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I_kwDODunzps5G93Ch

datasets.set_progress_bar_enabled(False) not working in datasets v2

closed

NONE

## Describe the bug datasets.set_progress_bar_enabled(False) not working in datasets v2 ## Steps to reproduce the bug ```python datasets.set_progress_bar_enabled(False) ``` ## Expected results datasets not using any progress bar ## Actual results AttributeError: module 'datasets' has no attribute 'set_progress_bar_enabled ## Environment info datasets version 2

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I_kwDODunzps5G7uF_

Errors in `Train with Datasets` Tensorflow code section on Huggingface.co

closed

NONE

## Describe the bug Hi ### Error 1 Running the Tensforlow code on [Huggingface](https://huggingface.co./docs/datasets/use_dataset) gives a TypeError: __init__() got an unexpected keyword argument 'return_tensors' ### Error 2 `DataCollatorWithPadding` isn't imported ## Steps to reproduce the bug ```python import tensorflow as tf from datasets import load_dataset from transformers import AutoTokenizer dataset = load_dataset('glue', 'mrpc', split='train') tokenizer = AutoTokenizer.from_pretrained('bert-base-cased') dataset = dataset.map(lambda e: tokenizer(e['sentence1'], truncation=True, padding='max_length'), batched=True) data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf") train_dataset = dataset["train"].to_tf_dataset( columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'], shuffle=True, batch_size=16, collate_fn=data_collator, ) ``` This is the same code on Huggingface.co ## Actual results TypeError: __init__() got an unexpected keyword argument 'return_tensors' ## Environment info - `datasets` version: 2.0.0 - Platform: Windows-10-10.0.19044-SP0 - Python version: 3.9.7 - PyArrow version: 6.0.0 - Pandas version: 1.4.1 >

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PR_kwDODunzps41gEbu

Add SacreBLEU Metric Card

closed

CONTRIBUTOR

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PR_kwDODunzps41f3fb

Add chrF(++) Metric Card

closed

CONTRIBUTOR

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PR_kwDODunzps41fsxW

Close parquet writer properly in `push_to_hub`

closed

MEMBER

We don’t call writer.close(), which causes https://github.com/huggingface/datasets/issues/4077. It can happen that we upload the file before the writer is garbage collected and writes the footer. I fixed this by explicitly closing the parquet writer. Close https://github.com/huggingface/datasets/issues/4077.

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I_kwDODunzps5G6OHg

NonMatchingChecksumError for downloading conll2012_ontonotesv5 dataset

closed

CONTRIBUTOR

## Steps to reproduce the bug ```python datasets.load_dataset("conll2012_ontonotesv5", "english_v12") ``` ## Actual results ``` Downloading builder script: 32.2kB [00:00, 9.72MB/s] Downloading metadata: 20.0kB [00:00, 10.4MB/s] Downloading and preparing dataset conll2012_ontonotesv5/english_v12 (download: 174.83 MiB, generated: 204.29 MiB, post-processed: Unknown size , total: 379.12 MiB) to ... Traceback (most recent call last): [315/390] File "/home/yisiang/lgtn/conll2012/run.py", line 86, in <module> train() File "/home/yisiang/lgtn/conll2012/run.py", line 65, in train trainer.fit(model, datamodule=dm) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 740, in fit self._call_and_handle_interrupt( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 685, in _call_and_handle_inte rrupt return trainer_fn(*args, **kwargs) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in _fit_impl self._run(model, ckpt_path=ckpt_path) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1131, in _run self._data_connector.prepare_data() File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py", line 154, in pre pare_data self.trainer.datamodule.prepare_data() File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/core/datamodule.py", line 474, in wrapped_fn fn(*args, **kwargs) File "/home/yisiang/lgtn/_abstract_task/data.py", line 43, in prepare_data raw_dsets = datasets.load_dataset(**load_dataset_kwargs) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/load.py", line 1687, in load_dataset builder_instance.download_and_prepare( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 605, in download_and_prepare self._download_and_prepare( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 1104, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 676, in _download_and_prepare verify_checksums( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/zmycy7t9h9-1.zip'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0

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https://github.com/huggingface/datasets/pull/4079

PR_kwDODunzps41eYRC

Increase max retries for GitHub datasets

closed

MEMBER

As GitHub recurrently raises connectivity issues, this PR increases the number of max retries to request GitHub datasets, as previously done for GitHub metrics: - #4063 Note that this is a temporary solution, while we decide when and how to load GitHub datasets from the Hub: - #4059 Fix #2048 Related to: - #4051 - #3210 - #2787 - #2075 - #2036 CC: @lhoestq

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PR_kwDODunzps41eWnl

Fix GithubMetricModuleFactory instantiation with None download_config

closed

MEMBER

Recent PR: - #4063 introduced a potential bug if `GithubMetricModuleFactory` is instantiated with None `download_config`. This PR add instantiation tests and fix that potential issue. CC: @lhoestq

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I_kwDODunzps5G5dXB

ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.

closed

CONTRIBUTOR

## Describe the bug When uploading a relatively large image dataset of > 1GB, reloading doesn't work for me, even though pushing to the hub went just fine. Basically, I do: ``` from datasets import load_dataset dataset = load_dataset("imagefolder", data_files="path_to_my_files") dataset.push_to_hub("dataset_name") # works fine, no errors reloaded_dataset = load_dataset("dataset_name") ``` and it returns: ``` /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file. ``` I created a Colab notebook to reproduce my error: https://colab.research.google.com/drive/141LJCcM2XyqprPY83nIQ-Zk3BbxWeahq?usp=sharing

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https://github.com/huggingface/datasets/pull/4076

PR_kwDODunzps41a1n2

Add ROUGE Metric Card

closed

CONTRIBUTOR

Add ROUGE metric card. I've left the 'Values from popular papers' section empty for the time being because I don't know the summarization literature very well and am therefore not sure which paper(s) to pull from (note that the original rouge paper does not seem to present specific values, just correlations with human judgements). Any suggestions on which paper(s) to pull from would be helpful! :)

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I_kwDODunzps5G1n5S

Add CCAgT dataset

closed

NONE

## Adding a Dataset - **Name:** CCAgT dataset: Images of Cervical Cells with AgNOR Stain Technique - **Description:** The dataset contains 2540 images (1600x1200 where each pixel is 0.111μm×0.111μm) from three different slides, having at least one nucleus per image. These images are from fields belonging to a sample cervical slide, colored with silver-stained, a method known as Argyrophilic Nucleolar Organizer Regions (AgNOR). - **Paper:** https://doi.org/10.1109/cbms49503.2020.00110 - **Data:** https://arquivos.ufsc.br/d/373be2177a33426a9e6c/ or https://drive.google.com/drive/u/4/folders/1TBpYCv6S1ydASLauSzcsvO7Wc5O-WUw0 - **Motivation:** This is a unique dataset (because of the stain), for a major health problem, cervical cancer, with real data. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Hi, this is a public version of the dataset that I have been working on, soon we will have another version of this dataset. But until this new version goes out, I thought I would add this dataset here, if it makes sense for the repository. You can assign the task to me if possible

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I_kwDODunzps5G1kt2

Error in google/xtreme_s dataset card

closed

NONE

**Link:** https://huggingface.co./datasets/google/xtreme_s Not a big deal but Hungarian is considered an Eastern European language, together with Serbian, Slovak, Slovenian (all correctly categorized; Slovenia is mostly to the West of Hungary, by the way).

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PR_kwDODunzps41adPA

Create a metric card for Competition MATH

closed

NONE

Proposing metric card for Competition MATH

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PR_kwDODunzps41aIUG

Add installation instructions to image_process doc

closed

COLLABORATOR

This PR adds the installation instructions for the Image feature to the image process doc.

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I_kwDODunzps5GySZj

Loading issue for xuyeliu/notebookCDG dataset

closed

NONE

## Dataset viewer issue for '*xuyeliu/notebookCDG*' **Link:** *[link to the dataset viewer page](https://huggingface.co./datasets/xuyeliu/notebookCDG)* *Couldn't load the xuyeliu/notebookCDG with provided scripts: * ``` from datasets import load_dataset dataset = load_dataset("xuyeliu/notebookCDG/dataset_notebook.pkl") ``` I get an error message as follows: FileNotFoundError: Couldn't find a dataset script at /home/code_documentation/code/xuyeliu/notebookCDG/notebookCDG.py or any data file in the same directory. Couldn't find 'xuyeliu/notebookCDG' on the Hugging Face Hub either: FileNotFoundError: Unable to resolve any data file that matches ['**train*'] in dataset repository xuyeliu/notebookCDG with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip'] Am I the one who added this dataset ? No

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PR_kwDODunzps41VMYq

Create metric card for seqeval

closed

NONE

Proposing metric card for seqeval. Not sure which values to report for Popular papers though.

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PR_kwDODunzps41VIMJ

Add support for metadata files to `imagefolder`

closed

COLLABORATOR

This PR adds support for metadata files to `imagefolder` to add an ability to specify image fields other than `image` and `label`, which are inferred from the directory structure in the loaded dataset. To be parsed as an image metadata file, a file should be named `"info.csv"` and should have the following structure: ``` image_id,some_col1_name,some_col2_name rel/path/to/image1.jpg,image1_col1_value,image1_col2_value rel/path/to/image2.jpg,image2_col1_value,image2_col2_value ... ``` This is how the resolution works: ``` - path/to/imagefolder/directory - info.csv - 10.jpg # referenced as 10.jpg in "info.csv" - Cat - 0.jpg # referenced as Cat/0.jpg in "info.csv" - 1.jpg # referenced as Cat/1.jpg in "info.csv" - Dog - 0.jpg # referenced as Dog/0.jpg in "info.csv" - 1.jpg # referenced as Dog/1.jpg in "info.csv" ``` Open questions: 1. IMO it makes more sense to store image metadata as JSON Lines than CSV. CSV is sufficient for textual metadata but not the best for representing bounding boxes, for instance. Also, JSON Lines is more strict, which is good in this case (CSV supports various delimiters, the header line is optional, etc., so it's easier to enforce rules on JSON Lines that it's on CSV) 2. A better name for the `image_id` column, which contains image identifiers? Maybe `image_file` or `image_filename`? 3. WDYT about making `with_metadata=True` the default behavior if the loaded repo/directory contains an `info.csv` file? An example repository: https://huggingface.co./datasets/mariosasko/PetImages. Can be loaded by installing `datasets` from the PR branch and running `load_dataset("mariosasko/PetImages", with_metadata=True)`. cc: @abhishekkrthakur (this PR should address https://huggingface.slack.com/archives/C02JB9L6JKF/p1645450017434029?thread_ts=1645157416.389499&cid=C02JB9L6JKF) TODOs: - [x] Test - [x] Metadata file nesting ``` - path/to/imagefolder/directory - info.csv - 10.jpg - Cat - info.csv # should have higher precedence in this directory than the top-level info.csv, but we choose the first "eligible" metadata file currently - 0.jpg - 1.jpg ```

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PR_kwDODunzps41VC0I

Improve out of bounds error message

closed

MEMBER

In 1.18.4 with https://github.com/huggingface/datasets/pull/3719 we introduced an error message for users using `select` with out of bounds indices. The message ended up being confusing for some users because it mentioned negative indices, which is not the main use case. I replaced it with a message that is very similar to the one you get with you try to access a list with an out-of-range index.

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PR_kwDODunzps41U7qc

Update datasets task tags to align tags with models

closed

MEMBER

**Requires https://github.com/huggingface/datasets/pull/4066 to be merged first** Following https://github.com/huggingface/datasets/pull/4066 we need to update many dataset tags to use the new ones. This PR takes case of this and is quite big - feel free to review only certain tags if you don't want to spend too much time on it. Note that the CI will never be green for this PR, because many dataset cards have missing tags or sections, and fixing them is out of scope of this PR (the CI on master will be green anyway)

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PR_kwDODunzps41U63x

Tasks alignment with models

closed

MEMBER

I updated our `tasks.json` file with the new task taxonomy that is aligned with models. The rule that defines a task is the following: **Two tasks are different if and only if the steps of their pipelines** are different, i.e. if they can’t reasonably be implemented using the same coherent code (level of granularity/complexity of the code to be defined - ideally I’d like to say “HF user’s level”) - this is the same definition in `transformers` I will update the tags of all the datasets in this repository [in another PR](https://github.com/huggingface/datasets/pull/4067) for readability. Main changes: - conditional-text-generation is split between summarization, translation, text-generation and text2text-generation - speech-processing is split into automatic-speech-recognition, audio-classification, etc. - structure-prediction is renamed token-classification - abstractive-qa now belongs to text2text-generation Here is just a simplified YAML dump of `tasks.json`: ```yaml audio-classification: - keyword-spotting - speaker-identification - speaker-intent-classification - emotion-recognition - speaker-language-identification audio-to-audio: [] automatic-speech-recognition: [] conversational: - dialogue-generation feature-extraction: [] fill-mask: - slot-filling - masked-language-modeling image-classification: - multi-label-image-classification - multi-class-image-classification image-segmentation: - instance-segmentation - semantic-segmentation - panoptic-segmentation image-to-text: - image-captioning multiple-choice: - multiple-choice-qa - multiple-choice-coreference-resolution object-detection: - face-detection - vehicle-detection question-answering: - extractive-qa - open-domain-qa - closed-domain-qa sentence-similarity: [] tabular-classification: [] tabular-to-text: - rdf-to-text summarization: - news-articles-summarization - news-articles-headline-generation table-to-text: [] table-question-answering: [] text-classification: - acceptability-classification - entity-linking-classification - fact-checking - intent-classification - multi-class-classification - multi-label-classification - natural-language-inference - semantic-similarity-classification - sentiment-classification - topic-classification - semantic-similarity-scoring - sentiment-scoring - sentiment-analysis - hate-speech-detection - text-scoring text-generation: - dialogue-modeling - language-modeling text-retrieval: - document-retrieval - utterance-retrieval - entity-linking-retrieval - fact-checking-retrieval text-to-image: [] text-to-tabular: - relation-extraction - semantic-role-labeling text-to-speech: [] text2text-generation: - text-simplification - explanation-generation - abstractive-qa - open-domain-abstractive-qa - closed-domain-qa - open-book-qa - closed-book-qa time-series-forecasting: - univariate-time-series-forecasting - multivariate-time-series-forecasting token-classification: - named-entity-recognition - part-of-speech-tagging - parsing - lemmatization - word-sense-disambiguation - coreference-resolution translation: [] visual-question-answering: [] voice-activity-detection: [] zero-shot-classification: [] zero-shot-image-classification: [] reinforcement-learning: [] other: [] ``` Feel free to comment and give suggestions, especially if you think we can also align this list with other projects cc @julien-c @osanseviero @severo @lewtun @yjernite @albertvillanova @mariosasko @polinaeterna

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PR_kwDODunzps41U5rq

Create metric card for METEOR

closed

NONE

Proposing a metric card for METEOR

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PR_kwDODunzps41UqXS

Contributing MedMCQA dataset

closed

CONTRIBUTOR

Adding MedMCQA dataset ( https://paperswithcode.com/dataset/medmcqa ) **Name**: MedMCQA **Description**: MedMCQA is a large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. MedMCQA has more than 194k high-quality AIIMS & NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity. The dataset contains questions about the following topics: Anesthesia, Anatomy, Biochemistry, Dental, ENT, Forensic Medicine (FM), Obstetrics and Gynecology (O&G), Medicine, Microbiology, Ophthalmology, Orthopedics Pathology, Pediatrics, Pharmacology, Physiology, Psychiatry, Radiology Skin, Preventive & Social Medicine (PSM), and Surgery **Code**: https://github.com/medmcqa/medmcqa All files are at place : **a dataset script** : medmcqa.py **a dataset card with tags and information** : README.md. **a metadata file** : dataset_infos.json **a dummy-data file** : Please help to generate this file, I was facing ` raise JSONDecodeError("Extra data", s, end)` error

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PR_kwDODunzps41UiDm

Increase max retries for GitHub metrics

closed

MEMBER

As GitHub recurrently raises connectivity issues, this PR increases the number of max retries to request GitHub metrics. Related to: - #3134 Also related to: - #4059

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I_kwDODunzps5Gtfhs

Loading mozilla-foundation/common_voice_7_0 dataset failed

closed

NONE

## Describe the bug I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN") ``` ## Expected results load `mozilla-foundation/common_voice_7_0` dataset succesfully ## Actual results ``` JSONDecodeError Traceback (most recent call last) /opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs) 909 try: --> 910 return complexjson.loads(self.text, **kwargs) 911 except JSONDecodeError as e: /opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw) 524 and not use_decimal and not kw): --> 525 return _default_decoder.decode(s) 526 if cls is None: /opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3) 369 s = str(s, self.encoding) --> 370 obj, end = self.raw_decode(s) 371 end = _w(s, end).end() /opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3) 399 idx += 3 --> 400 return self.scan_once(s, idx=_w(s, idx).end()) JSONDecodeError: Expecting value: line 1 column 1 (char 0) During handling of the above exception, another exception occurred: JSONDecodeError Traceback (most recent call last) /tmp/ipykernel_358/370980805.py in <module> 1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split ----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True) /opt/conda/lib/python3.7/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, **config_kwargs) 1690 ignore_verifications=ignore_verifications, 1691 try_from_hf_gcs=try_from_hf_gcs, -> 1692 use_auth_token=use_auth_token, 1693 ) 1694 /opt/conda/lib/python3.7/site-packages/datasets/builder.py in 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) 604 if not downloaded_from_gcs: 605 self._download_and_prepare( --> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 607 ) 608 # Sync info /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1102 1103 def _download_and_prepare(self, dl_manager, verify_infos): -> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1105 1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 670 split_dict = SplitDict(dataset_name=self.name) 671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 673 674 # Checksums verification ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager) 151 152 self._log_download(self.config.name, bundle_version, hf_auth_token) --> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template)) 154 155 if self.config.version < datasets.Version("5.0.0"): ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template) 130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'") 131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024 --> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json() 133 return response["url"] 134 /opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs) 915 raise RequestsJSONDecodeError(e.message) 916 else: --> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos) 918 919 @property JSONDecodeError: [Errno Expecting value] Not Found: 0 ``` ## Environment info - `datasets` version: 2.0.0 - Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid - Python version: 3.7.12 - PyArrow version: 5.0.0 - Pandas version: 1.3.5

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I_kwDODunzps5GtcMP

Loading cnn_dailymail dataset failed

closed

NONE

## Describe the bug I wanted to load cnn_dailymail dataset from huggingface datasets on jupyter lab, but I am getting an error ` NotADirectoryError:[Errno20] Not a directory ` while loading it. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('cnn_dailymail', '3.0.0') ``` ## Expected results load `cnn_dailymail` dataset succesfully ## Actual results failed to load and get error > NotADirectoryError: [Errno 20] Not a directory ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` 1.8.0: - Platform: Ubuntu-20.04 - Python version: 3.9.10 - PyArrow version: 3.0.0

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PR_kwDODunzps41Tbmg

Deprecate canonical Multilingual Librispeech

closed

CONTRIBUTOR

Deprecate canonical Multilingual Librispeech in favor of [the community one](https://huggingface.co./datasets/facebook/multilingual_librispeech) which supports streaming. However, there is a problem regarding new ASR template schema: since it's changed, I guess all community datasets that use this template do not work with new version of the library, including MLS. Should we somehow notify users about that or is it possible to change this line ourselves? For MLS specifically, I cannot change the code directly as I'm not the member of the Facebook org. Hm, and the code should be change after the release, no?

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PR_kwDODunzps41TC-o

Load GitHub datasets from Hub

closed

MEMBER

We have recurrently had connection errors when requesting GitHub because sometimes the site is not available. This PR requests the Hub instead, once all GitHub datasets are mirrored on the Hub. Fix #2048 Related to: - #4051 - #3210 - #2787 - #2075 - #2036

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PR_kwDODunzps41RPhl

Updated annotations for nli_tr dataset

closed

CONTRIBUTOR

This PR adds annotation tags for `nli_tr` dataset so that the dataset can be searchable wrt. relevant query parameters. The annotations in this PR are based on the existing annotations of `snli` and `multi_nli` datasets as `nli_tr` is a machine-generated extension of those datasets. This PR is intended only for updating the annotation labels but a followup PR will focus on updating the missing sections in the `README.md` as well. Thanks for all your time to review it.

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I_kwDODunzps5GqGjR

`load_dataset` consumes too much memory for audio + tar archives

closed

NONE

## Description `load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists. ## Steps to reproduce the bug Here's my implementation of `_generate_examples`: ```python class MyDatasetBuilder(datasets.GeneratorBasedBuilder): DEFAULT_WRITER_BATCH_SIZE = 1 ... def _split_generators(self, dl_manager): archive_path = dl_manager.download(_DL_URLS[self.config.name]) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "audio_tarfile_path": archive_path["audio_tarfile"] }, ), ] def _generate_examples(self, audio_tarfile_path): key = 0 with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile: for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 ``` I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened. I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times. ```python import tarfile def generate_examples(): audio_tarfile = tarfile.open("audios.tar", mode="r|") key = 0 for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 if __name__ == "__main__": examples = generate_examples() for example in examples: pass ``` ## Expected results Memory consumption should be similar to the non-huggingface script. ## Actual results Process is killed after consuming too much memory. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12 - Python version: 3.7.12 - PyArrow version: 6.0.1 - Pandas version: 1.3.5

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I_kwDODunzps5GpAq_

Unexpected behavior of _TempDirWithCustomCleanup

open

NONE

## Describe the bug This is not 100% a bug in `datasets`, but behavior that surprised me and I think this could be made more robust on the `datasets`side. When using `datasets.disable_caching()`, cache files are written to a temporary directory. This directory should be based on the environment variable TMPDIR. I want to set TMPDIR at runtime using os.ENVIRON["TMPDIR"] = something, but depending on other imported modules this can fail to take effect. ## Steps to reproduce the bug `_TempDirWithCustomCleanup` relies on `tempfile` to generate a path to a temporary directory. However, `tempfile` generates the path only once. This can be a problem when trying to set TMPDIR at runtime whenever other code imports `tempfile` first and does something unexpected. For example (after too much trial and error) I found out that a different part of the code base I work with defines a class `PatchedDataCollatorForLanguageModeling(transformers.DataCollatorForLanguageModeling)` based on a `transformers` class. This import is enough to trigger `tempfile` to generate `tempfile` to generate a temporary path and leading to the wrong path being cached in `tempfile.tempdir`. ## Suggestion: I could file this also as bug with `transformers`, but I think fixing this on the datasets would be much more robust: Datasets could recompute the temporary path once (technically possible via `tempfile._get_default_tempdir` or resetting the global variable `tempfile.tmpdir` to None) before setting its own global `_TEMP_DIR_FOR_TEMP_CACHE_FILES`.

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PR_kwDODunzps41PGF1

[DO NOT MERGE] Test doc-builder

closed

MEMBER

This is a test PR to ensure the changes in https://github.com/huggingface/doc-builder/pull/164 don't break anything in `datasets`

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PR_kwDODunzps41Nwjz

Support float data types in pearsonr/spearmanr metrics

closed

MEMBER

Fix #4053.

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I_kwDODunzps5Gmgqa

Modify datatype from `int32` to `float` for pearsonr, spearmanr.

closed

NONE

**Is your feature request related to a problem? Please describe.** - Now [Pearsonr](https://github.com/huggingface/datasets/blob/master/metrics/pearsonr/pearsonr.py) and [Spearmanr](https://github.com/huggingface/datasets/blob/master/metrics/spearmanr/spearmanr.py) both get input data as 'int32'. **Describe the solution you'd like** - Considering that those metrics are widely used for the STS task(labels are in 'float' data type), it would be better to modify datatype from 'int32' to 'float' for getting exact values of similarity.

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I_kwDODunzps5GmT3p

metric = metric_cls( TypeError: 'NoneType' object is not callable

closed

NONE

Hi, friend. I meet a problem. When I run the code: `metric = load_metric('glue', 'rte')` There is a problem raising: `metric = metric_cls( TypeError: 'NoneType' object is not callable ` I don't know why. Thanks for your help!

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I_kwDODunzps5GmIMz

ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py

closed

NONE

Hi, I meet a problem. When I run the code: `dataset = load_dataset('glue','sst2')` There is a issue raising: ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py I don't know why; it is ok when I use Google Chrome to view this url. Thanks for your help!

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PR_kwDODunzps41NAMF

Add RVL-CDIP dataset

closed

CONTRIBUTOR

Resolves #2762 Dataset Request : Add RVL-CDIP dataset [#2762](https://github.com/huggingface/datasets/issues/2762) This PR adds the RVL-CDIP dataset. The dataset contains Google Drive link for download and wasn't getting downloaded automatically, so I have provided manual_download_instructions. - I have added the dummy_data.zip as well. Needed inputs on how I can run the real data and the dummy data tests for datasets with manual download ? Inputs and suggestions for improvement are welcome. Thank you.

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PR_kwDODunzps41LSjv

Create metric card for the Code Eval metric

closed

NONE

Creating initial Code Eval metric card

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I_kwDODunzps5Gj2yg

Split size error on `amazon_us_reviews` / `PC_v1_00` dataset

closed

CONTRIBUTOR

## Describe the bug When downloading this subset as of 3-28-2022 you will encounter a split size error after the dataset is extracted. The extracted dataset has roughly ~6m rows while the split expects <1m. Upon digging a little deeper, I downloaded the raw files from `https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_us_PC_v1_00.tsv.gz` and extracted them. A line count via `wc -l` confirms the ~6m number that we see and the data looks valid at a glance (I did not check for duplicate rows). My guess is this file has either been updated in place or there is a bug in the dataset metadata. Happy to submit a PR and fix this up if turns out to be a metadata issue but wanted to get some other :eyes: on it first. ## Steps to reproduce the bug ```python load_dataset('amazon_us_reviews', 'PC_v1_00') ``` ## Expected results Dataset is downloaded and extracted successfully. ## Actual results An split size exception is thrown. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 7.0.0 - Pandas version: 1.4.1

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I_kwDODunzps5GjzC1

Dataset.unique(column: str) -> ArrowNotImplementedError

closed

NONE

## Describe the bug I'm trying to use `unique()` function, but it fails ## Steps to reproduce the bug 1. Get dataset 2. Call `unique` 3. Error # Sample code to reproduce the bug ```python !pip show datasets from datasets import load_dataset dataset = load_dataset('wikiann', 'en') dataset['train'].column_names dataset['train'].unique(dataset['train'].column_names[0]) ``` ## Expected results It would be nice to actually see unique items ## Actual results Error: ```python --------------------------------------------------------------------------- ArrowNotImplementedError Traceback (most recent call last) [<ipython-input-10-5e0de07ed42c>](https://s0qyv2vjaji-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20220324-060046-RC00_436956229#) in <module>() 6 7 dataset['train'].column_names ----> 8 dataset['train'].unique(dataset['train'].column_names[0]) 5 frames /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowNotImplementedError: Function unique has no kernel matching input types (array[list<item: string>]) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Google Collab - Python version: 3.7.13 - PyArrow version: 6.0.1

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PR_kwDODunzps41K6_H

Create metric card for XNLI

closed

NONE

Proposing a metric card for XNLI

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PR_kwDODunzps41KtfV

Fix CLI dummy data generation

closed

MEMBER

PR: - #3868 broke the CLI dummy data generation. Fix #4044.

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I_kwDODunzps5GjTO-

CLI dummy data generation is broken

closed

MEMBER

## Describe the bug We get a TypeError when running CLI dummy data generation: ```shell datasets-cli dummy_data datasets/<your-dataset-folder> --auto_generate ``` gives: ``` File ".../huggingface/datasets/src/datasets/commands/dummy_data.py", line 361, in _autogenerate_dummy_data dataset_builder._prepare_split(split_generator) TypeError: _prepare_split() missing 1 required positional argument: 'check_duplicate_keys' ```

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PR_kwDODunzps41Kl0b

Create metric card for CUAD

closed

NONE

Proposing a CUAD metric card

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I_kwDODunzps5GjEtl

Add support for IIIF in datasets

open

MEMBER

This is a feature request for support for IIIF in `datasets`. Apologies for the long issue. I have also used a different format to the usual feature request since I think that makes more sense but happy to use the standard template if preferred. ## What is [IIIF](https://iiif.io/)? IIIF (International Image Interoperability Framework) > is a set of open standards for delivering high-quality, attributed digital objects online at scale. It’s also an international community developing and implementing the IIIF APIs. IIIF is backed by a consortium of leading cultural institutions. The tl;dr is that IIIF provides various specifications for implementing useful functionality for: - Institutions to make available images for various use cases - Users to have a consistent way of interacting/requesting these images - For developers to have a common standard for developing tools for working with IIIF images that will work across all institutions that implement a particular IIIF standard (for example the image viewer for the BNF can also work for the Library of Congress if they both use IIIF). Some institutions that various levels of support IIF include: The British Library, Internet Archive, Library of Congress, Wikidata. There are also many smaller institutions that have IIIF support. An incomplete list can be found here: https://iiif.io/guides/finding_resources/ ## IIIF APIs IIIF consists of a number of APIs which could be integrated with datasets. I think the most obvious candidate for inclusion would be the [Image API](https://iiif.io/api/image/3.0/) ### IIIF Image API The Image API https://iiif.io/api/image/3.0/ is likely the most suitable first candidate for integration with datasets. The Image API offers a consistent protocol for requesting images via a URL: ```{scheme}://{server}{/prefix}/{identifier}/{region}/{size}/{rotation}/{quality}.{format}``` A concrete example of this: ```https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/full/0/default.jpg``` As you can see the scheme offers a number of options that can be specified in the URL, for example, size. Using the example URL we return:  We can change the size to request a size of 250 by 250, this is done by changing the size from `full` to `250,250` i.e. switching the URL to `https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/250,250/0/default.jpg`  We can also request the image with max width 250, max height 250 whilst maintaining the aspect ratio using `!w,h`. i.e. change the url to `https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg`  A full overview of the options for size can be found here: https://iiif.io/api/image/3.0/#42-size ## Why would/could this be useful for datasets? There are a few reasons why support for the IIIF Image API could be useful. Broadly the ability to have more control over how an image is returned from a server is useful for many ML workflows: - images can be requested in the right size, this prevents having to download/stream large images when the actual desired size is much smaller - can select a subset of an image: it is possible to select a sub-region of an image, this could be useful for example when you already have a bounding box for a subset of an image and then want to use this subset of an image for another task. For example, https://github.com/Living-with-machines/nnanno uses IIIF to request parts of a newspaper image that have been detected as 'photograph', 'illustration' etc for downstream use. - options for quality, rotation, the format can all be encoded in the URL request. These may become particularly useful when pre-training models on large image datasets where the cost of downloading images with 1600 pixel width when you actually want 240 has a larger impact. ## What could this look like in datasets? I think there are various ways in which support for IIIF could potentially be included in `datasets`. These suggestions aren't fully fleshed out but hopefully, give a sense of possible approaches that match existing `datasets` methods in their approach. ### Use through datasets scripts Loading images via URL is already supported. There are a few possible 'extras' that could be included when using IIIF. One option is to leverage the IIIF protocol in datasets scripts, i.e. the dataset script can expose the IIIF options via the dataset script: ```python ds = load_dataset("iiif_dataset", image_size="250,250", fmt="jpg") ``` This is already possible. The approach to parsing the IIIF URLs would be left to the person creating the dataset script. ### Support through dataset scripts (with some datasets support) This is similar to the above but `datasets` would offer some way of saying this is a iiif URL and then expose the options associated with IIIF images automatically. i.e. if you did something like: ```python features = {"label": ClassLabel(names=['dog','cat']), "url": datasets.IIIFURL()} ``` inside your loading script, you would automatically have exposed `size`, `fmt` etc. options when loading the dataset. ### Other possible integrations Some other possible pseudocode ways that a user could interact with IIIF URLs: The ability to cast to an `IIIFImage` feature type: ``` ds.cast_column('url', IIIFImage, download=False) ``` The ability to specify some options associated with IIIF urls. ``` ds = ds.set_iiif_options(column='url', size="250,250") ``` I think all of these would rely on having an `IIIFImage` feature type - this would be a little bit of a Frankenstein between a `string` and `datasets.Image`. I think most of the actual image behaviour would be exactly the same as `datasets.Image`, the difference would be that the underlying URL could be modified in various ways. ## prerequisite requirements There are a few pre-requisites that I can anticipate. This doesn't cover a full implementation of IIIF support which would have different requirements depending on the approach taken to implementing IIIF. Some of these features would be useful independently of adding IIIF support: ### support for handling failed images loaded via a URL (or a specific IIIFImage feature). Working with images via web requests will inevitably return the odd failed request. If these images are then requests and don't return it would be useful to have a `None` returned instead of an error. For example, when using `push_to_hub` `datasets` will try and include the image but currently fails with bad URLs. ```python from datasets import Dataset import datasets urls = ['https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg']*3 urls.append("badurl.com/image.jpg") data = {"url":urls} ds = Dataset.from_dict(data) ds = ds.cast_column('url', datasets.Image()) ds[3]['url'] ``` returns a `FileNotFoundError`, for streaming large datasets of images using their URLs it could be useful to have `None` returned instead. This has implications for the actual training loop i.e. you now need to somehow skip those examples because of this it might not be desirable to support this. ### Caching support Since IIIF requests images via a URL it would be great to have a way of not requesting the images multiple times. This is tracked in https://github.com/huggingface/datasets/issues/3142 and I think this would also be very desirable to have here particularly as one of the primary use cases of IIIF may be to do unsupervised pre-training on large datasets of IIIF URLs. ### Support for Parsing IIIF URLs This gets closer to the actual implementation. Here the requirement would be some way for `datasets` to parse a URL that the users specify is an IIIF URL. An example of a Python library that does this: https://github.com/Princeton-CDH/piffle. I also have a rough version that uses `dataclasses` which I can share. ## Why it might not be worthwhile/suitable for datasets There are some reasons that this might not be worth implementing: - currently, IIIF is mainly used by cultural heritage organizations (museums, archives etc.) The adoption of IIIF in this sector has been growing but it's possible that adoption won't be extended to other industries which may also be a source of image data for training ML models. - It may end up being better to leave this to the user. It would for example be possible for someone to write map functions to change an IIIF URL to the correct size etc. Adding direct support for IIIF in datasets may potentially not be worth the trouble. - The impact of different approaches to doing image scaling can impact the downstream model's performance, see: https://twitter.com/wightmanr/status/1479528581466243073?s=20. Since different IIIF image servers may implement different approaches to resizing images this could have a downstream impact on model performance. think this is something that could be flagged to the end-user in the documentation. This probably also falls into general "gotchas" that probably aren't the `datasets` libraries' role to protect users from. Some of the requirements outlined above would be useful for images anyway. These could be implemented prior to a final decision about whether IIIF support could/should be added to datasets. ## Suggested next steps: I realise this is a long and slightly open-ended issue. I am happy to clarify/answer questions on IIIF and possible integrations. If the prerequisite requirements seem worth exploring/are better explored in their own issues let me know and I can open new issues for those.

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PR_kwDODunzps41KFIf

Support streaming xcopa dataset

closed

MEMBER

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PR_kwDODunzps41JKUG

[DO NOT MERGE] Test doc-builder with skipped installation feature

closed

MEMBER

This PR is just for testing that we can build PR docs with changes made on the [`skip-install-for-real`](https://github.com/huggingface/doc-builder/tree/skip-install-for-real) branch of `doc-builder`.

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I_kwDODunzps5GhVom

Error while building documentation

closed

MEMBER

## Describe the bug Documentation building is failing: - https://github.com/huggingface/datasets/runs/5716300989?check_suite_focus=true ``` ValueError: There was an error when converting ../datasets/docs/source/package_reference/main_classes.mdx to the MDX format. Unable to find datasets.filesystems.S3FileSystem in datasets. Make sure the path to that object is correct. ```

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PR_kwDODunzps41I854

Fix building of documentation

closed

MEMBER

Documentation building is failing: - https://github.com/huggingface/datasets/runs/5716300989?check_suite_focus=true ``` ValueError: There was an error when converting ../datasets/docs/source/package_reference/main_classes.mdx to the MDX format. Unable to find datasets.filesystems.S3FileSystem in datasets. Make sure the path to that object is correct. ``` Fix #4037.

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PR_kwDODunzps41Iwb2

Add zero_division argument to precision and recall metrics

closed

MEMBER

Fix #4025.

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PR_kwDODunzps41IpN1

Fix null checksum in xcopa dataset

closed

MEMBER

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PR_kwDODunzps41Ie6w

Fix checksum error in cats_vs_dogs dataset

closed

MEMBER

Recent PR updated the metadata JSON file of cats_vs_dogs dataset: - #3878 However, that new JSON file contains a None checksum. This PR fixes it. Fix #4032.

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I_kwDODunzps5GfPpx

can't download cats_vs_dogs dataset

closed

NONE

## Describe the bug can't download cats_vs_dogs dataset. error: Checksums didn't match for dataset source files ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("cats_vs_dogs") ``` ## Expected results loaded successfully. ## Actual results NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip'] ## Environment info fresh google colab notebook

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I_kwDODunzps5GejkU

Cannot load the dataset conll2012_ontonotesv5

closed

NONE

## Describe the bug Cannot load the dataset conll2012_ontonotesv5 ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from datasets import load_dataset dataset = load_dataset('conll2012_ontonotesv5', 'english_v4', split="test") print(dataset) ``` ## Expected results The datasets should be downloaded successfully ## Actual results raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/zmycy7t9h9-1.zip'] ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-5.4.0-88-generic-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 7.0.0

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PR_kwDODunzps41FxjE

Use a constant for the articles regex in SQuAD v2

closed

CONTRIBUTOR

The main reason for doing this is to be able to change the articles list if using another language, for example. It's not the most elegant solution but at least it makes the metric more extensible with no drawbacks. BTW, what could be the best way to make this more generic (i.e., SQuAD in other languages)? Maybe receive a regex as an optional param, with the current value as the default? Similarly for SQuAD v1 (can't they re-use code?).

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I_kwDODunzps5GZX_z

Add FAISS .range_search() method for retrieving all texts from dataset above similarity threshold

closed

NONE

**Is your feature request related to a problem? Please describe.** I would like to retrieve all texts from a dataset, which are semantically similar to a specific input text (query), above a certain (cosine) similarity threshold. My dataset is very large (Wikipedia), so I need to use Datasets and FAISS for this. I would like to be able to repeat many different queries on the dataset quickly. **Describe the solution you'd like** dataset objects currently have the .get_nearest_examples() method for text retrieval via FAISS. But this only allows retrieving a specific number of K texts instead of everything above a specified similarity threshold. It would be great if HF Datasets would also support the FAISS method .range_search() for retrieving texts above a certain similarity threshold. see details here: https://github.com/facebookresearch/faiss/issues/1273 **Describe alternatives you've considered** I've considered using native FAISS, but doing this via HF datasets would be better. My assumption is that Dataset features like dataset streaming make it easier to work with large datasets **Additional context** The concrete use-case is: I have a large dataset (wikipedia) and I would like to retrieve all paragraphs which are similar to a query. I will use sentence-transformers for encoding the texts.

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PR_kwDODunzps41B429

Fix docs on audio feature installation

closed

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This PR: - Removes the explicit installation of `librosa` (this is installed with `pip install datasets[audio]` - Adds the warning for Linux users to install manually the non-Python package `libsndfile` - Explains that the installation of `torchaudio` is only necessary to support loading audio datasets containing MP3 audio files Related to #4000.

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I_kwDODunzps5GZH9w

ElasticSearch Indexing example: TypeError: __init__() missing 1 required positional argument: 'scheme'

closed

NONE

## Describe the bug I am following the example in the documentation for elastic search step by step (on google colab): https://huggingface.co./docs/datasets/faiss_es#elasticsearch ``` from datasets import load_dataset squad = load_dataset('crime_and_punish', split='train[:1000]') ``` When I run the line: `squad.add_elasticsearch_index("context", host="localhost", port="9200")` I get the error: `TypeError: __init__() missing 1 required positional argument: 'scheme'` ## Expected results No error message ## Actual results ``` TypeError Traceback (most recent call last) [<ipython-input-23-9205593edef3>](https://localhost:8080/#) in <module>() 1 import elasticsearch ----> 2 squad.add_elasticsearch_index("text", host="localhost", port="9200") 6 frames [/usr/local/lib/python3.7/dist-packages/elasticsearch/_sync/client/utils.py](https://localhost:8080/#) in host_mapping_to_node_config(host) 209 options["path_prefix"] = options.pop("url_prefix") 210 --> 211 return NodeConfig(**options) # type: ignore 212 213 TypeError: __init__() missing 1 required positional argument: 'scheme' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.0 - Platform: Linux, Google Colab - Python version: Google Colab (probably 3.7) - PyArrow version: ?

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PR_kwDODunzps41Btcm

Support streaming xtreme dataset for bucc18 config

closed

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Support streaming xtreme dataset for bucc18 config.

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I_kwDODunzps5GZBEh

Missing argument in precision/recall

closed

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** [`sklearn.metrics.precision_score`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) accepts an argument `zero_division`, but it is not available in [precision Metric](https://github.com/huggingface/datasets/blob/master/metrics/precision/precision.py#L117) Same issue is present for Recall. **Describe the solution you'd like** Support for **kwargs or adding a new field for `zero_division`. **Describe alternatives you've considered** I could filter the warnings myself, but that is not ideal. **Additional context** I can make the requested changes if this is approved.

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PR_kwDODunzps41Bp3V

Doc: image_process small tip

closed

NONE

I've added a small tip in the `image_process` doc

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PR_kwDODunzps41BSZT

Replace yahoo_answers_topics data url

closed

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I replaced the Google Drive URL of the dataset by the FastAI one, since we've had some issues with Google Drive.

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PR_kwDODunzps41BNeA

Replace dbpedia_14 data url

closed

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I replaced the Google Drive URL of the dataset by the FastAI one, since we've had some issues with Google Drive.

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PR_kwDODunzps41BLAf

Fix `map` remove_columns on empty dataset

closed

MEMBER

On an empty dataset, the `remove_columns` parameter of `map` currently doesn't actually remove the columns: ```python >>> ds = datasets.load_dataset("glue", "rte") >>> ds_filtered = ds.filter(lambda x: x["label"] != -1) >>> ds_mapped = ds_filtered.map(lambda x: x, remove_columns=["label"]) >>> print(repr(ds_mapped.column_names)) { 'train': ['sentence1', 'sentence2', 'idx'], 'validation': ['sentence1', 'sentence2', 'idx'], 'test': ['sentence1', 'sentence2', 'label', 'idx'] } ``` I fixed this error and updated the tests

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PR_kwDODunzps41Am4R

Replace amazon_polarity data URL

closed

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I replaced the Google Drive URL of the dataset by the FastAI one, since we've had some issues with Google Drive.

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PR_kwDODunzps41AlFk

Make yelp_polarity streamable

closed

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It was using `dl_manager.download_and_extract` on a TAR archive, which is not supported in streaming mode. I replaced this by `dl_manager.iter_archive`

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PR_kwDODunzps41Aj7g

Replace yelp_review_full data url

closed

MEMBER

I replaced the Google Drive URL of the Yelp review dataset by the FastAI one, since we've had some issues with Google Drive. Close https://github.com/huggingface/datasets/issues/4005

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PR_kwDODunzps41Ad_L

Support streaming scan dataset

closed

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PR_kwDODunzps41AWBk

Support streaming blimp dataset

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I_kwDODunzps5GXSqI

Can not correctly parse the classes with imagefolder

closed

NONE

## Describe the bug I try to load my own image dataset with imagefolder, but the parsing of classes is incorrect. ## Steps to reproduce the bug I organized my dataset (ImageNet) in the following structure: ``` - imagenet/ - train/ - n01440764/ - ILSVRC2012_val_00000293.jpg - ...... - n01695060/ - ...... - val/ - n01440764/ - n01695060/ - ...... ``` At first, I followed the instructions from the Huggingface [example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification#using-your-own-data) to load my data as: ``` from datasets import load_dataset data_files = {'train': 'imagenet/train', 'val': 'imagenet/val'} ds = load_dataset("nateraw/image-folder", data_files=data_files, task="image-classification") ``` but it resulted following error (I mask my personal path as <PERSONAL_PATH>): ``` FileNotFoundError: Unable to find 'https://huggingface.co./datasets/nateraw/image-folder/resolve/main/imagenet/train' at <PERSONAL_PATH>/ImageNet/https:/huggingface.co/datasets/nateraw/image-folder/resolve/main ``` Next, I followed a recent issue #3960 to load data as: ``` from datasets import load_dataset data_files = {'train': ['imagenet/train/**'], 'val': ['imagenet/val/**']} ds = load_dataset("imagefolder", data_files=data_files, task="image-classification") ``` and the data can be loaded without error as: (I copy val folder to train folder for illustration) ``` >>> ds DatasetDict({ train: Dataset({ features: ['image', 'labels'], num_rows: 50000 }) val: Dataset({ features: ['image', 'labels'], num_rows: 50000 }) }) ``` However, the parsed classes is wrong (should be 1000 classes): ``` >>> ds["train"].features {'image': Image(decode=True, id=None), 'labels': ClassLabel(num_classes=1, names=['val'], id=None)} ``` ## Expected results I expect that the "labels" in ds["train"].features should contain 1000 classes. ## Actual results The "labels" in ds["train"].features contains only 1 wrong class. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Ubuntu 18.04 - Python version: Python 3.7.12 - PyArrow version: 7.0.0

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PR_kwDODunzps41AGBu

Support streaming id_clickbait dataset

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