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https://api.github.com/repos/huggingface/datasets/issues/5549
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PR_kwDODunzps5KSsi3
5,549
Apply ruff flake8-comprehension checks
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-02-19T20:09:28Z
2023-02-23T14:06:39Z
2023-02-23T13:59:39Z
CONTRIBUTOR
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Fix #5548 Apply ruff's flake8-comprehension checks for better performance, and more readable code.
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1,067,481,390
PR_kwDODunzps4vM3wh
3,342
Fix ASSET dataset data URLs
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[ "> Hi @tianjianjiang, thanks for the fix.\r\n> The links should also be updated in the `dataset_infos.json` file.\r\n> The failing tests are due to the missing tag in the header of the `README.md` file:\r\n\r\nHi @albertvillanova, thank you for the info! My apologies for the messy PR.\r\n" ]
2021-11-30T17:13:30Z
2021-12-14T14:50:00Z
2021-12-14T14:50:00Z
CONTRIBUTOR
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Change the branch name "master" to "main" in the data URLs, since facebookresearch has changed that.
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4,698
Enable streaming dataset to use the "all" split
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4698). All of your documentation changes will be reflected on that endpoint.", "@albertvillanova \r\nAdding the validation split causes these two `assert_called_once` assertions to fail with `AssertionError: Expected 'ArrowWrit...
2022-07-18T07:47:39Z
2023-01-19T10:11:38Z
null
CONTRIBUTOR
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Fixes #4637
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[DO NOT MERGE] Test doc-builder
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Docs built successfully, so closing this." ]
2022-03-29T14:39:02Z
2022-03-30T12:31:14Z
2022-03-30T12:25:52Z
MEMBER
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This is a test PR to ensure the changes in https://github.com/huggingface/doc-builder/pull/164 don't break anything in `datasets`
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1,016,619,572
PR_kwDODunzps4std7u
3,033
Actual "proper" install of ruamel.yaml in the windows CI
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2021-10-05T17:52:07Z
2021-10-05T17:54:57Z
2021-10-05T17:54:57Z
MEMBER
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It was impossible to update the package directly with `pip`. Indeed it was installed with `distutils` which prevents `pip` or `conda` to uninstall it. I had to `rm` a directory from the `site-packages` python directory, and then do `pip install ruamel.yaml` It's not that "proper" but I couldn't find better solutions
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Rename config and environment variable for in memory max size
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[ "Thank you for the rename, @albertvillanova!" ]
2021-06-07T19:21:08Z
2021-06-07T20:43:46Z
2021-06-07T20:43:46Z
MEMBER
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As discussed in #2409, both config and environment variable have been renamed. cc: @stas00, huggingface/transformers#12056
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Avoid rate limit in update hub repositories
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[ "I also set GIT_LFS_SKIP_SMUDGE=1 to speed up git clones", "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-04-13T20:32:17Z
2022-04-13T20:56:41Z
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MEMBER
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use http.extraHeader to avoid rate limit
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4,632
'sort' method sorts one column only
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[ "Hi ! `ds.sort()` does sort the full dataset, not just one column:\r\n```python\r\nfrom datasets import *\r\n\r\nds = Dataset.from_dict({\"foo\": [3, 2, 1], \"bar\": [\"c\", \"b\", \"a\"]})\r\nprint(d.sort(\"foo\").to_pandas()\r\n# foo bar\r\n# 0 1 a\r\n# 1 2 b\r\n# 2 3 c\r\n```\r\n\r\nWhat made y...
2022-07-05T11:25:26Z
2023-07-25T15:04:27Z
2023-07-25T15:04:27Z
NONE
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The 'sort' method changes the order of one column only (the one defined by the argument 'column'), thus creating a mismatch between a sample fields. I would expect it to change the order of the samples as a whole, based on the 'column' order.
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Update DBRD dataset card and download URL
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[ "not sure why the CI was not triggered though" ]
2021-01-07T12:16:43Z
2021-01-07T13:41:39Z
2021-01-07T13:40:59Z
CONTRIBUTOR
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I've added the Dutch Bood Review Dataset (DBRD) during the recent sprint. This pull request makes two minor changes: 1. I'm changing the download URL from Google Drive to the dataset's GitHub release package. This is now possible because of PR #1316. 2. I've updated the dataset card. Cheers! 😄
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KeyError: '_indices_files' in `arrow_dataset.py`
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[ "Thanks for reporting @samsontmr.\r\n\r\nIt seems a backward compatibility issue...", "Thanks @samsontmr this should be fixed on master now\r\n\r\nFeel free to reopen if you're still having issues" ]
2021-04-09T01:37:12Z
2021-04-09T09:55:09Z
2021-04-09T09:54:39Z
NONE
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After pulling the latest master, I'm getting a crash when `load_from_disk` tries to load my local dataset. Trace: ``` Traceback (most recent call last): File "load_data.py", line 11, in <module> dataset = load_from_disk(SRC) File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/load.py", line 784, in load_from_disk return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory) File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/dataset_dict.py", line 692, in load_from_disk dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory) File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 634, in load_from_disk if state["_indices_files"]: KeyError: '_indices_files' ``` I believe this is the line causing the error since there may not be a `_indices_files` key in the older versions: https://github.com/huggingface/datasets/blob/b70141e3c5149430951773aaa0155555c5fb3e76/src/datasets/arrow_dataset.py#L634 May I suggest using `state.get()` instead of directly indexing the dictionary? @lhoestq
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Fix a typo in arrow_dataset.py
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2022-10-13T02:33:55Z
2022-10-14T09:47:28Z
2022-10-14T09:47:27Z
CONTRIBUTOR
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Align/fix license metadata info
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Thank you thank you! Let's merge and pray? 😱 ", "I just need to add `license_details` to the validator and yup we can merge" ]
2022-07-01T09:50:50Z
2022-07-01T12:53:57Z
2022-07-01T12:42:47Z
MEMBER
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fix bad "other-*" licenses and add the corresponding "license_details" when relevant
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2,833
IndexError when accessing first element of a Dataset if first RecordBatch is empty
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2021-08-24T16:49:20Z
2021-08-24T17:21:17Z
2021-08-24T17:21:17Z
MEMBER
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The computation of the offsets of the underlying Table of a Dataset has some issues if the first RecordBatch is empty. ```python from datasets import Dataset import pyarrow as pa pa_table = pa.Table.from_pydict({"a": [1]}) pa_table2 = pa.Table.from_pydict({"a": []}, schema=pa_table.schema) ds_table = pa.concat_tables([pa_table2, pa_table]) dataset = Dataset(ds_table) print([len(b) for b in dataset.data._batches]) # [0, 1] print(dataset.data._offsets) # [0 0 1] (should be [0, 1]) dataset[0] ``` raises ```python --------------------------------------------------------------------------- IndexError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/datasets/table.py in _interpolation_search(arr, x) 90 else: 91 i, j = i, k ---> 92 raise IndexError(f"Invalid query '{x}' for size {arr[-1] if len(arr) else 'none'}.") 93 94 IndexError: Invalid query '0' for size 1. ``` This can be fixed by ignoring empty batches when computing `table._batches` and `table._offsets` cc @SaulLu
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6,181
Fix import in `image_load` doc
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-08-25T13:12:19Z
2023-08-25T16:12:46Z
2023-08-25T16:02:24Z
CONTRIBUTOR
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Reported on [Discord](https://discord.com/channels/879548962464493619/1144295822209581168/1144295822209581168)
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Add HKCanCor
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2020-12-06T16:14:43Z
2020-12-06T20:23:17Z
2020-12-06T20:21:54Z
CONTRIBUTOR
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(Apologies, didn't manage the branches properly and the PR got too messy. Going to open a new PR with everything in order)
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Question/problem with dataset labels
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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...
2021-03-04T17:06:53Z
2023-07-24T14:39:33Z
2023-07-24T14:39:33Z
NONE
null
null
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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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3,927
Update main readme
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[ "_The documentation is not available anymore as the PR was closed or merged._", "What do you think @albertvillanova ?" ]
2022-03-15T18:09:59Z
2022-03-29T10:13:47Z
2022-03-29T10:08:20Z
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The main readme was still focused on text datasets - I extended it by mentioning that we also support image and audio datasets
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Adding UKP Argument Aspect Similarity Corpus
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[ "Adding a link to the guide on adding a dataset if someone want to give it a try: https://github.com/huggingface/datasets#add-a-new-dataset-to-the-hub\r\n\r\nwe should add this guide to the issue template @lhoestq ", "thanks @thomwolf , this is added now. The template is correct, sorry my mistake not to include i...
2020-12-24T11:01:31Z
2022-10-05T12:36:12Z
2022-10-05T12:36:12Z
CONTRIBUTOR
null
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Hi, this would be great to have this dataset included. ## Adding a Dataset - **Name:** UKP Argument Aspect Similarity Corpus - **Description:** The UKP Argument Aspect Similarity Corpus (UKP ASPECT) includes 3,595 sentence pairs over 28 controversial topics. Each sentence pair was annotated via crowdsourcing as either “high similarity”, “some similarity”, “no similarity” or “not related” with respect to the topic. - **Paper:** https://www.aclweb.org/anthology/P19-1054/ - **Data:** https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/1998 - **Motivation:** this is one of the datasets currently used frequently in recent adapter papers like https://arxiv.org/pdf/2005.00247.pdf Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Thank you
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Create README.md for WER metric
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3898). All of your documentation changes will be reflected on that endpoint.", "For ASR you can probably ping @patrickvonplaten ", "Ah only noticed now that ` # Values from popular papers` is from a template. @lhoestq @sashav...
2022-03-11T19:29:09Z
2022-03-15T17:05:00Z
2022-03-15T17:04:59Z
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Proposing a draft WER metric card, @lhoestq I'm not very certain about "Values from popular papers" -- I don't know ASR very well, what do you think of the examples I found?
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770,862,112
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1,604
Add tests for the download functions ?
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[ "We have some tests now for it under `tests/test_download_manager.py`." ]
2020-12-18T12:49:25Z
2022-10-05T13:04:24Z
2022-10-05T13:04:24Z
CONTRIBUTOR
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AFAIK the download functions in `DownloadManager` are not tested yet. It could be good to add some to ensure behavior is as expected.
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2,532
Tokenizer's normalization preprocessor cause misalignment in return_offsets_mapping for tokenizer classification task
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[ "Hi @jerryIsHere, thanks for reporting the issue. But are you sure this is a bug in HuggingFace **Datasets**?", "> Hi @jerryIsHere, thanks for reporting the issue. But are you sure this is a bug in HuggingFace **Datasets**?\r\n\r\nOh, I am sorry\r\nI would reopen the post on huggingface/transformers" ]
2021-06-22T10:08:18Z
2021-06-23T05:17:25Z
2021-06-23T05:17:25Z
CONTRIBUTOR
null
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[This colab notebook](https://colab.research.google.com/drive/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) implements a token classification input pipeline extending the logic from [this hugging example](https://huggingface.co/transformers/custom_datasets.html#tok-ner). The pipeline works fine with most instance in different languages, but unfortunately, [the Japanese Kana ligature (a form of abbreviation? I don't know Japanese well)](https://en.wikipedia.org/wiki/Kana_ligature) break the alignment of `return_offsets_mapping`: ![image](https://user-images.githubusercontent.com/50871412/122904371-db192700-d382-11eb-8917-1775db76db69.png) Without the try catch block, it riase `ValueError: NumPy boolean array indexing assignment cannot assign 88 input values to the 87 output values where the mask is true`, example shown here [(another colab notebook)](https://colab.research.google.com/drive/1MmOqf3ppzzdKKyMWkn0bJy6DqzOO0SSm?usp=sharing) It is clear that the normalizer is the process that break the alignment, as it is observed that `tokenizer._tokenizer.normalizer.normalize_str('ヿ')` return 'コト'. One workaround is to include `tokenizer._tokenizer.normalizer.normalize_str` before the tokenizer preprocessing pipeline, which is also provided in the [first colab notebook](https://colab.research.google.com/drive/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) with the name `udposTestDatasetWorkaround`. I guess similar logics should be included inside the tokenizer and the offsets_mapping generation process such that user don't need to include them in their code. But I don't understand the code of tokenizer well that I think I am not able to do this. p.s. **I am using my own dataset building script in the provided example, but the script should be equivalent to the changes made by this [update](https://github.com/huggingface/datasets/pull/2466)** `get_dataset `is just a simple wrapping for `load_dataset` and the `tokenizer` is just `XLMRobertaTokenizerFast.from_pretrained("xlm-roberta-large")`
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Dialogue action slot name and value are reversed in MultiWoZ 2.2
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[ "Hi\r\nGood catch ! Thanks for reporting\r\n\r\nIf you are interested in contributing, feel free to open a PR to fix this :) " ]
2021-03-29T06:34:02Z
2021-03-31T12:48:01Z
2021-03-31T12:48:01Z
CONTRIBUTOR
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Hi @yjernite, thank you for adding MultiWoZ 2.2 in the huggingface datasets platform. It is beneficial! I spot an error that the order of Dialogue action slot names and values are reversed. https://github.com/huggingface/datasets/blob/649b2c469779bc4221e1b6969aa2496d63eb5953/datasets/multi_woz_v22/multi_woz_v22.py#L251-L262
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Fix TF tests for 2.10
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-09-08T14:39:10Z
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2022-09-08T15:14:44Z
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Fixes #4953
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758,965,936
MDExOlB1bGxSZXF1ZXN0NTM0MDQyODYy
1,276
add One Million Posts Corpus
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[ "merging since the CI is fixed on master" ]
2020-12-08T00:50:08Z
2020-12-11T18:28:18Z
2020-12-11T18:28:18Z
CONTRIBUTOR
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- **Name:** One Million Posts Corpus - **Description:** The “One Million Posts” corpus is an annotated data set consisting of user comments posted to an Austrian newspaper website (in German language). - **Paper:** https://dl.acm.org/doi/10.1145/3077136.3080711 - **Data:** https://github.com/OFAI/million-post-corpus - **Motivation:** Big German (real-life) dataset containing different annotations around forum moderation with expert annotations. ### Checkbox - [X] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template - [X] Fill the `_DESCRIPTION` and `_CITATION` variables - [X] Implement `_infos()`, `_split_generators()` and `_generate_examples()` - [X] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class. - [X] Generate the metadata file `dataset_infos.json` for all configurations - [X] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB) - [X] Add the dataset card `README.md` using the template : fill the tags and the various paragraphs - [X] Both tests for the real data and the dummy data pass.
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PR_kwDODunzps4si4I2
2,994
Fix loading compressed CSV without streaming
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2021-10-01T07:28:59Z
2021-10-01T15:53:16Z
2021-10-01T15:53:16Z
MEMBER
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When implementing support to stream CSV files (https://github.com/huggingface/datasets/commit/ad489d4597381fc2d12c77841642cbeaecf7a2e0#diff-6f60f8d0552b75be8b3bfd09994480fd60dcd4e7eb08d02f721218c3acdd2782), a regression was introduced preventing loading compressed CSV files in non-streaming mode. This PR fixes it, allowing loading compressed/uncompressed CSV files in streaming/non-streaming mode. Fix #2977.
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Question (potential issue?) related to datasets caching
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[ "An educated guess: does this refer to the fact that depending on the custom column names in the dataset files (csv in this case), there is a dataset loader being created? and this dataset loader - using the \"custom data configuration\" is used among all jobs running using this particular csv files? (thinking out ...
2021-04-08T00:16:28Z
2023-01-03T18:30:38Z
null
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I thought I had disabled datasets caching in my code, as follows: ``` from datasets import set_caching_enabled ... def main(): # disable caching in datasets set_caching_enabled(False) ``` However, in my log files I see messages like the following: ``` 04/07/2021 18:34:42 - WARNING - datasets.builder - Using custom data configuration default-888a87931cbc5877 04/07/2021 18:34:42 - WARNING - datasets.builder - Reusing dataset csv (xxxx/cache-transformers/datasets/csv/default-888a87931cbc5877/0.0.0/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93 ``` Can you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you!
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760,499,576
MDExOlB1bGxSZXF1ZXN0NTM1MzIwNzA2
1,399
Add HoVer Dataset
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[ "@lhoestq all comments addressed :) ", "merging since the CI is fixed on master" ]
2020-12-09T16:55:39Z
2020-12-14T10:57:23Z
2020-12-14T10:57:22Z
MEMBER
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HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification https://arxiv.org/abs/2011.03088
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In v1.4.1, all TIMIT train transcripts are "Would such an act of refusal be useful?"
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[ "Hi @rcgale, thanks for reporting.\r\n\r\nPlease note that this bug was fixed on `datasets` version 1.5.0: https://github.com/huggingface/datasets/commit/a23c73e526e1c30263834164f16f1fdf76722c8c#diff-f12a7a42d4673bb6c2ca5a40c92c29eb4fe3475908c84fd4ce4fad5dc2514878\r\n\r\nIf you update `datasets` version, that shoul...
2021-09-07T18:53:45Z
2021-09-08T16:55:19Z
2021-09-08T09:12:28Z
NONE
null
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## Describe the bug Using version 1.4.1 of `datasets`, TIMIT transcripts are all the same. ## Steps to reproduce the bug I was following this tutorial - https://huggingface.co/blog/fine-tune-wav2vec2-english But here's a distilled repro: ```python !pip install datasets==1.4.1 from datasets import load_dataset timit = load_dataset("timit_asr", cache_dir="./temp") unique_transcripts = set(timit["train"]["text"]) print(unique_transcripts) assert len(unique_transcripts) > 1 ``` ## Expected results Expected the correct TIMIT data. Or an error saying that this version of `datasets` can't produce it. ## Actual results Every train transcript was "Would such an act of refusal be useful?" Every test transcript was "The bungalow was pleasantly situated near the shore." ## Environment info - `datasets` version: 1.4.1 - Platform: Darwin-18.7.0-x86_64-i386-64bit - Python version: 3.7.9 - PyTorch version (GPU?): 1.9.0 (False) - Tensorflow version (GPU?): not installed (NA) - Using GPU in script?: tried both - Using distributed or parallel set-up in script?: no -
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PR_kwDODunzps442P5L
4,433
Fix script fetching and local path handling in `inspect_dataset` and `inspect_metric`
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Added back the `[:]` and a comment to explain why this is needed. " ]
2022-06-01T12:09:56Z
2022-06-09T10:34:54Z
2022-06-09T10:26:07Z
CONTRIBUTOR
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Fix #4348
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I_kwDODunzps5NkC78
4,674
Issue loading datasets -- pyarrow.lib has no attribute
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[ "Hi @margotwagner, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your bug: in an environment with datasets-2.3.2 and pyarrow-8.0.0, I can load the datasets without any problem:\r\n```python\r\n>>> ds = load_dataset(\"glue\", \"cola\")\r\n>>> ds\r\nDatasetDict({\r\n train: Dataset({\r\n ...
2022-07-11T22:10:44Z
2023-02-28T18:06:55Z
2023-02-28T18:06:55Z
NONE
null
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## Describe the bug I am trying to load sentiment analysis datasets from huggingface, but any dataset I try to use via load_dataset, I get the same error: `AttributeError: module 'pyarrow.lib' has no attribute 'IpcReadOptions'` ## Steps to reproduce the bug ```python dataset = load_dataset("glue", "cola") ``` ## Expected results Download datasets without issue. ## Actual results `AttributeError: module 'pyarrow.lib' has no attribute 'IpcReadOptions'` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.5 - PyArrow version: 8.0.0 - Pandas version: 1.1.0
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2,638
Streaming for the Json loader
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[ "A note is that I think we should add a few indicator of status (as mentioned by @stas00 in #2649), probably at the (1) downloading, (2) extracting and (3) reading steps. In particular when loading many very large files it's interesting to know a bit where we are in the process.", "I tested locally, and the built...
2021-07-13T14:37:06Z
2021-07-16T15:59:32Z
2021-07-16T15:59:31Z
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It was not using `open` in the builder. Therefore `pyarrow.json.read_json` was downloading the full file to start yielding rows. Moreover, it appeared that `pyarrow.json.read_json` was not really suited for streaming as it was downloading too much data and failing if `block_size` was not properly configured (related to #2573). So I switched to using `open` which is extended to support reading from remote file progressively, and I removed the pyarrow json reader which was not practical. Instead, I'm using the classical `json.loads` from the standard library.
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Add HF.co for PRs/Issues for specific datasets
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-05-31T14:31:21Z
2022-06-01T12:37:42Z
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As in https://github.com/huggingface/transformers/pull/17485, issues and PR for datasets under a namespace have to be on the HF Hub
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evaluation result changes after `datasets` version change
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[ "Hi! The GH/no-namespace datasets versioning is synced with the version of the `datasets` lib, which means that the `wikiann` script was modified between the two compared versions. In this scenario, you can ensure reproducibility by pinning the script version, which is done by passing `revision=\"x.y.z\"` (e.g. `re...
2022-06-30T12:43:26Z
2023-07-25T15:05:26Z
2023-07-25T15:05:26Z
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## Describe the bug evaluation result changes after `datasets` version change ## Steps to reproduce the bug 1. Train a model on WikiAnn 2. reload the ckpt -> test accuracy becomes same as eval accuracy 3. such behavior is gone after downgrading `datasets` https://colab.research.google.com/drive/1kYz7-aZRGdayaq-gDTt30tyEgsKlpYOw?usp=sharing ## Expected results evaluation result shouldn't change before/after `datasets` version changes ## Actual results evaluation result changes before/after `datasets` version changes ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: colab - Python version: 3.7.13 - PyArrow version: 6.0.1 Q. How could the evaluation result change before/after `datasets` version changes?
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Add MultiEURLEX dataset
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[ "Hi @lhoestq, we have this new cool multilingual dataset coming at EMNLP 2021. It would be really nice if we could have it in Hugging Face asap. Thanks! ", "Hi @lhoestq, I adopted most of your suggestions:\r\n\r\n- Dummy data files reduced, including the 2 smallest documents per subset JSONL.\r\n- README was upda...
2021-09-02T09:42:24Z
2021-09-10T11:50:06Z
2021-09-10T11:50:06Z
CONTRIBUTOR
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**Add new MultiEURLEX Dataset** MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource). Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU. As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels); this is multi-label classification task (given the text, predict multiple labels).
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Allow ignoring kwargs inside fn_kwargs during dataset.map's fingerprinting
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[ "Hi ! In the `transformers` issue the object to not hash is a `Pool` - I think you can instantiate it inside your function instead of passing it as a parameter. It's good practice that your function and all its fn_kwargs are picklable, in case you want to parallelize `map` using `num_proc>1`\r\n\r\nFor the other ca...
2022-10-22T21:46:38Z
2022-11-01T22:19:07Z
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### Feature request `dataset.map` accepts a `fn_kwargs` that is passed to `fn`. Currently, the whole `fn_kwargs` is used by `fingerprint_transform` to calculate the new fingerprint. I'd like to be able to inform `fingerprint_transform` which `fn_kwargs` shoud/shouldn't be taken into account during hashing. Of course, users should be aware to properly use this new feature, just like the internal usages of `fingerprint_transform` [does](https://github.com/huggingface/datasets/blob/2699593b33ee63d17aad2a2bfddedd38a8df57b8/src/datasets/arrow_dataset.py#L2700). ### Motivation This is originally motivated by https://github.com/huggingface/transformers/pull/18351#issuecomment-1263588680. Nonetheless, consider a more general processing function that accepts a kwarg that does not influence it's output: ```python def fn(example, verbose=False): ... ``` Then `dataset.map(fn, verbose=True)` would not benefit from dataset caching. I'm not sure if other methods in the `Dataset` API could benefit from this feature. ### Your contribution Based on `fingerprint_transform `'s `wrapper` function [here](https://github.com/huggingface/datasets/blob/c59cc34fcd2a369d27b77cc678017f5976a926a9/src/datasets/fingerprint.py#L443), it seems to me that it should be possible to make `.map`/`._map_single` accept something like `fn_use_fingerprint_kwargs`/`fn_ignore_fingerprint_kwargs` (probably another arg name). This would then be used by `fingerprint_transform.wrapper` to better/more flexibly hash the transformation. I could contribute with a PR if this feature and approach look good to you.
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Whisper Event - RuntimeError: The size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1 100% 1000/1000 [2:52:21<00:00, 10.34s/it]
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[ "Hey @catswithbats! Super sorry for the late reply! This is happening because there is data with label length (504) that exceeds the model's max length (448). \r\n\r\nThere are two options here:\r\n1. Increase the model's `max_length` parameter: \r\n```python\r\nmodel.config.max_length = 512\r\n```\r\n2. Filter dat...
2022-12-25T15:17:14Z
2023-07-21T14:29:47Z
2023-07-21T14:29:47Z
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Done in a VM with a GPU (Ubuntu) following the [Whisper Event - PYTHON](https://github.com/huggingface/community-events/tree/main/whisper-fine-tuning-event#python-script) instructions. Attempted using [RuntimeError: he size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1 100% 1000/1000 - WEB](https://discuss.huggingface.co/t/trainer-runtimeerror-the-size-of-tensor-a-462-must-match-the-size-of-tensor-b-448-at-non-singleton-dimension-1/26010/10 ) - another person experiencing the same issue. But could not resolve the issue with the google/fleurs data. __Not clear what can be modified in the PY code to resolve the input data size mismatch, as the training data is already very small__. Tried posting on Discord, @sanchit-gandhi and @vaibhavs10. Was hoping that the event is over and some input/help is now available. [Hugging Face - whisper-small-amet](https://huggingface.co/drmeeseeks/whisper-small-amet). The paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) am_et is a low resource language (Table E), with the WER results ranging from 120-229, based on model size. (Whisper small WER=120.2). # ---> Initial Training Output /usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning warnings.warn( [INFO|trainer.py:1641] 2022-12-18 05:23:28,799 >> ***** Running training ***** [INFO|trainer.py:1642] 2022-12-18 05:23:28,799 >> Num examples = 446 [INFO|trainer.py:1643] 2022-12-18 05:23:28,799 >> Num Epochs = 72 [INFO|trainer.py:1644] 2022-12-18 05:23:28,799 >> Instantaneous batch size per device = 16 [INFO|trainer.py:1645] 2022-12-18 05:23:28,799 >> Total train batch size (w. parallel, distributed & accumulation) = 32 [INFO|trainer.py:1646] 2022-12-18 05:23:28,799 >> Gradient Accumulation steps = 2 [INFO|trainer.py:1647] 2022-12-18 05:23:28,800 >> Total optimization steps = 1000 [INFO|trainer.py:1648] 2022-12-18 05:23:28,801 >> Number of trainable parameters = 241734912 # ---> Error 14% 9/65 [07:07<48:34, 52.04s/it][INFO|configuration_utils.py:523] 2022-12-18 05:03:07,941 >> Generate config GenerationConfig { "begin_suppress_tokens": [ 220, 50257 ], "bos_token_id": 50257, "decoder_start_token_id": 50258, "eos_token_id": 50257, "max_length": 448, "pad_token_id": 50257, "transformers_version": "4.26.0.dev0", "use_cache": false } Traceback (most recent call last): File "run_speech_recognition_seq2seq_streaming.py", line 629, in <module> main() File "run_speech_recognition_seq2seq_streaming.py", line 578, in main train_result = trainer.train(resume_from_checkpoint=checkpoint) File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 1534, in train return inner_training_loop( File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 1859, in _inner_training_loop self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval) File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 2122, in _maybe_log_save_evaluate metrics = self.evaluate(ignore_keys=ignore_keys_for_eval) File "/usr/local/lib/python3.8/dist-packages/transformers/trainer_seq2seq.py", line 78, in evaluate return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix) File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 2818, in evaluate output = eval_loop( File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 3000, in evaluation_loop loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys) File "/usr/local/lib/python3.8/dist-packages/transformers/trainer_seq2seq.py", line 213, in prediction_step outputs = model(**inputs) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 1197, in forward outputs = self.model( File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 1066, in forward decoder_outputs = self.decoder( File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 873, in forward hidden_states = inputs_embeds + positions RuntimeError: The size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1 100% 1000/1000 [2:52:21<00:00, 10.34s/it]
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`udhr` doesn't load, dataset checksum mismatch
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2022-05-17T13:47:09Z
2022-06-08T19:11:21Z
2022-06-08T19:11:21Z
CONTRIBUTOR
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## Describe the bug Loading `udhr` fails due to a checksum mismatch for some source files. Looks like both of the source files on unicode.org have changed: size + checksum in datasets repo: ``` (hfdev) leon@blade:~/datasets/datasets/udhr$ jq .default.download_checksums < dataset_infos.json { "https://unicode.org/udhr/assemblies/udhr_xml.zip": { "num_bytes": 2273633, "checksum": "0565fa62c2ff155b84123198bcc967edd8c5eb9679eadc01e6fb44a5cf730fee" }, "https://unicode.org/udhr/assemblies/udhr_txt.zip": { "num_bytes": 2107471, "checksum": "087b474a070dd4096ae3028f9ee0b30dcdcb030cc85a1ca02e143be46327e5e5" } } ``` size + checksum regenerated from current source files: ``` (hfdev) leon@blade:~/datasets/datasets/udhr$ rm dataset_infos.json (hfdev) leon@blade:~/datasets/datasets/udhr$ datasets-cli test --save_infos udhr.py Using custom data configuration default Testing builder 'default' (1/1) Downloading and preparing dataset udhn/default (download: 4.18 MiB, generated: 6.15 MiB, post-processed: Unknown size, total: 10.33 MiB) to /home/leon/.cache/huggingface/datasets/udhn/default/0.0.0/ad74b91fa2b3c386e5751b0c52bdfda76d334f76731142fd432d4acc2e2fde66... Dataset udhn downloaded and prepared to /home/leon/.cache/huggingface/datasets/udhn/default/0.0.0/ad74b91fa2b3c386e5751b0c52bdfda76d334f76731142fd432d4acc2e2fde66. Subsequent calls will reuse this data. 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 686.69it/s] Dataset Infos file saved at dataset_infos.json Test successful. (hfdev) leon@blade:~/datasets/datasets/udhr$ jq .default.download_checksums < dataset_infos.json { "https://unicode.org/udhr/assemblies/udhr_xml.zip": { "num_bytes": 2389690, "checksum": "a3350912790196c6e1b26bfd1c8a50e8575f5cf185922ecd9bd15713d7d21438" }, "https://unicode.org/udhr/assemblies/udhr_txt.zip": { "num_bytes": 2215441, "checksum": "cb87ecb25b56f34e4fd6f22b323000524fd9c06ae2a29f122b048789cf17e9fe" } } (hfdev) leon@blade:~/datasets/datasets/udhr$ ``` --- is unicode.org a sustainable hosting solution for this dataset? ## Steps to reproduce the bug ```python from datasets import load_dataset udhr = load_dataset("udhr") ``` ## Expected results That a Dataset object containing the UDHR data will be returned. ## Actual results ``` >>> d = load_dataset('udhr') Using custom data configuration default Downloading and preparing dataset udhn/default (download: 4.18 MiB, generated: 6.15 MiB, post-processed: Unknown size, total: 10.33 MiB) to /home/leon/.cache/huggingface/datasets/udhn/default/0.0.0/ad74b91fa2b3c386e5751b0c52bdfda76d334f76731142fd432d4acc2e2fde66... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/leon/.local/lib/python3.9/site-packages/datasets/load.py", line 1731, in load_dataset builder_instance.download_and_prepare( File "/home/leon/.local/lib/python3.9/site-packages/datasets/builder.py", line 613, in download_and_prepare self._download_and_prepare( File "/home/leon/.local/lib/python3.9/site-packages/datasets/builder.py", line 1117, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/home/leon/.local/lib/python3.9/site-packages/datasets/builder.py", line 684, in _download_and_prepare verify_checksums( File "/home/leon/.local/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://unicode.org/udhr/assemblies/udhr_xml.zip', 'https://unicode.org/udhr/assemblies/udhr_txt.zip'] >>> ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.1 commit/4110fb6034f79c5fb470cf1043ff52180e9c63b7 - Platform: Linux Ubuntu 20.04 - Python version: 3.9.12 - PyArrow version: 8.0.0
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Fix streaming parquet with image feature in schema
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-06-01T15:23:10Z
2023-06-02T10:02:54Z
2023-06-02T09:53:11Z
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It was not reading the feature type from the parquet arrow schema
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adding fake-news-english-2
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2020-12-09T22:05:13Z
2020-12-13T00:48:49Z
2020-12-13T00:48:49Z
CONTRIBUTOR
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Running add_column() seems to corrupt existing sequence-type column info
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2022-09-17T17:42:05Z
2022-09-19T12:54:54Z
2022-09-19T12:54:54Z
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I have a dataset that contains a column ("foo") that is a sequence type of length 4. So when I run .to_pandas() on it, the resulting dataframe correctly contains 4 columns - foo_0, foo_1, foo_2, foo_3. So the 1st row of the dataframe might look like: ds = load_dataset(...) df = ds.to_pandas() df: foo_0 | foo_1 | foo_2 | foo_3 0.0 | 1.0 | 2.0 | 3.0 If I run .add_column("new_col", data) on the dataset, and then .to_pandas() on the resulting new dataset, the resulting dataframe contains only 2 columns - foo, new_col. The values in column foo are lists of length 4, the 4 elements that should have been split into separate columns. Dataframe 1st row would be: ds = load_dataset(...) new_ds = ds.add_column("new_col", data) df = new_ds.to_pandas() df: foo | new_col [0.0, 1.0, 2.0, 3.0] | new_val I've explored the 2 datasets in a debugger and haven't noticed any changes to any attributes related to the foo column, but I can't determine why the dataframes are so different.
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A descriptive name for my changes
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[ "I have noticed that the master branch of your fork has diverged from the one of the repo. This is probably what causes the mess in the github diff \"Files changed\".\r\n\r\nI would suggest to re-fork the `datasets` repo and recreate a new branch and a new PR. ", "You're pretty close to having all things ready to...
2020-12-10T06:47:24Z
2020-12-15T10:36:27Z
2020-12-15T10:36:26Z
CONTRIBUTOR
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hind encorp resubmited
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Paperswithcode dataset mapping
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[ "messed up my branch, repushing", "live mapping can be found at https://huggingface.co/api/pwc/datasets-mapping and will be kept up to date going forward" ]
2021-05-25T18:14:26Z
2021-05-26T11:21:25Z
2021-05-26T11:17:18Z
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This is a continuation of https://github.com/huggingface/huggingface_hub/pull/43, encoded directly inside dataset cards. As discussed: - `paperswithcode_id: null` when the dataset doesn't exist on paperswithcode's side. - I've added this new key at the end of the yaml instead of ordering all keys alphabetically as pyyaml's default. No strong opinion on that one though
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AttributeError: module 'datasets' has no attribute 'load_dataset'
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[ "I'm having the same issue,Can you tell me how to solve it?", "I have the same issue, can you tell me how to solve it? Thanks" ]
2022-06-08T13:59:20Z
2023-07-28T08:12:22Z
2022-06-08T14:41:00Z
NONE
null
null
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## Describe the bug I have piped install datasets, but this package doesn't have these attributes: load_dataset, load_metric. ## Environment info - `datasets` version: 1.9.0 - Platform: Linux-5.13.0-44-generic-x86_64-with-debian-bullseye-sid - Python version: 3.6.13 - PyArrow version: 6.0.1
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800,660,995
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1,816
Doc2dial rc update to latest version
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[ "- update data loader and readme for latest version 1.0.1" ]
2021-02-03T20:08:54Z
2021-02-15T15:15:24Z
2021-02-15T15:04:33Z
CONTRIBUTOR
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4,672
Support extract 7-zip compressed data files
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Cool! Can you please remove `Fix #3541` from the description as this PR doesn't add support for streaming/`iter_archive`, so it only partially addresses the issue?\r\n\r\nSide note:\r\nI think we can use `libarchive` (`libarchive-c` ...
2022-07-11T15:56:51Z
2022-07-15T13:14:27Z
2022-07-15T13:02:07Z
MEMBER
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Fix partially #3541, fix #4670.
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Datasets.map causes incorrect overflow_to_sample_mapping when used with tokenizers and small batch size
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[ "I've built a minimal example that shows this bug without `n_proc`. It seems like it's a problem any way of using **tokenizers, `overflow_to_sample_mapping`, and Dataset.map, with a small batch size**:\r\n\r\n```\r\nimport datasets\r\nimport transformers\r\npretrained = 'deepset/tinyroberta-squad2'\r\ntokenizer = t...
2022-07-27T14:54:11Z
2022-07-27T17:57:28Z
null
NONE
null
null
null
## Describe the bug When using `tokenizer`, we can retrieve the field `overflow_to_sample_mapping`, since long samples will be overflown into multiple token sequences. However, when tokenizing is done via `Dataset.map`, with `n_proc > 1`, the `overflow_to_sample_mapping` field is wrong. This seems to be because each tokenizer only looks at its share of the samples, and maps to the index _within its share_, but then `Dataset.map` collates them together. ## Steps to reproduce the bug 1. Make a dataset of 3 strings. 2. Tokenize via Dataset.map with n_proc = 8 3. Inspect the `overflow_to_sample_mapping` field ## Expected results `[0, 1, 2]` ## Actual results `[0, 0, 0]` Notes: 1. I have not yet extracted a minimal example, but the above works reliably 2. If the dataset is large, I've yet to determine if this bug still happens a. not at all b. always c. on the small, leftover batch at the end.
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2,870
Fix three typos in two files for documentation
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2021-09-04T11:49:43Z
2021-09-06T08:21:21Z
2021-09-06T08:19:35Z
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Changed "bacth_size" to "batch_size" (2x) Changed "intsructions" to "instructions"
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Updated WER metric implementation to avoid memory issues
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[ "Hi ! Thanks for suggesting this fix \r\nUnfortunately it looks like it's already been fixed by #2111 \r\n\r\nFeel free to share your thoughts about this PR !\r\n\r\nI'm closing this one if you don't mind." ]
2021-04-05T15:43:20Z
2021-04-06T15:02:58Z
2021-04-06T15:02:58Z
NONE
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This is in order to fix this issue: https://github.com/huggingface/datasets/issues/2078
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Adding NELL
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[ "hi @lhoestq, I wanted to push another change to this branch b/c I found a bug in the parsing. I need to swap arg1 and arg2. I tried to git push -u origin nell but it didn't work. So I tried to do git push --force -u origin nell which seems to work, but nothing is happening to this branch. I think this is because i...
2020-12-11T20:25:25Z
2021-01-07T08:37:07Z
2020-12-21T14:45:00Z
CONTRIBUTOR
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NELL is a knowledge base and knowledge graph along with sentences used to create the KB. See http://rtw.ml.cmu.edu/rtw/ for more details.
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data configuration hash suffix depends on uncanonicalized data_dir
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null
[ "It could even use `os.path.realpath` to resolve symlinks.", "Indeed, it makes sense to normalize `data_dir`. Feel free to submit a PR (this can be \"fixed\" [here](https://github.com/huggingface/datasets/blob/89f775226321ba94e5bf4670a323c0fb44f5f65c/src/datasets/builder.py#L173))", "#self-assign" ]
2023-05-16T18:56:04Z
2023-06-02T15:52:05Z
2023-06-02T15:52:05Z
CONTRIBUTOR
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### Describe the bug I am working with the `recipe_nlg` dataset, which requires manual download. Once it's downloaded, I've noticed that the hash in the custom data configuration is different if I add a trailing `/` to my `data_dir`. It took me a while to notice that the hashes were different, and to understand that that was the cause of my dataset being processed anew instead of the cached version being used. ### Steps to reproduce the bug 1. Follow the steps to manually download the `recipe_nlg` dataset to `/data/recipenlg`. 2. Load it using `load_dataset`, once without a trailing slash and once with one: ```python >>> ds = load_dataset("recipe_nlg", data_dir="/data/recipenlg") Using custom data configuration default-082278caeea85765 Downloading and preparing dataset recipe_nlg/default to /home/kyle/.cache/huggingface/datasets/recipe_nlg/default-082278caeea85765/1.0.0/aa4f120223637bedf7360cecb70a9bd108acfd64e38207ca90c9f385d21e5e74... Dataset recipe_nlg downloaded and prepared to /home/kyle/.cache/huggingface/datasets/recipe_nlg/default-082278caeea85765/1.0.0/aa4f120223637bedf7360cecb70a9bd108acfd64e38207ca90c9f385d21e5e74. Subsequent calls will reuse this data. 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.10s/it] DatasetDict({ train: Dataset({ features: ['id', 'title', 'ingredients', 'directions', 'link', 'source', 'ner'], num_rows: 2231142 }) }) >>> ds = load_dataset("recipe_nlg", data_dir="/data/recipenlg/") Using custom data configuration default-83e87680785d0493 Downloading and preparing dataset recipe_nlg/default to /home/user/.cache/huggingface/datasets/recipe_nlg/default-83e87680785d0493/1.0.0/aa4f120223637bedf7360cecb70a9bd108acfd64e38207ca90c9f385d21e5e74... Generating train split: 1%| | 12701/2231142 [00:04<13:15, 2790.25 examples/s ^C ``` 3. Observe that the hash suffix in the custom data configuration changes due to the altered string. ### Expected behavior I think I would expect the hash to remain constant if it actually points to the same location on disk. I would expect the use of `os.path.normpath` to canonicalize the paths. ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-5.4.0-147-generic-x86_64-with-glibc2.31 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2
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Add FACTCK.BR dataset
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[ "@lhoestq done!", "merging since the CI is fixed on master" ]
2020-12-09T13:06:22Z
2020-12-17T12:38:45Z
2020-12-15T15:34:11Z
CONTRIBUTOR
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This PR adds [FACTCK.BR](https://github.com/jghm-f/FACTCK.BR) dataset from [FACTCK.BR: a new dataset to study fake news](https://dl.acm.org/doi/10.1145/3323503.3361698).
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Error iterating over Dataset with DataLoader
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[ "Instead of:\r\n```python\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\n```\r\nIt should be:\r\n```python\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32)\r\n```\r\n\r\n`batch_sampler` accepts a Sampler object or an Iterable, so you get an error.", "@...
2021-01-21T22:56:45Z
2022-10-28T02:16:38Z
2021-01-23T03:44:14Z
NONE
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I have a Dataset that I've mapped a tokenizer over: ``` encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids']) encoded_dataset[:1] ``` ``` {'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])} ``` When I try to iterate as in the docs, I get errors: ``` dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) next(iter(dataloader)) ``` ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-45-05180ba8aa35> in <module>() 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) ----> 2 next(iter(dataloader)) 3 frames /usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader) 411 self._timeout = loader.timeout 412 self._collate_fn = loader.collate_fn --> 413 self._sampler_iter = iter(self._index_sampler) 414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item() 415 self._persistent_workers = loader.persistent_workers TypeError: 'int' object is not iterable ```
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Dataset Viewer issue for super_glue
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[ "Thanks for reporting @wzsxxa.\r\n\r\nHowever the \"super_glue\" dataset is rendered properly by the Dataset preview: https://huggingface.co/datasets/super_glue" ]
2022-08-16T01:34:56Z
2022-08-22T10:08:01Z
2022-08-22T10:07:45Z
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### Link https://huggingface.co/datasets/super_glue ### Description can't view super_glue dataset on the web page ### Owner _No response_
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Fix `array.values` handling in array cast/embed
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a...
2023-10-05T15:24:05Z
2023-10-06T13:46:13Z
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Fix #6280
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Update summary on PyPi beyond NLP
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2021-10-11T23:27:46Z
2021-10-13T08:55:54Z
2021-10-13T08:55:54Z
MEMBER
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More than just NLP now
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Add Indosum dataset
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[ "Hi @prasastoadi have you had a chance to take a look at my suggestions ?\r\n\r\nFeel free to ping ;e if you have questions or when you're ready for a review", "Thanks for your contribution, @prasastoadi. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub ...
2020-12-10T05:02:00Z
2022-10-03T09:38:54Z
2022-10-03T09:38:54Z
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How to convert torch.utils.data.Dataset to huggingface dataset?
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[ "Hi! I think you can use the newly-added `from_generator` method for that:\r\n```python\r\nfrom datasets import Dataset\r\n\r\ndef gen():\r\n for idx in len(torch_dataset):\r\n yield torch_dataset[idx] # this has to be a dictionary\r\n ## or if it's an IterableDataset\r\n # for ex in torch_dataset:...
2022-09-16T09:15:10Z
2023-05-05T14:20:07Z
2022-09-20T11:23:43Z
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I look through the huggingface dataset docs, and it seems that there is no offical support function to convert `torch.utils.data.Dataset` to huggingface dataset. However, there is a way to convert huggingface dataset to `torch.utils.data.Dataset`, like below: ```python from datasets import Dataset data = [[1, 2],[3, 4]] ds = Dataset.from_dict({"data": data}) ds = ds.with_format("torch") ds[0] ds[:2] ``` So is there something I miss, or there IS no function to convert `torch.utils.data.Dataset` to huggingface dataset. If so, is there any way to do this convert? Thanks.
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TydiQA dataset is mixed and is not split per language
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[ "You can filter the languages this way:\r\n```python\r\ntydiqa_en = tydiqa_dataset.filter(lambda x: x[\"language\"] == \"english\")\r\n```\r\n\r\nOtherwise maybe we can have one configuration per language ?\r\nWhat do you think of this for example ?\r\n\r\n```python\r\nload_dataset(\"tydiqa\", \"primary_task.en\")\...
2021-03-29T08:56:21Z
2021-04-04T09:57:15Z
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Hi @lhoestq Currently TydiQA is mixed and user can only access the whole training set of all languages: https://www.tensorflow.org/datasets/catalog/tydi_qa for using this dataset, one need to train/evaluate in each separate language, and having them mixed, makes it hard to use this dataset. This is much convenient for user to have them split and I appreciate your help on this. Meanwhile, till hopefully this is split per language, I greatly appreciate telling me how I can preprocess and get data per language. thanks a lot
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Add OPUS Ted Talks 2013
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[ "merging since the CI is fixed on master" ]
2020-12-08T12:38:38Z
2020-12-16T16:57:50Z
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3,241
Swap descriptions of v1 and raw-v1 configs of WikiText dataset and fix metadata
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2021-11-09T10:54:15Z
2022-02-14T15:46:00Z
2021-11-09T13:49:28Z
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Fix #3237, fix #795.
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Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory
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[ "Hi! You can load this dataset with:\r\n```python\r\ndata_files = {\r\n \"train\": \"/content/PUBHEALTH/train.tsv\",\r\n \"validation\": \"/content/PUBHEALTH/dev.tsv\",\r\n \"test\": \"/content/PUBHEALTH/test.tsv\",\r\n}\r\n\r\ntsv_datasets_reloaded = load_dataset(\"csv\", data_files=data_files, sep=\"\\t\...
2023-08-29T05:49:56Z
2023-08-29T16:21:45Z
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### Describe the bug ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) [<ipython-input-48-6a7b3e847019>](https://localhost:8080/#) in <cell line: 7>() 5 } 6 ----> 7 csv_datasets_reloaded = load_dataset("tsv", data_files=data_files) 8 csv_datasets_reloaded 2 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1489 raise e1 from None 1490 if isinstance(e1, FileNotFoundError): -> 1491 raise FileNotFoundError( 1492 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1493 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" FileNotFoundError: Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory. Couldn't find 'tsv' on the Hugging Face Hub either: FileNotFoundError: Dataset 'tsv' doesn't exist on the Hub ``` ### Steps to reproduce the bug ``` data_files = { "train": "/content/PUBHEALTH/train.tsv", "validation": "/content/PUBHEALTH/dev.tsv", "test": "/content/PUBHEALTH/test.tsv", } tsv_datasets_reloaded = load_dataset("tsv", data_files=data_files) tsv_datasets_reloaded ``` ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) <ipython-input-48-6a7b3e847019> in <cell line: 7>() 5 } 6 ----> 7 csv_datasets_reloaded = load_dataset("tsv", data_files=data_files) 8 csv_datasets_reloaded 2 frames /usr/local/lib/python3.10/dist-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1489 raise e1 from None 1490 if isinstance(e1, FileNotFoundError): -> 1491 raise FileNotFoundError( 1492 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1493 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" FileNotFoundError: Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory. Couldn't find 'tsv' on the Hugging Face Hub either: FileNotFoundError: Dataset 'tsv' doesn't exist on the Hub ``` ### Expected behavior load the data, push to hub ### Environment info jupyter notebook RTX 3090
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Autoeval
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4233). All of your documentation changes will be reflected on that endpoint." ]
2022-04-27T01:32:09Z
2022-04-27T05:29:30Z
2022-04-27T01:32:23Z
CONTRIBUTOR
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How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset
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[ "Hi,\r\n\r\na HF dataset can be converted to a Torch Dataset with a simple wrapper as follows:\r\n```python\r\nfrom torch.utils.data import Dataset\r\n \r\nclass HFDataset(Dataset):\r\n def __init__(self, dset):\r\n self.dset = dset\r\n\r\n def __getitem__(self, idx):\r\n return self.dset[idx]\r...
2021-04-04T01:01:48Z
2021-08-24T15:55:35Z
2021-04-07T15:06:04Z
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Hi, I'm trying to pretraine deep-speed model using HF arxiv dataset like: ``` train_ds = nlp.load_dataset('scientific_papers', 'arxiv') train_ds.set_format( type="torch", columns=["input_ids", "attention_mask", "global_attention_mask", "labels"], ) engine, _, _, _ = deepspeed.initialize( args=args, model=model, model_parameters=[p for p in model.parameters() if p.requires_grad], training_data=train_ds) ``` but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?
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Fix `DuplicatedKeysError` and improve card in `tweet_qa`
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2022-01-10T17:27:40Z
2022-01-12T15:13:58Z
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Fix #3555
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5,762
Not able to load the pile
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[ "Thanks for reporting, @surya-narayanan.\r\n\r\nI see you already started a discussion about this on the Community tab of the corresponding dataset: https://huggingface.co/datasets/EleutherAI/the_pile/discussions/10\r\nLet's continue the discussion there!" ]
2023-04-17T03:09:10Z
2023-04-17T09:37:27Z
2023-04-17T09:37:27Z
NONE
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### Describe the bug Got this error when I am trying to load the pile dataset ``` TypeError: Couldn't cast array of type struct<file: string, id: string> to {'id': Value(dtype='string', id=None)} ``` ### Steps to reproduce the bug Please visit the following sample notebook https://colab.research.google.com/drive/1JHcjawcHL6QHhi5VcqYd07W2QCEj2nWK#scrollTo=ulJP3eJCI-tB ### Expected behavior The pile should work ### Environment info - `datasets` version: 2.11.0 - Platform: Linux-5.10.147+-x86_64-with-glibc2.31 - Python version: 3.9.16 - Huggingface_hub version: 0.13.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3
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I_kwDODunzps5Mnlws
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Problem about wmt17 zh-en dataset
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[ "Running into the same error with `wmt17/zh-en`, `wmt18/zh-en` and `wmt19/zh-en`.", "@albertvillanova @lhoestq Could you take a look at this issue?", "@winterfell2021 Hi, I wonder where the code you provided should be added. I tried to add them in the `datasets/table.py` in `array_cast` function, however, the '...
2022-06-27T08:35:42Z
2022-08-23T10:01:02Z
2022-08-23T10:00:21Z
NONE
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It seems that in subset casia2015, some samples are like `{'c[hn]':'xxx', 'en': 'aa'}`. So when using `data = load_dataset('wmt17', "zh-en")` to load the wmt17 zh-en dataset, which will raise the exception: ``` Traceback (most recent call last): File "train.py", line 78, in <module> data = load_dataset(args.dataset, "zh-en") File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 1684, in load_dataset use_auth_token=use_auth_token, File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 705, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1221, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 793, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1215, in _prepare_split num_examples, num_bytes = writer.finalize() File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 533, in finalize self.write_examples_on_file() File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 410, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 503, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 198, in __arrow_array__ out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type) File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper return func(array, *args, **kwargs) File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1846, in cast_array_to_feature return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper return func(array, *args, **kwargs) File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1756, in array_cast raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}") TypeError: Couldn't cast array of type struct<c[hn]: string, en: string, zh: string> to struct<en: string, zh: string> ``` So the solution of this problem is to change the original array manually: ``` if 'c[hn]' in str(array.type): py_array = array.to_pylist() data_list = [] for vo in py_array: tmp = { 'en': vo['en'], } if 'zh' not in vo: tmp['zh'] = vo['c[hn]'] else: tmp['zh'] = vo['zh'] data_list.append(tmp) array = pa.array(data_list, type=pa.struct([ pa.field('en', pa.string()), pa.field('zh', pa.string()), ])) ``` Therefore, maybe a correct version of original casia2015 file need to be updated
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[docs] Custom decoding transforms
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5836). All of your documentation changes will be reflected on that endpoint.", "The error seems unrelated to the changes, so feel free to merge.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\...
2023-05-09T21:21:41Z
2023-05-15T07:36:12Z
2023-05-10T20:23:03Z
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Adds custom decoding transform solution to the docs to fix #5782.
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Remove deprecated identical_ok
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-09-06T15:01:24Z
2022-09-06T22:24:09Z
2022-09-06T22:21:57Z
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`huggingface-hub` says that the `identical_ok` argument of `HfApi.upload_file` is now deprecated, and will be removed soon. It even has no effect at the moment when it's passed: ```python Args: ... identical_ok (`bool`, *optional*, defaults to `True`): Deprecated: will be removed in 0.11.0. Changing this value has no effect. ... ``` There was only one occurence of `identical_ok=False` but it's maybe not worth adding a check ti verify if the files were the same. cc @mariosasko
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Add support for time, date, duration, and decimal dtypes
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[ "Is there a dataset which uses these four datatypes for tests purposes?\r\n", "@severo Not yet. I'll let you know if that changes." ]
2022-01-18T13:46:05Z
2022-01-31T18:29:34Z
2022-01-20T17:37:33Z
CONTRIBUTOR
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Add support for the pyarrow time (maps to `datetime.time` in python), date (maps to `datetime.time` in python), duration (maps to `datetime.timedelta` in python), and decimal (maps to `decimal.decimal` in python) dtypes. This should be helpful when writing scripts for time-series datasets.
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Add Wikimedia pre-processed datasets
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2021-12-07T17:33:19Z
2021-12-07T17:43:37Z
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## Adding a Dataset - **Name:** Add pre-processed data to: - *wikimedia/wikipedia*: https://huggingface.co/datasets/wikimedia/wikipedia - *wikimedia/wikisource*: https://huggingface.co/datasets/wikimedia/wikisource - **Description:** Add pre-processed data to the Hub for all languages - **Paper:** *link to the dataset paper if available* - **Data:** *link to the Github repository or current dataset location* - **Motivation:** This will be very useful for the NLP community, as the pre-processing has a high cost for lot of researchers (both in computation and in knowledge) Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). CC: @geohci, @yjernite
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Switch to huggingface_hub's HfFileSystem
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2023-07-11T16:24:40Z
2023-07-17T17:01:01Z
2023-07-17T17:01:01Z
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instead of the current datasets.filesystems.hffilesystem.HfFileSystem which can be slow in some cases related to https://github.com/huggingface/datasets/issues/5846 and https://github.com/huggingface/datasets/pull/5919
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Error in `Json(datasets.ArrowBasedBuilder)` class
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[ "I have additionally identified the source of the error, being that [this condition](https://github.com/huggingface/datasets/blob/fc46bba66ba4f432cc10501c16a677112e13984c/src/datasets/packaged_modules/json/json.py#L124-L126) in the file\r\n`python3.8/site-packages/datasets/packaged_modules/json/json.py` is not bein...
2021-11-07T05:50:32Z
2021-11-09T19:09:15Z
2021-11-09T19:09:15Z
NONE
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## Describe the bug When a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails. ## Steps to reproduce the bug Create a folder that contains the following: ``` . ├── testdata │   └── mydata.json └── test.py ``` Please download [this file](https://github.com/huggingface/datasets/files/7491797/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide) :exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`! `test.py` simply contains: ```python from datasets import load_dataset my_dataset = load_dataset("testdata") ``` To reproduce the error, simply run ``` python test.py ``` ## Expected results The data should load correctly without error. ## Actual results The dataset builder fails with: ``` Using custom data configuration testdata-d490389b8ab4fd82 Downloading and preparing dataset json/testdata to /home/junshern.chan/.cache/huggingface/datasets/json/testdata-d490389b8ab4fd82/0.0.0/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34... 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2264.74it/s] 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 447.01it/s] Failed to read file '/home/junshern.chan/hf-json-bug/testdata/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0 Traceback (most recent call last): File "test.py", line 28, in <module> my_dataset = load_dataset("testdata") File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 697, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 1156, in _prepare_split for key, table in utils.tqdm( File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/tqdm/std.py", line 1168, in __iter__ for obj in iterable: File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 146, in _generate_tables raise ValueError( ValueError: Not able to read records in the JSON file at /home/junshern.chan/hf-json-bug/testdata/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.1 - Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 6.0.0
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ELI5 supporting documents
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[ "Hi ! Please post your question on the [forum](https://discuss.huggingface.co/), more people will be able to help you there ;)" ]
2022-04-08T23:36:27Z
2022-04-13T13:52:46Z
null
NONE
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if i am using dense search to create supporting documents for eli5 how much time it will take bcz i read somewhere that it takes about 18 hrs??
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No Data format found
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[ "Hi, which dataset is giving you an error?" ]
2022-01-03T09:41:11Z
2022-01-17T13:26:05Z
2022-01-17T13:26:05Z
NONE
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## Dataset viewer issue for '*name of the dataset*' **Link:** *link to the dataset viewer page* *short description of the issue* Am I the one who added this dataset ? Yes-No
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Altered faiss installation comment
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Hi ! Can you explain why this change is needed ?", "Facebook recommends installing FAISS using conda (https://github.com/facebookresearch/faiss/blob/main/INSTALL.md). pip does not seem to have the latest version of FAISS. The lates...
2022-04-26T01:20:43Z
2022-05-09T17:29:34Z
2022-05-09T17:22:09Z
CONTRIBUTOR
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928,900,827
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2,544
Fix logging levels
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2021-06-24T06:41:36Z
2021-06-25T13:40:19Z
2021-06-25T13:40:19Z
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Sometimes default `datasets` logging can be too verbose. One approach could be reducing some logging levels, from info to debug, or from warning to info. Close #2543. cc: @stas00
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2,622
Integration with AugLy
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[ "Hi,\r\n\r\nyou can define your own custom formatting with `Dataset.set_transform()` and then run the tokenizer with the batches of augmented data as follows:\r\n```python\r\ndset = load_dataset(\"imdb\", split=\"train\") # Let's say we are working with the IMDB dataset\r\ndset.set_transform(lambda ex: {\"text\": ...
2021-07-10T00:03:09Z
2023-07-20T13:18:48Z
2023-07-20T13:18:47Z
NONE
null
null
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**Is your feature request related to a problem? Please describe.** Facebook recently launched a library, [AugLy](https://github.com/facebookresearch/AugLy) , that has a unified API for augmentations for image, video and text. It would be pretty exciting to have it hooked up to HF libraries so that we can make NLP models robust to misspellings or to punctuation, or emojis etc. Plus, with Transformers supporting more CV use cases, having augmentations support becomes crucial. **Describe the solution you'd like** The biggest difference between augmentations and preprocessing is that preprocessing happens only once, but you are running augmentations once per epoch. AugLy operates on text directly, so this breaks the typical workflow where we would run the tokenizer once, set format to pt tensors and be ready for the Dataloader. **Describe alternatives you've considered** One possible way of implementing these is to make a custom Dataset class where getitem(i) runs the augmentation and the tokenizer every time, though this would slow training down considerably given we wouldn't even run the tokenizer in batches.
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Are the `metadata JSON file` not on their way to deprecation? 😆😇\r\n\r\nIMO, more generally than this particular PR, the contribution process should be simplified now that many validation checks happen on the hub side.\r\n\r\nKeepi...
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An example or recommendations for creating large image datasets?
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[ "The beam utilities allow to prepare a dataset as parquet in your cloud storage. From my perspective this CLI is not super easy to use, but we've been working on a new python API to prepare a dataset in your cloud storage:\r\n```python\r\nfrom datasets import load_dataset_builder\r\n\r\nbuilder = load_dataset_build...
2022-11-01T07:38:38Z
2022-11-02T10:17:11Z
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I know that Apache Beam and `datasets` have [some connector utilities](https://huggingface.co/docs/datasets/beam). But it's a little unclear what we mean by "But if you want to run your own Beam pipeline with Dataflow, here is how:". What does that pipeline do? As a user, I was wondering if we have this support for creating large image datasets. If so, we should mention that [here](https://huggingface.co/docs/datasets/image_dataset). Cc @lhoestq
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Add oversampling strategies to interleave datasets
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4831). All of your documentation changes will be reflected on that endpoint.", "Hi @lhoestq, \r\nThanks for your review! I've added the requested mention in the documentation and corrected the Error type in `interleave_datasets...
2022-08-11T16:24:51Z
2023-07-11T15:57:48Z
2022-08-24T16:46:07Z
CONTRIBUTOR
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Hello everyone, Here is a proposal to improve `interleave_datasets` function. Following Issue #3064, and @lhoestq [comment](https://github.com/huggingface/datasets/issues/3064#issuecomment-1022333385), I propose here a code that performs oversampling when interleaving a `Dataset` list. I have myself encountered this problem while trying to implement training on a multilingual dataset following a training strategy similar to that of [XLSUM paper](https://arxiv.org/pdf/2106.13822.pdf), a multilingual abstract summary dataset where the multilingual training dataset is created by sampling from the languages following a smoothing strategy. The main idea is to sample languages that have a low number of samples more frequently than other languages. As in Issue #3064, the current default strategy is a undersampling strategy, which stops as soon as a dataset runs out of samples. The new `all_exhausted` strategy stops building the new dataset as soon as all samples in each dataset have been added at least once. How does it work in practice: - if ``probabilities`` is `None` and the strategy is `all_exhausted`, it simply performs a round robin interleaving that stops when the longest dataset is out of samples. Here the new dataset length will be $maxLengthDataset*nbDataset$. - if ``probabilities`` is not `None` and the strategy is `all_exhausted`, it keeps trace of the datasets which were out of samples but continues to add them to the new dataset, and stops as soons as every dataset runs out of samples at least once. - In the other cases, it is supposed to keep the same behaviour as before. Except that this time, when probabilities are precised, it really stops AS SOON AS a dataset is out of samples. More on the last sentence: The previous example of `interleave_datasets` was: >>> from datasets import Dataset, interleave_datasets >>> d1 = Dataset.from_dict({"a": [0, 1, 2]}) >>> d2 = Dataset.from_dict({"a": [10, 11, 12]}) >>> d3 = Dataset.from_dict({"a": [20, 21, 22]}) >>> dataset = interleave_datasets([d1, d2, d3]) >>> dataset["a"] [0, 10, 20, 1, 11, 21, 2, 12, 22] >>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42) >>> dataset["a"] [10, 0, 11, 1, 2, 20, 12] With my implementation, `dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)` gives: >>> dataset["a"] [10, 0, 11, 1, 2] because `d1` is already out of samples just after `2` is added. Example of the results of applying the different strategies: >>> from datasets import Dataset, interleave_datasets >>> d1 = Dataset.from_dict({"a": [0, 1, 2]}) >>> d2 = Dataset.from_dict({"a": [10, 11, 12]}) >>> d3 = Dataset.from_dict({"a": [20, 21, 22]}) >>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42, stopping_strategy="all_exhausted") >>> dataset["a"] [10, 0, 11, 1, 2, 20, 12, 10, 0, 1, 2, 21, 0, 11, 1, 2, 0, 1, 12, 2, 10, 0, 22] >>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42) >>> dataset["a"] [10, 0, 11, 1, 2] >>> dataset = interleave_datasets([d1, d2, d3]) >>> dataset["a"] [0, 10, 20, 1, 11, 21, 2, 12, 22] >>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted") >>> dataset["a"] [0, 10, 20, 1, 11, 21, 2, 12, 22] >>> 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]) >>> dataset["a"] [0, 10, 20, 1, 11, 21, 2, 12, 22] >>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted") >>> dataset["a"] [0, 10, 20, 1, 11, 21, 2, 12, 22, 0, 13, 23, 1, 0, 24] >>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42) >>> dataset["a"] [10, 0, 11, 1, 2] >>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42, stopping_strategy="all_exhausted") >>> dataset["a"] [10, 0, 11, 1, 2, 20, 12, 13, ..., 0, 1, 2, 0, 24] **Final note:** I've been using that code for a research project involving a large-scale multilingual dataset. One should be careful when using oversampling to avoid to avoid exploding the size of the dataset. For example, if a very large data set has a low probability of being sampled, the final dataset may be several times the size of that large data set.
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Add CCAgT dataset
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[ "Awesome ! Let us know if you have questions or if we can help ;) I'm assigning you\r\n\r\nPS: if possible, please try to not use Google Drive links in your dataset script, since Google Drive has download quotas and is not always reliable.", "HI, I was waiting to come out in the second version to do the implement...
2022-03-31T18:20:28Z
2022-07-06T19:03:42Z
2022-07-06T19:03:42Z
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## Adding a Dataset - **Name:** CCAgT dataset: Images of Cervical Cells with AgNOR Stain Technique - **Description:** The dataset contains 2540 images (1600x1200 where each pixel is 0.111μm×0.111μm) from three different slides, having at least one nucleus per image. These images are from fields belonging to a sample cervical slide, colored with silver-stained, a method known as Argyrophilic Nucleolar Organizer Regions (AgNOR). - **Paper:** https://doi.org/10.1109/cbms49503.2020.00110 - **Data:** https://arquivos.ufsc.br/d/373be2177a33426a9e6c/ or https://drive.google.com/drive/u/4/folders/1TBpYCv6S1ydASLauSzcsvO7Wc5O-WUw0 - **Motivation:** This is a unique dataset (because of the stain), for a major health problem, cervical cancer, with real data. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Hi, this is a public version of the dataset that I have been working on, soon we will have another version of this dataset. But until this new version goes out, I thought I would add this dataset here, if it makes sense for the repository. You can assign the task to me if possible
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Add IterableDataset.from_spark
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Hi again @lhoestq this is ready for review! Not sure I have permission to add people to the reviewers list...", "Cool ! I think you can define `IterableDataset.from_spark` instead of adding `streaming=` in `Dataset.from_spark`, it ...
2023-04-18T17:47:53Z
2023-05-17T14:07:32Z
2023-05-17T14:00:38Z
CONTRIBUTOR
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Follow-up from https://github.com/huggingface/datasets/pull/5701 Related issue: https://github.com/huggingface/datasets/issues/5678
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CI tests are broken: AttributeError: 'mappingproxy' object has no attribute 'target'
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2023-01-20T10:03:10Z
2023-01-20T10:28:44Z
2023-01-20T10:28:44Z
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CI tests are broken, raising `AttributeError: 'mappingproxy' object has no attribute 'target'`. See: https://github.com/huggingface/datasets/actions/runs/3966497597/jobs/6797384185 ``` ... ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://top_level-date=2019-10-0[1-4]/*-expected_paths4] - AttributeError: 'mappingproxy' object has no attribute 'target' ===== 2076 passed, 19 skipped, 15 warnings, 47 errors in 115.54s (0:01:55) ===== ```
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4,747
Shard parquet in `download_and_prepare`
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[ "_The documentation is not available anymore as the PR was closed or merged._", "This is ready for review cc @mariosasko :) please let me know what you think !" ]
2022-07-26T18:05:01Z
2022-09-15T13:43:55Z
2022-09-15T13:41:26Z
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Following https://github.com/huggingface/datasets/pull/4724 (needs to be merged first) It's good practice to shard parquet files to enable parallelism with spark/dask/etc. I added the `max_shard_size` parameter to `download_and_prepare` (default to 500MB for parquet, and None for arrow). ```python from datasets import * output_dir = "./output_dir" # also supports "s3://..." builder = load_dataset_builder("squad") builder.download_and_prepare(output_dir, file_format="parquet", max_shard_size="5MB") ``` ### Implementation details The examples are written to a parquet file until `ParquetWriter._num_bytes > max_shard_size`. When this happens, a new writer is instantiated to start writing the next shard. At the end, all the shards are renamed to include the total number of shards in their names: `{builder.name}-{split}-{shard_id:05d}-of-{num_shards:05d}.parquet` I also added the `MAX_SHARD_SIZE` config variable (default to 500MB) TODO: - [x] docstrings - [x] docs - [x] tests cc @severo
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Fix sem_eval_2018_task_1 download location
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[ "I fixed those two things, the two remaining failing checks seem to be due to some dependency missing in the tests." ]
2022-01-27T15:45:00Z
2022-02-04T15:15:26Z
2022-02-04T15:15:26Z
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As discussed with @lhoestq in https://github.com/huggingface/datasets/issues/3549#issuecomment-1020176931_ this is the new pull request to fix the download location.
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2,453
Keep original features order
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[ "The arrow writer was supposing that the columns were always in the sorted order. I just pushed a fix to reorder the arrays accordingly to the schema. It was failing for many datasets like squad", "and obviously it broke everything", "Feel free to revert my commit. I can investigate this in the coming days", ...
2021-06-07T16:26:38Z
2021-06-15T18:05:36Z
2021-06-15T15:43:48Z
MEMBER
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When loading a Dataset from a JSON file whose column names are not sorted alphabetically, we should get the same column name order, whether we pass features (in the same order as in the file) or not. I found this issue while working on #2366.
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Python Programming Puzzles
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[ "👀 @TalSchuster", "Thanks @VictorSanh!\r\nThere's also a [notebook](https://aka.ms/python_puzzles) and [demo](https://aka.ms/python_puzzles_study) available now to try out some of the puzzles" ]
2021-06-14T13:27:18Z
2021-06-15T18:14:14Z
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## Adding a Dataset - **Name:** Python Programming Puzzles - **Description:** Programming challenge called programming puzzles, as an objective and comprehensive evaluation of program synthesis - **Paper:** https://arxiv.org/pdf/2106.05784.pdf - **Data:** https://github.com/microsoft/PythonProgrammingPuzzles ([Scrolling through the data](https://github.com/microsoft/PythonProgrammingPuzzles/blob/main/problems/README.md)) - **Motivation:** Spans a large range of difficulty, problems, and domains. A useful resource for evaluation as we don't have a clear understanding of the abilities and skills of extremely large LMs. Note: it's a growing dataset (contributions are welcome), so we'll need careful versioning for 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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`GeneratorBasedBuilder` does not support `None` values
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[ "Hi,\r\n\r\nthanks for reporting and providing a minimal reproducible example. \r\n\r\nThis line of the PR I've linked in our discussion on the Forum will add support for `None` values:\r\nhttps://github.com/huggingface/datasets/blob/a53de01842aac65c66a49b2439e18fa93ff73ceb/src/datasets/features/features.py#L835\r\...
2021-11-11T19:51:21Z
2021-12-09T14:26:58Z
2021-12-09T14:26:58Z
NONE
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## Describe the bug `GeneratorBasedBuilder` does not support `None` values. ## Steps to reproduce the bug See [this repository](https://github.com/pavel-lexyr/huggingface-datasets-bug-reproduction) for minimal reproduction. ## Expected results Dataset is initialized with a `None` value in the `value` column. ## Actual results ``` Traceback (most recent call last): File "main.py", line 3, in <module> datasets.load_dataset("./bad-data") File ".../datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File ".../datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File ".../datasets/builder.py", line 697, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File ".../datasets/builder.py", line 1103, in _prepare_split example = self.info.features.encode_example(record) File ".../datasets/features/features.py", line 1033, in encode_example return encode_nested_example(self, example) File ".../datasets/features/features.py", line 808, in encode_nested_example return { File ".../datasets/features/features.py", line 809, in <dictcomp> k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) File ".../datasets/features/features.py", line 855, in encode_nested_example return schema.encode_example(obj) File ".../datasets/features/features.py", line 299, in encode_example return float(value) TypeError: float() argument must be a string or a number, not 'NoneType' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.1 - Platform: Linux-5.4.0-81-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 6.0.0
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5,218
Delta Tables usage using Datasets Library
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2022-11-09T02:42:18Z
2022-11-09T02:42:36Z
2022-11-09T02:42:36Z
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### Feature request Adding compatibility of Datasets library with Delta Format. Elevating the utilities of Datasets library from Machine Learning Scope to Data Engineering Scope as well. ### Motivation We know datasets library can absorb csv, json, parquet, etc. file formats but it would be great if Datasets library could work with Delta Tables (with delta format) as it has different features such as time travelling, layout optimization, query performance, aids in Data Engineering. This will help and enhance Datasets library from Machine Learning utility to Data Engineering utilities and expand horizons thereafter. I am totally using Datasets library in all my usecases and as my role expands so does the work, compatibility with Datasets library is something I don't want to lose. ### Your contribution Would love to work on this feature, even if this has to picked up from scratch, including design paradigms and patterns. I have basic idea about Delta Live Tables, would brush it easily for this feature.
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Fix f1 metric with None average
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2021-09-30T15:31:57Z
2021-10-01T14:17:39Z
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Fix #2979.
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CI fails on Windows with FileNotFoundError when stting up s3_base fixture
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2021-10-13T05:52:26Z
2021-10-13T08:05:49Z
2021-10-13T06:49:48Z
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## Describe the bug After commit 9353fc863d0c99ab0427f83cc5a4f04fcf52f1df, the CI fails on Windows with FileNotFoundError when stting up s3_base fixture. See: https://app.circleci.com/pipelines/github/huggingface/datasets/8151/workflows/5db8d154-badd-4d3d-b202-ca7a318997a2/jobs/50321 Error summary: ``` ERROR tests/test_arrow_dataset.py::test_dummy_dataset_serialize_s3 - FileNotF... ERROR tests/test_dataset_dict.py::test_dummy_dataset_serialize_s3 - FileNotFo... ``` Stack trace: ``` ______________ ERROR at setup of test_dummy_dataset_serialize_s3 ______________ [gw0] win32 -- Python 3.6.8 C:\tools\miniconda3\python.exe @pytest.fixture() def s3_base(): # writable local S3 system import shlex import subprocess # Mocked AWS Credentials for moto. old_environ = os.environ.copy() os.environ.update(S3_FAKE_ENV_VARS) > proc = subprocess.Popen(shlex.split("moto_server s3 -p %s" % s3_port)) tests\s3_fixtures.py:32: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ C:\tools\miniconda3\lib\subprocess.py:729: in __init__ restore_signals, start_new_session) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <subprocess.Popen object at 0x0000012BB8A4B908> args = 'moto_server s3 -p 5555', executable = None, preexec_fn = None close_fds = True, pass_fds = (), cwd = None, env = None startupinfo = <subprocess.STARTUPINFO object at 0x0000012BB8177630> creationflags = 0, shell = False, p2cread = -1, p2cwrite = -1, c2pread = -1 c2pwrite = -1, errread = -1, errwrite = -1, unused_restore_signals = True unused_start_new_session = False def _execute_child(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_start_new_session): """Execute program (MS Windows version)""" assert not pass_fds, "pass_fds not supported on Windows." if not isinstance(args, str): args = list2cmdline(args) # Process startup details if startupinfo is None: startupinfo = STARTUPINFO() if -1 not in (p2cread, c2pwrite, errwrite): startupinfo.dwFlags |= _winapi.STARTF_USESTDHANDLES startupinfo.hStdInput = p2cread startupinfo.hStdOutput = c2pwrite startupinfo.hStdError = errwrite if shell: startupinfo.dwFlags |= _winapi.STARTF_USESHOWWINDOW startupinfo.wShowWindow = _winapi.SW_HIDE comspec = os.environ.get("COMSPEC", "cmd.exe") args = '{} /c "{}"'.format (comspec, args) # Start the process try: hp, ht, pid, tid = _winapi.CreateProcess(executable, args, # no special security None, None, int(not close_fds), creationflags, env, os.fspath(cwd) if cwd is not None else None, > startupinfo) E FileNotFoundError: [WinError 2] The system cannot find the file specified C:\tools\miniconda3\lib\subprocess.py:1017: FileNotFoundError ```
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3,766
Fix head_qa data URL
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2022-02-21T13:52:50Z
2022-02-21T14:39:20Z
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Fix #3758.
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Fix `max_shard_size` docs
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2022-11-18T16:55:22Z
2022-11-18T17:28:58Z
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`part-of-speech-tagging` was not part of the tagging taxonomy under `structure-prediction`
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Better error msg if `len(predictions)` doesn't match `len(references)` in metrics
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[ "Can't test this now but it may be a good improvement indeed.", "I added a function, but it only works with the `list` type. For arrays/tensors, we delegate formatting to the frameworks. " ]
2021-10-25T15:25:05Z
2021-11-05T11:44:59Z
2021-11-05T09:31:02Z
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Improve the error message in `Metric.add_batch` if `len(predictions)` doesn't match `len(references)`. cc: @BramVanroy (feel free to test this code on your examples and review this PR)
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Missing Zenodo 1.13.3 release
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[ "Zenodo has fixed on their side the 1.13.3 release: https://zenodo.org/record/5589150" ]
2021-10-18T12:57:18Z
2021-10-22T13:22:25Z
2021-10-22T13:22:24Z
MEMBER
null
null
null
After `datasets` 1.13.3 release, this does not appear in Zenodo releases: https://zenodo.org/record/5570305 TODO: - [x] Contact Zenodo support - [x] Check it is fixed
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I_kwDODunzps5vD_oL
6,172
Make Dataset streaming queries retryable
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[ "Hi! The streaming mode also retries requests - `datasets.config.STREAMING_READ_MAX_RETRIES` (20 sec by default) controls the number of retries and `datasets.config.STREAMING_READ_RETRY_INTERVAL` (5 sec) the sleep time between retries.\r\n\r\n> At step 1800 I got a 504 HTTP status code error from Huggingface hub fo...
2023-08-23T13:15:38Z
2023-09-10T20:22:15Z
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### Feature request Streaming datasets, as intended, do not load the entire dataset in memory or disk. However, while querying the next data chunk from the remote, sometimes it is possible that the service is down or there might be other issues that may cause the query to fail. In such a scenario, it would be nice to make these queries retryable (perhaps with a backoff strategy). ### Motivation I was working on a model and the model checkpoints after every 1000 steps. At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch `dataloader`. Given the size of my model and data, it took around 2 hours to reach 1800 steps and now it will take about an hour to recover the lost 800. It would be better to get a retryable querying strategy. ### Your contribution It would be better if someone having experience in this area takes this up as this would require some testing.
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I_kwDODunzps5R1b1t
4,977
Providing dataset size
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[ "Hi @sashavor, thanks for your suggestion.\r\n\r\nUntil now we have the CLI command \r\n```\r\ndatasets-cli test datasets/<your-dataset-folder> --save_infos --all_configs\r\n```\r\nthat generates the `dataset_infos.json` with the size of the downloaded dataset, among other information.\r\n\r\nWe are currently in th...
2022-09-14T13:09:27Z
2022-09-15T16:03:58Z
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**Is your feature request related to a problem? Please describe.** Especially for big datasets like [LAION](https://huggingface.co/datasets/laion/laion2B-en/), it's hard to know exactly the downloaded size (because there are many files and you don't have their exact size when downloaded). **Describe the solution you'd like** Auto-populating the downloaded dataset size on the dataset page would be really useful, including that of each split (when there are some). **Describe alternatives you've considered** People should be adding this to dataset cards, but I don't think that is systematically the case :slightly_smiling_face: **Additional context** Mentioned to @lhoestq
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I_kwDODunzps5UQe-K
5,132
Depracate `num_proc` parameter in `DownloadManager.extract`
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[ "I can take this! #self-assign", "#self-assign", "@lazarust i'm already working on this issue :smile: ", "#self-assign", "hey @mariosasko , i made a pr for this issue. Could you please review it." ]
2022-10-18T17:41:05Z
2022-10-25T15:56:46Z
2022-10-25T15:56:46Z
CONTRIBUTOR
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The `num_proc` parameter is only present in `DownloadManager.extract` but not in `StreamingDownloadManager.extract`, making it impossible to support streaming in the dataset scripts that use it (`openwebtext` and `the_pile_stack_exchange`). We can avoid this situation by deprecating this parameter and passing `DownloadConfig`'s `num_proc` to `map_nested` instead, as it's done in `DownloadManager.download`.
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1,570
Documentation for loading CSV datasets misleads the user
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2020-12-14T19:04:37Z
2020-12-22T19:30:12Z
2020-12-21T13:47:09Z
CONTRIBUTOR
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Documentation for loading CSV datasets misleads the user into thinking setting `quote_char' to False will disable quoting. There are two problems here: i) `quote_char' is misspelled, must be `quotechar' ii) the documentation should mention `quoting'
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