Languages: English
Multilinguality: monolingual
Size Categories: 10K<n<100K
Language Creators: found
Annotations Creators: crowdsourced
Source Datasets: original
wiki_qa /
albertvillanova's picture
Replace YAML keys from int to str
  - crowdsourced
  - found
  - en
  - other
  - monolingual
  - 10K<n<100K
  - original
  - question-answering
  - open-domain-qa
paperswithcode_id: wikiqa
pretty_name: WikiQA
    - name: question_id
      dtype: string
    - name: question
      dtype: string
    - name: document_title
      dtype: string
    - name: answer
      dtype: string
    - name: label
            '0': '0'
            '1': '1'
    - name: test
      num_bytes: 1337903
      num_examples: 6165
    - name: train
      num_bytes: 4469148
      num_examples: 20360
    - name: validation
      num_bytes: 591833
      num_examples: 2733
  download_size: 7094233
  dataset_size: 6398884

Dataset Card for "wiki_qa"

Table of Contents

Dataset Description

Dataset Summary

Wiki Question Answering corpus from Microsoft.

The WikiQA corpus is a publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering.

Supported Tasks and Leaderboards

More Information Needed


More Information Needed

Dataset Structure

Data Instances


  • Size of downloaded dataset files: 6.77 MB
  • Size of the generated dataset: 6.10 MB
  • Total amount of disk used: 12.87 MB

An example of 'train' looks as follows.

    "answer": "Glacier caves are often called ice caves , but this term is properly used to describe bedrock caves that contain year-round ice.",
    "document_title": "Glacier cave",
    "label": 0,
    "question": "how are glacier caves formed?",
    "question_id": "Q1"

Data Fields

The data fields are the same among all splits.


  • question_id: a string feature.
  • question: a string feature.
  • document_title: a string feature.
  • answer: a string feature.
  • label: a classification label, with possible values including 0 (0), 1 (1).

Data Splits

name train validation test
default 20360 2733 6165

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


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


These license terms are an agreement between Microsoft Corporation (or based on where you live, one of its affiliates) and you. Please read them. They apply to the data associated with this license above, which includes the media on which you received it, if any. The terms also apply to any Microsoft:

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  11. DISCLAIMER OF WARRANTY. The Dataset is licensed “as-is.” You bear the risk of using it. Microsoft gives no express warranties, guarantees or conditions. You may have additional consumer rights or statutory guarantees under your local laws which this agreement cannot change. To the extent permitted under your local laws, Microsoft excludes the implied warranties of merchantability, fitness for a particular purpose and non- infringement.


This limitation applies to

  • anything related to the software, services, content (including code) on third party Internet sites, or third party programs; and Page 2 of 3
  • claims for breach of contract, breach of warranty, guarantee or condition, strict liability, negligence, or other tort to the extent permitted by applicable law.

It also applies even if Microsoft knew or should have known about the possibility of the damages. The above limitation or exclusion may not apply to you because your country may not allow the exclusion or limitation of incidental, consequential or other damages.

Citation Information

    title = "{W}iki{QA}: A Challenge Dataset for Open-Domain Question Answering",
    author = "Yang, Yi  and
      Yih, Wen-tau  and
      Meek, Christopher",
    booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "",
    doi = "10.18653/v1/D15-1237",
    pages = "2013--2018",


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