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


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Table of Contents

Dataset Description

Dataset Summary

SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales

Supported Tasks

More Information Needed


More Information Needed

Dataset Structure

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

Data Instances


  • Size of downloaded dataset files: 2.72 MB
  • Size of the generated dataset: 0.25 MB
  • Total amount of disk used: 2.97 MB

An example of 'validation' looks as follows.

    "cited_doc_ids": [14717500],
    "claim": "1,000 genomes project enables mapping of genetic sequence variation consisting of rare variants with larger penetrance effects than common variants.",
    "evidence_doc_id": "14717500",
    "evidence_label": "SUPPORT",
    "evidence_sentences": [2, 5],
    "id": 3


  • Size of downloaded dataset files: 2.72 MB
  • Size of the generated dataset: 7.63 MB
  • Total amount of disk used: 10.35 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

    "abstract": "[\"Alterations of the architecture of cerebral white matter in the developing human brain can affect cortical development and res...",
    "doc_id": 4983,
    "structured": false,
    "title": "Microstructural development of human newborn cerebral white matter assessed in vivo by diffusion tensor magnetic resonance imaging."

Data Fields

The data fields are the same among all splits.


  • id: a int32 feature.
  • claim: a string feature.
  • evidence_doc_id: a string feature.
  • evidence_label: a string feature.
  • evidence_sentences: a list of int32 features.
  • cited_doc_ids: a list of int32 features.


  • doc_id: a int32 feature.
  • title: a string feature.
  • abstract: a list of string features.
  • structured: a bool feature.

Data Splits Sample Size


train validation test
claims 1261 450 300


corpus 5183

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

  title={ Fact or Fiction: Verifying Scientific Claims},
  author={David,  Wadden and Kyle, Lo and Lucy Lu, Wang and Shanchuan, Lin and Madeleine van, Zuylen and Arman, Cohan and  Hannaneh, Hajishirzi},
  booktitle={2011 AAAI Spring Symposium Series},