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I_kwDODunzps5BW5qJ

Bug in `openbookqa` dataset

closed

CONTRIBUTOR

## Describe the bug Dataset entries contains a typo. ## Steps to reproduce the bug ```python >>> from datasets import load_dataset >>> obqa = load_dataset('openbookqa', 'main') >>> obqa['train'][0] ``` ## Expected results ```python {'id': '7-980', 'question_stem': 'The sun is responsible for', 'choices': {'text': ['puppies learning new tricks', 'children growing up and getting old', 'flowers wilting in a vase', 'plants sprouting, blooming and wilting'], 'label': ['A', 'B', 'C', 'D']}, 'answerKey': 'D'} ``` ## Actual results ```python {'id': '7-980', 'question_stem': 'The sun is responsible for', 'choices': {'text': ['puppies learning new tricks', 'children growing up and getting old', 'flowers wilting in a vase', 'plants sprouting, blooming and wilting'], 'label': ['puppies learning new tricks', 'children growing up and getting old', 'flowers wilting in a vase', 'plants sprouting, blooming and wilting']}, 'answerKey': 'D'} ``` The bug is present in all configs and all splits. ## Environment info - `datasets` version: 1.17.0 - Platform: Linux-5.4.0-1057-aws-x86_64-with-glibc2.27 - Python version: 3.9.7 - PyArrow version: 4.0.1

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https://github.com/huggingface/datasets/pull/3549

PR_kwDODunzps4wqkGt

Fix sem_eval_2018_task_1 download location

closed

CONTRIBUTOR

This changes the download location of sem_eval_2018_task_1 files to include the test set labels as discussed in https://github.com/huggingface/datasets/issues/2745#issuecomment-954588500_ with @lhoestq.

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I_kwDODunzps5BWeGo

Specify the feature types of a dataset on the Hub without needing a dataset script

closed

MEMBER

**Is your feature request related to a problem? Please describe.** Currently if I upload a CSV with paths to audio files, the column type is string instead of Audio. **Describe the solution you'd like** I'd like to be able to specify the types of the column, so that when loading the dataset I directly get the features types I want. The feature types could read from the `dataset_infos.json` for example. **Describe alternatives you've considered** Create a dataset script to specify the features, but that seems complicated for a simple thing. cc @abidlabs

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I_kwDODunzps5BWdIL

Datasets created with `push_to_hub` can't be accessed in offline mode

open

MEMBER

## Describe the bug In offline mode, one can still access previously-cached datasets. This fails with datasets created with `push_to_hub`. ## Steps to reproduce the bug in Python: ``` import datasets mpwiki = datasets.load_dataset("teven/matched_passages_wikidata") ``` in bash: ``` export HF_DATASETS_OFFLINE=1 ``` in Python: ``` import datasets mpwiki = datasets.load_dataset("teven/matched_passages_wikidata") ``` ## Expected results `datasets` should find the previously-cached dataset. ## Actual results ConnectionError: Couln't reach the Hugging Face Hub for dataset 'teven/matched_passages_wikidata': Offline mode is enabled ## Environment info - `datasets` version: 1.16.2.dev0 - Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17 - Python version: 3.8.10 - PyArrow version: 3.0.0

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https://github.com/huggingface/datasets/pull/3546

PR_kwDODunzps4wqYIV

Remove print statements in datasets

closed

CONTRIBUTOR

This is a second time I'm removing print statements in our datasets, so I've added a test to avoid these issues in the future.

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PR_kwDODunzps4wpziv

fix: 🐛 pass token when retrieving the split names

closed

CONTRIBUTOR

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I_kwDODunzps5BUFjp

Ability to split a dataset in multiple files.

open

CONTRIBUTOR

Hello, **Is your feature request related to a problem? Please describe.** My use case is that I have one writer that adds columns and multiple workers reading the same `Dataset`. Each worker should have access to columns added by the writer when they reload the dataset. I understand that we shouldn't overwrite an arrow file as this could cause Segfault and so on. Before 1.16, I was able to overwrite the dataset and that would work most of the time with some retries. **Describe the solution you'd like** I was thinking that if we could append `Dataset._data_files`, when the workers reload the Dataset, they would get the new columns. **Describe alternatives you've considered** I currently need to 1. Save multiple "versions" of the dataset and load the latest. 2. Try working with cache files to get the latest columns. **Additional context** I think this would be a great addition to HFDataset as Parquet supports multi-files input out of the box! I can make a PR myself with some pointers as needed :)

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I_kwDODunzps5BR9RG

Allow loading community metrics from the hub, just like datasets

closed

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** Currently, I can load a metric implemented by me by providing the local path to the file in `load_metric`. However, there is no option to do it with the metric uploaded to the hub. This means that if I want to allow other users to use it, they must download it first which makes the usage less smooth. **Describe the solution you'd like** Load metrics from the hub just like datasets are loaded. In order to not break stuff, the convention can be to put the metric file in a "metrics" folder in the hub.

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https://github.com/huggingface/datasets/pull/3542

PR_kwDODunzps4wmPIP

Update the CC-100 dataset card

closed

CONTRIBUTOR

* summary from the dataset homepage * more details about the data structure * this dataset does not contain annotations

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I_kwDODunzps5BROPk

Support 7-zip compressed data files

open

MEMBER

**Is your feature request related to a problem? Please describe.** We should support 7-zip compressed data files: - in `extract` - in `iter_archive` both in streaming and non-streaming modes.

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I_kwDODunzps5BQtpw

How to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset?

open

NONE

Hi, I use torch.utils.data.Dataset to define my own data, but I need to use the 'map' function of datasets.arrow_dataset.Dataset later, so I hope to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset. Here is an example. ``` from torch.utils.data import Dataset from datasets.arrow_dataset import Dataset as HFDataset class ADataset(Dataset): def __init__(self, data): super().__init__() self.data = data def __getitem__(self, index): return self.data[index] def __len__(self): return self.len class MDataset(): def __init__(self, tokenizer: AutoTokenizer, data_args, training_args): self.train_dataset = ADataset(data_args) self.tokenizer = tokenizer self.data_args = data_args self.train_dataset = self.train_dataset.map( self.process_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on train dataset", ) def process_function(self, examples): sentences = [" ".join(sample[0][3]) for sample in examples] tokenized = self.tokenizer( sentences, max_length=self.max_seq_len, padding=self.padding, truncation=True) ``` But it would raise an ERROR, AttributeError: 'ADataset' object has no attribute 'map'. so how to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset? Thanks in advance!

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https://github.com/huggingface/datasets/pull/3539

PR_kwDODunzps4wlXU4

Research wording for nc licenses

closed

CONTRIBUTOR

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PR_kwDODunzps4wlLmD

Readme usage update

closed

CONTRIBUTOR

Noticing that the recent commit throws a lot of errors in the automatic checks. It looks to me that those errors are simply errors that were already there (metadata issues), unrelated to what I've just changed, but worth another look to make sure.

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PR_kwDODunzps4wlH1d

added PII statements and license links to data cards

closed

CONTRIBUTOR

Updates for the following datacards: multilingual_librispeech openslr speech commands superb timit_asr vctk

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PR_kwDODunzps4wk0Yb

update `pretty_name` for all datasets

closed

CONTRIBUTOR

This PR updates `pretty_name` for all datasets. Previous PR #3498 had done this for only first 200 datasets

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PR_kwDODunzps4wkxv0

Add SVHN dataset

closed

CONTRIBUTOR

Add the SVHN dataset. Additional notes: * compared to the TFDS implementation, exposes additional the "full numbers" config * adds the streaming support for `os.path.splitext` and `scipy.io.loadmat` * adds `h5py` to the requirements list for the dummy data test

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PR_kwDODunzps4wj3LE

Update wiki_dpr README.md

closed

MEMBER

Some infos of wiki_dpr were missing as noted in https://github.com/huggingface/datasets/issues/3510, I added them and updated the tags and the examples Close #3510.

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I_kwDODunzps5BN39z

Task search function on hub not working correctly

open

MEMBER

When I want to look at all datasets of the category: `speech-processing` *i.e.* https://huggingface.co/datasets?task_categories=task_categories:speech-processing&sort=downloads , then the following dataset doesn't show up for some reason: - https://huggingface.co/datasets/speech_commands even thought it's task tags seem correct: https://raw.githubusercontent.com/huggingface/datasets/master/datasets/speech_commands/README.md

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https://github.com/huggingface/datasets/pull/3532

PR_kwDODunzps4wi1ft

Give clearer instructions to add the YAML tags

closed

MEMBER

Fix #3531. CC: @julien-c @VictorSanh

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I_kwDODunzps5BNZ-A

Give clearer instructions to add the YAML tags

closed

MEMBER

## Describe the bug As reported by @julien-c, many community datasets contain the line `YAML tags:` at the top of the YAML section in the header of the README file. See e.g.: https://huggingface.co/datasets/bigscience/P3/commit/a03bea08cf4d58f268b469593069af6aeb15de32 Maybe we should give clearer instruction/hints in the README template.

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https://github.com/huggingface/datasets/pull/3530

PR_kwDODunzps4wiZCw

Update README.md

closed

CONTRIBUTOR

Removing reference to "Common Voice" in Personal and Sensitive Information section. Adding link to license. Correct license type in metadata.

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PR_kwDODunzps4wiPA9

Update README.md

closed

CONTRIBUTOR

Updating licensing information & personal and sensitive information.

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PR_kwDODunzps4wiOqH

Update README.md

closed

CONTRIBUTOR

Updating license with appropriate capitalization & a link. Updating Personal and Sensitive Information to address PII concern.

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PR_kwDODunzps4wiN1w

Update README.md

closed

CONTRIBUTOR

Adding licensing information.

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PR_kwDODunzps4wiMaQ

Update README.md

open

CONTRIBUTOR

Not entirely sure, following the links here, but it seems the relevant license is at https://github.com/soskek/bookcorpus/blob/master/LICENSE

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https://github.com/huggingface/datasets/pull/3525

PR_kwDODunzps4wiL8p

Adding license information for Openbookcorpus

closed

CONTRIBUTOR

Not entirely sure, following the links here, but it seems the relevant license is at https://github.com/soskek/bookcorpus/blob/master/LICENSE

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https://github.com/huggingface/datasets/pull/3524

PR_kwDODunzps4wiK_v

Adding link to license.

closed

CONTRIBUTOR

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PR_kwDODunzps4wiJc2

Added links to licensing and PII message in vctk dataset

closed

CONTRIBUTOR

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I_kwDODunzps5BMi3i

wmt19 is broken (zh-en)

closed

NONE

## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("wmt19", 'zh-en') ``` ## Expected results The dataset should download. ## Actual results `ConnectionError: Couldn't reach ftp://cwmt-wmt:cwmt-wmt@datasets.nju.edu.cn/parallel/casia2015.zip` ## Environment info - `datasets` version: 1.15.1 - Platform: Linux - Python version: 3.8

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https://github.com/huggingface/datasets/pull/3521

PR_kwDODunzps4wiFCs

Vivos license update

closed

CONTRIBUTOR

Updated the license information with the link to the license text

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PR_kwDODunzps4wh6oD

Audio datacard update - first pass

closed

CONTRIBUTOR

Filling out data card "Personal and Sensitive Information" for speech datasets to note PII concerns

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PR_kwDODunzps4whnXH

CC100: Using HTTPS for the data source URL fixes load_dataset()

closed

CONTRIBUTOR

Without this change the following script (with any lang parameter) consistently fails. After changing to the HTTPS URL, the script works as expected. ```python from datasets import load_dataset dataset = load_dataset("cc100", lang="en") ``` This is the error produced by the previous script: ```sh Using custom data configuration en-lang=en Downloading and preparing dataset cc100/en to /home/antti/.cache/huggingface/datasets/cc100/en-lang=en/0.0.0/526ac20780de5e074cf73a7466e868cb67f960b48f6de42ff6a6c4e71910d71b... Traceback (most recent call last): File "/home/antti/tmp/cc100/cc100.py", line 3, in <module> dataset = load_dataset("cc100", lang="en") File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/load.py", line 1694, in load_dataset builder_instance.download_and_prepare( File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/builder.py", line 595, in download_and_prepare self._download_and_prepare( File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/builder.py", line 661, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/antti/.cache/huggingface/modules/datasets_modules/datasets/cc100/526ac20780de5e074cf73a7466e868cb67f960b48f6de42ff6a6c4e71910d71b/cc100.py", line 117, in _split_generators path = dl_manager.download_and_extract(download_url) File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 308, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 196, in download downloaded_path_or_paths = map_nested( File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 251, in map_nested return function(data_struct) File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 298, in cached_path output_path = get_from_cache( File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})") ConnectionError: Couldn't reach http://data.statmt.org/cc-100/en.txt.xz (error 503) ``` Note that I get the same behavior also using curl on the command line. The plain HTTP "curl -L http://data.statmt.org/cc-100/en.txt.xz" fails with "503 Service unavailable", but the with the HTTPS version of the URL curl starts downloading the file. My guess is that the server does overly aggressive rate-limitting. When a client requests an HTTP URL, it (sensibly) gets redirected to the HTTPS equivalent, but now the server notices two requests coming from the same client (the original HTTP and the redirected HTTPS) during a brief time windows and rate-limitter kicks in and blocks the second request! If the client initally uses the HTTPS URL there's only one incoming request which the rate-limitter allows.

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I_kwDODunzps5BJtMf

Add PubMed Central Open Access dataset

closed

MEMBER

## Adding a Dataset - **Name:** PubMed Central Open Access - **Description:** The PMC Open Access Subset includes more than 3.4 million journal articles and preprints that are made available under license terms that allow reuse. - **Paper:** *link to the dataset paper if available* - **Data:** https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/ - **Motivation:** *what are some good reasons to have this dataset* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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PR_kwDODunzps4wemwU

Add CPPE-5 dataset

closed

CONTRIBUTOR

Adds the recently released CPPE-5 dataset.

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PR_kwDODunzps4weYhE

dataset `asset` - change to raw.githubusercontent.com URLs

closed

MEMBER

Changed the URLs to the ones it was automatically re-directing. Before, the download was failing

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I_kwDODunzps5BICE3

`ExpectedMoreDownloadedFiles` for `evidence_infer_treatment`

closed

MEMBER

## Describe the bug I am trying to load a dataset called `evidence_infer_treatment`. The first subset (`1.1`) works fine but the second returns an error (`2.0`). It downloads a file but crashes during the checksums. ## Steps to reproduce the bug ```python >>> from datasets import load_dataset >>> load_dataset("evidence_infer_treatment", "2.0") Downloading and preparing dataset evidence_infer_treatment/2.0 (download: 34.84 MiB, generated: 91.46 MiB, post-processed: Unknown size, total: 126.30 MiB) to /home/victor_huggingface_co/.cache/huggingface/datasets/evidence_infer_treatment/2.0/2.0.0/6812655bfd26cbaa58c84eab098bf6403694b06c6ae2ded603c55681868a1e24... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/load.py", line 1669, in load_dataset use_auth_token=use_auth_token, File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 594, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 664, in _download_and_prepare self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files" File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 33, in verify_checksums raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'http://evidence-inference.ebm-nlp.com/v2.0.tar.gz'} ``` I did try to pass the argument `ignore_verifications=True` but run into an error when trying to build the dataset: ```python >>> load_dataset("evidence_infer_treatment", "2.0", ignore_verifications=True, download_mode="force_redownload") Downloading and preparing dataset evidence_infer_treatment/2.0 (download: 34.84 MiB, generated: 91.46 MiB, post-processed: Unknown size, total: 126.30 MiB) to /home/victor_huggingface_co/.cache/huggingface/datasets/evidence_infer_treatment/2.0/2.0.0/6812655bfd26cbaa58c84eab098bf6403694b06c6ae2ded603c55681868a1e24... Downloading: 164MB [00:23, 6.98MB/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/load.py", line 1669, in load_dataset use_auth_token=use_auth_token, File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 594, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 681, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 1080, in _prepare_split example = self.info.features.encode_example(record) File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 1032, in encode_example return encode_nested_example(self, example) File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 807, in encode_nested_example k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 807, in <dictcomp> k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 829, in encode_nested_example list_dict[k] = [encode_nested_example(dict_tuples[0], o) for o in dict_tuples[1:]] File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 829, in <listcomp> list_dict[k] = [encode_nested_example(dict_tuples[0], o) for o in dict_tuples[1:]] File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 828, in encode_nested_example for k, dict_tuples in utils.zip_dict(schema.feature, *obj): File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 136, in zip_dict yield key, tuple(d[key] for d in dicts) File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 136, in <genexpr> yield key, tuple(d[key] for d in dicts) KeyError: '' ``` ## Environment info - `datasets` version: 1.16.1 - Platform: Linux-5.0.0-1020-gcp-x86_64-with-debian-buster-sid - Python version: 3.7.11 - PyArrow version: 6.0.1

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https://github.com/huggingface/datasets/pull/3514

PR_kwDODunzps4weN9W

Fix to_tf_dataset references in docs

closed

CONTRIBUTOR

Fix the `to_tf_dataset` references in the docs. The currently failing example of usage will be fixed by #3338.

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PR_kwDODunzps4weGWl

Add desc parameter to filter

closed

CONTRIBUTOR

Fix #3317

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I_kwDODunzps5BHBcl

No Data format found

closed

NONE

## 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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I_kwDODunzps5BGTKr

Dataset

closed

NONE

## 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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I_kwDODunzps5BFo1M

`wiki_dpr` details for Open Domain Question Answering tasks

closed

NONE

Hey guys! Thanks for creating the `wiki_dpr` dataset! I am currently trying to use the dataset for context retrieval using DPR on NQ questions and need details about what each of the files and data instances mean, which version of the Wikipedia dump it uses, etc. Please respond at your earliest convenience regarding the same! Thanks a ton! P.S.: (If one of @thomwolf @lewtun @lhoestq could respond, that would be even better since they have the first-hand details of the dataset. If anyone else has those, please reach out! Thanks!)

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I_kwDODunzps5BCp3Y

Discuss whether support canonical datasets w/o dataset_infos.json and/or dummy data

open

MEMBER

I open this PR to have a public discussion about this topic and make a decision. As previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)? On the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However: - the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though) - we are migrating canonical datasets to the Hub Do we really need to continue testing them in out CI? Also note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data). Feel free to ping other people for the discussion. CC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw

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PR_kwDODunzps4wZpot

Allows DatasetDict.filter to have batching option

closed

MEMBER

- Related to: #3244 - Fixes: #3503 We extends `.filter( ... batched: bool)` support to DatasetDict.

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I_kwDODunzps5BCaPk

cast_column function not working with map function in streaming mode for Audio features

closed

NONE

## Describe the bug I am trying to use Audio class for loading audio features using custom dataset. I am able to cast 'audio' feature into 'Audio' format with cast_column function. On using map function, I am not getting 'Audio' casted feature but getting path of audio file only. I am getting features of 'audio' of string type with load_dataset call. After using cast_column 'audio' feature is converted into 'Audio' type. But in map function I am not able to get Audio type for audio feature & getting string type data containing path of file only. So I am not able to use processor in encode function. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from datasets import load_dataset, Audio from transformers import Wav2Vec2Processor def encode(batch, processor): print("Audio: ",batch['audio']) batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values return batch def print_ds(ds): iterator = iter(ds) for d in iterator: print("Data: ",d) break processor = Wav2Vec2Processor.from_pretrained(pretrained_model_path) dataset = load_dataset("custom_dataset.py","train",data_files={'train':'train_path.txt'}, data_dir="data", streaming=True, split="train") print("Features: ",dataset.features) print_ds(dataset) dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000)) print("Features: ",dataset.features) print_ds(dataset) dataset = dataset.map(lambda x: encode(x,processor)) print("Features: ",dataset.features) print_ds(dataset) ``` ## Expected results map function not printing Audio type features be used with processor function and getting error in processor call due to this. ## Actual results # after load_dataset call Features: {'sentence': Value(dtype='string', id=None), 'audio': Value(dtype='string', id=None)} Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': 'data/0116_003.wav'} # after cast_column call Features: {'sentence': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None)} Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': {'path': 'data/0116_003.wav', 'array': array([ 1.2662281e-06, 1.0264218e-06, -1.3615092e-06, ..., 1.3017889e-02, 1.0085563e-02, 4.8155054e-03], dtype=float32), 'sampling_rate': 16000}} # after map call Features: None Audio: data/0116_003.wav Traceback (most recent call last): File "demo2.py", line 36, in <module> print_ds(dataset) File "demo2.py", line 11, in print_ds for d in iterator: File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 341, in __iter__ for key, example in self._iter(): File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 338, in _iter yield from ex_iterable File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 192, in __iter__ yield key, self.function(example) File "demo2.py", line 32, in <lambda> dataset = dataset.map(lambda x: batch_encode(x,processor)) File "demo2.py", line 6, in batch_encode batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values TypeError: string indices must be integers ## Environment info - `datasets` version: 1.17.0 - Platform: Linux-4.14.243 with-debian-bullseye-sid - Python version: 3.7.9 - PyArrow version: 6.0.1

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I_kwDODunzps5BAn12

Unable to download PUBMED_title_abstracts_2019_baseline.jsonl.zst

closed

NONE

## Describe the bug I am unable to download the PubMed dataset from the link provided in the [Hugging Face Course (Chapter 5 Section 4)](https://huggingface.co/course/chapter5/4?fw=pt). https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from datasets import load_dataset # This takes a few minutes to run, so go grab a tea or coffee while you wait :) data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst" pubmed_dataset = load_dataset("json", data_files=data_files, split="train") pubmed_dataset ``` I also tried with `wget` as follows. ``` wget https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst ``` ## Expected results I expect to be able to download this file. ## Actual results Traceback ``` --------------------------------------------------------------------------- timeout Traceback (most recent call last) /usr/lib/python3/dist-packages/urllib3/connection.py in _new_conn(self) 158 try: --> 159 conn = connection.create_connection( 160 (self._dns_host, self.port), self.timeout, **extra_kw /usr/lib/python3/dist-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 83 if err is not None: ---> 84 raise err 85 /usr/lib/python3/dist-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 73 sock.bind(source_address) ---> 74 sock.connect(sa) 75 return sock timeout: timed out During handling of the above exception, another exception occurred: ConnectTimeoutError Traceback (most recent call last) /usr/lib/python3/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 664 # Make the request on the httplib connection object. --> 665 httplib_response = self._make_request( 666 conn, /usr/lib/python3/dist-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw) 375 try: --> 376 self._validate_conn(conn) 377 except (SocketTimeout, BaseSSLError) as e: /usr/lib/python3/dist-packages/urllib3/connectionpool.py in _validate_conn(self, conn) 995 if not getattr(conn, "sock", None): # AppEngine might not have `.sock` --> 996 conn.connect() 997 /usr/lib/python3/dist-packages/urllib3/connection.py in connect(self) 313 # Add certificate verification --> 314 conn = self._new_conn() 315 hostname = self.host /usr/lib/python3/dist-packages/urllib3/connection.py in _new_conn(self) 163 except SocketTimeout: --> 164 raise ConnectTimeoutError( 165 self, ConnectTimeoutError: (<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)') During handling of the above exception, another exception occurred: MaxRetryError Traceback (most recent call last) /usr/lib/python3/dist-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 438 if not chunked: --> 439 resp = conn.urlopen( 440 method=request.method, /usr/lib/python3/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 718 --> 719 retries = retries.increment( 720 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2] /usr/lib/python3/dist-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace) 435 if new_retry.is_exhausted(): --> 436 raise MaxRetryError(_pool, url, error or ResponseError(cause)) 437 MaxRetryError: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)')) During handling of the above exception, another exception occurred: ConnectTimeout Traceback (most recent call last) /tmp/ipykernel_15104/606583593.py in <module> 3 # This takes a few minutes to run, so go grab a tea or coffee while you wait :) 4 data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst" ----> 5 pubmed_dataset = load_dataset("json", data_files=data_files, split="train") 6 pubmed_dataset ~/.local/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1655 1656 # Create a dataset builder -> 1657 builder_instance = load_dataset_builder( 1658 path=path, 1659 name=name, ~/.local/lib/python3.8/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs) 1492 download_config = download_config.copy() if download_config else DownloadConfig() 1493 download_config.use_auth_token = use_auth_token -> 1494 dataset_module = dataset_module_factory( 1495 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files 1496 ) ~/.local/lib/python3.8/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs) 1116 # Try packaged 1117 if path in _PACKAGED_DATASETS_MODULES: -> 1118 return PackagedDatasetModuleFactory( 1119 path, data_files=data_files, download_config=download_config, download_mode=download_mode 1120 ).get_module() ~/.local/lib/python3.8/site-packages/datasets/load.py in get_module(self) 773 else get_patterns_locally(str(Path().resolve())) 774 ) --> 775 data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token) 776 module_path, hash = _PACKAGED_DATASETS_MODULES[self.name] 777 builder_kwargs = {"hash": hash, "data_files": data_files} ~/.local/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token) 576 for key, patterns_for_key in patterns.items(): 577 out[key] = ( --> 578 DataFilesList.from_local_or_remote( 579 patterns_for_key, 580 base_path=base_path, ~/.local/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token) 545 base_path = base_path if base_path is not None else str(Path().resolve()) 546 data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) --> 547 origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token) 548 return cls(data_files, origin_metadata) 549 ~/.local/lib/python3.8/site-packages/datasets/data_files.py in _get_origin_metadata_locally_or_by_urls(data_files, max_workers, use_auth_token) 492 data_files: List[Union[Path, Url]], max_workers=64, use_auth_token: Optional[Union[bool, str]] = None 493 ) -> Tuple[str]: --> 494 return thread_map( 495 partial(_get_single_origin_metadata_locally_or_by_urls, use_auth_token=use_auth_token), 496 data_files, ~/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py in thread_map(fn, *iterables, **tqdm_kwargs) 92 """ 93 from concurrent.futures import ThreadPoolExecutor ---> 94 return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs) 95 96 ~/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py in _executor_map(PoolExecutor, fn, *iterables, **tqdm_kwargs) 74 map_args.update(chunksize=chunksize) 75 with PoolExecutor(**pool_kwargs) as ex: ---> 76 return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs)) 77 78 ~/.local/lib/python3.8/site-packages/tqdm/notebook.py in __iter__(self) 252 def __iter__(self): 253 try: --> 254 for obj in super(tqdm_notebook, self).__iter__(): 255 # return super(tqdm...) will not catch exception 256 yield obj ~/.local/lib/python3.8/site-packages/tqdm/std.py in __iter__(self) 1171 # (note: keep this check outside the loop for performance) 1172 if self.disable: -> 1173 for obj in iterable: 1174 yield obj 1175 return /usr/lib/python3.8/concurrent/futures/_base.py in result_iterator() 617 # Careful not to keep a reference to the popped future 618 if timeout is None: --> 619 yield fs.pop().result() 620 else: 621 yield fs.pop().result(end_time - time.monotonic()) /usr/lib/python3.8/concurrent/futures/_base.py in result(self, timeout) 442 raise CancelledError() 443 elif self._state == FINISHED: --> 444 return self.__get_result() 445 else: 446 raise TimeoutError() /usr/lib/python3.8/concurrent/futures/_base.py in __get_result(self) 387 if self._exception: 388 try: --> 389 raise self._exception 390 finally: 391 # Break a reference cycle with the exception in self._exception /usr/lib/python3.8/concurrent/futures/thread.py in run(self) 55 56 try: ---> 57 result = self.fn(*self.args, **self.kwargs) 58 except BaseException as exc: 59 self.future.set_exception(exc) ~/.local/lib/python3.8/site-packages/datasets/data_files.py in _get_single_origin_metadata_locally_or_by_urls(data_file, use_auth_token) 483 if isinstance(data_file, Url): 484 data_file = str(data_file) --> 485 return (request_etag(data_file, use_auth_token=use_auth_token),) 486 else: 487 data_file = str(data_file.resolve()) ~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in request_etag(url, use_auth_token) 489 def request_etag(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> Optional[str]: 490 headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token) --> 491 response = http_head(url, headers=headers, max_retries=3) 492 response.raise_for_status() 493 etag = response.headers.get("ETag") if response.ok else None ~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries) 474 headers = copy.deepcopy(headers) or {} 475 headers["user-agent"] = get_datasets_user_agent(user_agent=headers.get("user-agent")) --> 476 response = _request_with_retry( 477 method="HEAD", 478 url=url, ~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params) 407 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err: 408 if tries > max_retries: --> 409 raise err 410 else: 411 logger.info(f"{method} request to {url} timed out, retrying... [{tries/max_retries}]") ~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params) 403 tries += 1 404 try: --> 405 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params) 406 success = True 407 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err: /usr/lib/python3/dist-packages/requests/api.py in request(method, url, **kwargs) 58 # cases, and look like a memory leak in others. 59 with sessions.Session() as session: ---> 60 return session.request(method=method, url=url, **kwargs) 61 62 /usr/lib/python3/dist-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json) 531 } 532 send_kwargs.update(settings) --> 533 resp = self.send(prep, **send_kwargs) 534 535 return resp /usr/lib/python3/dist-packages/requests/sessions.py in send(self, request, **kwargs) 644 645 # Send the request --> 646 r = adapter.send(request, **kwargs) 647 648 # Total elapsed time of the request (approximately) /usr/lib/python3/dist-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 502 # TODO: Remove this in 3.0.0: see #2811 503 if not isinstance(e.reason, NewConnectionError): --> 504 raise ConnectTimeout(e, request=request) 505 506 if isinstance(e.reason, ResponseError): ConnectTimeout: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)')) ``` ## Environment info - `datasets` version: 1.17.0 - Platform: Linux-5.11.0-43-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 6.0.1

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I_kwDODunzps5A_0sf

Batched in filter throws error

closed

NONE

I hope this is really a bug, I could not find it among the open issues ## Describe the bug using `batched=False` in DataSet.filter throws error ```python TypeError: filter() got an unexpected keyword argument 'batched' ``` but in the docs it is lister as an argument. ## Steps to reproduce the bug ```python task = "mnli" max_length = 128 tokenizer = AutoTokenizer.from_pretrained("./pretrained_models/pretrained_models_drozd/sl250.m.gsic.titech.ac.jp:8000/21.11.17_06.30.32_roberta-base_a0057/checkpoints/smpl_400M/hf/") dataset = load_dataset("glue", task) task_to_keys = { "cola": ("sentence", None), "mnli": ("premise", "hypothesis"), "mnli-mm": ("premise", "hypothesis"), "mrpc": ("sentence1", "sentence2"), "qnli": ("question", "sentence"), "qqp": ("question1", "question2"), "rte": ("sentence1", "sentence2"), "sst2": ("sentence", None), "stsb": ("sentence1", "sentence2"), "wnli": ("sentence1", "sentence2"), } ##### tokenization_parameters sentence1_key, sentence2_key = task_to_keys[task] def preprocess_function(examples, max_length): if sentence2_key is None: return tokenizer( examples[sentence1_key], truncation=True, max_length=max_length ) return tokenizer( examples[sentence1_key], examples[sentence2_key], truncation=False, padding="max_length", max_length=max_length, ) encoded_dataset = dataset.map( lambda x: preprocess_function(x, max_length=max_length), batched=False ) encoded_dataset.filter(lambda x: len(x['input_ids']) <= max_length, batched=False) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.16.1, 1.17.0 - Platform: ubuntu - Python version: 3.8.12

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PR_kwDODunzps4wXSLi

Add QuALITY

open

MEMBER

Fixes #3441.

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PR_kwDODunzps4wXM8H

Update pib dataset card

closed

MEMBER

Related to #3496

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PR_kwDODunzps4wXLTB

Docs: Add VCTK dataset description

closed

MEMBER

This PR is a very minor followup to #1837, with only docs changes (single comment string).

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I_kwDODunzps5A-hqK

Adjusting chunk size for streaming datasets

closed

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** I want to use mc4 which I cannot save locally, so I stream it. However, I want to process the entire dataset and filter some documents from it. With the current chunk size of around 1000 documents (right?) I hit a performance bottleneck because of the frequent decompressing. **Describe the solution you'd like** I would appreciate a parameter in the load_dataset function, that allows me to set the chunksize myself (to a value like 100'000 in my case). Like that, I hope to improve the processing time.

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PR_kwDODunzps4wWL5U

update `pretty_name` for first 200 datasets

closed

CONTRIBUTOR

I made a script some time back to fetch `pretty_names` from `papers_with_code` dataset along with some other rules incase that dataset wasn't available on `papers_with_code`. Updating them in the `README` of `datasets`. Took only the first 200 datasets into consideration and after some eyeballing, most of them were looking good to me!

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I_kwDODunzps5A-Nhk

Changing sampling rate in audio dataset and subsequently mapping with `num_proc > 1` leads to weird bug

closed

MEMBER

Running: ```python from datasets import load_dataset, DatasetDict import datasets from transformers import AutoFeatureExtractor raw_datasets = DatasetDict() raw_datasets["train"] = load_dataset("common_voice", "ab", split="train") feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base") raw_datasets = raw_datasets.cast_column( "audio", datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate) ) num_workers = 16 def prepare_dataset(batch): sample = batch["audio"] inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"]) batch["input_values"] = inputs.input_values[0] batch["input_length"] = len(batch["input_values"]) return batch raw_datasets.map( prepare_dataset, remove_columns=next(iter(raw_datasets.values())).column_names, num_proc=16, desc="preprocess datasets", ) ``` gives ```bash File "/home/patrick/experiments/run_bug.py", line 25, in <module> raw_datasets.map( File "/home/patrick/python_bin/datasets/dataset_dict.py", line 492, in map { File "/home/patrick/python_bin/datasets/dataset_dict.py", line 493, in <dictcomp> k: dataset.map( File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2139, in map shards = [ File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2140, in <listcomp> self.shard(num_shards=num_proc, index=rank, contiguous=True, keep_in_memory=keep_in_memory) File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 3164, in shard return self.select( File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 485, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/patrick/python_bin/datasets/fingerprint.py", line 411, in wrapper out = func(self, *args, **kwargs) File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2756, in select return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint) File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2667, in _new_dataset_with_indices return Dataset( File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 659, in __init__ raise ValueError( ValueError: External features info don't match the dataset: Got {'client_id': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None), 'down_votes': Value(dtype='int64', id=None), 'age': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'accent': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None)} with type struct<client_id: string, path: string, audio: string, sentence: string, up_votes: int64, down_votes: int64, age: string, gender: string, accent: string, locale: string, segment: string> but expected something like {'client_id': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'audio': {'path': Value(dtype='string', id=None), 'bytes': Value(dtype='binary', id=None)}, 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None), 'down_votes': Value(dtype='int64', id=None), 'age': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'accent': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None)} with type struct<client_id: string, path: string, audio: struct<path: string, bytes: binary>, sentence: string, up_votes: int64, down_votes: int64, age: string, gender: string, accent: string, locale: string, segment: string> ``` Versions: ```python - `datasets` version: 1.16.2.dev0 - Platform: Linux-5.15.8-76051508-generic-x86_64-with-glibc2.33 - Python version: 3.9.7 - PyArrow version: 6.0.1 ``` and `transformers`: ``` - `transformers` version: 4.16.0.dev0 - Platform: Linux-5.15.8-76051508-generic-x86_64-with-glibc2.33 - Python version: 3.9.7 ```

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PR_kwDODunzps4wV1_w

Update version of pib dataset and make it streamable

closed

MEMBER

This PR: - Updates version of pib dataset: from 0.0.0 to 1.3.0 - Makes the dataset streamable Fix #3491. CC: @severo

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I_kwDODunzps5A99SQ

Add VoxLingua107

open

MEMBER

## Adding a Dataset - **Name:** VoxLingua107 - **Description:** VoxLingua107 is a speech dataset for training spoken language identification models. - **Paper:** https://arxiv.org/abs/2011.12998 - **Data:** http://bark.phon.ioc.ee/voxlingua107/ - **Motivation:** 107 languages, totaling 6628 hours for the train split. 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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https://github.com/huggingface/datasets/pull/3494

PR_kwDODunzps4wV0vB

Clone full repo to detect new tags when mirroring datasets on the Hub

closed

MEMBER

The new releases of `datasets` were not detected because the shallow clone in the CI wasn't getting the git tags. By cloning the full repository we can properly detect a new release, and tag all the dataset repositories accordingly cc @SBrandeis

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https://github.com/huggingface/datasets/pull/3493

PR_kwDODunzps4wVxfr

Fix VCTK encoding

closed

MEMBER

utf-8 encoding was missing in the VCTK dataset builder added in #3351

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PR_kwDODunzps4wVufr

Add `gzip` for `to_json`

closed

CONTRIBUTOR

(Partially) closes #3480. I have added `gzip` compression for `to_json`. I realised we can run into this compression problem with `to_csv` as well. `IOHandler` can be used for `to_csv` too. Please let me know if any changes are required.

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I_kwDODunzps5A9tRC

Update version of pib dataset

closed

MEMBER

On the Hub we have v0, while there exists v1.3. Related to bigscience-workshop/data_tooling#130

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I_kwDODunzps5A8_aF

Does datasets support load text from HDFS?

open

NONE

The raw text data is stored on HDFS due to the dataset's size is too large to store on my develop machine, so I wander does datasets support read data from hdfs?

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https://github.com/huggingface/datasets/pull/3489

PR_kwDODunzps4wT97d

Avoid unnecessary list creations

open

CONTRIBUTOR

Like in `join([... for s in ...])`. Also changed other things that I saw: * Use a `with` statement for many `open` that missed them, so the files don't remain open. * Remove unused variables. * Many HTTP links converted into HTTPS (verified). * Remove unnecessary "r" mode arg in `open` (double-checked it was actually the default in each case). * Remove Python 2 style of using `super`. * Run `pyupgrade $(find . -name "*.py" -type f) --py36-plus` (which already does some of the previous points). * Run `dos2unix $(find . -name "*.py" -type f)` (CRLF to LF line endings). * Fix typos.

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I_kwDODunzps5A7hh1

URL query parameters are set as path in the compression hop for fsspec

open

MEMBER

## Describe the bug There is an ssue with `StreamingDownloadManager._extract`. I don't know how the test `test_streaming_gg_drive_gzipped` passes: For ```python TEST_GG_DRIVE_GZIPPED_URL = "https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz" urlpath = StreamingDownloadManager().download_and_extract(TEST_GG_DRIVE_GZIPPED_URL) ``` gives `urlpath`: ```python 'gzip://uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz::https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz' ``` The gzip path makes no sense: `gzip://uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz` ## Steps to reproduce the bug ```python from datasets.utils.streaming_download_manager import StreamingDownloadManager dl_manager = StreamingDownloadManager() urlpath = dl_manager.extract("https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz") print(urlpath) ``` ## Expected results The query parameters should not be set as part of the path.

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PR_kwDODunzps4wTVeN

Update ADD_NEW_DATASET.md

closed

CONTRIBUTOR

fixed make style prompt for Windows Terminal

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PR_kwDODunzps4wTNd1

Fix weird spacing in ManualDownloadError message

closed

CONTRIBUTOR

`textwrap.dedent` works based on the spaces at the beginning. Before this change, there wasn't any space.

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I_kwDODunzps5A6T39

skip columns which cannot set to specific format when set_format

closed

NONE

**Is your feature request related to a problem? Please describe.** When using `dataset.set_format("torch")`, I must make sure every columns in datasets can convert to `torch`, however, sometimes I want to keep some string columns. **Describe the solution you'd like** skip columns which cannot set to specific format when set_format instead of raise an error.

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I_kwDODunzps5A53RC

make shape verification to use ArrayXD instead of nested lists for map

open

NONE

As describe in https://github.com/huggingface/datasets/issues/2005#issuecomment-793716753 and mentioned by @mariosasko in [image feature example](https://colab.research.google.com/drive/1mIrTnqTVkWLJWoBzT1ABSe-LFelIep1c#scrollTo=ow3XHDvf2I0B&line=1&uniqifier=1), IMO make shape verifcaiton to use ArrayXD instead of nested lists for map can help user reduce unnecessary cast. I notice datasets have done something special for `input_ids` and `attention_mask` which is also unnecessary after this feature added.

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PR_kwDODunzps4wSAW4

Remove unused phony rule from Makefile

closed

CONTRIBUTOR

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PR_kwDODunzps4wQqE1

Fix duplicate keys in NewsQA

open

CONTRIBUTOR

* Fix duplicate keys in NewsQA when loading from CSV files. * Fix s/narqa/newsqa/ in the download manually error message. * Make the download manually error message show nicely when printed. Otherwise, is hard to read due to spacing issues. * Fix the format of the license text. * Reformat the code to make it simpler.

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PR_kwDODunzps4wQoJu

Fix overriding of filesystem info

closed

MEMBER

Previously, `BaseCompressedFileFileSystem.info` was overridden and transformed from function to dict. This generated a bug for filesystem methods that use `self.info()`, like e.g. `fs.isfile()`. This PR: - Adds tests for `fs.isfile` (that use `fs.info`). - Fixes custom `BaseCompressedFileFileSystem.info` by removing its overriding.

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I_kwDODunzps5A3aNm

the compression format requested when saving a dataset in json format is not respected

closed

NONE

## Describe the bug In the documentation of the `to_json` method, it is stated in the parameters that > **to_json_kwargs – Parameters to pass to pandas’s pandas.DataFrame.to_json. however when we pass for example `compression="gzip"`, the saved file is not compressed. Would you also have expected compression to be applied? :relaxed: ## Steps to reproduce the bug ```python my_dict = {"a": [1, 2, 3], "b": [1, 2, 3]} ``` ### Result with datasets ```python from datasets import Dataset dataset = Dataset.from_dict(my_dict) dataset.to_json("dic_with_datasets.jsonl.gz", compression="gzip") !cat dic_with_datasets.jsonl.gz ``` output ``` {"a":1,"b":1} {"a":2,"b":2} {"a":3,"b":3} ``` Note: I would expected to see binary data here ### Result with pandas ```python import pandas as pd df = pd.DataFrame(my_dict) df.to_json("dic_with_pandas.jsonl.gz", lines=True, orient="records", compression="gzip") !cat dic_with_pandas.jsonl.gz ``` output ``` 4��a�dic_with_pandas.jsonl��VJT�2�QJ��\� ��g��yƵ���������)��� ``` Note: It looks like binary data ## Expected results I would have expected that the saved result with datasets would also be a binary file ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.16.1 - Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17 - Python version: 3.8.11 - PyArrow version: 5.0.0

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I_kwDODunzps5A3R2w

Dataset preview is not available (I think for all Hugging Face datasets)

closed

NONE

## Dataset viewer issue for '*french_book_reviews*' **Link:** https://huggingface.co/datasets/Abirate/french_book_reviews **short description of the issue** For my dataset, the dataset preview is no longer functional (it used to work: The dataset had been added the day before and it was fine...) And, after looking over the datasets, I discovered that this issue affects all Hugging Face datasets (as of yesterday, December 23, 2021, around 10 p.m. (CET)). **Am I the one who added this dataset** : Yes **Note**: here a screenshot showing the issue  **And here for glue dataset :** 

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