qasc / README.md
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metadata
annotations_creators:
  - crowdsourced
language_creators:
  - found
language:
  - en
license:
  - cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - question-answering
  - multiple-choice
task_ids:
  - extractive-qa
  - multiple-choice-qa
paperswithcode_id: qasc
pretty_name: Question Answering via Sentence Composition (QASC)
dataset_info:
  features:
    - name: id
      dtype: string
    - name: question
      dtype: string
    - name: choices
      sequence:
        - name: text
          dtype: string
        - name: label
          dtype: string
    - name: answerKey
      dtype: string
    - name: fact1
      dtype: string
    - name: fact2
      dtype: string
    - name: combinedfact
      dtype: string
    - name: formatted_question
      dtype: string
  splits:
    - name: train
      num_bytes: 4891878
      num_examples: 8134
    - name: test
      num_bytes: 390534
      num_examples: 920
    - name: validation
      num_bytes: 559180
      num_examples: 926
  download_size: 2349698
  dataset_size: 5841592
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: validation
        path: data/validation-*

Dataset Card for "qasc"

Table of Contents

Dataset Description

Dataset Summary

QASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

default

  • Size of downloaded dataset files: 1.61 MB
  • Size of the generated dataset: 5.87 MB
  • Total amount of disk used: 7.49 MB

An example of 'validation' looks as follows.

{
    "answerKey": "F",
    "choices": {
        "label": ["A", "B", "C", "D", "E", "F", "G", "H"],
        "text": ["sand", "occurs over a wide range", "forests", "Global warming", "rapid changes occur", "local weather conditions", "measure of motion", "city life"]
    },
    "combinedfact": "Climate is generally described in terms of local weather conditions",
    "fact1": "Climate is generally described in terms of temperature and moisture.",
    "fact2": "Fire behavior is driven by local weather conditions such as winds, temperature and moisture.",
    "formatted_question": "Climate is generally described in terms of what? (A) sand (B) occurs over a wide range (C) forests (D) Global warming (E) rapid changes occur (F) local weather conditions (G) measure of motion (H) city life",
    "id": "3NGI5ARFTT4HNGVWXAMLNBMFA0U1PG",
    "question": "Climate is generally described in terms of what?"
}

Data Fields

The data fields are the same among all splits.

default

  • id: a string feature.
  • question: a string feature.
  • choices: a dictionary feature containing:
    • text: a string feature.
    • label: a string feature.
  • answerKey: a string feature.
  • fact1: a string feature.
  • fact2: a string feature.
  • combinedfact: a string feature.
  • formatted_question: a string feature.

Data Splits

name train validation test
default 8134 926 920

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 released under CC BY 4.0 license.

Citation Information

@article{allenai:qasc,
      author    = {Tushar Khot and Peter Clark and Michal Guerquin and Peter Jansen and Ashish Sabharwal},
      title     = {QASC: A Dataset for Question Answering via Sentence Composition},
      journal   = {arXiv:1910.11473v2},
      year      = {2020},
}

Contributions

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