Languages: en

Dataset Card for "cosmos_qa"

Dataset Summary

Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context

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: 23.27 MB
  • Size of the generated dataset: 23.37 MB
  • Total amount of disk used: 46.64 MB

An example of 'validation' looks as follows.

This example was too long and was cropped:

    "answer0": "If he gets married in the church he wo nt have to get a divorce .",
    "answer1": "He wants to get married to a different person .",
    "answer2": "He wants to know if he does nt like this girl can he divorce her ?",
    "answer3": "None of the above choices .",
    "context": "\"Do i need to go for a legal divorce ? I wanted to marry a woman but she is not in the same religion , so i am not concern of th...",
    "id": "3BFF0DJK8XA7YNK4QYIGCOG1A95STE##3180JW2OT5AF02OISBX66RFOCTG5J7##A2LTOS0AZ3B28A##Blog_56156##q1_a1##378G7J1SJNCDAAIN46FM2P7T6KZEW2",
    "label": 1,
    "question": "Why is this person asking about divorce ?"

Data Fields

The data fields are the same among all splits.


  • id: a string feature.
  • context: a string feature.
  • question: a string feature.
  • answer0: a string feature.
  • answer1: a string feature.
  • answer2: a string feature.
  • answer3: a string feature.
  • label: a int32 feature.

Data Splits

name train validation test
default 25262 2985 6963

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={COSMOS QA: Machine Reading Comprehension
    with Contextual Commonsense Reasoning},
    author={Lifu Huang and Ronan Le Bras and Chandra Bhagavatula and Yejin Choi},
    booktitle ={arXiv:1909.00277v2},


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

Models trained or fine-tuned on cosmos_qa