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PyArrow Dataset error when calling `load_dataset`
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## Describe the bug I am fine tuning a wav2vec2 model following the script here using my own dataset: https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py Loading my Audio dataset from the hub which was originally generated from disk results in the following PyArrow error: ```sh File "/home/ubuntu/w2v2/run_speech_recognition_ctc.py", line 227, in main raw_datasets = load_dataset( File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/load.py", line 1679, in load_dataset builder_instance.download_and_prepare( File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 793, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 1268, in _prepare_split for key, table in logging.tqdm( File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/tqdm/std.py", line 1195, in __iter__ for obj in iterable: File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/packaged_modules/parquet/parquet.py", line 68, in _generate_tables for batch_idx, record_batch in enumerate( File "pyarrow/_parquet.pyx", line 1309, in iter_batches File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs ``` ## Steps to reproduce the bug I created a dataset from a JSON lines manifest of `audio_filepath`, `text`, and `duration`. When creating the dataset, I do something like this: ```python import json from datasets import Dataset, Audio # manifest_lines is a list of dicts w/ "audio_filepath", "duration", and "text for line in manifest_lines: line = line.strip() if line: line_dict = json.loads(line) manifest_dict["audio"].append(f"{root_path}/{line_dict['audio_filepath']}") manifest_dict["duration"].append(line_dict["duration"]) manifest_dict["transcription"].append(line_dict["text"]) # Create a HF dataset dataset = Dataset.from_dict(manifest_dict).cast_column( "audio", Audio(sampling_rate=16_000), ) # From the docs for saving to disk # https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.save_to_disk def read_audio_file(example): with open(example["audio"]["path"], "rb") as f: return {"audio": {"bytes": f.read()}} dataset = dataset.map(read_audio_file, num_proc=70) dataset.save_to_disk(f"/audio-data/hf/{artifact_name}") dataset.push_to_hub(f"{org-name}/{artifact_name}", max_shard_size="5GB", private=True) ``` Then when I call `load_dataset()` in my training script, with the same dataset I generated above, and download from the huggingface hub I get the above stack trace. I am able to load the dataset fine if I use `load_from_disk()`. ## Expected results `load_dataset()` should behave just like `load_from_disk()` and not cause any errors. ## Actual results See above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> I am using the `huggingface/transformers-pytorch-gpu:latest` image - `datasets` version: 2.3.0 - Platform: Docker/Ubuntu 20.04 - Python version: 3.8 - PyArrow version: 8.0.0
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Dataset Viewer issue for shamikbose89/lancaster_newsbooks
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[ "It seems like the list of splits could not be obtained:\r\n\r\n```python\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names(\"shamikbose89/lancaster_newsbooks\", \"default\")\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 354, in get_dataset_config_info\r\n for split_generator in builder._split_generators(\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/shamikbose89--lancaster_newsbooks/2d1c63d269bf7b9342accce0a95960b1710ab4bc774248878bd80eb96c1afaf7/lancaster_newsbooks.py\", line 73, in _split_generators\r\n data_dir = dl_manager.download_and_extract(_URL)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 916, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 879, in extract\r\n urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 348, in map_nested\r\n return function(data_struct)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 884, in _extract\r\n protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 388, in _get_extraction_protocol\r\n return _get_extraction_protocol_with_magic_number(f)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 354, in _get_extraction_protocol_with_magic_number\r\n f.seek(0)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py\", line 684, in seek\r\n raise ValueError(\"Cannot seek streaming HTTP file\")\r\nValueError: Cannot seek streaming HTTP file\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 404, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 359, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```\r\n\r\nping @huggingface/datasets ", "Oh, I removed the 'split' key from `kwargs`. I put it back in, but there's still the same error" ]
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### Link https://huggingface.co/datasets/shamikbose89/lancaster_newsbooks ### Description Status code: 400 Exception: ValueError Message: Cannot seek streaming HTTP file I am able to use the dataset loading script locally and it also runs when I'm using the one from the hub, but the viewer still doesn't load ### Owner Yes
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Issue loading TheNoob3131/mosquito-data dataset
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[ "I am also getting a ValueError: 'Couldn't cast' at the bottom. Is this because of some delimiter issue? My dataset is on the Huggingface Hub. If you could look at it, that would be greatly appreciated.", "Hi @thenerd31, thanks for reporting.\r\n\r\nPlease note that your issue is not caused by the Hugging Face Datasets library, but it has to do with the specific implementation of your dataset on the Hub.\r\n\r\nTherefore, I'm transferring this discussion to your own dataset Community tab: https://huggingface.co/datasets/TheNoob3131/mosquito-data/discussions/1" ]
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![image](https://user-images.githubusercontent.com/53668030/179815591-d75fa7d3-3122-485f-a852-b06a68909066.png) So my dataset is public in the Huggingface Hub, but when I try to load it using the load_dataset command, it shows that it is downloading the files, but throws a ValueError. When I went to my directory to see if the files were downloaded, the folder was blank. Here is the error below: ValueError Traceback (most recent call last) Input In [8], in <cell line: 3>() 1 from datasets import load_dataset ----> 3 dataset = load_dataset("TheNoob3131/mosquito-data", split="train") File ~\Anaconda3\lib\site-packages\datasets\load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1678 # Download and prepare data -> 1679 builder_instance.download_and_prepare( 1680 download_config=download_config, 1681 download_mode=download_mode, 1682 ignore_verifications=ignore_verifications, 1683 try_from_hf_gcs=try_from_hf_gcs, 1684 use_auth_token=use_auth_token, 1685 ) 1687 # Build dataset for splits 1688 keep_in_memory = ( 1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1690 ) Is the dataset in the wrong format or is there some security permission that I should enable?
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Make Extractor accept Path as input
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4718). All of your documentation changes will be reflected on that endpoint." ]
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This PR: - Makes `Extractor` accept instance of `Path` as input - Removes unnecessary castings of `Path` to `str`
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Dataset Viewer issue for LawalAfeez/englishreview-ds-mini
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[ "It's currently working, as far as I understand\r\n\r\nhttps://huggingface.co/datasets/LawalAfeez/englishreview-ds-mini/viewer/LawalAfeez--englishreview-ds-mini/train\r\n\r\n<img width=\"1556\" alt=\"Capture d’écran 2022-07-19 à 09 24 01\" src=\"https://user-images.githubusercontent.com/1676121/179761130-2d7980b9-c0f6-4093-8b1d-f0a3872fef3f.png\">\r\n\r\n---\r\n\r\nWhat was your issue?" ]
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### Link _No response_ ### Description Unable to view the split data ### Owner _No response_
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Support "tags" yaml tag
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Added the "tags" YAML tag, so that users can specify data domain/topics keywords for dataset search
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Fix POS tags
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[ "_The documentation is not available anymore as the PR was closed or merged._", "CI failures are about missing content in the dataset cards or bad tags, and this is unrelated to this PR. Merging :)" ]
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We're now using `part-of-speech` and not `part-of-speech-tagging`, see discussion here: https://github.com/huggingface/datasets/commit/114c09aff2fa1519597b46fbcd5a8e0c0d3ae020#r78794777
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Fix named split casting and sorting
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This PR: - makes `NamedSplit` sortable: so that `sorted()` can be called on them - removes unnecessary `sorted()` on `dict.keys()`: `dict_keys` view is already like a `set` - removes unnecessary casting of `NamedSplit` to `str`
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Document installation of sox OS dependency for audio
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The `sox` OS package needs being installed manually using the distribution package manager. This PR adds this explanation to the docs.
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Update README.md
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Highlight that the licence granted by Amazon only covers non-commercial research use.
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Document how to create a dataset loading script for audio/vision
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Currently, in our docs for Audio/Vision/Text, we explain how to: - Load data - Process data However we only explain how to *Create a dataset loading script* for text data. I think it would be useful that we add the same for Audio/Vision as these have some specificities different from Text. See, for example: - #4697 CC: @stevhliu
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Add object detection processing tutorial
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4710). All of your documentation changes will be reflected on that endpoint.", "Great idea! Now that we have more than one task, it makes sense to separate image classification and object detection so it'll be easier for users to follow.", "@lhoestq do we want to do that in this PR, or should we merge it and let @stevhliu reorganize separately? " ]
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The following adds a quick guide on how to process object detection datasets with `albumentations`.
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WMT21 & WMT22
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[ "Hi ! That would be awesome to have them indeed, thanks for opening this issue\r\n\r\nI just added you to the WMT org on the HF Hub if you're interested in adding those datasets.\r\n\r\nFeel free to create a dataset repository for each dataset and upload the data files there :) preferably in ZIP archives instead of TAR archives (the current WMT scripts don't support streaming TAR archives, so it would break the dataset preview). We've also had issues with the `statmt.org` host (data unavailable, slow download speed), that's why I think it's better if we re-host the files on the Hub.\r\n\r\n`wmt21` (and wmt22) can be added in this GitHub repository I think, for consistency with the previous ones.\r\nTo add it, you can copy paste the code of the previous one (e.g. wmt19), and add the new data:\r\n- in wmt_utils.py, add the new data subsets. You need to provide the download URLs, as well as the target and source languages\r\n- in wmt21.py (renamed from wmt19.py), you can specify the subsets that WMT21 uses (i.e. the one you just added)\r\n- in wmt_utils.py, define the python function that must be used to parse the subsets you added. To do so, you must go in `_generate_examples` and chose the proper `sub_generator` based on the subset name. For example, the `paracrawl_v3` subset uses the `_parse_tmx` function:\r\n\r\nhttps://github.com/huggingface/datasets/blob/ede72d3f9796339701ec59899c7c31d2427046fb/datasets/wmt19/wmt_utils.py#L834-L835\r\n\r\nHopefully the data is in a format that is already supported and there's no need to write a new `_parse_*` function for the new subsets. Let me know if you have questions or if I can help :)" ]
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## Adding a Dataset - **Name:** WMT21 & WMT22 - **Description:** We are going to have three tracks: two small tasks and a large task. The small tracks evaluate translation between fairly related languages and English (all pairs). The large track uses 101 languages. - **Paper:** / - **Data:** https://statmt.org/wmt21/large-scale-multilingual-translation-task.html https://statmt.org/wmt22/large-scale-multilingual-translation-task.html - **Motivation:** Many more languages than previous WMT versions - Could be very high impact Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md). I could also tackle this. I saw the existing logic for WMT models is a bit complex (datasets are stored on the wmt account & retrieved in separate wmt datasets afaict). How long do you think it would take me? @lhoestq
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PR_kwDODunzps47lewm
4,708
Fix require torchaudio and refactor test requirements
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4708). All of your documentation changes will be reflected on that endpoint." ]
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Currently there is a bug in `require_torchaudio` (indeed it is requiring `sox` instead): ```python def require_torchaudio(test_case): if find_spec("sox") is None: ... ``` The bug was introduced by: - #3685 - Commit: https://github.com/huggingface/datasets/pull/3685/commits/b5a3e7122d49c4dcc9333b1d8d18a833fc04b940 which moved ```python require_sndfile = pytest.mark.skipif( # In Windows and OS X, soundfile installs sndfile (sys.platform != "linux" and find_spec("soundfile") is None) # In Linux, soundfile throws RuntimeError if sndfile not installed with distribution package manager or (sys.platform == "linux" and find_library("sndfile") is None), reason="Test requires 'sndfile': `pip install soundfile`; " "Linux requires sndfile installed with distribution package manager, e.g.: `sudo apt-get install libsndfile1`", ) require_sox = pytest.mark.skipif( find_library("sox") is None, reason="Test requires 'sox'; only available in non-Windows, e.g.: `sudo apt-get install sox`", ) require_torchaudio = pytest.mark.skipif(find_spec("torchaudio") is None, reason="Test requires 'torchaudio'") ``` to ```python def require_sndfile(test_case): """ Decorator marking a test that requires soundfile. These tests are skipped when soundfile isn't installed. """ if (sys.platform != "linux" and find_spec("soundfile") is None) or ( sys.platform == "linux" and find_library("sndfile") is None ): test_case = unittest.skip( "test requires 'sndfile': `pip install soundfile`; " "Linux requires sndfile installed with distribution package manager, e.g.: `sudo apt-get install libsndfile1`", )(test_case) return test_case def require_sox(test_case): """ Decorator marking a test that requires sox. These tests are skipped when sox isn't installed. """ if find_library("sox") is None: return unittest.skip("test requires 'sox'; only available in non-Windows, e.g.: `sudo apt-get install sox`")( test_case ) return test_case def require_torchaudio(test_case): """ Decorator marking a test that requires torchaudio. These tests are skipped when torchaudio isn't installed. """ if find_spec("sox") is None: return unittest.skip("test requires 'torchaudio'")(test_case) return test_case ``` This PR; - fixes the bug in `require_torchaudio` - refactors the test requirements back to using `pytest` instead of `unittest` - the text in `pytest.skipif` `reason` can be used if needed in a test body: `require_torchaudio.kwargs["reason"]`
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Dataset Viewer issue for TheNoob3131/mosquito-data
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[ "Thanks for reporting. I refreshed the dataset viewer and it now works as expected.\r\n\r\nhttps://huggingface.co/datasets/TheNoob3131/mosquito-data\r\n\r\n<img width=\"1135\" alt=\"Capture d’écran 2022-07-18 à 13 15 22\" src=\"https://user-images.githubusercontent.com/1676121/179566497-e47f1a27-fd84-4a8d-9d7f-2e0f2da803df.png\">\r\n\r\nWe will investigate why it occurred in the first place\r\n", "By chance, could you provide some details about the operations done on the dataset: was it private? gated?", "Yes, it was a private dataset, and when I made it public, the Dataset Preview did not work. \r\n\r\nHowever, now when I make the dataset private, it says that the Dataset Preview has been disabled. Why is this?", "Thanks for the details. For now, the dataset viewer is always disabled on private datasets (see https://huggingface.co/docs/hub/datasets-viewer for more details)", "Hi, it was working fine for a few hours, but then I can't see the dataset viewer again (public dataset). Why is this still happening?\r\nIt's the same error too:\r\n![image](https://user-images.githubusercontent.com/53668030/179602465-f220f971-d3aa-49ba-a31b-60510f4c2a89.png)\r\n", "OK? This is a bug, thanks for help spotting and reproducing it (it occurs when a dataset is switched to private, then to public). We will be working on it, meanwhile, I've restored the dataset viewer manually again." ]
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### Link _No response_ ### Description Getting this error when trying to view dataset preview: Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/TheNoob3131/mosquito-data/resolve/8aceebd6c4a359d216d10ef020868bd9e8c986dd/0_Africa_train.csv') ### Owner _No response_
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PR_kwDODunzps47lNBg
4,706
Fix empty examples in xtreme dataset for bucc18 config
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[ "_The documentation is not available anymore as the PR was closed or merged._", "I guess the report link is this instead: https://huggingface.co/datasets/xtreme/discussions/1" ]
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As reported in https://huggingface.co/muibk, there are empty examples in xtreme/bucc18.de I applied your fix @mustaszewski I also used a dict to make the dataset generation much faster
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Fix crd3
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4705). All of your documentation changes will be reflected on that endpoint." ]
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As reported in https://huggingface.co/datasets/crd3/discussions/1#62cc377073b2512b81662794, each split of the dataset was containing the same data. This issues comes from a bug in the dataset script I fixed it and also uploaded the data to hf.co to make the dataset work in streaming mode
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Skip tests only for lz4/zstd params if not installed
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Currently, if `zstandard` or `lz4` are not installed, `test_compression_filesystems` and `test_streaming_dl_manager_extract_all_supported_single_file_compression_types` are skipped for all compression format parameters. This PR fixes these tests, so that if `zstandard` or `lz4` are not installed, the tests are skipped only for the corresponding compression parameters (`zstd` or `lz4`), whereas the tests are not skipped for all the other compression parameters (`gzip`, `xz` and `bz2`). Related to: - #4688
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Make cast in `from_pandas` more robust
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Make the cast in `from_pandas` more robust (as it was done for the packaged modules in https://github.com/huggingface/datasets/pull/4364) This should be useful in situations like [this one](https://discuss.huggingface.co/t/loading-custom-audio-dataset-and-fine-tuning-model/8836/4).
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Domain specific dataset discovery on the Hugging Face hub
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[ "Hi! I added a link to this issue in our internal request for adding keywords/topics to the Hub, which is identical to the `topic tags` solution. The `collections` solution seems too complex (as you point out). Regarding the `domain tags` solution, we primarily focus on machine learning, so I'm not sure if it's a good idea to make our current taxonomy more complex.", "> Hi! I added a link to this issue in our internal request for adding keywords/topics to the Hub, which is identical to the `topic tags` solution. The `collections` solution seems too complex (as you point out). Regarding the `domain tags` solution, we primarily focus on machine learning, so I'm not sure if it's a good idea to make our current taxonomy more complex.\r\n\r\nThanks, for letting me know. Will you allow the topic tags to be user-generated or only chosen from a list?", "Thanks for opening this issue @davanstrien.\r\n\r\nAs we discussed last week, the tag approach would be in principle the simpler to be implemented, either the domain tag (with closed vocabulary: more reliable but also more rigid), or the topic tag (with open vocabulary: more flexible for user needs)", "Hi @davanstrien If i remember correctly this was also discussed inside a hf.co Discussion, would you be able to link it here too?\r\n\r\n(where i suggested using `tags: - foo - bar` IIRC.\r\n\r\nThanks a ton!", "> Hi @davanstrien If i remember correctly this was also discussed inside a hf.co Discussion, would you be able to link it here too?\r\n> \r\n> (where i suggested using `tags: - foo - bar` IIRC.\r\n> \r\n> Thanks a ton!\r\n\r\nThis doesn't ring a bell - I did a quick search of https://discuss.huggingface.co but didn't find anything. \r\n\r\nThe `tags: ` approach sounds like a good option for this. It would be especially nice if these could suggest existing tags, but this probably won't be easily possible through the current interface. \r\n", "I opened a PR to add \"tags\" to the YAML validator:\r\nhttps://github.com/huggingface/datasets/pull/4716\r\n\r\nI also added \"tags\" to the [tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging), with suggestions like \"bio\" or \"newspapers\"", "Thanks @lhoestq for the initiative.\r\n \r\nJust one question: are \"tags\" already supported on the Hub? \r\n\r\nI think they aren't. Thus, the Hub should support them so that they are properly displayed.", "I think they're not displayed, but at least it should enable users to filter by tag in using `huggingface_hub` or using the appropriate query params on the website (not sure if it's possible yet though)", "> I think they're not displayed, but at least it should enable users to filter by tag in using `huggingface_hub` or using the appropriate query params on the website (not sure if it's possible yet though)\r\n\r\nI think this would already be a helpful start. I'm happy to try this out with the datasets added to https://huggingface.co/organizations/biglam and use the `huggingface_hub` to filter those datasets using the tags. " ]
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CONTRIBUTOR
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**Is your feature request related to a problem? Please describe.** ## The problem The datasets hub currently has `8,239` datasets. These datasets span a wide range of different modalities and tasks (currently with a bias towards textual data). There are various ways of identifying datasets that may be relevant for a particular use case: - searching - various filters Currently, however, there isn't an easy way to identify datasets belonging to a specific domain. For example, I want to browse machine learning datasets related to 'social science' or 'climate change research'. The ability to identify datasets relating to a specific domain has come up in discussions around the [BigLA](https://github.com/bigscience-workshop/lam/) datasets hackathon https://github.com/bigscience-workshop/lam/discussions/31#discussioncomment-3123610. As part of the hackathon, we're currently collecting datasets related to Libraries, Archives and Museums and making them available via the hub. We currently do this under a Hugging Face organization (https://huggingface.co/biglam). However, going forward, I can see some of these datasets being migrated to sit under an organization that is the custodian of the dataset (for example, a national library the data was originally from). At this point, it becomes more difficult to quickly identify datasets from this domain without relying on search. This is also related to some existing issues on Github related to metadata on the hub: - https://github.com/huggingface/datasets/issues/3625 - https://github.com/huggingface/datasets/issues/3877 **Describe the solution you'd like** ### Some possible solutions that may help with this: #### Enable domain tags (from a controlled vocabulary) - This would add metadata field to the YAML for the domain a dataset relates to - Advantages: - the list is controlled, allowing it to be more easily integrated into the datasets tag app (https://huggingface.co/space/huggingface/datasets-tagging) - the controlled vocabulary could align with an existing controlled vocabulary - this additional metadata can be used to perform filtering by domain - disadvantages - choosing the best controlled vocab may be difficult - there are many datasets that are likely to fit into the 'machine learning' domain (i.e. there is a long tail of datasets that aren't in more 'generic' machine learning domain #### Enable topic tags (user-generated) Enable 'free form' topic tags for datasets and models. This would be closer to GitHub's repository topics which can be chosen from a controlled list (https://github.com/topics/) but can also be more user/org specific. This could potentially be useful for organizations to also manage their own models and datasets as the number they hold in their org grows. For example, they may create 'topic tags' for a specific project, so it's clearer which datasets /models are related to that project. #### Collections This solution would likely be the biggest shift and may require significant changes in the hub fronted. Collections could work in several different ways but would include: Users can curate particular datasets, models, spaces, etc., into a collection. For example, they may create a collection of 'historic newspapers suitable for training language models'. These collections would not be mutually exclusive, i.e. a dataset can belong to zero, one or many collections. Collections can also potentially be nested under other collections. This is fairly common on other data reposotiores for example the following collections: <img width="293" alt="Screenshot 2022-07-18 at 11 50 44" src="https://user-images.githubusercontent.com/8995957/179496445-963ed122-5e26-4574-96e8-41081bce3e2b.png"> all belong under a higher level collection (https://bl.iro.bl.uk/collections/353c908d-b495-4413-b047-87236d2573e3?locale=en). There are different models one could use for how these collections could be created: - only within an org - for any dataset/model - the owner or a dataset/model has to agree to be added to a collection - a collection owner can have people suggest additions to their collection - other models.... These collections could be thematic, related to particular training approaches, curate models with particular inference properties etc. Whilst some of these features may duplicate current/or future tag filters on the hub, they offer the advantage of being flexible and not having to predict what users will want to do upfront. There is also potential for automating the creation of these collections based on existing metadata. For example, one could collect models trained on a collection of datasets so for example, if we had a collection of 'historic newspapers suitable for training language models' that contained 30 datasets, we could create another collection 'historic newspaper language models' that takes any model on the hub whose metadata says it used one or more of those 30 datasets. There is also the option of exploring ML approaches to suggest models/datasets may be relevant to a particular collection. This approach is likely to be quite difficult to implement well and would require significant thought. There is also likely to be a benefit in doing quite a bit of upfront work in curating useful collections to demonstrate the benefits of collections. **Describe alternatives you've considered** A clear and concise description of any alternative solutions or features you've considered. It is possible to collate this information externally, i.e. one could link back to the relevant models/datasets from an external platform. **Additional context** Add any other context about the feature request here. I'm cc'ing others involved in the BigLAM hackathon who may also have thoughts @cakiki @clancyoftheoverflow @albertvillanova
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4,701
Added more information in the README about contributors of the Arabic Speech Corpus
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Added more information in the README about contributors and encouraged reading the thesis for more infos
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Support extract lz4 compressed data files
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
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MEMBER
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Fix Authentification Error while streaming
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I fixed a few errors when it occurs while streaming the private dataset on the Huggingface Hub. ``` from datasets import load_dataset dataset = load_dataset(<repo_id>, use_auth_token=<private_token>, streaming=True) for d in dataset['train']: print(d) break # this is for checking ``` This code is an example for streaming private datasets. when the version of the datasets is 2.2.2, it works well but datasets>2.2.2 occurs error like this, ``` /usr/local/lib/python3.7/dist-packages/aiohttp/client_reqrep.py in raise_for_status(self) 1007 status=self.status, 1008 message=self.reason, → 1009 headers=self.headers, 1010 ) 1011 ClientResponseError: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/.../train-00000-of-00001-168b451062c67c34.parquet') ``` (this is an example on the dataset has `parquet` extenstion) It seems that the `xisfile `module in `download/streaming_download_manager.py` couldn't recognize the file on "https://huggingface.co/~". so I add three lines. With this change, there is no error anymore(but this code is ad-hoc).
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Enable streaming dataset to use the "all" split
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4698). All of your documentation changes will be reflected on that endpoint.", "@albertvillanova \r\nAdding the validation split causes these two `assert_called_once` assertions to fail with `AssertionError: Expected 'ArrowWriter' to have been called once. Called 2 times`:\r\n\r\nhttps://github.com/huggingface/datasets/blob/main/tests/test_builder.py#L548-L562\r\n\r\nIt might be better to create a new dummy generator for the streaming tests, WDYT? Alternatively we could test for `self.call_count` equalling 2." ]
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Fixes #4637
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Trouble with streaming of vision dataset with TAR archive
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[ "Hi @frgfm, thanks for reporting.\r\n\r\nAs the error message says, streaming mode is not supported out of the box when the dataset contains TAR archive files.\r\n\r\nTo make the dataset streamable, you have to use `dl_manager.iter_archive`.\r\n\r\nThere are several examples in other datasets, e.g. food101: https://huggingface.co/datasets/food101/blob/main/food101.py\r\n\r\nAnd yes, as the link you pointed out, for the streaming to be possible, the metadata file must be loaded before all of the images:\r\n- either this is the case when iterating the archive (and you get the metadata file before the images)\r\n- or you have to extract the metadata file by hand and upload it separately to the Hub", "Hi @albertvillanova :wave:\r\n\r\nThanks! Yeah I saw that but since I didn't have any metadata, I wasn't sure whether I should create them myself.\r\n\r\nSo one last question:\r\nWhat is the metadata supposed to be for archives? The relative path of all files in it?\r\n_(Sorry I'm a bit confused since it's quite hard to debug using the single error message from the data preview :sweat_smile: )_" ]
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### Link https://huggingface.co/datasets/frgfm/imagenette ### Description Hello there :wave: Thanks for the amazing work you've done with HF Datasets! I've just started playing with it, and managed to upload my first dataset. But for the second one, I'm having trouble with the preview since there is some archive extraction involved :sweat_smile: Basically, I get a: ``` Status code: 400 Exception: NotImplementedError Message: Extraction protocol for TAR archives like 'https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead. ``` I've tried several things and checked this issue https://github.com/huggingface/datasets/issues/4181 as well, but no luck so far! Could you point me in the right direction please? :pray: ### Owner Yes
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Cannot load LinCE dataset
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[ "Hi @finiteautomata, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n ...: dataset = load_dataset(\"lince\", \"ner_spaeng\")\r\nDownloading builder script: 20.8kB [00:00, 9.09MB/s] \r\nDownloading metadata: 31.2kB [00:00, 13.5MB/s] \r\nDownloading and preparing dataset lince/ner_spaeng (download: 2.93 MiB, generated: 18.45 MiB, post-processed: Unknown size, total: 21.38 MiB) to .../.cache/huggingface/datasets/lince/ner_spaeng/1.0.0/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589...\r\nDownloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.08M/3.08M [00:01<00:00, 2.73MB/s]\r\nDataset lince downloaded and prepared to .../.cache/huggingface/datasets/lince/ner_spaeng/1.0.0/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 630.66it/s]\r\n\r\nIn [2]: dataset\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['idx', 'words', 'lid', 'ner'],\r\n num_rows: 33611\r\n })\r\n validation: Dataset({\r\n features: ['idx', 'words', 'lid', 'ner'],\r\n num_rows: 10085\r\n })\r\n test: Dataset({\r\n features: ['idx', 'words', 'lid', 'ner'],\r\n num_rows: 23527\r\n })\r\n})\r\n``` \r\n\r\nPlease note that for this dataset, the original data files are not hosted on the Hugging Face Hub, but on https://ritual.uh.edu\r\nAnd sometimes, the server might be temporarily unavailable, as your error message said (trying to connect to the server timed out):\r\n```\r\nConnectionError: Couldn't reach https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (ConnectTimeout(MaxRetryError(\"HTTPSConnectionPool(host='ritual.uh.edu', port=443): Max retries exceeded with url: /lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7feb1c45a690>, 'Connection to ritual.uh.edu timed out. (connect timeout=100)'))\")))\r\n```\r\nIn these cases you could:\r\n- either contact the owners of the data server where the data is hosted to inform them about the issue in their server\r\n- or re-try after waiting some time: usually these issues are just temporary", "Great, thanks for checking out!" ]
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## Describe the bug Cannot load LinCE dataset due to a connection error ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("lince", "ner_spaeng") ``` A notebook with this code and corresponding error can be found at https://colab.research.google.com/drive/1pgX3bNB9amuUwAVfPFm-XuMV5fEg-cD2 ## Expected results It should load the dataset ## Actual results ```python --------------------------------------------------------------------------- ConnectionError Traceback (most recent call last) <ipython-input-2-fc551ddcebef> in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("lince", "ner_spaeng") 10 frames /usr/local/lib/python3.7/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1682 ignore_verifications=ignore_verifications, 1683 try_from_hf_gcs=try_from_hf_gcs, -> 1684 use_auth_token=use_auth_token, 1685 ) 1686 /usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 703 if not downloaded_from_gcs: 704 self._download_and_prepare( --> 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 706 ) 707 # Sync info /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1219 1220 def _download_and_prepare(self, dl_manager, verify_infos): -> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1222 1223 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 769 split_dict = SplitDict(dataset_name=self.name) 770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 772 773 # Checksums verification /root/.cache/huggingface/modules/datasets_modules/datasets/lince/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589/lince.py in _split_generators(self, dl_manager) 481 def _split_generators(self, dl_manager): 482 """Returns SplitGenerators.""" --> 483 lince_dir = dl_manager.download_and_extract(f"{_LINCE_URL}/{self.config.name}.zip") 484 data_dir = os.path.join(lince_dir, self.config.data_dir) 485 return [ /usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download_and_extract(self, url_or_urls) 429 extracted_path(s): `str`, extracted paths of given URL(s). 430 """ --> 431 return self.extract(self.download(url_or_urls)) 432 433 def get_recorded_sizes_checksums(self): /usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download(self, url_or_urls) 313 num_proc=download_config.num_proc, 314 disable_tqdm=not is_progress_bar_enabled(), --> 315 desc="Downloading data files", 316 ) 317 duration = datetime.now() - start_time /usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc) 346 # Singleton 347 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 348 return function(data_struct) 349 350 disable_tqdm = disable_tqdm or not logging.is_progress_bar_enabled() /usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in _download(self, url_or_filename, download_config) 333 # append the relative path to the base_path 334 url_or_filename = url_or_path_join(self._base_path, url_or_filename) --> 335 return cached_path(url_or_filename, download_config=download_config) 336 337 def iter_archive(self, path_or_buf: Union[str, io.BufferedReader]): /usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 195 use_auth_token=download_config.use_auth_token, 196 ignore_url_params=download_config.ignore_url_params, --> 197 download_desc=download_config.download_desc, 198 ) 199 elif os.path.exists(url_or_filename): /usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc) 531 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") 532 if head_error is not None: --> 533 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})") 534 elif response is not None: 535 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})") ConnectionError: Couldn't reach https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (ConnectTimeout(MaxRetryError("HTTPSConnectionPool(host='ritual.uh.edu', port=443): Max retries exceeded with url: /lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7feb1c45a690>, 'Connection to ritual.uh.edu timed out. (connect timeout=100)'))"))) ``` ## Environment info - `datasets` version: 2.3.2 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5
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Add MANtIS dataset
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4695). All of your documentation changes will be reflected on that endpoint." ]
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This PR adds MANtIS dataset. Arxiv: [https://arxiv.org/abs/1912.04639](https://arxiv.org/abs/1912.04639) Github: [https://github.com/Guzpenha/MANtIS](https://github.com/Guzpenha/MANtIS) README and dataset tags are WIP.
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Distributed data parallel training for streaming datasets
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### Feature request Any documentations for the the `load_dataset(streaming=True)` for (multi-node multi-GPU) DDP training? ### Motivation Given a bunch of data files, it is expected to split them onto different GPUs. Is there a guide or documentation? ### Your contribution Does it requires manually split on data files for each worker in `DatasetBuilder._split_generator()`? What is`IterableDatasetShard` expected to do?
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update `samsum` script
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4693). All of your documentation changes will be reflected on that endpoint." ]
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update `samsum` script after #4672 was merged (citation is also updated)
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4,692
Unable to cast a column with `Image()` by using the `cast_column()` feature
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[ "Hi, thanks for reporting! A PR (https://github.com/huggingface/datasets/pull/4614) has already been opened to address this issue." ]
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## Describe the bug A clear and concise description of what the bug is. When I create a dataset, then add a column to the created dataset through the `dataset.add_column` feature and then try to cast a column of the dataset (this column contains image paths) with `Image()` by using the `cast_column()` feature, I get the following error - ``` TypeError: Couldn't cast array of type string to {'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)} ``` When I try and cast the same column, but without doing the `add_column` in the previous step, it works as expected. ## Steps to reproduce the bug ```python from datasets import Dataset, Image data_dict = { "img_path": ["https://picsum.photos/200/300"] } dataset = Dataset.from_dict(data_dict) #NOTE Comment out this line and use cast_column and it works properly dataset = dataset.add_column("yeet", [1]) #NOTE This line fails to execute properly if `add_column` is called before dataset = dataset.cast_column("img_path", Image()) # #NOTE This is my current workaround. This seems to work fine with/without `add_column`. While # # running this, make sure to comment out the `cast_column` line # new_features = dataset.features.copy() # new_features["img_path"] = Image() # dataset = dataset.cast(new_features) print(dataset) print(dataset.features) print(dataset[0]) ``` ## Expected results A clear and concise description of the expected results. Able to successfully use `cast_column` to cast a column containing img_paths to now be Image() features after modifying the dataset using `add_column` in a previous step ## Actual results Specify the actual results or traceback. ``` Traceback (most recent call last): File "/home/surya/Desktop/hf_bug_test.py", line 14, in <module> dataset = dataset.cast_column("img_path", Image()) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/fingerprint.py", line 458, in wrapper out = func(self, *args, **kwargs) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1580, in cast_column dataset._data = dataset._data.cast(dataset.features.arrow_schema) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1487, in cast new_tables.append(subtable.cast(subschema, *args, **kwargs)) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 834, in cast return InMemoryTable(table_cast(self.table, *args, **kwargs)) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1897, in table_cast return cast_table_to_schema(table, schema) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1880, in cast_table_to_schema arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1880, in <listcomp> arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1673, in wrapper return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1673, in <listcomp> return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1846, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type string to {'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Ubuntu 20.04.3 LTS - Python version: 3.9.7 - PyArrow version: 7.0.0
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4,691
Dataset Viewer issue for rajistics/indian_food_images
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[ "Hi, thanks for reporting. I triggered a refresh of the preview for this dataset, and it works now. I'm not sure what occurred.\r\n<img width=\"1019\" alt=\"Capture d’écran 2022-07-18 à 11 01 52\" src=\"https://user-images.githubusercontent.com/1676121/179541327-f62ecd5e-a18a-4d91-b316-9e2ebde77a28.png\">\r\n\r\n" ]
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### Link https://huggingface.co/datasets/rajistics/indian_food_images/viewer/rajistics--indian_food_images/train ### Description I have a train/test split in my dataset <img width="410" alt="Screen Shot 2022-07-15 at 11 44 42 AM" src="https://user-images.githubusercontent.com/6808012/179293215-7b419ec3-3527-46f2-8dad-adbc5568cfa0.png"> t The dataset viewer works for the test split (images of indian food), but does not show my train split. My guess is maybe there is some corrupt image file that is guessing this. But I have no idea. The original dataset was pulled from here: https://www.kaggle.com/datasets/l33tc0d3r/indian-food-classification?resource=download-directory ### Owner Yes
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Refactor base extractors
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This PR: - Refactors base extractors as subclasses of `BaseExtractor`: - this is an abstract class defining the interface with: - `is_extractable`: abstract class method - `extract`: abstract static method - Implements abstract `MagicNumberBaseExtractor` (as subclass of `BaseExtractor`): - this has a default implementation of `is_extractable` - this improves performance (reducing the number of file reads) by allowing passing already read `magic_number` - Refactors `Extractor`: - reads magic number from file only once This PR deprecates: ```python is_extractable, extractor = self.extractor.is_extractable(input_path, return_extractor=True) self.extractor.extract(input_path, output_path, extractor=extractor) ``` and uses more Pythonic instead: ```python extractor_format = self.extractor.infer_extractor_format(input_path) self.extractor.extract(input_path, output_path, extractor_format) ```
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Test extractors for all compression formats
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This PR: - Adds all compression formats to `test_extractor` - Tests each base extractor for all compression formats Note that all compression formats are tested except "rar".
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Skip test_extractor only for zstd param if zstandard not installed
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Currently, if `zstandard` is not installed, `test_extractor` is skipped for all compression format parameters. This PR fixes `test_extractor` so that if `zstandard` is not installed, `test_extractor` is skipped only for the `zstd` compression parameter, that is, it is not skipped for all the other compression parameters (`gzip`, `xz`,...).
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Trigger CI also on push to main
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Currently, new CI (on GitHub Actions) is only triggered on pull requests branches when the base branch is main. This PR also triggers the CI when a PR is merged to main branch.
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4,686
Align logging with Transformers (again)
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4686). All of your documentation changes will be reflected on that endpoint.", "I wasn't aware of https://github.com/huggingface/datasets/pull/1845 before opening this PR. This issue seems much more complex now ..." ]
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Fix #2832
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Fix mock fsspec
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This PR: - Removes an unused method from `DummyTestFS` - Refactors `mock_fsspec` to make it simpler
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How to assign new values to Dataset?
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[ "Hi! One option is use `map` with a function that overwrites the labels (`dset = dset.map(lamba _: {\"label\": 0}, features=dset.features`)). Or you can use the `remove_column` + `add_column` combination (`dset = dset.remove_columns(\"label\").add_column(\"label\", [0]*len(data)).cast(dset.features)`, but note that this approach creates an in-memory table for the added column instead of writing to disk, which could be problematic for large datasets." ]
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![image](https://user-images.githubusercontent.com/37113676/179149159-bbbda0c8-a661-403c-87ed-dc2b4219cd68.png) Hi, if I want to change some values of the dataset, or add new columns to it, how can I do it? For example, I want to change all the labels of the SST2 dataset to `0`: ```python from datasets import load_dataset data = load_dataset('glue','sst2') data['train']['label'] = [0]*len(data) ``` I will get the error: ``` TypeError: 'Dataset' object does not support item assignment ```
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Update create dataset card docs
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This PR proposes removing the [online dataset card creator](https://huggingface.co/datasets/card-creator/) in favor of simply copy/pasting a template and using the [Datasets Tagger app](https://huggingface.co/spaces/huggingface/datasets-tagging) to generate the tags. The Tagger app provides more guidance by showing all possible values a user can select in the dropdown menus, whereas the online dataset card creator doesn't, which can make it difficult to know what tag values to input. Let me know what you think! :)
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weird issue/bug with columns (dataset iterable/stream mode)
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I have a dataset online (CloverSearch/cc-news-mutlilingual) that has a bunch of columns, two of which are "score_title_maintext" and "score_title_description". the original files are jsonl formatted. I was trying to iterate through via streaming mode and grab all "score_title_description" values, but I kept getting key not found after a certain point of iteration. I found that some json objects in the file don't have "score_title_description". And in SOME cases, this returns a NONE and in others it just gets a key error. Why is there an inconsistency here and how can I fix it?
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IndexError when loading ImageFolder
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## Describe the bug Loading an image dataset with `imagefolder` throws `IndexError: list index out of range` when the given folder contains a non-image file (like a csv). ## Steps to reproduce the bug Put a csv file in a folder with images and load it: ```python import datasets datasets.load_dataset("imagefolder", data_dir=path/to/folder) ``` ## Expected results I would expect a better error message, like `Unsupported file` or even the dataset loader just ignoring every file that is not an image in that case. ## Actual results Here is the whole traceback: ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-5.11.0-051100-generic-x86_64-with-glibc2.27 - Python version: 3.9.9 - PyArrow version: 8.0.0 - Pandas version: 1.4.3
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Dataset Viewer issue for codeparrot/xlcost-text-to-code
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[ "There seems to be an issue with the `C++-snippet-level` config:\r\n\r\n```python\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names(\"codeparrot/xlcost-text-to-code\", \"C++-snippet-level\")\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 352, in get_dataset_config_info\r\n info.splits = {\r\nTypeError: 'NoneType' object is not iterable\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 404, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 359, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```\r\n\r\nI remove the dataset-viewer tag since it's not directly related.\r\n\r\nPinging @huggingface/datasets ", "Thanks I found that this subset wasn't properly defined the the config, I fixed it. Now I can see the subsets but I get this error for the viewer\r\n````\r\nStatus code: 400\r\nException: Status400Error\r\nMessage: The split cache is empty.\r\n```", "Yes, the cache is being refreshed, hopefully, it will work in some minutes for all the splits. Some are already here:\r\n\r\nhttps://huggingface.co/datasets/codeparrot/xlcost-text-to-code/viewer/Python-snippet-level/train\r\n\r\n<img width=\"1533\" alt=\"Capture d’écran 2022-07-18 à 12 04 06\" src=\"https://user-images.githubusercontent.com/1676121/179553933-64d874fa-ada9-4b82-900e-082619523c20.png\">\r\n", "I think all the splits are working as expected now", "Perfect, thank you!" ]
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### Link https://huggingface.co/datasets/codeparrot/xlcost-text-to-code ### Description Error ``` Server Error Status code: 400 Exception: TypeError Message: 'NoneType' object is not iterable ``` Before I did a minor change in the dataset script (removing some comments), the viewer was working but not properely, it wasn't showing the dataset subsets. But the data can be loaded successfully. Thanks! ### Owner Yes
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Added method to remove excess nesting in a DatasetDict
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[ "Hi ! I think the issue you linked is closed and suggests to use `remove_columns`.\r\n\r\nMoreover if you end up with a dataset with an unnecessarily nested data, please modify your processing functions to not output nested data, or use `map(..., batched=True)` if you function take batches as input", "Hi @lhoestq , you are right about the issues this pull has steered beyond that issue. I created this [colab notebook](https://colab.research.google.com/drive/16aLu6QrDSV_aUYRdpufl5E4iS08qkUGj?usp=sharing) to present the error. I tried using batch and that won't resolve it either. I'm looking into that error right now.", "I think you just need to pass one example at a time to your tokenizer, this way you don't end up with nested data:\r\n```python\r\n\r\ndef preprocessFunction(row):\r\n collatedContext = tokenizer.eos_token.join([row[\"context\"+str(i+1)] for i in range(int(AMT_OF_CONTEXT))])\r\n response = row[\"response\"]\r\n tokenizedContext = tokenizer(\r\n collatedContext, max_length=max_context_length, truncation=True # don't pass as a list here\r\n )\r\n with tokenizer.as_target_tokenizer():\r\n tokenized_response = tokenizer(\r\n response, max_length=max_response_length, truncation=True # don't pass a a list here\r\n )\r\n tokenizedContext[\"labels\"] = tokenized_response[\"input_ids\"]\r\n return tokenizedContext\r\n```", "Yes that is correct, the purpose of this pull is to advise of a more general solution like with `def remove_excess_nesting(self)` or maybe automate the solution (stas00 advised not to automate it as it could \"not be backwards compatible\").", "I'm not sure I understand how having `remove_excess_nesting` would make more sense than just fixing the preprocessFunction to simply not return nested samples, can you elaborate ?", "Figuring out the issue can be a bit difficult to figure out. Only until I added batch does it make a little more sense with the error\r\n\r\n> sequence item 0: expected str instance, list found\r\n\r\nbut batch was never intended.\r\n\r\nWhen you run the colab you will notice that only until collating do you learn there is this error. So i figured it would be better to address it during at the `DatasetDict` level.\r\nI think it would be ideal if the user could be notified at the preprocess function.", "I'm not arguing that `remove_excess_nesting` is the right solution but what I aim to address is dealing with unnecessary nesting as early as possible.", "> When you run the colab you will notice that only until collating do you learn there is this error.\r\n\r\nI think users can just check the `dataset.features` and they would notice that the data are nested\r\n```python\r\n{\r\n 'input_ids': Sequence(Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None), length=-1, id=None)\r\n ...\r\n}\r\n```\r\n\r\nSometime nested data are intentional, so you can't know in advance if it's a user's mistake or something planned." ]
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Added the ability for a DatasetDict to remove additional nested layers within its features to avoid conflicts when collating. It is meant to accompany [this PR](https://github.com/huggingface/transformers/pull/18119) to resolve the same issue [#15505](https://github.com/huggingface/transformers/issues/15505). @stas00 @lhoestq
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Cant pass streaming dataset to dataloader after take()
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[ "Hi! Calling `take` on an iterable/streamable dataset makes it not possible to shard the dataset, which in turn disables multi-process loading (attempts to split the workload over the shards), so to go past this limitation, you can either use single-process loading in `DataLoader` (`num_workers=None`) or fetch the first `50_000/batch_size` batches in the loop." ]
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## Describe the bug I am trying to pass a streaming version of c4 to a dataloader, but it can't be passed after I call `dataset.take(n)`. Some functions such as `shuffle()` can be applied without breaking the dataloader but not take. ## Steps to reproduce the bug ```python import datasets import torch dset = datasets.load_dataset(path='c4', name='en', split="train", streaming=True) dset = dset.take(50_000) dset = dset.with_format("torch") num_workers = 8 batch_size = 512 loader = torch.utils.data.DataLoader(dataset=dset, batch_size=batch_size, num_workers=num_workers) for batch in loader: ... ``` ## Expected results No error thrown when iterating over the dataloader ## Actual results Original Traceback (most recent call last): File "/usr/local/lib/python3.9/dist-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop data = fetcher.fetch(index) File "/usr/local/lib/python3.9/dist-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch data.append(next(self.dataset_iter)) File "/root/.local/lib/python3.9/site-packages/datasets/formatting/dataset_wrappers/torch_iterable_dataset.py", line 48, in __iter__ for key, example in self._iter_shard(shard_idx): File "/root/.local/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 586, in _iter_shard yield from ex_iterable.shard_data_sources(shard_idx) File "/root/.local/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 60, in shard_data_sources raise NotImplementedError(f"{type(self)} doesn't implement shard_data_sources yet") NotImplementedError: <class 'datasets.iterable_dataset.TakeExamplesIterable'> doesn't implement shard_data_sources yet ## Environment info - `datasets` version: 2.3.2 - Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.31 - Python version: 3.9.13 - PyArrow version: 8.0.0 - Pandas version: 1.4.3
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Random 400 Client Error when pushing dataset
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## Describe the bug When pushing a dataset, the client errors randomly with `Bad Request for url:...`. At the next call, a new parquet file is created for each shard. The client may fail at any random shard. ## Steps to reproduce the bug ```python dataset.push_to_hub("ORG/DATASET", private=True, branch="main") ``` ## Expected results Push all the dataset to the Hub with no duplicates. If it fails, it should retry or fail, but continue from the last failed shard. ## Actual results ``` --------------------------------------------------------------------------- HTTPError Traceback (most recent call last) testing.ipynb Cell 29 in <cell line: 1>() ----> [1](testing.ipynb?line=0) dataset.push_to_hub("ORG/DATASET", private=True, branch="main") File ~/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py:4297, in Dataset.push_to_hub(self, repo_id, split, private, token, branch, max_shard_size, shard_size, embed_external_files) 4291 warnings.warn( 4292 "'shard_size' was renamed to 'max_shard_size' in version 2.1.1 and will be removed in 2.4.0.", 4293 FutureWarning, 4294 ) 4295 max_shard_size = shard_size -> 4297 repo_id, split, uploaded_size, dataset_nbytes, repo_files, deleted_size = self._push_parquet_shards_to_hub( 4298 repo_id=repo_id, 4299 split=split, 4300 private=private, 4301 token=token, 4302 branch=branch, 4303 max_shard_size=max_shard_size, 4304 embed_external_files=embed_external_files, 4305 ) 4306 organization, dataset_name = repo_id.split("/") 4307 info_to_dump = self.info.copy() File ~/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py:4195, in Dataset._push_parquet_shards_to_hub(self, repo_id, split, private, token, branch, max_shard_size, embed_external_files) 4193 shard.to_parquet(buffer) 4194 uploaded_size += buffer.tell() -> 4195 _retry( 4196 api.upload_file, 4197 func_kwargs=dict( 4198 path_or_fileobj=buffer.getvalue(), 4199 path_in_repo=shard_path_in_repo, 4200 repo_id=repo_id, 4201 token=token, 4202 repo_type="dataset", 4203 revision=branch, 4204 identical_ok=False, 4205 ), 4206 exceptions=HTTPError, 4207 status_codes=[504], 4208 base_wait_time=2.0, 4209 max_retries=5, 4210 max_wait_time=20.0, 4211 ) 4212 shards_path_in_repo.append(shard_path_in_repo) 4214 # Cleanup to remove unused files File ~/.local/lib/python3.9/site-packages/datasets/utils/file_utils.py:284, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time) 282 except exceptions as err: 283 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes): --> 284 raise err 285 else: 286 sleep_time = min(max_wait_time, base_wait_time * 2**retry) # Exponential backoff File ~/.local/lib/python3.9/site-packages/datasets/utils/file_utils.py:281, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time) 279 while True: 280 try: --> 281 return func(*func_args, **func_kwargs) 282 except exceptions as err: 283 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes): File ~/.local/lib/python3.9/site-packages/huggingface_hub/hf_api.py:1967, in HfApi.upload_file(self, path_or_fileobj, path_in_repo, repo_id, token, repo_type, revision, identical_ok, commit_message, commit_description, create_pr) 1957 commit_message = ( 1958 commit_message 1959 if commit_message is not None 1960 else f"Upload {path_in_repo} with huggingface_hub" 1961 ) 1962 operation = CommitOperationAdd( 1963 path_or_fileobj=path_or_fileobj, 1964 path_in_repo=path_in_repo, 1965 ) -> 1967 pr_url = self.create_commit( 1968 repo_id=repo_id, 1969 repo_type=repo_type, 1970 operations=[operation], 1971 commit_message=commit_message, 1972 commit_description=commit_description, 1973 token=token, 1974 revision=revision, 1975 create_pr=create_pr, 1976 ) 1977 if pr_url is not None: 1978 re_match = re.match(REGEX_DISCUSSION_URL, pr_url) File ~/.local/lib/python3.9/site-packages/huggingface_hub/hf_api.py:1844, in HfApi.create_commit(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads) 1836 commit_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/commit/{revision}" 1838 commit_resp = requests.post( 1839 url=commit_url, 1840 headers={"Authorization": f"Bearer {token}"}, 1841 json=commit_payload, 1842 params={"create_pr": 1} if create_pr else None, 1843 ) -> 1844 _raise_for_status(commit_resp) 1845 return commit_resp.json().get("pullRequestUrl", None) File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:84, in _raise_for_status(request) 76 if request.status_code == 401: 77 # The repo was not found and the user is not Authenticated 78 raise RepositoryNotFoundError( 79 f"401 Client Error: Repository Not Found for url: {request.url}. If the" 80 " repo is private, make sure you are authenticated. (Request ID:" 81 f" {request_id})" 82 ) ---> 84 _raise_with_request_id(request) File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:95, in _raise_with_request_id(request) 92 if request_id is not None and len(e.args) > 0 and isinstance(e.args[0], str): 93 e.args = (e.args[0] + f" (Request ID: {request_id})",) + e.args[1:] ---> 95 raise e File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:90, in _raise_with_request_id(request) 88 request_id = request.headers.get("X-Request-Id") 89 try: ---> 90 request.raise_for_status() 91 except Exception as e: 92 if request_id is not None and len(e.args) > 0 and isinstance(e.args[0], str): File ~/.local/lib/python3.9/site-packages/requests/models.py:1021, in Response.raise_for_status(self) 1016 http_error_msg = ( 1017 f"{self.status_code} Server Error: {reason} for url: {self.url}" 1018 ) 1020 if http_error_msg: -> 1021 raise HTTPError(http_error_msg, response=self) HTTPError: 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/ORG/DATASET/commit/main (Request ID: a_F0IQAHJdxGKVRYyu1cF) ``` ## Environment info - `datasets` version: 2.3.2 - Platform: Linux-5.13.0-1025-aws-x86_64-with-glibc2.31 - Python version: 3.9.4 - PyArrow version: 8.0.0 - Pandas version: 1.4.3
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Dataset.map gets stuck on _cast_to_python_objects
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[ "Are you able to reproduce this? My example is small enough that it should be easy to try.", "Hi! Thanks for reporting and providing a reproducible example. Indeed, by default, `datasets` performs an expensive cast on the values returned by `map` to convert them to one of the types supported by PyArrow (the underlying storage format used by `datasets`). This cast is not needed on NumPy arrays as PyArrow supports them natively, so one way to make this transform faster is to add `return_tensors=\"np\"` to the tokenizer call. \r\n\r\nI think we should mention this in the docs (cc @stevhliu)", "I tested this tokenize function and indeed noticed a casting. However it seems to only concerns the `offset_mapping` field, which contains a list of tuples, that is converted to a list of lists. Since `pyarrow` also supports tuples, we actually don't need to convert the tuples to lists. \r\n\r\nI think this can be changed here: \r\n\r\nhttps://github.com/huggingface/datasets/blob/ede72d3f9796339701ec59899c7c31d2427046fb/src/datasets/features/features.py#L382-L383\r\n\r\n```diff\r\n- if isinstance(obj, list): \r\n+ if isinstance(obj, (list, tuple)): \r\n```\r\n\r\nand here: \r\n\r\nhttps://github.com/huggingface/datasets/blob/ede72d3f9796339701ec59899c7c31d2427046fb/src/datasets/features/features.py#L386-L387\r\n\r\n```diff\r\n- return obj if isinstance(obj, list) else [], isinstance(obj, tuple)\r\n+ return obj, False\r\n```\r\n\r\n@srobertjames can you try applying these changes and let us know if it helps ? If so, feel free to open a Pull Request to contribute this improvement if you want :)", "Wow, adding `return_tensors=\"np\"` sped up my example by a **factor 17x** of and completely eliminated the casting! I'd recommend not only to document it, but to make that the default.\r\n\r\nThe code at https://github.com/huggingface/notebooks/blob/main/examples/question_answering.ipynb does not specify `return_tensors=\"np\"` but yet avoids the casting penalty. How does it do that? (The ntbk seems to do `return_overflowing_tokens=True, return_offsets_mapping=True,`).\r\n\r\nAlso, surprisingly enough, using `return_tensors=\"pt\"` (which is my eventual application) yields this error:\r\n```\r\nTypeError: Provided `function` which is applied to all elements of table returns a `dict` of types \r\n[<class 'torch.Tensor'>, <class 'torch.Tensor'>, <class 'torch.Tensor'>, <class 'torch.Tensor'>]. \r\nWhen using `batched=True`, make sure provided `function` returns a `dict` of types like \r\n`(<class 'list'>, <class 'numpy.ndarray'>)`.\r\n```", "Setting the output to `\"np\"` makes the whole pipeline fast because it moves the data buffers from rust to python to arrow using zero-copy, and also because it does eliminate the casting completely ;)\r\n\r\nHave you had a chance to try eliminating the tuple casting using the trick above ?", "@lhoestq I just benchmarked the two edits to `features.py` above, and they appear to solve the problem, bringing my original example to within 20% the speed of the output `\"np\"` example. Nice!\r\n\r\nFor a pull request, do you suggest simply following https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md ?", "Cool ! Sure feel free to follow these instructions to open a PR :) thanks !" ]
1,657,638,598,000
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## Describe the bug `Dataset.map`, when fed a Huggingface Tokenizer as its map func, can sometimes spend huge amounts of time doing casts. A minimal example follows. Not all usages suffer from this. For example, I profiled the preprocessor at https://github.com/huggingface/notebooks/blob/main/examples/question_answering.ipynb , and it did _not_ have this problem. However, I'm at a loss to figure out how it avoids it, as the example below is simple and minimal and still has this problem. This casting, where it occurs, causes the `Dataset.map` to run approximately 7x slower than it runs for code which does not cause this casting. This may be related to https://github.com/huggingface/datasets/issues/1046 . However, the tokenizer is _not_ set to return Tensors. ## Steps to reproduce the bug A minimal, self-contained example to reproduce is below: ```python import transformers from transformers import AutoTokenizer from datasets import load_dataset import torch import cProfile pretrained = 'distilbert-base-uncased' tokenizer = AutoTokenizer.from_pretrained(pretrained) squad = load_dataset('squad') squad_train = squad['train'] squad_tiny = squad_train.select(range(5000)) assert isinstance(tokenizer, transformers.PreTrainedTokenizerFast) def tokenize(ds): tokens = tokenizer(text=ds['question'], text_pair=ds['context'], add_special_tokens=True, padding='max_length', truncation='only_second', max_length=160, stride=32, return_overflowing_tokens=True, return_offsets_mapping=True, ) return tokens cmd = 'squad_tiny.map(tokenize, batched=True, remove_columns=squad_tiny.column_names)' cProfile.run(cmd, sort='tottime') ``` ## Actual results The code works, but takes 10-25 sec per batch (about 7x slower than non-casting code), with the following profile. Note that `_cast_to_python_objects` is the culprit. ``` 63524075 function calls (58206482 primitive calls) in 121.836 seconds Ordered by: internal time ncalls tottime percall cumtime percall filename:lineno(function) 5274034/40 68.751 0.000 111.060 2.776 features.py:262(_cast_to_python_objects) 42223832 24.077 0.000 33.310 0.000 {built-in method builtins.isinstance} 16338/20 5.121 0.000 111.053 5.553 features.py:361(<listcomp>) 5274135 4.747 0.000 4.749 0.000 {built-in method _abc._abc_instancecheck} 80/40 4.731 0.059 116.292 2.907 {pyarrow.lib.array} 5274135 4.485 0.000 9.234 0.000 abc.py:96(__instancecheck__) 2661564/2645196 2.959 0.000 4.298 0.000 features.py:1081(_check_non_null_non_empty_recursive) 5 2.786 0.557 2.786 0.557 {method 'encode_batch' of 'tokenizers.Tokenizer' objects} 2668052 0.930 0.000 0.930 0.000 {built-in method builtins.len} 5000 0.930 0.000 0.938 0.000 tokenization_utils_fast.py:187(_convert_encoding) 5 0.750 0.150 0.808 0.162 {method 'to_pydict' of 'pyarrow.lib.Table' objects} 1 0.444 0.444 121.749 121.749 arrow_dataset.py:2501(_map_single) 40 0.375 0.009 116.291 2.907 arrow_writer.py:151(__arrow_array__) 10 0.066 0.007 0.066 0.007 {method 'write_batch' of 'pyarrow.lib._CRecordBatchWriter' objects} 1 0.060 0.060 121.835 121.835 fingerprint.py:409(wrapper) 11387/5715 0.049 0.000 0.175 0.000 {built-in method builtins.getattr} 36 0.049 0.001 0.049 0.001 {pyarrow._compute.call_function} 15000 0.040 0.000 0.040 0.000 _collections_abc.py:719(__iter__) 3 0.023 0.008 0.023 0.008 {built-in method _imp.create_dynamic} 77 0.020 0.000 0.020 0.000 {built-in method builtins.dir} 37 0.019 0.001 0.019 0.001 socket.py:543(send) 15 0.017 0.001 0.017 0.001 tokenization_utils_fast.py:460(<listcomp>) 432/421 0.015 0.000 0.024 0.000 traitlets.py:1388(_notify_observers) 5000 0.015 0.000 0.018 0.000 _collections_abc.py:672(keys) 51 0.014 0.000 0.042 0.001 traitlets.py:276(getmembers) 5 0.014 0.003 3.775 0.755 tokenization_utils_fast.py:392(_batch_encode_plus) 3/1 0.014 0.005 0.035 0.035 {built-in method _imp.exec_dynamic} 5 0.012 0.002 0.950 0.190 tokenization_utils_fast.py:438(<listcomp>) 31626 0.012 0.000 0.012 0.000 {method 'append' of 'list' objects} 1532/1001 0.011 0.000 0.189 0.000 traitlets.py:643(get) 5 0.009 0.002 3.796 0.759 arrow_dataset.py:2631(apply_function_on_filtered_inputs) 51 0.009 0.000 0.062 0.001 traitlets.py:1766(traits) 5 0.008 0.002 3.784 0.757 tokenization_utils_base.py:2632(batch_encode_plus) 368 0.007 0.000 0.044 0.000 traitlets.py:1715(_get_trait_default_generator) 26 0.007 0.000 0.022 0.001 traitlets.py:1186(setup_instance) 51 0.006 0.000 0.010 0.000 traitlets.py:1781(<listcomp>) 80/32 0.006 0.000 0.052 0.002 table.py:1758(cast_array_to_feature) 684 0.006 0.000 0.007 0.000 {method 'items' of 'dict' objects} 4344/1794 0.006 0.000 0.192 0.000 traitlets.py:675(__get__) ... ``` ## Environment info I observed this on both Google colab and my local workstation: ### Google colab - `datasets` version: 2.3.2 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 ### Local - `datasets` version: 2.3.2 - Platform: Windows-7-6.1.7601-SP1 - Python version: 3.8.10 - PyArrow version: 8.0.0 - Pandas version: 1.4.3
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4,675
Unable to use dataset with PyTorch dataloader
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[ "Hi! `para_crawl` has a single column of type `Translation`, which stores translation dictionaries. These dictionaries can be stored in a NumPy array but not in a PyTorch tensor since PyTorch only supports numeric types. In `datasets`, the conversion to `torch` works as follows: \r\n1. convert PyArrow table to NumPy arrays \r\n2. convert NumPy arrays to Torch tensors. \r\n\r\nThe 2nd step is problematic for your case as `datasets` attempts to convert the array of dictionaries to a PyTorch tensor. One way to fix this is to use the [preprocessing logic](https://github.com/huggingface/transformers/blob/8581a798c0a48fca07b29ce2ca2ef55adcae8c7e/examples/pytorch/translation/run_translation.py#L440-L458) from the Transformers translation script. And on our side, I think we can replace a NumPy array of dicts with a dict of NumPy array if the feature type is `Translation`/`TranslationVariableLanguages` (one array for each language) to get the official PyTorch error message for strings in such case." ]
1,657,638,244,000
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## Describe the bug When using `.with_format("torch")`, an arrow table is returned and I am unable to use it by passing it to a PyTorch DataLoader: please see the code below. ## Steps to reproduce the bug ```python from datasets import load_dataset from torch.utils.data import DataLoader ds = load_dataset( "para_crawl", name="enfr", cache_dir="/tmp/test/", split="train", keep_in_memory=True, ) dataloader = DataLoader(ds.with_format("torch"), num_workers=32) print(next(iter(dataloader))) ``` Is there something I am doing wrong? The documentation does not say much about the behavior of `.with_format()` so I feel like I am a bit stuck here :-/ Thanks in advance for your help! ## Expected results The code should run with no error ## Actual results ``` AttributeError: 'str' object has no attribute 'dtype' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-4.18.0-348.el8.x86_64-x86_64-with-glibc2.28 - Python version: 3.10.4 - PyArrow version: 8.0.0 - Pandas version: 1.4.3
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I_kwDODunzps5NkC78
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Issue loading datasets -- pyarrow.lib has no attribute
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[ "Hi @margotwagner, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your bug: in an environment with datasets-2.3.2 and pyarrow-8.0.0, I can load the datasets without any problem:\r\n```python\r\n>>> ds = load_dataset(\"glue\", \"cola\")\r\n>>> ds\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 8551\r\n })\r\n validation: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 1043\r\n })\r\n test: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 1063\r\n })\r\n})\r\n\r\n>>> import pyarrow\r\n>>> pyarrow.__version__\r\n8.0.0\r\n>>> from pyarrow.lib import IpcReadOptions\r\n>>> IpcReadOptions\r\npyarrow.lib.IpcReadOptions\r\n```\r\n\r\nI think you may have a problem in your Python environment: maybe you have also an old version of pyarrow that has precedence when importing it.\r\n\r\nCould you please check this (just after you tried to load the dataset and got the error)?\r\n```python\r\n>>> import pyarrow\r\n>>> pyarrow.__version__\r\n``` " ]
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## Describe the bug I am trying to load sentiment analysis datasets from huggingface, but any dataset I try to use via load_dataset, I get the same error: `AttributeError: module 'pyarrow.lib' has no attribute 'IpcReadOptions'` ## Steps to reproduce the bug ```python dataset = load_dataset("glue", "cola") ``` ## Expected results Download datasets without issue. ## Actual results `AttributeError: module 'pyarrow.lib' has no attribute 'IpcReadOptions'` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.5 - PyArrow version: 8.0.0 - Pandas version: 1.1.0
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4,673
load_datasets on csv returns everything as a string
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[ "Hi @courtneysprouse, thanks for reporting.\r\n\r\nYes, you are right: by default the \"csv\" loader loads all columns as strings. \r\n\r\nYou could tweak this behavior by passing the `feature` argument to `load_dataset`, but it is also true that currently it is not possible to perform some kind of casts, due to lacking of implementation in PyArrow. For example:\r\n```python\r\nimport datasets\r\n\r\nfeatures = datasets.Features(\r\n {\r\n \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\r\n \"ner_tags\": datasets.Sequence(datasets.Value(\"int32\")),\r\n }\r\n)\r\n\r\nnew_conll = datasets.load_dataset(\"csv\", data_files=\"ner_conll.csv\", features=features)\r\n```\r\ngives `ArrowNotImplementedError` error:\r\n```\r\n/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: Unsupported cast from string to list using function cast_list\r\n```\r\n\r\nOn the other hand, if you just would like to save and afterwards load your dataset, you could use `save_to_disk` and `load_from_disk` instead. These functions preserve all data types.\r\n```python\r\n>>> orig_conll.save_to_disk(\"ner_conll\")\r\n\r\n>>> from datasets import load_from_disk\r\n\r\n>>> new_conll = load_from_disk(\"ner_conll\")\r\n>>> new_conll\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 14042\r\n })\r\n validation: Dataset({\r\n features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 3251\r\n })\r\n test: Dataset({\r\n features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 3454\r\n })\r\n})\r\n>>> new_conll[\"train\"][0]\r\n{'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0],\r\n 'id': '0',\r\n 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0],\r\n 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7],\r\n 'tokens': ['EU',\r\n 'rejects',\r\n 'German',\r\n 'call',\r\n 'to',\r\n 'boycott',\r\n 'British',\r\n 'lamb',\r\n '.']}\r\n>>> new_conll[\"train\"].features\r\n{'chunk_tags': Sequence(feature=ClassLabel(num_classes=23, names=['O', 'B-ADJP', 'I-ADJP', 'B-ADVP', 'I-ADVP', 'B-CONJP', 'I-CONJP', 'B-INTJ', 'I-INTJ', 'B-LST', 'I-LST', 'B-NP', 'I-NP', 'B-PP', 'I-PP', 'B-PRT', 'I-PRT', 'B-SBAR', 'I-SBAR', 'B-UCP', 'I-UCP', 'B-VP', 'I-VP'], id=None), length=-1, id=None),\r\n 'id': Value(dtype='string', id=None),\r\n 'ner_tags': Sequence(feature=ClassLabel(num_classes=9, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC'], id=None), length=-1, id=None),\r\n 'pos_tags': Sequence(feature=ClassLabel(num_classes=47, names=['\"', \"''\", '#', '$', '(', ')', ',', '.', ':', '``', 'CC', 'CD', 'DT', 'EX', 'FW', 'IN', 'JJ', 'JJR', 'JJS', 'LS', 'MD', 'NN', 'NNP', 'NNPS', 'NNS', 'NN|SYM', 'PDT', 'POS', 'PRP', 'PRP$', 'RB', 'RBR', 'RBS', 'RP', 'SYM', 'TO', 'UH', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ', 'WDT', 'WP', 'WP$', 'WRB'], id=None), length=-1, id=None),\r\n 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}\r\n```", "Hi @albertvillanova!\r\n\r\nThanks so much for your suggestions! That worked! " ]
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## Describe the bug If you use: `conll_dataset.to_csv("ner_conll.csv")` It will create a csv file with all of your data as expected, however when you load it with: `conll_dataset = load_dataset("csv", data_files="ner_conll.csv")` everything is read in as a string. For example if I look at everything in 'ner_tags' I get back `['[3 0 7 0 0 0 7 0 0]', '[1 2]', '[5 0]']` instead of what I originally saved which was `[[3, 0, 7, 0, 0, 0, 7, 0, 0], [1, 2], [5, 0]]` I think maybe there is something funky going on with the csv delimiter ## Steps to reproduce the bug ```python # Sample code to reproduce the bug #load original conll dataset orig_conll = load_dataset("conll2003") #save original conll as a csv orig_conll.to_csv("ner_conll.csv") #reload conll data as a csv new_conll = load_dataset("csv", data_files="ner_conll.csv")` ``` ## Expected results A clear and concise description of the expected results. I would expect the data be returned as the data type I saved it as. I.e. if I save a list of ints [[3, 0, 7, 0, 0, 0, 7, 0, 0]], I shouldnt get back a string ['[3 0 7 0 0 0 7 0 0]'] I also get back a string when I pass a list of strings ['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.'] ## Actual results A list of strings `['[3 0 7 0 0 0 7 0 0]', '[1 2]', '[5 0]']` A string "['EU' 'rejects' 'German' 'call' 'to' 'boycott' 'British' 'lamb' '.']" ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.0-121-generic-x86_64-with-glibc2.17 - Python version: 3.8.13 - PyArrow version: 8.0.0
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Support extract 7-zip compressed data files
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Cool! Can you please remove `Fix #3541` from the description as this PR doesn't add support for streaming/`iter_archive`, so it only partially addresses the issue?\r\n\r\nSide note:\r\nI think we can use `libarchive` (`libarchive-c` is a Python package with the bindings) for streaming 7z archives. The only issue with this lib is that it's tricky to install on Windows/Mac." ]
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Fix partially #3541, fix #4670.
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Dataset Viewer issue for wmt16
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[ "Thanks for reporting, @lewtun.\r\n\r\n~We can't load the dataset locally, so I think this is an issue with the loading script (not the viewer).~\r\n\r\n We are investigating...", "Recently, there was a merged PR related to this dataset:\r\n- #4554\r\n\r\nWe are looking at this...", "Indeed, the above mentioned PR fixed the loading script (it was not working before).\r\n\r\nI'm forcing the refresh of the Viewer.", "Please note that the above mentioned PR also made an enhancement in the `datasets` library, required by this loading script. This enhancement will only be available to the Viewer once we make our next release." ]
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### Link https://huggingface.co/datasets/wmt16 ### Description [Reported](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/12#62cb83f14c7f35284e796f9c) by a user of AutoTrain Evaluate. AFAIK this dataset was working 1-2 weeks ago, and I'm not sure how to interpret this error. ``` Status code: 400 Exception: NotImplementedError Message: This is a abstract method ``` Thanks! ### Owner No
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Can't extract files from `.7z` zipfile using `download_and_extract`
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[ "Hi @bhavitvyamalik, thanks for reporting.\r\n\r\nYes, currently we do not support 7zip archive compression: I think we should.\r\n\r\nAs a workaround, you could uncompress it explicitly, like done in e.g. `samsum` dataset: \r\n\r\nhttps://github.com/huggingface/datasets/blob/fedf891a08bfc77041d575fad6c26091bc0fce52/datasets/samsum/samsum.py#L106-L110\r\n", "Related to this issue: https://github.com/huggingface/datasets/issues/3541", "Sure, let me look into and check what can be done. Will keep you guys updated here!", "Initially, I thought of solving this without any external dependency. Almost everywhere I saw `lzma` can be used for this but there is a caveat that lzma doesn’t work with 7z archives but only single files. In my case the 7z archive has multiple files so it didn't work. Is it fine to use external library here?", "Hi @bhavitvyamalik, thanks for your investigation.\r\n\r\nOn Monday, I started a PR that will eventually close this issue as well: I'm linking it to this.\r\n- #4672\r\n\r\nLet me know what you think. " ]
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CONTRIBUTOR
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## Describe the bug I'm adding a new dataset which is a `.7z` zip file in Google drive and contains 3 json files inside. I'm able to download the data files using `download_and_extract` but after downloading it throws this error: ``` >>> dataset = load_dataset("./datasets/mantis/") Using custom data configuration default Downloading and preparing dataset mantis/default to /Users/bhavitvyamalik/.cache/huggingface/datasets/mantis/default/1.1.0/611affa804ec53e2055a335cc1b8b213bb5a0b5142d919967729d5ee23c6bab4... Downloading data: 100%|█████████████████████████████████████████████████████████| 77.2M/77.2M [00:23<00:00, 3.28MB/s] /Users/bhavitvyamalik/.cache/huggingface/datasets/downloads/fc3d70123c9de8407587a59aa426c37819cf2bf016795d33270e8a1d558a34e6 Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/bhavitvyamalik/Desktop/work/hf/datasets/src/datasets/load.py", line 1745, in load_dataset use_auth_token=use_auth_token, File "/Users/bhavitvyamalik/Desktop/work/hf/datasets/src/datasets/builder.py", line 595, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/Users/bhavitvyamalik/Desktop/work/hf/datasets/src/datasets/builder.py", line 690, in _download_and_prepare ) from None OSError: Cannot find data file. Original error: [Errno 20] Not a directory: '/Users/bhavitvyamalik/.cache/huggingface/datasets/downloads/fc3d70123c9de8407587a59aa426c37819cf2bf016795d33270e8a1d558a34e6/merged_train.json' ``` just before generating the splits. I checked `fc3d70123c9de8407587a59aa426c37819cf2bf016795d33270e8a1d558a34e6` file and it's `7z` zip file (similar to downloaded Google drive file) which means it didn't get unzip. Do I need to unzip it separately and then pass the paths for train,dev,test files in `SplitGenerator`? ## Environment info - `datasets` version: 1.18.4.dev0 - Platform: Darwin-19.6.0-x86_64-i386-64bit - Python version: 3.7.8 - PyArrow version: 5.0.0
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loading oscar-corpus/OSCAR-2201 raises an error
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[ "I had to use the appropriate token for use_auth_token. Thank you." ]
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## Describe the bug load_dataset('oscar-2201', 'af') raises an error: Traceback (most recent call last): File "/usr/lib/python3.8/code.py", line 90, in runcode exec(code, self.locals) File "<input>", line 1, in <module> File "..python3.8/site-packages/datasets/load.py", line 1656, in load_dataset builder_instance = load_dataset_builder( File ".../lib/python3.8/site-packages/datasets/load.py", line 1439, in load_dataset_builder dataset_module = dataset_module_factory( File ".../lib/python3.8/site-packages/datasets/load.py", line 1189, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at .../oscar-2201/oscar-2201.py or any data file in the same directory. Couldn't find 'oscar-2201' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/oscar-2201/oscar-2201.py I've tried other permutations such as : oscar_22 = load_dataset('oscar-2201', 'af',use_auth_token=True) oscar_22 = load_dataset('oscar-corpus/OSCAR-2201', 'af',use_auth_token=True) oscar_22 = load_dataset('oscar-2201', 'af') oscar_22 = load_dataset('oscar-corpus/OSCAR-2201') with the same unfortunate result. ## Steps to reproduce the bug oscar_22 = load_dataset('oscar-2201', 'af',use_auth_token=True) oscar_22 = load_dataset('oscar-corpus/OSCAR-2201', 'af',use_auth_token=True) oscar_22 = load_dataset('oscar-2201', 'af') oscar_22 = load_dataset('oscar-corpus/OSCAR-2201') # Sample code to reproduce the bug ``` ## Expected results loaded data ## Actual results Traceback (most recent call last): File "/usr/lib/python3.8/code.py", line 90, in runcode exec(code, self.locals) File "<input>", line 1, in <module> File "..python3.8/site-packages/datasets/load.py", line 1656, in load_dataset builder_instance = load_dataset_builder( File ".../lib/python3.8/site-packages/datasets/load.py", line 1439, in load_dataset_builder dataset_module = dataset_module_factory( File ".../lib/python3.8/site-packages/datasets/load.py", line 1189, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at .../oscar-2201/oscar-2201.py or any data file in the same directory. Couldn't find 'oscar-2201' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/oscar-2201/oscar-2201.py ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-5.13.0-37-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 8.0.0 - Pandas version: 1.4.3
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Dataset Viewer issue for hungnm/multilingual-amazon-review-sentiment-processed
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[ "It seems like a private dataset. The viewer is currently not supported on the private datasets." ]
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### Link https://huggingface.co/hungnm/multilingual-amazon-review-sentiment ### Description _No response_ ### Owner Yes
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Dataset Viewer issue for hungnm/multilingual-amazon-review-sentiment-processed
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### Link _No response_ ### Description _No response_ ### Owner _No response_
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Issues with concatenating datasets
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[ "Hi! I agree we should improve the features equality checks to account for this particular case. However, your code fails due to `answer_start` having the dtype `int64` instead of `int32` after loading from JSON (it's not possible to embed type precision info into a JSON file; `save_to_disk` does that for arrow files), which would lead to the concatenation error as PyArrow does not support this sort of type promotion. This can be fixed as follows:\r\n```python\r\ntemp = load_dataset(\"json\", data_files={\"train\": \"output.jsonl\"}, features=squad[\"train\"].features)\r\n``` ", "That makes sense. I totally missed the `int64` and `int32` part. Thanks for pointing it out! Will close this issue for now." ]
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## Describe the bug It is impossible to concatenate datasets if a feature is sequence of dict in one dataset and a dict of sequence in another. But based on the document, it should be automatically converted. > A [datasets.Sequence](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Sequence) with a internal dictionary feature will be automatically converted into a dictionary of lists. This behavior is implemented to have a compatilbity layer with the TensorFlow Datasets library but may be un-wanted in some cases. If you don’t want this behavior, you can use a python list instead of the [datasets.Sequence](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Sequence). ## Steps to reproduce the bug ```python from datasets import concatenate_datasets, load_dataset squad = load_dataset("squad_v2") squad["train"].to_json("output.jsonl", lines=True) temp = load_dataset("json", data_files={"train": "output.jsonl"}) concatenate_datasets([temp["train"], squad["train"]]) ``` ## Expected results No error executing that code ## Actual results ``` ValueError: The features can't be aligned because the key answers of features {'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None)} has unexpected type - Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None) (expected either {'text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'answer_start': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)} or Value("null"). ``` ## Environment info - `datasets` version: 2.3.2 - Platform: macOS-12.4-arm64-arm-64bit - Python version: 3.8.11 - PyArrow version: 6.0.1 - Pandas version: 1.3.5
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Unable to create dataset having Python dataset script only
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null
[ "Hi @aleSuglia, thanks for reporting.\r\n\r\nWe are having a look at it. \r\n\r\nWe transfer this issue to the Community tab of the corresponding Hub dataset: https://huggingface.co/datasets/Heriot-WattUniversity/dialog-babi/discussions" ]
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CONTRIBUTOR
null
## Describe the bug Hi there, I'm trying to add the following dataset to Huggingface datasets: https://huggingface.co/datasets/Heriot-WattUniversity/dialog-babi/blob/ I'm trying to do so using the CLI commands but seems that this command generates the wrong `dataset_info.json` file (you can find it in the repo already): ``` datasets-cli test Heriot-WattUniversity/dialog-babi/dialog_babi.py --save_infos --all-configs ``` while it errors when I remove the python script: ``` datasets-cli test Heriot-WattUniversity/dialog-babi/ --save_infos --all-configs ``` The error message is the following: ``` FileNotFoundError: Unable to resolve any data file that matches '['**']' at /Users/as2180/workspace/Heriot-WattUniversity/dialog-babi with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip'] ``` ## Environment info - `datasets` version: 2.3.2 - Platform: macOS-12.4-arm64-arm-64bit - Python version: 3.9.9 - PyArrow version: 8.0.0 - Pandas version: 1.4.3
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Add stanford dog dataset
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Hi @khushmeeet, thanks for your contribution.\r\n\r\nBut wouldn't it be better to add this dataset to the Hub? \r\n- https://huggingface.co/docs/datasets/share\r\n- https://huggingface.co/docs/datasets/dataset_script", "Hi @albertvillanova \r\n\r\nDataset is added to Hub - https://huggingface.co/datasets/dgrnd4/stanford_dog_dataset", "Great, so I guess we can close this issue, as the dataset is already available on the Hub.", "OK I read the discussion on:\r\n- #4504\r\n\r\nCurrently, priority is adding datasets to the Hub, not here on GitHub.\r\n\r\nIf you would like to contribute the loading script and all the metadata you generated (README + JSON files), you could:\r\n- Either make a PR to the existing dataset on the Hub\r\n- Create a new dataset on the Hub:\r\n - Either under your personal namespace\r\n - or even more professionally, under the namespace `stanfordSVL` (Stanford Vision and Learning Lab: https://svl.stanford.edu/)\r\n\r\nYou can use the Community tab to ping us if you need help or have any questions." ]
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CONTRIBUTOR
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This PR is for adding dataset, related to issue #4504. We are adding Stanford dog breed dataset. It is a multi class image classification dataset. Details can be found here - http://vision.stanford.edu/aditya86/ImageNetDogs/ Tests on dummy data is failing currently, which I am looking into.
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
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This PR adds some decoration to text about different modalities to make it more obvious separate guides exist for audio, vision, and text. The goal is to make it easier for users to discover these guides! ![underline](https://user-images.githubusercontent.com/59462357/178044392-9596693e-9a4a-479a-a282-f1edbd90be1a.png) TODO: - [x] Open PR to support new Tailwind classes
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Fix: conll2003 - fix empty example
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As reported in https://huggingface.co/datasets/conll2003/discussions/2#62c45a14f93fc97e8260532f, there was an extra empty example at the end of the dataset
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Concurrency bug when using same cache among several jobs
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[ "I can confirm that if I run one job first that processes the dataset, then I can run any jobs in parallel with no problem (no write-concurrency anymore...). ", "Hi! That's weird. It seems like the error points to the `mkstemp` function, but the official docs state the following:\r\n```\r\nThere are no race conditions in the file’s creation, assuming that the platform properly implements the [os.O_EXCL](https://docs.python.org/3/library/os.html#os.O_EXCL) flag for [os.open()](https://docs.python.org/3/library/os.html#os.open)\r\n```\r\nSo this could mean your platform doesn't support that flag.\r\n\r\n~~Can you please check if wrapping the temp file creation (the line `tmp_file = tempfile.NamedTemporaryFile(\"wb\", dir=os.path.dirname(cache_file_name), delete=False)` in `_map_single`) with the `multiprocess.Lock` fixes the issue?~~\r\nPerhaps wrapping the temp file creation in `_map_single` with `filelock` could work:\r\n```python\r\nwith FileLock(lock_path):\r\n tmp_file = tempfile.NamedTemporaryFile(\"wb\", dir=os.path.dirname(cache_file_name), delete=False)\r\n```\r\nCan you please check if that helps?" ]
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## Describe the bug I used to see this bug with an older version of the datasets. It seems to persist. This is my concrete scenario: I launch several evaluation jobs on a cluster in which I share the file system and I share the cache directory used by huggingface libraries. The evaluation jobs read the same *.csv files. If my jobs get all scheduled pretty much at the same time, there are all kinds of weird concurrency errors. Sometime it crashes silently. This time I got lucky that it crashed with a stack trace that I can share and maybe you get to the bottom of this. If you don't have a similar setup available, it may be hard to reproduce as you really need two jobs accessing the same file at the same time to see this type of bug. ## Steps to reproduce the bug I'm running a modified version of `run_glue.py` script adapted to my use case. I've seen the same problem when running some glue datasets as well (so it's not specific to loading the datasets from csv files). ## Expected results No crash, concurrent access to the (intermediate) files just fine. ## Actual results Crashes due to races/concurrency bugs. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-4.18.0-348.23.1.el8_5.x86_64-x86_64-with-glibc2.10 - Python version: 3.8.5 - PyArrow version: 8.0.0 - Pandas version: 1.1.0 Stack trace that I just got with the crash (I've obfuscated some names, it should still be quite informative): ``` Running tokenizer on dataset: 0%| | 0/3 [00:00<?, ?ba/s] Traceback (most recent call last): File "../../src/models//run_*******.py", line 600, in <module> main() File "../../src/models//run_*******.py", line 444, in main raw_datasets = raw_datasets.map( File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/dataset_dict.py", line 770, in map { File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/dataset_dict.py", line 771, in <dictcomp> k: dataset.map( File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2376, in map return self._map_single( File "/*******/envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 551, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 518, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/*******/envs/tr-crt/lib/python3.8/site-packages/datasets/fingerprint.py", line 458, in wrapper out = func(self, *args, **kwargs) File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2776, in _map_single buf_writer, writer, tmp_file = init_buffer_and_writer() File "/*******//envs/tr-crt/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2696, in init_buffer_and_writer tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(cache_file_name), delete=False) File "/*******//envs/tr-crt/lib/python3.8/tempfile.py", line 541, in NamedTemporaryFile (fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type) File "/*******//envs/tr-crt/lib/python3.8/tempfile.py", line 250, in _mkstemp_inner fd = _os.open(file, flags, 0o600) FileNotFoundError: [Errno 2] No such file or directory: '/*******/cache-transformers//transformers/csv/default-ef9cd184210742a7/0.0.0/51cce309a08df9c4d82ffd9363bbe090bf173197fc01a71b034e8594995a1a58/tmps8l6j5yc' ``` As I ran 100s of experiments last year for an empirical paper, I ran into this type of bugs several times. I found several bandaid/work-arounds, e.g., run one job first that caches the dataset => eliminate concurrency; OR use unique caches => eliminate concurrency (but increase storage space), etc. and it all works fine. I'd like to help you fixing this bug as it's really annoying to always apply the work arounds. Let me know what other info from my side could help you figure out the issue. Thanks for your help!
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Fix _resolve_single_pattern_locally on Windows with multiple drives
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Good catch ! Sorry I forgot (again) about windows paths when writing this x)" ]
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Currently, when `_resolve_single_pattern_locally` is called from a different drive than the one in `pattern`, it raises an exception: ``` _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\io\parquet.py:35: in __init__ **kwargs, C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\builder.py:287: in __init__ sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:761: in from_local_or_remote if not isinstance(patterns_for_key, DataFilesList) C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:723: in from_local_or_remote data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:321: in resolve_patterns_locally_or_by_urls for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions): C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:239: in _resolve_single_pattern_locally for filepath in glob_iter C:\hostedtoolcache\windows\Python\3.6.8\x64\lib\site-packages\datasets\data_files.py:242: in <listcomp> os.path.relpath(filepath, base_path), os.path.relpath(pattern, base_path) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ path = 'C:\\Users\\runneradmin\\AppData\\Local\\Temp\\pytest-of-runneradmin\\pytest-0\\popen-gw0\\data6\\dataset.parquet' start = '/' ... E ValueError: path is on mount 'C:', start on mount 'D:' ``` This PR makes sure that `base_path` is in the same drive as `pattern`.
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Transfer CI to GitHub Actions
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks a lot @albertvillanova ! I hope we're finally done with flakiness on windows ^^\r\n\r\nAlso thanks for paying extra attention to billing and avoiding running unnecessary jobs. Though for certain aspects (see my comments), I think it's worth having the extra jobs to make our life easier", "~@lhoestq I think you forgot to add your comments?~\r\n\r\nI had missed it among all the other comments...", "@lhoestq, I'm specially enthusiastic with the fail-fast policy: it was in my TODO list for a long time. I really think it will have a positive impact (I would love to know the spent time saving it will enable, besides the carbon footprint reduction). :wink: \r\n\r\nSo yes, as you said above, let's give it a try at least. If we encounter any inconvenience, we can easily disable it.\r\n\r\nQuestion: I guess I have to disable CircleCI CI before merging this PR?\r\n\r\n" ]
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This PR transfers CI from CircleCI to GitHub Actions. The implementation in GitHub Actions tries to be as faithful as possible to the implementation in CircleCI and get the same output results (exceptions below). **IMPORTANT NOTE**: The fast-fail policy (described below) is not finally implemented, so that: - we can continue merging PRs with CI in red because of some random error returned by the Hub - it is not annoying for maintainers to have to relaunch failed CI jobs See comments here: https://github.com/huggingface/datasets/pull/4659#discussion_r918802348 Differences in the implementation in GitHub Actions compared to the CircleCI one: - This PR introduces some *fail-fast* mechanisms to significantly reduce the total time CI is running, both because of environmental impact and because CI in GitHub Actions billing depends on the minutes per month running time (see [About billing for GitHub Actions](https://docs.github.com/en/billing/managing-billing-for-github-actions/about-billing-for-github-actions)): - All tests *depend* on `check_code_quality` job: only if `check_code_quality` passes, then the other test jobs are launched - The tests are implemented with a matrix strategy (cross-product: OS and PyArrow versions) and fail-fast: if any of the 4 processes fails, the others are cancelled - OS dependencies for Linux (see table below) | OS dependencies | Passed tests | Skipped tests | | --- | ---: | ---: | | libsndfile1-dev | 4786 | 3119 | | libsndfile1 | 4786 | 3119 | | libsndfile1, sox | 4788 | 3117 | - This PR replaces `libsndfile1-dev` with `libsndfile1`: the same number of passing tests but less packages installed - This PR adds `sox`: required by MP3 tests (2 more tests are passed: 4788 instead of 4786) - For tests using PyArrow 6, this PR uses 6.0.1 instead of 6.0.0 TO DO: - [ ] Remove old CircleCI CI: kept for the moment to compare stability and performance Close #4658. ## Comparison between CircleCI and GitHub Actions | | | CircleCI | GitHub Actions | | --- | --- | ---: | ---: | | Ubuntu, pyarrow-latest |||| || Passed tests | 4786 | 4788 | || Duration | 11m 0s | 10m 10s | | Windows, pyarrow-latest |||| || Passed tests | 4783 | 4783 | || Duration | 29m 59s | 22m 56s |
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null
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1,657,181,450,000
1,657,624,705,000
1,657,624,705,000
MEMBER
null
Let's try CI tests using GitHub Actions to see if they are more stable than on CircleCI.
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1,296,743,133
I_kwDODunzps5NSrrd
4,657
Add SQuAD2.0 Dataset
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[ "Hey, It's already present [here](https://huggingface.co/datasets/squad_v2) ", "Hi! This dataset is indeed already available on the Hub. Closing." ]
1,657,163,976,000
1,657,642,492,000
1,657,642,492,000
NONE
null
## Adding a Dataset - **Name:** *SQuAD2.0* - **Description:** *Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.* - **Paper:** *https://aclanthology.org/P18-2124.pdf* - **Data:** *https://rajpurkar.github.io/SQuAD-explorer/* - **Motivation:** *Dataset for training and evaluating models of conversational response*
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1,296,740,266
I_kwDODunzps5NSq-q
4,656
Add Amazon-QA Dataset
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[]
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[ "uploaded dataset [here](https://huggingface.co/datasets/embedding-data/Amazon-QA)." ]
1,657,163,711,000
1,657,765,212,000
1,657,765,212,000
NONE
null
## Adding a Dataset - **Name:** *Amazon-QA* - **Description:** *The dataset is .jsonl format, where each line in the file is a json string that corresponds to a question, existing answers to the question and the extracted review snippets (relevant to the question).* - **Paper:** *https://github.com/amazonqa/amazonqa/tree/master/paper* - **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/amazon-qa.jsonl.gz* - **Motivation:** *Dataset for training and evaluating models of conversational response*
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1,296,720,896
I_kwDODunzps5NSmQA
4,655
Simple Wikipedia
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null
[]
null
[ "uploaded dataset [here](https://huggingface.co/datasets/embedding-data/simple-wiki)." ]
1,657,162,286,000
1,657,764,993,000
1,657,764,993,000
NONE
null
## Adding a Dataset - **Name:** *Simple Wikipedia* - **Description:** *Two different versions of the data set now exist. Both were generated by aligning Simple English Wikipedia and English Wikipedia. A complete description of the extraction process can be found in "Simple English Wikipedia: A New Simplification Task", William Coster and David Kauchak (2011).* - **Paper:** *https://aclanthology.org/P11-2117/* - **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/SimpleWiki.jsonl.gz* - **Motivation:** *Dataset for training and evaluating models of conversational response*
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1,296,716,119
I_kwDODunzps5NSlFX
4,654
Add Quora Question Triplets Dataset
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false
null
[]
null
[ "uploaded dataset [here](https://huggingface.co/datasets/embedding-data/QQP_triplets)." ]
1,657,161,822,000
1,657,764,830,000
1,657,764,830,000
NONE
null
## Adding a Dataset - **Name:** *Quora Question Triplets* - **Description:** *This dataset consists of over 400,000 lines of potential question duplicate pairs. Each line contains IDs for each question in the pair, the full text for each question, and a binary value that indicates whether the line truly contains a duplicate pair.* - **Paper:** - **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/quora_duplicates_triplets.jsonl.gz* - **Motivation:** *Dataset for training and evaluating models of conversational response*
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1,296,702,834
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4,653
Add Altlex dataset
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null
[]
null
[ "uploaded dataset [here](https://huggingface.co/datasets/embedding-data/altlex)." ]
1,657,160,582,000
1,657,764,759,000
1,657,764,759,000
NONE
null
## Adding a Dataset - **Name:** *Altlex* - **Description:** *Git repository for software associated with the 2016 ACL paper "Identifying Causal Relations Using Parallel Wikipedia Articles.”* - **Paper:** *https://aclanthology.org/P16-1135.pdf* - **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/altlex.jsonl.gz* - **Motivation:** *Dataset for training and evaluating models of conversational response*
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1,296,697,498
I_kwDODunzps5NSgia
4,652
Add Sentence Compression Dataset
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null
[]
null
[ "uploaded dataset [here](https://huggingface.co/datasets/embedding-data/sentence-compression)." ]
1,657,160,026,000
1,657,764,708,000
1,657,764,708,000
NONE
null
## Adding a Dataset - **Name:** *Sentence Compression* - **Description:** *Large corpus of uncompressed and compressed sentences from news articles.* - **Paper:** *https://www.aclweb.org/anthology/D13-1155/* - **Data:** *https://github.com/google-research-datasets/sentence-compression/tree/master/data* - **Motivation:** *Dataset for training and evaluating models of conversational response*
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1,296,689,414
I_kwDODunzps5NSekG
4,651
Add Flickr 30k Dataset
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null
[]
null
[ "uploaded dataset [here](https://huggingface.co/datasets/embedding-data/flickr30k-captions)." ]
1,657,159,148,000
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1,657,764,585,000
NONE
null
## Adding a Dataset - **Name:** *Flickr 30k* - **Description:** *To produce the denotation graph, we have created an image caption corpus consisting of 158,915 crowd-sourced captions describing 31,783 images. This is an extension of our previous Flickr 8k Dataset. The new images and captions focus on people involved in everyday activities and events.* - **Paper:** *https://transacl.org/ojs/index.php/tacl/article/view/229/33* - **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/flickr30k_captions.jsonl.gz* - **Motivation:** *Dataset for training and evaluating models of conversational response*
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1,296,680,037
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4,650
Add SPECTER dataset
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[ "uploaded dataset [here](https://huggingface.co/datasets/embedding-data/SPECTER)" ]
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## Adding a Dataset - **Name:** *SPECTER* - **Description:** *SPECTER: Document-level Representation Learning using Citation-informed Transformers* - **Paper:** *https://doi.org/10.18653/v1/2020.acl-main.207* - **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/specter_train_triples.jsonl.gz* - **Motivation:** *Dataset for training and evaluating models of conversational response*
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1,296,673,712
I_kwDODunzps5NSauw
4,649
Add PAQ dataset
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[ "uploaded dataset [here](https://huggingface.co/datasets/embedding-data/PAQ_pairs)" ]
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## Adding a Dataset - **Name:** *PAQ* - **Description:** *This repository contains code and models to support the research paper PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them* - **Paper:** *https://arxiv.org/abs/2102.07033* - **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/PAQ_pairs.jsonl.gz* - **Motivation:** *Dataset for training and evaluating models of conversational response*
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1,296,659,335
I_kwDODunzps5NSXOH
4,648
Add WikiAnswers dataset
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[ "uploaded dataset [here](https://huggingface.co/datasets/embedding-data/WikiAnswers)" ]
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## Adding a Dataset - **Name:** *WikiAnswers* - **Description:** *The WikiAnswers corpus contains clusters of questions tagged by WikiAnswers users as paraphrases. Each cluster optionally contains an answer provided by WikiAnswers users.* - **Paper:** *https://dl.acm.org/doi/10.1145/2623330.2623677* - **Data:** *https://github.com/afader/oqa#wikianswers-corpus* - **Motivation:** *Dataset for training and evaluating models of conversational response*
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1,296,311,270
I_kwDODunzps5NRCPm
4,647
Add Reddit dataset
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## Adding a Dataset - **Name:** *Reddit comments (2015-2018)* - **Description:** *Reddit is an American social news aggregation website, where users can post links, and take part in discussions on these posts. These threaded discussions provide a large corpus, which is converted into a conversational dataset using the tools in this directory.* - **Paper:** *https://arxiv.org/abs/1904.06472* - **Data:** *https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit* - **Motivation:** *Dataset for training and evaluating models of conversational response*
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1,296,027,785
PR_kwDODunzps468oZ6
4,645
Set HF_SCRIPTS_VERSION to main
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
1,657,122,201,000
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MEMBER
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After renaming "master" to "main", the CI fails with ``` AssertionError: 'https://raw.githubusercontent.com/huggingface/datasets/main/datasets/_dummy/_dummy.py' not found in "Couldn't find a dataset script at /home/circleci/datasets/_dummy/_dummy.py or any data file in the same directory. Couldn't find '_dummy' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/_dummy/_dummy.py" ``` This is because in the CI we were still using `HF_SCRIPTS_VERSION=master`. I changed it to "main"
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[Minor fix] Typo correction
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
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CONTRIBUTOR
null
recieve -> receive
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4,643
Rename master to main
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[ "_The documentation is not available anymore as the PR was closed or merged._", "All the mentions I found on google were simple URLs that will be redirected, so it's fine. I also checked the spaces and we should be good:\r\n- dalle-mini used to install the master branch but [it's no longer the case](https://huggingface.co/spaces/flax-community/dalle-mini/commit/b78c972afd5c2d2bed087be6479fe5c9c6cfa741)\r\n- same for [logo generator](https://huggingface.co/spaces/tom-doerr/logo_generator/commit/a9ea330e518870d0ca8f65abb56f71d86750d8e4)\r\n- I opened a PR to fix [vision-datasets-viewer](https://huggingface.co/spaces/nateraw/vision-datasets-viewer/discussions/1)\r\n", "Ok let's rename the branch, and then we can merge this PR" ]
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MEMBER
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This PR renames mentions of "master" by "main" in the code base for several cases: - set the default dataset script version to "main" if the local installation of `datasets` is a dev installation - update URLs to this github repository to use "main" - update the DVC benchmark - update the github workflows - update docstrings - update tests to compare the changes in dataset cards against "main"
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I_kwDODunzps5NO4vz
4,642
Streaming issue for ccdv/pubmed-summarization
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[ "Thanks for reporting @lewtun.\r\n\r\nI confirm there is an issue with streaming: it does not stream locally. ", "Oh, after investigation, the source of the issue is in the Hub dataset loading script.\r\n\r\nI'm opening a PR on the Hub dataset.", "I've opened a PR on their Hub dataset to support streaming: https://huggingface.co/datasets/ccdv/pubmed-summarization/discussions/2" ]
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### Link https://huggingface.co/datasets/ccdv/pubmed-summarization ### Description This was reported by a [user of AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/7). It seems like streaming doesn't work due to the way the dataset loading script is defined? ``` Status code: 400 Exception: FileNotFoundError Message: https://huggingface.co/datasets/ccdv/pubmed-summarization/resolve/main/train.zip/train.txt ``` ### Owner No
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Dataset Viewer issue for kmfoda/booksum
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[ "Thanks for reporting, @lewtun.\r\n\r\nIt works locally in streaming mode:\r\n```\r\n{'bid': 27681,\r\n 'is_aggregate': True,\r\n 'source': 'cliffnotes',\r\n 'chapter_path': 'all_chapterized_books/27681-chapters/chapters_1_to_2.txt',\r\n 'summary_path': 'finished_summaries/cliffnotes/The Last of the Mohicans/section_1_part_0.txt',\r\n 'book_id': 'The Last of the Mohicans.chapters 1-2',\r\n 'summary_id': 'chapters 1-2',\r\n 'content': None,\r\n 'summary': '{\"name\": \"Chapters 1-2\", \"url\": \"https://web.archive.org/web/20201101053205/https://www.cliffsnotes.com/literature/l/the-last-of-the-mohicans/summary-and-analysis/chapters-12\", \"summary\": \"Before any characters appear, the time and geography are made clear. Though it is the last war that England and France waged for a country that neither would retain, the wilderness between the forces still has to be...\r\n```\r\n\r\nI'm forcing the refresh of the preview. ", "The preview appears as expected once the refresh forced.", "Thank you @albertvillanova 🤗 !" ]
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### Link https://huggingface.co/datasets/kmfoda/booksum ### Description A [user of AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/9) discovered this dataset cannot be streamed due to: ``` Status code: 400 Exception: ClientResponseError Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/kmfoda/booksum/resolve/47953f583d6967f086cb16a2f4d2346e9834024d/test.csv') ``` I'm not sure why it says "Unauthorized" since it's just a bunch of CSV files in a repo ### Owner No
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Support all split in streaming mode
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4640). All of your documentation changes will be reflected on that endpoint." ]
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Fix #4637.
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Add HaGRID -- HAnd Gesture Recognition Image Dataset
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## Adding a Dataset - **Name:** HaGRID -- HAnd Gesture Recognition Image Dataset - **Description:** We introduce a large image dataset HaGRID (HAnd Gesture Recognition Image Dataset) for hand gesture recognition (HGR) systems. You can use it for image classification or image detection tasks. Proposed dataset allows to build HGR systems, which can be used in video conferencing services (Zoom, Skype, Discord, Jazz etc.), home automation systems, the automotive sector, etc. - **Paper:** https://arxiv.org/abs/2206.08219 - **Data:** https://github.com/hukenovs/hagrid 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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The speechocean762 dataset
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[ "CircleCL reported two errors, but I didn't find the reason. The error message:\r\n```\r\n_________________ ERROR collecting tests/test_dataset_cards.py _________________\r\ntests/test_dataset_cards.py:53: in <module>\r\n @pytest.mark.parametrize(\"dataset_name\", get_changed_datasets(repo_path))\r\ntests/test_dataset_cards.py:35: in get_changed_datasets\r\n diff_output = check_output([\"git\", \"diff\", \"--name-only\", \"origin/master...HEAD\"], cwd=repo_path)\r\n../.pyenv/versions/3.6.15/lib/python3.6/subprocess.py:356: in check_output\r\n **kwargs).stdout\r\n../.pyenv/versions/3.6.15/lib/python3.6/subprocess.py:438: in run\r\n output=stdout, stderr=stderr)\r\nE subprocess.CalledProcessError: Command '['git', 'diff', '--name-only', 'origin/master...HEAD']' returned non-zero exit status 128.\r\n\r\n=========================== short test summary info ============================\r\nERROR tests/test_dataset_cards.py - subprocess.CalledProcessError: Command '[...\r\nERROR tests/test_dataset_cards.py - subprocess.CalledProcessError: Command '[...\r\n= 4011 passed, 2357 skipped, 2 xfailed, 1 xpassed, 116 warnings, 2 errors in 284.32s (0:04:44) =\r\n\r\nExited with code exit status 1\r\n```\r\nI'm not sure if it was caused by this PR ...\r\n\r\nI ran `tests/test_dataset_cards.py` in my local environment, and it passed:\r\n```\r\n(venv)$ pytest tests/test_dataset_cards.py\r\n============================== test session starts ==============================\r\nplatform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0\r\nrootdir: /home/zhangjunbo/src/datasets\r\nplugins: forked-1.4.0, datadir-1.3.1, xdist-2.5.0\r\ncollected 1531 items\r\n\r\ntests/test_dataset_cards.py ..... [100%]\r\n======================= 766 passed, 765 skipped in 2.55s ========================\r\n```\r\n" ]
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[speechocean762](https://www.openslr.org/101/) is a non-native English corpus for pronunciation scoring tasks. It is free for both commercial and non-commercial use. I believe it will be easier to use if it could be available on Hugging Face.
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The "all" split breaks streaming
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[ "Thanks for reporting @cakiki.\r\n\r\nYes, this is a bug. We are investigating it.", "@albertvillanova Nice! Let me know if it's something I can fix my self; would love to contribtue!", "@cakiki I was working on this but if you would like to contribute, go ahead. I will close my PR. ;)\r\n\r\nFor the moment I just pushed the test (to see if it impacts other tests).", "It impacted the test `test_generator_based_download_and_prepare` and I have fixed this.\r\n\r\nSo that you can copy the test I implemented in my PR and then implement a fix for this issue that passes the test `tests/test_builder.py::test_builder_as_streaming_dataset`.", "Hi @cakiki are you still interested in working on this? Are you planning to open a PR?", "Hi @albertvillanova ! Sorry it took so long; I wanted to spend this weekend working on it." ]
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CONTRIBUTOR
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## Describe the bug Not sure if this is a bug or just the way streaming works, but setting `streaming=True` did not work when setting `split="all"` ## Steps to reproduce the bug The following works: ```python ds = load_dataset('super_glue', 'wsc.fixed', split='all') ``` The following throws `ValueError: Bad split: all. Available splits: ['train', 'validation', 'test']`: ```python ds = load_dataset('super_glue', 'wsc.fixed', split='all', streaming=True) ``` ## Expected results An iterator over all splits. ## Actual results I had to do the following to achieve the desired result: ```python from itertools import chain ds = load_dataset('super_glue', 'wsc.fixed', streaming=True) it = chain.from_iterable(ds.values()) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-4.15.0-176-generic-x86_64-with-glibc2.31 - Python version: 3.10.5 - PyArrow version: 8.0.0 - Pandas version: 1.4.3
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Add info in docs about behavior of download_config.num_proc
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**Is your feature request related to a problem? Please describe.** I went to override `download_config.num_proc` and was confused about what was happening under the hood. It would be nice to have the behavior documented a bit better so folks know what's happening when they use it. **Describe the solution you'd like** - Add note about how the default number of workers is 16. Related code: https://github.com/huggingface/datasets/blob/7bcac0a6a0fc367cc068f184fa132b8de8dfa11d/src/datasets/download/download_manager.py#L299-L302 - Add note that if the number of workers is higher than the number of files to download, it won't use multiprocessing. **Describe alternatives you've considered** maybe it would also be nice to set `num_proc` = `num_files` when `num_proc` > `num_files`. **Additional context** ...
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Dataset Viewer issue for vadis/sv-ident
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[ "Thanks for reporting, @e-tornike \r\n\r\nSome context:\r\n- #4527 \r\n\r\nThe dataset loads locally in streaming mode:\r\n```python\r\nIn [2]: from datasets import load_dataset; ds = load_dataset(\"vadis/sv-ident\", split=\"validation\", streaming=True); item = next(iter(ds)); item\r\nUsing custom data configuration default\r\nOut[2]: \r\n{'sentence': 'Im Falle von Umweltbelastungen kann selten eindeutig entschieden werden, ob Unbedenklichkeitswerte bereits erreicht oder überschritten sind, die die menschliche Gesundheit oder andere Wohlfahrts»güter« beeinträchtigen.',\r\n 'is_variable': 0,\r\n 'variable': [],\r\n 'research_data': [],\r\n 'doc_id': '51971',\r\n 'uuid': 'ee3d7f88-1a3e-4a59-997f-e986b544a604',\r\n 'lang': 'de'}\r\n```", "~~I have forced the refresh of the split in the preview without success.~~\r\n\r\nI have forced the refresh of the split in the preview, and now it works.", "Preview seems to work now. \r\n\r\nhttps://huggingface.co/datasets/vadis/sv-ident/viewer/default/validation", "OK, thank you @e-tornike.\r\n\r\nApparently, after forcing the refresh, we just had to wait a little until it is effectively refreshed. ", "I'm closing this issue as it was solved after forcing the refresh of the split in the preview.", "Thanks a lot! :)" ]
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### Link https://huggingface.co/datasets/vadis/sv-ident/viewer/default/validation ### Description Error message when loading validation split in the viewer: ``` Status code: 400 Exception: Status400Error Message: The split cache is empty. ``` ### Owner _No response_
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Can't load the Hausa audio dataset
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[ "Could you provide the error details. It is difficult to debug otherwise. Also try other config. `ha` is not a valid." ]
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common_voice_train = load_dataset("common_voice", "ha", split="train+validation")
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[data_files] Only match separated split names
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[ "_The documentation is not available anymore as the PR was closed or merged._", "I ran a script to find affected datasets (just did it on non-private non-gated). Adding \"testing\" and \"evaluation\" fixes all of of them except one:\r\n- projecte-aina/cat_manynames:\thuman_annotated_testset.tsv\r\n\r\nLet me open a PR on their repository to fix it\r\nEDIT: pr [here](https://huggingface.co/datasets/projecte-aina/cat_manynames/discussions/2)", "Feel free to merge @albertvillanova if it's all good to you :)", "Thanks for the feedback @albertvillanova I took your comments into account :)\r\n- added numbers as supported delimiters\r\n- used list comprehension to create the patterns list\r\n- updated the docs and the tests according to your comments\r\n\r\nLet me know what you think !", "I ended up removing the patching and the context manager :) merging" ]
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MEMBER
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As reported in https://github.com/huggingface/datasets/issues/4477, the current pattern matching to infer which file goes into which split is too permissive. For example a file "contest.py" would be considered part of a test split (it contains "test") and "seqeval.py" as well (it contains "eval"). In this PR I made the pattern matching more robust by only matching split names **between separators**. The supported separators are dots, dashes, spaces and underscores. I updated the docs accordingly. One detail about the tests: I had to update one test because it was using `PurePath.match` as a reference for globbing, but it doesn't support the `[..]` glob pattern. Therefore I added a `mock_fs` context manager that can be used to easily define a dummy filesystem with certain files in it and run pattern matching tests. Its code comes mostly from test_streaming_download_manager.py Close https://github.com/huggingface/datasets/issues/4477
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'sort' method sorts one column only
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[ "Hi ! `ds.sort()` does sort the full dataset, not just one column:\r\n```python\r\nfrom datasets import *\r\n\r\nds = Dataset.from_dict({\"foo\": [3, 2, 1], \"bar\": [\"c\", \"b\", \"a\"]})\r\nprint(d.sort(\"foo\").to_pandas()\r\n# foo bar\r\n# 0 1 a\r\n# 1 2 b\r\n# 2 3 c\r\n```\r\n\r\nWhat made you think it was not the case ? Did you experience a situation where it was only sorting one column ?", "Hi! thank you for your quick reply!\r\nI wanted to sort the `cnn_dailymail` dataset by the length of the labels (num of characters). I added a new column to the dataset (`ds.add_column`) with the lengths and then sorted by this new column. Only the new length column was sorted, the reset left in their original order. ", "That's unexpected, can you share the code you used to get this ?" ]
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The 'sort' method changes the order of one column only (the one defined by the argument 'column'), thus creating a mismatch between a sample fields. I would expect it to change the order of the samples as a whole, based on the 'column' order.
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Update WinoBias README
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
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I'm adding some information about Winobias that I got from the paper :smile: I think this makes it a bit clearer!
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fix(dataset_wrappers): Fixes access to fsspec.asyn in torch_iterable_dataset.py.
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Fix #4612. Apparently, newest `fsspec` versions do not allow access to attribute-based modules if they are not imported, such as `fsspec.async`. Thus, @mariosasko suggested to add the missing part to the module import to allow for its access.
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Rename repo default branch to main
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Rename repository default branch to `main` (instead of current `master`). Once renamed, users will have to manually update their local repos: - [ ] Upstream: ``` git branch -m master main git fetch upstream main git branch -u upstream/main main git remote set-head upstream -a ``` - [ ] Origin: Rename fork default branch as well at: https://github.com/USERNAME/lam/settings/branches Then: ``` git fetch origin main git remote set-head origin -a ``` CC: @sgugger
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Fix time type `_arrow_to_datasets_dtype` conversion
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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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fixed duplicate calculation of spearmanr function in metrics wrapper.
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[ "Great, can open a PR in `evaluate` as well to optimize this.\r\n\r\nRelatedly, I wanted to add a new metric, Kendall Tau (https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kendalltau.html). If I were to open a PR with the wrapper, description, citation, docstrings, readme, etc. would it make more sense to do that in the `datasets` or `evaluate` repo (or both)?\r\n\r\nThanks!", "PR opened in`evaluate` library with same minor adjustment: https://github.com/huggingface/evaluate/pull/176 ", "> If I were to open a PR with the wrapper, description, citation, docstrings, readme, etc. would it make more sense to do that in the datasets or evaluate repo (or both)?\r\n\r\nI think you could just add it to `evaluate`, we're not adding new metrics in this repo anymore" ]
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During _compute, the scipy.stats spearmanr function was called twice, redundantly, once for calculating the score and once for calculating the p-value, under the conditional branch where return_pvalue=True. I adjusted the _compute function to execute the spearmanr function once, store the results tuple in a temporary variable, and then pass the indexed contents to the expected keys of the returned dictionary.
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Add non-commercial licensing info for datasets for which we removed tags
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[ "yep plus `license_details` also makes sense for this IMO" ]
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MEMBER
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We removed several YAML tags saying that certain datasets can't be used for commercial purposes: https://github.com/huggingface/datasets/pull/4613#discussion_r911919753 Reason for this is that we only allow tags that are part of our [supported list of licenses](https://github.com/huggingface/datasets/blob/84fc3ad73c85de4eda5d152dfede7671491449cb/src/datasets/utils/resources/standard_licenses.tsv) We should update the Licensing Information section of the concerned dataset cards, now that the non-commercial tag doesn't exist anymore for certain datasets
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Unpack `dl_manager.iter_files` to allow parallization
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Cool thanks ! Yup it sounds like the right solution.\r\n\r\nIt looks like `_generate_tables` needs to be updated as well to fix the CI" ]
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CONTRIBUTOR
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Iterate over data files outside `dl_manager.iter_files` to allow parallelization in streaming mode. (The issue reported [here](https://discuss.huggingface.co/t/dataset-only-have-n-shard-1-when-has-multiple-shards-in-repo/19887)) PS: Another option would be to override `FilesIterable.__getitem__` to make it indexable and check for that type in `_shard_kwargs` and `n_shards,` but IMO this solution adds too much unnecessary complexity.
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Remove all paperswithcode_id: null
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[ "_The documentation is not available anymore as the PR was closed or merged._", "> We've been using `null` to specify that we checked on pwc but the dataset doesn't exist there.\r\n\r\n@lhoestq maybe it's better to accept it on the Hub side then? Let me know if you want us to do it Hub-side", "Yup it's maybe better to support it on the Hub side then indeed, thanks ! Closing this one" ]
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MEMBER
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On the Hub there is a validation error on the `paperswithcode_id` tag when the value is `null`: <img width="686" alt="image" src="https://user-images.githubusercontent.com/42851186/177151825-93d341c5-25bd-41ab-96c2-c0b516d51c68.png"> We've been using `null` to specify that we checked on pwc but the dataset doesn't exist there. To have the validation working again we can simply remove all the `paperswithcode_id: null`. cc @julien-c
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Loading MNIST as Pytorch Dataset
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[ "Hi ! We haven't implemented the conversion from images data to PyTorch tensors yet I think\r\n\r\ncc @mariosasko ", "So I understand:\r\n\r\nset_format() does not properly do the conversion to pytorch tensors from PIL images.\r\n\r\nSo that someone who stumbles on this can use the package:\r\n\r\n```python\r\ndataset = load_dataset(\"mnist\", split=\"train\")\r\ndef transform_func(examples):\r\n examples[\"image\"] = [np.array(img) for img in examples[\"image\"]]\r\n return examples\r\ndataset = dataset.with_transform(transform_func)\r\ndataset[0]\r\n``` ", "This then appears to work with pytorch dataloaders as:\r\n```\r\ndataloader=torch.utils.data.DataLoader(dataset,batch_size=1)\r\n```\r\n\r\nand tensorflow as:\r\n```\r\ndataset=dataset.to_tf_dataset(batch_size=1)\r\n```", "Hi! `set_transform`/`with_transform` is indeed the correct solution for the conversion. Improving this part of the API is one of the things I'm working on currently, so stay tuned!" ]
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## Describe the bug Conversion of MNIST dataset to pytorch fails with bug ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("mnist", split="train") dataset.set_format('torch') dataset[0] print() ``` ## Expected results Expect to see torch tensors image and label ## Actual results Traceback (most recent call last): File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\pydevd.py", line 1491, in _exec pydev_imports.execfile(file, globals, locals) # execute the script File "C:\Program Files\JetBrains\PyCharm 2020.3.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "C:/Users/chapm/PycharmProjects/multiviewdata/multiviewdata/huggingface/mnist.py", line 13, in <module> dataset[0] File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2154, in __getitem__ return self._getitem( File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\arrow_dataset.py", line 2139, in _getitem formatted_output = format_table( File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 532, in format_table return formatter(pa_table, query_type=query_type) File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\formatting.py", line 281, in __call__ return self.format_row(pa_table) File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 58, in format_row return self.recursive_tensorize(row) File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 54, in recursive_tensorize return map_nested(self._recursive_tensorize, data_struct, map_list=False) File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 356, in map_nested mapped = [ File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 357, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in _single_map_nested return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 309, in <dictcomp> return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\utils\py_utils.py", line 293, in _single_map_nested return function(data_struct) File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 51, in _recursive_tensorize return self._tensorize(data_struct) File "C:\Users\chapm\PycharmProjects\multiviewdata\venv\lib\site-packages\datasets\formatting\torch_formatter.py", line 38, in _tensorize if np.issubdtype(value.dtype, np.integer): AttributeError: 'bytes' object has no attribute 'dtype' python-BaseException ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Windows-10-10.0.22579-SP0 - Python version: 3.9.2 - PyArrow version: 8.0.0 - Pandas version: 1.4.1
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4,622
Fix ImageFolder with parameters drop_metadata=True and drop_labels=False (when metadata.jsonl is present)
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[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq @mariosasko pls take a look at https://github.com/huggingface/datasets/pull/4622/commits/769e4c046a5bd5e3a4dbd09cfad1f4cf60677869. I modified `_generate_examples()` according to the same logic too: removed checking if `metadata_files` are not empty for the case when `self.config.drop_metadata=True` because I think we should be aligned with the config and preserve labels if `self.config.drop_labels=False` (the default value) and `self.config.drop_metadata=True` but `metadata_files` are passed. This is an extremely unlikely use case (when `self.config.drop_metadata=True`, but `metadata_files` are passed to `_generate_examples()`) since users usually do not use `_generate_examples()` alone but I believe it would be consistent to have the same behavior as in `_splits_generators()`. This change requires change in tests too if we suppose that we want to preserve labels (default value of `self.config.drop_labels` is False) when `self.config.drop_metadata=True`, even if `metadata_files` are for some reason provided (as it is done in tests). \r\n\r\nwdyt about this change?\r\n", "@lhoestq it wouldn't raise an error if we check `example.keys() == {\"image\", \"label\"}` as test checks only `_generate_examples`, not `encode_example`. and in the implementation of this PR `_generate_examples` would return both `image` and `label` key in the case when `drop_metadata=True` and `drop_labels=False` (default) as it seems that we agreed on that :)", "and on the other hand it would raise an error if `label` column is missing in _generate_examples when `drop_metadata=True` and `drop_labels=False`\r\n\r\nby \"it\" i mean tests :D (`test_generate_examples_with_metadata_that_misses_one_image`, `test_generate_examples_with_metadata_in_wrong_location` and `test_generate_examples_drop_metadata`)", "Perhaps we could make `self.config.drop_metadata = None` and `self.config.drop_labels = None` the defaults to see explicitly what the user wants. This would then turn into `self.config.drop_metadata = False` and `self.config.drop_labels = True` if metadata files are present and `self.config.drop_metadata = True` and `self.config.drop_labels = False` if not. And if the user wants to have the `label` column alongside metadata columns, it can do so by passing `drop_labels = False` explicitely (in that scenario we have to check that the `label` column is not already present in metadata files). And maybe we can also improve the logging messages.\r\n\r\nI find it problematic that the current implementation drops labels in some scenarios even if `self.config.drop_labels = False`, and the user doesn't have control over this behavior.\r\n\r\nLet me know what you think." ]
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CONTRIBUTOR
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Will fix #4621 ImageFolder raises `KeyError: 'label'` with params `drop_metadata=True` and `drop_labels=False` (if there is at least one metadata.jsonl file a data directory). This happens because metadata files are collected inside `analyze()` function regardless of `drop_metadata` value. And then the following condition doesn't pass: https://github.com/huggingface/datasets/blob/master/src/datasets/packaged_modules/imagefolder/imagefolder.py#L167 So I suggest to double check it inside `analyze()` not to collect metadata files if they are not needed. (and labels too, to be consistent) --- Also, I added a test to check if labels are inferred correctly from directories names in general (because we didn't have it) :)
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4,621
ImageFolder raises an error with parameters drop_metadata=True and drop_labels=False when metadata.jsonl is present
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CONTRIBUTOR
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## Describe the bug If you pass `drop_metadata=True` and `drop_labels=False` when a `data_dir` contains at least one `matadata.jsonl` file, you will get a KeyError. This is probably not a very useful case but we shouldn't get an error anyway. Asking users to move metadata files manually outside `data_dir` or pass features manually (when there is a tool that can infer them automatically) don't look like a good idea to me either. ## Steps to reproduce the bug ### Clone an example dataset from the Hub ```bash git clone https://huggingface.co/datasets/nateraw/test-imagefolder-metadata ``` ### Try to load it ```python from datasets import load_dataset ds = load_dataset("test-imagefolder-metadata", drop_metadata=True, drop_labels=False) ``` or even just ```python ds = load_dataset("test-imagefolder-metadata", drop_metadata=True) ``` as `drop_labels=False` is a default value. ## Expected results A DatasetDict object with two features: `"image"` and `"label"`. ## Actual results ``` Traceback (most recent call last): File "/home/polina/workspace/datasets/debug.py", line 18, in <module> ds = load_dataset( File "/home/polina/workspace/datasets/src/datasets/load.py", line 1732, in load_dataset builder_instance.download_and_prepare( File "/home/polina/workspace/datasets/src/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/home/polina/workspace/datasets/src/datasets/builder.py", line 1227, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/home/polina/workspace/datasets/src/datasets/builder.py", line 793, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/polina/workspace/datasets/src/datasets/builder.py", line 1218, in _prepare_split example = self.info.features.encode_example(record) File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1596, in encode_example return encode_nested_example(self, example) File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1165, in encode_nested_example { File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1165, in <dictcomp> { File "/home/polina/workspace/datasets/src/datasets/utils/py_utils.py", line 249, in zip_dict yield key, tuple(d[key] for d in dicts) File "/home/polina/workspace/datasets/src/datasets/utils/py_utils.py", line 249, in <genexpr> yield key, tuple(d[key] for d in dicts) KeyError: 'label' ``` ## Environment info `datasets` master branch - `datasets` version: 2.3.3.dev0 - Platform: Linux-5.14.0-1042-oem-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 6.0.1 - Pandas version: 1.4.1
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