Dataset:

Task Categories: question-answering
Languages: en
Multilinguality: monolingual
Size Categories: 10K<n<100K
Licenses: cc-by-4.0
Language Creators: crowdsourcedfound
Annotations Creators: crowdsourced
Source Datasets: extended|wikipedia

Dataset Card for "squad"

Dataset Summary

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.

Supported Tasks

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Languages

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Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances

plain_text

  • Size of downloaded dataset files: 33.51 MB
  • Size of the generated dataset: 85.75 MB
  • Total amount of disk used: 119.27 MB

An example of 'train' looks as follows.

{
    "answers": {
        "answer_start": [1],
        "text": ["This is a test text"]
    },
    "context": "This is a test context.",
    "id": "1",
    "question": "Is this a test?",
    "title": "train test"
}

Data Fields

The data fields are the same among all splits.

plain_text

  • id: a string feature.
  • title: a string feature.
  • context: a string feature.
  • question: a string feature.
  • answers: a dictionary feature containing:
    • text: a string feature.
    • answer_start: a int32 feature.

Data Splits Sample Size

name train validation
plain_text 87599 10570

Dataset Creation

Curation Rationale

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Source Data

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Annotations

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

@article{2016arXiv160605250R,
       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
                 Konstantin and {Liang}, Percy},
        title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
      journal = {arXiv e-prints},
         year = 2016,
          eid = {arXiv:1606.05250},
        pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
       eprint = {1606.05250},
}

Contributions

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

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Models trained or fine-tuned on squad