metadata

annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
language:
  - en
license:
  - mit
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - question-answering
task_ids:
  - open-domain-qa
paperswithcode_id: commonsenseqa
pretty_name: 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: 2207794
      num_examples: 9741
    - name: validation
      num_bytes: 273848
      num_examples: 1221
    - name: test
      num_bytes: 257842
      num_examples: 1140
  download_size: 1558570
  dataset_size: 2739484
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Usage

from datasets import load_dataset

dataset=load_dataset(
    "Sadanto3933/commonsense_qa",
    split="train",
    )

# ...

Dataset Card for "commonsense_qa"

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. 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

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.

Dataset Creation

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",
}