README.md

metadata

annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
language:
  - fr
language_bcp47:
  - fr-FR
license:
  - mit
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - question-answering
task_ids:
  - extractive-qa
  - open-domain-qa
paperswithcode_id: null
pretty_name: Piaf
dataset_info:
  features:
    - name: id
      dtype: string
    - name: title
      dtype: string
    - name: context
      dtype: string
    - name: question
      dtype: string
    - name: answers
      sequence:
        - name: text
          dtype: string
        - name: answer_start
          dtype: int32
  config_name: plain_text
  splits:
    - name: train
      num_bytes: 3332905
      num_examples: 3835
  download_size: 1370384
  dataset_size: 3332905

Dataset Card for Piaf

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://piaf.etalab.studio
• Repository: More Information Needed
• Paper: More Information Needed
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 1.31 MB
• Size of the generated dataset: 3.18 MB
• Total amount of disk used: 4.49 MB

Dataset Summary

Piaf is a reading comprehension dataset. This version, published in February 2020, contains 3835 questions on French Wikipedia.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

plain_text

• Size of downloaded dataset files: 1.31 MB
• Size of the generated dataset: 3.18 MB
• Total amount of disk used: 4.49 MB

An example of 'train' looks as follows.

{
    "answers": {
        "answer_start": [0],
        "text": ["Voici"]
    },
    "context": "Voici le contexte du premier paragraphe du deuxième article.",
    "id": "p140295460356960",
    "question": "Suis-je la troisième question ?",
    "title": "Jakob Böhme"
}

Data Fields

The data fields are the same among all splits.

plain_text

• id: a string feature.
• title: a string feature.
• context: a string feature.
• question: a string feature.
• answers: a dictionary feature containing:
  • text: a string feature.
  • answer_start: a int32 feature.

Data Splits

name	train
plain_text	3835

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

Citation Information

@InProceedings{keraron-EtAl:2020:LREC,
  author    = {Keraron, Rachel  and  Lancrenon, Guillaume  and  Bras, Mathilde  and  Allary, Frédéric  and  Moyse, Gilles  and  Scialom, Thomas  and  Soriano-Morales, Edmundo-Pavel  and  Staiano, Jacopo},
  title     = {Project PIAF: Building a Native French Question-Answering Dataset},
  booktitle      = {Proceedings of The 12th Language Resources and Evaluation Conference},
  month          = {May},
  year           = {2020},
  address        = {Marseille, France},
  publisher      = {European Language Resources Association},
  pages     = {5483--5492},
  abstract  = {Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.},
  url       = {https://www.aclweb.org/anthology/2020.lrec-1.673}
}

Contributions

Thanks to @lewtun, @lhoestq, @thomwolf, @albertvillanova, @RachelKer for adding this dataset.