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Update files from the datasets library (from 1.6.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.6.0

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  1. README.md +54 -22
  2. fquad.py +0 -1
README.md CHANGED
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  # Dataset Card for "fquad"
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  ## Dataset Description
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  - **Homepage:** [https://fquad.illuin.tech/](https://fquad.illuin.tech/)
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- - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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- - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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- - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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  - **Size of downloaded dataset files:** 3.14 MB
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  - **Size of the generated dataset:** 6.62 MB
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  - **Total amount of disk used:** 9.76 MB
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  ### Dataset Summary
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  FQuAD: French Question Answering Dataset
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- We introduce FQuAD, a native French Question Answering Dataset. FQuAD contains 25,000+ question and answer pairs.
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- Finetuning CamemBERT on FQuAD yields a F1 score of 88% and an exact match of 77.9%.
 
 
 
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  ### Supported Tasks
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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  ### Languages
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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  ## Dataset Structure
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  ### Data Splits Sample Size
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- | name |train|validation|
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- |-------|----:|---------:|
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- |default| 4921| 768|
 
 
 
 
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  ## Dataset Creation
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  ### Curation Rationale
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-
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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  ### Source Data
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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-
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  ### Annotations
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 
 
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  ### Personal and Sensitive Information
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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  ## Considerations for Using the Data
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  ### Social Impact of Dataset
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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  ### Discussion of Biases
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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  ### Other Known Limitations
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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  ## Additional Information
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  ### Dataset Curators
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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  ### Licensing Information
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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  ### Citation Information
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  ### Contributions
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- Thanks to [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
 
 
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  ---
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+ annotations_creators:
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+ - crowdsourced
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+ extended:
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+ - original
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+ language_creators:
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+ - crowdsourced
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+ - found
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+ languages:
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+ - fr
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+ licenses:
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+ - cc-by-nc-sa-3-0
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+ multilinguality:
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+ - monolingual
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+ size_categories:
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+ - 1K<n<10K
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - question-answering
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+ - text-retrieval
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+ task_ids:
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+ - extractive-qa
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+ - closed-domain-qa
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  ---
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  # Dataset Card for "fquad"
 
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  ## Dataset Description
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  - **Homepage:** [https://fquad.illuin.tech/](https://fquad.illuin.tech/)
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+ - **Paper:** [FQuAD: French Question Answering Dataset](https://arxiv.org/abs/2002.06071)
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+ - **Point of Contact:** [https://www.illuin.tech/contact/](https://www.illuin.tech/contact/)
 
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  - **Size of downloaded dataset files:** 3.14 MB
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  - **Size of the generated dataset:** 6.62 MB
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  - **Total amount of disk used:** 9.76 MB
 
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  ### Dataset Summary
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  FQuAD: French Question Answering Dataset
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+ We introduce FQuAD, a native French Question Answering Dataset.
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+
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+ FQuAD contains 25,000+ question and answer pairs.
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+ Finetuning CamemBERT on FQuAD yields a F1 score of 88% and an exact match of 77.9%.
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+ Developped to provide a SQuAD equivalent in the French language. Questions are original and based on high quality Wikipedia articles.
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  ### Supported Tasks
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+ - `closed-domain-qa`, `text-retrieval`: This dataset is intended to be used for `closed-domain-qa`, but can also be used for information retrieval tasks.
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  ### Languages
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+ This dataset is exclusively in French, with context data from Wikipedia and questions from French university students (`fr`).
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  ## Dataset Structure
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  ### Data Splits Sample Size
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+ The FQuAD dataset has 3 splits: _train_, _validation_, and _test_. The _test_ split is however not released publicly at the moment. The splits contain disjoint sets of articles. The following table contains stats about each split.
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+ Dataset Split | Number of Articles in Split | Number of paragraphs in split | Number of questions in split
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+ --------------|------------------------------|--------------------------|-------------------------
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+ Train | 117 | 4921 | 20731
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+ Validation | 768 | 51.0% | 3188
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+ Test | 10 | 532 | 2189
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  ## Dataset Creation
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  ### Curation Rationale
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+ The FQuAD dataset was created by Illuin technology. It was developped to provide a SQuAD equivalent in the French language. Questions are original and based on high quality Wikipedia articles.
 
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  ### Source Data
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+ The text used for the contexts are from the curated list of French High-Quality Wikipedia [articles](https://fr.wikipedia.org/wiki/Cat%C3%A9gorie:Article_de_qualit%C3%A9).
 
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  ### Annotations
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+ Annotations (spans and questions) are written by students of the CentraleSupélec school of engineering.
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+ Wikipedia articles were scraped and Illuin used an internally-developped tool to help annotators ask questions and indicate the answer spans.
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+ Annotators were given paragraph sized contexts and asked to generate 4/5 non-trivial questions about information in the context.
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  ### Personal and Sensitive Information
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+ No personal or sensitive information is included in this dataset. This has been manually verified by the dataset curators.
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  ## Considerations for Using the Data
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+ Users should consider this dataset is sampled from Wikipedia data which might not be representative of all QA use cases.
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+
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  ### Social Impact of Dataset
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+ The social biases of this dataset have not yet been investigated.
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  ### Discussion of Biases
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+ The social biases of this dataset have not yet been investigated, though articles have been selected by their quality and objectivity.
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  ### Other Known Limitations
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+ The limitations of the FQuAD dataset have not yet been investigated.
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  ## Additional Information
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  ### Dataset Curators
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+ Illuin Technology: [https://fquad.illuin.tech/](https://fquad.illuin.tech/)
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  ### Licensing Information
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+ The FQuAD dataset is licensed under the [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/fr/) license.
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  ### Citation Information
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  ### Contributions
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+ Thanks to [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
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+ Thanks to [@ManuelFay](https://github.com/manuelfay) for providing information on the dataset creation process.
fquad.py CHANGED
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  """TODO(fquad): Add a description here."""
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- from __future__ import absolute_import, division, print_function
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  import json
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  import os
 
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  """TODO(fquad): Add a description here."""
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  import json
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  import os