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Cannot download TOTTO dataset
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2021-09-01T11:04:10Z
2021-09-02T06:47:40Z
2021-09-02T06:47:40Z
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Error: Couldn't find file at https://storage.googleapis.com/totto/totto_data.zip `datasets version: 1.11.0` # How to reproduce: ```py from datasets import load_dataset dataset = load_dataset('totto') ```
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[ "Hola @mrm8488, thanks for reporting.\r\n\r\nApparently, the data source host changed their URL one week ago: https://github.com/google-research-datasets/ToTTo/commit/cebeb430ec2a97747e704d16a9354f7d9073ff8f\r\n\r\nI'm fixing it." ]
https://api.github.com/repos/huggingface/datasets/issues/5874
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I_kwDODunzps5mQ6QC
5,874
Using as_dataset on a "parquet" builder
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closed
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2023-05-18T14:09:03Z
2023-05-31T13:23:55Z
2023-05-31T13:23:55Z
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### Describe the bug I used a custom builder to ``download_and_prepare`` a dataset. The first (very minor) issue is that the doc seems to suggest ``download_and_prepare`` will return the dataset, while it does not ([builder.py](https://github.com/huggingface/datasets/blob/main/src/datasets/builder.py#L718-L738)). ``` >>> from datasets import load_dataset_builder >>> builder = load_dataset_builder("rotten_tomatoes") >>> ds = builder.download_and_prepare("./output_dir", file_format="parquet") ``` The main issue I am facing is loading the dataset from those parquet files. I used the `as_dataset` method suggested by the doc, however it returns: ` FileNotFoundError: [Errno 2] Failed to open local file 'output_dir/__main__-train-00000-of-00245.arrow'. Detail: [errno 2] No such file or directory. ` ### Steps to reproduce the bug 1. Create a custom builder of some sort: `builder = CustomBuilder()`. 2. Run `download_and_prepare` with the parquet format: `builder.download_and_prepare("./output_dir", file_format="parquet")`. 3. Run `dataset = builder.as_dataset()`. ### Expected behavior I guess I'd expect `as_dataset` to generate the dataset in arrow format if it has to, or to suggest an alternative way to load the dataset (I've also tried other methods with `load_dataset` to no avail, probably due to misunderstandings on my part). ### Environment info ``` - `datasets` version: 2.12.0 - Platform: Linux-5.15.0-1027-gcp-x86_64-with-glibc2.31 - Python version: 3.10.0 - Huggingface_hub version: 0.14.1 - PyArrow version: 8.0.0 - Pandas version: 1.5.3 ```
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[ "Hi! You can refer to [this doc](https://huggingface.co/docs/datasets/filesystems#load-and-save-your-datasets-using-your-cloud-storage-filesystem) to see the intended usage (basically, it skips the Arrow -> Parquet conversion step in `ds = load_dataset(...); ds.to_parquet(\"path/to/parquet\")`) and allows writing P...
https://api.github.com/repos/huggingface/datasets/issues/5841
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1,705,286,639
I_kwDODunzps5lpJvv
5,841
Abusurdly slow on iteration
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closed
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4
2023-05-11T08:04:09Z
2023-05-15T15:38:13Z
2023-05-15T15:38:13Z
null
### 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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[ "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...
https://api.github.com/repos/huggingface/datasets/issues/5827
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1,698,891,246
I_kwDODunzps5lQwXu
5,827
load json dataset interrupt when dtype cast problem occured
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open
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2023-05-07T04:52:09Z
2023-05-10T12:32:28Z
null
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### Describe the bug i have a json like this: [ {"id": 1, "name": 1}, {"id": 2, "name": "Nan"}, {"id": 3, "name": 3}, .... ] ,which have several problematic rows data like row 2, then i load it with datasets.load_dataset('json', data_files=['xx.json'], split='train'), it will report like this: Generating train split: 0 examples [00:00, ? examples/s]Failed to read file 'C:\Users\gawinjunwu\Downloads\test\data\a.json' with error <class 'pyarrow.lib.ArrowInvalid'>: Could not convert '2' with type str: tried to convert to int64 Traceback (most recent call last): File "D:\Python3.9\lib\site-packages\datasets\builder.py", line 1858, in _prepare_split_single for _, table in generator: File "D:\Python3.9\lib\site-packages\datasets\packaged_modules\json\json.py", line 146, in _generate_tables raise ValueError(f"Not able to read records in the JSON file at {file}.") from None ValueError: Not able to read records in the JSON file at C:\Users\gawinjunwu\Downloads\test\data\a.json. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "c:\Users\gawinjunwu\Downloads\test\scripts\a.py", line 4, in <module> ds = load_dataset('json', data_dir='data', split='train') File "D:\Python3.9\lib\site-packages\datasets\load.py", line 1797, in load_dataset builder_instance.download_and_prepare( File "D:\Python3.9\lib\site-packages\datasets\builder.py", line 890, in download_and_prepare self._download_and_prepare( File "D:\Python3.9\lib\site-packages\datasets\builder.py", line 985, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "D:\Python3.9\lib\site-packages\datasets\builder.py", line 1746, in _prepare_split for job_id, done, content in self._prepare_split_single( File "D:\Python3.9\lib\site-packages\datasets\builder.py", line 1891, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset. Could datasets skip those problematic data row? ### Steps to reproduce the bug prepare a json file like this: [ {"id": 1, "name": 1}, {"id": 2, "name": "Nan"}, {"id": 3, "name": 3} ] then use datasets.load_dataset('json', dir_files=['xxx.json']) to load the json file ### Expected behavior skip the problematic data row and load row1 and row3 ### Environment info python3.9
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[ "Indeed the JSON dataset builder raises an error when it encounters an unexpected type.\r\n\r\nThere's an old PR open to add away to ignore such elements though, if it can help: https://github.com/huggingface/datasets/pull/2838" ]
https://api.github.com/repos/huggingface/datasets/issues/5949
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5,949
Replace metadata utils with `huggingface_hub`'s RepoCard API
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2023-06-13T13:03:19Z
2023-06-27T16:47:51Z
2023-06-27T16:38:32Z
null
Use `huggingface_hub`'s RepoCard API instead of `DatasetMetadata` for modifying the card's YAML, and deprecate `datasets.utils.metadata` and `datasets.utils.readme`. After removing these modules, we can also delete `datasets.utils.resources` since the moon landing repo now stores its own version of these resources for the metadata UI. PS: this change requires bumping `huggingface_hub` to 0.13.0 (Transformers requires 0.14.0, so should be ok)
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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...
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3,684
[fix]: iwslt2017 download urls
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2022-02-06T07:56:55Z
2022-09-22T16:20:19Z
2022-09-22T16:20:18Z
null
Fixes #2076.
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[ "Hi ! Thanks for the fix ! Do you know where this new URL comes from ?\r\n\r\nAlso we try to not use Google Drive if possible, since it has download quota limitations. Do you know if the data is available from another host than Google Drive ?", "Oh, I found it just by following the link from the [IWSLT2017 homepa...
https://api.github.com/repos/huggingface/datasets/issues/3347
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3,347
iter_archive for zip files
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2021-11-30T22:34:17Z
2021-12-04T00:22:22Z
2021-12-04T00:22:11Z
null
* In this PR, I added the option to iterate through zipfiles for `download_manager.py` only. * Next PR will be the same applied to `streaming_download_manager.py`. * Related issue #3272. ## Comments : * There is no `.isreg()` equivalent in zipfile library to check if file is Regular so I used `.is_dir()` instead to skip directories. * For now I got `streaming_download_manager.py` working for local zip files, but not for urls. I get the following error when I test it on an archive in google drive, so still working on it. `BlockSizeError: Got more bytes so far (>2112) than requested (22)` ## Tasks : - [x] download_manager.py - [ ] streaming_download_manager.py
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[ "And also don't always try streaming with Google Drive - it can have issues because of how Google Drive works (with quotas, restrictions, etc.) and it can indeed cause `BlockSizeError`.\r\n\r\nFeel free to host your test data elsewhere, such as in a dataset repository on https://huggingface.co (see [here](https://h...
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4,584
Add binary classification task IDs
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2022-06-28T07:30:39Z
2023-01-26T09:27:53Z
2023-01-26T09:27:52Z
null
As a precursor to aligning the task IDs in `datasets` and AutoTrain, we need a way to distinguish binary vs multiclass vs multilabel classification. This PR adds binary classification to the task IDs to enable this. Related AutoTrain issue: https://github.com/huggingface/autonlp-backend/issues/597 cc @abhishekkrthakur @SBrandeis
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4584). All of your documentation changes will be reflected on that endpoint.", "> Awesome thanks ! Can you add it to https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts first please ? This is where ...
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755,292,066
MDExOlB1bGxSZXF1ZXN0NTMxMDMxMTE1
1,000
UM005: Urdu <> English Translation Dataset
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2020-12-02T13:51:35Z
2020-12-04T15:34:30Z
2020-12-04T15:34:29Z
null
Adds Urdu-English dataset for machine translation: http://ufal.ms.mff.cuni.cz/umc/005-en-ur/
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MDExOlB1bGxSZXF1ZXN0NjQ5Nzc3MDMz
2,389
Insert task templates for text classification
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closed
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null
6
2021-05-21T08:36:26Z
2021-05-28T15:28:58Z
2021-05-28T15:26:28Z
null
This PR inserts text-classification templates for datasets with the following properties: * Only one config * At most two features of `(Value, ClassLabel)` type Note that this misses datasets like `sentiment140` which only has `Value` type features - these will be handled in a separate PR
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[ "Update: found a few datasets that slipped through the net. Adding them shortly!", "You might have thought about this already, but would it make sense to use the `datasets.features.ClassLabel` values when possible instead of declaring the list once for the `feature` and once for the `template`?", "> You might h...
https://api.github.com/repos/huggingface/datasets/issues/6013
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[FR] `map` should reuse unchanged columns from the previous dataset to avoid disk usage
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### Feature request Currently adding a new column with `map` will cause all the data in the dataset to be duplicated and stored/cached on the disk again. It should reuse unchanged columns. ### Motivation This allows having datasets with different columns but sharing some basic columns. Currently, these datasets would become too expensive to store and one would need some kind of on-the-fly join; which also doesn't seem implemented. ### Your contribution _
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[ "You can use the `remove_columns` parameter in `map` to avoid duplicating the columns (and save disk space) and then concatenate the original dataset with the map result:\r\n```python\r\nfrom datasets import concatenate_datasets\r\n# dummy example\r\nds_new = ds.map(lambda x: {\"new_col\": x[\"col\"] + 2}, remove_c...
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Better filenotfound for gated
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2023-06-14T10:33:10Z
2023-06-14T12:33:27Z
2023-06-14T12:26:31Z
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close https://github.com/huggingface/datasets/issues/5953 <img width="1292" alt="image" src="https://github.com/huggingface/datasets/assets/42851186/270fe5bc-1739-4878-b7bc-ab6d35336d4d">
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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...
https://api.github.com/repos/huggingface/datasets/issues/5049
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1,392,361,381
PR_kwDODunzps4_7zOY
5,049
Add `kwargs` to `Dataset.from_generator`
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2022-09-30T12:24:27Z
2022-10-03T11:00:11Z
2022-10-03T10:58:15Z
null
Add the `kwargs` param to `from_generator` to align it with the rest of the `from_` methods (this param allows passing custom `writer_batch_size` for instance).
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
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1,383
added conv ai 2
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2020-12-09T13:30:12Z
2020-12-13T18:54:42Z
2020-12-13T18:54:41Z
null
Dataset : https://github.com/DeepPavlov/convai/tree/master/2018
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[ "@lhoestq Thank you for the suggestions. I added the changes to the branch and seems after rebasing it to master, all the commits previous commits got added. Should I create a new PR or should I keep this one only ? ", "closing this one in favor of #1527 " ]
https://api.github.com/repos/huggingface/datasets/issues/734
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734
Fix GLUE metric description
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2020-10-14T20:44:14Z
2020-10-15T09:27:43Z
2020-10-15T09:27:42Z
null
Small typo: the description says translation instead of prediction.
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Add DER metric for SUPERB speaker diarization task
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2021-08-06T09:12:36Z
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[ "Metrics are deprecated in `datasets` and `evaluate` should be used instead: https://github.com/huggingface/evaluate" ]
https://api.github.com/repos/huggingface/datasets/issues/2072
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2,072
Fix docstring issues
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2
2021-03-17T18:13:44Z
2021-03-24T08:20:57Z
2021-03-18T12:41:21Z
null
Fix docstring issues.
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[ "I think I will stop pushing to this PR, so that it can me merged for today release. \r\n\r\nI will open another PR for further fixing docs.\r\n\r\nDo you agree, @lhoestq ?", "Sounds good thanks !" ]
https://api.github.com/repos/huggingface/datasets/issues/3501
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3,501
Update pib dataset card
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2021-12-29T10:14:40Z
2021-12-29T11:13:21Z
2021-12-29T11:13:21Z
null
Related to #3496
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890,595,693
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2,357
Adding Microsoft CodeXGlue Datasets
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2021-05-13T00:43:01Z
2021-06-08T09:29:57Z
2021-06-08T09:29:57Z
null
Hi there, this is a new pull request to get the CodeXGlue datasets into the awesome HF datasets lib. Most of the work has been done in this PR #997 by the awesome @madlag. However, that PR has been stale for a while now and so I spoke with @lhoestq about finishing up the final mile and so he told me to open a new PR with the final changes :smile:. I believe I've met all of the changes still left in the old PR to do, except for the change to the languages. I believe the READMEs should include the different programming languages used rather than just using the tag "code" as when searching for datasets, SE researchers may specifically be looking only for what type of programming language and so being able to quickly filter will be very valuable. Let me know what you think of that or if you still believe it should be the "code" tag @lhoestq.
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[ "Oh one other thing. Mentioned in the PR was that I would need to regenerate the dataset_infos.json once the camel casing was done. However, I am unsure why this is the case since there is no reference to any object names in the dataset_infos.json file.\r\n\r\nIf it needs to be reran, I can try it do it on my own m...
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572
Add CLUE Benchmark (11 datasets)
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2020-09-04T01:57:40Z
2020-09-07T09:59:11Z
2020-09-07T09:59:10Z
null
Add 11 tasks of [CLUE](https://github.com/CLUEbenchmark/CLUE).
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[ "Thanks, @lhoestq! I've addressed the comments. \r\nAlso, I have tried to use `ClassLabel` [when possible](https://github.com/huggingface/nlp/pull/572/files#diff-1026ac7d7b78bf029cb0ebe63162c77dR297). Is there still somewhere else we can use `ClassLabel`? ", "I believe CI failure is unrelated.", "Great job! " ]
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2,573
Finding right block-size with JSON loading difficult for user
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2021-07-01T08:48:35Z
2021-07-01T19:10:53Z
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As reported by @thomwolf, while loading a JSON Lines file with "json" loading script, he gets > json.decoder.JSONDecodeError: Extra data: line 2 column 1 (char 383)
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[ "This was actually a second error arising from a too small block-size in the json reader.\r\n\r\nFinding the right block size is difficult for the layman user" ]
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Altered faiss installation comment
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2022-04-26T01:20:43Z
2022-05-09T17:29:34Z
2022-05-09T17:22:09Z
null
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Hi ! Can you explain why this change is needed ?", "Facebook recommends installing FAISS using conda (https://github.com/facebookresearch/faiss/blob/main/INSTALL.md). pip does not seem to have the latest version of FAISS. The lates...
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541
Best practices for training tokenizers with nlp
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1
2020-08-29T12:06:49Z
2022-10-04T17:28:04Z
2022-10-04T17:28:04Z
null
Hi, thank you for developing this library. What do you think are the best practices for training tokenizers using `nlp`? In the document and examples, I could only find pre-trained tokenizers used.
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[ "Docs that explain how to train a tokenizer with `datasets` are available here: https://huggingface.co/docs/tokenizers/training_from_memory#using-the-datasets-library" ]
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Add OpenAI's HumanEval dataset
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2021-09-11T09:37:47Z
2021-09-16T15:02:11Z
2021-09-16T15:02:11Z
null
This PR adds OpenAI's [HumanEval](https://github.com/openai/human-eval) dataset. The dataset consists of 164 handcrafted programming problems with solutions and unittests to verify solution. This dataset is useful to evaluate code generation models.
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[ "I just fixed the class name, and added `[More Information Needed]` in empty sections in case people want to complete the dataset card :)" ]
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755,047,470
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986
Add SciTLDR Dataset
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2020-12-02T08:11:16Z
2020-12-02T18:37:22Z
2020-12-02T18:02:59Z
null
Adds the SciTLDR Dataset by AI2 Added README card with tags to the best of my knowledge Multi-target summaries or TLDRs of Scientific Documents
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[ "CI failures seem to be unrelated (related to `norwegian_ner`)\r\n```\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_class_norwegian_ner\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_configs_norwegian_ner\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::t...
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1,052,346,381
I_kwDODunzps4-uYgN
3,261
Scifi_TV_Shows: Having trouble getting viewer to find appropriate files
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2021-11-12T19:25:19Z
2021-12-21T10:24:10Z
2021-12-21T10:24:10Z
null
## 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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[ "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...
https://api.github.com/repos/huggingface/datasets/issues/2921
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2,921
Using a list of multi-dim numpy arrays raises an error "can only convert 1-dimensional array values"
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2021-09-15T17:12:11Z
2021-09-15T17:21:45Z
2021-09-15T17:21:45Z
null
This error has been introduced in https://github.com/huggingface/datasets/pull/2361 To reproduce: ```python import numpy as np from datasets import Dataset d = Dataset.from_dict({"a": [np.zeros((2, 2))]}) ``` raises ```python Traceback (most recent call last): File "playground/ttest.py", line 5, in <module> d = Dataset.from_dict({"a": [np.zeros((2, 2))]}).with_format("torch") File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/arrow_dataset.py", line 458, in from_dict pa_table = InMemoryTable.from_pydict(mapping=mapping) File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/table.py", line 365, in from_pydict return cls(pa.Table.from_pydict(*args, **kwargs)) File "pyarrow/table.pxi", line 1639, in pyarrow.lib.Table.from_pydict File "pyarrow/array.pxi", line 332, in pyarrow.lib.asarray File "pyarrow/array.pxi", line 223, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/arrow_writer.py", line 107, in __arrow_array__ out = pa.array(self.data, type=type) File "pyarrow/array.pxi", line 306, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Can only convert 1-dimensional array values
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1,336,177,937
PR_kwDODunzps49Cdro
4,830
Fix task tags in dataset cards
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2022-08-11T16:06:06Z
2022-08-11T16:37:27Z
2022-08-11T16:23:00Z
null
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[ "_The documentation is not available anymore as the PR was closed or merged._", "The non-passing tests are caused by other missing information in the dataset cards." ]
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984,093,938
MDExOlB1bGxSZXF1ZXN0NzIzNTY5OTE4
2,857
Update: Openwebtext - update size
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2021-08-31T17:11:03Z
2022-02-15T10:38:03Z
2021-09-07T09:44:32Z
null
Update the size of the Openwebtext dataset I also regenerated the dataset_infos.json but the data file checksum didn't change, and the number of examples either (8013769 examples) Close #2839, close #726.
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[ "merging since the CI error in unrelated to this PR and fixed on master" ]
https://api.github.com/repos/huggingface/datasets/issues/4628
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1,293,361,308
PR_kwDODunzps46zvFJ
4,628
Fix time type `_arrow_to_datasets_dtype` conversion
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2022-07-04T16:20:15Z
2022-07-07T14:08:38Z
2022-07-07T13:57:12Z
null
Fix #4620 The issue stems from the fact that `pa.array([time_data]).type` returns `DataType(time64[unit])`, which doesn't expose the `unit` attribute, instead of `Time64Type(time64[unit])`. I believe this is a bug in PyArrow. Luckily, the both types have the same `str()`, so in this PR I call `pa.type_for_alias(str(type))` to convert them both to the `Time64Type(time64[unit])` format. cc @severo
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
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4,369
Add redirect to dataset script in the repo structure page
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2022-05-18T17:05:33Z
2022-05-19T08:19:01Z
2022-05-19T08:10:51Z
null
Following https://github.com/huggingface/hub-docs/pull/146 I added a redirection to the dataset scripts documentation in the repository structure page.
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
https://api.github.com/repos/huggingface/datasets/issues/4588
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1,287,368,751
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4,588
Host head_qa data on the Hub and fix NonMatchingChecksumError
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2022-06-28T13:39:28Z
2022-07-05T16:01:15Z
2022-07-05T15:49:52Z
null
This PR: - Hosts head_qa data on the Hub instead of Google Drive - Fixes NonMatchingChecksumError Fix https://huggingface.co/datasets/head_qa/discussions/1
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Hi @albertvillanova ! Thanks for the fix ;)\r\nCan I safely checkout from this branch to build `datasets` or it is preferable to wait until all CI tests pass?\r\nThanks 🙏 ", "@younesbelkada we have just merged this PR." ]
https://api.github.com/repos/huggingface/datasets/issues/2038
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830,036,875
MDU6SXNzdWU4MzAwMzY4NzU=
2,038
outdated dataset_infos.json might fail verifications
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2021-03-12T11:41:54Z
2021-03-16T16:27:40Z
2021-03-16T16:27:40Z
null
The [doc2dial/dataset_infos.json](https://github.com/huggingface/datasets/blob/master/datasets/doc2dial/dataset_infos.json) is outdated. It would fail data_loader when verifying download checksum etc.. Could you please update this file or point me how to update this file? Thank you.
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[ "Hi ! Thanks for reporting.\r\n\r\nTo update the dataset_infos.json you can run:\r\n```\r\ndatasets-cli test ./datasets/doc2dial --all_configs --save_infos --ignore_verifications\r\n```", "Fixed by #2041, thanks again @songfeng !" ]
https://api.github.com/repos/huggingface/datasets/issues/727
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719,386,366
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727
Parallel downloads progress bar flickers
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2020-10-12T13:36:05Z
2020-10-12T13:36:05Z
null
null
When there are parallel downloads using the download manager, the tqdm progress bar flickers since all the progress bars are on the same line. To fix that we could simply specify `position=i` for i=0 to n the number of files to download when instantiating the tqdm progress bar. Another way would be to have one "master" progress bar that tracks the number of finished downloads, and then one progress bar per process that show the current downloads.
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PR_kwDODunzps5K9K1i
5,589
Revert "pass the dataset features to the IterableDataset.from_generator"
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2023-02-28T17:52:04Z
2023-03-21T14:21:45Z
2023-03-21T14:18:18Z
null
This reverts commit b91070b9c09673e2e148eec458036ab6a62ac042 (temporarily) It hurts iterable dataset performance a lot (e.g. x4 slower because it encodes+decodes images unnecessarily). I think we need to fix this before re-adding it cc @mariosasko @Hubert-Bonisseur
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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...
https://api.github.com/repos/huggingface/datasets/issues/1663
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775,914,320
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1,663
update saving and loading methods for faiss index so to accept path l…
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2020-12-29T14:15:37Z
2021-01-18T09:27:23Z
2021-01-18T09:27:23Z
null
- Update saving and loading methods for faiss index so to accept path like objects from pathlib The current code only supports using a string type to save and load a faiss index. This change makes it possible to use a string type OR a Path from [pathlib](https://docs.python.org/3/library/pathlib.html). The codes becomes a more intuitive this way I think.
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[ "Seems ok for me, what do you think @lhoestq ?" ]
https://api.github.com/repos/huggingface/datasets/issues/2809
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971,902,613
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2,809
Add Beans Dataset
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closed
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2021-08-16T16:22:33Z
2021-08-26T11:42:27Z
2021-08-26T11:42:27Z
null
Adds the [beans](https://github.com/AI-Lab-Makerere/ibean/) image classification dataset.
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https://api.github.com/repos/huggingface/datasets/issues/4993
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4,993
fix: avoid casting tuples after Dataset.map
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2022-09-20T08:45:16Z
2022-09-20T16:11:27Z
2022-09-20T13:08:29Z
null
This PR updates features.py to avoid casting tuples to lists when reading the results of Dataset.map as suggested by @lhoestq [here](https://github.com/huggingface/datasets/issues/4676#issuecomment-1187371367) in https://github.com/huggingface/datasets/issues/4676.
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
https://api.github.com/repos/huggingface/datasets/issues/987
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987
Add OPUS DOGC dataset
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2020-12-02T08:30:32Z
2020-12-04T13:27:41Z
2020-12-04T13:27:41Z
null
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[ "merging since the CI is fixed on master" ]
https://api.github.com/repos/huggingface/datasets/issues/4580
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I_kwDODunzps5Mq5PQ
4,580
Dataset Viewer issue for multi_news
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2022-06-27T20:25:25Z
2022-06-28T14:08:48Z
2022-06-28T14:08:48Z
null
### Link https://huggingface.co/datasets/multi_news ### Description Not sure what the index error is referring to here: ``` Status code: 400 Exception: IndexError Message: list index out of range ``` ### Owner No
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[ "Thanks for reporting, @lewtun.\r\n\r\nI forced the refreshing of the preview and it worked OK for train and validation splits.\r\n\r\nI guess the error has to do with the data files being hosted at Google Drive: this gives errors when requested automatically using scripts.\r\nWe should host them to fix the error. ...
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1,988
Readme.md is misleading about kinds of datasets?
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2021-03-04T17:04:20Z
2021-08-04T18:05:23Z
2021-08-04T18:05:23Z
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Hi! At the README.MD, you say: "efficient data pre-processing: simple, fast and reproducible data pre-processing for the above public datasets as well as your own local datasets in CSV/JSON/text. " But here: https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py#L82-L117 You mention other kinds of datasets, with images and so on. I'm confused. Is it possible to use it to store, say, imagenet locally?
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[ "Hi ! Yes it's possible to use image data. There are already a few of them available (MNIST, CIFAR..)" ]
https://api.github.com/repos/huggingface/datasets/issues/1648
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1,648
Update README.md
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2020-12-28T18:59:06Z
2020-12-29T10:39:14Z
2020-12-29T10:39:14Z
null
added dataset summary
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3,994
Change audio column from string path to Audio feature in ASR task
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2022-03-23T14:34:52Z
2022-03-23T15:43:43Z
2022-03-23T15:43:43Z
null
Will fix #3990
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3,111
concatenate_datasets removes ClassLabel typing.
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2021-10-19T18:05:31Z
2021-10-21T14:50:21Z
2021-10-21T14:50:21Z
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## Describe the bug When concatenating two datasets, we lose typing of ClassLabel columns. I can work on this if this is a legitimate bug, ## Steps to reproduce the bug ```python import datasets from datasets import Dataset, ClassLabel, Value, concatenate_datasets DS_LEN = 100 my_dataset = Dataset.from_dict( { "sentence": [f"{chr(i % 10)}" for i in range(DS_LEN)], "label": [i % 2 for i in range(DS_LEN)] } ) my_predictions = Dataset.from_dict( { "pred": [(i + 1) % 2 for i in range(DS_LEN)] } ) my_dataset = my_dataset.cast(datasets.Features({"sentence": Value("string"), "label": ClassLabel(2, names=["POS", "NEG"])})) print("Original") print(my_dataset) print(my_dataset.features) concat_ds = concatenate_datasets([my_dataset, my_predictions], axis=1) print("Concatenated") print(concat_ds) print(concat_ds.features) ``` ## Expected results The features of `concat_ds` should contain ClassLabel. ## Actual results On master, I get: ``` {'sentence': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None), 'pred': Value(dtype='int64', id=None)} ``` ## Environment info - `datasets` version: 1.14.1.dev0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.11 - PyArrow version: 4.0.1
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[ "Something like this would fix it I think: https://github.com/huggingface/datasets/compare/master...Dref360:HF-3111/concatenate_types?expand=1" ]
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2,230
Keys yielded while generating dataset are not being checked
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2021-04-16T13:29:47Z
2021-05-10T17:31:21Z
2021-05-10T17:31:21Z
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The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not. Currently, the keys are not being checked for any of these, as evident from `xnli' dataset generation: https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/datasets/xnli/xnli.py#L196 Even after having a tuple as key, the dataset is generated without any warning. Also, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example): ``` >>> import datasets >>> nik = datasets.load_dataset('anli') Downloading and preparing dataset anli/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\Users\nikhil\.cache\huggingface\datasets\anli\plain_text\0.1.0\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299... 0 examples [00:00, ? examples/s]1 {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: "Rete filoviaria di Parma" ) forms part of the public transport network of the city and "comune" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''} 2021-04-16 12:38:14.483968: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll 1 examples [00:01, 1.87s/ examples]1 {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': "Sharron Macready was a popular character through the 1980's.", 'label': 'neutral', 'reason': ''} 1 {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': "Bastedo didn't keep any pets because of her views on animal rights.", 'label': 'neutral', 'reason': ''} 1 {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''} 1 {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''} ``` Here also, the dataset was generated successfuly even hough it had same keys without any warning. The reason appears to stem from here: https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/src/datasets/builder.py#L988 Here, although it has access to every key, but it is not being checked and the example is written directly: https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/src/datasets/builder.py#L992 I would like to take this issue if you allow me. Thank You!
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[ "Hi ! Indeed there's no verification on the uniqueness nor the types of the keys.\r\nDo you already have some ideas of what you would like to implement and how ?", "Hey @lhoestq, thank you so much for the opportunity.\r\nAlthough I haven't had much experience with the HF Datasets code, after a careful look at how...
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4,282
Don't do unnecessary list type casting to avoid replacing None values by empty lists
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2022-05-04T16:37:01Z
2022-05-06T10:43:58Z
2022-05-06T10:37:00Z
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In certain cases, `None` values are replaced by empty lists when casting feature types. It happens every time you cast an array of nested lists like [None, [0, 1, 2, 3]] to a different type (to change the integer precision for example). In this case you'd get [[], [0, 1, 2, 3]] for example. This issue comes from PyArrow, see the discussion in https://github.com/huggingface/datasets/issues/3676 This issue also happens when no type casting is needed, because casting is supposed to be a no-op in this case. But as https://github.com/huggingface/datasets/issues/3676 shown, it's not the case and `None` are replaced by empty lists even if we cast to the exact same type. In this PR I just workaround this bug in the case where no type casting is needed. In particular, I only call `pa.ListArray.from_arrays` only when necessary. I also added a warning when some `None` are effectively replaced by empty lists. I wanted to raise an error in this case, but maybe we should wait a major update to do so This PR fixes this particular case, that is occurring in `run_qa.py` in `transformers`: ```python from datasets import Dataset ds = Dataset.from_dict({"a": range(4)}) ds = ds.map(lambda x: {"b": [[None, [0]]]}, batched=True, batch_size=1, remove_columns=["a"]) print(ds.to_pandas()) # before: # b # 0 [None, [0]] # 1 [[], [0]] # 2 [[], [0]] # 3 [[], [0]] # # now: # b # 0 [None, [0]] # 1 [None, [0]] # 2 [None, [0]] # 3 [None, [0]] ``` cc @sgugger
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Quick question about the message in the warning. You say \"will be fixed in a future major version\" but don't you mean \"will raise an error in a future major version\"?", "Right ! Good catch, thanks, I updated the message to say ...
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Return Python float instead of numpy.float64 in sklearn metrics
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3
2021-07-09T09:48:09Z
2021-07-12T14:12:53Z
2021-07-09T13:03:54Z
null
This PR converts the return type of all `sklearn` metrics to be Python `float` instead of `numpy.float64`. The reason behind this is that our Hub evaluation framework relies on converting benchmark-specific metrics to YAML ([example](https://huggingface.co/datasets/autonlp/autonlp-benchmark-raft-neelalex__raft-test-neelalex__raft-predictions-3/blob/main/README.md#L11)) and the `numpy.float64` format produces garbage like: ```python import yaml from datasets import load_metric metric = load_metric("accuracy") score = metric.compute(predictions=[0,1], references=[0,1]) print(yaml.dump(score["accuracy"])) # output below # !!python/object/apply:numpy.core.multiarray.scalar # - !!python/object/apply:numpy.dtype # args: # - f8 # - false # - true # state: !!python/tuple # - 3 # - < # - null # - null # - null # - -1 # - -1 # - 0 # - !!binary | # AAAAAAAA8D8= ```
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[ "I opened an issue on the `sklearn` repo to understand why `numpy.float64` is the default: https://github.com/scikit-learn/scikit-learn/discussions/20490", "It could be surprising at first to use `tolist()` on numpy scalars but it works ^^", "did the same for Pearsonr here: https://github.com/huggingface/datase...
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1,793,838,152
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6,010
Improve `Dataset`'s string representation
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2023-07-07T16:38:03Z
2023-07-16T13:00:18Z
null
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Currently, `Dataset.__repr__` outputs a dataset's column names and the number of rows. We could improve it by printing its features and the first few rows. We should also implement `_repr_html_` to have a rich HTML representation in notebooks/Streamlit.
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[ "I want to take a shot at this if possible ", "Yes, feel free to work on this.\r\n\r\nYou can check the PyArrow Table `__repr__` and Polars DataFrame `__repr__`/`_repr_html_` implementations for some pointers/ideas." ]
https://api.github.com/repos/huggingface/datasets/issues/2029
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Loading a faiss index KeyError
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4
2021-03-11T12:16:13Z
2021-03-12T00:21:09Z
2021-03-12T00:21:09Z
null
I've recently been testing out RAG and DPR embeddings, and I've run into an issue that is not apparent in the documentation. The basic steps are: 1. Create a dataset (dataset1) 2. Create an embeddings column using DPR 3. Add a faiss index to the dataset 4. Save faiss index to a file 5. Create a new dataset (dataset2) with the same text and label information as dataset1 6. Try to load the faiss index from file to dataset2 7. Get `KeyError: "Column embeddings not in the dataset"` I've made a colab notebook that should show exactly what I did. Please switch to GPU runtime; I didn't check on CPU. https://colab.research.google.com/drive/1X0S9ZuZ8k0ybcoei4w7so6dS_WrABmIx?usp=sharing Ubuntu Version VERSION="18.04.5 LTS (Bionic Beaver)" datasets==1.4.1 faiss==1.5.3 faiss-gpu==1.7.0 torch==1.8.0+cu101 transformers==4.3.3 NVIDIA-SMI 460.56 Driver Version: 460.32.03 CUDA Version: 11.2 Tesla K80 I was basically following the steps here: https://huggingface.co/docs/datasets/faiss_and_ea.html#adding-a-faiss-index I included the exact code from the documentation at the end of the notebook to show that they don't work either.
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[ "In your code `dataset2` doesn't contain the \"embeddings\" column, since it is created from the pandas DataFrame with columns \"text\" and \"label\".\r\n\r\nTherefore when you call `dataset2[embeddings_name]`, you get a `KeyError`.\r\n\r\nIf you want the \"embeddings\" column back, you can create `dataset2` with\r...
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I_kwDODunzps5Kqfdg
4,419
Update `unittest` assertions over tuples from `assertEqual` to `assertTupleEqual`
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2022-05-30T12:13:18Z
2022-09-30T16:01:37Z
2022-09-30T16:01:37Z
null
**Is your feature request related to a problem? Please describe.** So this is more a readability improvement rather than a proposal, wouldn't it be better to use `assertTupleEqual` over the tuples rather than `assertEqual`? As `unittest` added that function in `v3.1`, as detailed at https://docs.python.org/3/library/unittest.html#unittest.TestCase.assertTupleEqual, so maybe it's worth updating. Find an example of an `assertEqual` over a tuple in 🤗 `datasets` unit tests over an `ArrowDataset` at https://github.com/huggingface/datasets/blob/0bb47271910c8a0b628dba157988372307fca1d2/tests/test_arrow_dataset.py#L570 **Describe the solution you'd like** Start slowly replacing all the `assertEqual` statements with `assertTupleEqual` if the assertion is done over a Python tuple, as we're doing with the Python lists using `assertListEqual` rather than `assertEqual`. **Additional context** If so, please let me know and I'll try to go over the tests and create a PR if applicable, otherwise, if you consider this should stay as `assertEqual` rather than `assertSequenceEqual` feel free to close this issue! Thanks 🤗
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[ "Hi! If the only goal is to improve readability, it's better to use `assertTupleEqual` than `assertSequenceEqual` for Python tuples. Also, note that this function is called internally by `assertEqual`, but I guess we can accept a PR to be more verbose.", "Hi @mariosasko, right! I'll update the issue title/desc wi...
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2,892
Error when encoding a dataset with None objects with a Sequence feature
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2021-09-10T14:11:43Z
2021-09-13T14:18:13Z
2021-09-13T14:17:42Z
null
There is an error when encoding a dataset with None objects with a Sequence feature To reproduce: ```python from datasets import Dataset, Features, Value, Sequence data = {"a": [[0], None]} features = Features({"a": Sequence(Value("int32"))}) dataset = Dataset.from_dict(data, features=features) ``` raises ```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-24-40add67f8751> in <module> 2 data = {"a": [[0], None]} 3 features = Features({"a": Sequence(Value("int32"))}) ----> 4 dataset = Dataset.from_dict(data, features=features) [...] ~/datasets/features.py in encode_nested_example(schema, obj) 888 if isinstance(obj, str): # don't interpret a string as a list 889 raise ValueError("Got a string but expected a list instead: '{}'".format(obj)) --> 890 return [encode_nested_example(schema.feature, o) for o in obj] 891 # Object with special encoding: 892 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks TypeError: 'NoneType' object is not iterable ``` Instead, if should run without error, as if the `features` were not passed
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[ "This has been fixed by https://github.com/huggingface/datasets/pull/2900\r\nWe're doing a new release 1.12 today to make the fix available :)" ]
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950,483,980
MDExOlB1bGxSZXF1ZXN0Njk1MDIzMTEz
2,704
Fix pick default config name message
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2021-07-22T09:49:43Z
2021-07-22T10:02:41Z
2021-07-22T10:02:40Z
null
The error message to tell which config name to load is not displayed. This is because in the code it was considering the config kwargs to be non-empty, which is a special case for custom configs created on the fly. It appears after this change: https://github.com/huggingface/datasets/pull/2659 I fixed that by making the config kwargs empty by default, even if default parameters are passed Fix https://github.com/huggingface/datasets/issues/2703
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3,301
Add wikipedia tags
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2021-11-19T16:39:25Z
2021-11-19T16:49:30Z
2021-11-19T16:49:29Z
null
Add the missing tags to the wikipedia dataset card. I also added the missing languages code in our language codes list. This should also fix the code snippet that is presented on the Hub to load the dataset: fix https://github.com/huggingface/datasets/issues/3292
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Added conv ai 2 (Again)
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6
2020-12-10T18:21:55Z
2020-12-13T00:21:32Z
2020-12-13T00:21:31Z
null
The original PR -> https://github.com/huggingface/datasets/pull/1383 Reason for creating again - The reason I had to create the PR again was due to the master rebasing issue. After rebasing the changes, all the previous commits got added to the branch.
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[ "Looking perfect to me, need to rerun the tests\r\n", "Thanks, @tanmoyio. \r\nHow do I rerun the tests? Should I change something or push a new commit?", "@rkc007 you don't need to rerun it, @lhoestq @yjernite will rerun it, as there are huge number of PRs in the queue it might take lil bit of time. ", "ive j...
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Fix save_to_disk nested features order in dataset_info.json
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2021-05-28T15:03:28Z
2021-05-28T15:26:57Z
2021-05-28T15:26:56Z
null
Fix issue https://github.com/huggingface/datasets/issues/2267 The order of the nested features matters (pyarrow limitation), but the save_to_disk method was saving the features types as JSON with `sort_keys=True`, which was breaking the order of the nested features.
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Add ImageNet-Sketch dataset
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2
2022-05-09T23:38:45Z
2022-05-23T18:14:14Z
2022-05-23T18:05:29Z
null
This PR adds the ImageNet-Sketch dataset and resolves #3953 .
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[ "_The documentation is not available anymore as the PR was closed or merged._", "I think you can go ahead with uploading the data, and also ping the author in parallel. I think the images may subject to copyright anyway (scrapped from google image) so the dataset author is not allowed to set a license to the data...
https://api.github.com/repos/huggingface/datasets/issues/5222
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1,442,412,507
I_kwDODunzps5V-Xfb
5,222
HuggingFace website is incorrectly reporting that my datasets are pickled
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2022-11-09T16:41:16Z
2022-11-09T18:10:46Z
2022-11-09T18:06:57Z
null
### Describe the bug HuggingFace is incorrectly reporting that my datasets are pickled. They are not picked, they are simple ZIP files containing PNG images. Hopefully this is the right location to report this bug. ### Steps to reproduce the bug Inspect my dataset respository here: https://huggingface.co/datasets/ProGamerGov/StableDiffusion-v1-5-Regularization-Images ### Expected behavior They should not be reported as being pickled. ### Environment info N/A
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[ "cc @McPatate maybe you know what's happening ?", "Yes I think I know what is happening. We check in zips for pickles, and the UI must display the pickle jar when a scan has an associated list of imports, even when empty.\r\n~I'll fix ASAP !~", "> I'll fix ASAP !\r\n\r\nActually I'd rather leave it like that f...
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6,051
Skipping shard in the remote repo and resume upload
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2023-07-19T09:25:26Z
2023-07-20T18:16:01Z
2023-07-20T18:16:00Z
null
### Describe the bug For some reason when I try to resume the upload of my dataset, it is very slow to reach the index of the shard from which to resume the uploading. From my understanding, the problem is in this part of the code: arrow_dataset.py ```python for index, shard in logging.tqdm( enumerate(itertools.chain([first_shard], shards_iter)), desc="Pushing dataset shards to the dataset hub", total=num_shards, disable=not logging.is_progress_bar_enabled(), ): shard_path_in_repo = path_in_repo(index, shard) # Upload a shard only if it doesn't already exist in the repository if shard_path_in_repo not in data_files: ``` In particular, iterating the generator is slow during the call: ```python self._select_contiguous(start, length, new_fingerprint=new_fingerprint) ``` I wonder if it is possible to avoid calling this function for shards that are already uploaded and just start from the correct shard index. ### Steps to reproduce the bug 1. Start the upload ```python dataset = load_dataset("imagefolder", data_dir=DATA_DIR, split="train", drop_labels=True) dataset.push_to_hub("repo/name") ``` 2. Stop and restart the upload after hundreds of shards ### Expected behavior Skip the uploaded shards faster. ### Environment info - `datasets` version: 2.5.1 - Platform: Linux-4.18.0-193.el8.x86_64-x86_64-with-glibc2.17 - Python version: 3.8.16 - PyArrow version: 12.0.1 - Pandas version: 2.0.2
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[ "Hi! `_select_contiguous` fetches a (zero-copy) slice of the dataset's Arrow table to build a shard, so I don't think this part is the problem. To me, the issue seems to be the step where we embed external image files' bytes (a lot of file reads). You can use `.map` with multiprocessing to perform this step before ...
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3,128
Support Audio feature for TAR archives in sequential access
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2021-10-21T08:23:01Z
2021-11-17T17:42:07Z
2021-11-17T17:42:07Z
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Currently, Audio feature accesses each audio file by their file path. However, streamed TAR archive files do not allow random access to their archived files. Therefore, we should enhance the Audio feature to support TAR archived files in sequential access.
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Allow to run CI on push to ci-branch
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2023-04-25T13:57:26Z
2023-04-26T13:43:08Z
2023-04-26T13:35:47Z
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This PR allows to run the CI on push to a branch named "ci-*", without needing to open a PR. - This will allow to make CI tests without opening a PR, e.g., for future `huggingface-hub` releases, future dependency releases (like `fsspec`, `pandas`,...) Note that to build the documentation, we already allow it on push to a branch named "doc-builder*". See: - #5788 CC: @Wauplin
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Remove `extended` field from dataset tagger
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2021-06-01T17:18:42Z
2021-06-09T09:06:31Z
2021-06-09T09:06:30Z
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## Describe the bug While working on #2435 I used the [dataset tagger](https://huggingface.co/datasets/tagging/) to generate the missing tags for the YAML metadata of each README.md file. However, it seems that our CI raises an error when the `extended` field is included: ``` dataset_name = 'arcd' @pytest.mark.parametrize("dataset_name", get_changed_datasets(repo_path)) def test_changed_dataset_card(dataset_name): card_path = repo_path / "datasets" / dataset_name / "README.md" assert card_path.exists() error_messages = [] try: ReadMe.from_readme(card_path) except Exception as readme_error: error_messages.append(f"The following issues have been found in the dataset cards:\nREADME:\n{readme_error}") try: DatasetMetadata.from_readme(card_path) except Exception as metadata_error: error_messages.append( f"The following issues have been found in the dataset cards:\nYAML tags:\n{metadata_error}" ) if error_messages: > raise ValueError("\n".join(error_messages)) E ValueError: The following issues have been found in the dataset cards: E YAML tags: E __init__() got an unexpected keyword argument 'extended' tests/test_dataset_cards.py:70: ValueError ``` Consider either removing this tag from the tagger or including it as part of the validation step in the CI. cc @yjernite
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[ "The tagger also doesn't insert the value for the `size_categories` field automatically, so this should be fixed too", "Thanks for reporting. Indeed the `extended` tag doesn't exist. Not sure why we had that in the tagger.\r\nThe repo of the tagger is here if someone wants to give this a try: https://github.com/h...
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Check that array is not Float as nan != nan
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2021-09-17T16:16:41Z
2021-09-21T09:39:05Z
2021-09-21T09:39:04Z
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The Exception wants to check for issues with StructArrays/ListArrays but catches FloatArrays with value nan as nan != nan. Pass on FloatArrays as we should not raise an Exception for them.
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Make get_from_cache use custom temp filename that is locked
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2023-06-09T09:01:13Z
2023-06-14T13:35:38Z
2023-06-14T13:27:24Z
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This PR ensures that the temporary filename created is the same as the one that is locked, while writing to the cache. This PR stops using `tempfile` to generate the temporary filename. Additionally, the behavior now is aligned for both `resume_download` `True` and `False`. Refactor temp_file_manager so that it uses the filename that is locked: - Use: `cache_path + ".incomplete"`, when the locked one is `cache_path + ".lock"` Before it was using `tempfile` inside `cache_dir`, which was not locked: although very improbable name collision (8 random characters), this was not impossible when huge number of multiple processes. Maybe related to "Stale file handle" issues caused by `tempfile`: - [ ] https://huggingface.co/datasets/tapaco/discussions/4 - [ ] https://huggingface.co/datasets/xcsr/discussions/1 - [ ] https://huggingface.co/datasets/covost2/discussions/3 ``` Error code: ConfigNamesError Exception: OSError Message: [Errno 116] Stale file handle Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 61, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, use_auth_token=use_auth_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 323, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1219, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1188, in dataset_module_factory return HubDatasetModuleFactoryWithScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 907, in get_module dataset_readme_path = self.download_dataset_readme_file() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 896, in download_dataset_readme_file return cached_path( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 183, in cached_path output_path = get_from_cache( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 611, in get_from_cache http_get( File "/usr/local/lib/python3.9/tempfile.py", line 496, in __exit__ result = self.file.__exit__(exc, value, tb) OSError: [Errno 116] Stale file handle ``` - the stale file handle error can be raised when `tempfile` tries to close (when exiting its context manager) a filename that has been already closed by other process - note that `tempfile` filenames are randomly generated but not locked in our code CC: @severo
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Clone full repo to detect new tags when mirroring datasets on the Hub
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2021-12-28T15:50:47Z
2021-12-28T16:07:21Z
2021-12-28T16:07:20Z
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The new releases of `datasets` were not detected because the shallow clone in the CI wasn't getting the git tags. By cloning the full repository we can properly detect a new release, and tag all the dataset repositories accordingly cc @SBrandeis
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[ "Good catch !!", "The CI fail is unrelated to this PR and fixed on master, merging :)" ]
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Replace assertTrue(isinstance with assertIsInstance in tests
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2021-04-06T14:41:09Z
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Replaces all the occurrences of the `assertTrue(isinstance(` pattern with `assertIsInstance`.
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add crd3(ACL 2020) dataset
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2020-08-03T11:28:52Z
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This PR adds the **Critical Role Dungeons and Dragons Dataset** published at ACL 2020
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Hotfix failing CI tests on Windows
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2022-04-07T07:38:46Z
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This PR makes a hotfix for our CI Windows tests: https://app.circleci.com/pipelines/github/huggingface/datasets/11092/workflows/9cfdb1dd-0fec-4fe0-8122-5f533192ebdc/jobs/67414 Fix #4118 I guess this issue is related to this PR: - huggingface/huggingface_hub#815
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fsspec lock reset in multiprocessing
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`fsspec` added a clean way of resetting its lock - instead of doing it manually
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add DoQA dataset (ACL 2020)
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add DoQA dataset (ACL 2020) http://ixa.eus/node/12931
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Remove README link to deprecated Colab notebook
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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...
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760,724,914
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1,424
Add yoruba wordsim353
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2020-12-09T22:37:42Z
2020-12-09T22:39:45Z
2020-12-09T22:39:45Z
null
Added WordSim-353 evaluation dataset for Yoruba
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[WIP] Add ArrayXD support for fixed size list.
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2021-04-16T13:04:08Z
2022-07-06T15:19:48Z
null
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Add support for fixed size list for ArrayXD when shape is known . See https://github.com/huggingface/datasets/issues/2146 Since offset are not stored anymore, the file size is now roughly equal to the actual data size.
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[ "Awesome thanks ! To fix the CI you just need to merge master into your branch.\r\nThe error is unrelated to your PR" ]
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New Preprocessing Feature - Deduplication [Request]
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**Is your feature request related to a problem? Please describe.** Many large datasets are full of duplications and it has been shown that deduplicating datasets can lead to better performance while training, and more truthful evaluation at test-time. A feature that allows one to easily deduplicate a dataset can be cool! **Describe the solution you'd like** We can define a function and keep only the first/last data-point that yields the value according to this function. **Describe alternatives you've considered** The clear alternative is to repeat a clear boilerplate every time someone want to deduplicate a dataset.
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[ "Hi! The [datasets_sql](https://github.com/mariosasko/datasets_sql) package lets you easily find distinct rows in a dataset (an example with `SELECT DISTINCT` is in the readme). Deduplication is (still) not part of the official API because it's hard to implement for datasets bigger than RAM while only using the nat...
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2,530
Fixed label parsing in the ProductReviews dataset
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2021-06-22T09:12:45Z
2021-06-22T12:55:20Z
2021-06-22T12:52:40Z
null
Fixed issue with parsing dataset labels.
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[ "@lhoestq, can you please review this PR?\r\nWhat exactly is the problem in the test case? Should it matter?", "Hi ! Thanks for fixing this :)\r\n\r\nThe CI fails for two reasons:\r\n- the `pretty_name` tag is missing in yaml tags in ./datasets/turkish_product_reviews/README.md. You can fix that by adding this in...
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447
[BugFix] fix wrong import of DEFAULT_TOKENIZER
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2020-07-28T07:41:10Z
2020-07-28T12:58:01Z
2020-07-28T12:52:05Z
null
Fixed the path to `DEFAULT_TOKENIZER` #445
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2,600
Crash when using multiprocessing (`num_proc` > 1) on `filter` and all samples are discarded
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2021-07-06T16:53:25Z
2021-07-07T12:50:31Z
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## Describe the bug If `filter` is applied to a dataset using multiprocessing (`num_proc` > 1) and all sharded datasets are empty afterwards (due to all samples being discarded), the program crashes. ## Steps to reproduce the bug ```python from datasets import Dataset data = Dataset.from_dict({'id': [0,1]}) data.filter(lambda x: False, num_proc=2) ``` ## Expected results An empty table should be returned without crashing. ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 185, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/user/venv/lib/python3.8/site-packages/datasets/fingerprint.py", line 397, in wrapper out = func(self, *args, **kwargs) File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2143, in filter return self.map( File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1738, in map result = concatenate_datasets(transformed_shards) File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3267, in concatenate_datasets table = concat_tables(tables_to_concat, axis=axis) File "/home/user/venv/lib/python3.8/site-packages/datasets/table.py", line 853, in concat_tables return ConcatenationTable.from_tables(tables, axis=axis) File "/home/user/venv/lib/python3.8/site-packages/datasets/table.py", line 713, in from_tables blocks = to_blocks(tables[0]) IndexError: list index out of range ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.0 - Platform: Linux-5.12.11-300.fc34.x86_64-x86_64-with-glibc2.2.5 - Python version: 3.8.10 - PyArrow version: 3.0.0
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3,674
Add FrugalScore metric
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2022-02-03T12:28:52Z
2022-02-21T15:58:44Z
2022-02-21T15:58:44Z
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This pull request add FrugalScore metric for NLG systems evaluation. FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance. Paper: https://arxiv.org/abs/2110.08559?context=cs Github: https://github.com/moussaKam/FrugalScore @lhoestq
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[ "@lhoestq \r\n\r\nThe model used by default (`moussaKam/frugalscore_tiny_bert-base_bert-score`) is a tiny model.\r\n\r\nI still want to make one modification before merging.\r\nI would like to load the model checkpoint once. Do you think it's a good idea if I load it in `_download_and_prepare`? In this case should ...
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dutch_social can't be loaded
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2021-01-01T17:37:08Z
2022-10-05T13:03:26Z
2022-10-05T13:03:26Z
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Hi all, I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social). However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links. ``` (base) Koens-MacBook-Pro:~ koenvandenberge$ python Python 3.7.4 (default, Aug 13 2019, 15:17:50) [Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset dataset = load_dataset( 'dutch_social') >>> dataset = load_dataset( ... 'dutch_social') Traceback (most recent call last): File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 2, in <module> File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module combined_path, github_file_path, file_path FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py ```
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[ "exactly the same issue in some other datasets.\r\nDid you find any solution??\r\n", "Hi @koenvandenberge and @alighofrani95!\r\nThe datasets you're experiencing issues with were most likely added recently to the `datasets` library, meaning they have not been released yet. They will be released with the v2 of the...
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4,572
Dataset Viewer issue for mlsum
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2022-06-26T20:24:17Z
2022-07-21T12:40:01Z
2022-07-21T12:40:01Z
null
### Link https://huggingface.co/datasets/mlsum/viewer/de/train ### Description There's seems to be a problem with the download / streaming of this dataset: ``` Server error Status code: 400 Exception: BadZipFile Message: File is not a zip file ``` ### Owner No
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[ "Thanks for reporting, @lewtun.\r\n\r\nAfter investigation, it seems that the server https://gitlab.lip6.fr does not allow HTTP Range requests.\r\n\r\nWe are trying to find a workaround..." ]
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2,698
Ignore empty batch when writing
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2021-07-21T22:35:30Z
2021-07-26T14:56:03Z
2021-07-26T13:25:26Z
null
This prevents an schema update with unknown column types, as reported in #2644. This is my first attempt at fixing the issue. I tested the following: - First batch returned by a batched map operation is empty. - An intermediate batch is empty. - `python -m unittest tests.test_arrow_writer` passes. However, `arrow_writer` looks like a pretty generic interface, I'm not sure if there are other uses I may have overlooked. Let me know if that's the case, or if a better approach would be preferable.
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Add CelebA Dataset
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2021-02-05T20:20:55Z
2021-02-18T14:17:07Z
2021-02-18T14:17:07Z
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Trying to add CelebA Dataset. Need help with testing. Loading examples takes a lot of time so I am unable to generate the `dataset_infos.json` and unable to test. Also, need help with creating `dummy_data.zip`. Additionally, trying to load a few examples using `load_dataset('./datasets/celeb_a',split='train[10:20]')` still loads all the examples (doesn't stop at 10).
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[ "Hi @gchhablani! Thanks for all the contributions! We definitely want more image datasets, but Face datasets are tricky in general, in this one includes predicting attributes such as Attractiveness, Gender, or Race, which can be pretty problematic.\r\n\r\nWould you be up for starting with only object classification...
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🇸🇪 Added Swedish Reviews dataset for sentiment classification in Sw…
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2020-12-06T21:02:54Z
2020-12-08T10:54:56Z
2020-12-08T10:54:56Z
null
perhaps: @lhoestq 🤗
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cast_column function not working with map function in streaming mode for Audio features
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2021-12-30T14:52:01Z
2022-01-18T19:54:07Z
2022-01-18T19:54:07Z
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## Describe the bug I am trying to use Audio class for loading audio features using custom dataset. I am able to cast 'audio' feature into 'Audio' format with cast_column function. On using map function, I am not getting 'Audio' casted feature but getting path of audio file only. I am getting features of 'audio' of string type with load_dataset call. After using cast_column 'audio' feature is converted into 'Audio' type. But in map function I am not able to get Audio type for audio feature & getting string type data containing path of file only. So I am not able to use processor in encode function. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from datasets import load_dataset, Audio from transformers import Wav2Vec2Processor def encode(batch, processor): print("Audio: ",batch['audio']) batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values return batch def print_ds(ds): iterator = iter(ds) for d in iterator: print("Data: ",d) break processor = Wav2Vec2Processor.from_pretrained(pretrained_model_path) dataset = load_dataset("custom_dataset.py","train",data_files={'train':'train_path.txt'}, data_dir="data", streaming=True, split="train") print("Features: ",dataset.features) print_ds(dataset) dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000)) print("Features: ",dataset.features) print_ds(dataset) dataset = dataset.map(lambda x: encode(x,processor)) print("Features: ",dataset.features) print_ds(dataset) ``` ## Expected results map function not printing Audio type features be used with processor function and getting error in processor call due to this. ## Actual results # after load_dataset call Features: {'sentence': Value(dtype='string', id=None), 'audio': Value(dtype='string', id=None)} Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': 'data/0116_003.wav'} # after cast_column call Features: {'sentence': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None)} Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': {'path': 'data/0116_003.wav', 'array': array([ 1.2662281e-06, 1.0264218e-06, -1.3615092e-06, ..., 1.3017889e-02, 1.0085563e-02, 4.8155054e-03], dtype=float32), 'sampling_rate': 16000}} # after map call Features: None Audio: data/0116_003.wav Traceback (most recent call last): File "demo2.py", line 36, in <module> print_ds(dataset) File "demo2.py", line 11, in print_ds for d in iterator: File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 341, in __iter__ for key, example in self._iter(): File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 338, in _iter yield from ex_iterable File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 192, in __iter__ yield key, self.function(example) File "demo2.py", line 32, in <lambda> dataset = dataset.map(lambda x: batch_encode(x,processor)) File "demo2.py", line 6, in batch_encode batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values TypeError: string indices must be integers ## Environment info - `datasets` version: 1.17.0 - Platform: Linux-4.14.243 with-debian-bullseye-sid - Python version: 3.7.9 - PyArrow version: 6.0.1
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[ "Hi! This is probably due to the fact that `IterableDataset.map` sets `features` to `None` before mapping examples. We can fix the issue by passing the old `features` dict to the map generator and performing encoding/decoding there (before calling the map transform function)." ]
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CC100: Using HTTPS for the data source URL fixes load_dataset()
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2022-01-04T18:45:54Z
2022-01-05T17:28:34Z
2022-01-05T17:28:34Z
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Without this change the following script (with any lang parameter) consistently fails. After changing to the HTTPS URL, the script works as expected. ```python from datasets import load_dataset dataset = load_dataset("cc100", lang="en") ``` This is the error produced by the previous script: ```sh Using custom data configuration en-lang=en Downloading and preparing dataset cc100/en to /home/antti/.cache/huggingface/datasets/cc100/en-lang=en/0.0.0/526ac20780de5e074cf73a7466e868cb67f960b48f6de42ff6a6c4e71910d71b... Traceback (most recent call last): File "/home/antti/tmp/cc100/cc100.py", line 3, in <module> dataset = load_dataset("cc100", lang="en") File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/load.py", line 1694, in load_dataset builder_instance.download_and_prepare( File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/builder.py", line 595, in download_and_prepare self._download_and_prepare( File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/builder.py", line 661, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/antti/.cache/huggingface/modules/datasets_modules/datasets/cc100/526ac20780de5e074cf73a7466e868cb67f960b48f6de42ff6a6c4e71910d71b/cc100.py", line 117, in _split_generators path = dl_manager.download_and_extract(download_url) File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 308, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 196, in download downloaded_path_or_paths = map_nested( File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 251, in map_nested return function(data_struct) File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 298, in cached_path output_path = get_from_cache( File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})") ConnectionError: Couldn't reach http://data.statmt.org/cc-100/en.txt.xz (error 503) ``` Note that I get the same behavior also using curl on the command line. The plain HTTP "curl -L http://data.statmt.org/cc-100/en.txt.xz" fails with "503 Service unavailable", but the with the HTTPS version of the URL curl starts downloading the file. My guess is that the server does overly aggressive rate-limitting. When a client requests an HTTP URL, it (sensibly) gets redirected to the HTTPS equivalent, but now the server notices two requests coming from the same client (the original HTTP and the redirected HTTPS) during a brief time windows and rate-limitter kicks in and blocks the second request! If the client initally uses the HTTPS URL there's only one incoming request which the rate-limitter allows.
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4,826
Fix language tags in dataset cards
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2022-08-11T13:47:14Z
2022-08-11T14:17:48Z
2022-08-11T14:03:12Z
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Fix language tags in all dataset cards, so that they are validated (aligned with our `languages.json` resource).
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[ "_The documentation is not available anymore as the PR was closed or merged._", "The non-passing tests are caused by other missing information in the dataset cards." ]
https://api.github.com/repos/huggingface/datasets/issues/3020
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3,020
Add a metric for the MATH dataset (competition_math).
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2021-10-04T16:52:16Z
2021-10-22T10:29:31Z
2021-10-22T10:29:31Z
null
This metric computes accuracy for the MATH dataset (https://arxiv.org/abs/2103.03874) after canonicalizing the prediction and the reference (e.g., converting "1/2" to "\\\\frac{1}{2}").
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[ "I believe the only failed test related to this PR is tests/test_metric_common.py::LocalMetricTest::test_load_metric_competition_math. It gives the following error:\r\n\r\nImportError: To be able to use this dataset, you need to install the following dependencies['math_equivalence'] using 'pip install git+https://g...
https://api.github.com/repos/huggingface/datasets/issues/2100
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MDExOlB1bGxSZXF1ZXN0NTk4NzMzOTM0
2,100
Fix deprecated warning message and docstring
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2021-03-23T10:27:52Z
2021-03-24T08:19:41Z
2021-03-23T18:03:49Z
null
Fix deprecated warnings: - Use deprecated Sphinx directive in docstring - Fix format of deprecated message - Raise FutureWarning
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[ "I have a question: what about `dictionary_encode_column_`?\r\n- It is deprecated in Dataset, but it recommends using a non-existing method instead: `Dataset.dictionary_encode_column` does not exist.\r\n- It is NOT deprecated in DatasetDict.", "`dictionary_encode_column_ ` should be deprecated since it never work...
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567
Fix BLEURT metrics for backward compatibility
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2020-09-02T21:22:35Z
2020-09-03T07:29:52Z
2020-09-03T07:29:50Z
null
Fix #565
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Add KorQPair dataset
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2020-12-07T14:33:57Z
2021-12-29T00:49:40Z
2020-12-08T15:11:41Z
null
This PR adds a [Korean paired question dataset](https://github.com/songys/Question_pair) containing labels indicating whether two questions in a given pair are semantically identical. This dataset was used to evaluate the performance of [KoGPT2](https://github.com/SKT-AI/KoGPT2#subtask-evaluations) on a phrase detection downstream task.
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[ "dummy data is missing", "Hey @cceyda, thanks for pointing that out. I thought I'd added it, but seems like that wasn't the case. Just pushed a new commit with the dummy data." ]
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773,669,700
MDU6SXNzdWU3NzM2Njk3MDA=
1,624
Cannot download ade_corpus_v2
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2020-12-23T10:58:14Z
2021-08-03T05:08:54Z
2021-08-03T05:08:54Z
null
I tried this to get the dataset following this url : https://huggingface.co/datasets/ade_corpus_v2 but received this error : `Traceback (most recent call last): File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/ade_corpus_v2/ade_corpus_v2.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/ade_corpus_v2/ade_corpus_v2.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module combined_path, github_file_path, file_path FileNotFoundError: Couldn't find file locally at ade_corpus_v2/ade_corpus_v2.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/ade_corpus_v2/ade_corpus_v2.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/ade_corpus_v2/ade_corpus_v2.py`
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[ "Hi @him1411, the dataset you are trying to load has been added during the community sprint and has not been released yet. It will be available with the v2 of `datasets`.\r\nFor now, you should be able to load the datasets after installing the latest (master) version of `datasets` using pip:\r\n`pip install git+htt...
https://api.github.com/repos/huggingface/datasets/issues/580
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580
nlp re-creates already-there caches when using a script, but not within a shell
[]
closed
false
null
2
2020-09-07T10:23:50Z
2020-09-07T15:19:09Z
2020-09-07T14:26:41Z
null
`nlp` keeps creating new caches for the same file when launching `filter` from a script, and behaves correctly from within the shell. Example: try running ``` import nlp hans_easy_data = nlp.load_dataset('hans', split="validation").filter(lambda x: x['label'] == 0) hans_hard_data = nlp.load_dataset('hans', split="validation").filter(lambda x: x['label'] == 1) ``` twice. If launched from a `file.py` script, the cache will be re-created the second time. If launched as 3 shell/`ipython` commands, `nlp` will correctly re-use the cache. As observed with @lhoestq.
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[ "Couln't reproduce on my side :/ \r\nlet me know if you manage to reproduce on another env (colab for example)", "Fixed with a clean re-install!" ]
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987,139,146
MDU6SXNzdWU5ODcxMzkxNDY=
2,868
Add Common Objects in 3D (CO3D)
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2021-09-02T20:36:12Z
2021-12-08T12:02:10Z
null
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## Adding a Dataset - **Name:** *Common Objects in 3D (CO3D)* - **Description:** *See blog post [here](https://ai.facebook.com/blog/common-objects-in-3d-dataset-for-3d-reconstruction)* - **Paper:** *[link to paper](https://arxiv.org/abs/2109.00512)* - **Data:** *[link to data](https://ai.facebook.com/datasets/co3d-downloads/)* - **Motivation:** *excerpt from above blog post:* > As the first data set of its kind, CO3D will aptly enable reconstruction of real-life 3D objects. Indeed, CO3D already provides training data to enable our NeRFormer to tackle the new-view synthesis (NVS) task. Here, photorealistic NVS is a major step on the path to fully immersive AR/VR effects, where objects can be virtually transported across different environments, which will allow connecting users by sharing or recollecting their experiences. > > Besides practical applications in AR/VR, we hope that the data set will become a standard testbed for the recent proliferation of methods (including NeRFormer, Implicit Differentiable Renderer, NeRF, and others) that reconstruct 3D scenes by means of an implicit shape model. > 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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2,144
Loading wikipedia 20200501.en throws pyarrow related error
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open
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2021-03-30T10:38:31Z
2021-04-01T09:21:17Z
null
null
**Problem description** I am getting the following error when trying to load wikipedia/20200501.en dataset. **Error log** Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931... Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s] Downloading: 59%|███████████████████████████████████████████████████████████████████████████████████████▊ | 10.7G/18.3G [11:30<08:08, 15.5MB/s] Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data. Traceback (most recent call last): File "load_wiki.py", line 2, in <module> ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache') File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset map_tuple=True, File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename pa_table = ArrowReader.read_table(filename, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table pa_table = f.read_all() File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: Expected to be able to read 9176784 bytes for message body, got 4918712 **Detailed version info** datasets==1.5.0 - dataclasses [required: Any, installed: 0.8] - dill [required: Any, installed: 0.3.3] - fsspec [required: Any, installed: 0.8.7] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - huggingface-hub [required: <0.1.0, installed: 0.0.7] - filelock [required: Any, installed: 3.0.12] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - requests [required: Any, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: Any, installed: 4.49.0] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - multiprocess [required: Any, installed: 0.70.11.1] - dill [required: >=0.3.3, installed: 0.3.3] - numpy [required: >=1.17, installed: 1.17.0] - pandas [required: Any, installed: 1.1.5] - numpy [required: >=1.15.4, installed: 1.17.0] - python-dateutil [required: >=2.7.3, installed: 2.8.0] - six [required: >=1.5, installed: 1.15.0] - pytz [required: >=2017.2, installed: 2020.1] - pyarrow [required: >=0.17.1, installed: 3.0.0] - numpy [required: >=1.16.6, installed: 1.17.0] - requests [required: >=2.19.0, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0] - xxhash [required: Any, installed: 2.0.0]
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[ "That's how I loaded the dataset\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache')\r\n```", "Hi ! It looks like the arrow file in the folder\r\n`/usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa...
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1,774,134,091
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5,989
Set a rule on the config and split names
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3
2023-06-26T07:34:14Z
2023-07-19T14:22:54Z
null
null
> should we actually allow characters like spaces? maybe it's better to add validation for whitespace symbols and directly in datasets and raise https://github.com/huggingface/datasets-server/issues/853
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[ "in this case we need to decide what to do with the existing datasets with white space characters (there shouldn't be a lot of them I think)", "I imagine that we should stop supporting them, and help the user fix them?", "See a report where the datasets server fails: https://huggingface.co/datasets/poloclub/dif...
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1,586,930,643
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5,536
Failure to hash function when using .map()
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closed
false
null
8
2023-02-16T03:12:07Z
2023-05-22T20:02:16Z
2023-02-16T14:56:41Z
null
### Describe the bug _Parameter 'function'=<function process at 0x7f1ec4388af0> of the transform datasets.arrow_dataset.Dataset.\_map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed._ This issue with `.map()` happens for me consistently, as also described in closed issue #4506 Dataset indices can be individually serialized using dill and pickle without any errors. I'm using tiktoken to encode in the function passed to map(). Similarly, indices can be individually encoded without error. ### Steps to reproduce the bug ```py from datasets import load_dataset import tiktoken dataset = load_dataset("stas/openwebtext-10k") enc = tiktoken.get_encoding("gpt2") tokenized = dataset.map( process, remove_columns=['text'], desc="tokenizing the OWT splits", ) def process(example): ids = enc.encode(example['text']) ids.append(enc.eot_token) out = {'ids': ids, 'len': len(ids)} return out ``` ### Expected behavior Should encode simple text objects. ### Environment info Python versions tried: both 3.8 and 3.10.10 `PYTHONUTF8=1` as env variable Datasets tried: - stas/openwebtext-10k - rotten_tomatoes - local text file OS: Ubuntu Linux 20.04 Package versions: - torch 1.13.1 - dill 0.3.4 (if using 0.3.6 - same issue) - datasets 2.9.0 - tiktoken 0.2.0
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[ "Hi ! `enc` is not hashable:\r\n```python\r\nimport tiktoken\r\nfrom datasets.fingerprint import Hasher\r\n\r\nenc = tiktoken.get_encoding(\"gpt2\")\r\nHasher.hash(enc)\r\n# raises TypeError: cannot pickle 'builtins.CoreBPE' object\r\n```\r\nIt happens because it's not picklable, and because of that it's not possib...
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756,652,427
MDExOlB1bGxSZXF1ZXN0NTMyMTY4Nzky
1,079
nkjp-ner
[]
closed
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null
0
2020-12-03T22:47:26Z
2020-12-04T09:42:06Z
2020-12-04T09:42:06Z
null
- **Name:** *nkjp-ner* - **Description:** *The NKJP-NER is based on a human-annotated part of NKJP. We extracted sentences with named entities of exactly one type. The task is to predict the type of the named entity.* - **Data:** *https://klejbenchmark.com/tasks/* - **Motivation:** *The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language understanding.*
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3,790
Add doc builder scripts
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closed
false
null
3
2022-02-25T16:38:47Z
2022-03-01T15:55:42Z
2022-03-01T15:55:41Z
null
I added the three scripts: - build_dev_documentation.yml - build_documentation.yml - delete_dev_documentation.yml I got them from `transformers` and did a few changes: - I removed the `transformers`-specific dependencies - I changed all the paths to be "datasets" instead of "transformers" - I passed the `--library_name datasets` arg to the `doc-builder build` command (according to https://github.com/huggingface/doc-builder/pull/94/files#diff-bcc33cf7c223511e498776684a9a433810b527a0a38f483b1487e8a42b6575d3R26) cc @LysandreJik @mishig25
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[ "I think we're only missing the hosted runner to be configured for this repository and we should be good", "Regarding the self-hosted runner, I actually encourage using the approach defined here: https://github.com/huggingface/transformers/pull/15710, which doesn't leverage a self-hosted runner. This prevents que...
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PR_kwDODunzps48TRUH
4,768
Unpin rouge_score test dependency
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null
1
2022-07-29T08:17:40Z
2022-07-29T16:42:28Z
2022-07-29T16:29:17Z
null
Once `rouge-score` has made the 0.1.2 release to fix their issue https://github.com/google-research/google-research/issues/1212, we can unpin it. Related to: - #4735
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
https://api.github.com/repos/huggingface/datasets/issues/185
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https://github.com/huggingface/datasets/pull/185
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185
[Commands] In-detail instructions to create dummy data folder
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2020-05-22T12:26:25Z
2020-05-22T14:06:35Z
2020-05-22T14:06:34Z
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### Dummy data command This PR adds a new command `python nlp-cli dummy_data <path_to_dataset_folder>` that gives in-detail instructions on how to add the dummy data files. It would be great if you can try it out by moving the current dummy_data folder of any dataset in `./datasets` with `mv datasets/<dataset_script>/dummy_data datasets/<dataset_name>/dummy_data_copy` and running the command `python nlp-cli dummy_data ./datasets/<dataset_name>` to see if you like the instructions. ### CONTRIBUTING.md Also the CONTRIBUTING.md is made cleaner including a new section on "How to add a dataset". ### Current PRs It would be nice if we can try out if this command helps current PRs, *e.g.* #169 to add a dataset. I comment on those PRs.
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