https://github.com/huggingface/huggingface_hub/blob/e28646c977fc9304a4c3576ce61ff07f9778950b/src/huggingface_hub/_commit_api.py#L164) LFS files are uploaded 1 by 1 (multithreaded) and then [the commit is pushed](https://github.com/huggingface/huggingface_hub/blob/e28646c977fc9304a4c3576ce61ff07f9778950b/src/huggingface_hub/hf_api.py#L1926) to the Hub once all files have been uploaded. This is pretty much what you need, right ?\r\n\r\nI can help you if you have questions how to do it in `datasets`. If that makes sense we could then move the implementation from `datasets` to `huggingface_hub` once it's mature. Next week I'm on holidays but feel free to start without my input.\r\n\r\n(also cc @coyotte508 and @SBrandeis who implemented LFS upload in `hfh`)", "> Could you share the error you got please ? Maybe the full stack trace if you have it ?\r\n\r\nHere’s part of the stack trace, that I can reproduce at the moment from a photo I took (potential typos from OCR):\r\n```\r\nValueError\r\nTraceback (most recent call last)\r\n<ipython-input-4-274613b7d3f5> in <module>\r\nfrom datasets import load dataset\r\nds = load_dataset('jrahn/chessv6', use_auth_token-True)\r\n\r\n/us/local/1ib/python3.7/dist-packages/datasets/table.py in cast_table _to_schema (table, schema)\r\nLine 2005 raise ValueError()\r\n\r\nValueError: Couldn't cast \r\nfen: string \r\nmove: string \r\nres: string \r\neco: string \r\nmove_id: int64\r\nres_num: int64 to\r\n{ 'fen': Value(dtype='string', id=None), \r\n'move': Value(dtype=' string', id=None),\r\n'res': Value(dtype='string', id=None),\r\n'eco': Value(dtype='string', id=None), \r\n'hc': Value(dtype='string', id=None), \r\n'move_ id': Value(dtype='int64', id=None),\r\n'res_num': Value(dtype= 'int64' , id=None) }\r\nbecause column names don't match \r\n```\r\n\r\nThe column 'hc' was removed before the interrupted push_to_hub(). It appears in the column list in curly brackets but not in the column list above.\r\n\r\nLet me know, if I can be of any help." ]

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[ "Hi @JulesBelveze, thanks for your question.\r\n\r\nNote that 🤗 `datasets` internally store their data in Apache Arrow format.\r\n\r\nHowever, when accessing dataset columns, by default they are returned as native Python objects (lists in this case).\r\n\r\nIf you would like their columns to be returned in a more suitable format for your use case (torch arrays), you can use the method `set_format()`:\r\n```python\r\nconcat_dataset.set_format(type=\"torch\")\r\n```\r\n\r\nYou have detailed information in our docs:\r\n- [Using a Dataset with PyTorch/Tensorflow](https://huggingface.co/docs/datasets/torch_tensorflow.html)\r\n- [Dataset.set_format()](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.set_format)", "Thanks @albertvillanova it indeed did the job 😃 \r\nThanks for your answer!" ]

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[ "I'll take a look!" ]

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[ "Version 2.3.2 is over one year old, so please use the latest release (2.14.0) to get the expected behavior. Version 2.3.2 does not contain some fixes we made to fix resolving hidden files/directories (starting with a dot)." ]

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[ "> Ok, so we're getting rid of the `FaissGpuOptions`?\r\n\r\nWe support `device=...` because it's simple, but faiss GPU options can be used in so many ways (you can set different gpu options for the different parts of your index for example) that it's probably better to let the user create and configure its index and then use `custom_index=...`" ]

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[ "Hi! This code works for me locally or in Colab. What's the output of `python -c \"import pyarrow as pa; print(pa.__version__)\"` when you run it inside your environment?", "Thanks for looking into this!\r\nThe output of `python -c \"import pyarrow as pa; print(pa.__version__)\"` is:\r\n```\r\n11.0.0\r\n```\r\n\r\nI did the following to setup the environment:\r\n```\r\nconda create -n datasets_debug python=3.9\r\nconda activate datasets_debug\r\npip install datasets==2.9.0\r\n```\r\n\r\nI just tested this on another machine (Ubuntu 18.04.6 LTS) with the same result as mentioned in the issue description.\r\n" ]

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[ "Thanks for reporting !\r\n\r\nYou're right, `string_to_arrow` should be able to take `\"timestamp[ns]\"` as input and return the right pyarrow timestamp type.\r\nFeel free to suggest a fix for `string_to_arrow` and open a PR if you want to contribute ! This would be very appreciated :)\r\n\r\nTo give you more context:\r\n\r\nAs you may know we define the features types of a dataset using the `Features` object in combination with feature types like `Value`. For example\r\n```python\r\nfeatures = Features({\r\n \"age\": Value(\"int32\")\r\n})\r\n```\r\nHowever under the hood we are actually using pyarrow to store the data, and so we have a mapping between the feature types of `datasets` and the types of pyarrow.\r\n\r\nFor example, the `Value` feature types are created from a pyarrow type with `Value(str(pa_type))`.\r\nHowever it looks like the conversion back to a pyarrow type doesn't work with `\"timestamp[ns]\"`.\r\nThis is the `string_to_arrow` function you highlighted that does this conversion, so we should fix that.\r\n\r\n", "Thanks for the clarification @lhoestq !\r\n\r\nThis may be a little bit of a stupid question, but I wanted to clarify one more thing before I took a stab at this:\r\n\r\nWhen the features get inferred, I believe they already have a pyarrow schema (https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L234).\r\n\r\nWe then convert it to a string (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L778) only to convert it back into the arrow type (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L143, and https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L35). Is there a reason for this round-trip?\r\n\r\nI'll open a PR later to add `timestamp` support to `string_to_arrow`, but I'd be curious to understand since it feels like there may be some opportunities to simplify!", "The objective in terms of design is to make it easy to create Features in a pythonic way. So for example we use a string to define a Value type.\r\nThat's why when inferring the Features from an arrow schema we have to find the right string definitions for Value types. I guess we could also have a constructor `Value.from_arrow_type` to avoid recreating the arrow type, but this could create silent errors if the pyarrow type doesn't have a valid mapping with the string definition. The \"round-trip\" is used to enforce that the ground truth is the string definition, not the pyarrow type, and also as a sanity check.\r\n\r\nLet me know if that makes sense ", "OK I think I understand now:\r\n\r\nFeatures are datasets' internal representation of a schema type, distinct from pyarrow's schema.\r\nValue() corresponds to pyarrow's \"primitive\" types (e.g. `int` or `string`, but not things like `list` or `dict`).\r\n`get_nested_type()` (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L698) and `generate_from_arrow_type()` (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L778) *should* be inverses of each other, and similarly, for the primitive values, `string_to_arrow()` and `Value.__call__` (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L146) should be inverses of each other?\r\n\r\nThanks for taking the time to answer - I just wanted to make sure I understood before opening a PR so I'm not disrupting anything about how the codebase is expected to work!", "Yes you're totally right :)" ]

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[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" }, { "color": "2edb81", "default": false, "description": "A bug in a dataset script provided in the library", "id": 2067388877, "name": "dataset bug", "node_id": "MDU6TGFiZWwyMDY3Mzg4ODc3", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug" } ]

[ "I came to open the same issue.", "Thanks for reporting !\r\n\r\nI pushed a temporary fix on `master` that uses an URL from a previous commit to access the dataset for now, until we have a better solution", "I changed the URL again to use another host, the fix is available on `master` and we'll probably do a new release of `datasets` tomorrow.\r\n\r\nIn the meantime, feel free to do `load_dataset(..., revision=\"master\")` to use the fixed script", "We just released a new version of `datasets` with a working URL. Feel free to update `datasets` and try again :)", "Hello! Unfortunately, this URL does not work for me. \r\nCould you please tell me how I can solve the problem?\r\n\r\n`>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"conll2003\")\r\nDownloading and preparing dataset conll2003/conll2003 (download: 4.63 MiB, generated: 9.78 MiB, post-processed: Unknown size, total: 14.41 MiB) to /home/dafedo/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/40e7cb6bcc374f7c349c83acd1e9352a4f09474eb691f64f364ee62eb65d0ca6...\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/load.py\", line 745, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/builder.py\", line 574, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/builder.py\", line 630, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/home/dafedo/.cache/huggingface/modules/datasets_modules/datasets/conll2003/40e7cb6bcc374f7c349c83acd1e9352a4f09474eb691f64f364ee62eb65d0ca6/conll2003.py\", line 196, in _split_generators\r\n downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/download_manager.py\", line 287, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/download_manager.py\", line 195, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 203, in map_nested\r\n mapped = [\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 204, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 142, in _single_map_nested\r\n return function(data_struct)\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/download_manager.py\", line 218, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 281, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 621, in get_from_cache\r\n raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https://github.com/davidsbatista/NER-datasets/raw/master/CONLL2003/train.txt\r\n`\r\n\r\nI receive the same error when I run \"itrain run_configs/conll2003.json\" from https://github.com/adapter-hub/efficient-task-transfer\r\n\r\nThank you very much in advance!\r\n\r\nRegards, \r\nDaria\r\n", "Can you try updating `datasets` and try again ?\r\n```\r\npip install -U datasets\r\n```", "@lhoestq Thank you very much for your answer! \r\n\r\nIt works this way, but for my research I need datasets==1.6.3 or closest to it because otherwise the other package would not work as it is built on this version.\r\nDo you have any other suggestion? I would really appreciate it. Maybe which version of the datasets is without hard-coded link but closest to 1.6.3\r\n", "No problem, I have solved it. \r\nThank you anyway.", "Out of curiosity, which package has the `datasets==1.6.3` requirement ?" ]

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[ "Thank you for all the feedback! I have updated the dummy data with a zip under 30KB, which needs to include at least one data instance from both document domain and dialog domain. Please let me know if it is still too big. Thanks!", "Thank you again for the feedback! I am not too sure what the preferable style for data instance in readme, but still added my edits. Thanks!" ]

[ { "color": "e99695", "default": false, "description": "Requesting to add a new dataset", "id": 2067376369, "name": "dataset request", "node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request" }, { "color": "d93f0b", "default": false, "description": "", "id": 2725241052, "name": "speech", "node_id": "MDU6TGFiZWwyNzI1MjQxMDUy", "url": "https://api.github.com/repos/huggingface/datasets/labels/speech" }, { "color": "bfdadc", "default": false, "description": "Vision datasets", "id": 3608941089, "name": "vision", "node_id": "LA_kwDODunzps7XHBIh", "url": "https://api.github.com/repos/huggingface/datasets/labels/vision" } ]

[ "The IEMOCAP dataset is private and available only on request.\r\n```\r\nTo obtain the IEMOCAP data you just need to fill out an electronic release form below.\r\n```\r\n\r\n- [Request form](https://sail.usc.edu/iemocap/release_form.php)\r\n- [License ](https://sail.usc.edu/iemocap/Data_Release_Form_IEMOCAP.pdf)\r\n\r\n\r\n> We do not share the dataset for commercial purposes due to privacy concerns surrounding the participants of the research. The login details will only be emailed to the given academic email address.\r\n\r\nI think it won't be possible to add this dataset to 🤗 datasets.", "Hi @dnaveenr ! We can contact the authors to see if they are interested in hosting the dataset on the Hub. In the meantime, feel free to work on a script with manual download.", "Hi @mariosasko . Thanks for your response. Sure, I will mail them and find out if they're open to this.\r\n\r\nWork on a script with manual download ? This is new to me, any guidelines would be helpful here.\r\n", "> Thanks for your response. Sure, I will mail them and find out if they're open to this.\r\n\r\nIt's best to leave this part to us because we have to explain how login would work and (potentially) set up a custom verification for the dataset.\r\n\r\n> Work on a script with manual download ? This is new to me, any guidelines would be helpful here.\r\n\r\nFor instance, this is one of the scripts with manual download: https://huggingface.co/datasets/arxiv_dataset. Compared to the standard dataset, it has the `manual_download_instructions` attribute and uses `dl_manager.manual_dir` (derived from `load_dataset(..., data_dir=\"path/to/data\")`) to access the dataset's data files.", "> It's best to leave this part to us because we have to explain how login would work and (potentially) set up a custom verification for the dataset.\r\n\r\nYes. That would be perfect. Thanks.\r\n\r\n----\r\nOkay. Thanks for giving a reference. This is helpful. I will go through it.\r\n\r\n", "@mariosasko has this been solved? I would like to use login and custom verification for training on my private dataset.", "@flckv I think the [gating mechanism](https://huggingface.co/docs/hub/datasets-gated) is what you are looking for. ", "@mariosasko Thanks, but no. I would like to keep my HuggingFace Dataset private and train a model on it. Is this possible?" ]

[ { "color": "e99695", "default": false, "description": "Requesting to add a new dataset", "id": 2067376369, "name": "dataset request", "node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request" } ]

[ "Dataset added in release [1.5.0](https://github.com/huggingface/datasets/releases/tag/1.5.0), I think I can close this." ]

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[ "Dupe" ]

[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]

[ "Hi @kolk, thanks for reporting.\r\n\r\nIndeed, Google Drive service recently changed their service and we had to add a fix to our library to cope with that change:\r\n- #3787 \r\n\r\nWe just made patch release last week: 1.18.4 https://github.com/huggingface/datasets/releases/tag/1.18.4\r\n\r\nPlease, feel free to update your local `datasets` version, so that you get the fix:\r\n```shell\r\npip install -U datasets\r\n```" ]

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[ "The CI fail are caused by some missing sections or tags, which is unrelated to this PR. Merging !" ]

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[ "merging since the CI is fixed on master" ]

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[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]

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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 been added in #957 " ]

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[ { "color": "e99695", "default": false, "description": "Requesting to add a new dataset", "id": 2067376369, "name": "dataset request", "node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request" } ]

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[ "Hii please follow me", "merging since the CI is fixed on master" ]

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[ "`iter_archive` is about to support ZIP archives. I think we should use this no ?\r\n\r\nsee #3347 https://github.com/huggingface/datasets/pull/3379", "I was interested in the support for isfile/dir in remote.\r\n\r\nAnyway, `iter_files` will be available for community users.", "I'm not a big fan of having two functions that do the same thing. What do you think ?", "They do not do the same thing:\r\n- One iterates over files in a directory\r\n- The other I guess will iterate over the members of an archive", "Makes sense ! Sounds good then - sorry for my misunderstanding\r\n\r\nNote that `iter_archive` will be more performant for data streaming that `iter_files` thanks to the buffering so maybe in the future we can `iter_archive` for some of these datasets", "Yes, @lhoestq I agree with you: once `iter_archive` supports zip files, it will be more suitable than `iter_files` for these 2 datasets.\r\n\r\nAnyway, this PR also implements `isfile`/`isdir` in streaming mode, besides fixing `iter_files`. And I'm interested in having those in master.\r\n\r\nMaybe, could we merge this PR into master and take note to refactor the datasets to use `iter_archive` once zip is supported?\r\nOther option could be to split this PR into 2..." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "IMO `DatasetMetadata` shouldn't crash with attributes that it doesn't know, btw", "Yea this PR is mostly to have a validation that this field contains a list of strings.\r\n\r\nRegarding unknown fields, the tagging app currently returns an error if a field is unknown using the `DatasetMetadata`. We can change that though" ]

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[ "closing since #1379 got merged" ]

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[ "The glue metric for cola is a metric for classification. It expects label ids as integers as inputs.", "I want to evaluate a sentence pair whether they are semantically equivalent, so I used MRPC and it gives the same error, does that mean we have to encode the sentences and parse as input?\r\n\r\nusing BertTokenizer;\r\n```\r\nencoded_reference=tokenizer.encode(reference, add_special_tokens=False)\r\nencoded_prediction=tokenizer.encode(prediction, add_special_tokens=False)\r\n```\r\n\r\n`glue_score = glue_metric.compute(encoded_prediction, encoded_reference)`\r\n```\r\n\r\nValueError Traceback (most recent call last)\r\n<ipython-input-9-4c3a3ce7b583> in <module>()\r\n----> 1 glue_score = glue_metric.compute(encoded_prediction, encoded_reference)\r\n\r\n6 frames\r\n/usr/local/lib/python3.6/dist-packages/nlp/metric.py in compute(self, predictions, references, timeout, **metrics_kwargs)\r\n 198 predictions = self.data[\"predictions\"]\r\n 199 references = self.data[\"references\"]\r\n--> 200 output = self._compute(predictions=predictions, references=references, **metrics_kwargs)\r\n 201 return output\r\n 202 \r\n\r\n/usr/local/lib/python3.6/dist-packages/nlp/metrics/glue/27b1bc63e520833054bd0d7a8d0bc7f6aab84cc9eed1b576e98c806f9466d302/glue.py in _compute(self, predictions, references)\r\n 101 return pearson_and_spearman(predictions, references)\r\n 102 elif self.config_name in [\"mrpc\", \"qqp\"]:\r\n--> 103 return acc_and_f1(predictions, references)\r\n 104 elif self.config_name in [\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]:\r\n 105 return {\"accuracy\": simple_accuracy(predictions, references)}\r\n\r\n/usr/local/lib/python3.6/dist-packages/nlp/metrics/glue/27b1bc63e520833054bd0d7a8d0bc7f6aab84cc9eed1b576e98c806f9466d302/glue.py in acc_and_f1(preds, labels)\r\n 60 def acc_and_f1(preds, labels):\r\n 61 acc = simple_accuracy(preds, labels)\r\n---> 62 f1 = f1_score(y_true=labels, y_pred=preds)\r\n 63 return {\r\n 64 \"accuracy\": acc,\r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in f1_score(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)\r\n 1097 pos_label=pos_label, average=average,\r\n 1098 sample_weight=sample_weight,\r\n-> 1099 zero_division=zero_division)\r\n 1100 \r\n 1101 \r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in fbeta_score(y_true, y_pred, beta, labels, pos_label, average, sample_weight, zero_division)\r\n 1224 warn_for=('f-score',),\r\n 1225 sample_weight=sample_weight,\r\n-> 1226 zero_division=zero_division)\r\n 1227 return f\r\n 1228 \r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)\r\n 1482 raise ValueError(\"beta should be >=0 in the F-beta score\")\r\n 1483 labels = _check_set_wise_labels(y_true, y_pred, average, labels,\r\n-> 1484 pos_label)\r\n 1485 \r\n 1486 # Calculate tp_sum, pred_sum, true_sum ###\r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\r\n 1314 raise ValueError(\"Target is %s but average='binary'. Please \"\r\n 1315 \"choose another average setting, one of %r.\"\r\n-> 1316 % (y_type, average_options))\r\n 1317 elif pos_label not in (None, 1):\r\n 1318 warnings.warn(\"Note that pos_label (set to %r) is ignored when \"\r\n\r\nValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\r\n\r\n```", "MRPC is also a binary classification task, so its metric is a binary classification metric.\r\n\r\nTo evaluate if pairs of sentences are semantically equivalent, maybe you could take a look at models that compute if one sentence entails the other or not (typically the kinds of model that could work well on the MRPC task).", "Closing this one. Feel free to re-open if you have other questions :)" ]

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

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[ "You can list the dataset's columns with `ds.column_names` before `.map` to check whether the dataset has an `image` column. If it doesn't, then this is a bug. Otherwise, please paste the line with the `.map` call.\r\n\r\n\r\n", "This is the piece of code I am running:\r\n```\r\ndata_transforms = utils.get_data_augmentation(args)\r\nimage_dataset = utils.load_image_dataset(args.dataset)\r\n\r\ndef resize(examples):\r\n examples[\"pixel_values\"] = [image.convert(\"RGB\").resize((300, 300)) for image in examples[\"image\"]]\r\n return examples\r\n\r\ndef preprocess_train(example_batch):\r\n print(f\"Example batch: \\n{example_batch}\")\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"train\"](image.convert(\"RGB\")) for image in example_batch[\"pixel_values\"]\r\n ]\r\n return example_batch\r\n\r\ndef preprocess_val(example_batch):\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"val\"](image.convert(\"RGB\")) for image in example_batch[\"pixel_values\"]\r\n ]\r\n return example_batch\r\n\r\nimage_dataset = image_dataset.map(resize, remove_columns=[\"image\"], batched=True)\r\n\r\nimage_dataset[\"train\"].set_transform(preprocess_train)\r\nimage_dataset[\"validation\"].set_transform(preprocess_val)\r\n```\r\n\r\nWhen I print ds.column_names I get the following\r\n`{'train': ['image', 'label'], 'validation': ['image', 'label'], 'test': ['image', 'label']}`\r\n\r\nThe `print(f\"Example batch: \\n{example_batch}\")` in the `preprocess_train` function outputs only labels without images:\r\n```\r\nExample batch: \r\n{'label': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]}\r\n```\r\n\r\nThe weird part of it all is that a sample code runs in a jupyter lab notebook without any bugs, but when I run my scripts from the terminal I get the bug. The same code.", "The `remove_columns=[\"image\"]` argument in the `.map` call removes the `image` column from the output, so drop this argument to preserve it.", "The problem is not with the removal of the image key. The bug is why only the labels are sent to be process, instead of all the featues or dictionary keys.\r\n\r\nP.S. I just dropped the removal argument as you've suggested, but that didn't solve the problem, because only the labels are being sent to be processed", "All the `image_dataset.column_names` after the `map` call should also be present in `preprocess_train `/`preprocess_val` unless (input) `columns` in `set_transform` are specified.\r\n\r\nIf that's not the case, we need a full reproducer (not snippets) with the environment info.", "I have resolved the error after including a collate function as indicated in the Quick Start session of the Datasets docs.:\r\n\r\nHere is what I did:\r\n```\r\ndata_transforms = utils.get_data_augmentation(args)\r\nimage_dataset = utils.load_image_dataset(args.dataset)\r\n\r\ndef preprocess_train(example_batch):\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"train\"](image.convert(\"RGB\")) for image in example_batch[\"image\"]\r\n ]\r\n return example_batch\r\n\r\ndef preprocess_val(example_batch):\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"val\"](image.convert(\"RGB\")) for image in example_batch[\"image\"]\r\n ]\r\n return example_batch\r\n\r\ndef collate_fn(examples):\r\n images = []\r\n labels = []\r\n for example in examples:\r\n images.append((example[\"pixel_values\"]))\r\n labels.append(example[\"label\"])\r\n\r\n pixel_values = torch.stack(images)\r\n labels = torch.tensor(labels)\r\n return {\"pixel_values\": pixel_values, \"label\": labels}\r\n\r\ntrain_dataset = image_dataset[\"train\"].with_transform(preprocess_train)\r\nval_dataset = image_dataset[\"validation\"].with_transform(preprocess_val)\r\n\r\nimage_datasets = {\r\n \"train\": train_dataset,\r\n \"val\": val_dataset\r\n}\r\n\r\nsamplers = {\r\n \"train\": data.RandomSampler(train_dataset),\r\n \"val\": data.SequentialSampler(val_dataset),\r\n}\r\n\r\ndataloaders = {\r\n x: data.DataLoader(\r\n image_datasets[x],\r\n collate_fn=collate_fn,\r\n batch_size=batch_size,\r\n sampler=samplers[x],\r\n num_workers=args.num_workers,\r\n worker_init_fn=utils.set_seed_for_worker,\r\n generator=g,\r\n pin_memory=True,\r\n )\r\n for x in [\"train\", \"val\"]\r\n}\r\n\r\ntrain_loader, val_loader = dataloaders[\"train\"], dataloaders[\"val\"]\r\n```\r\nEverything runs fine without any bug now. " ]

[]

[ "Can you run `make style` to format the code and fix the CI please ?", "> Can you run `make style` to format the code and fix the CI please ?\r\n\r\nI ran `make style` before this PR and just a few minutes ago. No changes to the code. Not sure why the CI is failing.", "Also, I attempted to see if I can get the source Chinese sentences from `wmt17` dataset. But this call `data = load_dataset('wmt17', \"zh-en\")` failed with this error: `FileNotFoundError: Couldn't find file at https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-zh.tar.gz`. I think it should be possible and fairly straightforward to get the pairing source sentences from it. I just can not test it right now.", "The `RemoteDatasetTest ` errors in the CI are fixed on master so it's fine", "merging since the CI is fixed on master" ]

[]

[ "@lhoestq, I think this reaches some memory limit on Linux instances... (?)", "It looks like the `comet` metric test fails because it tries to load a model in memory.\r\nIn the tests I think we have `patch_comet` that mocks the model download + inference. Not sure why it didn't work though.\r\nI can take a look tomorrow (this afternoon is the pytorch ecosystem day)", "@lhoestq thanks for the hint: I'm going to have a look at that mock... ;)", "@lhoestq finally I did not find out why the mock is not used... If you can give me some other hint tomorrow..." ]

[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]

[ "Hi ! Are you having this issue only with this specific dataset, or it also happens with other ones like `squad` ?", "@lhoestq It happens also on `squad`. It successfully downloads the whole dataset and then crashes on: \r\n\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\nI tested it on Ubuntu and its working OK. Didn't test on non-preview version of Windows 11, `Windows-10-10.0.22504-SP0` is a preview version, not sure if this is causing it.", "I see the same error in Windows-10.0.19042 as of a few days ago:\r\n\r\n`Fatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS`\r\n\r\npython 3.8.12 h7840368_2_cpython conda-forge\r\nboto3 1.20.11 pyhd8ed1ab_0 conda-forge\r\nbotocore 1.23.11 pyhd8ed1ab_0 conda-forge\r\n\r\n...but I am not using `datasets` (although I might take a look now that I know about it!)\r\n\r\nThe error has occurred a few times over the last two days, but not consistently enough for me to get it with DEBUG. If there is any interest I can report back here, but it seems not unique to `datasets`.", "I'm not sure what `datasets` has to do with a crash that seems related to `aws-c-io`, could it be an issue with your environment ?", "> I'm not sure what `datasets` has to do with a crash that seems related to `aws-c-io`, could it be an issue with your environment ?\r\n\r\nAgreed, this issue is not likely a bug in datasets, since I get the identical error without datasets installed.", "Will close this issue. Bug in `aws-c-io` shouldn't be in `datasets` repo. Nevertheless, it can be useful to know that it happens. Thanks @leehaust @lhoestq ", "I have also had this issue since a few days, when running scripts using PyCharm in particular, but it does not seem to affect the script from running, only reporting this error at the end of the run.", "I also get this issue, It appears after my script has finished running. I get the following error message\r\n```\r\nFatal error condition occurred in /home/conda/feedstock_root/build_artifacts/aws-c-io_1637179816120/work/source/event_loop.c:72: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n################################################################################\r\nStack trace:\r\n################################################################################\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_backtrace_print+0x59) [0x2aabe0479579]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_fatal_assert+0x48) [0x2aabe04696c8]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././././libaws-c-io.so.1.0.0(+0x13ad3) [0x2aabe0624ad3]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_ref_count_release+0x1d) [0x2aabe047b60d]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././././libaws-c-io.so.1.0.0(+0x113ca) [0x2aabe06223ca]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_ref_count_release+0x1d) [0x2aabe047b60d]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-crt-cpp.so(_ZN3Aws3Crt2Io15ClientBootstrapD1Ev+0x3a) [0x2aabe041cf5a]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././libaws-cpp-sdk-core.so(+0x5f570) [0x2aabe00eb570]\r\n/lib64/libc.so.6(+0x39ce9) [0x2aaaab835ce9]\r\n/lib64/libc.so.6(+0x39d37) [0x2aaaab835d37]\r\n/lib64/libc.so.6(__libc_start_main+0xfc) [0x2aaaab81e55c]\r\npython(+0x1c721d) [0x55555571b21d]\r\nAborted\r\n```\r\nI don't get this issue when running my code in a container, and it seems more relevant to PyArrow but thought a more complete stack trace might be helpful to someone\r\n", "I created an issue on JIRA:\r\nhttps://issues.apache.org/jira/browse/ARROW-15141", "@CallumMcMahon Do you have a small reproducer for this problem on Linux? I can reproduce this on Windows but sadly not with linux.", "Any updates on this issue? I started receiving the same error a few days ago on the amazon reviews", "Hi,\r\n\r\nI also ran into this issue, Windows only. It caused our massive binary to minidump left and right, very annoying.\r\nWhen the program is doing an exit, the destructors in the exit-handlers want to do cleanup, leading to code in event_loop.c, on line 73-ish:\r\n\r\nAWS_FATAL_ASSERT(\r\n aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) ==\r\n AWS_OP_SUCCESS);\r\n\r\nThe fatal_assert end in an abort/minidump.\r\n\r\nDigging through the code, I found that aws_thread_launch in the Windows version (aws-c-common/source/windows/thread.c) has only ONE reason to return anything other than AWS_OP_SUCCESS:\r\n\r\nreturn aws_raise_error(AWS_ERROR_THREAD_INSUFFICIENT_RESOURCE);\r\n\r\non line 263, when CreateThread fails. Our conclusion was that, apparently, Windows dislikes launching a new thread while already handling the exit-handlers. And while I appreciate the the fatal_assert is there in case of problems, the cure here is worse than the problem.\r\n\r\nI \"fixed\" this in our (Windows) environment by (bluntly) removing the AWS_FATAL_ASSERT. If Windows cannot start a thread, the program is in deep trouble anyway and the chances of that actually happening are acceptable (to us).\r\nThe exit is going to clean up all resources anyway.\r\n\r\nA neater fix would probably be to detect somehow that the program is actually in the process of exiting and then not bother (on windows, anyway) to start a cleanup thread. Alternatively, try to start the thread but not fatal-assert when it fails during exit. Or perhaps Windows can be convinced somehow to start the thread under these circumstances?\r\n\r\n@xhochy : The problem is Windows-only, the aws_thread_launch has two implementations (posix and windows). The problem is in the windows CreateThread which fails.\r\n", "I also encountered the same problem, but I made an error in the multi gpu training environment on Linux, and the single gpu training environment will not make an error.\r\ni use accelerate package to do multi gpu training.", "> I also get this issue, It appears after my script has finished running. I get the following error message\r\n> \r\n> ```\r\n> Fatal error condition occurred in /home/conda/feedstock_root/build_artifacts/aws-c-io_1637179816120/work/source/event_loop.c:72: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\n> Exiting Application\r\n> ################################################################################\r\n> Stack trace:\r\n> ################################################################################\r\n> /home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_backtrace_print+0x59) [0x2aabe0479579]\r\n> /home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_fatal_assert+0x48) [0x2aabe04696c8]\r\n> /home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././././libaws-c-io.so.1.0.0(+0x13ad3) [0x2aabe0624ad3]\r\n> /home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_ref_count_release+0x1d) [0x2aabe047b60d]\r\n> /home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././././libaws-c-io.so.1.0.0(+0x113ca) [0x2aabe06223ca]\r\n> /home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_ref_count_release+0x1d) [0x2aabe047b60d]\r\n> /home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-crt-cpp.so(_ZN3Aws3Crt2Io15ClientBootstrapD1Ev+0x3a) [0x2aabe041cf5a]\r\n> /home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././libaws-cpp-sdk-core.so(+0x5f570) [0x2aabe00eb570]\r\n> /lib64/libc.so.6(+0x39ce9) [0x2aaaab835ce9]\r\n> /lib64/libc.so.6(+0x39d37) [0x2aaaab835d37]\r\n> /lib64/libc.so.6(__libc_start_main+0xfc) [0x2aaaab81e55c]\r\n> python(+0x1c721d) [0x55555571b21d]\r\n> Aborted\r\n> ```\r\n> \r\n> I don't get this issue when running my code in a container, and it seems more relevant to PyArrow but thought a more complete stack trace might be helpful to someone\r\n\r\nAny updates for your issue because I'm getting the same one ", "Potentially related AWS issue: https://github.com/aws/aws-sdk-cpp/issues/1809\r\n\r\nRan into this issue today while training a BPE tokenizer on a dataset.\r\n\r\nTrain code:\r\n\r\n```python\r\n\"\"\"Train a ByteLevelBPETokenizer based on a given dataset. The dataset must be on the HF Hub.\r\nThis script is adaptated from the Transformers example in https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling\r\n\"\"\"\r\nfrom os import PathLike\r\nfrom pathlib import Path\r\nfrom typing import Sequence, Union\r\n\r\nfrom datasets import load_dataset\r\nfrom tokenizers import ByteLevelBPETokenizer\r\n\r\n\r\ndef train_tokenizer(dataset_name: str = \"oscar\", dataset_config_name: str = \"unshuffled_deduplicated_nl\",\r\n dataset_split: str = \"train\", dataset_textcol: str = \"text\",\r\n vocab_size: int = 50265, min_frequency: int = 2,\r\n special_tokens: Sequence[str] = (\"<s>\", \"<pad>\", \"</s>\", \"<unk>\", \"<mask>\"),\r\n dout: Union[str, PathLike] = \".\"):\r\n # load dataset\r\n dataset = load_dataset(dataset_name, dataset_config_name, split=dataset_split)\r\n # Instantiate tokenizer\r\n tokenizer = ByteLevelBPETokenizer()\r\n\r\n def batch_iterator(batch_size=1024):\r\n for i in range(0, len(dataset), batch_size):\r\n yield dataset[i: i + batch_size][dataset_textcol]\r\n\r\n # Customized training\r\n tokenizer.train_from_iterator(batch_iterator(), vocab_size=vocab_size, min_frequency=min_frequency,\r\n special_tokens=special_tokens)\r\n\r\n # Save to disk\r\n pdout = Path(dout).resolve()\r\n pdout.mkdir(exist_ok=True, parents=True)\r\n tokenizer.save_model(str(pdout))\r\n\r\n\r\ndef main():\r\n import argparse\r\n cparser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter)\r\n\r\n cparser.add_argument(\"dataset_name\", help=\"Name of dataset to use for tokenizer training\")\r\n cparser.add_argument(\"--dataset_config_name\", default=None,\r\n help=\"Name of the config to use for tokenizer training\")\r\n cparser.add_argument(\"--dataset_split\", default=None,\r\n help=\"Name of the split to use for tokenizer training (typically 'train')\")\r\n cparser.add_argument(\"--dataset_textcol\", default=\"text\",\r\n help=\"Name of the text column to use for tokenizer training\")\r\n cparser.add_argument(\"--vocab_size\", type=int, default=50265, help=\"Vocabulary size\")\r\n cparser.add_argument(\"--min_frequency\", type=int, default=2, help=\"Minimal frequency of tokens\")\r\n cparser.add_argument(\"--special_tokens\", nargs=\"+\", default=[\"<s>\", \"<pad>\", \"</s>\", \"<unk>\", \"<mask>\"],\r\n help=\"Special tokens to add. Useful for specific training objectives. Note that if you wish\"\r\n \" to use this tokenizer with a default transformers.BartConfig, then make sure that the\"\r\n \" order of at least these special tokens are correct: BOS (0), padding (1), EOS (2)\")\r\n cparser.add_argument(\"--dout\", default=\".\", help=\"Path to directory to save tokenizer.json file\")\r\n\r\n train_tokenizer(**vars(cparser.parse_args()))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\nCommand:\r\n\r\n```sh\r\n$WDIR=\"your_tokenizer\"\r\npython prepare_tokenizer.py dbrd --dataset_config_name plain_text --dataset_split unsupervised --dout $WDIR\r\n```\r\n\r\nOutput:\r\n\r\n```\r\nReusing dataset dbrd (cache/datasets/dbrd/plain_text/3.0.0/2b12e31348489dfe586c2d0f40694e5d9f9454c9468457ac9f1b51abf686eeb3)\r\n[00:00:30] Pre-processing sequences ████████ 0 / 0\r\n[00:00:00] Tokenize words ████████ 333319 / 333319\r\n[00:01:06] Count pairs ████████ 333319 / 333319\r\n[00:00:03] Compute merges ████████ 50004 / 50004\r\n\r\nFatal error condition occurred in /opt/vcpkg/buildtrees/aws-c-io/src/9e6648842a-364b708815.clean/source/event_loop.c:72: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n################################################################################\r\nStack trace:\r\n################################################################################\r\nvenv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x200af06) [0x155106589f06]\r\nvenv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x20028e5) [0x1551065818e5]\r\nvenv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x1f27e09) [0x1551064a6e09]\r\nvenv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x200ba3d) [0x15510658aa3d]\r\nvenv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x1f25948) [0x1551064a4948]\r\nvenv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x200ba3d) [0x15510658aa3d]\r\nvenv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x1ee0b46) [0x15510645fb46]\r\nvenv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x194546a) [0x155105ec446a]\r\n/lib64/libc.so.6(+0x39b0c) [0x1551075b8b0c]\r\n/lib64/libc.so.6(on_exit+0) [0x1551075b8c40]\r\n/lib64/libc.so.6(__libc_start_main+0xfa) [0x1551075a249a]\r\npython(_start+0x2e) [0x4006ce]\r\nAborted (core dumped)\r\n```\r\n\r\nRunning on datasets==2.4.0 and pyarrow==9.0.0 on RHEL 8.\r\n", "There is also a discussion here https://issues.apache.org/jira/browse/ARROW-15141 where it is suggested for conda users to use an older version of aws-sdk-cpp: `aws-sdk-cpp=1.8.186`", "Downgrading pyarrow to 6.0.1 solves the issue for me.\r\n\r\n`pip install pyarrow==6.0.1`", "First of all, I’d never call a downgrade a solution, at most a (very) temporary workaround.\r\nFurthermore: This bug also happens outside pyarrow, I incorporate AWS in a standalone Windows C-program and that crashes during exit.\r\n\r\nFrom: Bo-Ru (Roy) Lu ***@***.***>\r\nSent: Thursday, 15 September 2022 01:12\r\nTo: huggingface/datasets ***@***.***>\r\nCc: Ruurd Beerstra ***@***.***>; Comment ***@***.***>\r\nSubject: Re: [huggingface/datasets] Fatal error condition occurred in aws-c-io (Issue #3310)\r\n\r\nSent by an external sender. Please be cautious about clicking on links and opening attachments.\r\n--------------------------------------------------------------------------------------------------------------------------------\r\n\r\n\r\nDowngrading pyarrow to 6.0.1 solves the issue.\r\n\r\n—\r\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/3310#issuecomment-1247390774>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AKYUE3WBCSMHKJOOA2RQELLV6JLSVANCNFSM5IQ3WG7Q>.\r\nYou are receiving this because you commented.Message ID: ***@***.******@***.***>>\r\n", "> First of all, I’d never call a downgrade a solution, at most a (very) temporary workaround.\r\n\r\nVery much so! It looks like an apparent fix for the underlying problem [might](https://github.com/awslabs/aws-c-io/pull/515) have landed, but it sounds like it might still be a bit of a [lift](https://github.com/aws/aws-sdk-cpp/issues/1809#issuecomment-1289859795) to get it into aws-sdk-cpp.\r\n\r\n> Downgrading pyarrow to 6.0.1 solves the issue for me.\r\n\r\nSidenote: On conda-forge side, all recent pyarrow releases (all the way up to v9 and soon v10) have carried the respective pin and will not run into this issue.\r\n\r\n```\r\nconda install -c conda-forge pyarrow\r\n```\r\n\r\n", "For pip people, I confirmed that installing the nightly version of pyarrow also solves this by: `pip install --extra-index-url https://pypi.fury.io/arrow-nightlies/ --prefer-binary --pre pyarrow --upgrade`. (See https://arrow.apache.org/docs/python/install.html#installing-nightly-packages)\r\nAny version after https://github.com/apache/arrow/pull/14157 would work fine.", "> Furthermore: This bug also happens outside pyarrow, I incorporate AWS in a standalone Windows C-program and that crashes during exit.\r\n\r\nDo you have a reproducer you could share? I'd like to test if the new versions that supposedly solve this actually do, but we don't have a way to test it...", "Hi,\r\n\r\nNo – sorry. It is part of a massive eco-system which cannot easily be shared.\r\nBut I think the problem was summarized quite clearly: Windows does not allow a CreateThread while doing ExitProcess.\r\nThe cleanup that gets called as part of the exit handler code tries to start a thread, the fatal-assert on that causes the crash, and in windows we get a very big dump file.\r\nThe fix I applied simply removes that fatal assert, that solves the problem for me.\r\nI did not delve into the what the thread was trying to achieve and if that might cause issues when not executed during exit of the process. We did not notice anything of the kind.\r\nHowever, we *did* notice the many, many gigabytes of accumulated dumps of hundreds of processes 😊\r\n\r\nI’ll try and upgrade to the latest AWS version and report my findings, but that will be after I return from a month of vacationing…\r\n\r\n\r\n * Regards – Ruurd Beerstra\r\n\r\n\r\nFrom: h-vetinari ***@***.***>\r\nSent: Friday, 28 October 2022 02:09\r\nTo: huggingface/datasets ***@***.***>\r\nCc: Ruurd Beerstra ***@***.***>; Comment ***@***.***>\r\nSubject: Re: [huggingface/datasets] Fatal error condition occurred in aws-c-io (Issue #3310)\r\n\r\nSent by an external sender. Please be cautious about clicking on links and opening attachments.\r\n--------------------------------------------------------------------------------------------------------------------------------\r\n\r\n\r\nFurthermore: This bug also happens outside pyarrow, I incorporate AWS in a standalone Windows C-program and that crashes during exit.\r\n\r\nDo you have a reproducer you could share? I'd like to test if the new versions that supposedly solve this actually do, but we don't have a way to test it...\r\n\r\n—\r\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/3310#issuecomment-1294251331>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AKYUE3SHHPC5AT7KQ4GDAJDWFMKRTANCNFSM5IQ3WG7Q>.\r\nYou are receiving this because you commented.Message ID: ***@***.******@***.***>>\r\n", "> No – sorry. It is part of a massive eco-system which cannot easily be shared.\r\n\r\nOK, was worth a try...\r\n\r\n> The fix I applied simply removes that fatal assert, that solves the problem for me.\r\n\r\nThis seems to be what https://github.com/awslabs/aws-c-io/pull/515 did upstream.\r\n\r\n> I’ll try and upgrade to the latest AWS version and report my findings, but that will be after I return from a month of vacationing…\r\n\r\ncaution: aws-sdk-cpp hasn't yet upgraded its bundled(?) aws-c-io and hence doesn't contain the fix AFAICT", "Hi, I also encountered the same problem, but I made an error on Ubuntu without using `datasets` as @Crabzmatic he wrote.\r\n\r\nAt that time, I find my version of pyarrow is 9.0.0, which is different from as follow:\r\n> https://github.com/huggingface/datasets/issues/3310#issuecomment-1247390774\r\n> Downgrading pyarrow to 6.0.1 solves the issue for me.\r\n> \r\n> `pip install pyarrow==6.0.1`\r\n\r\nAs it happens, I found this error message when I introduced the [`Trainer`](https://huggingface.co/docs/transformers/main_classes/trainer) of HuggingFace\r\n\r\nFor example, I write following code:\r\n```python\r\nfrom transformers import Trainer\r\nprint('Hugging Face')\r\n```\r\n I get the following error message:\r\n```python\r\nHugging Face\r\nFatal error condition occurred in /opt/vcpkg/buildtrees/aws-c-io/src/9e6648842a-364b708815.clean/source/event_loop.c:72: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n################################################################################\r\nStack trace:\r\n################################################################################\r\n/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x200af06) [0x7fa9add1df06]\r\n/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x20028e5) [0x7fa9add158e5]\r\n/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x1f27e09) [0x7fa9adc3ae09]\r\n/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x200ba3d) [0x7fa9add1ea3d]\r\n/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x1f25948) [0x7fa9adc38948]\r\n/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x200ba3d) [0x7fa9add1ea3d]\r\n/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x1ee0b46) [0x7fa9adbf3b46]\r\n/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x194546a) [0x7fa9ad65846a]\r\n/lib/x86_64-linux-gnu/libc.so.6(+0x468d7) [0x7faa2fcfe8d7]\r\n/lib/x86_64-linux-gnu/libc.so.6(on_exit+0) [0x7faa2fcfea90]\r\n/lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xfa) [0x7faa2fcdc0ba]\r\n/home/ubuntu/anaconda3/envs/pytorch38/bin/python(+0x1f9ad7) [0x5654571d1ad7]\r\n```\r\nBut, when I remove the `Trainer` module from transformers, **everthing is OK**.\r\n\r\nSo Why ?\r\n\r\n**Environment info**\r\n- Platform: Ubuntu 18\r\n- Python version: 3.8\r\n- PyArrow version: 9.0.0\r\n- transformers: 4.22.1\r\n- simpletransformers: 0.63.9", "> I get the following error message:\r\n\r\nNot sure what's going on, but that shouldn't happen, especially as we're pinning to a version that should avoid this.\r\n\r\nCan you please open an issue https://github.com/conda-forge/arrow-cpp-feedstock, including the requested output of `conda list` & `conda info`?", "pyarrow 10.0.1 was just released in conda-forge, which is the first release where we're building against aws-sdk-cpp 1.9.* again after more than a year. Since we cannot test the failure reported here on our infra, I'd be very grateful if someone could verify that the problem does or doesn't reappear. 🙃 \r\n\r\n```\r\nconda install -c conda-forge pyarrow=10\r\n```", "> pyarrow 10.0.1 was just released in conda-forge, which is the first release where we're building against aws-sdk-cpp 1.9.* again after more than a year. Since we cannot test the failure reported here on our infra, I'd be very grateful if someone could verify that the problem does or doesn't reappear. 🙃\r\n> \r\n> ```\r\n> conda install -c conda-forge pyarrow=10\r\n> ```\r\n\r\nThe problem is gone after I install the new version. Thanks!\r\npip install pyarrow==10", "@liuchaoqun, with `pip install pyarrow` you don't get aws-bindings, they're too complicated to package into wheels as far as I know. And even if they're packaged, at the time of the release of pyarrow 10 it would have still been pinned to aws 1.8 for the same reasons as in this issue." ]

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

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[ "Oh yeah, for some reason I thought you had to do it after the merge, I'll get on it", "Weird, now the CI seems to fail because of other datasets (XGLUE, Norwegian_NER)", "Yea you just need to rebase from master", "Re-opening a PR without the messed-up rebase" ]

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[ "For one dataset you can have different configurations that each have their own `nlp.Features`.\r\nWe imagine having one configuration per subtask for example.\r\nThey are loaded with `nlp.load_dataset(\"my_dataset\", \"my_config\")`.\r\n\r\nFor example the `glue` dataset has many configurations. It is a bit different from your case though because each configuration is a dataset by itself (sst2, mnli).\r\nAnother example is `wikipedia` that has one configuration per language." ]

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[ "Can you try to remove \"drop_labels=false\" ? It may force the loader to infer the labels instead of reading the metadata", "Hi, thanks for responding. I tried that, but it does not change anything.", "Can you try updating `datasets` ? Metadata support was added in `datasets` 2.4", "Probably the issue, will report back asap!", "Okay, now it seems to actually load the metadata and create the train_split, but it still says only returns \"image\" and \"label\", which is always 0 since all images are from same folder", "> Can you try updating `datasets` ? Metadata support was added in `datasets` 2.4\r\n\r\nUpdate: This was the issue." ]

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