Dataset:



Dataset Card for "xnli"

Dataset Summary

XNLI is a subset of a few thousand examples from MNLI which has been translated into a 14 different languages (some low-ish resource). As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).

Supported Tasks

More Information Needed

Languages

More Information Needed

Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances

all_languages

  • Size of downloaded dataset files: 461.54 MB
  • Size of the generated dataset: 1535.82 MB
  • Total amount of disk used: 1997.37 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "hypothesis": "{\"language\": [\"ar\", \"bg\", \"de\", \"el\", \"en\", \"es\", \"fr\", \"hi\", \"ru\", \"sw\", \"th\", \"tr\", \"ur\", \"vi\", \"zh\"], \"translation\": [\"احد اع...",
    "label": 0,
    "premise": "{\"ar\": \"واحدة من رقابنا ستقوم بتنفيذ تعليماتك كلها بكل دقة\", \"bg\": \"един от нашите номера ще ви даде инструкции .\", \"de\": \"Eine ..."
}

ar

  • Size of downloaded dataset files: 461.54 MB
  • Size of the generated dataset: 104.26 MB
  • Total amount of disk used: 565.81 MB

An example of 'validation' looks as follows.

{
    "hypothesis": "اتصل بأمه حالما أوصلته حافلة المدرسية.",
    "label": 1,
    "premise": "وقال، ماما، لقد عدت للمنزل."
}

bg

  • Size of downloaded dataset files: 461.54 MB
  • Size of the generated dataset: 122.38 MB
  • Total amount of disk used: 583.92 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "hypothesis": "\"губиш нещата на следното ниво , ако хората си припомнят .\"...",
    "label": 0,
    "premise": "\"по време на сезона и предполагам , че на твоето ниво ще ги загубиш на следващото ниво , ако те решат да си припомнят отбора на ..."
}

de

  • Size of downloaded dataset files: 461.54 MB
  • Size of the generated dataset: 82.18 MB
  • Total amount of disk used: 543.73 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "hypothesis": "Man verliert die Dinge auf die folgende Ebene , wenn sich die Leute erinnern .",
    "label": 0,
    "premise": "\"Du weißt , während der Saison und ich schätze , auf deiner Ebene verlierst du sie auf die nächste Ebene , wenn sie sich entschl..."
}

el

  • Size of downloaded dataset files: 461.54 MB
  • Size of the generated dataset: 135.71 MB
  • Total amount of disk used: 597.25 MB

An example of 'validation' looks as follows.

This example was too long and was cropped:

{
    "hypothesis": "\"Τηλεφώνησε στη μαμά του μόλις το σχολικό λεωφορείο τον άφησε.\"...",
    "label": 1,
    "premise": "Και είπε, Μαμά, έφτασα στο σπίτι."
}

Data Fields

The data fields are the same among all splits.

all_languages

  • premise: a multilingual string variable, with possible languages including ar, bg, de, el, en.
  • hypothesis: a multilingual string variable, with possible languages including ar, bg, de, el, en.
  • label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).

ar

  • premise: a string feature.
  • hypothesis: a string feature.
  • label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).

bg

  • premise: a string feature.
  • hypothesis: a string feature.
  • label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).

de

  • premise: a string feature.
  • hypothesis: a string feature.
  • label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).

el

  • premise: a string feature.
  • hypothesis: a string feature.
  • label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).

Data Splits Sample Size

name train validation test
all_languages 392702 2490 5010
ar 392702 2490 5010
bg 392702 2490 5010
de 392702 2490 5010
el 392702 2490 5010

Dataset Creation

Curation Rationale

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Source Data

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Annotations

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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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},
}

Contributions

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

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