Dataset Card for "squad_v1_pt"

Dataset Summary

Portuguese translation of the SQuAD dataset. The translation was performed automatically using the Google Cloud API.

Supported Tasks

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Dataset Structure

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

Data Instances


  • Size of downloaded dataset files: 37.70 MB
  • Size of the generated dataset: 92.24 MB
  • Total amount of disk used: 129.94 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

    "answers": {
        "answer_start": [0],
        "text": ["Saint Bernadette Soubirous"]
    "context": "\"Arquitetonicamente, a escola tem um caráter católico. No topo da cúpula de ouro do edifício principal é uma estátua de ouro da ...",
    "id": "5733be284776f41900661182",
    "question": "A quem a Virgem Maria supostamente apareceu em 1858 em Lourdes, na França?",
    "title": "University_of_Notre_Dame"

Data Fields

The data fields are the same among all splits.


  • 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
default 87599 10570

Dataset Creation

Curation Rationale

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Source Data

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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

       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},


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

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Models trained or fine-tuned on squad_v1_pt