The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider removing the loading script and relying on automated data support (you can use convert_to_parquet from the datasets library). If this is not possible, please open a discussion for direct help.

Dataset Card for "mlqa"

Dataset Summary

MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.

Supported Tasks and Leaderboards

More Information Needed

Languages

MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese.

Dataset Structure

Data Instances

mlqa-translate-test.ar

  • Size of downloaded dataset files: 10.08 MB
  • Size of the generated dataset: 5.48 MB
  • Total amount of disk used: 15.56 MB

An example of 'test' looks as follows.


mlqa-translate-test.de

  • Size of downloaded dataset files: 10.08 MB
  • Size of the generated dataset: 3.88 MB
  • Total amount of disk used: 13.96 MB

An example of 'test' looks as follows.


mlqa-translate-test.es

  • Size of downloaded dataset files: 10.08 MB
  • Size of the generated dataset: 3.92 MB
  • Total amount of disk used: 13.99 MB

An example of 'test' looks as follows.


mlqa-translate-test.hi

  • Size of downloaded dataset files: 10.08 MB
  • Size of the generated dataset: 4.61 MB
  • Total amount of disk used: 14.68 MB

An example of 'test' looks as follows.


mlqa-translate-test.vi

  • Size of downloaded dataset files: 10.08 MB
  • Size of the generated dataset: 6.00 MB
  • Total amount of disk used: 16.07 MB

An example of 'test' looks as follows.


Data Fields

The data fields are the same among all splits.

mlqa-translate-test.ar

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

mlqa-translate-test.de

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

mlqa-translate-test.es

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

mlqa-translate-test.hi

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

mlqa-translate-test.vi

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

Data Splits

name test
mlqa-translate-test.ar 5335
mlqa-translate-test.de 4517
mlqa-translate-test.es 5253
mlqa-translate-test.hi 4918
mlqa-translate-test.vi 5495

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

@article{lewis2019mlqa,
  title = {MLQA: Evaluating Cross-lingual Extractive Question Answering},
  author = {Lewis, Patrick and Oguz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger},
  journal = {arXiv preprint arXiv:1910.07475},
  year = 2019,
  eid = {arXiv: 1910.07475}
}

Contributions

Thanks to @patrickvonplaten, @M-Salti, @lewtun, @thomwolf, @mariamabarham, @lhoestq for adding this dataset.

Downloads last month
9,897

Models trained or fine-tuned on facebook/mlqa