Datasets:

Ayon128

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PR_kwDODunzps49k9Rm

Fix: wmt datasets - fix CWMT zh subsets

closed

MEMBER

Fix https://github.com/huggingface/datasets/issues/4575 TODO: run `datasets-cli test`: - [x] wmt17 - [x] wmt18 - [x] wmt19

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PR_kwDODunzps49jGxD

audio folder check CI

closed

CONTRIBUTOR

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PR_kwDODunzps49hBGY

Fix typos in documentation

closed

CONTRIBUTOR

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PR_kwDODunzps49gBk0

adding mafand to datasets

closed

CONTRIBUTOR

I'm addding the MAFAND dataset by Masakhane based on the paper/repository below: Paper: https://aclanthology.org/2022.naacl-main.223/ Code: https://github.com/masakhane-io/lafand-mt Please, help merge this Everything works except for creating dummy data file

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PR_kwDODunzps49fZle

Complete tags of superglue dataset card

closed

CONTRIBUTOR

Related to #4479 .

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PR_kwDODunzps49e1CP

amend docstring for dunder

open

NONE

display dunder method in docsting with underlines an not bold markdown.

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I_kwDODunzps5QJD6y

Dataset Viewer issue for MoritzLaurer/multilingual_nli

closed

NONE

### Link _No response_ ### Description I've just uploaded a new dataset to the hub and the viewer does not work for some reason, see here: https://huggingface.co/datasets/MoritzLaurer/multilingual_nli It displays the error: ``` Status code: 400 Exception: Status400Error Message: The dataset does not exist. ``` Weirdly enough the dataviewer works for an earlier version of the same dataset. The only difference is that it is smaller, but I'm not aware of other changes I have made: https://huggingface.co/datasets/MoritzLaurer/multilingual_nli_test Do you know why the dataviewer is not working? ### Owner _No response_

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I_kwDODunzps5QIhG7

Allow pathlib PoxisPath in Dataset.read_json

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** ``` from pathlib import Path from datasets import Dataset ds = Dataset.read_json(Path('data.json')) ``` causes an error ``` AttributeError: 'PosixPath' object has no attribute 'decode' ``` **Describe the solution you'd like** It should be able to accept PosixPath and read the json from inside.

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I_kwDODunzps5QF89E

TFDS wiki_dialog dataset to Huggingface dataset

closed

NONE

## Adding a Dataset - **Name:** *Wiki_dialog* - **Description: https://github.com/google-research/dialog-inpainting#:~:text=JSON%20object%2C%20for-,example,-%3A - **Paper: https://arxiv.org/abs/2205.09073 - **Data: https://github.com/google-research/dialog-inpainting - **Motivation:** *Research and Development on biggest corpus of dialog data* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).

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I_kwDODunzps5QE6T7

Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code

closed

NONE

## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug # The dataset function is as follows： from pathlib import Path from typing import Dict, List, Tuple import datasets import pandas as pd _CITATION = """\ """ _DATASETNAME = "jadi_ide" _DESCRIPTION = """\ """ _HOMEPAGE = "" _LICENSE = "Unknown" _URLS = { _DATASETNAME: "https://github.com/fathanick/Javanese-Dialect-Identification-from-Twitter-Data/raw/main/Update 16K_Dataset.xlsx", } _SOURCE_VERSION = "1.0.0" class JaDi_Ide(datasets.GeneratorBasedBuilder): SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BUILDER_CONFIGS = [ NusantaraConfig( name="jadi_ide_source", version=SOURCE_VERSION, description="JaDi-Ide source schema", schema="source", subset_id="jadi_ide", ), ] DEFAULT_CONFIG_NAME = "source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "id": datasets.Value("string"), "text": datasets.Value("string"), "label": datasets.Value("string") } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: """Returns SplitGenerators.""" # Dataset does not have predetermined split, putting all as TRAIN urls = _URLS[_DATASETNAME] base_dir = Path(dl_manager.download_and_extract(urls)) data_files = {"train": base_dir} return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": data_files["train"], "split": "train", }, ), ] def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" df = pd.read_excel(filepath, engine='openpyxl') df.columns = ["id", "text", "label"] if self.config.schema == "source": for row in df.itertuples(): ex = { "id": str(row.id), "text": row.text, "label": row.label, } yield row.id, ex ``` ## Expected results Expecting to load the dataset smoothly. ## Actual results File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 1751, in load_dataset use_auth_token=use_auth_token, File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 705, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1227, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 793, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1216, in _prepare_split desc=f"Generating {split_info.name} split", File "/home/xuyan/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__ for obj in iterable: File "/home/xuyan/.cache/huggingface/modules/datasets_modules/datasets/jadi_ide/7a539f2b6f726defea8fbe36ceda17bae66c370f6d6c418e3a08d760ebef7519/jadi_ide.py", line 107, in _generate_examples df = pd.read_excel(filepath, engine='openpyxl') File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/download/streaming_download_manager.py", line 701, in xpandas_read_excel return pd.read_excel(BytesIO(filepath_or_buffer.read()), **kwargs) AttributeError: 'xPath' object has no attribute 'read' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-4.15.0-142-generic-x86_64-with-debian-stretch-sid - Python version: 3.7.4 - PyArrow version: 9.0.0 - Pandas version: 0.25.1

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I_kwDODunzps5QEIY8

Using disk for memory with the method `from_dict`

open

MEMBER

**Is your feature request related to a problem? Please describe.** I start with an empty dataset. In a loop, at each iteration, I create a new dataset with the method `from_dict` (based on some data I load) and I concatenate this new dataset with the one at the previous iteration. After some iterations, I have an OOM error. **Describe the solution you'd like** The method `from_dict` loads the data in RAM. It could be good to add an option to use the disk instead. **Describe alternatives you've considered** To solve the problem, I have to do an intermediate step where I save the new datasets at each iteration with `save_to_disk`. Once it's done, I open them all and concatenate them.

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PR_kwDODunzps49WjEu

Add collection3 dataset

closed

NONE

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I_kwDODunzps5QANHo

can't install using conda on Windows 10

open

NONE

## Describe the bug I wanted to install using conda or Anaconda navigator. That didn't work, so I had to install using pip. ## Steps to reproduce the bug conda install -c huggingface -c conda-forge datasets ## Expected results Should have indicated successful installation. ## Actual results Solving environment: failed with initial frozen solve. Retrying with flexible solve. Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source. ... took forever, so I cancelled it with ctrl-c ## Environment info - `datasets` version: 2.4.0 # after installing with pip - Platform: Windows-10-10.0.19044-SP0 - Python version: 3.9.12 - PyArrow version: 9.0.0 - Pandas version: 1.4.2 - conda version: 4.13.0 conda info active environment : base active env location : G:\anaconda2022 shell level : 1 user config file : C:\Users\michael\.condarc populated config files : C:\Users\michael\.condarc conda version : 4.13.0 conda-build version : 3.21.8 python version : 3.9.12.final.0 virtual packages : __cuda=11.1=0 __win=0=0 __archspec=1=x86_64 base environment : G:\anaconda2022 (writable) conda av data dir : G:\anaconda2022\etc\conda conda av metadata url : None channel URLs : https://conda.anaconda.org/pytorch/win-64 https://conda.anaconda.org/pytorch/noarch https://conda.anaconda.org/huggingface/win-64 https://conda.anaconda.org/huggingface/noarch https://conda.anaconda.org/conda-forge/win-64 https://conda.anaconda.org/conda-forge/noarch https://conda.anaconda.org/anaconda-fusion/win-64 https://conda.anaconda.org/anaconda-fusion/noarch https://repo.anaconda.com/pkgs/main/win-64 https://repo.anaconda.com/pkgs/main/noarch https://repo.anaconda.com/pkgs/r/win-64 https://repo.anaconda.com/pkgs/r/noarch https://repo.anaconda.com/pkgs/msys2/win-64 https://repo.anaconda.com/pkgs/msys2/noarch package cache : G:\anaconda2022\pkgs C:\Users\michael\.conda\pkgs C:\Users\michael\AppData\Local\conda\conda\pkgs envs directories : G:\anaconda2022\envs C:\Users\michael\.conda\envs C:\Users\michael\AppData\Local\conda\conda\envs platform : win-64 user-agent : conda/4.13.0 requests/2.27.1 CPython/3.9.12 Windows/10 Windows/10.0.19044 administrator : False netrc file : None offline mode : False

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I_kwDODunzps5P6-XN

map() function removes columns when input_columns is not None

closed

NONE

## Describe the bug The map function, removes features from the dataset that are not present in the _input_columns_ list of columns, despite the columns being removed not mentioned in the _remove_columns_ argument. ## Steps to reproduce the bug ```python from datasets import Dataset ds = Dataset.from_dict({"a" : [1,2,3],"b" : [0,1,0], "c" : [2,4,5]}) def double(x,y): x = x*2 y = y*2 return {"d" : x, "e" : y} ds.map(double, input_columns=["a","c"]) ``` ## Expected results ``` Dataset({ features: ['a', 'b', 'c', 'd', 'e'], num_rows: 3 }) ``` ## Actual results ``` Dataset({ features: ['a', 'c', 'd', 'e'], num_rows: 3 }) ``` In this specific example feature **b** should not be removed. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: linux (colab) - Python version: 3.7.13 - PyArrow version: 6.0.1

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I_kwDODunzps5P5NZh

No preprocessed wikipedia is working on huggingface/datasets

closed

NONE

## Describe the bug 20220301 wikipedia dump has been deprecated, so now there is no working wikipedia dump on huggingface https://huggingface.co/datasets/wikipedia https://dumps.wikimedia.org/enwiki/

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I_kwDODunzps5P22t1

file missing when load_dataset with openwebtext on windows

closed

NONE

## Describe the bug 0015896-b1054262f7da52a0518521e29c8e352c.txt is missing when I run run_mlm.py with openwebtext. I check the cache_path and can not find 0015896-b1054262f7da52a0518521e29c8e352c.txt. but I can find this file in the 17ecf461bfccd469a1fbc264ccb03731f8606eea7b3e2e8b86e13d18040bf5b3/urlsf_subset00-16_data.xz with 7-zip. ## Steps to reproduce the bug ```sh python run_mlm.py --model_type roberta --tokenizer_name roberta-base --dataset_name openwebtext --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --do_train --do_eval --output_dir F:/model/roberta-base ``` or ```python from datasets import load_dataset load_dataset("openwebtext", None, cache_dir=None, use_auth_token=None) ``` ## Expected results Loading is successful ## Actual results Traceback (most recent call last): File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 704, in download_and_prepare self._download_and_prepare( File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 1227, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 795, in _download_and_prepare raise OSError( OSError: Cannot find data file. Original error: [Errno 22] Invalid argument: 'F://huggingface/datasets/downloads/extracted/0901d27f43b7e9ac0577da0d0061c8c632ba0b70ecd1b4bfb21562d9b7486faa/0015896-b1054262f7da52a0518521e29c8e352c.txt' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: windows - Python version: 3.8.5 - PyArrow version: 9.0.0

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I_kwDODunzps5P2jMH

Dataset Viewer issue for super_glue

closed

NONE

### 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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PR_kwDODunzps49NFNL

Fix bug and checksums in exams dataset

closed

MEMBER

Fix #4852.

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I_kwDODunzps5P1mZv

Bug in multilingual_with_para config of exams dataset and checksums error

closed

MEMBER

## Describe the bug There is a bug for "multilingual_with_para" config in exams dataset: ```python ds = load_dataset("./datasets/exams", split="train") ``` raises: ``` KeyError: 'choices' ``` Moreover, there is a NonMatchingChecksumError: ``` NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/train_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/dev_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/test_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/test_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_bg_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_bg_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_hr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_hr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_hu_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_hu_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_it_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_it_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_mk_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_mk_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_pl_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_pl_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_pt_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_pt_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_sq_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_sq_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_sr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_sr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_tr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_tr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_vi_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_vi_with_para.jsonl.tar.gz'] ``` CC: @thesofakillers

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PR_kwDODunzps49L6ee

Fix license tag and Source Data section in billsum dataset card

closed

CONTRIBUTOR

Fixed the data source and license fields

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PR_kwDODunzps49KnZ8

Fix test of _get_extraction_protocol for TAR files

closed

MEMBER

While working in another PR, I discovered an xpass test (a test that is supposed to xfail but nevertheless passes) when testing `_get_extraction_protocol`: https://github.com/huggingface/datasets/runs/7818845285?check_suite_focus=true ``` XPASS tests/test_streaming_download_manager.py::test_streaming_dl_manager_get_extraction_protocol_throws[https://foo.bar/train.tar] ``` This PR: - refactors the test so that it tests the raise of the exceptions instead of xfailing - fixes the test for TAR files: it does not raise an exception, but returns "tar" - fixes some tests wrongly named: exchange `test_streaming_dl_manager_get_extraction_protocol` with `test_streaming_dl_manager_get_extraction_protocol_gg_drive`

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PR_kwDODunzps49JN8d

1.18.x

closed

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PR_kwDODunzps49JNj_

a

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PR_kwDODunzps49JNWX

Test win ci

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NONE

aa

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PR_kwDODunzps49IYSC

Update documentation card of miam dataset

closed

CONTRIBUTOR

Hi ! Paper has been published at EMNLP.

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PR_kwDODunzps49IOjf

Mark CI tests as xfail if Hub HTTP error

closed

MEMBER

In order to make testing more robust (and avoid merges to master with red tests), we could mark tests as xfailed (instead of failed) when the Hub raises some temporary HTTP errors. This PR: - marks tests as xfailed only if the Hub raises a 500 error for: - test_upstream_hub - makes pytest report the xfailed/xpassed tests. More tests could also be marked if needed. Examples of CI failures due to temporary Hub HTTP errors: - FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_multiple_files - https://github.com/huggingface/datasets/runs/7806855399?check_suite_focus=true `requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://hub-ci.huggingface.co/api/datasets/__DUMMY_TRANSFORMERS_USER__/test-16603108028233/commit/main (Request ID: aZeAQ5yLktoGHQYBcJ3zo)` - FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_no_token - https://github.com/huggingface/datasets/runs/7840022996?check_suite_focus=true `requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://s3.us-east-1.amazonaws.com/lfs-staging.huggingface.co/repos/81/e3/81e3b831fa9bf23190ec041f26ef7ff6d6b71c1a937b8ec1ef1f1f05b508c089/caae596caa179cf45e7c9ac0c6d9a9cb0fe2d305291bfbb2d8b648ae26ed38b6?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20220815%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220815T144713Z&X-Amz-Expires=900&X-Amz-Signature=5ddddfe8ef2b0601e80ab41c78a4d77d921942b0d8160bcab40ff894095e6823&X-Amz-SignedHeaders=host&x-id=PutObject` - FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_private - https://github.com/huggingface/datasets/runs/7835921082?check_suite_focus=true `requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://hub-ci.huggingface.co/api/repos/create (Request ID: gL_1I7i2dii9leBhlZen-) - Internal Error - We're working hard to fix that as soon as possible!` - FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_to_hub_custom_features_image_list - https://github.com/huggingface/datasets/runs/7835920900?check_suite_focus=true - This is not 500, but 404: `requests.exceptions.HTTPError: 404 Client Error: Not Found for url: [https://hub-ci.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/test-16605586458339.git/info/lfs/objects](https://hub-ci.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/test-16605586458339.git/info/lfs/objects/batch)`

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PR_kwDODunzps49IFLa

Add 'val' to VALIDATION_KEYWORDS.

closed

CONTRIBUTOR

This PR fixes #4839 by adding the word `"val"` to the `VALIDATION_KEYWORDS` so that the `load_dataset()` method with `imagefolder` (and probably, some other directives as well) reads folders named `"val"` as well. I think the supported keywords have to be mentioned in the documentation as well, but I couldn't think of a proper place to add that.

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PR_kwDODunzps49HaWT

Fix typo in streaming docs

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CONTRIBUTOR

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PR_kwDODunzps49G8CC

Update stackexchange license

closed

CONTRIBUTOR

The correct license of the stackexchange subset of the Pile is `cc-by-sa-4.0`, as can for example be seen here: https://stackoverflow.com/help/licensing

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PR_kwDODunzps49Gf0I

Update ted_talks_iwslt license to include ND

closed

CONTRIBUTOR

Excerpt from the paper's abstract: "Aside from its cultural and social relevance, this content, which is published under the Creative Commons BY-NC-ND license, also represents a precious language resource for the machine translation research community"

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I_kwDODunzps5PtjrQ

Dataset Viewer issue for darragh/demo_data_raw3

open

CONTRIBUTOR

### Link https://huggingface.co/datasets/darragh/demo_data_raw3 ### Description ``` Exception: ValueError Message: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent. ``` reported by @NielsRogge ### Owner No

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I_kwDODunzps5PtCZp

ImageFolder dataset builder does not read the validation data set if it is named as "val"

closed

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** Currently, the `'imagefolder'` data set builder in [`load_dataset()`](https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/load.py#L1541] ) only [supports](https://github.com/huggingface/datasets/blob/6c609a322da994de149b2c938f19439bca99408e/src/datasets/data_files.py#L31) the following names as the validation data set directory name: `["validation", "valid", "dev"]`. When the validation directory is named as `'val'`, the Data set will not have a validation split. I expected this to be a trivial task but ended up spending a lot of time before knowing that only the above names are supported. Here's a minimal example of `val` not being recognized: ```python import os import numpy as np import cv2 from datasets import load_dataset # creating a dummy data set with the following structure: # ROOT # | -- train # | ---- class_1 # | ---- class_2 # | -- val # | ---- class_1 # | ---- class_2 ROOT = "data" for which in ["train", "val"]: for class_name in ["class_1", "class_2"]: dir_name = os.path.join(ROOT, which, class_name) if not os.path.exists(dir_name): os.makedirs(dir_name) for i in range(10): cv2.imwrite( os.path.join(dir_name, f"{i}.png"), np.random.random((224, 224)) ) # trying to create a data set dataset = load_dataset( "imagefolder", data_dir=ROOT ) >> dataset DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 20 }) }) # ^ note how the dataset only has a 'train' subset ``` **Describe the solution you'd like** The suggestion is to include `"val"` to [that list ](https://github.com/huggingface/datasets/blob/6c609a322da994de149b2c938f19439bca99408e/src/datasets/data_files.py#L31) as that's a commonly used phrase to name the validation directory. Also, In the documentation, explicitly mention that only such directory names are supported as train/val/test directories to avoid confusion. **Describe alternatives you've considered** In the documentation, explicitly mention that only such directory names are supported as train/val/test directories without adding `val` to the above list. **Additional context** A question asked in the forum: [ Loading an imagenet-style image dataset with train/val directories](https://discuss.huggingface.co/t/loading-an-imagenet-style-image-dataset-with-train-val-directories/21554)

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PR_kwDODunzps49F08R

Fix documentation card of adv_glue dataset

closed

MEMBER

Fix documentation card of adv_glue dataset.

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PR_kwDODunzps49Fb6l

Add support for CSV metadata files to ImageFolder

closed

CONTRIBUTOR

Fix #4814

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I_kwDODunzps5Psghw

Is it possible to pass multiple links to a split in load script?

open

NONE

**Is your feature request related to a problem? Please describe.** I wanted to use a python loading script in hugging face datasets that use different sources of text (it's somehow a compilation of multiple datasets + my own dataset) based on how `load_dataset` [works](https://huggingface.co/docs/datasets/loading) I assumed I could do something like bellow in my loading script: ```python ... _URL = "MY_DATASET_URL/resolve/main/data/" _URLS = { "train": [ "FIRST_URL_TO.txt", _URL + "train-00000-of-00001-676bfebbc8742592.parquet" ] } ... ``` but when loading the dataset it raises the following error: ```python File ~/.local/lib/python3.8/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 702 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 703 if not downloaded_from_gcs: --> 704 self._download_and_prepare( 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs ... 668 if isinstance(a, str): 669 # Force-cast str subclasses to str (issue #21127) 670 parts.append(str(a)) TypeError: expected str, bytes or os.PathLike object, not list ``` **Describe the solution you'd like** I believe since it's possible for `load_dataset` to get list of URLs instead of just a URL for `train` split it can be possible here too. **Describe alternatives you've considered** An alternative solution would be to download all needed datasets locally and `push_to_hub` them all, but since the datasets I'm talking about are huge it's not among my options. **Additional context** I think loading `text` beside the `parquet` is completely a different issue but I believe I can figure it out by proposing a config for my dataset to load each entry of `_URLS['train']` separately either by `load_dataset("text", ...` or `load_dataset("parquet", ...`.

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PR_kwDODunzps49FJg9

Fix documentation card of ethos dataset

closed

MEMBER

Fix documentation card of ethos dataset.

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PR_kwDODunzps49FJOu

Fix documentation card of recipe_nlg dataset

closed

MEMBER

Fix documentation card of recipe_nlg dataset

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PR_kwDODunzps49E_Nk

Fix missing tags in dataset cards

closed

MEMBER

Fix missing tags in dataset cards: - boolq - break_data - definite_pronoun_resolution - emo - kor_nli - pg19 - quartz - sciq - squad_es - wmt14 - wmt15 - wmt16 - wmt17 - wmt18 - wmt19 - wmt_t2t This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task.

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PR_kwDODunzps49EQav

Fix tags in dataset cards

closed

MEMBER

Fix wrong tags in dataset cards.

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PR_kwDODunzps49Cibf

Add oversampling strategies to interleave datasets

closed

CONTRIBUTOR

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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PR_kwDODunzps49Cdro

Fix task tags in dataset cards

closed

MEMBER

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I_kwDODunzps5Posfk

Misalignment between card tag validation and docs

open

MEMBER

## Describe the bug As pointed out in other issue: https://github.com/huggingface/datasets/pull/4827#discussion_r943536284 the validation of the dataset card tags is not aligned with its documentation: e.g. - implementation: `license: List[str]` - docs: `license: Union[str, List[str]]` They should be aligned. CC: @julien-c

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PR_kwDODunzps49B_vb

Support PIL Image objects in `add_item`/`add_column`

open

CONTRIBUTOR

Fix #4796 PS: We should also improve the type inference in `OptimizedTypeSequence` to make it possible to also infer the complex types (only `Image` currently) in nested arrays (e.g. `[[pil_image], [pil_image, pil_image]]` or `[{"img": pil_image}`]), but I plan to address this in a separate PR.

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PR_kwDODunzps49B1zi

Add license metadata to pg19

closed

MEMBER

As reported over email by Roy Rijkers

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PR_kwDODunzps49B0V3

Fix language tags in dataset cards

closed

MEMBER

Fix language tags in all dataset cards, so that they are validated (aligned with our `languages.json` resource).

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PR_kwDODunzps49BYWL

[Windows] Fix Access Denied when using os.rename()

closed

CONTRIBUTOR

In this PR, we are including an additional step when `os.rename()` raises a PermissionError. Basically, we will use `shutil.move()` on the temp files. Fix #2937

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PR_kwDODunzps49BR5H

Fix titles in dataset cards

closed

MEMBER

Fix all the titles in the dataset cards, so that they conform to the required format.

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PR_kwDODunzps49A0O_

Update data URL in mkqa dataset

closed

MEMBER

Update data URL in mkqa dataset. Fix #4817.

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I_kwDODunzps5PnMnY

Moving dataset between namespaces breaks dataset viewer

open

CONTRIBUTOR

## Describe the bug I moved a dataset from my own namespace to an org and that broke the dataset viewer. To fix it I had to manually edit the `dataset_info.json` file and change the first key in the json from `username--datasetname` to `orgname--datasetname` ## Steps to reproduce the bug What I did was: 1- Upload a dataset to my own namespace using `push_to_hub` 2- Move the dataset from my namespace to an org using the web interface. ## Expected results For the file to be changed accordingly. ## Actual results Broken dataset viewer. ## Environment info - `datasets` version: 2.3.3.dev0 - Platform: Linux-4.15.0-189-generic-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.5 - PyArrow version: 7.0.0 - Pandas version: 1.3.5

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PR_kwDODunzps49AvaE

Fix train_test_split docs

closed

CONTRIBUTOR

I saw that `stratify` is added to the `train_test_split` method as per #4322, hence the docs can be updated.

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I_kwDODunzps5PlEVM

Terminating: fork() called from a process already using GNU OpenMP, this is unsafe.

closed

NONE

Hi, when i try to run prepare_dataset function in [fine tuning ASR tutorial 4](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tuning_Wav2Vec2_for_English_ASR.ipynb) , i got this error. I got this error Terminating: fork() called from a process already using GNU OpenMP, this is unsafe. There is no other logs available, so i have no clue what is the cause of it. ``` def prepare_dataset(batch): audio = batch["path"] # batched output is "un-batched" batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0] batch["input_length"] = len(batch["input_values"]) with processor.as_target_processor(): batch["labels"] = processor(batch["text"]).input_ids return batch data = data.map(prepare_dataset, remove_columns=data.column_names["train"], num_proc=4) ``` Specify the actual results or traceback. There is no traceback except `Terminating: fork() called from a process already using GNU OpenMP, this is unsafe.` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-5.15.0-43-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 9.0.0 - Pandas version: 1.4.3

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PR_kwDODunzps48-xc6

Add missing language tags to resources

closed

MEMBER

Add missing language tags to resources, required by existing datasets on GitHub.

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PR_kwDODunzps48-W7a

Add add cc-by-sa-2.5 license tag

closed

CONTRIBUTOR

- [ ] add it to moon-landing - [ ] add it to hub-docs

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I_kwDODunzps5Pi_SD

Outdated Link for mkqa Dataset

closed

NONE

## Describe the bug The URL used to download the mkqa dataset is outdated. It seems the URL to download the dataset is currently https://github.com/apple/ml-mkqa/blob/main/dataset/mkqa.jsonl.gz instead of https://github.com/apple/ml-mkqa/raw/master/dataset/mkqa.jsonl.gz (master branch has been renamed to main). ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("mkqa") ``` ## Expected results downloads the dataset ## Actual results ```python Downloading builder script: 4.79k/? [00:00<00:00, 201kB/s] Downloading metadata: 13.2k/? [00:00<00:00, 504kB/s] Downloading and preparing dataset mkqa/mkqa (download: 11.35 MiB, generated: 34.29 MiB, post-processed: Unknown size, total: 45.65 MiB) to /home/lhr/.cache/huggingface/datasets/mkqa/mkqa/1.0.0/5401489c674c81257cf563417aaaa5de2c7e26a1090ce9b10eb0404f10003d4d... Downloading data files: 0% 0/1 [00:00<?, ?it/s] --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Input In [3], in <cell line: 3>() 1 from datasets import load_dataset ----> 3 dataset = load_dataset("mkqa") File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/load.py:1746, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1743 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1745 # Download and prepare data -> 1746 builder_instance.download_and_prepare( 1747 download_config=download_config, 1748 download_mode=download_mode, 1749 ignore_verifications=ignore_verifications, 1750 try_from_hf_gcs=try_from_hf_gcs, 1751 use_auth_token=use_auth_token, 1752 ) 1754 # Build dataset for splits 1755 keep_in_memory = ( 1756 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1757 ) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 702 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 703 if not downloaded_from_gcs: --> 704 self._download_and_prepare( 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 706 ) 707 # Sync info 708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:1227, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos) 1226 def _download_and_prepare(self, dl_manager, verify_infos): -> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 769 split_dict = SplitDict(dataset_name=self.name) 770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 773 # Checksums verification 774 if verify_infos and dl_manager.record_checksums: File ~/.cache/huggingface/modules/datasets_modules/datasets/mkqa/5401489c674c81257cf563417aaaa5de2c7e26a1090ce9b10eb0404f10003d4d/mkqa.py:130, in Mkqa._split_generators(self, dl_manager) 128 # download and extract URLs 129 urls_to_download = _URLS --> 130 downloaded_files = dl_manager.download_and_extract(urls_to_download) 132 return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]})] File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:431, in DownloadManager.download_and_extract(self, url_or_urls) 415 def download_and_extract(self, url_or_urls): 416 """Download and extract given url_or_urls. 417 418 Is roughly equivalent to: (...) 429 extracted_path(s): `str`, extracted paths of given URL(s). 430 """ --> 431 return self.extract(self.download(url_or_urls)) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:309, in DownloadManager.download(self, url_or_urls) 306 download_func = partial(self._download, download_config=download_config) 308 start_time = datetime.now() --> 309 downloaded_path_or_paths = map_nested( 310 download_func, 311 url_or_urls, 312 map_tuple=True, 313 num_proc=download_config.num_proc, 314 disable_tqdm=not is_progress_bar_enabled(), 315 desc="Downloading data files", 316 ) 317 duration = datetime.now() - start_time 318 logger.info(f"Downloading took {duration.total_seconds() // 60} min") File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:393, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc) 391 num_proc = 1 392 if num_proc <= 1 or len(iterable) <= num_proc: --> 393 mapped = [ 394 _single_map_nested((function, obj, types, None, True, None)) 395 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 396 ] 397 else: 398 split_kwds = [] # We organize the splits ourselve (contiguous splits) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:394, in <listcomp>(.0) 391 num_proc = 1 392 if num_proc <= 1 or len(iterable) <= num_proc: 393 mapped = [ --> 394 _single_map_nested((function, obj, types, None, True, None)) 395 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 396 ] 397 else: 398 split_kwds = [] # We organize the splits ourselve (contiguous splits) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:330, in _single_map_nested(args) 328 # Singleton first to spare some computation 329 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 330 return function(data_struct) 332 # Reduce logging to keep things readable in multiprocessing with tqdm 333 if rank is not None and logging.get_verbosity() < logging.WARNING: File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:335, in DownloadManager._download(self, url_or_filename, download_config) 332 if is_relative_path(url_or_filename): 333 # append the relative path to the base_path 334 url_or_filename = url_or_path_join(self._base_path, url_or_filename) --> 335 return cached_path(url_or_filename, download_config=download_config) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py:185, in cached_path(url_or_filename, download_config, **download_kwargs) 181 url_or_filename = str(url_or_filename) 183 if is_remote_url(url_or_filename): 184 # URL, so get it from the cache (downloading if necessary) --> 185 output_path = get_from_cache( 186 url_or_filename, 187 cache_dir=cache_dir, 188 force_download=download_config.force_download, 189 proxies=download_config.proxies, 190 resume_download=download_config.resume_download, 191 user_agent=download_config.user_agent, 192 local_files_only=download_config.local_files_only, 193 use_etag=download_config.use_etag, 194 max_retries=download_config.max_retries, 195 use_auth_token=download_config.use_auth_token, 196 ignore_url_params=download_config.ignore_url_params, 197 download_desc=download_config.download_desc, 198 ) 199 elif os.path.exists(url_or_filename): 200 # File, and it exists. 201 output_path = url_or_filename File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py:530, in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc) 525 raise FileNotFoundError( 526 f"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been" 527 " disabled. To enable file online look-ups, set 'local_files_only' to False." 528 ) 529 elif response is not None and response.status_code == 404: --> 530 raise FileNotFoundError(f"Couldn't find file at {url}") 531 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") 532 if head_error is not None: FileNotFoundError: Couldn't find file at https://github.com/apple/ml-mkqa/raw/master/dataset/mkqa.jsonl.gz ``` ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.13.0-40-generic-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 9.0.0 - Pandas version: 1.4.2

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PR_kwDODunzps487kpq

Update version of opus_paracrawl dataset

closed

MEMBER

This PR updates OPUS ParaCrawl from 7.1 to 9 version. Fix #4815.

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I_kwDODunzps5PhGtf

Outdated loading script for OPUS ParaCrawl dataset

closed

MEMBER

## Describe the bug Our loading script for OPUS ParaCrawl loads its 7.1 version. Current existing version is 9.

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I_kwDODunzps5PeWbG

Support CSV as metadata file format in AudioFolder/ImageFolder

closed

CONTRIBUTOR

Requested here: https://discuss.huggingface.co/t/how-to-structure-an-image-dataset-repo-using-the-image-folder-approach/21004. CSV is also used in AutoTrain for specifying metadata in image datasets.

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PR_kwDODunzps48446r

Fix loading example in opus dataset cards

closed

MEMBER

This PR: - fixes the examples to load the datasets, with the corrected dataset name, in their dataset cards for: - opus_dgt - opus_paracrawl - opus_wikipedia - fixes their dataset cards with the missing required information: title, data instances/fields/splits - enumerates the supported languages - adds a missing citation reference for opus_wikipedia Related to: - #4806

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PR_kwDODunzps484Fzq

Fix bug in function validate_type for Python >= 3.9

closed

MEMBER

Fix `validate_type` function, so that it uses `get_origin` instead. This makes the function forward compatible. This fixes #4811 because: ```python In [4]: typing.Optional[str] Out[4]: typing.Optional[str] In [5]: get_origin(typing.Optional[str]) Out[5]: typing.Union ``` Fix #4811.

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I_kwDODunzps5PdKDd

Bug in function validate_type for Python >= 3.9

closed

MEMBER

## Describe the bug The function `validate_type` assumes that the type `typing.Optional[str]` is automatically transformed to `typing.Union[str, NoneType]`. ```python In [4]: typing.Optional[str] Out[4]: typing.Union[str, NoneType] ``` However, this is not the case for Python 3.9: ```python In [3]: typing.Optional[str] Out[3]: typing.Optional[str] ```

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PR_kwDODunzps484C9l

Add description to hellaswag dataset

closed

MEMBER

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PR_kwDODunzps483Y4h

Complete the mlqa dataset card

closed

CONTRIBUTOR

I fixed the issue #4808 Details of PR: - Added languages included in the dataset. - Added task id and task category. - Updated the citation information. Fix #4808.

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I_kwDODunzps5PcYcZ

Add more information to the dataset card of mlqa dataset

closed

CONTRIBUTOR

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PR_kwDODunzps483MSH

document fix in opus_gnome dataset

closed

CONTRIBUTOR

I fixed a issue #4805. I changed `"gnome"` to `"opus_gnome"` in[ README.md](https://github.com/huggingface/datasets/tree/main/datasets/opus_gnome#dataset-summary).

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PR_kwDODunzps482yiS

Fix opus_gnome dataset card

closed

CONTRIBUTOR

I fixed a issue #4805. I changed `"gnome"` to `"opus_gnome"` in[ README.md](https://github.com/huggingface/datasets/tree/main/datasets/opus_gnome#dataset-summary). Fix #4805

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I_kwDODunzps5Pbq3b

Wrong example in opus_gnome dataset card

closed

CONTRIBUTOR

## Describe the bug I found that [the example on opus_gone dataset ](https://github.com/huggingface/datasets/tree/main/datasets/opus_gnome#dataset-summary) doesn't work. ## Steps to reproduce the bug ```python load_dataset("gnome", lang1="it", lang2="pl") ``` `"gnome"` should be `"opus_gnome"` ## Expected results ```bash 100% 1/1 [00:00<00:00, 42.09it/s] DatasetDict({ train: Dataset({ features: ['id', 'translation'], num_rows: 8368 }) }) ``` ## Actual results ```bash Couldn't find 'gnome' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/main/datasets/gnome/gnome.py ``` ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.27 - Python version: 3.9.13 - PyArrow version: 9.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5PblNW

streaming dataset with concatenating splits raises an error

open

NONE

## Describe the bug streaming dataset with concatenating splits raises an error ## Steps to reproduce the bug ```python from datasets import load_dataset # no error repo = "nateraw/ade20k-tiny" dataset = load_dataset(repo, split="train+validation") ``` ```python from datasets import load_dataset # error repo = "nateraw/ade20k-tiny" dataset = load_dataset(repo, split="train+validation", streaming=True) ``` ```sh --------------------------------------------------------------------------- ValueError Traceback (most recent call last) [<ipython-input-4-a6ae02d63899>](https://localhost:8080/#) in <module>() 3 # error 4 repo = "nateraw/ade20k-tiny" ----> 5 dataset = load_dataset(repo, split="train+validation", streaming=True) 1 frames [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path) 1030 splits_generator = splits_generators[split] 1031 else: -> 1032 raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}") 1033 1034 # Create a dataset for each of the given splits ValueError: Bad split: train+validation. Available splits: ['validation', 'train'] ``` [Colab](https://colab.research.google.com/drive/1wMj08_0bym9jnGgByib4lsBPu8NCZBG9?usp=sharing) ## Expected results load successfully or throws an error saying it is not supported. ## Actual results above ## Environment info - `datasets` version: 2.4.0 - Platform: Windows-10-10.0.22000-SP0 (windows11 x64) - Python version: 3.9.13 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5PZevK

Support `pipeline` argument in inspect.py functions

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** The `wikipedia` dataset requires a `pipeline` argument to build the list of splits: https://huggingface.co/datasets/wikipedia/blob/main/wikipedia.py#L937 But this is currently not supported in `get_dataset_config_info`: https://github.com/huggingface/datasets/blob/main/src/datasets/inspect.py#L373-L375 which is called by other functions, e.g. `get_dataset_split_names`. **Additional context** The dataset viewer is not working out-of-the-box on `wikipedia` for this reason: https://huggingface.co/datasets/wikipedia/viewer <img width="637" alt="Capture d’écran 2022-08-08 à 12 01 16" src="https://user-images.githubusercontent.com/1676121/183461838-5330783b-0269-4ba7-a999-314cde2023d8.png">

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I_kwDODunzps5PX8YT

`with_format` behavior is inconsistent on different datasets

open

CONTRIBUTOR

## Describe the bug I found a case where `with_format` does not transform the dataset to the requested format. ## Steps to reproduce the bug Run: ```python from transformers import AutoTokenizer, AutoFeatureExtractor from datasets import load_dataset raw = load_dataset("glue", "sst2", split="train") raw = raw.select(range(100)) tokenizer = AutoTokenizer.from_pretrained("philschmid/tiny-bert-sst2-distilled") def preprocess_func(examples): return tokenizer(examples["sentence"], padding=True, max_length=256, truncation=True) data = raw.map(preprocess_func, batched=True) print(type(data[0]["input_ids"])) data = data.with_format("torch", columns=["input_ids"]) print(type(data[0]["input_ids"])) ``` printing as expected: ```python <class 'list'> <class 'torch.Tensor'> ``` Then run: ```python raw = load_dataset("beans", split="train") raw = raw.select(range(100)) preprocessor = AutoFeatureExtractor.from_pretrained("nateraw/vit-base-beans") def preprocess_func(examples): imgs = [img.convert("RGB") for img in examples["image"]] return preprocessor(imgs) data = raw.map(preprocess_func, batched=True) print(type(data[0]["pixel_values"])) data = data.with_format("torch", columns=["pixel_values"]) print(type(data[0]["pixel_values"])) ``` Printing, unexpectedly ```python <class 'list'> <class 'list'> ``` ## Expected results `with_format` should transform into the requested format; it's not the case. ## Actual results `type(data[0]["pixel_values"])` should be `torch.Tensor` in the example above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: dev version, commit 44af3fafb527302282f6b6507b952de7435f0979 - Platform: Linux - Python version: 3.9.12 - PyArrow version: 7.0.0

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PR_kwDODunzps48yTYu

Fix fine classes in trec dataset

closed

MEMBER

This PR: - replaces the fine labels, so that there are 50 instead of 47 - once more labels are added, all they (fine and coarse) have been re-ordered, so that they align with the order in: https://cogcomp.seas.upenn.edu/Data/QA/QC/definition.html - the feature names have been fixed: `fine_label` instead of `label-fine` - to sneak-case (underscores instead of hyphens) - words have been reordered Fix #4790.

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PR_kwDODunzps48yIss

support LargeListArray in pyarrow

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```python import numpy as np import datasets a = np.zeros((5000000, 768)) res = datasets.Dataset.from_dict({'embedding': a}) ''' File '/home/wenjiaxin/anaconda3/envs/data/lib/python3.8/site-packages/datasets/arrow_writer.py', line 178, in __arrow_array__ out = numpy_to_pyarrow_listarray(data) File "/home/wenjiaxin/anaconda3/envs/data/lib/python3.8/site-packages/datasets/features/features.py", line 1173, in numpy_to_pyarrow_listarray offsets = pa.array(np.arange(n_offsets + 1) * step_offsets, type=pa.int32()) File "pyarrow/array.pxi", line 312, in pyarrow.lib.array File "pyarrow/array.pxi", line 83, in pyarrow.lib._ndarray_to_array File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Integer value 2147483904 not in range: -2147483648 to 2147483647 ''' ``` Loading a large numpy array currently raises the error above as the type of offsets is `int32`. And pyarrow has supported [LargeListArray](https://arrow.apache.org/docs/python/generated/pyarrow.LargeListArray.html) for this case.

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