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[WER] Better error message for wer
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[ "Hi ! I don't think this would solve this issue.\r\nCurrently it looks like there's a bug that converts the list `[\"hello it's nice\"]` to a string `'[\"hello it's nice\"]'` since this is what the metric expects as input. The conversion is done before the data are passed to `_compute()`.\r\n\r\nThis is `Value(\"st...
2021-12-20T11:38:40Z
2021-12-20T16:53:37Z
2021-12-20T16:53:36Z
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Currently we have the following problem when using the WER. When the input format to the WER metric is wrong, instead of throwing an error message a word-error-rate is computed which is incorrect. E.g. when doing the following: ```python from datasets import load_metric wer = load_metric("wer") target_str = ["hello this is nice", "hello the weather is bloomy"] pred_str = [["hello it's nice"], ["hello it's the weather"]] print("Wrong:", wer.compute(predictions=pred_str, references=target_str)) print("Correct", wer.compute(predictions=[x[0] for x in pred_str], references=target_str)) ``` We get: ``` Wrong: 1.0 Correct 0.5555555555555556 ``` meaning that we get a word-error rate for incorrectly passed input formats. We should raise an error here instead so that people don't spent hours fixing a model while it's their incorrect evaluation metric is the problem for a low WER.
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Can we parallelized the add_faiss_index process over dataset shards ?
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[ "Hi !\r\nAs far as I know not all faiss indexes can be computed in parallel and then merged. \r\nFor example [here](https://github.com/facebookresearch/faiss/wiki/Special-operations-on-indexes#splitting-and-merging-indexes) is is mentioned that only IndexIVF indexes can be merged.\r\nMoreover faiss already works us...
2021-03-01T12:47:34Z
2021-03-04T19:40:56Z
2021-03-04T19:40:42Z
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I am thinking of making the **add_faiss_index** process faster. What if we run the add_faiss_index process on separate dataset shards and then combine them before (dataset.concatenate) saving the faiss.index file ? I feel theoretically this will reduce the accuracy of retrieval since it affects the indexing process. @lhoestq
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Fix JSON generation in benchmarks CI
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a...
2023-06-19T16:56:06Z
2023-06-19T17:29:11Z
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Related to changes made in https://github.com/iterative/dvc/pull/9475
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Small fixes in ROC AUC docs
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-04-27T12:15:50Z
2022-05-02T13:28:57Z
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The list of use cases did not render on GitHub with the prepended spacing. Additionally, some typo's we're fixed.
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Adding `sep` to TextConfig
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[ "Hi @omar-araboghli, thanks for your proposal.\r\n\r\nHave you tried to use \"csv\" loader instead of \"text\"? That already has a `sep` argument.", "Hi @albertvillanova, thanks for the quick response!\r\n\r\nIndeed, I have been trying to use `csv` instead of `text`. However I am still not able to define range of...
2023-01-31T10:39:53Z
2023-01-31T14:50:18Z
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I have a local a `.txt` file that follows the `CONLL2003` format which I need to load using `load_script`. However, by using `sample_by='line'`, one can only split the dataset into lines without splitting each line into columns. Would it be reasonable to add a `sep` argument in combination with `sample_by='paragraph'` to parse a paragraph into an array for each column ? If so, I am happy to contribute! ## Environment * `python 3.8.10` * `datasets 2.9.0` ## Snippet of `train.txt` ```txt Distribution NN O O and NN O O dynamics NN O O of NN O O electron NN O B-RP complexes NN O I-RP in NN O O cyanobacterial NN O B-R membranes NN O I-R The NN O O occurrence NN O O of NN O O prostaglandin NN O B-R F2α NN O I-R in NN O O Pharbitis NN O B-R seedlings NN O I-R grown NN O O under NN O O short NN O B-P days NN O I-P or NN O I-P days NN O I-P ``` ## Current Behaviour ```python # defining 4 features ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags'] here would fail with `ValueError: Length of names (4) does not match length of arrays (1)` dataset = datasets.load_dataset(path='text', features=features, data_files={'train': 'train.txt'}, sample_by='line') dataset['train']['tokens'][0] >>> 'Distribution\tNN\tO\tO' ``` ## Expected Behaviour / Suggestion ```python # suppose we defined 4 features ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags'] dataset = datasets.load_dataset(path='text', features=features, data_files={'train': 'train.txt'}, sample_by='paragraph', sep='\t') dataset['train']['tokens'][0] >>> ['Distribution', 'and', 'dynamics', ... ] dataset['train']['ner_tags'][0] >>> ['O', 'O', 'O', ... ] ```
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Raise FileNotFoundError when passing data_files that don't exist
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e.g. when running `load_dataset("parquet", data_files="doesnt_exist.parquet")`
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Add SQL guide
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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...
2022-11-09T19:10:27Z
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This PR adapts @nateraw's awesome SQL notebook as a guide for the docs!
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adding hate-speech-and-offensive-language
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[ "made suggested changes and opened PR https://github.com/huggingface/datasets/pull/1628" ]
2020-12-17T18:35:15Z
2020-12-23T23:27:17Z
2020-12-23T23:27:16Z
CONTRIBUTOR
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VIVOS dataset for Vietnamese ASR
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2021-08-10T09:47:36Z
2021-08-12T11:09:30Z
2021-08-12T11:09:30Z
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Add missing parquet known extension
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2021-07-30T13:01:20Z
2021-07-30T13:24:31Z
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This code was failing because the parquet extension wasn't recognized: ```python from datasets import load_dataset base_url = "https://storage.googleapis.com/huggingface-nlp/cache/datasets/wikipedia/20200501.en/1.0.0/" data_files = {"train": base_url + "wikipedia-train.parquet"} wiki = load_dataset("parquet", data_files=data_files, split="train", streaming=True) ``` It raises ```python NotImplementedError: Extraction protocol for file at https://storage.googleapis.com/huggingface-nlp/cache/datasets/wikipedia/20200501.en/1.0.0/wikipedia-train.parquet is not implemented yet ``` I added `parquet` to the list of known extensions EDIT: added pickle, conllu, xml extensions as well
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ERROR: File "setup.py" or "setup.cfg" not found. Directory cannot be installed in editable mode: /content
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[ "Hi! The `cd` command in Jupyer/Colab needs to start with `%`, so this should work:\r\n```\r\n!git clone https://github.com/huggingface/datasets.git\r\n%cd datasets\r\n!pip install -e \".[streaming]\"\r\n```", "thanks @mariosasko i had the same mistake and your solution is what was needed" ]
2022-01-19T13:19:28Z
2022-08-05T12:35:51Z
2022-02-14T08:46:34Z
NONE
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## Bug The install of streaming dataset is giving following error. ## Steps to reproduce the bug ```python ! git clone https://github.com/huggingface/datasets.git ! cd datasets ! pip install -e ".[streaming]" ``` ## Actual results Cloning into 'datasets'... remote: Enumerating objects: 50816, done. remote: Counting objects: 100% (2356/2356), done. remote: Compressing objects: 100% (1606/1606), done. remote: Total 50816 (delta 834), reused 1741 (delta 525), pack-reused 48460 Receiving objects: 100% (50816/50816), 72.47 MiB | 27.68 MiB/s, done. Resolving deltas: 100% (22541/22541), done. Checking out files: 100% (6722/6722), done. ERROR: File "setup.py" or "setup.cfg" not found. Directory cannot be installed in editable mode: /content
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PR_kwDODunzps44cdR5
4,402
Skip identical files in `push_to_hub` instead of overwriting
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-05-25T13:12:51Z
2022-05-25T15:16:36Z
2022-05-25T15:08:03Z
CONTRIBUTOR
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Skip identical files instead of overwriting them to save bandwidth and circumvent (user-side/server-side) errors, which can arise when working with large datasets due to long-lasting HTTP connections, by repeating calls to `push_to_hub` to resume an upload. To be able to check if an upload can be resumed, this PR modifies the shard naming scheme from: ``` data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].parquet ``` to: ``` data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]-<SHARD_FINGERPRINT>.parquet ``` cc @LysandreJik
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fix: 🐛 pass token when retrieving the split names
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[ "Currently, it does not work with https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0/blob/main/common_voice_7_0.py#L146 (which was the goal), because `dl_manager.download_config.use_auth_token` is ignored, and the authentication is required to be use `huggingface-cli login`.\r\nIn my use case (data...
2022-01-07T10:29:22Z
2022-01-10T10:51:47Z
2022-01-10T10:51:46Z
CONTRIBUTOR
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[BUG] With Pandas 2.0.0, `load_dataset` raises `TypeError: read_csv() got an unexpected keyword argument 'mangle_dupe_cols'`
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[ "Thanks for reporting, @keyboardAnt.\r\n\r\nWe haven't noticed any crash in our CI tests. Could you please indicate specifically the `load_dataset` command that crashes in your side, so that we can reproduce it?", "This has been fixed in `datasets` 2.11" ]
2023-04-13T20:21:28Z
2023-07-06T17:01:59Z
2023-07-06T17:01:59Z
NONE
null
null
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The `load_dataset` function with Pandas `1.5.3` has no issue (just a FutureWarning) but crashes with Pandas `2.0.0`. For your convenience, I opened a draft Pull Request to fix it quickly: https://github.com/huggingface/datasets/pull/5745 --- * The FutureWarning mentioned above: ``` FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols' ```
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Fix project description in PyPI
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2021-10-18T12:47:29Z
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Fix project description appearing in PyPI, so that it contains the content of the README.md file (like transformers). Currently, `datasets` project description appearing in PyPI shows the release instructions addressed to core maintainers: https://pypi.org/project/datasets/1.13.3/ Fix #3102.
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I_kwDODunzps5YUNP8
5,337
Support webdataset format
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[ "I like the idea of having `webdataset` as an optional dependency to ensure our loader generates web datasets the same way as the main project.", "Webdataset is the one of the most popular dataset formats for large scale computer vision tasks. Upvote for this issue. ", "Any updates on this?", "We haven't had ...
2022-12-07T11:32:25Z
2023-05-26T10:34:45Z
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Webdataset is an efficient format for iterable datasets. It would be nice to support it in `datasets`, as discussed in https://github.com/rom1504/img2dataset/issues/234. In particular it would be awesome to be able to load one using `load_dataset` in streaming mode (either from a local directory, or from a dataset on the Hugging Face Hub). Some datasets on the Hub are already in webdataset format. It terms of implementation, we can have something similar to the Parquet loader. I also think it's fine to have webdataset as an optional dependency.
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Dataset.get_nearest_examples does not return all feature values for the k most similar datapoints - side effect of Dataset.set_format
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[ "Actually, my bad -- specifying\r\n```python\r\nfoo.set_format('numpy', ['vectors'], output_all_columns=True)\r\n```\r\nfixes it." ]
2023-07-28T20:31:59Z
2023-07-28T20:49:58Z
2023-07-28T20:49:58Z
NONE
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### Describe the bug Hi team! I observe that there seems to be a side effect of `Dataset.set_format`: after setting a format and creating a FAISS index, the method `get_nearest_examples` from the `Dataset` class, fails to retrieve anything else but the embeddings themselves - not super useful. This is not the case if not using the `set_format` method: you can also retrieve any other feature value, such as an index/id/etc. Are you able to reproduce what I observe? ### Steps to reproduce the bug ```python from datasets import Dataset import numpy as np foo = {'vectors': np.random.random((100,1024)), 'ids': [str(u) for u in range(100)]} foo = Dataset.from_dict(foo) foo.set_format('numpy', ['vectors']) foo.add_faiss_index('vectors') new_vector = np.random.random(1024) scores, res = foo.get_nearest_examples('vectors', new_vector, k=3) ``` This will return, for the resulting most similar vectors to `new_vector` - in particular it will not return the `ids` feature: ``` {'vectors': array([[random values ...]])} ``` ### Expected behavior The expected behavior happens when the `set_format` method is not called: ```python from datasets import Dataset import numpy as np foo = {'vectors': np.random.random((100,1024)), 'ids': [str(u) for u in range(100)]} foo = Dataset.from_dict(foo) # foo.set_format('numpy', ['vectors']) foo.add_faiss_index('vectors') new_vector = np.random.random(1024) scores, res = foo.get_nearest_examples('vectors', new_vector, k=3) ``` This *will* return the `ids` of the similar vectors - with unfortunately a list of lists in lieu of the array I think for caching reasons - read it elsewhere ``` {'vectors': [[random values on multiple lines...]], 'ids': ['x', 'y', 'z']} ``` ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.4.0-155-generic-x86_64-with-glibc2.31 - Python version: 3.10.6 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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Would like to Contribute a dataset
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[ "Hi! The process of contributing a dataset is explained here: https://huggingface.co/docs/datasets/upload_dataset. Also, check https://huggingface.co/docs/datasets/image_dataset for a more detailed explanation of how to share an image dataset." ]
2023-10-02T07:00:21Z
2023-10-10T16:27:54Z
2023-10-10T16:27:54Z
NONE
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I have a dataset of 2500 images that can be used for color-blind machine-learning algorithms. Since , there was no dataset available online , I made this dataset myself and would like to contribute this now to community
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Warn about checksums
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[ "_The documentation is not available anymore as the PR was closed or merged._", "I'm also in favor of disabling this by default - it's kinda impractical", "Great, thanks for the quick turnaround on this!" ]
2022-11-22T10:58:48Z
2022-11-23T11:43:50Z
2022-11-23T09:47:02Z
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It takes a lot of time on big datasets to compute the checksums, we should at least add a warning to notify the user about this step. I also mentioned how to disable it, and added a tqdm bar (delay=5 seconds) cc @ola13
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1,535,856,503
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5,430
Support Apache Beam >= 2.44.0
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[ "Some of the shard files now have 0 number of rows.\r\n\r\nWe have opened an issue in the Apache Beam repo:\r\n- https://github.com/apache/beam/issues/25041" ]
2023-01-17T06:42:12Z
2023-01-17T16:12:18Z
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Once we find out the root cause of: - #5426 we should revert the temporary pin on apache-beam introduced by: - #5429
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Reduce default max `writer_batch_size`
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2022-10-25T14:14:52Z
2022-10-27T12:19:27Z
2022-10-27T12:16:47Z
CONTRIBUTOR
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Reduce the default writer_batch_size from 10k to 1k examples. Additionally, align the default values of `batch_size` and `writer_batch_size` in `Dataset.cast` with the values from the corresponding docstring.
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[INVALID PR]
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2023-02-10T19:35:50Z
2023-02-10T19:51:45Z
2023-02-10T19:49:12Z
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Hi to whoever is reading this! 🤗 ## What's in this PR? ~~Basically, I've removed the 🤗`datasets` installation as `python -m pip install ".[quality]" in the `check_code_quality` job in `.github/workflows/ci.yaml`, as we don't need to install the whole package to run the CI, unless that's done on purpose e.g. to check that the Python package installation succeeds before running the tests over the matrix of os?~~ ~~So I just wanted to check whether the time was reduced doing this (which I assume it will), plus whether this is something that can be improved, or just discarded in case you're also using that step to make sure that the package can be installed.~~ ## What's missing? ~~I was just wondering whether you consider replacing `isort` and `flake8` with `ruff` (if possible), since it's way faster, more information at [`ruff`](https://github.com/charliermarsh/ruff). Before creating this PR the average time of the `check_code_quality` job was around 40s.~~ ## Edit Sorry for the inconvenience this may have caused, didn't realise that the config is defined in `setup.cfg` and `pyproject.toml`, so running those without installing the Python package leads to failure, my bad 😞
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759,354,627
MDExOlB1bGxSZXF1ZXN0NTM0Mzc0MzQ3
1,292
arXiv dataset added
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2020-12-08T11:08:28Z
2020-12-08T14:02:13Z
2020-12-08T14:02:13Z
CONTRIBUTOR
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1,160,181,404
I_kwDODunzps5FJvac
3,830
Got error when load cnn_dailymail dataset
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[ "Was able to reproduce the issue on Colab; full logs below. \r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNotADirectoryError Traceback (most recent call last)\r\n[<ipython-input-2-39967739ba7f>](https://localhost:8080/#) in <module>()\r\n 1...
2022-03-05T01:43:12Z
2022-03-07T06:53:41Z
2022-03-07T06:53:41Z
NONE
null
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When using datasets.load_dataset method to load cnn_dailymail dataset, got error as below: - windows os: FileNotFoundError: [WinError 3] 系统找不到指定的路径。: 'D:\\SourceCode\\DataScience\\HuggingFace\\Data\\downloads\\1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\\cnn\\stories' - google colab: NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' The code is to load dataset: windows os: ``` from datasets import load_dataset dataset = load_dataset("cnn_dailymail", "3.0.0", cache_dir="D:\\SourceCode\\DataScience\\HuggingFace\\Data") ``` google colab: ``` import datasets train_data = datasets.load_dataset("cnn_dailymail", "3.0.0", split="train") ```
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I_kwDODunzps5aPXGl
5,394
CI error: TypeError: dataclass_transform() got an unexpected keyword argument 'field_specifiers'
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[ "I still getting the same error :\r\n\r\n`python -m spacy download fr_core_news_lg\r\n`.\r\n`import spacy`", "@MFatnassi, this issue and the corresponding fix only affect our Continuous Integration testing environment.\r\n\r\nNote that `datasets` does not depend on `spacy`." ]
2022-12-29T18:58:44Z
2022-12-30T10:40:51Z
2022-12-29T21:00:27Z
MEMBER
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### Describe the bug While installing the dependencies, the CI raises a TypeError: ``` Traceback (most recent call last): File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/runpy.py", line 183, in _run_module_as_main mod_name, mod_spec, code = _get_module_details(mod_name, _Error) File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/runpy.py", line 142, in _get_module_details return _get_module_details(pkg_main_name, error) File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/runpy.py", line 109, in _get_module_details __import__(pkg_name) File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/spacy/__init__.py", line 6, in <module> from .errors import setup_default_warnings File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/spacy/errors.py", line 2, in <module> from .compat import Literal File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/spacy/compat.py", line 3, in <module> from thinc.util import copy_array File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/thinc/__init__.py", line 5, in <module> from .config import registry File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/thinc/config.py", line 2, in <module> import confection File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/confection/__init__.py", line 10, in <module> from pydantic import BaseModel, create_model, ValidationError, Extra File "pydantic/__init__.py", line 2, in init pydantic.__init__ File "pydantic/dataclasses.py", line 46, in init pydantic.dataclasses # | None | Attribute is set to None. | File "pydantic/main.py", line 121, in init pydantic.main TypeError: dataclass_transform() got an unexpected keyword argument 'field_specifiers' ``` See: https://github.com/huggingface/datasets/actions/runs/3793736481/jobs/6466356565 ### Steps to reproduce the bug ```shell pip install .[tests,metrics-tests] python -m spacy download en_core_web_sm ``` ### Expected behavior No error. ### Environment info See: https://github.com/huggingface/datasets/actions/runs/3793736481/jobs/6466356565
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972,522,480
MDExOlB1bGxSZXF1ZXN0NzE0MTAzNDIy
2,811
Fix stream oscar
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[ "One additional note: if we can try to not change the code of oscar.py too often, I'm sure users that have it in their cache directory will be happy to not have to redownload it every time they update the library ;)\r\n\r\n(since changing the code changes the cache directory of the dataset)", "I don't think this ...
2021-08-17T10:10:59Z
2021-08-26T10:26:15Z
2021-08-26T10:26:14Z
MEMBER
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Previously, an additional `open` was added to oscar to make it stream-compatible: 587bbb94e891b22863b312b99696e32708c379f4. This was argued that might be problematic: https://github.com/huggingface/datasets/pull/2786#discussion_r690045921 This PR: - removes that additional `open` - patches `gzip.open` with `xopen` + `compression="gzip"`
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1,170,087,793
PR_kwDODunzps40e_fb
3,930
Create README.md
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-03-15T19:16:59Z
2022-04-04T15:23:15Z
2022-04-04T15:17:28Z
NONE
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Creating a README for IndicGLUE cc @mcmillanmajora for fact checking in terms of languages (also, are there any limitations of the dataset or eval metric that I'm not aware of?)
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1,403,379,816
I_kwDODunzps5TpeBo
5,096
Transfer some canonical datasets under an organization namespace
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[ "The transfer of the dummy dataset to the dummy org works as expected:\r\n```python\r\nIn [1]: from datasets import load_dataset; ds = load_dataset(\"dummy_canonical_dataset\", download_mode=\"force_redownload\"); ds\r\nDownloading builder script: 100%|███████████████████████████████████████████████████████████████...
2022-10-10T15:44:31Z
2023-09-15T14:01:02Z
null
MEMBER
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As discussed during our @huggingface/datasets meeting, we are planning to move some "canonical" dataset scripts under their corresponding organization namespace (if this does not exist). On the contrary, if the dataset already exists under the organization namespace, we are deprecating the canonical one (and eventually delete it). First, we should test it using a dummy dataset/organization. TODO: - [x] Test with a dummy dataset - [x] Create dummy canonical dataset: https://huggingface.co/datasets/dummy_canonical_dataset - [x] Create dummy organization: https://huggingface.co/dummy-canonical-org - [x] Transfer dummy canonical dataset to dummy organization - [ ] Transfer datasets - [x] babi_qa => facebook - [x] cord19 => allenai - [x] emotion => dair-ai - [ ] gem => GEM - [x] hendrycks_test => cais/mmlu - [x] indonlu => indonlp - [ ] multilingual_librispeech => facebook - It already exists "facebook/multilingual_librispeech" - [ ] oscar => oscar-corpus - [x] peer_read => allenai - [x] qasper => allenai - [x] reddit => webis/tldr-17 - [x] russian_super_glue => russiannlp - [x] rvl_cdip => aharley - [x] s2orc => allenai - [x] scicite => allenai - [x] scifact => allenai - [x] scitldr => allenai - [x] swiss_judgment_prediction => rcds - [x] the_pile => EleutherAI - [ ] wmt14, wmt15, wmt16, wmt17, wmt18, wmt19,... => wmt - [ ] Deprecate (and eventually remove) datasets that cannot be transferred because they already exist - [x] banking77 => PolyAI - [x] common_voice => mozilla-foundation - [x] german_legal_entity_recognition => elenanereiss - ... EDIT: the list above is continuously being updated
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Fix google drive streaming for small files
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Actually the CI fails because of this\r\n![image](https://user-images.githubusercontent.com/42851186/159281771-78e611b1-6b04-4a87-8324-b6ba2d8c6a6a.png)\r\n\r\nIt looks like we can't have a proper way to test google drive in the CI r...
2022-03-21T11:38:46Z
2023-09-24T09:55:19Z
2022-03-21T14:25:58Z
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Google drive did another change recently, following #3787 #3843 . In particular Google Drive now returns 403 for GET requests with `confirm=t` when a files doesn't have a virus warning message. I fixed this by passing `confirm=t` if and only if when there is one (i.e. when status code is 200 for HEAD)
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testing doc-builder
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a...
2023-10-10T15:17:29Z
2023-10-13T08:57:14Z
2023-10-13T08:56:48Z
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testing https://github.com/huggingface/doc-builder/pull/426
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Improve ReadInstruction logic and update docs
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[ "Ready for the final review" ]
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Improve ReadInstruction logic and docs.
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Abusurdly slow on iteration
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[ "Hi ! You can try to use the [Image](https://huggingface.co/docs/datasets/v2.12.0/en/package_reference/main_classes#datasets.Image) type which [decodes images on-the-fly](https://huggingface.co/docs/datasets/v2.12.0/en/about_dataset_features#image-feature) into pytorch tensors :)\r\n\r\n```python\r\nds = Dataset.fr...
2023-05-11T08:04:09Z
2023-05-15T15:38:13Z
2023-05-15T15:38:13Z
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### Describe the bug I am attempting to iterate through an image dataset, but I am encountering a significant slowdown in the iteration speed. In order to investigate this issue, I conducted the following experiment: ```python a=torch.randn(100,224) a=torch.stack([a] * 10000) a.shape # %% ds=Dataset.from_dict({"tensor":a}) for i in tqdm(ds.with_format("numpy")): pass for i in tqdm(ds.with_format("torch")): pass ``` I noticed that the dataset in numpy format performs significantly faster than the one in torch format. My hypothesis is that the dataset undergoes a transformation process of torch->python->numpy(torch) in the background, which might be causing the slowdown. Is there any way to expedite the process by bypassing such transformations? Furthermore, if I increase the size of a to an image shape, like: ```python a=torch.randn(3,224,224) ``` the iteration speed becomes absurdly slow, around 100 iterations per second, whereas the speed with numpy format is approximately 250 iterations per second. This level of speed would be unacceptable for large image datasets, as it could take several hours just to iterate through a single epoch. ### Steps to reproduce the bug ```python a=torch.randn(100,224) a=torch.stack([a] * 10000) a.shape # %% ds=Dataset.from_dict({"tensor":a}) for i in tqdm(ds.with_format("numpy")): pass for i in tqdm(ds.with_format("torch")): pass ``` ### Expected behavior iteration faster ### Environment info - `datasets` version: 2.11.0 - Platform: Linux-5.4.0-148-generic-x86_64-with-glibc2.10 - Python version: 3.8.16 - Huggingface_hub version: 0.13.4 - PyArrow version: 11.0.0 - Pandas version: 2.0.0
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Concat only unique fields in DatasetInfo.from_merge
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[ "Hi @mariosasko,\r\nJust came across this PR and I was wondering if we can use\r\n`description = \"\\n\\n\".join(OrderedDict.fromkeys([info.description for info in dataset_infos]))`\r\n\r\nThis will obviate the need for `unique` and is almost as fast as `set`. We could have used `dict` inplace of `OrderedDict` but ...
2021-04-03T14:31:30Z
2021-04-06T14:40:00Z
2021-04-06T14:39:59Z
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I thought someone from the community with less experience would be interested in fixing this issue, but that wasn't the case. Fixes #2103
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ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py
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[ "Hi ! Do you have some kind of proxy in your browser that gives you access to internet ?\r\n\r\nMaybe you're having this error because you don't have access to this URL from python ?", "Hi,do you fixed this error?\r\nI still have this issue when use \"use_auth_token=True\"", "You don't need authentication to ac...
2021-11-04T10:47:26Z
2022-03-30T08:26:35Z
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when I use python examples/pytorch/translation/run_translation.py --model_name_or_path examples/pytorch/translation/opus-mt-en-ro --do_train --do_eval --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config_name ro-en --output_dir /tmp/tst-translation --per_device_train_batch_size=4 --per_device_eval_batch_size=4 --overwrite_output_dir --predict_with_generate to finetune translation model on huggingface, I get the issue"ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py".But I can open the https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py by using website. What should I do to solve the issue?
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Filter unsupported extensions
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a...
2023-06-21T15:43:01Z
2023-06-22T14:23:29Z
2023-06-22T14:16:26Z
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I used a regex to filter the data files based on their extension for packaged builders. I tried and a regex is 10x faster that using `in` to check if the extension is in the list of supported extensions. Supersedes https://github.com/huggingface/datasets/pull/5850 Close https://github.com/huggingface/datasets/issues/5849 I also did a small change to favor the parquet module in case of a draw in the extension counter.
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Can't reach "https://storage.googleapis.com/illuin/fquad/train.json.zip" when trying to load fquad dataset
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[ "Hi ! Apparently the data are not available from this url anymore. We'll replace it with the new url when it's available", "I saw this on their website when we request to download the dataset:\r\n![image](https://user-images.githubusercontent.com/19718818/114879600-fa458680-9e1e-11eb-9e05-f0963d68ff0f.png)\r\n\r\...
2021-04-12T13:49:56Z
2023-10-03T16:09:19Z
2023-10-03T16:09:18Z
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I'm trying to load the [fquad dataset](https://huggingface.co/datasets/fquad) by running: ```Python fquad = load_dataset("fquad") ``` which produces the following error: ``` Using custom data configuration default Downloading and preparing dataset fquad/default (download: 3.14 MiB, generated: 6.62 MiB, post-processed: Unknown size, total: 9.76 MiB) to /root/.cache/huggingface/datasets/fquad/default/0.1.0/778dc2c85813d05ddd0c17087294d5f8f24820752340958070876b677af9f061... --------------------------------------------------------------------------- ConnectionError Traceback (most recent call last) <ipython-input-48-a2721797e23b> in <module>() ----> 1 fquad = load_dataset("fquad") 11 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token) 614 raise FileNotFoundError("Couldn't find file at {}".format(url)) 615 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") --> 616 raise ConnectionError("Couldn't reach {}".format(url)) 617 618 # Try a second time ConnectionError: Couldn't reach https://storage.googleapis.com/illuin/fquad/train.json.zip ``` Does anyone know why that is and how to fix it?
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Remove redundant code from some dataset module factories
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-09-28T07:06:26Z
2022-09-28T16:57:51Z
2022-09-28T16:55:12Z
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This PR removes some redundant code introduced by mistake after a refactoring in: - #4576
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Overwrite legacy default config name in `dataset_infos.json` in packaged datasets
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6231). All of your documentation changes will be reflected on that endpoint.", "realized that this pr is still not merged, @lhoestq maybe you can take a look at it? ", "I think https://github.com/huggingface/datasets/pull/621...
2023-09-11T16:27:09Z
2023-09-26T11:19:36Z
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Currently if we push data as default config with `.push_to_hub` to a repo that has a legacy `dataset_infos.json` file containing a legacy default config name like `{username}--{dataset_name}`, new key `"default"` is added to `dataset_infos.json` along with the legacy one. I think the legacy one should be dropped in this case. Also, in `load.py` I suggest to check if a legacy config name is indeed a legacy config name because after this fix it might not be the case (this check was first introduced in https://github.com/huggingface/datasets/pull/6218)
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Remove redundant prepare_module
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2021-07-06T13:47:45Z
2021-07-12T14:10:52Z
2021-07-07T13:01:46Z
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I have noticed that after implementing `load_dataset_builder` (#2500), there is a redundant call to `prepare_module`.
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Adding Text-based NP Enrichment (TNE) dataset
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[ "Hey @lhoestq, can you please have a look? 🙏", "Great, thanks again @lhoestq! I think we're good to go now", "Done" ]
2022-04-12T15:47:03Z
2022-05-03T14:05:48Z
2022-05-03T14:05:48Z
CONTRIBUTOR
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Added the [TNE](https://github.com/yanaiela/TNE) dataset to the library
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ci: 🎡 remove two obsolete issue templates
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-01-13T12:58:43Z
2023-01-13T13:36:00Z
2023-01-13T13:29:01Z
CONTRIBUTOR
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add-dataset is not needed anymore since the "canonical" datasets are on the Hub. And dataset-viewer is managed within the datasets-server project. See https://github.com/huggingface/datasets/issues/new/choose <img width="1245" alt="Capture d’écran 2023-01-13 à 13 59 58" src="https://user-images.githubusercontent.com/1676121/212325813-2d4c30e2-343e-4aa2-8cce-b2b77f45628e.png">
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Fix google bleu typos, examples
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Extend support for streaming datasets that use pd.read_excel
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[ "TODO in the future: https://github.com/huggingface/datasets/pull/3355#discussion_r761138011\r\n- If we finally find a use case where the `pd.read_excel()` can work in streaming mode (using fsspec), that is, without using the `.read()`, I propose to try this first, catch the ValueError and then try with `.read`, bu...
2021-12-01T14:22:43Z
2021-12-17T07:24:19Z
2021-12-17T07:24:18Z
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This PR fixes error: ``` ValueError: Cannot seek streaming HTTP file ``` CC: @severo
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[DOCS] Fix typo: Elasticsearch
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-09-25T12:50:59Z
2023-09-26T14:55:35Z
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Not ElasticSearch :)
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Make HANS dataset streamable
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-04-12T17:34:13Z
2022-04-13T12:03:46Z
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Fix #4133
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Add Arrow type casting to struct for Image and Audio + Support nested casting
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[ "Regarding the tests I'm just missing the FixedSizeListType type casting for ListArray objects, will to it tomorrow as well as adding new tests + docstrings\r\n\r\nand also adding soundfile in the CI", "While writing some tests I noticed that the ExtensionArray can't be directly concatenated - maybe we can get ri...
2022-01-13T15:36:59Z
2022-11-29T11:14:16Z
2022-01-21T13:22:27Z
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## Intro 1. Currently, it's not possible to have nested features containing Audio or Image. 2. Moreover one can keep an Arrow array as a StringArray to store paths to images, but such arrays can't be directly concatenated to another image array if it's stored an another Arrow type (typically, a StructType). 3. Allowing several Arrow types for a single HF feature type also leads to bugs like this one #3497 4. Issues like #3247 are quite frequent and happen when Arrow fails to reorder StructArrays. 5. Casting Audio feature type is blocking preparation for the ASR task template: https://github.com/huggingface/datasets/pull/3364 All those issues are linked together by the fact that: - we are limited by the Arrow type casting which is lacking features for nested types. - and especially for Audio and Image: they are not robust enough for concatenation and feature inference. ## Proposed solution To fix 1 and 4 I implemented nested array type casting (which is missing in PyArrow). To fix 2, 3 and 5 while having a simple implementation for nested array type casting, I changed the storage type of Audio and Image to always be a StructType. Also casting from StringType is directly implemented via a new function `cast_storage` that is defined individually for Audio and Image. I also added nested decoding. ## Implementation details ### I. Better Arrow data type casting for nested data structures I implemented new functions `array_cast` and `table_cast` that do the exact same as `pyarrow.Array.cast` or `pyarrow.Table.cast` but support nested struct casting and array re-ordering. These functions can be used on PyArrow objects, and are already integrated in our own `datasets.table.Table.cast` functions. So one can do `my_dataset.data.cast(pyarrow_schema_with_custom_hf_types)` directly. ### II. New image and audio extension types with custom casting I used PyArrow extension types to be able to define what casting is allowed or not. For example both StringType->ImageExtensionType and StructType->ImageExtensionType are allowed, via the `cast_storage` method. I factorized all the PyArrow + Pandas extension stuff in the `base_extension.py` file. This aims at separating the front-facing API code of `datasets` from the Arrow back-end which requires advanced knowledge. ### III. Nested feature decoding I added a new function `decode_nested_example` to decode image and audio data in nested data structures. For optimization's sake, this function is only called if a column has at least one feature that requires decoding. ## Alternative considered The casting to struct type could have been done directly with python objects using some Audio and Image methods, but bringing arrow data to python objects is expensive. The Audio and Image types could also have been able to convert the arrow data directly, but this is not convenient to use when casting a full Arrow Table with nested fields. Therefore I decided to keep the Arrow data casting logic in Arrow extension types. ## Future work This work can be used to allow the ArrayND feature types to be nested too (see issue #887) ## TODO - [x] fix current tests - [x] add new tests - [x] docstrings/comments
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Fix comet metric
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Finally I'm done updating the dependencies ^^'\r\n\r\ncc @sashavor can you review my changes in the metric card please ?", "Looks good to me! Just fixed a tiny typo :wink: ", "Thanks !" ]
2022-03-16T15:56:47Z
2022-03-22T15:10:12Z
2022-03-22T15:05:30Z
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The COMET metric has been broken for a while since big breaking changes happened. We did not catch them in the CI because the slow test mocks the download_model function that was changed. This PR fixes the metric, updates the download_model mock and updates the doctest.
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Allow to use tqdm>=4.50.0
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2021-06-11T14:49:21Z
2021-06-11T15:11:51Z
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We used to have permission errors on windows whith the latest versions of tqdm (see [here](https://app.circleci.com/pipelines/github/huggingface/datasets/6365/workflows/24f7c960-3176-43a5-9652-7830a23a981e/jobs/39232)) They were due to open arrow files not properly closed by pyarrow. Since https://github.com/huggingface/datasets/commit/42320a110d9d072703814e1f630a0d90d626a1e6 gc.collect is called each time we don't need an arrow file to make sure that the files are closed. close https://github.com/huggingface/datasets/issues/2471 cc @lewtun
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`with_format` behavior is inconsistent on different datasets
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[ "Hi! You can get a `torch.Tensor` if you do the following:\r\n```python\r\nraw = load_dataset(\"beans\", split=\"train\")\r\nraw = raw.select(range(100))\r\n\r\npreprocessor = AutoFeatureExtractor.from_pretrained(\"nateraw/vit-base-beans\")\r\n\r\nfrom datasets import Array3D\r\nfeatures = raw.features.copy()\r\nfe...
2022-08-08T10:41:34Z
2022-08-09T16:49:09Z
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## Describe the bug I found a case where `with_format` does not transform the dataset to the requested format. ## Steps to reproduce the bug Run: ```python from transformers import AutoTokenizer, AutoFeatureExtractor from datasets import load_dataset raw = load_dataset("glue", "sst2", split="train") raw = raw.select(range(100)) tokenizer = AutoTokenizer.from_pretrained("philschmid/tiny-bert-sst2-distilled") def preprocess_func(examples): return tokenizer(examples["sentence"], padding=True, max_length=256, truncation=True) data = raw.map(preprocess_func, batched=True) print(type(data[0]["input_ids"])) data = data.with_format("torch", columns=["input_ids"]) print(type(data[0]["input_ids"])) ``` printing as expected: ```python <class 'list'> <class 'torch.Tensor'> ``` Then run: ```python raw = load_dataset("beans", split="train") raw = raw.select(range(100)) preprocessor = AutoFeatureExtractor.from_pretrained("nateraw/vit-base-beans") def preprocess_func(examples): imgs = [img.convert("RGB") for img in examples["image"]] return preprocessor(imgs) data = raw.map(preprocess_func, batched=True) print(type(data[0]["pixel_values"])) data = data.with_format("torch", columns=["pixel_values"]) print(type(data[0]["pixel_values"])) ``` Printing, unexpectedly ```python <class 'list'> <class 'list'> ``` ## Expected results `with_format` should transform into the requested format; it's not the case. ## Actual results `type(data[0]["pixel_values"])` should be `torch.Tensor` in the example above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: dev version, commit 44af3fafb527302282f6b6507b952de7435f0979 - Platform: Linux - Python version: 3.9.12 - PyArrow version: 7.0.0
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seqeval metric does not work with a recent version of sklearn: classification_report() got an unexpected keyword argument 'output_dict'
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[ "Sorry, I was using an old version of sequeval" ]
2021-06-17T15:36:02Z
2021-06-17T15:46:07Z
2021-06-17T15:46:07Z
NONE
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## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python from datasets import load_dataset, load_metric seqeval = load_metric("seqeval") seqeval.compute(predictions=[['A']], references=[['A']]) ``` ## Expected results The function computes a dict with metrics ## Actual results ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-39-69a57f5cf06f> in <module> 1 from datasets import load_dataset, load_metric 2 seqeval = load_metric("seqeval") ----> 3 seqeval.compute(predictions=[['A']], references=[['A']]) ~/p3/lib/python3.7/site-packages/datasets/metric.py in compute(self, *args, **kwargs) 396 references = self.data["references"] 397 with temp_seed(self.seed): --> 398 output = self._compute(predictions=predictions, references=references, **kwargs) 399 400 if self.buf_writer is not None: ~/.cache/huggingface/modules/datasets_modules/metrics/seqeval/81eda1ff004361d4fa48754a446ec69bb7aa9cf4d14c7215f407d1475941c5ff/seqeval.py in _compute(self, predictions, references, suffix) 95 96 def _compute(self, predictions, references, suffix=False): ---> 97 report = classification_report(y_true=references, y_pred=predictions, suffix=suffix, output_dict=True) 98 report.pop("macro avg") 99 report.pop("weighted avg") TypeError: classification_report() got an unexpected keyword argument 'output_dict' ``` ## Environment info sklearn=0.24 datasets=1.1.3
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IWSLT-17 Link Broken
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[ "Sorry, this is a duplicate of #1287. Not sure why it didn't come up when I searched `iwslt` in the issues list.", "Closing this since its a duplicate" ]
2020-12-17T00:46:42Z
2020-12-18T08:06:36Z
2020-12-18T08:05:28Z
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``` FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnmted//texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.tgz ```
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Doc readme improvements
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a...
2023-10-11T21:51:12Z
2023-10-12T12:47:15Z
2023-10-12T12:38:19Z
CONTRIBUTOR
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Changes in the doc READMe: * adds two new sections (to be aligned with `transformers` and `hfh`): "Previewing the documentation" and "Writing documentation examples" * replaces the mentions of `transformers` with `datasets` * fixes some dead links
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Re-add code and und language tags
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-08-26T09:48:57Z
2022-08-26T10:27:18Z
2022-08-26T10:24:20Z
MEMBER
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This PR fixes the removal of 2 language tags done by: - #4882 The tags are: - "code": this is not a IANA tag but needed - "und": this is one of the special scoped tags removed by 0d53202b9abce6fd0358cb00d06fcfd904b875af - used in "mc4" and "udhr" datasets
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Add Belebele multiple-choice machine reading comprehension (MRC) dataset
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[ "This dataset is already available on the Hub: https://huggingface.co/datasets/facebook/belebele.\r\n" ]
2023-10-06T06:58:03Z
2023-10-06T13:26:51Z
2023-10-06T13:26:51Z
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### Feature request Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the [FLORES-200](https://github.com/facebookresearch/flores/tree/main/flores200) dataset. The human annotation procedure was carefully curated to create questions that discriminate between different levels of generalizable language comprehension and is reinforced by extensive quality checks. While all questions directly relate to the passage, the English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. Belebele opens up new avenues for evaluating and analyzing the multilingual abilities of language models and NLP systems. Please refer to paper for more details, [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://arxiv.org/abs/2308.16884). ## Composition - 900 questions per language variant - 488 distinct passages, there are 1-2 associated questions for each. - For each question, there is 4 multiple-choice answers, exactly 1 of which is correct. - 122 language/language variants (including English). - 900 x 122 = 109,800 total questions. ### Motivation official repo https://github.com/facebookresearch/belebele ### Your contribution -
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Scifi_TV_Shows: Having trouble getting viewer to find appropriate files
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[ "Hi ! I think this is because `iter_archive` doesn't support ZIP files yet. See https://github.com/huggingface/datasets/issues/3272\r\n\r\nYou can navigate into the archive this way instead:\r\n```python\r\n# in split_generators\r\ndata_dir = dl_manager.download_and_extract(url)\r\ntrain_filepath = os.path.join(dat...
2021-11-12T19:25:19Z
2021-12-21T10:24:10Z
2021-12-21T10:24:10Z
NONE
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## Dataset viewer issue for '*Science Fiction TV Show Plots Corpus (Scifi_TV_Shows)*' **Link:** [link](https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows) I tried adding both a script (https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/blob/main/Scifi_TV_Shows.py) and some dummy examples (https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/tree/main/dummy), but the viewer still has a 404 error ("Not found. Maybe the cache is missing, or maybe the ressource does not exist."). I'm not sure what to try next. Thanks in advance! Am I the one who added this dataset? Yes
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Check builder cls default config name in inspect
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2023-09-21T10:15:32Z
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Fix https://github.com/huggingface/datasets-server/issues/1812 this was causing this issue: ```ipython In [1]: from datasets import * In [2]: inspect.get_dataset_config_names("aakanksha/udpos") Out[2]: ['default'] In [3]: load_dataset_builder("aakanksha/udpos").config.name Out[3]: 'en' ```
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758,454,438
MDExOlB1bGxSZXF1ZXN0NTMzNjI0ODY5
1,248
Update step-by-step guide about the dataset cards
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2020-12-07T12:12:12Z
2020-12-07T13:19:24Z
2020-12-07T13:19:23Z
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Small update in the step-by-step guide about the dataset cards to indicate it can be created and completing while exploring the dataset.
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944,672,565
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[load_dataset] shard and parallelize the process
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[ "I need the same feature for distributed training", "I think @TevenLeScao is exploring adding multiprocessing in `GeneratorBasedBuilder._prepare_split` - feel free to post updates here :)", "Posted a PR to address the building side, still needs something to load sharded arrow files + tests" ]
2021-07-14T18:04:58Z
2022-10-12T20:05:07Z
null
CONTRIBUTOR
null
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- Some huge datasets take forever to build the first time. (e.g. oscar/en) as it's done in a single cpu core. - If the build crashes, everything done up to that point gets lost Request: Shard the build over multiple arrow files, which would enable: - much faster build by parallelizing the build process - if the process crashed, the completed arrow files don't need to be re-built again Thank you! @lhoestq
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811,384,174
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1,905
Standardizing datasets.dtypes
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[ "Also - I took a stab at updating the docs, but I'm not sure how to actually check the outputs to see if it's formatted properly." ]
2021-02-18T19:15:31Z
2021-02-20T22:01:30Z
2021-02-20T22:01:30Z
CONTRIBUTOR
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This PR was further branched off of jdy-str-to-pyarrow-parsing, so it depends on https://github.com/huggingface/datasets/pull/1900 going first for the diff to be up-to-date (I'm not sure if there's a way for me to use jdy-str-to-pyarrow-parsing as a base branch while having it appear in the pull requests here). This moves away from `str(pyarrow.DataType)` as the method of choice for creating dtypes, favoring an explicit mapping to a list of supported Value dtypes. I believe in practice this should be backward compatible, since anyone previously using Value() would only have been able to use dtypes that had an identically named pyarrow factory function, which are all explicitly supported here.
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load_dataset in seaborn is not working for me. getting this error.
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[ "Hi! `seaborn`'s `load_dataset` pulls datasets from [here](https://github.com/mwaskom/seaborn-data) and not from our Hub, so this issue is not related to our library in any way and should be reported in their repo instead." ]
2023-02-16T14:01:58Z
2023-02-16T14:44:36Z
2023-02-16T14:44:36Z
NONE
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TimeoutError Traceback (most recent call last) ~\anaconda3\lib\urllib\request.py in do_open(self, http_class, req, **http_conn_args) 1345 try: -> 1346 h.request(req.get_method(), req.selector, req.data, headers, 1347 encode_chunked=req.has_header('Transfer-encoding')) ~\anaconda3\lib\http\client.py in request(self, method, url, body, headers, encode_chunked) 1278 """Send a complete request to the server.""" -> 1279 self._send_request(method, url, body, headers, encode_chunked) 1280 ~\anaconda3\lib\http\client.py in _send_request(self, method, url, body, headers, encode_chunked) 1324 body = _encode(body, 'body') -> 1325 self.endheaders(body, encode_chunked=encode_chunked) 1326 ~\anaconda3\lib\http\client.py in endheaders(self, message_body, encode_chunked) 1273 raise CannotSendHeader() -> 1274 self._send_output(message_body, encode_chunked=encode_chunked) 1275 ~\anaconda3\lib\http\client.py in _send_output(self, message_body, encode_chunked) 1033 del self._buffer[:] -> 1034 self.send(msg) 1035 ~\anaconda3\lib\http\client.py in send(self, data) 973 if self.auto_open: --> 974 self.connect() 975 else: ~\anaconda3\lib\http\client.py in connect(self) 1440 -> 1441 super().connect() 1442 ~\anaconda3\lib\http\client.py in connect(self) 944 """Connect to the host and port specified in __init__.""" --> 945 self.sock = self._create_connection( 946 (self.host,self.port), self.timeout, self.source_address) ~\anaconda3\lib\socket.py in create_connection(address, timeout, source_address) 843 try: --> 844 raise err 845 finally: ~\anaconda3\lib\socket.py in create_connection(address, timeout, source_address) 831 sock.bind(source_address) --> 832 sock.connect(sa) 833 # Break explicitly a reference cycle TimeoutError: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond During handling of the above exception, another exception occurred: URLError Traceback (most recent call last) ~\AppData\Local\Temp/ipykernel_12220/2927704185.py in <module> 1 import seaborn as sn ----> 2 iris = sn.load_dataset('iris') ~\anaconda3\lib\site-packages\seaborn\utils.py in load_dataset(name, cache, data_home, **kws) 594 if name not in get_dataset_names(): 595 raise ValueError(f"'{name}' is not one of the example datasets.") --> 596 urlretrieve(url, cache_path) 597 full_path = cache_path 598 else: ~\anaconda3\lib\urllib\request.py in urlretrieve(url, filename, reporthook, data) 237 url_type, path = _splittype(url) 238 --> 239 with contextlib.closing(urlopen(url, data)) as fp: 240 headers = fp.info() 241 ~\anaconda3\lib\urllib\request.py in urlopen(url, data, timeout, cafile, capath, cadefault, context) 212 else: 213 opener = _opener --> 214 return opener.open(url, data, timeout) 215 216 def install_opener(opener): ~\anaconda3\lib\urllib\request.py in open(self, fullurl, data, timeout) 515 516 sys.audit('urllib.Request', req.full_url, req.data, req.headers, req.get_method()) --> 517 response = self._open(req, data) 518 519 # post-process response ~\anaconda3\lib\urllib\request.py in _open(self, req, data) 532 533 protocol = req.type --> 534 result = self._call_chain(self.handle_open, protocol, protocol + 535 '_open', req) 536 if result: ~\anaconda3\lib\urllib\request.py in _call_chain(self, chain, kind, meth_name, *args) 492 for handler in handlers: 493 func = getattr(handler, meth_name) --> 494 result = func(*args) 495 if result is not None: 496 return result ~\anaconda3\lib\urllib\request.py in https_open(self, req) 1387 1388 def https_open(self, req): -> 1389 return self.do_open(http.client.HTTPSConnection, req, 1390 context=self._context, check_hostname=self._check_hostname) 1391 ~\anaconda3\lib\urllib\request.py in do_open(self, http_class, req, **http_conn_args) 1347 encode_chunked=req.has_header('Transfer-encoding')) 1348 except OSError as err: # timeout error -> 1349 raise URLError(err) 1350 r = h.getresponse() 1351 except: URLError: <urlopen error [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond>
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adding covid-tweets-japanese (again)
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[ "README.md is not created yet. I'll add it soon.", "Thank you for your detailed code review! It's so helpful.\r\nI'll reflect them to the code in 24 hours.\r\n\r\nYou may have told me in Slack (I cannot find the conversation log though I've looked through threads), but I'm sorry it seems I'm still misunderstandin...
2020-12-09T17:46:46Z
2020-12-13T17:54:14Z
2020-12-13T17:47:36Z
CONTRIBUTOR
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I had mistaken use git rebase, I was so hurried to fix it. However, I didn't fully consider the use of git reset , so I unintendedly stopped PR (#1367) altogether. Sorry about that. I'll make a new PR.
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Update ADD_NEW_DATASET.md
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2021-12-27T12:24:51Z
2021-12-27T15:00:45Z
2021-12-27T15:00:45Z
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fixed make style prompt for Windows Terminal
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4,357
Fix warning in push_to_hub
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-05-16T11:50:17Z
2022-05-16T15:18:49Z
2022-05-16T15:10:41Z
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Fix warning: ``` FutureWarning: 'shard_size' was renamed to 'max_shard_size' in version 2.1.1 and will be removed in 2.4.0. ```
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844,603,518
MDExOlB1bGxSZXF1ZXN0NjAzODMxOTE2
2,145
Implement Dataset add_column
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[ "#2274 has been merged. You can now merge master into this branch and use `assert_arrow_metadata_are_synced_with_dataset_features(dset)` to make sure that the metadata are good :)" ]
2021-03-30T14:02:14Z
2021-04-29T14:50:44Z
2021-04-29T14:50:43Z
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Implement `Dataset.add_column`. Close #1954.
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1,366,356,514
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4,953
CI test of TensorFlow is failing
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2022-09-08T13:39:29Z
2022-09-08T15:14:45Z
2022-09-08T15:14:45Z
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## Describe the bug The following CI test fails: https://github.com/huggingface/datasets/runs/8246722693?check_suite_focus=true ``` FAILED tests/test_py_utils.py::TempSeedTest::test_tensorflow - AssertionError: ``` Details: ``` _________________________ TempSeedTest.test_tensorflow _________________________ [gw0] linux -- Python 3.7.13 /opt/hostedtoolcache/Python/3.7.13/x64/bin/python self = <tests.test_py_utils.TempSeedTest testMethod=test_tensorflow> @require_tf def test_tensorflow(self): import tensorflow as tf from tensorflow.keras import layers def gen_random_output(): model = layers.Dense(2) x = tf.random.uniform((1, 3)) return model(x).numpy() with temp_seed(42, set_tensorflow=True): out1 = gen_random_output() with temp_seed(42, set_tensorflow=True): out2 = gen_random_output() out3 = gen_random_output() > np.testing.assert_equal(out1, out2) E AssertionError: E Arrays are not equal E E Mismatched elements: 2 / 2 (100%) E Max absolute difference: 0.84619296 E Max relative difference: 16.083529 E x: array([[-0.793581, 0.333286]], dtype=float32) E y: array([[0.052612, 0.539708]], dtype=float32) tests/test_py_utils.py:149: AssertionError ```
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TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe'
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[ "Hi! Casting class labels from strings is currently not supported in the CSV loader, but you can get the same result with an additional map as follows:\r\n```python\r\nfrom datasets import load_dataset,Features,Value,ClassLabel\r\nclass_names = [\"cmn\",\"deu\",\"rus\",\"fra\",\"eng\",\"jpn\",\"spa\",\"ita\",\"kor\...
2022-04-25T07:28:42Z
2022-05-31T12:16:31Z
2022-05-31T12:16:31Z
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### System Info ```shell - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.10.0+cu111 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> ``` ### Who can help? @LysandreJik ### Information - [ ] The official example scripts - [X] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [X] My own task or dataset (give details below) ### Reproduction ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], features = features ``` ERROR: ``` ClassLabel(num_classes=403, names=['cmn', 'deu', 'rus', 'fra', 'eng', 'jpn', 'spa', 'ita', 'kor', 'vie', 'nld', 'epo', 'por', 'tur', 'heb', 'hun', 'ell', 'ind', 'ara', 'arz', 'fin', 'bul', 'yue', 'swe', 'ukr', 'bel', 'que', 'ces', 'swh', 'nno', 'wuu', 'nob', 'zsm', 'est', 'kat', 'pol', 'lat', 'urd', 'sqi', 'isl', 'fry', 'afr', 'ron', 'fao', 'san', 'bre', 'tat', 'yid', 'uig', 'uzb', 'srp', 'qya', 'dan', 'pes', 'slk', 'eus', 'cycl', 'acm', 'tgl', 'lvs', 'kaz', 'hye', 'hin', 'lit', 'ben', 'cat', 'bos', 'hrv', 'tha', 'orv', 'cha', 'mon', 'lzh', 'scn', 'gle', 'mkd', 'slv', 'frm', 'glg', 'vol', 'ain', 'jbo', 'tok', 'ina', 'nds', 'mal', 'tlh', 'roh', 'ltz', 'oss', 'ido', 'gla', 'mlt', 'sco', 'ast', 'jav', 'oci', 'ile', 'ota', 'xal', 'tel', 'sjn', 'nov', 'khm', 'tpi', 'ang', 'aze', 'tgk', 'tuk', 'chv', 'hsb', 'dsb', 'bod', 'sme', 'cym', 'mri', 'ksh', 'kmr', 'ewe', 'kab', 'ber', 'tpw', 'udm', 'lld', 'pms', 'lad', 'grn', 'mlg', 'xho', 'pnb', 'grc', 'hat', 'lao', 'npi', 'cor', 'nah', 'avk', 'mar', 'guj', 'pan', 'kir', 'myv', 'prg', 'sux', 'crs', 'ckt', 'bak', 'zlm', 'hil', 'cbk', 'chr', 'nav', 'lkt', 'enm', 'arq', 'lin', 'abk', 'pcd', 'rom', 'gsw', 'tam', 'zul', 'awa', 'wln', 'amh', 'bar', 'hbo', 'mhr', 'bho', 'mrj', 'ckb', 'osx', 'pfl', 'mgm', 'sna', 'mah', 'hau', 'kan', 'nog', 'sin', 'glv', 'dng', 'kal', 'liv', 'vro', 'apc', 'jdt', 'fur', 'che', 'haw', 'yor', 'crh', 'pdc', 'ppl', 'kin', 'shs', 'mnw', 'tet', 'sah', 'kum', 'ngt', 'nya', 'pus', 'hif', 'mya', 'moh', 'wol', 'tir', 'ton', 'lzz', 'oar', 'lug', 'brx', 'non', 'mww', 'hak', 'nlv', 'ngu', 'bua', 'aym', 'vec', 'ibo', 'tkl', 'bam', 'kha', 'ceb', 'lou', 'fuc', 'smo', 'gag', 'lfn', 'arg', 'umb', 'tyv', 'kjh', 'oji', 'cyo', 'urh', 'kzj', 'pam', 'srd', 'lmo', 'swg', 'mdf', 'gil', 'snd', 'tso', 'sot', 'zza', 'tsn', 'pau', 'som', 'egl', 'ady', 'asm', 'ori', 'dtp', 'cho', 'max', 'kam', 'niu', 'sag', 'ilo', 'kaa', 'fuv', 'nch', 'hoc', 'iba', 'gbm', 'sun', 'war', 'mvv', 'pap', 'ary', 'kxi', 'csb', 'pag', 'cos', 'rif', 'kek', 'krc', 'aii', 'ban', 'ssw', 'tvl', 'mfe', 'tah', 'bvy', 'bcl', 'hnj', 'nau', 'nst', 'afb', 'quc', 'min', 'tmw', 'mad', 'bjn', 'mai', 'cjy', 'got', 'hsn', 'gan', 'tzl', 'dws', 'ldn', 'afh', 'sgs', 'krl', 'vep', 'rue', 'tly', 'mic', 'ext', 'izh', 'sma', 'jam', 'cmo', 'mwl', 'kpv', 'koi', 'bis', 'ike', 'run', 'evn', 'ryu', 'mnc', 'aoz', 'otk', 'kas', 'aln', 'akl', 'yua', 'shy', 'fkv', 'gos', 'fij', 'thv', 'zgh', 'gcf', 'cay', 'xmf', 'tig', 'div', 'lij', 'rap', 'hrx', 'cpi', 'tts', 'gaa', 'tmr', 'iii', 'ltg', 'bzt', 'syc', 'emx', 'gom', 'chg', 'osp', 'stq', 'frr', 'fro', 'nys', 'toi', 'new', 'phn', 'jpa', 'rel', 'drt', 'chn', 'pli', 'laa', 'bal', 'hdn', 'hax', 'mik', 'ajp', 'xqa', 'pal', 'crk', 'mni', 'lut', 'ayl', 'ood', 'sdh', 'ofs', 'nus', 'kiu', 'diq', 'qxq', 'alt', 'bfz', 'klj', 'mus', 'srn', 'guc', 'lim', 'zea', 'shi', 'mnr', 'bom', 'sat', 'szl'], id=None) Value(dtype='string', id=None) Using custom data configuration loretoparisi--tatoeba-sentences-7b2c5e991f398f39 Downloading and preparing dataset csv/loretoparisi--tatoeba-sentences to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-7b2c5e991f398f39/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519... Downloading data files: 100% 2/2 [00:18<00:00, 8.06s/it] Downloading data: 100% 391M/391M [00:13<00:00, 35.3MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 36.5MB/s] Failed to read file '/root/.cache/huggingface/datasets/downloads/933132df9905194ea9faeb30cabca8c49318795612f6495fcb941a290191dd5d' with error <class 'ValueError'>: invalid literal for int() with base 10: 'cmn' --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe' During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) 15 frames /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() ValueError: invalid literal for int() with base 10: 'cmn' ``` while loading without `features` it loads without errors ``` sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'] ) ``` but the `label` col seems to be wrong (without the `ClassLabel` object): ``` sentences['train'].features {'label': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None)} ``` The dataset was https://huggingface.co/datasets/loretoparisi/tatoeba-sentences Dataset format is: ``` ces Nechci vědět, co je tam uvnitř. ces Kdo o tom chce slyšet? deu Tom sagte, er fühle sich nicht wohl. ber Mel-iyi-d anida-t tura ? hun Gondom lesz rá rögtön. ber Mel-iyi-d anida-tt tura ? deu Ich will dich nicht reden hören. ``` ### Expected behavior ```shell correctly load train and test files. ```
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1,404,458,586
I_kwDODunzps5TtlZa
5,100
datasets[s3] sagemaker can't run a model - datasets issue with Value and ClassLabel and cast() method
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2022-10-11T11:16:31Z
2022-10-11T13:48:26Z
2022-10-11T13:48:26Z
NONE
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1,172,872,695
I_kwDODunzps5F6J33
3,959
Medium-sized dataset conversion from pandas causes a crash
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[ "Hi ! It looks like an issue with pyarrow, could you try updating pyarrow and try again ?", "@albertvillanova did you find a solution to this?", "I´m getting the same problem with some files, @albertvillanova did you find a solution to this?" ]
2022-03-17T20:20:35Z
2022-12-12T17:14:06Z
2022-04-20T12:35:37Z
NONE
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Hi, I am suffering from the following issue: ## Describe the bug Conversion to arrow dataset from pandas dataframe of a certain size deterministically causes the following crash: ``` File "/home/datasets_crash.py", line 7, in <module> arrow=datasets.Dataset.from_pandas(d) File "/home/.conda/envs/tools/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 783, in from_pandas table = InMemoryTable.from_pandas( File "/home/.conda/envs/tools/lib/python3.9/site-packages/datasets/table.py", line 379, in from_pandas return cls(pa.Table.from_pandas(*args, **kwargs)) File "pyarrow/table.pxi", line 1487, in pyarrow.lib.Table.from_pandas File "pyarrow/table.pxi", line 1532, in pyarrow.lib.Table.from_arrays File "pyarrow/table.pxi", line 1181, in pyarrow.lib.Table.validate File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Column 1: In chunk 0: Invalid: List child array invalid: Invalid: Struct child array #1 has length smaller than expected for struct array (1192457 < 1192458) ``` ## Steps to reproduce the bug I have a dataset made from replicated single example mocking a dict representation of a publication. I copy over this example 140k times and create a pandas frame. I use 'Dataset.from_pandas' and boom ```python # Sample code to reproduce the bug import copy import datasets import pandas # serialized dict is quite long to be realistic representation of a publication content paper_as_dict=eval("{'article_id': '2020-11-05T14:25:05.321Z02bc3286-91b7-486a-9c74-4f457fbc586a', 'sections': [{'section_id': 'body.0', 'paragraphs': [{'sentences': ['11010111001000000011010011110011101110111011000100001010011100101001111010110111101011101111101010101110001111011110111010111', '1101100110110010010101010100110011000111001100100000011100010111010000011100001101111000000011010111001111001010101111110011010010111011000110100110010', '101011011000010100000010011001011011000000110011011110000101001110110000010001100110111100011100110101010010110000101', '1101101110101010101000000010101011111001111000101000110001110100111000100000011001110100110000110100111011001010110011101001001110']}]}, {'section_id': 'body.1', 'paragraphs': [{'sentences': 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'0101101001010101001101010100011000111011001000100001110100110011100000001001010110001101010110011100111111100101101111101111011001111111110010111010011011011111011011110000101011010', '11000001110111000001100100001110000111001010000101011011101010111001011100010010010111111111000011111110010111100011100110001001100011111010100111110111001110010', '0100010110100001010101110111100011100100010111111011101001100101111110101011010010101111001000101001111000001110001100011001110010100110101100110100100000001010101101011110011001000101100111001001001110100', '100000100010011111001101010000100110011110001100000010010110110100000111111011010100101111010111001110101000100001111101001110000011010110000010100', '00100110000011100101000110110001000011101000011010101000010001111011100001111111001011100111101000001000000110110001000101111010010010001100111', '0110110100011001110011001111100010101001011111011001011001101101010010101101110101010100001000100100000111101110001001110111000110011101101010100000101', '0011111010010011011101010110100110000011000011100100101011011001110110001110001111000011010111011000110100111111011101110111000010010000011011010011011100000011101100110110100100000010110101110100110101001100111011101001010111011011110100110101110010011011010001010111110011001000010100010101010010110010010110000100110001000011010011000100101011010100100111010']}]}]}") d=pandas.DataFrame.from_records(copy.deepcopy(paper_as_dict) for _ in range(140_100)) arrow=datasets.Dataset.from_pandas(d) ``` ## Expected results The dataset should be converted without error. ## Actual results Error `pyarrow.lib.ArrowInvalid: Column 1: In chunk 0: Invalid: List child array invalid: Invalid: Struct child array #1 has length smaller than expected for struct array (1192457 < 1192458)` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: datasets==1.18.4 pandas==1.3.5 - Platform: macOS 11.6 or CentOS Linux 7 (Core) - Python version: Python 3.9.7 - PyArrow version: pyarrow==3.0.0
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I_kwDODunzps5kuEKt
5,809
wiki_dpr details for Open Domain Question Answering tasks
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[ "Hi ! I don't remember exactly how it was done, but maybe you have to embed `f\"{title}<sep>{text}\"` ?\r\n\r\nUsing a HF tokenizer it corresponds to doing\r\n```python\r\ntokenized = tokenizer(titles, texts)\r\n```" ]
2023-04-30T06:12:04Z
2023-07-21T14:11:00Z
2023-07-21T14:11:00Z
NONE
null
null
null
Hey guys! Thanks for creating the wiki_dpr dataset! I am currently trying to combine wiki_dpr and my own datasets. but I don't know how to make the embedding value the same way as wiki_dpr. As an experiment, I embeds the text of id="7" of wiki_dpr, but this result was very different from wiki_dpr.
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I_kwDODunzps5NwZ7y
4,680
Dataset Viewer issue for codeparrot/xlcost-text-to-code
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[ "There seems to be an issue with the `C++-snippet-level` config:\r\n\r\n```python\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names(\"codeparrot/xlcost-text-to-code\", \"C++-snippet-level\")\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/...
2022-07-14T09:45:50Z
2022-07-18T16:37:00Z
2022-07-18T16:04:36Z
NONE
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### Link https://huggingface.co/datasets/codeparrot/xlcost-text-to-code ### Description Error ``` Server Error Status code: 400 Exception: TypeError Message: 'NoneType' object is not iterable ``` Before I did a minor change in the dataset script (removing some comments), the viewer was working but not properely, it wasn't showing the dataset subsets. But the data can be loaded successfully. Thanks! ### Owner Yes
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1,170,882,331
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3,939
Source links broken
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[ "Thanks for reporting @qqaatw.\r\n\r\n@mishig25 @sgugger do you think this can be tweaked in the new doc framework?\r\n- From: https://github.com/huggingface/datasets/blob/v2.0.0/\r\n- To: https://github.com/huggingface/datasets/blob/2.0.0/", "@qqaatw thanks a lot for notifying about this issue!\r\n\r\nin compari...
2022-03-16T11:17:47Z
2022-03-19T04:41:32Z
2022-03-19T04:41:32Z
CONTRIBUTOR
null
null
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## Describe the bug The source links of v2.0.0 docs are broken: For exmaple, clicking the source button of this [class](https://huggingface.co/docs/datasets/v2.0.0/en/package_reference/main_classes#datasets.ClassLabel) will direct users to `https://github.com/huggingface/datasets/blob/v2.0.0/src/datasets/features/features.py#L747` here, the `v2.0.0` should be `2.0.0`. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug ``` ## Expected results Redirecting to this link: `https://github.com/huggingface/datasets/blob/2.0.0/src/datasets/features/features.py#L747` ## Actual results Described above. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: - Python version: - PyArrow version:
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4,681
IndexError when loading ImageFolder
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[ "Hi, thanks for reporting! If there are no examples in ImageFolder, the `label` column is of type `ClassLabel(names=[])`, which leads to an error in [this line](https://github.com/huggingface/datasets/blob/c15b391942764152f6060b59921b09cacc5f22a6/src/datasets/arrow_writer.py#L387) as `asdict(info)` calls `Features(...
2022-07-14T10:57:55Z
2022-07-25T12:37:54Z
2022-07-25T12:37:54Z
NONE
null
null
null
## Describe the bug Loading an image dataset with `imagefolder` throws `IndexError: list index out of range` when the given folder contains a non-image file (like a csv). ## Steps to reproduce the bug Put a csv file in a folder with images and load it: ```python import datasets datasets.load_dataset("imagefolder", data_dir=path/to/folder) ``` ## Expected results I would expect a better error message, like `Unsupported file` or even the dataset loader just ignoring every file that is not an image in that case. ## Actual results Here is the whole traceback: ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-5.11.0-051100-generic-x86_64-with-glibc2.27 - Python version: 3.9.9 - PyArrow version: 8.0.0 - Pandas version: 1.4.3
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1,004,306,367
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2,956
Cache problem in the `load_dataset` method for local compressed file(s)
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[ "The problem is still present. \r\nOne solution would be to add the `download_mode=\"force_redownload\"` argument to load_dataset. \r\nHowever, doing so may lead to a `DatasetGenerationError: An error occurred while generating the dataset`. To mitigate, just do:\r\n`rm -r ~/.cache/huggingface/datasets/*`" ]
2021-09-22T13:34:32Z
2023-08-31T16:49:01Z
null
CONTRIBUTOR
null
null
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## Describe the bug Cache problem in the `load_dataset` method: when modifying a compressed file in a local folder `load_dataset` doesn't detect the change and load the previous version. ## Steps to reproduce the bug To test it directly, I have prepared a [Google Colaboratory notebook](https://colab.research.google.com/drive/11Em_Amoc-aPGhSBIkSHU2AvEh24nVayy?usp=sharing) that shows this behavior. For this example, I have created a toy dataset at: https://huggingface.co/datasets/SaulLu/toy_struc_dataset This dataset is composed of two versions: - v1 on commit `a6beb46` which has a single example `{'id': 1, 'value': {'tag': 'a', 'value': 1}}` in file `train.jsonl.gz` - v2 on commit `e7935f4` (`main` head) which has a single example `{'attr': 1, 'id': 1, 'value': 'a'}` in file `train.jsonl.gz` With a terminal, we can start to get the v1 version of the dataset ```bash git lfs install git clone https://huggingface.co/datasets/SaulLu/toy_struc_dataset cd toy_struc_dataset git checkout a6beb46 ``` Then we can load it with python and look at the content: ```python from datasets import load_dataset path = "/content/toy_struc_dataset" dataset = load_dataset(path, data_files={"train": "*.jsonl.gz"}) print(dataset["train"][0]) ``` Output ``` {'id': 1, 'value': {'tag': 'a', 'value': 1}} # This is the example in v1 ``` With a terminal, we can now start to get the v1 version of the dataset ```bash git checkout main ``` Then we can load it with python and look at the content: ```python from datasets import load_dataset path = "/content/toy_struc_dataset" dataset = load_dataset(path, data_files={"train": "*.jsonl.gz"}) print(dataset["train"][0]) ``` Output ``` {'id': 1, 'value': {'tag': 'a', 'value': 1}} # This is the example in v1 (not v2) ``` ## Expected results The last output should have been ``` {"id":1, "value": "a", "attr": 1} # This is the example in v2 ``` ## Ideas As discussed offline with Quentin, if the cache hash was ever sensitive to changes in a compressed file we would probably not have the problem anymore. This situation leads me to suggest 2 other features: - to also have an `load_from_cache_file` argument in the "load_dataset" method - to reorganize the cache so that we can delete the caches related to a dataset (cf issue #ToBeFilledSoon) And thanks again for this great library :hugs: ## Environment info - `datasets` version: 1.12.1 - Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 3.0.0
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2,170
Wikipedia historic dumps are deleted but hf/datasets hardcodes dump date
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[ "It seems that this can be fixed from user's end by including a `date` argument, like this:\r\n\r\n`dataset = datasets.load_dataset('wikipedia', '20200501.en', date='20210420')`\r\n\r\nYou can get available dates from [here](https://dumps.wikimedia.org/enwiki/).\r\n\r\nThis is not a proper fix however as all the fi...
2021-04-06T03:13:18Z
2021-06-16T01:10:50Z
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Wikimedia does not keep all historical dumps. For example, as of today https://dumps.wikimedia.org/kowiki/ only provides ``` 20201220/ 02-Feb-2021 01:36 - 20210101/ 21-Feb-2021 01:26 - 20210120/ 02-Mar-2021 01:25 - 20210201/ 21-Mar-2021 01:26 - 20210220/ 02-Apr-2021 01:26 - 20210301/ 03-Mar-2021 08:10 - 20210320/ 21-Mar-2021 18:13 - 20210401/ 03-Apr-2021 10:08 - latest/ 03-Apr-2021 10:08 - ``` However, the wikipedia dataset provided in the library, only supports the following configs, none of which are applicable anymore when disregarding the cached datasets: ``` ValueError: BuilderConfig 20210401.ko not found. Available: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu'] ``` The cached datasets: ``` % aws s3 --no-sign-request --endpoint-url https://storage.googleapis.com ls s3://huggingface-nlp/cache/datasets/wikipedia/ PRE 20200501.de/ PRE 20200501.en/ PRE 20200501.fr/ PRE 20200501.frr/ PRE 20200501.it/ PRE 20200501.simple/ ```
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3,376
Update clue benchmark
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[ "The CI error is due to missing tags in the CLUE dataset card - merging !" ]
2021-12-03T12:06:01Z
2021-12-08T14:14:42Z
2021-12-08T14:14:41Z
CONTRIBUTOR
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Fix #3374
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1,911
Saving processed dataset running infinitely
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[ "@thomwolf @lhoestq can you guys please take a look and recommend some solution.", "am suspicious of this thing? what's the purpose of this? pickling and unplickling\r\n`self = pickle.loads(pickle.dumps(self))`\r\n\r\n```\r\n def save_to_disk(self, dataset_path: str, fs=None):\r\n \"\"\"\r\n Save...
2021-02-19T13:09:19Z
2021-02-23T07:34:44Z
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I have a text dataset of size 220M. For pre-processing, I need to tokenize this and filter rows with the large sequence. My tokenization took roughly 3hrs. I used map() with batch size 1024 and multi-process with 96 processes. filter() function was way to slow, so I used a hack to use pyarrow filter table function, which is damm fast. Mentioned [here](https://github.com/huggingface/datasets/issues/1796) ```dataset._data = dataset._data.filter(...)``` It took 1 hr for the filter. Then i use `save_to_disk()` on processed dataset and it is running forever. I have been waiting since 8 hrs, it has not written a single byte. Infact it has actually read from disk more than 100GB, screenshot below shows the stats using `iotop`. Second process is the one. <img width="1672" alt="Screenshot 2021-02-19 at 6 36 53 PM" src="https://user-images.githubusercontent.com/20911334/108508197-7325d780-72e1-11eb-8369-7c057d137d81.png"> I am not able to figure out, whether this is some issue with dataset library or that it is due to my hack for filter() function.
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774,921,836
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1,640
Fix "'BertTokenizerFast' object has no attribute 'max_len'"
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2020-12-26T19:25:41Z
2020-12-28T17:26:35Z
2020-12-28T17:26:35Z
CONTRIBUTOR
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Tensorflow 2.3.0 gives: FutureWarning: The `max_len` attribute has been deprecated and will be removed in a future version, use `model_max_length` instead. Tensorflow 2.4.0 gives: AttributeError 'BertTokenizerFast' object has no attribute 'max_len'
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1,743,922,028
I_kwDODunzps5n8iNs
5,926
Uncaught exception when generating the splits from a dataset that miss data
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[ "Thanks for reporting, @severo.\r\n\r\nThis is a known issue with `fsspec`:\r\n- #5862\r\n- https://github.com/fsspec/filesystem_spec/issues/1265" ]
2023-06-06T13:51:01Z
2023-06-07T07:53:16Z
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### Describe the bug Dataset https://huggingface.co/datasets/blog_authorship_corpus has an issue with its hosting platform, since https://drive.google.com/u/0/uc?id=1cGy4RNDV87ZHEXbiozABr9gsSrZpPaPz&export=download returns 404 error. But when trying to generate the split names, we get an exception which is now correctly caught. Seen originally in https://github.com/huggingface/datasets-server/blob/adbdcd6710ffed4e2eb2e4cd905b5e0dff530a15/services/worker/src/worker/job_runners/config/parquet_and_info.py#L435 ### Steps to reproduce the bug ```python >>> from datasets import StreamingDownloadManager, load_dataset_builder >>> builder = load_dataset_builder(path="blog_authorship_corpus") Downloading builder script: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.60k/5.60k [00:00<00:00, 23.1MB/s] Downloading metadata: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.81k/2.81k [00:00<00:00, 14.7MB/s] Downloading readme: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7.30k/7.30k [00:00<00:00, 30.8MB/s] >>> dl_manager = StreamingDownloadManager(base_path=builder.base_path) >>> builder._split_generators(dl_manager) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/blog_authorship_corpus/6f5d78241afd8313111956f877a57db7a0e9fc6718255dc85df0928197feb683/blog_authorship_corpus.py", line 79, in _split_generators data = dl_manager.download_and_extract(_DATA_URL) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1087, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1039, in extract urlpaths = map_nested(self._extract, url_or_urls, map_tuple=True) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 435, in map_nested return function(data_struct) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1044, in _extract protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 433, in _get_extraction_protocol with fsspec.open(urlpath, **kwargs) as f: File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 439, in open return open_files( File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 194, in __getitem__ out = super().__getitem__(item) IndexError: list index out of range ``` ### Expected behavior We should have an Exception raised by the datasets library. ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.19.0-1026-aws-x86_64-with-glibc2.35 - Python version: 3.9.15 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 2.0.2
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I_kwDODunzps5KUuvL
4,399
LocalDatasetModuleFactoryWithoutScript extracts invalid builder name
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[ "Ok, so\r\n```\r\nos.path.basename(\"/home/user/\")\r\n```\r\ngives `''` while \r\n```\r\nos.path.basename(\"/home/user\")\r\n```\r\ngives `user`. \r\nThe code should check if the last char is a slash.\r\n", "The fix is:\r\n```\r\n\"name\": os.path.basename(self.path[:-1] if self.path[-1] == \"/\" else self.path)...
2022-05-24T18:03:01Z
2022-09-12T15:30:43Z
2022-09-12T15:30:43Z
CONTRIBUTOR
null
null
null
## Describe the bug Trying to load a local dataset raises an error indicating that the config builder has to have a name. No error should be reported, since the call is completly valid. ## Steps to reproduce the bug ```python load_dataset("./data/some-dataset/", name="some-name") ``` ## Expected results The dataset should be loaded. ## Actual results ``` Traceback (most recent call last): File "train_lquad.py", line 19, in <module> load(tokenize_target_function, tokenize_target_function, {}, tokenizer) File "train_lquad.py", line 14, in load dataset = load_dataset("./data/lquad/", name="lquad") File "/net/pr2/scratch/people/plgapohl/python-3.8.6/lib/python3.8/site-packages/datasets/load.py", line 1708, in load_dataset builder_instance = load_dataset_builder( File "/net/pr2/scratch/people/plgapohl/python-3.8.6/lib/python3.8/site-packages/datasets/load.py", line 1560, in load_dataset_builder builder_instance: DatasetBuilder = builder_cls( File "/net/pr2/scratch/people/plgapohl/python-3.8.6/lib/python3.8/site-packages/datasets/builder.py", line 269, in __init__ self.config, self.config_id = self._create_builder_config( File "/net/pr2/scratch/people/plgapohl/python-3.8.6/lib/python3.8/site-packages/datasets/builder.py", line 403, in _create_builder_config raise ValueError(f"BuilderConfig must have a name, got {builder_config.name}") ValueError: BuilderConfig must have a name, got ``` ## Environment info - `datasets` version: 2.2.2 - Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-glibc2.2.5 - Python version: 3.8.6 - PyArrow version: 8.0.0 - Pandas version: 1.4.2 The error is probably in line 795 in load.py: ``` builder_kwargs = { "hash": hash, "data_files": data_files, "name": os.path.basename(self.path), "base_path": self.path, **builder_kwargs, } ``` `os.path.basename` for a directory returns an empty string, rather than the name of the directory.
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I_kwDODunzps5ygLAo
6,274
FileNotFoundError for dataset with multiple builder config
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[ "Please tell me if the above info is not enough for solving the problem. I will then make my dataset public temporarily so that you can really reproduce the bug. " ]
2023-10-01T23:45:56Z
2023-10-02T20:09:38Z
2023-10-02T20:09:38Z
NONE
null
null
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### Describe the bug When there is only one config and only the dataset name is entered when using datasets.load_dataset(), it works fine. But if I create a second builder_config for my dataset and enter the config name when using datasets.load_dataset(), the following error will happen. FileNotFoundError: [Errno 2] No such file or directory: 'C:/Users/chenx/.cache/huggingface/datasets/my_dataset/0_shot_multiple_choice/1.0.0/97c3854a012cfd6b045e3be4c864739902af2d818bb9235b047baa94c302e9a2.incomplete/my_dataset-test-00000-00000-of-NNNNN.arrow' The "XXX.incomplete folder" in the cache folder of my dataset will disappear before "generating test split", which does not happen when config name is not entered and the config name is "default" C:\Users\chenx\.cache\huggingface\datasets\my_dataset\0_shot_multiple_choice\1.0.0 The folder that is supposed to remain under the above directory will disappear, and the data generator will not have a place to generate data into. ### Steps to reproduce the bug test = load_dataset('my_dataset', '0_shot_multiple_choice') ### Expected behavior FileNotFoundError: [Errno 2] No such file or directory: 'C:/Users/chenx/.cache/huggingface/datasets/my_dataset/0_shot_multiple_choice/1.0.0/97c3854a012cfd6b045e3be4c864739902af2d818bb9235b047baa94c302e9a2.incomplete/my_dataset-test-00000-00000-of-NNNNN.arrow' ### Environment info datasets 2.14.5 python 3.8.18
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Adds XED dataset
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[ "Hi @lhoestq @yjernite, requesting you to review this for any changes needed. Thanks! :)" ]
2020-12-12T09:47:00Z
2020-12-14T21:20:59Z
2020-12-14T21:20:59Z
CONTRIBUTOR
null
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PR_kwDODunzps44q9pG
4,418
Add dataset MMChat
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2022-05-30T10:10:40Z
2022-05-30T14:58:18Z
2022-05-30T14:58:18Z
CONTRIBUTOR
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5,334
Clean up docstrings
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks ! Let us know if we can help :)\r\n\r\nSmall pref for having multiple PRs", "Awesome, thanks! Sorry this one is a little big, I'll open some smaller ones next :)" ]
2022-12-05T20:56:08Z
2022-12-09T01:44:25Z
2022-12-09T01:41:44Z
MEMBER
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As raised by @polinaeterna in #5324, some of the docstrings are a bit of a mess because it has both Markdown and Sphinx syntax. This PR fixes the docstring for `DatasetBuilder`. I'll start working on cleaning up the rest of the docstrings and removing the old Sphinx syntax (let me know if you prefer one big PR with all the cleaned changes or multiple smaller ones)! 🧼
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Unpack `dl_manager.iter_files` to allow parallization
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Cool thanks ! Yup it sounds like the right solution.\r\n\r\nIt looks like `_generate_tables` needs to be updated as well to fix the CI" ]
2022-07-04T13:16:58Z
2022-07-05T11:11:54Z
2022-07-05T11:00:48Z
CONTRIBUTOR
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Iterate over data files outside `dl_manager.iter_files` to allow parallelization in streaming mode. (The issue reported [here](https://discuss.huggingface.co/t/dataset-only-have-n-shard-1-when-has-multiple-shards-in-repo/19887)) PS: Another option would be to override `FilesIterable.__getitem__` to make it indexable and check for that type in `_shard_kwargs` and `n_shards,` but IMO this solution adds too much unnecessary complexity.
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Dataset is slower after calling `to_iterable_dataset`
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[ "@lhoestq ", "It's roughly the same code between the two so we can expected roughly the same speed, could you share a benchmark ?" ]
2023-08-10T06:36:23Z
2023-08-16T09:18:54Z
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CONTRIBUTOR
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### Describe the bug Can anyone explain why looping over a dataset becomes slower after calling `to_iterable_dataset` to convert to `IterableDataset` ### Steps to reproduce the bug Any dataset after converting to `IterableDataset` ### Expected behavior Maybe it should be faster on big dataset? I only test on small dataset ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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Release: 2.14.3
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a...
2023-08-03T10:18:32Z
2023-08-03T15:08:02Z
2023-08-03T10:24:57Z
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Error loading music files using `load_dataset`
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[ "This code behaves as expected on my local machine or in Colab. Which version of `soundfile` do you have installed? MP3 requires `soundfile>=0.12.1`.", "I upgraded the `soundfile` and it's working now! \r\nThanks @mariosasko for the help!" ]
2023-07-26T12:44:05Z
2023-07-26T13:08:08Z
2023-07-26T13:08:08Z
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### Describe the bug I tried to load a music file using `datasets.load_dataset()` from the repository - https://huggingface.co/datasets/susnato/pop2piano_real_music_test I got the following error - ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__ return self._getitem(key) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2788, in _getitem formatted_output = format_table( File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 629, in format_table return formatter(pa_table, query_type=query_type) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 398, in __call__ return self.format_column(pa_table) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 442, in format_column column = self.python_features_decoder.decode_column(column, pa_table.column_names[0]) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 218, in decode_column return self.features.decode_column(column, column_name) if self.features else column File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1924, in decode_column [decode_nested_example(self[column_name], value) if value is not None else None for value in column] File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1924, in <listcomp> [decode_nested_example(self[column_name], value) if value is not None else None for value in column] File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1325, in decode_nested_example return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/audio.py", line 184, in decode_example array, sampling_rate = sf.read(f) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 372, in read with SoundFile(file, 'r', samplerate, channels, File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 740, in __init__ self._file = self._open(file, mode_int, closefd) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 1264, in _open _error_check(_snd.sf_error(file_ptr), File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 1455, in _error_check raise RuntimeError(prefix + _ffi.string(err_str).decode('utf-8', 'replace')) RuntimeError: Error opening <_io.BufferedReader name='/home/susnato/.cache/huggingface/datasets/downloads/d2b09cb974b967b13f91553297c40c0f02f3c0d4c8356350743598ff48d6f29e'>: Format not recognised. ``` ### Steps to reproduce the bug Code to reproduce the error - ```python from datasets import load_dataset ds = load_dataset("susnato/pop2piano_real_music_test", split="test") print(ds[0]) ``` ### Expected behavior I should be able to read the music file without any error. ### Environment info - `datasets` version: 2.14.0 - Platform: Linux-5.19.0-50-generic-x86_64-with-glibc2.35 - Python version: 3.9.16 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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Support streaming UDHR dataset
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-06-14T09:33:33Z
2022-06-15T05:09:22Z
2022-06-15T04:59:49Z
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This PR: - Adds support for streaming UDHR dataset - Adds the BCP 47 language code as feature
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Adding to_tf_dataset method
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[ "This seems to be working reasonably well in testing, and performance is way better. `tf.py_function` has been dropped for an input generator, but I moved as much of the code as possible outside the generator to allow TF to compile it correctly. I also avoid `tf.RaggedTensor` at all costs, and do the shuffle in the...
2021-07-29T18:10:25Z
2021-09-16T13:50:54Z
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Oh my **god** do not merge this yet, it's just a draft. I've added a method (via a mixin) to the `arrow_dataset.Dataset` class that automatically converts our Dataset classes to TF Dataset classes ready for training. It hopefully has most of the features we want, including streaming from disk (no need to load the whole dataset in memory!), correct shuffling, variable-length batches to reduce compute, and correct support for unusual padding. It achieves that by calling the tokenizer `pad` method in the middle of a TF compute graph via a very hacky call to `tf.py_function`, which is heretical but seems to work. A number of issues need to be resolved before it's ready to merge, though: 1) Is a MixIn the right way to do this? Do other classes besides `arrow_dataset.Dataset` need this method too? 2) Needs an argument to support constant-length batches for TPU training - this is easy to add and I'll do it soon. 3) Needs the user to supply the list of columns to drop from the arrow `Dataset`. Is there some automatic way to get the columns we want, or see which columns were added by the tokenizer? 4) Assumes the label column is always present and always called "label" - this is probably not great, but I'm not sure what the 'correct' thing to do here is.
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Incorrect Russian filenames encoding after extraction by datasets.DownloadManager.download_and_extract()
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[ "To reproduce:\r\n\r\n```python\r\n>>> import datasets\r\n>>> datasets.get_dataset_split_names('MalakhovIlya/RuREBus', config_name='raw_txt')\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 280, in get_dataset_...
2022-04-11T02:07:13Z
2022-04-19T03:15:46Z
2022-04-16T15:46:29Z
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## Dataset viewer issue for 'MalakhovIlya/RuREBus' **Link:** https://huggingface.co/datasets/MalakhovIlya/RuREBus **Description** Using os.walk(topdown=False) in DatasetBuilder causes following error: Status code: 400 Exception: TypeError Message: xwalk() got an unexpected keyword argument 'topdown' Couldn't find where "xwalk" come from. How can I fix this? Am I the one who added this dataset ? Yes
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Add ability to read-write to SQL databases.
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Ah CI runs with `pandas=1.3.5` which doesn't return the number of row inserted.", "wow this is super cool!", "@lhoestq I'm getting error in integration tests, not sure if it's related to my PR. Any help would be appreciated :) \r...
2022-09-03T19:09:08Z
2022-10-03T16:34:36Z
2022-10-03T16:32:28Z
CONTRIBUTOR
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Fixes #3094 Add ability to read/write to SQLite files and also read from any SQL database supported by SQLAlchemy. I didn't add SQLAlchemy as a dependence as it is fairly big and it remains optional. I also recorded a Loom to showcase the feature. https://www.loom.com/share/f0e602c2de8a46f58bca4b43333d541f
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4,707
Dataset Viewer issue for TheNoob3131/mosquito-data
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[ "Thanks for reporting. I refreshed the dataset viewer and it now works as expected.\r\n\r\nhttps://huggingface.co/datasets/TheNoob3131/mosquito-data\r\n\r\n<img width=\"1135\" alt=\"Capture d’écran 2022-07-18 à 13 15 22\" src=\"https://user-images.githubusercontent.com/1676121/179566497-e47f1a27-fd84-4a8d-9d7f-2e...
2022-07-18T17:07:19Z
2022-07-18T19:44:46Z
2022-07-18T17:15:50Z
NONE
null
null
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### Link _No response_ ### Description Getting this error when trying to view dataset preview: Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/TheNoob3131/mosquito-data/resolve/8aceebd6c4a359d216d10ef020868bd9e8c986dd/0_Africa_train.csv') ### Owner _No response_
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Preserve member order by MockDownloadManager.iter_archive
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2022-07-01T05:48:20Z
2022-07-01T16:59:11Z
2022-07-01T16:48:28Z
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Currently, `MockDownloadManager.iter_archive` yields paths to archive members in an order given by `path.rglob("*")`, which migh not be the same order as in the original archive. See issue in: - https://github.com/huggingface/datasets/pull/4579#issuecomment-1172135027 This PR fixes the order of the members yielded by `MockDownloadManager.iter_archive` so that it is the same as in the original archive.
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Refactor conftest fixtures
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2022-07-20T12:15:22Z
2022-07-21T14:37:11Z
2022-07-21T14:24:18Z
MEMBER
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Previously, fixture modules `hub_fixtures` and `s3_fixtures`: - were both at the root test directory - were imported using `import *` - as a side effect, the modules `os` and `pytest` were imported from `s3_fixtures` into `conftest` This PR: - puts both fixture modules in a dedicated directory `fixtures` - renames both to: `fixtures.hub` and `fixtures.s3` - imports them into `conftest` as plugins, using the `pytest_plugins`: this avoids the `import *` - additionally creates a new fixture module `fixtures.files` with all file-related fixtures
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Make `Version` hashable
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2022-11-14T14:52:55Z
2022-11-14T15:30:02Z
2022-11-14T15:27:35Z
CONTRIBUTOR
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Add `__hash__` to the `Version` class to make it hashable (and remove the unneeded methods), as `Version("0.0.0")` is the default value of `BuilderConfig.version` and the default fields of a dataclass need to be hashable in Python 3.11. Fix https://github.com/huggingface/datasets/issues/5230
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English wikipedia datasets is not clean
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[ "Hi ! Certain users might need these data (for training or simply to explore/index the dataset).\r\n\r\nFeel free to implement a map function that gets rid of these paragraphs and process the wikipedia dataset with it before training" ]
2021-08-05T14:37:24Z
2023-07-25T17:43:04Z
2023-07-25T17:43:04Z
CONTRIBUTOR
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## Describe the bug Wikipedia english dumps contain many wikipedia paragraphs like "References", "Category:" and "See Also" that should not be used for training. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from datasets import load_dataset w = load_dataset('wikipedia', '20200501.en') print(w['train'][0]['text']) ``` > 'Yangliuqing () is a market town in Xiqing District, in the western suburbs of Tianjin, People\'s Republic of China. Despite its relatively small size, it has been named since 2006 in the "famous historical and cultural market towns in China".\n\nIt is best known in China for creating nianhua or Yangliuqing nianhua. For more than 400 years, Yangliuqing has in effect specialised in the creation of these woodcuts for the New Year. wood block prints using vivid colourschemes to portray traditional scenes of children\'s games often interwoven with auspiciouse objects.\n\n, it had 27 residential communities () and 25 villages under its administration.\n\nShi Family Grand Courtyard\n\nShi Family Grand Courtyard (Tiānjīn Shí Jiā Dà Yuàn, 天津石家大院) is situated in Yangliuqing Town of Xiqing District, which is the former residence of wealthy merchant Shi Yuanshi - the 4th son of Shi Wancheng, one of the eight great masters in Tianjin. First built in 1875, it covers over 6,000 square meters, including large and small yards and over 200 folk houses, a theater and over 275 rooms that served as apartments and places of business and worship for this powerful family. Shifu Garden, which finished its expansion in October 2003, covers 1,200 square meters, incorporates the elegance of imperial garden and delicacy of south garden. Now the courtyard of Shi family covers about 10,000 square meters, which is called the first mansion in North China. Now it serves as the folk custom museum in Yangliuqing, which has a large collection of folk custom museum in Yanliuqing, which has a large collection of folk art pieces like Yanliuqing New Year pictures, brick sculpture.\n\nShi\'s ancestor came from Dong\'e County in Shandong Province, engaged in water transport of grain. As the wealth gradually accumulated, the Shi Family moved to Yangliuqing and bought large tracts of land and set up their residence. Shi Yuanshi came from the fourth generation of the family, who was a successful businessman and a good household manager, and the residence was thus enlarged for several times until it acquired the present scale. It is believed to be the first mansion in the west of Tianjin.\n\nThe residence is symmetric based on the axis formed by a passageway in the middle, on which there are four archways. On the east side of the courtyard, there are traditional single-story houses with rows of rooms around the four sides, which was once the living area for the Shi Family. The rooms on north side were the accountants\' office. On the west are the major constructions including the family hall for worshipping Buddha, theater and the south reception room. On both sides of the residence are side yard rooms for maids and servants.\n\nToday, the Shi mansion, located in the township of Yangliuqing to the west of central Tianjin, stands as a surprisingly well-preserved monument to China\'s pre-revolution mercantile spirit. It also serves as an on-location shoot for many of China\'s popular historical dramas. Many of the rooms feature period furniture, paintings and calligraphy, and the extensive Shifu Garden.\n\nPart of the complex has been turned into the Yangliuqing Museum, which includes displays focused on symbolic aspects of the courtyards\' construction, local folk art and customs, and traditional period furnishings and crafts.\n\n**See also \n\nList of township-level divisions of Tianjin\n\nReferences \n\n http://arts.cultural-china.com/en/65Arts4795.html\n\nCategory:Towns in Tianjin'** ## Expected results I expect no junk in the data. ## Actual results Specify the actual results or traceback. ## Environment info - `datasets` version: 1.10.2 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.5 - PyArrow version: 3.0.0
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How to train BERT model with next sentence prediction?
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[ "Hi !\r\nWe're not using `TextDatasetForNextSentencePrediction` in `datasets`.\r\nAlthough you can probably use the `TextDatasetForNextSentencePrediction.create_examples_from_document` on a dataset to prepare it for next sentence prediction.", "Thanks.\r\n\r\nDo you mean that `TextDatasetForNextSentencePrediction...
2021-03-29T06:48:03Z
2021-04-01T04:58:40Z
2021-04-01T04:58:40Z
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Hello. I'm trying to pretrain the BERT model with next sentence prediction. Is there any function that supports next sentence prediction like ` TextDatasetForNextSentencePrediction` of `huggingface/transformers` ?
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FileNotFoundError: Couldn't find a module script at /content/paws-x/paws-x.py. Module 'paws-x' doesn't exist on the Hugging Face Hub either.
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[ "`evaluate.load(\"paws-x\", \"es\")` throws the error because there is no such metric in the `evaluate` lib.\r\n\r\nSo, this is unrelated to our lib." ]
2023-10-04T22:01:25Z
2023-10-08T17:05:46Z
2023-10-08T17:05:46Z
NONE
null
null
null
### Describe the bug I'm encountering a "FileNotFoundError" while attempting to use the "paws-x" dataset to retrain the DistilRoBERTa-base model. The error message is as follows: FileNotFoundError: Couldn't find a module script at /content/paws-x/paws-x.py. Module 'paws-x' doesn't exist on the Hugging Face Hub either. ### Steps to reproduce the bug https://colab.research.google.com/drive/11xUUFxloClpmqLvDy_Xxfmo3oUzjY5nx#scrollTo=kUn74FigzhHm ### Expected behavior The the trained model ### Environment info colab, "paws-x" dataset , DistilRoBERTa-base model
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2021-09-26T09:50:29Z
2021-09-26T10:38:59Z
2021-09-26T10:38:59Z
NONE
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## Adding a Dataset - **Name:** *name of the dataset* - **Description:** *short description of the dataset (or link to social media or blog post)* - **Paper:** *link to the dataset paper if available* - **Data:** *link to the Github repository or current dataset location* - **Motivation:** *what are some good reasons to have this dataset* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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[ "This could be solved by going to the glue/ directory and delete sst2 directory, then load the dataset again will help you redownload the dataset.", "Hi ! Not sure why this file was missing, but yes the way to fix this is to delete the sst2 directory and to reload the dataset" ]
2021-07-28T03:52:07Z
2022-03-21T08:27:51Z
2022-03-21T08:27:51Z
NONE
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Strangely missing cache file after I restart my program again. `glue_dataset = datasets.load_dataset('glue', 'sst2')` `FileNotFoundError: [Errno 2] No such file or directory: /Users/chris/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96d6053ad/dataset_info.json'`
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