README.md

metadata

annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
language:
  - en
license:
  - mit
multilinguality:
  - monolingual
pretty_name: CommonsenseQA
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - question-answering
task_ids:
  - open-domain-qa
paperswithcode_id: commonsenseqa
dataset_info:
  features:
    - name: id
      dtype: string
    - name: question
      dtype: string
    - name: question_concept
      dtype: string
    - name: choices
      sequence:
        - name: label
          dtype: string
        - name: text
          dtype: string
    - name: answerKey
      dtype: string
  splits:
    - name: train
      num_bytes: 2209044
      num_examples: 9741
    - name: validation
      num_bytes: 274033
      num_examples: 1221
    - name: test
      num_bytes: 258017
      num_examples: 1140
  download_size: 4680691
  dataset_size: 2741094

Dataset Card for "commonsense_qa"

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://www.tau-nlp.org/commonsenseqa
• Repository: https://github.com/jonathanherzig/commonsenseqa
• Paper: https://arxiv.org/abs/1811.00937
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 4.68 MB
• Size of the generated dataset: 2.18 MB
• Total amount of disk used: 6.86 MB

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

More Information Needed

Languages

The dataset is in English (en).

Dataset Structure

Data Instances

default

• Size of downloaded dataset files: 4.68 MB
• Size of the generated dataset: 2.18 MB
• Total amount of disk used: 6.86 MB

An example of 'train' looks as follows:

{'id': '075e483d21c29a511267ef62bedc0461',
 'question': 'The sanctions against the school were a punishing blow, and they seemed to what the efforts the school had made to change?',
 'question_concept': 'punishing',
 'choices': {'label': ['A', 'B', 'C', 'D', 'E'],
  'text': ['ignore', 'enforce', 'authoritarian', 'yell at', 'avoid']},
 'answerKey': 'A'}

Data Fields

The data fields are the same among all splits.

default

• id (str): Unique ID.
• question: a string feature.
• question_concept (str): ConceptNet concept associated to the question.
• choices: a dictionary feature containing:
  • label: a string feature.
  • text: a string feature.
• answerKey: a string feature.

Data Splits

name	train	validation	test
default	9741	1221	1140

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

The dataset is licensed under the MIT License.

See: https://github.com/jonathanherzig/commonsenseqa/issues/5

Citation Information

@inproceedings{talmor-etal-2019-commonsenseqa,
    title = "{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge",
    author = "Talmor, Alon  and
      Herzig, Jonathan  and
      Lourie, Nicholas  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N19-1421",
    doi = "10.18653/v1/N19-1421",
    pages = "4149--4158",
    archivePrefix = "arXiv",
    eprint        = "1811.00937",
    primaryClass  = "cs",
}

Contributions

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