Dataset Overview

Dataset Description

• Name: CommonsenseQA
• Language: English (en)
• License: MIT
• Multilinguality: Monolingual
• Size Category: 1K<n<10K
• Source Dataset: Original data
• Task Category: Question Answering
• Task ID: Open‑domain QA
• PapersWithCode ID: commonsenseqa
• Alias: CommonsenseQA

Dataset Structure

Features

• id (string): Unique ID
• question (string): Question
• question_concept (string): ConceptNet concept related to the question
• choices (dictionary):
  • label (string): Option label
  • text (string): Option text
• answerKey (string): Answer

Splits

• train
  • Bytes: 2,207,794
  • Samples: 9,741
• validation
  • Bytes: 273,848
  • Samples: 1,221
• test
  • Bytes: 257,842
  • Samples: 1,140

Configurations

• default
  • Data files:
    • train: data/train-*
    • validation: data/validation-*
    • test: data/test-*

Dataset Creation

License Information

The dataset is released under the MIT license.

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", }