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Dataset Card for "commonsense_qa"

Dataset Summary

CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers. The dataset is provided in two major training/validation/testing set splits: "Random split" which is the main evaluation split, and "Question token split", see paper for details.

Supported Tasks and Leaderboards

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Languages

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Dataset Structure

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

Data Instances

default

  • Size of downloaded dataset files: 4.46 MB
  • Size of the generated dataset: 2.08 MB
  • Total amount of disk used: 6.54 MB

An example of 'train' looks as follows.

{
    "answerKey": "B",
    "choices": {
        "label": ["A", "B", "C", "D", "E"],
        "text": ["mildred's coffee shop", "mexico", "diner", "kitchen", "canteen"]
    },
    "question": "In what Spanish speaking North American country can you get a great cup of coffee?"
}

Data Fields

The data fields are the same among all splits.

default

  • answerKey: a string feature.
  • question: a string feature.
  • choices: a dictionary feature containing:
    • label: a string feature.
    • text: a string feature.

Data Splits

name train validation test
default 9741 1221 1140

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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Annotations

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

@InProceedings{commonsense_QA,
title={COMMONSENSEQA: A Question Answering Challenge Targeting Commonsense Knowledge},
author={Alon, Talmor and Jonathan, Herzig and Nicholas, Lourie and Jonathan ,Berant},
journal={arXiv preprint arXiv:1811.00937v2},
year={2019}

Contributions

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

Models trained or fine-tuned on commonsense_qa