google/xtreme

Dataset Card for "xtreme"

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://github.com/google-research/xtreme
• —Repository: More Information Needed
• —Paper: More Information Needed
• —Point of Contact: More Information Needed
• —Size of downloaded dataset files: 15.88 GB
• —Size of the generated dataset: 1.08 GB
• —Total amount of disk used: 16.96 GB

Dataset Summary

The Cross-lingual Natural Language Inference (XNLI) corpus is a crowd-sourced collection of 5,000 test and 2,500 dev pairs for the MultiNLI corpus. The pairs are annotated with textual entailment and translated into 14 languages: French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi, Swahili and Urdu. This results in 112.5k annotated pairs. Each premise can be associated with the corresponding hypothesis in the 15 languages, summing up to more than 1.5M combinations. The corpus is made to evaluate how to perform inference in any language (including low-resources ones like Swahili or Urdu) when only English NLI data is available at training time. One solution is cross-lingual sentence encoding, for which XNLI is an evaluation benchmark. The Cross-lingual TRansfer Evaluation of Multilingual Encoders (XTREME) benchmark is a benchmark for the evaluation of the cross-lingual generalization ability of pre-trained multilingual models. It covers 40 typologically diverse languages (spanning 12 language families) and includes nine tasks that collectively require reasoning about different levels of syntax and semantics. The languages in XTREME are selected to maximize language diversity, coverage in existing tasks, and availability of training data. Among these are many under-studied languages, such as the Dravidian languages Tamil (spoken in southern India, Sri Lanka, and Singapore), Telugu and Malayalam (spoken mainly in southern India), and the Niger-Congo languages Swahili and Yoruba, spoken in Africa.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

MLQA.ar.ar

• —Size of downloaded dataset files: 75.72 MB
• —Size of the generated dataset: 9.20 MB
• —Total amount of disk used: 84.91 MB

An example of 'validation' looks as follows.

MLQA.ar.de

• —Size of downloaded dataset files: 75.72 MB
• —Size of the generated dataset: 2.55 MB
• —Total amount of disk used: 78.27 MB

An example of 'validation' looks as follows.

MLQA.ar.en

• —Size of downloaded dataset files: 75.72 MB
• —Size of the generated dataset: 9.04 MB
• —Total amount of disk used: 84.76 MB

An example of 'validation' looks as follows.

MLQA.ar.es

• —Size of downloaded dataset files: 75.72 MB
• —Size of the generated dataset: 3.27 MB
• —Total amount of disk used: 78.99 MB

An example of 'validation' looks as follows.

MLQA.ar.hi

• —Size of downloaded dataset files: 75.72 MB
• —Size of the generated dataset: 3.32 MB
• —Total amount of disk used: 79.04 MB

An example of 'validation' looks as follows.

Data Fields

The data fields are the same among all splits.

MLQA.ar.ar

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

MLQA.ar.de

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

MLQA.ar.en

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

MLQA.ar.es

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

MLQA.ar.hi

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

Data Splits

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{conneau2018xnli,
  author = {Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin},
  title = {XNLI: Evaluating Cross-lingual Sentence Representations},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing},
  year = {2018},
  publisher = {Association for Computational Linguistics},
  location = {Brussels, Belgium},
}
@article{hu2020xtreme,
      author    = {Junjie Hu and Sebastian Ruder and Aditya Siddhant and Graham Neubig and Orhan Firat and Melvin Johnson},
      title     = {XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization},
      journal   = {CoRR},
      volume    = {abs/2003.11080},
      year      = {2020},
      archivePrefix = {arXiv},
      eprint    = {2003.11080}
}

Contributions

Thanks to @thomwolf, @jplu, @lewtun, @lvwerra, @lhoestq, @patrickvonplaten, @mariamabarham for adding this dataset.