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Dataset Card for "anli"

Dataset Summary

The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset, The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure. ANLI is much more difficult than its predecessors including SNLI and MNLI. It contains three rounds. Each round has train/dev/test splits.

Supported Tasks and Leaderboards

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Dataset Structure

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

Data Instances


  • Size of downloaded dataset files: 17.76 MB
  • Size of the generated dataset: 73.55 MB
  • Total amount of disk used: 91.31 MB

An example of 'train_r2' looks as follows.

This example was too long and was cropped:

    "hypothesis": "Idris Sultan was born in the first month of the year preceding 1994.",
    "label": 0,
    "premise": "\"Idris Sultan (born January 1993) is a Tanzanian Actor and comedian, actor and radio host who won the Big Brother Africa-Hotshot...",
    "reason": "",
    "uid": "ed5c37ab-77c5-4dbc-ba75-8fd617b19712"

Data Fields

The data fields are the same among all splits.


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

Data Splits

name train_r1 dev_r1 train_r2 dev_r2 train_r3 dev_r3 test_r1 test_r2 test_r3
plain_text 16946 1000 45460 1000 100459 1200 1000 1000 1200

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotation process

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Who are the annotators?

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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

    title={Adversarial NLI: A New Benchmark for Natural Language Understanding},
    author={Nie, Yixin
                and Williams, Adina
                and Dinan, Emily
                and Bansal, Mohit
                and Weston, Jason
                and Kiela, Douwe},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    year = "2020",
    publisher = "Association for Computational Linguistics",


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

Models trained or fine-tuned on anli