README.md

metadata

pretty_name: SQuAD2.0
annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
language:
  - en
license:
  - cc-by-sa-4.0
multilinguality:
  - monolingual
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - question-answering
task_ids:
  - open-domain-qa
  - extractive-qa
paperswithcode_id: squad
train-eval-index:
  - config: squad_v2
    task: question-answering
    task_id: extractive_question_answering
    splits:
      train_split: train
      eval_split: validation
    col_mapping:
      question: question
      context: context
      answers:
        text: text
        answer_start: answer_start
    metrics:
      - type: squad_v2
        name: SQuAD v2

Dataset Card for "squad_v2"

Table of Contents

• Dataset Card for "squad_v2"
  • Table of Contents
  • Dataset Description
    • Dataset Summary
    • Supported Tasks and Leaderboards
    • Languages
  • Dataset Structure
    • Data Instances
      • squad_v2
    • Data Fields
      • squad_v2
    • Data Splits
  • Dataset Creation
    • Curation Rationale
    • Source Data
      • Initial Data Collection and Normalization
      • Who are the source language producers?
    • Annotations
      • Annotation process
      • Who are the annotators?
    • Personal and Sensitive Information
  • Considerations for Using the Data
    • Social Impact of Dataset
    • Discussion of Biases
    • Other Known Limitations
  • Additional Information
    • Dataset Curators
    • Licensing Information
    • Citation Information
    • Contributions

Dataset Description

• Homepage: https://rajpurkar.github.io/SQuAD-explorer/
• Repository: More Information Needed
• Paper: More Information Needed
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 44.34 MB
• Size of the generated dataset: 122.57 MB
• Total amount of disk used: 166.91 MB

Dataset Summary

combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

squad_v2

• Size of downloaded dataset files: 44.34 MB
• Size of the generated dataset: 122.57 MB
• Total amount of disk used: 166.91 MB

An example of 'validation' looks as follows.

This example was too long and was cropped:

{
    "answers": {
        "answer_start": [94, 87, 94, 94],
        "text": ["10th and 11th centuries", "in the 10th and 11th centuries", "10th and 11th centuries", "10th and 11th centuries"]
    },
    "context": "\"The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave thei...",
    "id": "56ddde6b9a695914005b9629",
    "question": "When were the Normans in Normandy?",
    "title": "Normans"
}

Data Fields

The data fields are the same among all splits.

squad_v2

• 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

name	train	validation
squad_v2	130319	11873

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

More Information Needed

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.