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https://api.github.com/repos/huggingface/datasets/issues/3142
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1,033,566,034
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3,142
Provide a way to write a streamed dataset to the disk
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2021-10-22T13:09:53Z
2021-10-29T11:14:39Z
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**Is your feature request related to a problem? Please describe.** The streaming mode allows to get the 100 first rows of a dataset very quickly. But it does not cache the answer, so a posterior call to get the same 100 rows will send a request to the server again and again. **Describe the solution you'd like** Provide a way to write the streamed rows of a dataset on the disk, and to load from it later. **Describe alternatives you've considered** Provide a third mode: `lazy`, which would use the local cache for the data that have already been fetched previously, and use streaming to get the rest of the requested data.
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[ "Yes, I agree this feature is much needed. We could do something similar to what TF does (https://www.tensorflow.org/api_docs/python/tf/data/Dataset#cache). \r\n\r\nIdeally, if the entire streamed dataset is consumed/cached, the generated cache should be reusable for the Arrow dataset." ]
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5,558
Remove instructions for `ffmpeg` system package installation on Colab
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2023-02-21T15:13:36Z
2023-03-01T13:46:04Z
2023-02-23T13:50:27Z
null
Colab now has Ubuntu 20.04 which already has `ffmpeg` of required (>4) version.
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https://api.github.com/repos/huggingface/datasets/issues/218
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218
Add Natual Questions and C4 scripts
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2020-05-29T10:40:30Z
2020-05-29T12:31:01Z
2020-05-29T12:31:00Z
null
Scripts are ready ! However they are not processed nor directly available from gcp yet.
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1,623
Add CLIMATE-FEVER dataset
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2020-12-22T13:34:05Z
2020-12-22T17:53:53Z
2020-12-22T17:53:53Z
null
As suggested by @SBrandeis , fresh PR that adds CLIMATE-FEVER. Replaces PR #1579. --- A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs. The dataset features challenging claims that relate multiple facets and disputed cases of claims where both supporting and refuting evidence are present. More information can be found at: * Homepage: http://climatefever.ai * Paper: https://arxiv.org/abs/2012.00614
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[ "Thank you @lhoestq for your comments! 😄 I added your suggested changes, ran the tests and regenerated `dataset_infos.json` and `dummy_data`." ]
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6,042
Fix unused DatasetInfosDict code in push_to_hub
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2023-07-17T11:03:09Z
2023-07-18T16:17:52Z
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https://api.github.com/repos/huggingface/datasets/issues/2763
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English wikipedia datasets is not clean
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2021-08-05T14:37:24Z
2023-07-25T17:43:04Z
2023-07-25T17:43:04Z
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## Describe the bug Wikipedia english dumps contain many wikipedia paragraphs like "References", "Category:" and "See Also" that should not be used for training. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from datasets import load_dataset w = load_dataset('wikipedia', '20200501.en') print(w['train'][0]['text']) ``` > 'Yangliuqing () is a market town in Xiqing District, in the western suburbs of Tianjin, People\'s Republic of China. Despite its relatively small size, it has been named since 2006 in the "famous historical and cultural market towns in China".\n\nIt is best known in China for creating nianhua or Yangliuqing nianhua. For more than 400 years, Yangliuqing has in effect specialised in the creation of these woodcuts for the New Year. wood block prints using vivid colourschemes to portray traditional scenes of children\'s games often interwoven with auspiciouse objects.\n\n, it had 27 residential communities () and 25 villages under its administration.\n\nShi Family Grand Courtyard\n\nShi Family Grand Courtyard (Tiānjīn Shí Jiā Dà Yuàn, 天津石家大院) is situated in Yangliuqing Town of Xiqing District, which is the former residence of wealthy merchant Shi Yuanshi - the 4th son of Shi Wancheng, one of the eight great masters in Tianjin. First built in 1875, it covers over 6,000 square meters, including large and small yards and over 200 folk houses, a theater and over 275 rooms that served as apartments and places of business and worship for this powerful family. Shifu Garden, which finished its expansion in October 2003, covers 1,200 square meters, incorporates the elegance of imperial garden and delicacy of south garden. Now the courtyard of Shi family covers about 10,000 square meters, which is called the first mansion in North China. Now it serves as the folk custom museum in Yangliuqing, which has a large collection of folk custom museum in Yanliuqing, which has a large collection of folk art pieces like Yanliuqing New Year pictures, brick sculpture.\n\nShi\'s ancestor came from Dong\'e County in Shandong Province, engaged in water transport of grain. As the wealth gradually accumulated, the Shi Family moved to Yangliuqing and bought large tracts of land and set up their residence. Shi Yuanshi came from the fourth generation of the family, who was a successful businessman and a good household manager, and the residence was thus enlarged for several times until it acquired the present scale. It is believed to be the first mansion in the west of Tianjin.\n\nThe residence is symmetric based on the axis formed by a passageway in the middle, on which there are four archways. On the east side of the courtyard, there are traditional single-story houses with rows of rooms around the four sides, which was once the living area for the Shi Family. The rooms on north side were the accountants\' office. On the west are the major constructions including the family hall for worshipping Buddha, theater and the south reception room. On both sides of the residence are side yard rooms for maids and servants.\n\nToday, the Shi mansion, located in the township of Yangliuqing to the west of central Tianjin, stands as a surprisingly well-preserved monument to China\'s pre-revolution mercantile spirit. It also serves as an on-location shoot for many of China\'s popular historical dramas. Many of the rooms feature period furniture, paintings and calligraphy, and the extensive Shifu Garden.\n\nPart of the complex has been turned into the Yangliuqing Museum, which includes displays focused on symbolic aspects of the courtyards\' construction, local folk art and customs, and traditional period furnishings and crafts.\n\n**See also \n\nList of township-level divisions of Tianjin\n\nReferences \n\n http://arts.cultural-china.com/en/65Arts4795.html\n\nCategory:Towns in Tianjin'** ## Expected results I expect no junk in the data. ## Actual results Specify the actual results or traceback. ## Environment info - `datasets` version: 1.10.2 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.5 - PyArrow version: 3.0.0
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[ "Hi ! Certain users might need these data (for training or simply to explore/index the dataset).\r\n\r\nFeel free to implement a map function that gets rid of these paragraphs and process the wikipedia dataset with it before training" ]
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2,844
Fix: wikicorpus - fix keys
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2021-08-27T15:56:06Z
2021-09-06T14:07:28Z
2021-09-06T14:07:27Z
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As mentioned in https://github.com/huggingface/datasets/issues/2552, there is a duplicate keys error in `wikicorpus`. I fixed that by taking into account the file index in the keys
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[ "The CI error is unrelated to this PR\r\n\r\n... merging !" ]
https://api.github.com/repos/huggingface/datasets/issues/4929
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4,929
Fixes a typo in loading documentation
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2022-09-05T07:18:54Z
2022-09-06T02:11:03Z
2022-09-05T13:06:38Z
null
As show in the [documentation page](https://huggingface.co/docs/datasets/loading) here the `"tr"in` should be `"train`. ![image](https://user-images.githubusercontent.com/7144772/188390445-e1f04d54-e3e3-4762-8686-63ecbe4087e5.png)
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811,260,904
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1,904
Fix to_pandas for boolean ArrayXD
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2021-02-18T16:30:46Z
2021-02-18T17:10:03Z
2021-02-18T17:10:01Z
null
As noticed in #1887 the conversion of a dataset with a boolean ArrayXD feature types fails because of the underlying ListArray conversion to numpy requires `zero_copy_only=False`. zero copy is available for all primitive types except booleans see https://arrow.apache.org/docs/python/generated/pyarrow.Array.html#pyarrow.Array.to_numpy and https://issues.apache.org/jira/browse/ARROW-2871?jql=text%20~%20%22boolean%20to_numpy%22 cc @SBrandeis
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[ "Thanks!" ]
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4,303
Fix: Add missing comma
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2022-05-10T09:21:38Z
2022-05-11T08:50:15Z
2022-05-11T08:50:14Z
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[ "The CI failure is unrelated to this PR and fixed on master, merging :)" ]
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Create XML dummy data without loading all dataset in memory
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2020-12-10T08:32:07Z
2020-12-17T09:59:43Z
2020-12-17T09:59:43Z
null
While I was adding one XML dataset, I noticed that all the dataset was loaded in memory during the dummy data generation process (using nearly all my laptop RAM). Looking at the code, I have found that the origin is the use of `ET.parse()`. This method loads **all the file content in memory**. In order to fix this, I have refactorized the code and use `ET.iterparse()` instead, which **parses the file content incrementally**. I have also implemented a test.
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2,092
How to disable making arrow tables in load_dataset ?
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2021-03-21T04:50:07Z
2022-06-01T16:49:52Z
2022-06-01T16:49:52Z
null
Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?
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[ "Hi ! We plan to add streaming features in the future.\r\n\r\nThis should allow to load a dataset instantaneously without generating the arrow table. The trade-off is that accessing examples from a streaming dataset must be done in an iterative way, and with an additional (but hopefully minor) overhead.\r\nWhat do ...
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793
[Datasets] fix discofuse links
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2020-11-03T08:03:45Z
2020-11-03T08:16:41Z
2020-11-03T08:16:40Z
null
The discofuse links were changed: https://github.com/google-research-datasets/discofuse/commit/d27641016eb5b3eb2af03c7415cfbb2cbebe8558. The old links are broken I changed the links and created the new dataset_infos.json. Pinging @thomwolf @lhoestq for notification.
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1,248
Update step-by-step guide about the dataset cards
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2020-12-07T12:12:12Z
2020-12-07T13:19:24Z
2020-12-07T13:19:23Z
null
Small update in the step-by-step guide about the dataset cards to indicate it can be created and completing while exploring the dataset.
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318
Multitask
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2020-06-27T13:27:29Z
2022-07-06T15:19:57Z
2022-07-06T15:19:57Z
null
Following our discussion in #217, I've implemented a first working version of `MultiDataset`. There's a function `build_multitask()` which takes either individual `nlp.Dataset`s or `dicts` of splits and constructs `MultiDataset`(s). I've added a notebook with example usage. I've implemented many of the `nlp.Dataset` methods (cache_files, columns, nbytes, num_columns, num_rows, column_names, schema, shape). Some of the other methods are complicated as they change the number of examples. These raise `NotImplementedError`s at the moment. This will need some tests which I haven't written yet. There's definitely room for improvements but I think the general approach is sound.
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[ "It's definitely going in the right direction ! Thanks for giving it a try\r\n\r\nI really like the API.\r\nIMO it's fine right now if we don't have all the dataset transforms (map, filter, etc.) as it can be done before building the multitask dataset, but it will be important to have them in the end.\r\nAll the fo...
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4,286
Add Lahnda language tag
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2022-05-05T14:34:20Z
2022-05-10T12:10:04Z
2022-05-10T12:02:38Z
null
This language is present in [Wikimedia's WIT](https://huggingface.co/datasets/wikimedia/wit_base) dataset.
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2,174
Pin docutils for better doc
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2021-04-06T12:40:20Z
2021-04-06T12:55:53Z
2021-04-06T12:55:53Z
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The latest release of docutils make the navbar in the documentation weird and the Markdown wrongly interpreted: ![image](https://user-images.githubusercontent.com/35901082/113711773-5be55280-96b3-11eb-9b3b-9794f17709aa.png) We had the same problem in Transformers and solved it by pinning docutils (a dep of sphinx). You can see the version after the change [here](https://32769-250213286-gh.circle-artifacts.com/0/docs/_build/html/index.html).
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600
Pickling error when loading dataset
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2020-09-10T06:28:08Z
2020-09-25T14:31:54Z
2020-09-25T14:31:54Z
null
Hi, I modified line 136 in the original [run_language_modeling.py](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) as: ``` # line 136: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size) dataset = load_dataset("text", data_files=file_path, split="train") dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True, truncation=True, max_length=args.block_size), batched=True) dataset.set_format(type='torch', columns=['input_ids']) return dataset ``` When I run this with transformers (3.1.0) and nlp (0.4.0), I get the following error: ``` Traceback (most recent call last): File "src/run_language_modeling.py", line 319, in <module> main() File "src/run_language_modeling.py", line 248, in main get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None File "src/run_language_modeling.py", line 139, in get_dataset dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True, truncation=True, max_length=args.block_size), batched=True) File "/data/nlp/src/nlp/arrow_dataset.py", line 1136, in map new_fingerprint=new_fingerprint, File "/data/nlp/src/nlp/fingerprint.py", line 158, in wrapper self._fingerprint, transform, kwargs_for_fingerprint File "/data/nlp/src/nlp/fingerprint.py", line 105, in update_fingerprint hasher.update(transform_args[key]) File "/data/nlp/src/nlp/fingerprint.py", line 57, in update self.m.update(self.hash(value).encode("utf-8")) File "/data/nlp/src/nlp/fingerprint.py", line 53, in hash return cls.hash_default(value) File "/data/nlp/src/nlp/fingerprint.py", line 46, in hash_default return cls.hash_bytes(dumps(value)) File "/data/nlp/src/nlp/utils/py_utils.py", line 362, in dumps dump(obj, file) File "/data/nlp/src/nlp/utils/py_utils.py", line 339, in dump Pickler(file, recurse=True).dump(obj) File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump StockPickler.dump(self, obj) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 409, in dump self.save(obj) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function obj.__dict__, fkwdefaults), obj=obj) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell pickler.save_reduce(_create_cell, (f,), obj=obj) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 521, in save self.save_reduce(obj=obj, *rv) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 605, in save_reduce save(cls) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type obj.__bases__, _dict), obj=obj) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 507, in save self.save_global(obj, rv) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 927, in save_global (obj, module_name, name)) _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union ```
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[ "When I change from python3.6 to python3.8, it works! ", "Does it work when you install `nlp` from source on python 3.6?", "No, still the pickling error.", "I wasn't able to reproduce on google colab (python 3.6.9 as well) with \r\n\r\npickle==4.0\r\ndill=0.3.2\r\ntransformers==3.1.0\r\ndatasets=1.0.1 (also t...
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601
check if trasnformers has PreTrainedTokenizerBase
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2020-09-10T07:54:56Z
2020-09-10T11:01:37Z
2020-09-10T11:01:36Z
null
Fix #598
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4,392
remove int documentation from logging docs
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2022-05-23T09:24:55Z
2022-05-23T15:16:55Z
2022-05-23T15:08:32Z
null
Removes the `int` documentation from the [logging section](https://huggingface.co/docs/datasets/package_reference/logging_methods#levels) of the docs.
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3,669
Common voice validated partition
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2022-02-02T20:04:43Z
2022-02-08T17:26:52Z
2022-02-08T17:23:12Z
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This patch adds access to the 'validated' partitions of CommonVoice datasets (provided by the dataset creators but not available in the HuggingFace interface yet). As 'validated' contains significantly more data than 'train' (although it contains both test and validation, so one needs to be careful there), it can be useful to train better models where no strict comparison with the previous work is intended.
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[ "Hi @patrickvonplaten - could you please advise whether this would be a welcomed change, and if so, who I consult regarding the unit-tests?", "I'd be happy with adding this change. @anton-l @lhoestq - what do you think?", "Cool ! I just fixed the tests by adding a dummy `validated.tsv` file in the dummy data ar...
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1,256
adding LiMiT dataset
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2020-12-07T14:00:41Z
2020-12-08T14:58:28Z
2020-12-08T14:42:51Z
null
Adding LiMiT: The Literal Motion in Text Dataset https://github.com/ilmgut/limit_dataset
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how to get all the options of a property in datasets
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2020-12-12T16:24:08Z
2022-05-25T16:27:29Z
2022-05-25T16:27:29Z
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Hi could you tell me how I can get all unique options of a property of dataset? for instance in case of boolq, if the user wants to know which unique labels it has, is there a way to access unique labels without getting all training data lables and then forming a set i mean? thanks
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[ "In a dataset, labels correspond to the `ClassLabel` feature that has the `names` property that returns string represenation of the integer classes (or `num_classes` to get the number of different classes).", "I think the `features` attribute of the dataset object is what you are looking for:\r\n```\r\n>>> datase...
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Update builder docstring for deprecated/added arguments
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2022-05-31T17:37:25Z
2022-06-08T11:40:18Z
2022-06-08T11:31:45Z
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This PR updates the builder docstring with deprecated/added directives for arguments name/config_name. Follow up of: - #4414 - huggingface/doc-builder#233 First merge: - #4432
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Add the DKTC dataset (Extension of #3564)
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2022-01-12T15:31:29Z
2022-10-01T06:43:05Z
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New pull request of #3564. (for DKTC)
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[ "I reflect your comment! @lhoestq ", "Wait, the format of the data just changed, so I'll take it into consideration and commit it.", "I update the code according to the dataset structure change.", "Thanks ! I think the dummy data are not valid yet - the dummy train.csv file only contains a partial example (th...
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Support streaming iwslt2017 dataset
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2022-09-20T09:27:55Z
2022-09-20T09:15:24Z
null
Support streaming iwslt2017 dataset. Once this PR is merged: - [x] Remove old ".tgz" data files from the Hub.
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Logo float left
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2022-03-07T08:38:34Z
2022-03-07T20:21:11Z
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<img width="1000" alt="Screenshot 2022-03-07 at 09 35 29" src="https://user-images.githubusercontent.com/11827707/156996422-339ba43e-932b-4849-babf-9321cb99c922.png">
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3836). All of your documentation changes will be reflected on that endpoint.", "Weird, the logo doesn't seem to be floating on my side (using Chrome) at https://huggingface.co/docs/datasets/master/en/index", "https://huggingf...
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CI is broken: ModuleNotFoundError: jax requires jaxlib to be installed
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2023-03-23T09:39:43Z
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CI test_py310 is broken: see https://github.com/huggingface/datasets/actions/runs/4498945505/jobs/7916194236?pr=5662 ``` FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_jax_in_memory - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions. FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_jax_on_disk - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions. FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions. FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_audio - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions. FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_device - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions. FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_image - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions. FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_jnp_array_kwargs - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions. FAILED tests/features/test_features.py::CastToPythonObjectsTest::test_cast_to_python_objects_jax - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions. ===== 8 failed, 2147 passed, 10 skipped, 37 warnings in 228.69s (0:03:48) ====== ```
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Fix URL in CITATION file
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2021-11-07T10:04:35Z
2021-11-07T10:04:46Z
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Currently the BibTeX citation parsed from the CITATION file has wrong URL (it shows the repo URL instead of the proceedings paper URL): ``` @inproceedings{Lhoest_Datasets_A_Community_2021, author = {Lhoest, Quentin and Villanova del Moral, Albert and von Platen, Patrick and Wolf, Thomas and Šaško, Mario and Jernite, Yacine and Thakur, Abhishek and Tunstall, Lewis and Patil, Suraj and Drame, Mariama and Chaumond, Julien and Plu, Julien and Davison, Joe and Brandeis, Simon and Sanh, Victor and Le Scao, Teven and Canwen Xu, Kevin and Patry, Nicolas and Liu, Steven and McMillan-Major, Angelina and Schmid, Philipp and Gugger, Sylvain and Raw, Nathan and Lesage, Sylvain and Lozhkov, Anton and Carrigan, Matthew and Matussière, Théo and von Werra, Leandro and Debut, Lysandre and Bekman, Stas and Delangue, Clément}, booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}, month = {11}, pages = {175--184}, publisher = {Association for Computational Linguistics}, title = {{Datasets: A Community Library for Natural Language Processing}}, url = {https://github.com/huggingface/datasets}, year = {2021} } ```
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[doc] nlp/viewer ➡️datasets/viewer
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2020-12-07T13:58:41Z
2020-12-08T17:17:54Z
2020-12-08T17:17:53Z
null
cc @srush
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Add Microsoft Building Footprints dataset
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## Adding a Dataset - **Name:** Microsoft Building Footprints - **Description:** With the goal to increase the coverage of building footprint data available as open data for OpenStreetMap and humanitarian efforts, we have released millions of building footprints as open data available to download free of charge. - **Paper:** *link to the dataset paper if available* - **Data:** https://www.microsoft.com/en-us/maps/building-footprints - **Motivation:** this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Reported by: @sashavor
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[ "Motivation: this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc. I'll see if I can figure out how to add it!" ]
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Fix docs on audio feature installation
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2022-03-25T16:55:11Z
2022-03-31T16:20:47Z
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This PR: - Removes the explicit installation of `librosa` (this is installed with `pip install datasets[audio]` - Adds the warning for Linux users to install manually the non-Python package `libsndfile` - Explains that the installation of `torchaudio` is only necessary to support loading audio datasets containing MP3 audio files Related to #4000.
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Fix iter_archive getting reset
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2022-03-03T15:58:52Z
2022-03-03T18:06:37Z
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The `DownloadManager.iter_archive` method currently returns an iterator - which is **empty** once you iter over it once. This means you can't pass the same archive iterator to several splits. To fix that, I changed the ouput of `DownloadManager.iter_archive` to be an iterable that you can iterate over several times, instead of a one-time-use iterator. The `StreamingDownloadManager.iter_archive` already returns an appropriate iterable, and the code added in this PR is inspired from the one in `streaming_download_manager.py`
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ArrowIndexError on Dataset.select
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2020-09-08T07:43:15Z
2020-09-08T07:43:15Z
null
If the indices table consists in several chunks, then `dataset.select` results in an `ArrowIndexError` error for pyarrow < 1.0.0 Example: ```python from nlp import load_dataset mnli = load_dataset("glue", "mnli", split="train") shuffled = mnli.shuffle(seed=42) mnli.select(list(range(len(mnli)))) ``` raises: ```python --------------------------------------------------------------------------- ArrowIndexError Traceback (most recent call last) <ipython-input-64-006a5d38d418> in <module> ----> 1 mnli.shuffle(seed=42).select(list(range(len(mnli)))) ~/Desktop/hf/nlp/src/nlp/fingerprint.py in wrapper(*args, **kwargs) 161 # Call actual function 162 --> 163 out = func(self, *args, **kwargs) 164 165 # Update fingerprint of in-place transforms + update in-place history of transforms ~/Desktop/hf/nlp/src/nlp/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint) 1653 if self._indices is not None: 1654 if PYARROW_V0: -> 1655 indices_array = self._indices.column(0).chunk(0).take(indices_array) 1656 else: 1657 indices_array = self._indices.column(0).take(indices_array) ~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.Array.take() ~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowIndexError: take index out of bounds ``` This is because the `take` method is only done on the first chunk which only contains 1000 elements by default (mnli has ~400 000 elements). Shall we change that to use ```python pa.concat_tables(self._indices._indices.slice(i, 1) for i in indices_array) ``` instead of `take` ? @thomwolf
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I_kwDODunzps5sFFMI
6,054
Multi-processed `Dataset.map` slows down a lot when `import torch`
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2023-07-20T06:36:14Z
2023-07-21T15:19:37Z
2023-07-21T15:19:37Z
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### Describe the bug When using `Dataset.map` with `num_proc > 1`, the speed slows down much if I add `import torch` to the start of the script even though I don't use it. I'm not sure if it's `torch` only or if any other package that is "large" will also cause the same result. BTW, `import lightning` also slows it down. Below are the progress bars of `Dataset.map`, the only difference between them is with or without `import torch`, but the speed varies by 6-7 times. - without `import torch` ![image](https://github.com/huggingface/datasets/assets/47121592/0233055a-ced4-424a-9f0f-32a2afd802c2) - with `import torch` ![image](https://github.com/huggingface/datasets/assets/47121592/463eafb7-b81e-4eb9-91ca-fd7fe20f3d59) ### Steps to reproduce the bug Below is the code I used, but I don't think the dataset and the mapping function have much to do with the phenomenon. ```python3 from datasets import load_from_disk, disable_caching from transformers import AutoTokenizer # import torch # import lightning def rearrange_datapoints( batch, tokenizer, sequence_length, ): datapoints = [] input_ids = [] for x in batch['input_ids']: input_ids += x while len(input_ids) >= sequence_length: datapoint = input_ids[:sequence_length] datapoints.append(datapoint) input_ids[:sequence_length] = [] if input_ids: paddings = [-1] * (sequence_length - len(input_ids)) datapoint = paddings + input_ids if tokenizer.padding_side == 'left' else input_ids + paddings datapoints.append(datapoint) batch['input_ids'] = datapoints return batch if __name__ == '__main__': disable_caching() tokenizer = AutoTokenizer.from_pretrained('...', use_fast=False) dataset = load_from_disk('...') dataset = dataset.map( rearrange_datapoints, fn_kwargs=dict( tokenizer=tokenizer, sequence_length=2048, ), batched=True, num_proc=8, ) ``` ### Expected behavior The multi-processed `Dataset.map` function speed between with and without `import torch` should be the same. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1127.el7.x86_64-x86_64-with-glibc2.31 - Python version: 3.10.11 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1
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[ "A duplicate of https://github.com/huggingface/datasets/issues/5929" ]
https://api.github.com/repos/huggingface/datasets/issues/5224
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5,224
Seems to freeze when loading audio dataset with wav files from local folder
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2022-11-10T10:29:31Z
2023-04-25T09:54:05Z
2022-11-22T11:24:19Z
null
### Describe the bug I'm following the instructions in [https://huggingface.co/docs/datasets/audio_load#audiofolder-with-metadata](url) to be able to load a dataset from a local folder. I have everything into a folder, into a train folder and then the audios and csv. When I try to load the dataset and run from terminal, seems to work but then freezes with no apparent reason. The metadata.csv file contains a few columns but the important ones, `file_name` with the filename and `transcription` with the transcription are okay. The audios are `.wav` files, I don't know if that might be the problem (I will proceed to try to change them all to `.mp3` and try again). ### Steps to reproduce the bug The code I'm using: ```python from datasets import load_dataset dataset = load_dataset("audiofolder", data_dir="../archive/Dataset") dataset[0]["audio"] ``` The output I obtain: ``` Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 311135.43it/s] Using custom data configuration default-38d4546ffd010f3e Downloading and preparing dataset audiofolder/default to /Users/mine/.cache/huggingface/datasets/audiofolder/default-38d4546ffd010f3e/0.0.0/6cbdd16f8688354c63b4e2a36e1585d05de285023ee6443ffd71c4182055c0fc... Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 166467.72it/s] Using custom data configuration default-38d4546ffd010f3e Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 187772.74it/s] Using custom data configuration default-38d4546ffd010f3e Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 59623.71it/s] Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 138090.55it/s] Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 106065.64it/s] Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 56036.38it/s] Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 74004.24it/s] Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 162343.45it/s] Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 101881.23it/s] Using custom data configuration default-38d4546ffd010f3e Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 60145.67it/s] Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 80890.02it/s] Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 54036.67it/s] Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 95851.09it/s] Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 155897.00it/s] Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 137656.96it/s] Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 131230.81it/s] Using custom data configuration default-38d4546ffd010f3e Using custom data configuration default-38d4546ffd010f3e Using custom data configuration default-38d4546ffd010f3e Using custom data configuration default-38d4546ffd010f3e Using custom data configuration default-38d4546ffd010f3e Using custom data configuration default-38d4546ffd010f3e Using custom data configuration default-38d4546ffd010f3e Using custom data configuration default-38d4546ffd010f3e Using custom data configuration default-38d4546ffd010f3e Using custom data configuration default-38d4546ffd010f3e Using custom data configuration default-38d4546ffd010f3e Using custom data configuration default-38d4546ffd010f3e Using custom data configuration default-38d4546ffd010f3e ``` And then here it just freezes and nothing more happens. ### Expected behavior Load the dataset. ### Environment info Datasets version: datasets 2.6.1 pypi_0 pypi
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[ "I just tried to do the same but changing the `.wav` files to `.mp3` files and that doesn't fix it.", "I don't know if anyone will ever read this but I've tried to upload the same dataset with google colab and the output seems more clarifying. I didn't specify the train/test split so the dataset wasn't fully uplo...
https://api.github.com/repos/huggingface/datasets/issues/4586
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PR_kwDODunzps46e9xB
4,586
Host pn_summary data on the Hub instead of Google Drive
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2022-06-28T10:05:05Z
2022-06-28T14:52:56Z
2022-06-28T14:42:03Z
null
Fix #4581.
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
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1,658,613,092
I_kwDODunzps5i3G1k
5,716
Handle empty audio
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1
2023-04-07T09:51:40Z
2023-04-13T17:33:36Z
null
null
Some audio paths exist, but they are empty, and an error will be reported when reading the audio path.How to use the filter function to avoid the empty audio path? when a audio is empty, when do resample , it will break: `array, sampling_rate = sf.read(f) array = librosa.resample(array, orig_sr=sampling_rate, target_sr=self.sampling_rate)`
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[ "Hi! Can you share one of the problematic audio files with us?\r\n\r\nI tried to reproduce the error with the following code: \r\n```python\r\nimport soundfile as sf\r\nimport numpy as np\r\nfrom datasets import Audio\r\n\r\nsf.write(\"empty.wav\", np.array([]), 16000)\r\nAudio(sampling_rate=24000).decode_example(...
https://api.github.com/repos/huggingface/datasets/issues/5903
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5,903
Relax `ci.yml` trigger for `pull_request` based on modified paths
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2023-05-26T10:46:52Z
2023-05-26T10:51:37Z
null
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## What's in this PR? As of a previous PR at #5902, I've seen that the CI was automatically trigger on any file, in that case when modifying a Jupyter Notebook (.ipynb), which IMO could be skipped, as the modification on the Jupyter Notebook has no effect/impact on the `ci.yml` outcome. So this PR controls the paths that trigger the `ci.yml` to avoid wasting resources when not needed. ## What's pending in this PR? I would like to confirm whether this should affect both `push` and `pull_request`, since just modifications in those files won't change the `ci.yml` outcome, so maybe it's worth skipping it too in the `push` trigger.
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[ "Also this could be extended to the rest of the GitHub Action `yml` files, so let me know whether you want me to have a look into it! 🤗", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5903). All of your documentation changes will be reflected on that endpoint." ]
https://api.github.com/repos/huggingface/datasets/issues/3666
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PR_kwDODunzps4x-ULz
3,666
process .opus files (for Multilingual Spoken Words)
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2022-02-02T15:21:48Z
2022-02-22T10:04:03Z
2022-02-22T10:03:53Z
null
Opus files requires `libsndfile>=1.0.30`. Add check for this version and tests. **outdated:** Add [Multillingual Spoken Words dataset](https://mlcommons.org/en/multilingual-spoken-words/) You can specify multiple languages for downloading 😌: ```python ds = load_dataset("datasets/ml_spoken_words", languages=["ar", "tt"]) ``` 1. I didn't take into account that each time you pass a set of languages the data for a specific language is downloaded even if it was downloaded before (since these are custom configs like `ar+tt` and `ar+tt+br`. Maybe that wasn't a good idea? 2. The script will have to be slightly changed after merge of https://github.com/huggingface/datasets/pull/3664 2. Just can't figure out what wrong with dummy files... 😞 Maybe we should get rid of them at some point 😁
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[ "@lhoestq I still have problems with processing `.opus` files with `soundfile` so I actually cannot fully check that it works but it should... Maybe this should be investigated in case of someone else would also have problems with that.\r\n\r\nAlso, as the data is in a private repo on the hub (before we come to a ...
https://api.github.com/repos/huggingface/datasets/issues/5486
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1,564,059,749
I_kwDODunzps5dOahl
5,486
Adding `sep` to TextConfig
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2023-01-31T10:39:53Z
2023-01-31T14:50:18Z
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I have a local a `.txt` file that follows the `CONLL2003` format which I need to load using `load_script`. However, by using `sample_by='line'`, one can only split the dataset into lines without splitting each line into columns. Would it be reasonable to add a `sep` argument in combination with `sample_by='paragraph'` to parse a paragraph into an array for each column ? If so, I am happy to contribute! ## Environment * `python 3.8.10` * `datasets 2.9.0` ## Snippet of `train.txt` ```txt Distribution NN O O and NN O O dynamics NN O O of NN O O electron NN O B-RP complexes NN O I-RP in NN O O cyanobacterial NN O B-R membranes NN O I-R The NN O O occurrence NN O O of NN O O prostaglandin NN O B-R F2α NN O I-R in NN O O Pharbitis NN O B-R seedlings NN O I-R grown NN O O under NN O O short NN O B-P days NN O I-P or NN O I-P days NN O I-P ``` ## Current Behaviour ```python # defining 4 features ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags'] here would fail with `ValueError: Length of names (4) does not match length of arrays (1)` dataset = datasets.load_dataset(path='text', features=features, data_files={'train': 'train.txt'}, sample_by='line') dataset['train']['tokens'][0] >>> 'Distribution\tNN\tO\tO' ``` ## Expected Behaviour / Suggestion ```python # suppose we defined 4 features ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags'] dataset = datasets.load_dataset(path='text', features=features, data_files={'train': 'train.txt'}, sample_by='paragraph', sep='\t') dataset['train']['tokens'][0] >>> ['Distribution', 'and', 'dynamics', ... ] dataset['train']['ner_tags'][0] >>> ['O', 'O', 'O', ... ] ```
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[ "Hi @omar-araboghli, thanks for your proposal.\r\n\r\nHave you tried to use \"csv\" loader instead of \"text\"? That already has a `sep` argument.", "Hi @albertvillanova, thanks for the quick response!\r\n\r\nIndeed, I have been trying to use `csv` instead of `text`. However I am still not able to define range of...
https://api.github.com/repos/huggingface/datasets/issues/949
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754,317,777
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949
Add GermaNER Dataset
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1
2020-12-01T11:33:31Z
2020-12-03T14:06:41Z
2020-12-03T14:06:40Z
null
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[ "@lhoestq added. " ]
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3,991
Add Lung Image Database Consortium image collection (LIDC-IDRI) dataset
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2022-03-22T22:16:05Z
2022-03-23T12:57:16Z
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## Adding a Dataset - **Name:** *Lung Image Database Consortium image collection (LIDC-IDRI)* - **Description:** *Consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions. It is a web-accessible international resource for development, training, and evaluation of computer-assisted diagnostic (CAD) methods for lung cancer detection and diagnosis. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.* - **Data:** *[link to the Github repository or current dataset location](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI)* - **Motivation:** *Key dataset in the healthcare community* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). FYI @osanseviero @abidlabs
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1,639
bug with sst2 in glue
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2020-12-26T16:57:23Z
2022-10-05T12:40:16Z
2022-10-05T12:40:16Z
null
Hi I am getting very low accuracy on SST2 I investigate this and observe that for this dataset sentences are tokenized, while this is correct for the other datasets in GLUE, please see below. Is there any alternatives I could get untokenized sentences? I am unfortunately under time pressure to report some results on this dataset. thank you for your help. @lhoestq ``` >>> a = datasets.load_dataset('glue', 'sst2', split="validation", script_version="master") Reusing dataset glue (/julia/datasets/glue/sst2/1.0.0/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4) >>> a[:10] {'idx': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'label': [1, 0, 1, 1, 0, 1, 0, 0, 1, 0], 'sentence': ["it 's a charming and often affecting journey . ", 'unflinchingly bleak and desperate ', 'allows us to hope that nolan is poised to embark a major career as a commercial yet inventive filmmaker . ', "the acting , costumes , music , cinematography and sound are all astounding given the production 's austere locales . ", "it 's slow -- very , very slow . ", 'although laced with humor and a few fanciful touches , the film is a refreshingly serious look at young women . ', 'a sometimes tedious film . ', "or doing last year 's taxes with your ex-wife . ", "you do n't have to know about music to appreciate the film 's easygoing blend of comedy and romance . ", "in exactly 89 minutes , most of which passed as slowly as if i 'd been sitting naked on an igloo , formula 51 sank from quirky to jerky to utter turkey . "]} ```
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[ "Maybe you can use nltk's treebank detokenizer ?\r\n```python\r\nfrom nltk.tokenize.treebank import TreebankWordDetokenizer\r\n\r\nTreebankWordDetokenizer().detokenize(\"it 's a charming and often affecting journey . \".split())\r\n# \"it's a charming and often affecting journey.\"\r\n```", "I am looking for alte...
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661
Replace pa.OSFile by open
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2020-09-22T15:05:59Z
2021-05-05T18:24:36Z
2020-09-22T15:15:25Z
null
It should fix #643
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[Feature request] Add cos-e v1.0
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2020-05-18T22:05:26Z
2020-06-16T23:15:25Z
2020-06-16T18:52:06Z
null
I noticed the second release of cos-e (v1.11) is included in this repo. I wanted to request inclusion of v1.0, since this is the version on which results are reported on in [the paper](https://www.aclweb.org/anthology/P19-1487/), and v1.11 has noted [annotation](https://github.com/salesforce/cos-e/issues/2) [issues](https://arxiv.org/pdf/2004.14546.pdf).
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[ "Sounds good, @mariamabarham do you want to give a look?\r\nI think we should have two configurations so we can allow either version of the dataset to be loaded with the `1.0` version being the default maybe.\r\n\r\nCc some authors of the great cos-e: @nazneenrajani @bmccann", "cos_e v1.0 is related to CQA v1.0 b...
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1,208,001,118
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4,179
Dataset librispeech_asr fails to load
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2022-04-19T08:45:48Z
2022-07-27T16:10:00Z
2022-07-27T16:10:00Z
null
## Describe the bug The dataset librispeech_asr (standard Librispeech) fails to load. ## Steps to reproduce the bug ```python datasets.load_dataset("librispeech_asr") ``` ## Expected results It should download and prepare the whole dataset (all subsets). In [the doc](https://huggingface.co/datasets/librispeech_asr), it says it has two configurations (clean and other). However, the dataset doc says that not specifying `split` should just load the whole dataset, which is what I want. Also, in case of this specific dataset, this is also the standard what the community uses. When you look at any publications with results on Librispeech, they always use the whole train dataset for training. ## Actual results ``` ... File "/home/az/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c/librispeech_asr.py", line 119, in LibrispeechASR._split_generators line: archive_path = dl_manager.download(_DL_URLS[self.config.name]) locals: archive_path = <not found> dl_manager = <local> <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160> dl_manager.download = <local> <bound method DownloadManager.download of <datasets.utils.download_manager.DownloadManager object at 0x7fc07b426160>> _DL_URLS = <global> {'clean': {'dev': 'http://www.openslr.org/resources/12/dev-clean.tar.gz', 'test': 'http://www.openslr.org/resources/12/test-clean.tar.gz', 'train.100': 'http://www.openslr.org/resources/12/train-clean-100.tar.gz', 'train.360': 'http://www.openslr.org/resources/12/train-clean-360.tar.gz'}, 'other'... self = <local> <datasets_modules.datasets.librispeech_asr.1f4602f6b5fed8d3ab3e3382783173f2e12d9877e98775e34d7780881175096c.librispeech_asr.LibrispeechASR object at 0x7fc12a633310> self.config = <local> BuilderConfig(name='default', version=0.0.0, data_dir='/home/az/i6/setups/2022-03-20--sis/work/i6_core/datasets/huggingface/DownloadAndPrepareHuggingFaceDatasetJob.TV6Nwm6dFReF/output/data_dir', data_files=None, description=None) self.config.name = <local> 'default', len = 7 KeyError: 'default' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.1.0 - Platform: Linux-5.4.0-107-generic-x86_64-with-glibc2.31 - Python version: 3.9.9 - PyArrow version: 6.0.1 - Pandas version: 1.4.2
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[ "@patrickvonplaten Hi! I saw that you prepared this? :)", "Another thing, but maybe this should be a separate issue: As I see from the code, it would try to use up to 16 simultaneous downloads? This is problematic for Librispeech or anything on OpenSLR. On [the homepage](https://www.openslr.org/), it says:\r\n\r\...
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331
Loading CNN/Daily Mail dataset produces `nlp.utils.info_utils.NonMatchingSplitsSizesError`
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2020-06-30T22:21:33Z
2020-07-09T13:03:40Z
2020-07-09T13:03:40Z
null
``` >>> import nlp >>> nlp.load_dataset('cnn_dailymail', '3.0.0') Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/p/qdata/jm8wx/datasets/nlp/src/nlp/load.py", line 520, in load_dataset builder_instance.download_and_prepare( File "/p/qdata/jm8wx/datasets/nlp/src/nlp/builder.py", line 431, in download_and_prepare self._download_and_prepare( File "/p/qdata/jm8wx/datasets/nlp/src/nlp/builder.py", line 488, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/p/qdata/jm8wx/datasets/nlp/src/nlp/utils/info_utils.py", line 70, in verify_splits raise NonMatchingSplitsSizesError(str(bad_splits)) nlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}] ```
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[ "I couldn't reproduce on my side.\r\nIt looks like you were not able to generate all the examples, and you have the problem for each split train-test-validation.\r\nCould you try to enable logging, try again and send the logs ?\r\n```python\r\nimport logging\r\nlogging.basicConfig(level=logging.INFO)\r\n```", "he...
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ConnectionError: Couldn't reach https://raw.githubusercontent.com
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2021-08-11T16:19:01Z
2021-11-24T06:25:38Z
2021-08-18T15:09:18Z
null
Hello, I am trying to run run_glue.py and it gives me this error - Traceback (most recent call last): File "E:/BERT/pytorch_hugging/transformers/examples/pytorch/text-classification/run_glue.py", line 546, in <module> main() File "E:/BERT/pytorch_hugging/transformers/examples/pytorch/text-classification/run_glue.py", line 250, in main datasets = load_dataset("glue", data_args.task_name, cache_dir=model_args.cache_dir) File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\load.py", line 718, in load_dataset use_auth_token=use_auth_token, File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\load.py", line 320, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\utils\file_utils.py", line 291, in cached_path use_auth_token=download_config.use_auth_token, File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\utils\file_utils.py", line 623, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py Trying to do python run_glue.py --model_name_or_path bert-base-cased --task_name mrpc --do_train --do_eval --max_seq_length 128 --per_device_train_batch_size 32 --learning_rate 2e-5 --num_train_epochs 3 --output_dir ./tmp/mrpc/ Is this something on my end? From what I can tell, this was re-fixeded by @fullyz a few months ago. Thank you!
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[ "the bug code locate in :\r\n if data_args.task_name is not None:\r\n # Downloading and loading a dataset from the hub.\r\n datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)", "Hi @jinec,\r\n\r\nFrom time to time we get this kind of `ConnectionError` coming fr...
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PR_kwDODunzps5ArjCZ
5,107
Multiprocessed dataset builder
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17
2022-10-12T19:59:17Z
2022-12-01T15:37:09Z
2022-11-09T17:11:43Z
null
This PR adds the multiprocessing part of #2650 (but not the caching of already-computed arrow files). On the other side, loading of sharded arrow files still needs to be implemented (sharded parquet files can already be loaded).
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[ "I would also like to add a test, but am not sure whether it should go into `test_builder` (more natural imo) or `test_load` (which already contains a lot of the things I have to import to run my current testing setup). For reference, what I run to test that it works looks like:\r\n\r\n```\r\nimport os\r\nfrom path...
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Fix VCTK encoding
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2021-12-28T15:23:36Z
2021-12-28T15:48:18Z
2021-12-28T15:48:17Z
null
utf-8 encoding was missing in the VCTK dataset builder added in #3351
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wmt19 is broken
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2021-02-16T18:39:58Z
2021-03-03T17:42:02Z
2021-03-03T17:42:02Z
null
1. Check which lang pairs we have: `--dataset_name wmt19`: Please pick one among the available configs: ['cs-en', 'de-en', 'fi-en', 'gu-en', 'kk-en', 'lt-en', 'ru-en', 'zh-en', 'fr-de'] 2. OK, let's pick `ru-en`: `--dataset_name wmt19 --dataset_config "ru-en"` no cookies: ``` Traceback (most recent call last): File "./run_seq2seq.py", line 661, in <module> main() File "./run_seq2seq.py", line 317, in main datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 740, in load_dataset builder_instance.download_and_prepare( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/builder.py", line 572, in download_and_prepare self._download_and_prepare( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/builder.py", line 628, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/stas/.cache/huggingface/modules/datasets_modules/datasets/wmt19/436092de5f3faaf0fc28bc84875475b384e90a5470fa6afaee11039ceddc5052/wmt_utils.py", line 755, in _split_generators downloaded_files = dl_manager.download_and_extract(urls_to_download) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/download_manager.py", line 276, in download_and_extract return self.extract(self.download(url_or_urls)) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/download_manager.py", line 191, in download downloaded_path_or_paths = map_nested( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 233, in map_nested mapped = [ File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 234, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 190, in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 190, in <listcomp> mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 172, in _single_map_nested return function(data_struct) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/download_manager.py", line 211, in _download return cached_path(url_or_filename, download_config=download_config) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 274, in cached_path output_path = get_from_cache( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 584, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-ru.tar.gz ```
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[ "This was also mentioned in https://github.com/huggingface/datasets/issues/488 \r\n\r\nThe bucket where is data was stored seems to be unavailable now. Maybe we can change the URL to the ones in https://conferences.unite.un.org/uncorpus/en/downloadoverview ?", "Closing since this has been fixed by #1912" ]
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1,440,334,978
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5,214
Update github pr docs actions
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2022-11-08T14:43:37Z
2022-11-08T15:39:58Z
2022-11-08T15:39:57Z
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5214). All of your documentation changes will be reflected on that endpoint." ]
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1,009,873,482
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2,980
OpenSLR 25: ASR data for Amharic, Swahili and Wolof
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2021-09-28T15:04:36Z
2021-09-29T17:25:14Z
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## Adding a Dataset - **Name:** *SLR25* - **Description:** *Subset 25 from OpenSLR. Other subsets have been added to https://huggingface.co/datasets/openslr, 25 covers Amharic, Swahili and Wolof data* - **Paper:** *https://www.openslr.org/25/ has citations for each of the three subsubsets. * - **Data:** *Currently the three links to the .tar.bz2 files can be found a thttps://www.openslr.org/25/* - **Motivation:** *Increase ASR data for underrepresented African languages. Also, other subsets of OpenSLR speech recognition have been uploaded, so this would be easy.* https://github.com/huggingface/datasets/blob/master/datasets/openslr/openslr.py already has been created for various other OpenSLR subsets, this should be relatively straightforward to do.
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[ "Whoever handles this just needs to: \r\n\r\n- [ ] fork the HuggingFace Datasets repo\r\n- [ ] update the [existing dataset script](https://github.com/huggingface/datasets/blob/master/datasets/openslr/openslr.py) to add SLR25. Lots of copypasting from other sections of the script should make that easy. \r\nAmharic ...
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1,067
add xquad-r dataset
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2020-12-03T17:50:01Z
2020-12-03T17:53:21Z
2020-12-03T17:53:15Z
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Dataset viewer issue for Winoground
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2022-04-11T06:11:41Z
2022-06-21T16:43:58Z
2022-06-21T16:43:58Z
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## Dataset viewer issue for 'Winoground' **Link:** [*link to the dataset viewer page*](https://huggingface.co/datasets/facebook/winoground/viewer/facebook--winoground/train) *short description of the issue* Getting 401, message='Unauthorized' The dataset is subject to authorization, but I can access the files from the interface, so I assume I'm granted to access it. I'd assume the permission somehow doesn't propagate to the dataset viewer tool. Am I the one who added this dataset ? No
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[ "related (same dataset): https://github.com/huggingface/datasets/issues/4149. But the issue is different. Looking at it", "I thought this issue was related to the error I was seeing, but upon consideration I'd think the dataset viewer would return a 500 (unable to create the split like me) or a 404 (unable to loa...
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2,713
Enumerate all ner_tags values in WNUT 17 dataset
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2021-07-26T05:22:16Z
2021-07-26T09:30:55Z
2021-07-26T09:30:55Z
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This PR does: - Enumerate all ner_tags in dataset card Data Fields section - Add all metadata tags to dataset card Close #2709.
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Logic bug in arrow_writer?
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10
2022-06-15T14:50:00Z
2022-06-18T15:15:51Z
2022-06-18T15:15:51Z
null
https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488 I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows: ``` - if batch_examples and len(next(iter(batch_examples.values()))) == 0: + if not batch_examples or len(next(iter(batch_examples.values()))) == 0: return ``` @lhoestq
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[ "Hi @cccntu you're right, as when `batch_examples={}` the current if-statement won't be triggered as the condition won't be satisfied, I'll prepare a PR to address it as well as add the regression tests so that this issue is handled properly.", "Hi @alvarobartt ,\r\nThanks for answering. Do you know when and why ...
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Fix task reloading from cache
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2021-10-11T12:03:04Z
2021-10-11T12:23:39Z
2021-10-11T12:23:39Z
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When reloading a dataset from the cache when doing `map`, the tasks templates were kept instead of being updated regarding the output of the `map` function. This is an issue because we drop the tasks templates that are not compatible anymore after `map`, for example if a column of the template was removed. This PR fixes this and for convenience introduces a decorator `@transmit_tasks` that takes care of doing this verification, similar to the `@transmit_format` decorator. This should fix issue https://github.com/huggingface/datasets/issues/3047 cc @sgugger
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Add args description to DatasetInfo
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2021-05-20T13:53:10Z
2021-05-22T09:26:16Z
2021-05-22T09:26:14Z
null
Closes #2354 I am not sure what `post_processed` and `post_processing_size` correspond to, so have left them empty for now. I also took a guess at some of the other fields like `dataset_size` vs `size_in_bytes`, so might have misunderstood their meaning.
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[ "Thanks for the suggestions! I've included them and made a few minor tweaks along the way", "Please merge master into this branch to fix the CI, I just fixed metadata validation tests." ]
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added opus GNOME data
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1
2020-12-11T21:21:51Z
2020-12-17T14:20:23Z
2020-12-17T14:20:23Z
null
Dataset : http://opus.nlpl.eu/GNOME.php
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[ "merging since the Ci is fixed on master" ]
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Update map docs
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2022-07-25T20:59:35Z
2022-07-27T16:22:04Z
2022-07-27T16:10:04Z
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This PR updates the `map` docs for processing text to include `return_tensors="np"` to make it run faster (see #4676).
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
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5,479
audiofolder works on local env, but creates empty dataset in a remote one, what dependencies could I be missing/outdated
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2023-01-27T20:01:22Z
2023-01-29T05:23:14Z
2023-01-29T05:23:14Z
null
### Describe the bug I'm using a custom audio dataset (400+ audio files) in the correct format for audiofolder. Although loading the dataset with audiofolder works in one local setup, it doesn't in a remote one (it just creates an empty dataset). I have both ffmpeg and libndfile installed on both computers, what could be missing/need to be updated in the one that doesn't work? On the remote env, libsndfile is 1.0.28 and ffmpeg is 4.2.1. from datasets import load_dataset ds = load_dataset("audiofolder", data_dir="...") Here is the output (should be generating 400+ rows): Downloading and preparing dataset audiofolder/default to ... Downloading data files: 0%| | 0/2 [00:00<?, ?it/s] Downloading data files: 0it [00:00, ?it/s] Extracting data files: 0it [00:00, ?it/s] Generating train split: 0 examples [00:00, ? examples/s] Dataset audiofolder downloaded and prepared to ... Subsequent calls will reuse this data. 0%| | 0/1 [00:00<?, ?it/s] DatasetDict({ train: Dataset({ features: ['audio', 'transcription'], num_rows: 1 }) }) Here is my pip environment in the one that doesn't work (uses torch 1.11.a0 from shared env): Package Version ------------------- ------------------- aiofiles 22.1.0 aiohttp 3.8.3 aiosignal 1.3.1 altair 4.2.1 anyio 3.6.2 appdirs 1.4.4 argcomplete 2.0.0 argon2-cffi 20.1.0 astunparse 1.6.3 async-timeout 4.0.2 attrs 21.2.0 audioread 3.0.0 backcall 0.2.0 bleach 4.0.0 certifi 2021.10.8 cffi 1.14.6 charset-normalizer 2.0.12 click 8.1.3 contourpy 1.0.7 cycler 0.11.0 datasets 2.9.0 debugpy 1.4.1 decorator 5.0.9 defusedxml 0.7.1 dill 0.3.6 distlib 0.3.4 entrypoints 0.3 evaluate 0.4.0 expecttest 0.1.3 fastapi 0.89.1 ffmpy 0.3.0 filelock 3.6.0 fonttools 4.38.0 frozenlist 1.3.3 fsspec 2023.1.0 future 0.18.2 gradio 3.16.2 h11 0.14.0 httpcore 0.16.3 httpx 0.23.3 huggingface-hub 0.12.0 idna 3.3 ipykernel 6.2.0 ipython 7.26.0 ipython-genutils 0.2.0 ipywidgets 7.6.3 jedi 0.18.0 Jinja2 3.0.1 jiwer 2.5.1 joblib 1.2.0 jsonschema 3.2.0 jupyter 1.0.0 jupyter-client 6.1.12 jupyter-console 6.4.0 jupyter-core 4.7.1 jupyterlab-pygments 0.1.2 jupyterlab-widgets 1.0.0 kiwisolver 1.4.4 Levenshtein 0.20.2 librosa 0.9.2 linkify-it-py 1.0.3 llvmlite 0.39.1 markdown-it-py 2.1.0 MarkupSafe 2.0.1 matplotlib 3.6.3 matplotlib-inline 0.1.2 mdit-py-plugins 0.3.3 mdurl 0.1.2 mistune 0.8.4 multidict 6.0.4 multiprocess 0.70.14 nbclient 0.5.4 nbconvert 6.1.0 nbformat 5.1.3 nest-asyncio 1.5.1 notebook 6.4.3 numba 0.56.4 numpy 1.20.3 orjson 3.8.5 packaging 21.0 pandas 1.5.3 pandocfilters 1.4.3 parso 0.8.2 pexpect 4.8.0 pickleshare 0.7.5 Pillow 9.4.0 pip 22.3.1 pipx 1.1.0 platformdirs 2.5.2 pooch 1.6.0 prometheus-client 0.11.0 prompt-toolkit 3.0.19 psutil 5.9.0 ptyprocess 0.7.0 pyarrow 10.0.1 pycparser 2.20 pycryptodome 3.16.0 pydantic 1.10.4 pydub 0.25.1 Pygments 2.10.0 pyparsing 2.4.7 pyrsistent 0.18.0 python-dateutil 2.8.2 python-multipart 0.0.5 pytz 2022.7.1 PyYAML 6.0 pyzmq 22.2.1 qtconsole 5.1.1 QtPy 1.10.0 rapidfuzz 2.13.7 regex 2022.10.31 requests 2.27.1 resampy 0.4.2 responses 0.18.0 rfc3986 1.5.0 scikit-learn 1.2.1 scipy 1.6.3 Send2Trash 1.8.0 setuptools 65.5.1 shiboken6 6.3.1 shiboken6-generator 6.3.1 six 1.16.0 sniffio 1.3.0 soundfile 0.11.0 starlette 0.22.0 terminado 0.11.0 testpath 0.5.0 threadpoolctl 3.1.0 tokenizers 0.13.2 toolz 0.12.0 torch 1.11.0a0+gitunknown tornado 6.1 tqdm 4.64.1 traitlets 5.0.5 transformers 4.27.0.dev0 types-dataclasses 0.6.4 typing_extensions 4.1.1 uc-micro-py 1.0.1 urllib3 1.26.9 userpath 1.8.0 uvicorn 0.20.0 virtualenv 20.14.1 wcwidth 0.2.5 webencodings 0.5.1 websockets 10.4 wheel 0.37.1 widgetsnbextension 3.5.1 xxhash 3.2.0 yarl 1.8.2 ### Steps to reproduce the bug Create a pip environment with the packages listed above (make sure ffmpeg and libsndfile is installed with same versions listed above). Create a custom audio dataset and load it in with load_dataset("audiofolder", ...) ### Expected behavior load_dataset should create a dataset with 400+ rows. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.9.0 - PyArrow version: 10.0.1 - Pandas version: 1.5.3
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3,823
500 internal server error when trying to open a dataset composed of Zarr stores
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2022-03-04T10:37:14Z
2022-03-08T09:47:39Z
2022-03-08T09:47:39Z
null
## Describe the bug The dataset [openclimatefix/mrms](https://huggingface.co/datasets/openclimatefix/mrms) gives a 500 server error when trying to open it on the website, or through code. The dataset doesn't have a loading script yet, and I did push two [xarray](https://docs.xarray.dev/en/stable/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine. In general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format? For context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https://github.com/openclimatefix/metnet) [DGMR](https://github.com/openclimatefix/skillful_nowcasting), and [others](https://github.com/openclimatefix/graph_weather), which all have large, complex datasets. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("openclimatefix/mrms") ``` ## Expected results The dataset should be downloaded or open up ## Actual results A 500 internal server error ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35 - Python version: 3.9.10 - PyArrow version: 7.0.0
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[ "Hi @jacobbieker, thanks for reporting!\r\n\r\nI have transferred this issue to our Hub team and they are investigating it. I keep you informed. ", "Hi @jacobbieker, we are investigating this issue on our side and we'll see if we can fix it, but please note that your repo is considered problematic for git. Here a...
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4,333
Adding eval metadata for Banking 77
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2022-05-12T14:05:05Z
2022-05-12T21:03:32Z
2022-05-12T21:03:31Z
null
Adding eval metadata for Banking 77
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[ "@lhoestq , Circle CI is giving me an error, saying that ['extended'] is a key that shouldn't be in the dataset metadata, but it was there before my modification (so I don't want to remove it)" ]
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Saving processed dataset running infinitely
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2021-02-19T13:09:19Z
2021-02-23T07:34:44Z
null
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I have a text dataset of size 220M. For pre-processing, I need to tokenize this and filter rows with the large sequence. My tokenization took roughly 3hrs. I used map() with batch size 1024 and multi-process with 96 processes. filter() function was way to slow, so I used a hack to use pyarrow filter table function, which is damm fast. Mentioned [here](https://github.com/huggingface/datasets/issues/1796) ```dataset._data = dataset._data.filter(...)``` It took 1 hr for the filter. Then i use `save_to_disk()` on processed dataset and it is running forever. I have been waiting since 8 hrs, it has not written a single byte. Infact it has actually read from disk more than 100GB, screenshot below shows the stats using `iotop`. Second process is the one. <img width="1672" alt="Screenshot 2021-02-19 at 6 36 53 PM" src="https://user-images.githubusercontent.com/20911334/108508197-7325d780-72e1-11eb-8369-7c057d137d81.png"> I am not able to figure out, whether this is some issue with dataset library or that it is due to my hack for filter() function.
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[ "@thomwolf @lhoestq can you guys please take a look and recommend some solution.", "am suspicious of this thing? what's the purpose of this? pickling and unplickling\r\n`self = pickle.loads(pickle.dumps(self))`\r\n\r\n```\r\n def save_to_disk(self, dataset_path: str, fs=None):\r\n \"\"\"\r\n Save...
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Consistent caching between python and jupyter
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2022-10-25T01:34:33Z
2022-11-02T15:43:22Z
2022-11-02T15:43:22Z
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### Feature request I hope this is not my mistake, currently if I use `load_dataset` from a python session on a custom dataset to do the preprocessing, it will be saved in the cache and in other python sessions it will be loaded from the cache, however calling the same from a jupyter notebook does not work, meaning the preprocessing starts from scratch. If adjusting the hashes is impossible, is there a way to manually set dataset fingerprint to "force" this behaviour? ### Motivation If this is not already the case and I am doing something wrong, it would be useful to have the two fingerprints consistent so one can create the dataset once and then try small things on jupyter without preprocessing everything again. ### Your contribution I am happy to try a PR if you give me some pointers where the changes should happen
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[ "Hi ! Maybe it's possible to have a consistent hash for a function defined in `__main__` and a function define in a notebook.\r\n\r\nHowever for functions imported from another location, pickle uses the location to identify the code, so in that case we can't do much I believe.\r\n\r\nWould it be ok for you if we on...
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DuplicatedKeysError: FAILURE TO GENERATE DATASET ! load_dataset('head_qa', 'en')
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2021-05-19T15:49:48Z
2021-05-30T13:26:16Z
2021-05-30T13:26:16Z
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Hello everyone, I try to use head_qa dataset in [https://huggingface.co/datasets/viewer/?dataset=head_qa&config=en](url) ``` !pip install datasets from datasets import load_dataset dataset = load_dataset( 'head_qa', 'en') ``` When I write above load_dataset(.), it throws the following: ``` DuplicatedKeysError Traceback (most recent call last) <ipython-input-6-ea87002d32f0> in <module>() 2 from datasets import load_dataset 3 dataset = load_dataset( ----> 4 'head_qa', 'en') 5 frames /usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in check_duplicate_keys(self) 347 for hash, key in self.hkey_record: 348 if hash in tmp_record: --> 349 raise DuplicatedKeysError(key) 350 else: 351 tmp_record.add(hash) DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 1 Keys should be unique and deterministic in nature ``` How can I fix the error? Thanks
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3,611
Indexing bug after dataset.select()
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2022-01-21T12:09:30Z
2022-01-27T18:16:22Z
2022-01-27T18:16:22Z
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## Describe the bug A clear and concise description of what the bug is. Dataset indexing is not working as expected after `dataset.select(range(100))` ## Steps to reproduce the bug ```python # Sample code to reproduce the bug import datasets task_to_keys = { "cola": ("sentence", None), "mnli": ("premise", "hypothesis"), "mrpc": ("sentence1", "sentence2"), "qnli": ("question", "sentence"), "qqp": ("question1", "question2"), "rte": ("sentence1", "sentence2"), "sst2": ("sentence", None), "stsb": ("sentence1", "sentence2"), "wnli": ("sentence1", "sentence2"), } task_name = "sst2" raw_datasets = datasets.load_dataset("glue", task_name) train_dataset = raw_datasets["train"] print("before select: ",train_dataset[-2:]) # before select: {'sentence': ['a patient viewer ', 'this new jangle of noise , mayhem and stupidity must be a serious contender for the title . '], 'label': [1, 0], 'idx': [67347, 67348]} train_dataset = train_dataset.select(range(100)) print("after select: ",train_dataset[-2:]) # after select: {'sentence': [], 'label': [], 'idx': []} ``` link to colab: https://colab.research.google.com/drive/1LngeRC9f0jE7eSQ4Kh1cIeb411lRXQD-?usp=sharing ## Expected results A clear and concise description of the expected results. showing 98, 99 index data ## Actual results Specify the actual results or traceback. empty ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.17.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 3.0.0
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[ "Hi! Thanks for reporting! I've opened a PR with the fix." ]
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Update link to Datasets Tagging app in Spaces
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2021-11-02T08:13:50Z
2021-11-08T10:36:23Z
2021-11-08T10:36:22Z
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Fix #3193.
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Fix bleurt logging import
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2020-09-01T03:01:25Z
2020-09-03T18:04:46Z
2020-09-03T09:04:20Z
null
Bleurt started throwing an error in some code we have. This looks like the fix but... It's also unnerving that even a prebuilt docker image with pinned versions can be working 1 day and then fail the next (especially for production systems). Any way for us to pin your metrics code so that they are guaranteed not to to change and possibly fail on repository changes? Thanks (and also for your continued work on the lib...)
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[ "That’s a good point that we started to discuss internally as well. We should pin the dataset en metrics code by default indeed.\r\nLet’s update this in the coming release.", "Ok closed this with #567 and we are working on a more general solution to pin dataset version in #562 (should be in the coming release)." ...
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Trouble with streaming frgfm/imagenette vision dataset with TAR archive
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2022-07-18T02:51:09Z
2022-08-01T15:10:57Z
2022-08-01T15:10:57Z
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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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[ "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:/...
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319
Nested sequences with dicts
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2020-06-27T23:45:17Z
2020-07-03T10:22:00Z
2020-07-03T10:22:00Z
null
Am pretty much finished [adding a dataset](https://github.com/ghomasHudson/nlp/blob/DocRED/datasets/docred/docred.py) for [DocRED](https://github.com/thunlp/DocRED), but am getting an error when trying to add a nested `nlp.features.sequence(nlp.features.sequence({key:value,...}))`. The original data is in this format: ```python { 'title': "Title of wiki page", 'vertexSet': [ [ { 'name': "mention_name", 'sent_id': "mention in which sentence", 'pos': ["postion of mention in a sentence"], 'type': "NER_type"}, {another mention} ], [another entity] ] ... } ``` So to represent this I've attempted to write: ``` ... features=nlp.Features({ "title": nlp.Value("string"), "vertexSet": nlp.features.Sequence(nlp.features.Sequence({ "name": nlp.Value("string"), "sent_id": nlp.Value("int32"), "pos": nlp.features.Sequence(nlp.Value("int32")), "type": nlp.Value("string"), })), ... }), ... ``` This is giving me the error: ``` pyarrow.lib.ArrowTypeError: Could not convert [{'pos': [[0,2], [2,4], [3,5]], "type": ["ORG", "ORG", "ORG"], "name": ["Lark Force", "Lark Force", "Lark Force", "sent_id": [0, 3, 4]}..... with type list: was not a dict, tuple, or recognized null value for conversion to struct type ``` Do we expect the pyarrow stuff to break when doing this deeper nesting? I've checked that it still works when you do `nlp.features.Sequence(nlp.features.Sequence(nlp.Value("string"))` or `nlp.features.Sequence({key:value,...})` just not nested sequences with a dict. If it's not possible, I can always convert it to a shallower structure. I'd rather not change the DocRED authors' structure if I don't have to though.
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[ "Oh yes, this is a backward compatibility feature with tensorflow_dataset in which a `Sequence` or `dict` is converted in a `dict` of `lists`, unfortunately it is not very intuitive, see here: https://github.com/huggingface/nlp/blob/master/src/nlp/features.py#L409\r\n\r\nTo avoid this behavior, you can just define ...
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Error loading json dataset with multiple splits if keys in nested dicts have a different order
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2021-10-15T09:33:25Z
2022-04-10T14:06:29Z
2022-04-10T14:06:29Z
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## Describe the bug Loading a json dataset with multiple splits that have nested dicts with keys in different order results in the error below. If the keys in the nested dicts always have the same order or even if you just load a single split in which the nested dicts don't have the same order, everything works fine. ## Steps to reproduce the bug Create two json files: train.json ``` {"a": {"c": 8, "b": 5}} {"a": {"b": 7, "c": 6}} ``` test.json ``` {"a": {"b": 1, "c": 2}} {"a": {"b": 3, "c": 4}} ``` ```python from datasets import load_dataset # Loading the files individually works (even though the keys in train.json don't have the same order) load_dataset('json', data_files={"test": "test.json"}) load_dataset('json', data_files={"train": "train.json"}) # Loading both splits fails load_dataset('json', data_files={"train": "train.json", "test": "test.json"}) ``` ## Expected results Loading both splits should not give an error whether the nested dicts are have the same order or not. ## Actual results ``` >>> load_dataset('json', data_files={"train": "train.json", "test": "test.json"}) Using custom data configuration default-f1bc76fd07398c4c Downloading and preparing dataset json/default to /home/dthulke/.cache/huggingface/datasets/json/default-f1bc76fd07398c4c/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426... 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 8839.42it/s] 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 477.82it/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/load.py", line 1632, in load_dataset use_auth_token=use_auth_token, File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 608, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 697, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 1159, in _prepare_split writer.write_table(table) File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/arrow_writer.py", line 428, in write_table pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema) File "pyarrow/table.pxi", line 1596, in pyarrow.lib.Table.from_arrays File "pyarrow/table.pxi", line 592, in pyarrow.lib._sanitize_arrays File "pyarrow/array.pxi", line 329, in pyarrow.lib.asarray File "pyarrow/table.pxi", line 277, in pyarrow.lib.ChunkedArray.cast File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/pyarrow/compute.py", line 297, in cast return call_function("cast", [arr], options) File "pyarrow/_compute.pyx", line 527, in pyarrow._compute.call_function File "pyarrow/_compute.pyx", line 337, in pyarrow._compute.Function.call File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 120, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct ``` ## Environment info - `datasets` version: 1.13.2 - Platform: Linux-4.15.0-147-generic-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.6.9 - PyArrow version: 5.0.0
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[ "Hi, \r\n\r\neven Pandas, which is less strict compared to PyArrow when it comes to reading JSON, doesn't support different orderings:\r\n```python\r\nimport io\r\nimport pandas as pd\r\n\r\ns = \"\"\"\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n\"\"\"\r\n\r\nbuffer = io.StringIO(s)\r\ndf =...
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4,115
ImageFolder add option to ignore some folders like '.ipynb_checkpoints'
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2022-04-06T17:29:43Z
2022-06-01T13:04:16Z
2022-06-01T13:04:16Z
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**Is your feature request related to a problem? Please describe.** I sometimes like to peek at the dataset images from jupyterlab. thus '.ipynb_checkpoints' folder appears where my dataset is and (just realized) leads to accidental duplicate image additions. I think this is an easy enough thing to miss especially if the dataset is very large. **Describe the solution you'd like** maybe have an option `ignore` or something .gitignore style `dataset = load_dataset("imagefolder", data_dir="./data/original", ignore="regex?")` **Describe alternatives you've considered** Could filter out manually
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[ "Maybe it would be nice to ignore private dirs like this one (ones starting with `.`) by default. \r\n\r\nCC @mariosasko ", "Maybe we can add a `ignore_hidden_files` flag to the builder configs of our packaged loaders (to be consistent across all of them), wdyt @lhoestq @albertvillanova? ", "I think they should...
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4,043
Create metric card for CUAD
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2022-03-28T15:38:58Z
2022-03-29T15:20:56Z
2022-03-29T15:15:19Z
null
Proposing a CUAD metric card
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770
Fix custom builder caching
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2020-10-28T13:32:24Z
2020-10-29T09:36:03Z
2020-10-29T09:36:01Z
null
The cache directory of a dataset didn't take into account additional parameters that the user could specify such as `features` or any parameter of the builder configuration kwargs (ex: `encoding` for the `text` dataset). To fix that, the cache directory name now has a suffix that depends on all of them. Fix #730 Fix #750
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4,291
Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message
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2022-05-06T12:03:27Z
2022-05-09T08:25:58Z
2022-05-09T08:25:58Z
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### Link https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train ### Description The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss? ### Owner Yes
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[ "Hi @leondz, thanks for reporting.\r\n\r\nIndeed, the dataset viewer relies on the dataset being streamable (passing `streaming=True` to `load_dataset`). Whereas most of the datastes are streamable out of the box (thanks to our implementation of streaming), there are still some exceptions.\r\n\r\nIn particular, in ...
https://api.github.com/repos/huggingface/datasets/issues/6060
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1,816,614,120
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6,060
Dataset.map() execute twice when in PyTorch DDP mode
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open
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2023-07-22T05:06:43Z
2023-07-24T19:29:55Z
null
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### Describe the bug I use `torchrun --standalone --nproc_per_node=2 train.py` to start training. And write the code following the [docs](https://huggingface.co/docs/datasets/process#distributed-usage). The trick about using `torch.distributed.barrier()` to only execute map at the main process doesn't always work. When I am training model, it will map twice. When I am running a test for dataset and dataloader (just print the batches), it can work. Their code about loading dataset are same. And on another server with 30 CPU cores, I use 2 GPUs and it can't work neither. I have tried to use `rank` and `local_rank` to check, they all didn't make sense. ### Steps to reproduce the bug use `torchrun --standalone --nproc_per_node=2 train.py` or `torchrun --standalone train.py` to run This is my code: ```python if args.distributed and world_size > 1: if args.local_rank > 0: print(f"Rank {args.rank}: Gpu {args.gpu} waiting for main process to perform the mapping", force=True) torch.distributed.barrier() print("Mapping dataset") dataset = dataset.map(lambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=True), num_proc=8, desc="cut_reorder_keys") dataset = dataset.map(lambda x: random_shift(x, shift_range=(-160, 0), feature_scale=16), num_proc=8, desc="random_shift") dataset_test = dataset_test.map(lambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=False), num_proc=8, desc="cut_reorder_keys") if args.local_rank == 0: print("Mapping finished, loading results from main process") torch.distributed.barrier() ``` ### Expected behavior Only the main process will execute `map`, while the sub process will load cache from disk. ### Environment info server with 64 CPU cores (AMD Ryzen Threadripper PRO 5995WX 64-Cores) and 2 RTX 4090 - `python==3.9.16` - `datasets==2.13.1` - `torch==2.0.1+cu117` - `22.04.1-Ubuntu` server with 30 CPU cores (Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz) and 2 RTX 4090 - `python==3.9.0` - `datasets==2.13.1` - `torch==2.0.1+cu117` - `Ubuntu 20.04`
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[ "Sorry for asking a duplicate question about `num_proc`, I searched the forum and find the solution.\r\n\r\nBut I still can't make the trick with `torch.distributed.barrier()` to only map at the main process work. The [post on forum]( https://discuss.huggingface.co/t/slow-processing-with-map-when-using-deepspeed-or...
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HTTP error 504 Server Error: Gateway Time-out
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2022-03-09T12:03:37Z
2022-03-15T16:19:50Z
2022-03-15T16:19:50Z
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I am trying to push a large dataset(450000+) records with the help of `push_to_hub()` While pushing, it gives some error like this. ``` Traceback (most recent call last): File "data_split_speech.py", line 159, in <module> data_new_2.push_to_hub("user-name/dataset-name",private=True) File "/opt/conda/lib/python3.8/site-packages/datasets/dataset_dict.py", line 951, in push_to_hub repo_id, split, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub( File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3556, in _push_parquet_shards_to_hub api.upload_file( File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1017, in upload_file raise err File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1008, in upload_file r.raise_for_status() File "/opt/conda/lib/python3.8/site-packages/requests/models.py", line 953, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/api/datasets/user-name/dataset-name/upload/main/data/train2-00041-of-00064.parquet ``` Can anyone help me to resolve this issue.
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[ "is pushing directly with git (and git-lfs) an option for you?", "I have installed git-lfs and doing this push with that\r\n", "yes but is there any way you could try pushing with `git` command line directly instead of `push_to_hub`?", "Okay. I didnt saved the dataset to my local machine. So, I processed the ...
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4,086
Dataset viewer issue for McGill-NLP/feedbackQA
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2022-04-04T07:27:20Z
2022-04-04T22:29:53Z
2022-04-04T08:01:45Z
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## Dataset viewer issue for '*McGill-NLP/feedbackQA*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/McGill-NLP/feedbackQA)* *short description of the issue* The dataset can be loaded correctly with `load_dataset` but the preview doesn't work. Error message: ``` Status code: 400 Exception: Status400Error Message: Not found. Maybe the cache is missing, or maybe the dataset does not exist. ``` Am I the one who added this dataset ? Yes
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[ "Hi @cslizc, thanks for reporting.\r\n\r\nI have just forced the refresh of the corresponding cache and the preview is working now.", "thank you so much" ]
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5,512
Speed up batched PyTorch DataLoader
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2023-02-08T13:38:59Z
2023-02-19T18:35:09Z
2023-02-19T18:27:29Z
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I implemented `__getitems__` to speed up batched data loading in PyTorch close https://github.com/huggingface/datasets/issues/5505
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
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Add description to hellaswag dataset
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2022-08-09T10:21:14Z
2022-09-23T11:35:38Z
2022-09-23T11:33:44Z
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Are the `metadata JSON file` not on their way to deprecation? 😆😇\r\n\r\nIMO, more generally than this particular PR, the contribution process should be simplified now that many validation checks happen on the hub side.\r\n\r\nKeepi...
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1,048,165,525
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3,237
wikitext description wrong
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2021-11-09T04:06:52Z
2022-02-14T15:45:11Z
2021-11-09T13:49:28Z
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## Describe the bug Descriptions of the wikitext datasests are wrong. ## Steps to reproduce the bug Please see: https://github.com/huggingface/datasets/blob/f6dcafce996f39b6a4bbe3a9833287346f4a4b68/datasets/wikitext/wikitext.py#L50 ## Expected results The descriptions for raw-v1 and v1 should be switched.
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[ "Hi @hongyuanmei, thanks for reporting.\r\n\r\nI'm fixing it.", "Duplicate of:\r\n- #795" ]
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1,319,333,645
I_kwDODunzps5Oo28N
4,750
Easily create loading script for benchmark comprising multiple huggingface datasets
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2022-07-27T10:13:38Z
2022-07-27T13:58:07Z
2022-07-27T13:58:07Z
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Hi, I would like to create a loading script for a benchmark comprising multiple huggingface datasets. The function _split_generators needs to return the files for the respective dataset. However, the files are not always in the same location for each dataset. I want to just make a wrapper dataset that provides a single interface to all the underlying datasets. I thought about downloading the files with the load_dataset function and then providing the link to the cached file. But this seems a bit inelegant to me. What approach would you propose to do this? Please let me know if you have any questions. Cheers, Joel
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[ "Hi ! I think the simplest is to copy paste the `_split_generators` code from the other datasets and do a bunch of if-else, as in the glue dataset: https://huggingface.co/datasets/glue/blob/main/glue.py#L467", "Ok, I see. Thank you" ]
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Close `PIL.Image` file handler in `Image.decode_example`
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2022-03-23T14:51:48Z
2022-03-23T18:24:52Z
2022-03-23T18:19:27Z
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Closes the file handler of the PIL image object in `Image.decode_example` to avoid the `Too many open files` error. To pass [the image equality checks](https://app.circleci.com/pipelines/github/huggingface/datasets/10774/workflows/d56670e6-16bb-4c64-b601-a152c5acf5ed/jobs/65825) in CI, `Image.decode_example` calls `image.load()` regardless of how the image object is created (not only for the `PIL.Image.open(local_path)` case). This is needed because `load()` sets the `readonly` attribute of a `PIL.Image` object to 0 (it's 1 after `PIL.Image.open(file_like)`), and in the older PIL versions (only fixed on main), that attribute is considered in `PIL.Image.__eq__`. More info can be found here: https://github.com/python-pillow/Pillow/issues/5926. Fix #3985
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
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Start community-provided dataset docs V2
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2020-10-23T13:07:30Z
2020-10-23T13:15:37Z
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Generate metadata JSON for telugu_books dataset
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2021-08-03T13:14:52Z
2021-08-04T08:49:02Z
2021-08-04T08:49:02Z
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Related to #2743.
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636
Consistent ner features
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2020-09-16T15:56:25Z
2020-09-17T09:52:59Z
2020-09-17T09:52:58Z
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As discussed in #613 , this PR aims at making NER feature names consistent across datasets. I changed the feature names of LinCE and XTREME/PAN-X
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2,358
Roman Urdu Stopwords List
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2021-05-13T18:29:27Z
2021-05-19T08:50:43Z
2021-05-17T14:05:10Z
null
A list of most frequently used Roman Urdu words with different spellings and usages. This is a very basic effort to collect some basic stopwords for Roman Urdu to help efforts of analyzing text data in roman Urdu which makes up a huge part of daily internet interaction of Roman-Urdu users.
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[ "Hi ! Thanks for sharing :)\r\nI think the best place to share this is probably the `Languages at Hugging Face` section of the forum:\r\nhttps://discuss.huggingface.co/c/languages-at-hugging-face/15\r\n\r\nSince this is not a dataset, I'm closing this PR if you don't mind", "Thank you I will look into the link th...
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3,278
Proposed update to the documentation for WER
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2021-11-15T23:28:31Z
2021-11-16T11:19:37Z
2021-11-16T11:19:37Z
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I wanted to submit a minor update to the description of WER for your consideration. Because of the possibility of insertions, the numerator in the WER formula can be larger than N, so the value of WER can be greater than 1.0: ``` >>> from datasets import load_metric >>> metric = load_metric("wer") >>> metric.compute(predictions=["hello how are you"], references=["hello"]) 3.0 ``` and similarly from the underlying jiwer module's `wer` function: ``` >>> from jiwer import wer >>> wer("hello", "hello how are you") 3.0 ```
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Dataset Viewer issue for Pavithree/test
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2022-06-14T13:23:10Z
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### Link https://huggingface.co/datasets/Pavithree/test ### Description I have extracted the subset of original eli5 dataset found at hugging face. However, while loading the dataset It throws ArrowNotImplementedError: Unsupported cast from string to null using function cast_null error. Is there anything missing from my end? Kindly help. ### Owner _No response_
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[ "This issue can be resolved according to this post https://stackoverflow.com/questions/70566660/parquet-with-null-columns-on-pyarrow. It looks like first data entry in the json file must not have any null values as pyarrow uses this first file to infer schema for entire dataset." ]
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[ "whoops", "please delete this" ]
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Fix to_tf_dataset references in docs
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2022-01-03T15:31:39Z
2022-01-05T18:52:48Z
2022-01-05T18:52:48Z
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Fix the `to_tf_dataset` references in the docs. The currently failing example of usage will be fixed by #3338.
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[ "The code snippet in [this section](https://huggingface.co/docs/datasets/master/use_dataset.html?highlight=to_tf_dataset#tensorflow) is missing an import (`DataCollatorWithPadding`) and doesn't initialize the TF model before the `model.fit` call." ]
https://api.github.com/repos/huggingface/datasets/issues/3325
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1,064,663,075
PR_kwDODunzps4vEaGO
3,325
Update conda dependencies
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2021-11-26T16:08:07Z
2021-11-26T16:20:37Z
2021-11-26T16:20:36Z
null
Some dependencies minimum versions were outdated. For example `pyarrow` and `huggingface_hub`
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https://api.github.com/repos/huggingface/datasets/issues/1299
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759,414,566
MDU6SXNzdWU3NTk0MTQ1NjY=
1,299
can't load "german_legal_entity_recognition" dataset
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closed
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3
2020-12-08T12:42:01Z
2020-12-16T16:03:13Z
2020-12-16T16:03:13Z
null
FileNotFoundError: Couldn't find file locally at german_legal_entity_recognition/german_legal_entity_recognition.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/german_legal_entity_recognition/german_legal_entity_recognition.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/german_legal_entity_recognition/german_legal_entity_recognition.py
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[ "Please if you could tell me more about the error? \r\n\r\n1. Please check the directory you've been working on\r\n2. Check for any typos", "> Please if you could tell me more about the error?\r\n> \r\n> 1. Please check the directory you've been working on\r\n> 2. Check for any typos\r\n\r\nError happens during t...
https://api.github.com/repos/huggingface/datasets/issues/4850
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PR_kwDODunzps49KnZ8
4,850
Fix test of _get_extraction_protocol for TAR files
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2022-08-15T08:37:58Z
2022-08-15T09:42:56Z
2022-08-15T09:28:46Z
null
While working in another PR, I discovered an xpass test (a test that is supposed to xfail but nevertheless passes) when testing `_get_extraction_protocol`: https://github.com/huggingface/datasets/runs/7818845285?check_suite_focus=true ``` XPASS tests/test_streaming_download_manager.py::test_streaming_dl_manager_get_extraction_protocol_throws[https://foo.bar/train.tar] ``` This PR: - refactors the test so that it tests the raise of the exceptions instead of xfailing - fixes the test for TAR files: it does not raise an exception, but returns "tar" - fixes some tests wrongly named: exchange `test_streaming_dl_manager_get_extraction_protocol` with `test_streaming_dl_manager_get_extraction_protocol_gg_drive`
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
https://api.github.com/repos/huggingface/datasets/issues/2632
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942,293,727
MDExOlB1bGxSZXF1ZXN0Njg4MDQyMjcw
2,632
add image-classification task template
[]
closed
false
null
2
2021-07-12T17:41:03Z
2021-07-13T15:44:28Z
2021-07-13T15:28:16Z
null
Snippet below is the tl;dr, but you can try it out directly here: [![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/nateraw/005c025d41f0e48ae3d4ee61c0f20b70/image-classification-task-template-demo.ipynb) ```python from datasets import load_dataset ds = load_dataset('nateraw/image-folder', data_files='PetImages/') # DatasetDict({ # train: Dataset({ # features: ['file', 'labels'], # num_rows: 23410 # }) # }) ds = ds.prepare_for_task('image-classification') # DatasetDict({ # train: Dataset({ # features: ['image_file_path', 'labels'], # num_rows: 23410 # }) # }) ```
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[ "Awesome!", "Thanks for adding a new task template - great work @nateraw 🚀 !" ]
https://api.github.com/repos/huggingface/datasets/issues/1517
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764,045,214
MDExOlB1bGxSZXF1ZXN0NTM4MzAyNDM1
1,517
Kd conv smangrul
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closed
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2020-12-12T16:51:30Z
2020-12-16T14:56:14Z
2020-12-16T14:56:14Z
null
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[ "Hii please follow me", "merging since the CI is fixed on master" ]
https://api.github.com/repos/huggingface/datasets/issues/1303
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759,440,484
MDExOlB1bGxSZXF1ZXN0NTM0NDQ2NDg0
1,303
adding opus_openoffice
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2020-12-08T13:20:21Z
2020-12-10T09:37:10Z
2020-12-10T09:37:10Z
null
Adding Opus OpenOffice: http://opus.nlpl.eu/OpenOffice.php 8 languages, 28 bitexts
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