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Dataset: coqa 🏷
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from datasets import load_dataset dataset = load_dataset("coqa")

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Models trained or fine-tuned on coqa



Dataset Card for "coqa"

Table of Contents

Dataset Description

Dataset Summary

CoQA: A Conversational Question Answering Challenge

Supported Tasks

More Information Needed

Languages

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Dataset Structure

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

Data Instances

default

  • Size of downloaded dataset files: 55.40 MB
  • Size of the generated dataset: 18.35 MB
  • Total amount of disk used: 73.75 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "answers": "{\"answer_end\": [179, 494, 511, 545, 879, 1127, 1128, 94, 150, 412, 1009, 1046, 643, -1, 764, 724, 125, 1384, 881, 910], \"answer_...",
    "questions": "[\"When was the Vat formally opened?\", \"what is the library for?\", \"for what subjects?\", \"and?\", \"what was started in 2014?\", \"ho...",
    "source": "wikipedia",
    "story": "\"The Vatican Apostolic Library (), more commonly called the Vatican Library or simply the Vat, is the library of the Holy See, l..."
}

Data Fields

The data fields are the same among all splits.

default

  • source: a string feature.
  • story: a string feature.
  • questions: a list of string features.
  • answers: a dictionary feature containing:
    • input_text: a string feature.
    • answer_start: a int32 feature.
    • answer_end: a int32 feature.

Data Splits Sample Size

name train validation
default 7199 500

Dataset Creation

Curation Rationale

More Information Needed

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

@InProceedings{SivaAndAl:Coca,
       author = {Siva, Reddy and Danqi, Chen and  Christopher D., Manning},
        title = {WikiQA: A Challenge Dataset for Open-Domain Question Answering},
      journal = { arXiv},
         year = {2018},

}