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779,107,313
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1,689
Fix ade_corpus_v2 config names
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2021-01-05T14:33:28Z
2021-01-05T14:55:09Z
2021-01-05T14:55:08Z
null
There are currently some typos in the config names of the `ade_corpus_v2` dataset, I fixed them: - Ade_corpos_v2_classificaion -> Ade_corpus_v2_classification - Ade_corpos_v2_drug_ade_relation -> Ade_corpus_v2_drug_ade_relation - Ade_corpos_v2_drug_dosage_relation -> Ade_corpus_v2_drug_dosage_relation
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5,862
IndexError: list index out of range with data hosted on Zenodo
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2023-05-15T13:47:19Z
2023-06-16T14:54:02Z
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The dataset viewer sometimes raises an `IndexError`: ``` IndexError: list index out of range ``` See: - huggingface/datasets-server#1151 - https://huggingface.co/datasets/reddit/discussions/5 - huggingface/datasets-server#1118 - https://huggingface.co/datasets/krr-oxford/OntoLAMA/discussions/1 - https://huggingface.co/datasets/hyperpartisan_news_detection/discussions/3 - https://huggingface.co/datasets/um005/discussions/2 - https://huggingface.co/datasets/tapaco/discussions/2 - https://huggingface.co/datasets/common_language/discussions/3 - https://huggingface.co/datasets/pass/discussions/1 After investigation: - This happens with data files hosted on Zenodo - Indeed, there is an underlying 429 HTTP error: Too Many Requests Note that some time ago, it also happened with data files hosted on Google Drive. See: - #4581 - #4580 The reason then was that there was a 403 HTTP error: Forbidden
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[ "This error is also raised when data is hosted on Google Drive:\r\n- https://huggingface.co/datasets/docred/discussions/5\r\n- https://huggingface.co/datasets/linnaeus/discussions/3\r\n- https://huggingface.co/datasets/poleval2019_mt/discussions/3\r\n- https://huggingface.co/datasets/reddit_tifu/discussions/2\r\n- ...
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2,639
Refactor patching to specific submodule
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2021-07-13T15:08:45Z
2021-07-13T16:52:49Z
2021-07-13T16:52:49Z
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Minor reorganization of the code, so that additional patching functions (not related to streaming) might be created. In relation with the initial approach followed in #2631.
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4,347
Support remote cache_dir
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2022-05-13T14:26:35Z
2022-05-25T16:35:23Z
2022-05-25T16:27:03Z
null
This PR implements complete support for remote `cache_dir`. Before, the support was just partial. This is useful to create datasets using Apache Beam (parallel data processing) builder with `cache_dir` in a remote bucket, e.g., for Wikipedia dataset.
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[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq thanks for your review.\r\n\r\nPlease note that `xjoin` cannot be used in this context, as it always returns a POSIX path string and this is not suitable on Windows machines.", "<s>`xjoin` returns windows paths (not posix)...
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741,531,121
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843
use_custom_baseline still produces errors for bertscore
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2020-11-12T11:44:32Z
2021-08-31T10:06:44Z
2021-02-09T14:21:48Z
null
`metric = load_metric('bertscore')` `a1 = "random sentences"` `b1 = "random sentences"` `metric.compute(predictions = [a1], references = [b1], lang = 'en')` `Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/stephen_chan/.local/lib/python3.6/site-packages/datasets/metric.py", line 393, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/stephen_chan/.cache/huggingface/modules/datasets_modules/metrics/bertscore/361e597a01a41d6cf95d94bbfb01dea16261687abc0c6c74cc9930f80488f363/bertscore.py", line 108, in _compute hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline) TypeError: get_hash() missing 1 required positional argument: 'use_custom_baseline'` Adding 'use_custom_baseline = False' as an argument produces this error `Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/stephen_chan/.local/lib/python3.6/site-packages/datasets/metric.py", line 393, in compute output = self._compute(predictions=predictions, references=references, **kwargs) TypeError: _compute() got an unexpected keyword argument 'use_custom_baseline'` This is on Ubuntu 18.04, Python 3.6.9, datasets version 1.1.2
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[ "Thanks for reporting ! That's a bug indeed\r\nIf you want to contribute, feel free to fix this issue and open a PR :)", "This error is because of a mismatch between `datasets` and `bert_score`. With `datasets=1.1.2` and `bert_score>=0.3.6` it works ok. So `pip install -U bert_score` should fix the problem. ", ...
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878,490,927
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2,330
Allow passing `desc` to `tqdm` in `Dataset.map()`
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2021-05-07T05:52:54Z
2021-05-26T14:59:21Z
2021-05-26T14:59:21Z
null
It's normal to have many `map()` calls, and some of them can take a few minutes, it would be nice to have a description on the progress bar. Alternative solution: Print the description before/after the `map()` call.
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[ "Hi @lhoestq,\r\nShould we change `desc` in [pbar](https://github.com/huggingface/datasets/blob/81fcf88172ed5e3026ef68aed4c0ec6980372333/src/datasets/arrow_dataset.py#L1860) to something meaningful?", "I think the user could pass the `desc` parameter to `map` so that it can be displayed in the tqdm progress bar, ...
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112
Qa4mre - add dataset
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2020-05-14T22:17:51Z
2020-05-15T09:16:43Z
2020-05-15T09:16:42Z
null
Added dummy data test only for the first config. Will do the rest later. I had to do add some minor hacks to an important function to make it work. There might be a cleaner way to handle it - can you take a look @thomwolf ?
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4,517
Add tags for task_ids:summarization-* and task_categories:summarization*
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2022-06-16T18:52:25Z
2022-07-08T15:14:23Z
2022-07-08T15:02:31Z
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yaml header at top of README.md file was edited to add task tags because I couldn't find the existing tags in the json separate Pull Request will modify dataset_infos.json to add these tags The Enron dataset (dataset id aeslc) is only tagged with: arxiv:1906.03497' languages:en pretty_name:AESLC Using the email subject_line field as a label or target variable it possible to create models for the following task_ids (in order of relevance): 'task_ids:summarization' 'task_ids:summarization-other-conversations-summarization' "task_ids:other-other-query-based-multi-document-summarization" 'task_ids:summarization-other-aspect-based-summarization' 'task_ids:summarization--other-headline-generation' The subject might also be used for the task_category "task_categories:summarization" E-mail chains might be used for the task category "task_categories:dialogue-system"
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[ "Associated community discussion is [here](https://huggingface.co/datasets/aeslc/discussions/1).\r\nPaper referenced in the `dataset_infos.json` is [here](https://arxiv.org/pdf/1906.03497.pdf). It mentions the _email-subject-generation_ task, which is not a tag mentioned in any other dataset so it was not added in...
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2,974
Actually disable dummy labels by default
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2021-09-27T14:50:20Z
2021-09-29T09:04:42Z
2021-09-29T09:04:41Z
null
So I might have just changed the docstring instead of the actual default argument value and not realized. @lhoestq I'm sorry >.>
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564
Wait for writing in distributed metrics
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2020-09-02T12:58:50Z
2020-09-09T09:13:23Z
2020-09-09T09:13:22Z
null
There were CI bugs where a distributed metric would try to read all the files in process 0 while the other processes haven't started writing. To fix that I added a custom locking mechanism that waits for the file to exist before trying to read it
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[ "I agree this fix the problem for the CI where the files are always created in a new and clean temporary directory.\r\n\r\nHowever, in a general setting of a succession of fast distributed operation, the files could already exist from previous metrics runs but one process may still finish before another has even st...
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6,078
resume_download with streaming=True
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2023-07-26T14:08:22Z
2023-07-26T21:10:40Z
null
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### Describe the bug I used: ``` dataset = load_dataset( "oscar-corpus/OSCAR-2201", token=True, language="fr", streaming=True, split="train" ) ``` Unfortunately, the server had a problem during the training process. I saved the step my training stopped at. But how can I resume download from step 1_000_´000 without re-streaming all the first 1 million docs of the dataset? `download_config=DownloadConfig(resume_download=True)` seems to not work with streaming=True. ### Steps to reproduce the bug ``` from datasets import load_dataset, DownloadConfig dataset = load_dataset( "oscar-corpus/OSCAR-2201", token=True, language="fr", streaming=True, # optional split="train", download_config=DownloadConfig(resume_download=True) ) # interupt the run and try to relaunch it => this restart from scratch ``` ### Expected behavior I would expect a parameter to start streaming from a given index in the dataset. ### Environment info - `datasets` version: 2.14.0 - Platform: Linux-5.19.0-45-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.0
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[ "Currently, it's not possible to efficiently resume streaming after an error. Eventually, we plan to support this for Parquet (see https://github.com/huggingface/datasets/issues/5380). ", "Ok thank you for your answer", "I'm closing this as a duplicate of #5380" ]
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4,495
Fix patching module that doesn't exist
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2022-06-15T08:17:50Z
2022-06-15T16:40:49Z
2022-06-15T08:54:09Z
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Reported in https://github.com/huggingface/huggingface_hub/runs/6894703718?check_suite_focus=true When trying to patch `scipy.io.loadmat`: ```python ModuleNotFoundError: No module named 'scipy' ``` Instead it shouldn't raise an error and do nothing Bug introduced by #4375 Fix https://github.com/huggingface/datasets/issues/4494
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1,110
Using a feature named "_type" fails with certain operations
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2020-12-04T12:56:33Z
2022-01-14T18:07:00Z
2022-01-14T18:07:00Z
null
A column named `_type` leads to a `TypeError: unhashable type: 'dict'` for certain operations: ```python from datasets import Dataset, concatenate_datasets ds = Dataset.from_dict({"_type": ["whatever"]}).map() concatenate_datasets([ds]) # or simply Dataset(ds._data) ``` Context: We are using datasets to persist data coming from elasticsearch to feed to our pipeline, and elasticsearch has a `_type` field, hence the strange name of the column. Not sure if you wish to support this specific column name, but if you do i would be happy to try a fix and provide a PR. I already had a look into it and i think the culprit is the `datasets.features.generate_from_dict` function. It uses the hard coded `_type` string to figure out if it reached the end of the nested feature object from a serialized dict. Best wishes and keep up the awesome work!
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[ "Thanks for reporting !\r\n\r\nIndeed this is a keyword in the library that is used to encode/decode features to a python dictionary that we can save/load to json.\r\nWe can probably change `_type` to something that is less likely to collide with user feature names.\r\nIn this case we would want something backward ...
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1,903
Initial commit for the addition of TIMIT dataset
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2021-02-18T14:23:12Z
2021-03-01T09:39:12Z
2021-03-01T09:39:12Z
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Below points needs to be addressed: - Creation of dummy dataset is failing - Need to check on the data representation - License is not creative commons. Copyright: Portions © 1993 Trustees of the University of Pennsylvania Also the links (_except the download_) point to the ami corpus! ;-) @patrickvonplaten Requesting your comments, will be happy to address them!
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[ "@patrickvonplaten could you please review and help me close this PR?", "@lhoestq Thank you so much for your comments and for patiently reviewing the code. Have _hopefully_ included all the suggested changes. Let me know if any more changes are required.\r\n\r\nSorry the code had lots of silly errors from my sid...
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Add PubMed Central Open Access dataset
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2022-01-04T06:54:35Z
2022-01-17T15:25:57Z
2022-01-17T15:25:57Z
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## Adding a Dataset - **Name:** PubMed Central Open Access - **Description:** The PMC Open Access Subset includes more than 3.4 million journal articles and preprints that are made available under license terms that allow reuse. - **Paper:** *link to the dataset paper if available* - **Data:** https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/ - **Motivation:** *what are some good reasons to have this dataset* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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[ "In the framework of BigScience:\r\n- bigscience-workshop/data_tooling#121\r\n\r\nI have created this dataset as a community dataset: https://huggingface.co/datasets/albertvillanova/pmc_open_access\r\n\r\nHowever, I was wondering that it may be more appropriate to move it under an org namespace: `pubmed_central` or...
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4,723
Refactor conftest fixtures
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2022-07-20T12:15:22Z
2022-07-21T14:37:11Z
2022-07-21T14:24:18Z
null
Previously, fixture modules `hub_fixtures` and `s3_fixtures`: - were both at the root test directory - were imported using `import *` - as a side effect, the modules `os` and `pytest` were imported from `s3_fixtures` into `conftest` This PR: - puts both fixture modules in a dedicated directory `fixtures` - renames both to: `fixtures.hub` and `fixtures.s3` - imports them into `conftest` as plugins, using the `pytest_plugins`: this avoids the `import *` - additionally creates a new fixture module `fixtures.files` with all file-related fixtures
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336
[Dataset requests] New datasets for Open Question Answering
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2020-07-02T13:03:03Z
2020-07-16T09:04:22Z
2020-07-16T09:04:22Z
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We are still a few datasets missing for Open-Question Answering which is currently a field in strong development. Namely, it would be really nice to add: - WebQuestions (Berant et al., 2013) [done] - CuratedTrec (Baudis et al. 2015) [not open-source] - MS-MARCO (NGuyen et al. 2016) [done] - SearchQA (Dunn et al. 2017) [done] - FEVER (Thorne et al. 2018) - [ done] All these datasets are cited in http://arxiv.org/abs/2005.11401
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5,355
Clean up Table class docstrings
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2022-12-13T00:29:47Z
2022-12-13T18:17:56Z
2022-12-13T18:14:42Z
null
This PR cleans up the `Table` class docstrings :)
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The speechocean762 dataset
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2022-07-06T06:17:30Z
2022-10-03T09:34:36Z
2022-10-03T09:34:36Z
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[speechocean762](https://www.openslr.org/101/) is a non-native English corpus for pronunciation scoring tasks. It is free for both commercial and non-commercial use. I believe it will be easier to use if it could be available on Hugging Face.
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[ "CircleCL reported two errors, but I didn't find the reason. The error message:\r\n```\r\n_________________ ERROR collecting tests/test_dataset_cards.py _________________\r\ntests/test_dataset_cards.py:53: in <module>\r\n @pytest.mark.parametrize(\"dataset_name\", get_changed_datasets(repo_path))\r\ntests/test_d...
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2,123
Problem downloading GEM wiki_auto_asset_turk dataset
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2021-03-27T18:41:28Z
2021-05-12T16:15:18Z
2021-05-12T16:15:17Z
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@yjernite ### Summary I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code. ### Steps to reproduce Code snippet: from datasets import load_dataset #dataset = load_dataset('gem', 'web_nlg_en') dataset = load_dataset('gem', 'wiki_auto_asset_turk') ``` **Expected behavior:** I expect the dataset to start downloading (download bar appears and progresses toward 100%) **Actual behavior:** Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more: Downloading: 36.6kB [00:00, 37.2MB/s] Downloading: 41.7kB [00:00, ?B/s] Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d... ### Is this a regression? No, it was the first time I was trying to download this dataset (same for the other ones). ### Debug info - Python version: Python 3.8.2 - OS version: Windows 10 Family
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[ "Hi,\r\n\r\nsadly I can't replicate the problem on my Windows machine. Try to update the library to the newest version with:\r\n```bash\r\npip install git+https://github.com/huggingface/datasets\r\n``` ", "Thanks for the answer! I updated the library but unfortunately it didn't solve the problem.", "Is there an...
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4,035
Add zero_division argument to precision and recall metrics
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2022-03-28T08:19:14Z
2022-03-28T09:53:07Z
2022-03-28T09:53:06Z
null
Fix #4025.
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Can't import cc100 dataset
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2021-01-03T07:12:56Z
2022-10-05T12:42:25Z
2022-10-05T12:42:25Z
null
There is some issue to import cc100 dataset. ``` from datasets import load_dataset dataset = load_dataset("cc100") ``` FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py During handling of the above exception, another exception occurred: FileNotFoundError Traceback (most recent call last) FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py During handling of the above exception, another exception occurred: FileNotFoundError Traceback (most recent call last) /usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs) 280 raise FileNotFoundError( 281 "Couldn't find file locally at {}, or remotely at {} or {}".format( --> 282 combined_path, github_file_path, file_path 283 ) 284 ) FileNotFoundError: Couldn't find file locally at cc100/cc100.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py
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[ "cc100 was added recently, that's why it wasn't available yet.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `cc100` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nlang = \"en\"\r\ndataset = load_dataset(\"cc100\", la...
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4,189
Document how to use FAISS index for special operations
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2022-04-20T15:51:56Z
2022-05-06T08:43:10Z
2022-05-06T08:35:52Z
null
Document how to use FAISS index for special operations, by accessing the index itself. Close #4029.
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Add dataset Yoruba BBC Topic Classification
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2020-12-08T19:12:18Z
2020-12-10T11:27:41Z
2020-12-10T11:27:41Z
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Added new dataset Yoruba BBC Topic Classification Contains loading script as well as dataset card including YAML tags.
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Use Pandas' `read_json` in the JSON builder
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2023-04-20T17:15:49Z
2023-04-20T17:15:49Z
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Instead of PyArrow's `read_json`, we should use `pd.read_json` in the JSON builder for consistency with the CSV and SQL builders (e.g., to address https://github.com/huggingface/datasets/issues/5725). In Pandas2.0, to get the same performance, we can set the `engine` to "pyarrow". The issue is that Colab still doesn't install Pandas 2.0 by default, so I think it's best to wait for this to be resolved on their side to avoid downgrading decoding performance in scenarios when Pandas 2.0 is not installed.
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CI fails after bulk edit of canonical datasets
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2022-11-04T10:51:20Z
2023-02-16T09:11:10Z
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``` ______ test_get_dataset_config_info[paws-labeled_final-expected_splits2] _______ [gw0] linux -- Python 3.7.15 /opt/hostedtoolcache/Python/3.7.15/x64/bin/python path = 'paws', config_name = 'labeled_final' expected_splits = ['train', 'test', 'validation'] @pytest.mark.parametrize( "path, config_name, expected_splits", [ ("squad", "plain_text", ["train", "validation"]), ("dalle-mini/wit", "dalle-mini--wit", ["train"]), ("paws", "labeled_final", ["train", "test", "validation"]), ], ) def test_get_dataset_config_info(path, config_name, expected_splits): info = get_dataset_config_info(path, config_name=config_name) assert info.config_name == config_name > assert list(info.splits.keys()) == expected_splits E AssertionError: assert ['test', 'tra... 'validation'] == ['train', 'te... 'validation'] E At index 0 diff: 'test' != 'train' E Full diff: E - ['train', 'test', 'validation'] E + ['test', 'train', 'validation'] tests/test_inspect.py:45: AssertionError _ test_get_dataset_info[paws-expected_configs2-expected_splits_in_first_config2] _ [gw0] linux -- Python 3.7.15 /opt/hostedtoolcache/Python/3.7.15/x64/bin/python path = 'paws' expected_configs = ['labeled_final', 'labeled_swap', 'unlabeled_final'] expected_splits_in_first_config = ['train', 'test', 'validation'] @pytest.mark.parametrize( "path, expected_configs, expected_splits_in_first_config", [ ("squad", ["plain_text"], ["train", "validation"]), ("dalle-mini/wit", ["dalle-mini--wit"], ["train"]), ("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]), ], ) def test_get_dataset_info(path, expected_configs, expected_splits_in_first_config): infos = get_dataset_infos(path) assert list(infos.keys()) == expected_configs expected_config = expected_configs[0] assert expected_config in infos info = infos[expected_config] assert info.config_name == expected_config > assert list(info.splits.keys()) == expected_splits_in_first_config E AssertionError: assert ['test', 'tra... 'validation'] == ['train', 'te... 'validation'] E At index 0 diff: 'test' != 'train' E Full diff: E - ['train', 'test', 'validation'] E + ['test', 'train', 'validation'] tests/test_inspect.py:90: AssertionError ______ test_get_dataset_split_names[paws-labeled_final-expected_splits2] _______ [gw0] linux -- Python 3.7.15 /opt/hostedtoolcache/Python/3.7.15/x64/bin/python path = 'paws', expected_config = 'labeled_final' expected_splits = ['train', 'test', 'validation'] @pytest.mark.parametrize( "path, expected_config, expected_splits", [ ("squad", "plain_text", ["train", "validation"]), ("dalle-mini/wit", "dalle-mini--wit", ["train"]), ("paws", "labeled_final", ["train", "test", "validation"]), ], ) def test_get_dataset_split_names(path, expected_config, expected_splits): infos = get_dataset_infos(path) assert expected_config in infos info = infos[expected_config] assert info.config_name == expected_config > assert list(info.splits.keys()) == expected_splits E AssertionError: assert ['test', 'tra... 'validation'] == ['train', 'te... 'validation'] E At index 0 diff: 'test' != 'train' E Full diff: E - ['train', 'test', 'validation'] E + ['test', 'train', 'validation'] ```
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[ "Fixed by: https://huggingface.co/datasets/paws/discussions/1" ]
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606
Quick fix :)
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2020-09-10T14:32:06Z
2020-09-10T16:18:32Z
2020-09-10T16:18:30Z
null
`nlp` => `datasets`
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Add SVHN dataset
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2022-01-05T18:29:09Z
2022-01-12T14:14:35Z
2022-01-12T14:14:35Z
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Add the SVHN dataset. Additional notes: * compared to the TFDS implementation, exposes additional the "full numbers" config * adds the streaming support for `os.path.splitext` and `scipy.io.loadmat` * adds `h5py` to the requirements list for the dummy data test
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Update BibTeX entry
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2021-09-14T10:16:17Z
2021-09-14T12:25:37Z
2021-09-14T12:25:37Z
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Update BibTeX entry.
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FileNotFound remotly, can't load a dataset
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2020-12-10T09:14:47Z
2020-12-15T17:41:14Z
2020-12-15T17:41:14Z
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```py !pip install datasets import datasets as ds corpus = ds.load_dataset('large_spanish_corpus') ``` gives the error > FileNotFoundError: Couldn't find file locally at large_spanish_corpus/large_spanish_corpus.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/large_spanish_corpus/large_spanish_corpus.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/large_spanish_corpus/large_spanish_corpus.py not just `large_spanish_corpus`, `zest` too, but `squad` is available. this was using colab and localy
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[ "This dataset will be available in version-2 of the library. If you want to use this dataset now, install datasets from `master` branch rather.\r\n\r\nCommand to install datasets from `master` branch:\r\n`!pip install git+https://github.com/huggingface/datasets.git@master`", "Closing this, thanks @VasudevGupta7 "...
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2021-04-30T17:53:49Z
2021-05-03T08:17:31Z
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## Adding a Dataset - **Name:** *name of the dataset* - **Description:** *short description of the dataset (or link to social media or blog post)* - **Paper:** *link to the dataset paper if available* - **Data:** *link to the Github repository or current dataset location* - **Motivation:** *what are some good reasons to have this dataset* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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add telugu-news corpus
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2020-12-07T09:08:48Z
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Adding Telugu News Corpus to datasets.
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Creating dataset consumes too much memory
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2020-10-18T06:07:06Z
2022-02-15T17:03:10Z
2022-02-15T17:03:10Z
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Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)
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[ "Thanks for reporting.\r\nIn theory since the dataset script is just made to yield examples to write them into an arrow file, it's not supposed to create memory issues.\r\n\r\nCould you please try to run this exact same loop in a separate script to see if it's not an issue with `PIL` ?\r\nYou can just copy paste wh...
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Filter unsupported extensions
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2023-06-21T15:43:01Z
2023-06-22T14:23:29Z
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I used a regex to filter the data files based on their extension for packaged builders. I tried and a regex is 10x faster that using `in` to check if the extension is in the list of supported extensions. Supersedes https://github.com/huggingface/datasets/pull/5850 Close https://github.com/huggingface/datasets/issues/5849 I also did a small change to favor the parquet module in case of a draw in the extension counter.
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a...
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Fix metric compute (original_instructions missing)
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2020-06-16T08:52:01Z
2020-06-18T07:41:45Z
2020-06-18T07:41:44Z
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When loading arrow data we added in cc8d250 a way to specify the instructions that were used to store them with the loaded dataset. However metrics load data the same way but don't need instructions (we use one single file). In this PR I just make `original_instructions` optional when reading files to load a `Dataset` object. This should fix #269
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[ "Awesome! This is working now:\r\n\r\n```python\r\nimport nlp \r\nseqeval = nlp.load_metric(\"seqeval\") \r\ny_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] \r\ny_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] ...
https://api.github.com/repos/huggingface/datasets/issues/4410
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4,410
Remove Google Drive URL in spider dataset
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2022-05-26T06:17:35Z
2022-05-26T06:48:42Z
2022-05-26T06:40:12Z
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The `spider` dataset is distributed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode) license. Fix #4401.
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1,317,260,663
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4,742
Dummy data nowhere to be found
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2022-07-25T19:18:42Z
2022-11-04T14:04:24Z
2022-11-04T14:04:10Z
null
## Describe the bug To finalize my dataset, I wanted to create dummy data as per the guide and I ran ```shell datasets-cli dummy_data datasets/hebban-reviews --auto_generate ``` where hebban-reviews is [this repo](https://huggingface.co/datasets/BramVanroy/hebban-reviews). And even though the scripts runs and shows a message at the end that it succeeded, I cannot find the dummy data anywhere. Where is it? ## Expected results To see the dummy data in the datasets' folder or in the folder where I ran the command. ## Actual results I see the following message but I cannot find the dummy data anywhere. ``` Dummy data generation done and dummy data test succeeded for config 'filtered''. Automatic dummy data generation succeeded for all configs of '.\datasets\hebban-reviews\' ``` ## Environment info - `datasets` version: 2.4.1.dev0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.8.8 - PyArrow version: 8.0.0 - Pandas version: 1.4.3
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[ "Hi @BramVanroy, thanks for reporting.\r\n\r\nFirst of all, please note that you do not need the dummy data: this was the case when we were adding datasets to the `datasets` library (on this GitHub repo), so that we could test the correct loading of all datasets with our CI. However, this is no longer the case for ...
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3,022
MeDAL dataset: Add further description and update download URL
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2021-10-05T00:13:28Z
2021-10-13T09:03:09Z
2021-10-13T09:03:09Z
null
Added more details in the following sections: * Dataset Structure * Data Instances * Data Splits * Source Data * Annotations * Discussions of Biases * LIcensing Information
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[ "@lhoestq I'm a bit confused by the error message. I haven't touched the YAML code at all - do you have any insight on that?", "I just added the missing `pretty_name` tag in the YAML - sorry about that ;)", "Thanks! Seems like it did the trick since the tests are passing. Let me know if there's anything else I ...
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4,901
Raise ManualDownloadError from get_dataset_config_info
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2022-08-26T15:45:56Z
2022-08-30T10:42:21Z
2022-08-30T10:40:04Z
null
This PRs raises a specific `ManualDownloadError` when `get_dataset_config_info` is called for a dataset that requires manual download. Related to: - #4898 CC: @severo
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4,288
Add missing `faiss` import to fix https://github.com/huggingface/datasets/issues/4287
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2022-05-05T15:21:49Z
2022-05-10T12:55:06Z
2022-05-10T12:09:48Z
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This PR fixes the issue recently mentioned in https://github.com/huggingface/datasets/issues/4287 🤗
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1,115,133,279
I_kwDODunzps5Cd5Vf
3,634
Dataset.shuffle(seed=None) gives fixed row permutation
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2022-01-26T15:13:08Z
2022-01-27T18:16:07Z
2022-01-27T18:16:07Z
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## Describe the bug Repeated attempts to `shuffle` a dataset without specifying a seed give the same results. ## Steps to reproduce the bug ```python import datasets # Some toy example data = datasets.Dataset.from_dict( {"feature": [1, 2, 3, 4, 5], "label": ["a", "b", "c", "d", "e"]} ) # Doesn't work as expected print("Shuffle dataset") for _ in range(3): print(data.shuffle(seed=None)[:]) # This seems to work with pandas print("\nShuffle via pandas") for _ in range(3): df = data.to_pandas().sample(frac=1.0) print(datasets.Dataset.from_pandas(df, preserve_index=False)[:]) ``` ## Expected results I assumed that the default setting would initialize a new/random state of a `np.random.BitGenerator` (see [docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=shuffle#datasets.Dataset.shuffle)). Wouldn't that reshuffle the rows each time I call `data.shuffle()`? ## Actual results ```bash Shuffle dataset {'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']} {'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']} {'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']} Shuffle via pandas {'feature': [4, 2, 3, 1, 5], 'label': ['d', 'b', 'c', 'a', 'e']} {'feature': [2, 5, 3, 4, 1], 'label': ['b', 'e', 'c', 'd', 'a']} {'feature': [5, 2, 3, 1, 4], 'label': ['e', 'b', 'c', 'a', 'd']} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.0 - Platform: Linux-5.13.0-27-generic-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 6.0.1
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[ "I'm not sure if this is expected behavior.\r\n\r\nAm I supposed to work with a copy of the dataset, i.e. `shuffled_dataset = data.shuffle(seed=None)`?\r\n\r\n```diff\r\nimport datasets\r\n\r\n# Some toy example\r\ndata = datasets.Dataset.from_dict(\r\n {\"feature\": [1, 2, 3, 4, 5], \"label\": [\"a\", \"b\", \"...
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3,424
Add RedCaps dataset
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2021-12-13T13:38:13Z
2022-01-12T14:13:16Z
2022-01-12T14:13:15Z
null
Add the RedCaps dataset. I'm not adding the generated `dataset_infos.json` file for now due to its size (11 MB). TODOs: - [x] dummy data - [x] dataset card Close #3316
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[ "Cool ! If you want you can include `dataset_infos.json` but only for the main configurations. That's what we do for example for translation datasets when there are too many configs", "@lhoestq I've added an example that uses `map` to download the images." ]
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1,311,907,096
I_kwDODunzps5OMh0Y
4,725
the_pile datasets URL broken.
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2022-07-20T20:57:30Z
2022-07-22T06:09:46Z
2022-07-21T07:38:19Z
null
https://github.com/huggingface/datasets/pull/3627 changed the Eleuther AI Pile dataset URL from https://the-eye.eu/ to https://mystic.the-eye.eu/ but the latter is now broken and the former works again. Note that when I git clone the repo and use `pip install -e .` and then edit the URL back the codebase doesn't seem to use this edit so the mystic URL is also cached somewhere else that I can't find?
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[ "Thanks for reporting, @TrentBrick. We are addressing the change with their data host server.\r\n\r\nOn the meantime, if you would like to work with your fixed local copy of the_pile script, you should use:\r\n```python\r\nload_dataset(\"path/to/your/local/the_pile/the_pile.py\",...\r\n```\r\ninstead of just `load_...
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4,867
Complete tags of superglue dataset card
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2022-08-19T23:44:39Z
2022-08-22T09:14:03Z
2022-08-22T08:58:31Z
null
Related to #4479 .
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MDExOlB1bGxSZXF1ZXN0NTM1Mzg2ODA1
1,405
Adding TaPaCo Dataset with README.md
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2020-12-09T18:42:58Z
2020-12-13T19:11:18Z
2020-12-13T19:11:18Z
null
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[ "We want to keep the repo as light as possible so that it doesn't take ages to clone, that's why we ask for small dummy data files (especially when there are many of them). Let me know if you have questions or if we can help you on this", "Hello @lhoestq , made the changes as you suggested and pushed, please revi...
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5,526
Allow loading/saving of FAISS index using fsspec
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2023-02-10T23:37:14Z
2023-03-27T15:26:46Z
2023-03-27T15:18:20Z
null
Fixes #5428 Allow loading/saving of FAISS index using fsspec: 1. Simply use BufferedIOWriter/Reader to Read/Write indices on fsspec stream. 2. Needed `mockfs` in the test, so I took it out of the `TestCase`. Let me know if that makes sense. I can work on the documentation once the code changes are approved.
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks for the quick review! I updated the code with your suggestion", "Thanks for the quick review @albertvillanova! I updated the code with your suggestions", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n...
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1,706,616,634
I_kwDODunzps5luOc6
5,847
Streaming IterableDataset not working with translation pipeline
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2023-05-11T21:52:38Z
2023-05-16T15:59:55Z
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### Describe the bug I'm trying to use a streaming dataset for translation inference to avoid downloading the training data. I'm using a pipeline and a dataset, and following the guidance in the tutorial. Instead I get an exception that IterableDataset has no len(). ### Steps to reproduce the bug CODE: ``` from transformers import pipeline from transformers.pipelines.pt_utils import KeyDataset from datasets import load_dataset ds = load_dataset(path="wmt14", name="fr-en", split="test", streaming=True) bs=1 mt = pipeline("translation_en_to_fr", model="t5-base", batch_size=bs) #print(mt("hello")) THIS WORKS ks = KeyDataset(ds, "translation") print(f"{ks}") xx= mt(ks) for x in xx: print(x) ``` RUN: ``` (watnlp) [jlquinn@bertdev01 hf]$ python ende.t5.pipe.py 2023-05-11 16:48:08.817572: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2023-05-11 16:48:08.821388: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory 2023-05-11 16:48:08.821407: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. <transformers.pipelines.pt_utils.KeyDataset object at 0x7f61ed5da9d0> Traceback (most recent call last): File "/home/jlquinn/models/hf/ende.t5.pipe.py", line 11, in <module> for x in xx: File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/pt_utils.py", line 111, in __next__ item = next(self.iterator) File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/pt_utils.py", line 111, in __next__ item = next(self.iterator) File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 681, in __next__ data = self._next_data() File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 720, in _next_data index = self._next_index() # may raise StopIteration File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 671, in _next_index return next(self._sampler_iter) # may raise StopIteration File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/sampler.py", line 247, in __iter__ for idx in self.sampler: File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/sampler.py", line 76, in __iter__ return iter(range(len(self.data_source))) File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/pt_utils.py", line 13, in __len__ return len(self.dataset) File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/pt_utils.py", line 289, in __len__ return len(self.dataset) TypeError: object of type 'IterableDataset' has no len() ``` ### Expected behavior I'm expecting french translations of the english test set to be printed. ### Environment info Run on CPU with no GPU. RHEL 8.7 x86_64 python 3.9.0 transformers 4.17.0 datasets 2.0.0 tokenizers 0.12.1 ``` (watnlp) [jlquinn@bertdev01 hf]$ datasets-cli env Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-372.19.1.el8_6.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.0 - PyArrow version: 8.0.0 - Pandas version: 1.4.4 ```
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[ "I wasn't sure to file this against transformers or datasets.", "[`KeyDataset`](https://github.com/huggingface/transformers/blob/7f8b909189547944617741d8d3c6c84504701693/src/transformers/pipelines/pt_utils.py#L296) doesn't support iterable datasets, so you either need to implement a version that does (and also in...
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1,277,897,197
PR_kwDODunzps46AFK_
4,534
Add `tldr_news` dataset
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2
2022-06-21T05:02:43Z
2022-06-23T14:33:54Z
2022-06-21T14:21:11Z
null
This PR aims at adding support for a news dataset: `tldr news`. This dataset is based on the daily [tldr tech newsletter](https://tldr.tech/newsletter) and contains a `headline` as well as a `content` for every piece of news contained in a newsletter.
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[ "Hey @lhoestq, \r\nSorry for opening a PR, I was following the guide [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)! Thanks for the review anyway, I will follow the instructions you sent 😃 ", "Thanks, we will update the guide ;)" ]
https://api.github.com/repos/huggingface/datasets/issues/2286
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871,032,393
MDExOlB1bGxSZXF1ZXN0NjI2MTE5MTE2
2,286
Fix metadata validation with config names
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2021-04-29T13:44:32Z
2021-04-29T14:07:29Z
2021-04-29T14:07:28Z
null
I noticed in https://github.com/huggingface/datasets/pull/2280 that the metadata validator doesn't parse the tags in the readme properly when then contain the tags per config.
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5,026
patch CI_HUB_TOKEN_PATH with Path instead of str
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2022-09-26T13:19:01Z
2022-09-26T14:30:55Z
2022-09-26T14:28:45Z
null
Should fix the tests for `huggingface_hub==0.10.0rc0` prerelease (see [failed CI](https://github.com/huggingface/datasets/actions/runs/3127805250/jobs/5074879144)). Related to [this thread](https://huggingface.slack.com/archives/C02V5EA0A95/p1664195165294559) (internal link). Note: this should be a backward compatible fix (e.g. works also with previous versions of `huggingface_hub`) I am not sure where to put the changes so feel free to cherry-pick the commit and close this one without merging. cc @lhoestq
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
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Dataset caching policy
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2022-04-27T10:42:11Z
2022-04-27T16:29:25Z
2022-04-27T16:28:50Z
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## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ```
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[ "Hi @loretoparisi, thanks for reporting.\r\n\r\nThere is an option to force the redownload of the data files (and thus not using previously download and cached data files): `load_dataset(..., download_mode=\"force_redownload\")`.\r\n\r\nPlease, let me know if this fixes your problem.\r\n\r\nI can confirm you that y...
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4,699
Fix Authentification Error while streaming
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2022-07-18T08:03:41Z
2022-07-20T13:10:44Z
2022-07-20T13:10:43Z
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I fixed a few errors when it occurs while streaming the private dataset on the Huggingface Hub. ``` from datasets import load_dataset dataset = load_dataset(<repo_id>, use_auth_token=<private_token>, streaming=True) for d in dataset['train']: print(d) break # this is for checking ``` This code is an example for streaming private datasets. when the version of the datasets is 2.2.2, it works well but datasets>2.2.2 occurs error like this, ``` /usr/local/lib/python3.7/dist-packages/aiohttp/client_reqrep.py in raise_for_status(self) 1007 status=self.status, 1008 message=self.reason, → 1009 headers=self.headers, 1010 ) 1011 ClientResponseError: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/.../train-00000-of-00001-168b451062c67c34.parquet') ``` (this is an example on the dataset has `parquet` extenstion) It seems that the `xisfile `module in `download/streaming_download_manager.py` couldn't recognize the file on "https://huggingface.co/~". so I add three lines. With this change, there is no error anymore(but this code is ad-hoc).
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[ "Hi, thanks for working on this, but the fix for this has already been merged in https://github.com/huggingface/datasets/pull/4608." ]
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Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True`
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2021-06-29T07:43:03Z
2022-08-04T11:58:36Z
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## Describe the bug If i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets. ## Steps to reproduce the bug - Create a local dataset builder class - load the local builder class file using `load_dataset` and let the cache build - update the file's content - The cache should rebuilt. ## Expected results With `ignore_verifications=True`, `load_dataset` should pick up existing cache. ## Actual results Creates new cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.8.0 - Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid - Python version: 3.7.7 - PyArrow version: 3.0.0
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[ "Hi ! I just tried to reproduce what you said:\r\n- create a local builder class\r\n- use `load_dataset`\r\n- update the builder class code\r\n- use `load_dataset` again (with or without `ignore_verifications=True`)\r\nAnd it creates a new cache, as expected.\r\n\r\nWhat modifications did you do to your builder's c...
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2,317
Fix incorrect version specification for the pyarrow package
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2021-05-04T19:30:20Z
2021-05-05T10:09:16Z
2021-05-05T09:21:58Z
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This PR addresses the bug in the pyarrow version specification, which is detailed in #2316 . Simply, I put a comma between the version bounds. Fix #2316.
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Missing original answers in kilt-TriviaQA
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2021-05-21T14:57:07Z
2021-06-14T17:29:11Z
2021-06-14T17:29:11Z
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I previously opened an issue at https://github.com/facebookresearch/KILT/issues/42 but from the answer of @fabiopetroni it seems that the problem comes from HF-datasets ## Describe the bug The `answer` field in kilt-TriviaQA, e.g. `kilt_tasks['train_triviaqa'][0]['output']['answer']` contains a list of alternative answer which are accepted for the question. However it'd be nice to know the original answer to the question (the only fields in `output` are `'answer', 'meta', 'provenance'`) ## How to fix It can be fixed by retrieving the original answer from the original TriviaQA (e.g. `trivia_qa['train'][0]['answer']['value']`), perhaps at the same place as here where one retrieves the questions https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md#loading-the-kilt-knowledge-source-and-task-data cc @yjernite who previously answered to an issue about KILT and TriviaQA :)
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[ "That could be useful indeed! Feel free to open a PR on the dataset card if you already have some code that runs, otherwise we'll take care of it soon :) ", "I can open a PR but there is 2 details to fix:\r\n- the name for the corresponding key (e.g. `original_answer`)\r\n- how to implement it: I’m not sure what ...
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5,680
Fix a description error for interleave_datasets.
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2023-03-29T09:50:23Z
2023-03-30T13:14:19Z
2023-03-30T13:07:18Z
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There is a description mistake in the annotation of interleave_dataset with "all_exhausted" stopping_strategy. ``` python d1 = Dataset.from_dict({"a": [0, 1, 2]}) d2 = Dataset.from_dict({"a": [10, 11, 12, 13]}) d3 = Dataset.from_dict({"a": [20, 21, 22, 23, 24]}) dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted") ``` According to the interleave way, the correct output of `dataset["a"]` is `[0, 10, 20, 1, 11, 21, 2, 12, 22, 0, 13, 23, 1, 10, 24]`, not `[0, 10, 20, 1, 11, 21, 2, 12, 22, 0, 13, 23, 1, 0, 24]`
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[ "_The documentation is not available anymore as the PR was closed or merged._", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_a...
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3,675
Add CodeContests dataset
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2022-02-03T13:20:00Z
2022-07-20T11:07:05Z
2022-07-20T11:07:05Z
null
## Adding a Dataset - **Name:** CodeContests - **Description:** CodeContests is a competitive programming dataset for machine-learning. - **Paper:** - **Data:** https://github.com/deepmind/code_contests - **Motivation:** This dataset was used when training [AlphaCode](https://deepmind.com/blog/article/Competitive-programming-with-AlphaCode). Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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[ "@mariosasko Can I take this up?", "This dataset is now available here: https://huggingface.co/datasets/deepmind/code_contests." ]
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5,581
[DOC] Mistaken docs on set_format
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2023-02-27T08:03:09Z
2023-02-28T19:19:17Z
2023-02-28T19:19:17Z
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### Describe the bug https://huggingface.co/docs/datasets/v2.10.0/en/package_reference/main_classes#datasets.Dataset.set_format <img width="700" alt="image" src="https://user-images.githubusercontent.com/36224762/221506973-ae2e3991-60a7-4d4e-99f8-965c6eb61e59.png"> While actually running it will result in: <img width="1094" alt="image" src="https://user-images.githubusercontent.com/36224762/221507032-007dab82-8781-4319-b21a-e6e4d40d97b3.png"> ### Steps to reproduce the bug _ ### Expected behavior _ ### Environment info - `datasets` version: 2.10.0 - Platform: Linux-5.10.147+-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 9.0.0 - Pandas version: 1.3.5
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5,350
Clean up Loading methods docstrings
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2022-12-09T22:25:30Z
2022-12-12T17:27:20Z
2022-12-12T17:24:01Z
null
Clean up for the docstrings in Loading methods!
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3,616
Make streamable the BnL Historical Newspapers dataset
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2022-01-22T14:52:36Z
2022-02-04T14:05:23Z
2022-02-04T14:05:21Z
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I've refactored the code in order to make the dataset streamable and to avoid it takes too long: - I've used `iter_files` Close #3615
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can't load local dataset: pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries
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2020-07-09T16:16:53Z
2020-12-15T23:07:22Z
2020-07-10T14:52:06Z
null
Trying to load a local SQuAD-formatted dataset (from a JSON file, about 60MB): ``` dataset = nlp.load_dataset(path='json', data_files={nlp.Split.TRAIN: ["./path/to/file.json"]}) ``` causes ``` Traceback (most recent call last): File "dataloader.py", line 9, in <module> ["./path/to/file.json"]}) File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/load.py", line 524, in load_dataset save_infos=save_infos, File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 432, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 483, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 719, in _prepare_split for key, table in utils.tqdm(generator, unit=" tables", leave=False): File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/tqdm/std.py", line 1129, in __iter__ for obj in iterable: File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/datasets/json/88c1bc5c68489f7eda549ed05a5a738527c613b3e7a4ee3524d9d233353a949b/json.py", line 53, in _generate_tables file, read_options=self.config.pa_read_options, parse_options=self.config.pa_parse_options, File "pyarrow/_json.pyx", line 191, in pyarrow._json.read_json File "pyarrow/error.pxi", line 85, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?) ``` I haven't been able to find any reports of this specific pyarrow error here or elsewhere.
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[ "I am able to reproduce this with the official SQuAD `train-v2.0.json` file downloaded directly from https://rajpurkar.github.io/SQuAD-explorer/", "I am facing this issue in transformers library 3.0.2 while reading a csv using datasets.\r\nIs this fixed in latest version? \r\nI updated the latest version 4.0.1 bu...
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4,377
Fix checksum and bug in irc_disentangle dataset
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2022-05-20T07:29:28Z
2022-05-20T09:34:36Z
2022-05-20T09:26:32Z
null
There was a bug in filepath segment: - wrong: `jkkummerfeld-irc-disentanglement-fd379e9` - right: `jkkummerfeld-irc-disentanglement-35f0a40` Also there was a bug in the checksum of the downloaded file. This PR fixes these issues. Fix partially #4376.
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User permissions
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2021-03-31T19:33:48Z
2021-03-31T19:34:24Z
2021-03-31T19:34:24Z
null
Updated user permissions based on running user's umask. Let me know if `0o666` is looking good or should I change it to `~umask` only (to give execute permissions as well)
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Move Dataset.to_csv to csv module
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2021-03-23T14:35:46Z
2021-03-24T14:07:35Z
2021-03-24T14:07:34Z
null
Move the implementation of `Dataset.to_csv` to module `datasets.io.csv`.
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add books3
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2021-08-14T07:04:25Z
2021-08-19T16:43:09Z
2021-08-18T15:36:59Z
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books3 is part of EleutherAI/The Pile, but AFAIK, The Pile dataset blend all sub datasets together thus we are not able to use just one of its sub dataset from The Pile data. So I create an independent dataset using The Pile preliminary components. When I was creating dataset card. I found there is room for creating / editing dataset card. I've made it an issue. #2797 Also I am wondering whether the import of The Pile dataset is actively undertaken (because I may need it recently)? #1675
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[ "> When I was creating dataset card. I found there is room for creating / editing dataset card. I've made it an issue. #2797\r\n\r\nThanks for the message, we'll definitely improve this\r\n\r\n> Also I am wondering whether the import of The Pile dataset is actively undertaken (because I may need it recently)? #1675...
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562
[Reproductibility] Allow to pin versions of datasets/metrics
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2020-09-02T10:30:13Z
2020-09-09T13:04:54Z
2020-09-09T13:04:54Z
null
Repurpose the `version` attribute in datasets and metrics to let the user pin a specific version of datasets and metric scripts: ``` dataset = nlp.load_dataset('squad', version='1.0.0') metric = nlp.load_metric('squad', version='1.0.0') ``` Notes: - version number are the release version of the library - currently only possible for canonical datasets/metrics, ie. integrated in the GitHub repo of the library
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[ "Closing this one in favor of #584 " ]
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512
Delete CONTRIBUTING.md
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2020-08-18T15:33:25Z
2020-08-18T15:48:21Z
2020-08-18T15:39:07Z
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[ "😱", "Yeah, this is spammy behavior. I've reported the user handle." ]
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2,402
PermissionError on Windows when using temp dir for caching
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2021-05-24T21:22:59Z
2021-05-26T16:39:29Z
2021-05-26T16:39:29Z
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Currently, the following code raises a PermissionError on master if working on Windows: ```python # run as a script or call exit() in REPL to initiate the temp dir cleanup from datasets import * d = load_dataset("sst", split="train", keep_in_memory=False) set_caching_enabled(False) d.map(lambda ex: ex) ``` Error stack trace: ``` Traceback (most recent call last): File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\weakref.py", line 624, in _exitfunc f() File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\weakref.py", line 548, in __call__ return info.func(*info.args, **(info.kwargs or {})) File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\tempfile.py", line 799, in _cleanup _shutil.rmtree(name) File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 500, in rmtree return _rmtree_unsafe(path, onerror) File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 395, in _rmtree_unsafe onerror(os.unlink, fullname, sys.exc_info()) File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 393, in _rmtree_unsafe os.unlink(fullname) PermissionError: [WinError 5] Access is denied: 'C:\\Users\\Mario\\AppData\\Local\\Temp\\tmp20epyhmq\\cache-87a87ffb5a956e68.arrow' ```
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5,483
Unable to upload dataset
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2023-01-28T15:18:26Z
2023-01-29T08:09:49Z
2023-01-29T08:09:49Z
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### Describe the bug Uploading a simple dataset ends with an exception ### Steps to reproduce the bug I created a new conda env with python 3.10, pip installed datasets and: ```python >>> from datasets import load_dataset, load_from_disk, Dataset >>> d = Dataset.from_dict({"text": ["hello"] * 2}) >>> d.push_to_hub("ttt111") /home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/utils/_hf_folder.py:92: UserWarning: A token has been found in `/a/home/cc/students/cs/kirstain/.huggingface/token`. This is the old path where tokens were stored. The new location is `/home/olab/kirstain/.cache/huggingface/token` which is configurable using `HF_HOME` environment variable. Your token has been copied to this new location. You can now safely delete the old token file manually or use `huggingface-cli logout`. warnings.warn( Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 279.94ba/s] Upload 1 LFS files: 0%| | 0/1 [00:02<?, ?it/s] Pushing dataset shards to the dataset hub: 0%| | 0/1 [00:04<?, ?it/s] Traceback (most recent call last): File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 264, in hf_raise_for_status response.raise_for_status() File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/requests/models.py", line 1021, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://s3.us-east-1.amazonaws.com/lfs.huggingface.co/repos/cf/0c/cf0c5ab8a3f729e5f57a8b79a36ecea64a31126f13218591c27ed9a1c7bd9b41/ece885a4bb6bbc8c1bb51b45542b805283d74590f72cd4c45d3ba76628570386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230128%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230128T151640Z&X-Amz-Expires=900&X-Amz-Signature=89e78e9a9d70add7ed93d453334f4f93c6f29d889d46750a1f2da04af73978db&X-Amz-SignedHeaders=host&x-amz-storage-class=INTELLIGENT_TIERING&x-id=PutObject The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/_commit_api.py", line 334, in _inner_upload_lfs_object return _upload_lfs_object( File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/_commit_api.py", line 391, in _upload_lfs_object lfs_upload( File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/lfs.py", line 273, in lfs_upload _upload_single_part( File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/lfs.py", line 305, in _upload_single_part hf_raise_for_status(upload_res) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 318, in hf_raise_for_status raise HfHubHTTPError(str(e), response=response) from e huggingface_hub.utils._errors.HfHubHTTPError: 403 Client Error: Forbidden for url: https://s3.us-east-1.amazonaws.com/lfs.huggingface.co/repos/cf/0c/cf0c5ab8a3f729e5f57a8b79a36ecea64a31126f13218591c27ed9a1c7bd9b41/ece885a4bb6bbc8c1bb51b45542b805283d74590f72cd4c45d3ba76628570386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230128%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230128T151640Z&X-Amz-Expires=900&X-Amz-Signature=89e78e9a9d70add7ed93d453334f4f93c6f29d889d46750a1f2da04af73978db&X-Amz-SignedHeaders=host&x-amz-storage-class=INTELLIGENT_TIERING&x-id=PutObject The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 4909, in push_to_hub repo_id, split, uploaded_size, dataset_nbytes, repo_files, deleted_size = self._push_parquet_shards_to_hub( File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 4804, in _push_parquet_shards_to_hub _retry( File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 281, in _retry return func(*func_args, **func_kwargs) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 124, in _inner_fn return fn(*args, **kwargs) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2537, in upload_file commit_info = self.create_commit( File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 124, in _inner_fn return fn(*args, **kwargs) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2346, in create_commit upload_lfs_files( File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 124, in _inner_fn return fn(*args, **kwargs) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/_commit_api.py", line 346, in upload_lfs_files thread_map( File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/tqdm/contrib/concurrent.py", line 94, in thread_map return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/tqdm/contrib/concurrent.py", line 76, in _executor_map return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs)) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/tqdm/std.py", line 1195, in __iter__ for obj in iterable: File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/concurrent/futures/_base.py", line 621, in result_iterator yield _result_or_cancel(fs.pop()) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/concurrent/futures/_base.py", line 319, in _result_or_cancel return fut.result(timeout) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/concurrent/futures/_base.py", line 458, in result return self.__get_result() File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result raise self._exception File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/concurrent/futures/thread.py", line 58, in run result = self.fn(*self.args, **self.kwargs) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/_commit_api.py", line 338, in _inner_upload_lfs_object raise RuntimeError( RuntimeError: Error while uploading 'data/train-00000-of-00001-6df93048e66df326.parquet' to the Hub. ``` ### Expected behavior The dataset should be uploaded without any exceptions ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-4.15.0-65-generic-x86_64-with-glibc2.27 - Python version: 3.10.9 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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[ "Seems to work now, perhaps it was something internal with our university's network." ]
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## Describe the L L ## Expected L A clear and concise lmll Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: - Python version: - PyArrow version:
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Add NoReC: Norwegian Review Corpus
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Question/problem with dataset labels
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2021-03-04T17:06:53Z
2023-07-24T14:39:33Z
2023-07-24T14:39:33Z
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Hi, I'm using a dataset with two labels "nurse" and "not nurse". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are "nurse" and "surgeon". This is the trace I get: ``` File "../../../models/tr-4.3.2/run_puppets.py", line 523, in <module> main() File "../../../models/tr-4.3.2/run_puppets.py", line 249, in main datasets = load_dataset("csv", data_files=data_files) File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/load.py", line 740, in load_dataset builder_instance.download_and_prepare( File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/builder.py", line 572, in download_and_prepare self._download_and_prepare( File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/builder.py", line 650, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/builder.py", line 1028, in _prepare_split writer.write_table(table) File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/arrow_writer.py", line 292, in write_table pa_table = pa_table.cast(self._schema) File "pyarrow/table.pxi", line 1311, in pyarrow.lib.Table.cast File "pyarrow/table.pxi", line 265, in pyarrow.lib.ChunkedArray.cast File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/pyarrow/compute.py", line 87, in cast return call_function("cast", [arr], options) File "pyarrow/_compute.pyx", line 298, in pyarrow._compute.call_function File "pyarrow/_compute.pyx", line 192, in pyarrow._compute.Function.call File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Failed to parse string: not nurse ``` Any ideas how to fix this? For now, I'll probably make them numeric.
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[ "It seems that I get parsing errors for various fields in my data. For example now I get this:\r\n```\r\n File \"../../../models/tr-4.3.2/run_puppets.py\", line 523, in <module>\r\n main()\r\n File \"../../../models/tr-4.3.2/run_puppets.py\", line 249, in main\r\n datasets = load_dataset(\"csv\", data_files...
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Fix conda release
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2021-05-03T14:52:59Z
2021-05-03T16:01:17Z
2021-05-03T16:01:17Z
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There were a few issues with conda releases (they've been failing for a while now). To fix this I had to: - add the --single-version-externally-managed tag to the build stage (suggestion from [here](https://stackoverflow.com/a/64825075)) - set the python version of the conda build stage to 3.8 since 3.9 isn't supported - sync the evrsion requirement of `huggingface_hub` With these changes I'm working on uploading all missing versions until 1.6.2 to conda EDIT: I managed to build and upload all missing versions until 1.6.2 to conda :)
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Iterable torch formatting
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2023-05-12T16:48:49Z
2023-06-13T16:04:05Z
2023-06-13T15:57:05Z
null
Used the TorchFormatter to get torch tensors in iterable dataset with format set to "torch". It uses the data from Arrow if possible, otherwise applies recursive_tensorize. When set back to format_type=None, cast_to_python_objects is used. requires https://github.com/huggingface/datasets/pull/5821 close https://github.com/huggingface/datasets/issues/5793
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a...
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2,648
Add web_split dataset for Paraphase and Rephrase benchmark
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2021-07-14T14:24:36Z
2021-07-14T14:26:12Z
null
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## Describe: For getting simple sentences from complex sentence there are dataset and task like wiki_split that is available in hugging face datasets. This web_split is a very similar dataset. There some research paper which states that by combining these two datasets we if we train the model it will yield better results on both tests data. This dataset is made from web NLG data. All the dataset related details are provided in the below repository Github link: https://github.com/shashiongithub/Split-and-Rephrase
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Add MultiBooked dataset
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1
2020-12-08T22:49:36Z
2020-12-15T17:02:09Z
2020-12-15T17:02:09Z
null
Add dataset.
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[ "There' still an issue with the dummy data, let me take a look" ]
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1,743
Issue while Creating Custom Metric
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2021-01-17T07:01:14Z
2022-06-01T15:49:34Z
2022-06-01T15:49:34Z
null
Hi Team, I am trying to create a custom metric for my training as follows, where f1 is my own metric: ```python def _info(self): # TODO: Specifies the datasets.MetricInfo object return datasets.MetricInfo( # This is the description that will appear on the metrics page. description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, # This defines the format of each prediction and reference features = datasets.Features({'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")),"offset_mapping":datasets.Sequence(datasets.Value("int32")),'text':datasets.Sequence(datasets.Value('string')),"ground":datasets.Sequence(datasets.Value("int32")),}), # Homepage of the metric for documentation homepage="http://metric.homepage", # Additional links to the codebase or references codebase_urls=["http://github.com/path/to/codebase/of/new_metric"], reference_urls=["http://path.to.reference.url/new_metric"] ) def _compute(self,predictions,references,text,offset_mapping,spans): pred_spans = [] for i,preds in enumerate(predictions): current_preds = [] for j,token_preds in enumerate(preds): if (preds>0.5): current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1])) pred_spans.append(current_spans) return { "Token Wise F1": f1_score(references,predictions,labels=[0,1]), "Offset Wise F1": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)]) } ``` I believe this is not correct. But that's not the issue I am facing right now. I get this error : ```python --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-144-ed7349b50821> in <module>() ----> 1 new_metric.compute(predictions=inputs["labels"],references=inputs["labels"], text=inputs["text"], offset_mapping=inputs["offset_mapping"],ground=inputs["ground"] ) 2 frames /usr/local/lib/python3.6/dist-packages/datasets/features.py in encode_batch(self, batch) 802 encoded_batch = {} 803 if set(batch) != set(self): --> 804 print(batch) 805 print(self) 806 raise ValueError("Column mismatch between batch {} and features {}".format(set(batch), set(self))) ValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'} ``` On checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references. How do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work? Thanks, Gunjan
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[ "Currently it's only possible to define the features for the two columns `references` and `predictions`.\r\nThe data for these columns can then be passed to `metric.add_batch` and `metric.compute`.\r\nInstead of defining more columns `text`, `offset_mapping` and `ground` you must include them in either references a...
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Protect master branch to force contributions via Pull Requests
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2021-10-06T07:34:17Z
2021-10-07T06:51:47Z
2021-10-07T06:49:52Z
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In order to have a clearer Git history in the master branch, I propose to protect it so that all contributions must be done through a Pull Request and no direct commits to master are allowed. - The Pull Request allows to give context, discuss any potential issues and improve the quality of the contribution - The Pull Request will eventually be squashed and merged into master with a single commit that links to the Pull Request page (with all the context/discussions) Note that we already implemented a protection in the master branch to avoid *merge* commits and ensure a linear history. This proposal goes one step further by avoiding all kind of direct commits and forcing contributions **only** through Pull Requests. Please note that we can temporarily deactivate this protection if we need to make a direct commit, e.g. at each new version release. The only way GitHub allows this kind or protection is by requiring a minimal number (at least one) of approvals of the Pull Request. The inconvenient is that the PR creator cannot approve their own PR: another person must approve it before it can be merged into master. To circumvent this, we could eventually disable this protection in the master branch when an urgent commit is needed (e.g. for a hotfix) and there is no other person available at that time to approve the PR.
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[ "It would be nice to protect the master from direct commits, but still having a way to merge our own PRs when no review is required (for example when updating a dataset_infos.json file, or minor bug fixes - things that happen quite often actually).\r\nDo you know if there's a way ?", "you can if you're an admin o...
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5,307
Use correct dataset type in `from_generator` docs
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2022-11-28T13:59:10Z
2022-11-28T15:30:37Z
2022-11-28T15:27:26Z
null
Use the correct dataset type in the `from_generator` docs (example with sharding).
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
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wmt cannot be downloaded
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2020-11-16T01:04:41Z
2020-11-16T09:31:58Z
2020-11-16T09:31:58Z
null
Hi, I appreciate your help with the following error, thanks >>> from datasets import load_dataset >>> dataset = load_dataset("wmt16", "ro-en", split="train") Downloading and preparing dataset wmt16/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/root/.cache/huggingface/modules/datasets_modules/datasets/wmt16/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308/wmt_utils.py", line 755, in _split_generators downloaded_files = dl_manager.download_and_extract(urls_to_download) File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 181, in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 181, in <listcomp> mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-ro.tmx.gz
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423
Change features vs schema logic
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2020-07-21T14:52:47Z
2020-07-25T09:08:34Z
2020-07-23T10:15:17Z
null
## New logic for `nlp.Features` in datasets Previously, it was confusing to have `features` and pyarrow's `schema` in `nlp.Dataset`. However `features` is supposed to be the front-facing object to define the different fields of a dataset, while `schema` is only used to write arrow files. Changes: - Remove `schema` field in `nlp.Dataset` - Make `features` the source of truth to read/write examples - `features` can no longer be `None` in `nlp.Dataset` - Update `features` after each dataset transform such as `nlp.Dataset.map` Todo: change the tests to take these changes into account
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Docs are not generated after latest release
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2022-11-16T14:59:31Z
2022-11-22T16:27:50Z
2022-11-22T16:27:50Z
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After the latest `datasets` release version 0.7.0, the docs were not generated. As we have changed the release procedure (so that now we do not push directly to main branch), maybe we should also change the corresponding GitHub action: https://github.com/huggingface/datasets/blob/edf1902f954c5568daadebcd8754bdad44b02a85/.github/workflows/build_documentation.yml#L3-L8 Related to: - #5250 CC: @mishig25
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[ "After a discussion with @mishig25:\r\n- He said that this action should be triggered if we call our release branch according to the regex `v*-release`, as transformers does\r\n- I said that our procedure is different: our release branch is *temporary* and it is deleted just after the release PR is merged to main\r...
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datasets map() handles all data at a stroke and takes long time
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2022-08-30T02:25:56Z
2023-04-06T09:43:58Z
2022-09-06T09:23:35Z
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**1. Background** Huggingface datasets package advises using `map()` to process data in batches. In the example code on pretraining masked language model, they use `map()` to tokenize all data at a stroke before the train loop. The corresponding code: ``` with accelerator.main_process_first(): tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not args.overwrite_cache, desc="Running tokenizer on every text in dataset" ) ``` **2. The problem** Thus, when I try the same pertaining code with a much larger corpus, it takes quite a long time to tokenize. Also, we can choose to tokenize data in `data-collator`. In this way, the program only tokenizes one batch in the next training step and avoids getting stuck in tokenization. **3. My question** As described above, my questions are: * **Which is better? Process in `map()` or in `data-collator`** * **Why huggingface advises `map()` function?** There should be some advantages to using `map()` Thanks for your answers!
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[ "Hi ! Interesting question ;)\r\n\r\n> Which is better? Process in map() or in data-collator\r\n\r\nAs you said, both can be used in practice: map() if you want to preprocess before training, or a data-collator (or the equivalent `dataset.set_transform`) if you want to preprocess on-the-fly during training. Both op...
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Add KDE4 Dataset
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2020-12-09T15:32:58Z
2020-12-14T10:22:33Z
2020-12-14T10:22:32Z
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[ "@lhoestq fixed :) " ]
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Fix docstring of inspect_dataset
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2022-06-02T14:21:10Z
2022-06-02T16:40:55Z
2022-06-02T16:32:27Z
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As pointed out by @sgugger: - huggingface/doc-builder/issues/235
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`ExpectedMoreDownloadedFiles` for `evidence_infer_treatment`
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2022-01-03T15:58:38Z
2022-02-14T13:21:43Z
2022-02-14T13:21:43Z
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## Describe the bug I am trying to load a dataset called `evidence_infer_treatment`. The first subset (`1.1`) works fine but the second returns an error (`2.0`). It downloads a file but crashes during the checksums. ## Steps to reproduce the bug ```python >>> from datasets import load_dataset >>> load_dataset("evidence_infer_treatment", "2.0") Downloading and preparing dataset evidence_infer_treatment/2.0 (download: 34.84 MiB, generated: 91.46 MiB, post-processed: Unknown size, total: 126.30 MiB) to /home/victor_huggingface_co/.cache/huggingface/datasets/evidence_infer_treatment/2.0/2.0.0/6812655bfd26cbaa58c84eab098bf6403694b06c6ae2ded603c55681868a1e24... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/load.py", line 1669, in load_dataset use_auth_token=use_auth_token, File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 594, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 664, in _download_and_prepare self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files" File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 33, in verify_checksums raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'http://evidence-inference.ebm-nlp.com/v2.0.tar.gz'} ``` I did try to pass the argument `ignore_verifications=True` but run into an error when trying to build the dataset: ```python >>> load_dataset("evidence_infer_treatment", "2.0", ignore_verifications=True, download_mode="force_redownload") Downloading and preparing dataset evidence_infer_treatment/2.0 (download: 34.84 MiB, generated: 91.46 MiB, post-processed: Unknown size, total: 126.30 MiB) to /home/victor_huggingface_co/.cache/huggingface/datasets/evidence_infer_treatment/2.0/2.0.0/6812655bfd26cbaa58c84eab098bf6403694b06c6ae2ded603c55681868a1e24... Downloading: 164MB [00:23, 6.98MB/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/load.py", line 1669, in load_dataset use_auth_token=use_auth_token, File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 594, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 681, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 1080, in _prepare_split example = self.info.features.encode_example(record) File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 1032, in encode_example return encode_nested_example(self, example) File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 807, in encode_nested_example k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 807, in <dictcomp> k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 829, in encode_nested_example list_dict[k] = [encode_nested_example(dict_tuples[0], o) for o in dict_tuples[1:]] File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 829, in <listcomp> list_dict[k] = [encode_nested_example(dict_tuples[0], o) for o in dict_tuples[1:]] File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 828, in encode_nested_example for k, dict_tuples in utils.zip_dict(schema.feature, *obj): File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 136, in zip_dict yield key, tuple(d[key] for d in dicts) File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 136, in <genexpr> yield key, tuple(d[key] for d in dicts) KeyError: '' ``` ## Environment info - `datasets` version: 1.16.1 - Platform: Linux-5.0.0-1020-gcp-x86_64-with-debian-buster-sid - Python version: 3.7.11 - PyArrow version: 6.0.1
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[ "Thanks for reporting @VictorSanh.\r\n\r\nI'm looking at it... " ]
https://api.github.com/repos/huggingface/datasets/issues/1286
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[libprotobuf FATAL /sentencepiece/src/../third_party/protobuf-lite/google/protobuf/repeated_field.h:1505] CHECK failed: (index) >= (0): terminate called after throwing an instance of 'google::protobuf::FatalException' what(): CHECK failed: (index) >= (0): Aborted
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2020-12-08T09:44:15Z
2020-12-12T19:36:22Z
2020-12-12T16:22:36Z
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Hi I am getting this error when evaluating on wmt16-ro-en using finetune_trainer.py of huggingface repo. thank for your help {'epoch': 20.0} 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:16<00:00, 1.22it/s] 12/08/2020 10:41:19 - INFO - seq2seq.trainers.trainer - Saving model checkpoint to outputs/experiment/joint/finetune/lr-2e-5 12/08/2020 10:41:24 - INFO - __main__ - {'wmt16-en-ro': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1998), 'qnli': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 5462), 'scitail': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1303)} 12/08/2020 10:41:24 - INFO - __main__ - *** Evaluate *** 12/08/2020 10:41:24 - INFO - seq2seq.utils.utils - using task specific params for wmt16-en-ro: {'max_length': 300, 'num_beams': 4} 12/08/2020 10:41:24 - INFO - seq2seq.trainers.trainer - ***** Running Evaluation ***** 12/08/2020 10:41:24 - INFO - seq2seq.trainers.trainer - Num examples = 1998 12/08/2020 10:41:24 - INFO - seq2seq.trainers.trainer - Batch size = 64 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:37<00:00, 1.19s/it][libprotobuf FATAL /sentencepiece/src/../third_party/protobuf-lite/google/protobuf/repeated_field.h:1505] CHECK failed: (index) >= (0): terminate called after throwing an instance of 'google::protobuf::FatalException' what(): CHECK failed: (index) >= (0): Aborted
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[ "I remember also getting the same issue for several other translation datasets like all the iwslt2017 group, this is blokcing me and I really need to fix it and I was wondering if you have an idea on this. @lhoestq thanks,. ", "maybe there is an empty line or something inside these datasets? could you tell me wh...
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4,108
Perplexity Speedup
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2022-04-06T12:57:21Z
2022-04-20T13:00:54Z
2022-04-20T12:54:42Z
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This PR makes necessary changes to perplexity such that: - it runs much faster (via batching) - it throws an error when input is empty, or when input is one word without <BOS> token - it adds the option to add a <BOS> token Issues: - The values returned are extremely high, and I'm worried they aren't correct. Even if they are correct, they are sometimes returned as `inf`, which is not very useful (see [comment below](https://github.com/huggingface/datasets/pull/4108#discussion_r843931094) for some of the output values). - If the values are not correct, can you help me find the error? - If the values are correct, it might be worth it to measure something like perplexity per word, which would allow us to get actual values for the larger perplexities, instead of just `inf` Future: - `stride` is not currently implemented here. I have some thoughts on how to make it happen with batching, but I think it would be better to get another set of eyes to look at any possible errors causing such large values now rather than later.
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[ "WRT the high values, can you add some unit tests with some [string, model] pairs and their resulting perplexity code, and @TristanThrush can run the same pairs through his version of the code?", "_The documentation is not available anymore as the PR was closed or merged._", "I thought that the perplexity metri...
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5,507
Optimise behaviour in respect to indices mapping
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2023-02-06T14:25:55Z
2023-02-28T18:19:18Z
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_Originally [posted](https://huggingface.slack.com/archives/C02V51Q3800/p1675443873878489?thread_ts=1675418893.373479&cid=C02V51Q3800) on Slack_ Considering all this, perhaps for Datasets 3.0, we can do the following: * [ ] have `continuous=True` by default in `.shard` (requested in the survey and makes more sense for us since it doesn't create an indices mapping) * [x] allow calling `save_to_disk` on "unflattened" datasets * [ ] remove "hidden" expensive calls in `save_to_disk`, `unique`, `concatenate_datasets`, etc. For instance, instead of silently calling `flatten_indices` where it's needed, it's probably better to be explicit (considering how expensive these ops can be) and raise an error instead
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4,024
Doc: image_process small tip
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2022-03-25T15:44:32Z
2022-03-31T15:35:35Z
2022-03-31T15:30:20Z
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I've added a small tip in the `image_process` doc
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[ "_The documentation is not available anymore as the PR was closed or merged._", "This tip is unnecessary, i.e., Pillow will already be installed since the `Image` feature requires it for encoding and decoding. Thanks anyway.\r\n\r\ncc @stevhliu I've noticed we are missing the installation section in the doc (`pip...
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4,388
Set builder name from module instead of class
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2022-05-23T06:26:35Z
2022-05-25T05:24:43Z
2022-05-25T05:16:15Z
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Now the builder name attribute is set from from the builder class name. This PR sets the builder name attribute from the module name instead. Some motivating reasons: - The dataset ID is relevant and unique among all datasets and this is directly related to the repository name, i.e., the name of the directory containing the dataset - The name of the module (i.e. the file containing the loading loading script) is already relevant for loading: it must have the same name as its containing directory (related to the dataset ID), as we search for it using its directory name - On the other hand, the name of the builder class is not relevant for loading: in our code, we just search for a class which is subclass of `DatasetBuilder` (independently of its name). We do not put any constraint on the naming of the builder class and indeed it can have a name completely different from its module/direcotry/dataset_id IMO it makes more sense to align the caching directory name with the dataset_id/directory/module name instead of the builder class name. Fix #4381.
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2021-03-02T07:12:34Z
2021-03-10T16:36:57Z
2021-03-10T16:36:57Z
null
Current implementation of ArrowWriter does not properly release the `stream` resource (by closing it) if its `finalize()` method is not called and/or an Exception is raised before/during the call to its `finalize()` method. Therefore, ArrowWriter should be used as a context manager that properly closes its `stream` resource at exit.
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[ "Oh nice thanks for adding the context manager ! All the streams and RecordBatchWriter will be properly closed now. Hopefully this gives a better experience on windows on which it's super important to close stuff.\r\n\r\nNot sure about the error, it looks like a process crashed silently.\r\nLet me take a look", "...
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5,858
Throw an error when dataset improperly indexed
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closed
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2023-05-15T05:15:53Z
2023-05-25T16:23:19Z
2023-05-25T16:23:19Z
null
### Describe the bug Pandas-style subset indexing on dataset does not throw an error, when maybe it should. Instead returns the first instance of the dataset regardless of index condition. ### Steps to reproduce the bug Steps to reproduce the behavior: 1. `squad = datasets.load_dataset("squad_v2", split="validation")` 2. `item = squad[squad['question'] == "Who was the Norse leader?"]` or `it = squad[squad['id'] == '56ddde6b9a695914005b962b']` 3. returns the first item in the dataset, which does not satisfy the above conditions: `{'id': '56ddde6b9a695914005b9628', 'title': 'Normans', 'context': 'The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave their name to Normandy, a region in France. They were descended from Norse ("Norman" comes from "Norseman") raiders and pirates from Denmark, Iceland and Norway who, under their leader Rollo, agreed to swear fealty to King Charles III of West Francia. Through generations of assimilation and mixing with the native Frankish and Roman-Gaulish populations, their descendants would gradually merge with the Carolingian-based cultures of West Francia. The distinct cultural and ethnic identity of the Normans emerged initially in the first half of the 10th century, and it continued to evolve over the succeeding centuries.', 'question': 'In what country is Normandy located?', 'answers': {'text': ['France', 'France', 'France', 'France'], 'answer_start': [159, 159, 159, 159]}}` ### Expected behavior Should either throw an error message, or return the dataset item that satisfies the condition. ### Environment info - `datasets` version: 2.9.0 - Platform: macOS-13.3.1-arm64-arm-64bit - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.3
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[ "Thanks for reporting, @sarahwie.\r\n\r\nPlease note that in `datasets` we do not have vectorized operation like `pandas`. Therefore, your equality comparisons above are `False`:\r\n- For example: `squad['question']` returns a `list`, and this list is not equal to `\"Who was the Norse leader?\"`\r\n\r\nThe `False` ...
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2,744
Fix key by recreating metadata JSON for journalists_questions dataset
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2021-08-02T13:27:53Z
2021-08-03T09:25:34Z
2021-08-03T09:25:33Z
null
Close #2743.
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1,414
Adding BioCreative II Gene Mention corpus
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closed
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0
2020-12-09T19:49:28Z
2020-12-11T11:17:40Z
2020-12-11T11:17:40Z
null
Adding BioCreative II Gene Mention corpus
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6,024
Don't reference self in Spark._validate_cache_dir
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2023-07-12T20:31:16Z
2023-07-13T16:58:32Z
2023-07-13T12:37:09Z
null
Fix for https://github.com/huggingface/datasets/issues/5963
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[ "Ptal @lhoestq :) I tested this manually on a multi-node Databricks cluster", "Hm looks like the check_code_quality failures are unrelated to me change... https://github.com/huggingface/datasets/actions/runs/5536162850/jobs/10103451883?pr=6024", "_The documentation is not available anymore as the PR was closed ...
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2,170
Wikipedia historic dumps are deleted but hf/datasets hardcodes dump date
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open
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2021-04-06T03:13:18Z
2021-06-16T01:10:50Z
null
null
Wikimedia does not keep all historical dumps. For example, as of today https://dumps.wikimedia.org/kowiki/ only provides ``` 20201220/ 02-Feb-2021 01:36 - 20210101/ 21-Feb-2021 01:26 - 20210120/ 02-Mar-2021 01:25 - 20210201/ 21-Mar-2021 01:26 - 20210220/ 02-Apr-2021 01:26 - 20210301/ 03-Mar-2021 08:10 - 20210320/ 21-Mar-2021 18:13 - 20210401/ 03-Apr-2021 10:08 - latest/ 03-Apr-2021 10:08 - ``` However, the wikipedia dataset provided in the library, only supports the following configs, none of which are applicable anymore when disregarding the cached datasets: ``` ValueError: BuilderConfig 20210401.ko not found. Available: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu'] ``` The cached datasets: ``` % aws s3 --no-sign-request --endpoint-url https://storage.googleapis.com ls s3://huggingface-nlp/cache/datasets/wikipedia/ PRE 20200501.de/ PRE 20200501.en/ PRE 20200501.fr/ PRE 20200501.frr/ PRE 20200501.it/ PRE 20200501.simple/ ```
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[ "It seems that this can be fixed from user's end by including a `date` argument, like this:\r\n\r\n`dataset = datasets.load_dataset('wikipedia', '20200501.en', date='20210420')`\r\n\r\nYou can get available dates from [here](https://dumps.wikimedia.org/enwiki/).\r\n\r\nThis is not a proper fix however as all the fi...
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1,783
Dataset Examples Explorer
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2021-01-26T20:39:02Z
2021-02-01T13:58:44Z
2021-02-01T13:58:44Z
null
In the Older version of the Dataset, there are a useful Dataset Explorer that allow user to visualize the examples (training, test and validation) of a particular dataset, it is no longer there in current version. Hope HuggingFace can re-enable the feature that at least allow viewing of the first 20 examples of a particular dataset, or alternatively can extract 20 examples for each datasets and make those part of the Dataset Card Documentation.
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[ "Hi @ChewKokWah,\r\n\r\nWe're working on it! In the meantime, you can still find the dataset explorer at the following URL: https://huggingface.co/datasets/viewer/", "Glad to see that it still exist, this existing one is more than good enough for me, it is feature rich, simple to use and concise. \r\nHope similar...
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Monero
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2020-12-13T19:56:48Z
2022-10-03T09:38:35Z
2022-10-03T09:38:35Z
null
Biomedical Romanian dataset :)
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[ "Hi @iliemihai - you need to add the Readme file! Otherwise seems good. \r\nAlso don't forget to run `make style` & `flake8 datasets` locally, from the datasets folder", "@skyprince999 I will add the README.d for it. Thank you :D ", "Thanks for your contribution, @iliemihai. Are you still interested in adding ...
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4,420
Metric evaluation problems in multi-node, shared file system
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2022-05-30T13:24:05Z
2023-07-11T09:33:18Z
2023-07-11T09:33:17Z
null
## Describe the bug Metric evaluation fails in multi-node within a shared file system, because the master process cannot find the lock files from other nodes. (This issue was originally mentioned in the transformers repo https://github.com/huggingface/transformers/issues/17412) ## Steps to reproduce the bug 1. clone [this huggingface model](https://huggingface.co/PereLluis13/wav2vec2-xls-r-300m-ca-lm) and replace the `run_speech_recognition_ctc.py` script with the version in the gist [here](https://gist.github.com/gullabi/3f66094caa8db1c1e615dd35bd67ec71#file-run_speech_recognition_ctc-py). 2. Setup the `venv` according to the requirements of the model file plus `datasets==2.0.0`, `transformers==4.18.0` and `torch==1.9.0` 3. Launch the runner in a distributed environment which has a shared file system for two nodes, preferably with SLURM. Example [here](https://gist.github.com/gullabi/3f66094caa8db1c1e615dd35bd67ec71) Specifically for the datasets, for the distributed setup the `load_metric` is called as: ``` process_id=int(os.environ["RANK"]) num_process=int(os.environ["WORLD_SIZE"]) eval_metrics = {metric: load_metric(metric, process_id=process_id, num_process=num_process, experiment_id="slurm") for metric in data_args.eval_metrics} ``` ## Expected results The training should not fail, due to the failure of the `Metric.compute()` step. ## Actual results For the test I am executing the world size is 4, with 2 GPUs in 2 nodes. However the process is not finding the necessary lock files ``` File "/gpfs/projects/bsc88/speech/asr/wav2vec2-xls-r-300m-ca-lm/run_speech_recognition_ctc.py", line 841, in <module> main() File "/gpfs/projects/bsc88/speech/asr/wav2vec2-xls-r-300m-ca-lm/run_speech_recognition_ctc.py", line 792, in main train_result = trainer.train(resume_from_checkpoint=checkpoint) File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/transformers/trainer.py", line 1497, in train self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval) File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/transformers/trainer.py", line 1624, in _maybe_log_save_evaluate metrics = self.evaluate(ignore_keys=ignore_keys_for_eval) File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/transformers/trainer.py", line 2291, in evaluate metric_key_prefix=metric_key_prefix, File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/transformers/trainer.py", line 2535, in evaluation_loop metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels)) File "/gpfs/projects/bsc88/speech/asr/wav2vec2-xls-r-300m-ca-lm/run_speech_recognition_ctc.py", line 742, in compute_metrics metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()} File "/gpfs/projects/bsc88/speech/asr/wav2vec2-xls-r-300m-ca-lm/run_speech_recognition_ctc.py", line 742, in <dictcomp> metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()} File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/datasets/metric.py", line 419, in compute self.add_batch(**inputs) File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/datasets/metric.py", line 465, in add_batch self._init_writer() File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/datasets/metric.py", line 552, in _init_writer self._check_rendez_vous() # wait for master to be ready and to let everyone go File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/datasets/metric.py", line 342, in _check_rendez_vous ) from None ValueError: Expected to find locked file /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-0.arrow.lock from process 3 but it doesn't exist. ``` When I look at the cache directory, I can see all the lock files in principle: ``` /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-0.arrow /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-0.arrow.lock /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-1.arrow /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-1.arrow.lock /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-2.arrow /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-2.arrow.lock /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-3.arrow /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-3.arrow.lock /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-rdv.lock ``` I see that there was another related issue here https://github.com/huggingface/datasets/issues/1942, but it seems to have resolved via https://github.com/huggingface/datasets/pull/1966. Let me know if there is problem with how I am calling the `load_metric` or whether I need to make changes to the `.compute()` steps. ## Environment info - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-147.8.1.el8_1.x86_64-x86_64-with-centos-8.1.1911-Core - Python version: 3.7.4 - PyArrow version: 7.0.0 - Pandas version: 1.3.0
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[ "If you call `metric.compute` in a distributed setup like yours, then `metric.compute` is called in each process. `metric.compute` first calls `metric.add_batch`, and it looks like your error appears at that stage.\r\n\r\nTo make sure that all the processes have started writing their predictions/references at the s...