Dataset: cosmos_qa


Dataset Card for "cosmos_qa"

Table of Contents

Dataset Description

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

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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: 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.

default

  • 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 Sample Size

name train validation test
default 25262 2985 6963

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{cosmos,
    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},
    year={2019}
}

Models trained or fine-tuned on cosmos_qa

None yet