

# Dataset Card for SciFact

## Dataset Description

- **Homepage:** https://scifact.apps.allenai.org/
- **Pubmed:** False
- **Public:** True
- **Tasks:** TXT2CLASS


### Scifact Corpus Source

        SciFact is a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales.
     This config has abstracts and document ids.
    

### Scifact Claims Source

    {_DESCRIPTION_BASE} This config connects the claims to the evidence and doc ids.
    

### Scifact Rationale Bigbio Pairs

    {_DESCRIPTION_BASE} This task is the following: given a claim and a text span composed of one or more sentences from an abstract, predict a label from ("rationale", "not_rationale") indicating if the span is evidence (can be supporting or refuting) for the claim. This roughly corresponds to the second task outlined in Section 5 of the paper."
    

### Scifact Labelprediction Bigbio Pairs

    {_DESCRIPTION_BASE} This task is the following: given a claim and a text span composed of one or more sentences from an abstract, predict a label from ("SUPPORT", "NOINFO", "CONTRADICT") indicating if the span supports, provides no info, or contradicts the claim. This roughly corresponds to the thrid task outlined in Section 5 of the paper.
    




## Citation Information

```
@article{wadden2020fact,
  author    = {David Wadden and Shanchuan Lin and Kyle Lo and Lucy Lu Wang and Madeleine van Zuylen and Arman Cohan and Hannaneh Hajishirzi},
  title     = {Fact or Fiction: Verifying Scientific Claims},
  year      = {2020},
  address   = {Online},
  publisher = {Association for Computational Linguistics},
  url       = {https://aclanthology.org/2020.emnlp-main.609},
  doi       = {10.18653/v1/2020.emnlp-main.609},
  pages     = {7534--7550},
  biburl    = {},
  bibsource = {}
}

```

multilinguality:monolingual language:en license:cc-by-nc-2.0 region:us
{"language": ["en"], "license": "cc-by-nc-2.0", "multilinguality": "monolingual", "pretty_name": "SciFact", "bigbio_language": ["English"], "bigbio_license_shortname": "CC_BY_NC_2p0", "homepage": "https://scifact.apps.allenai.org/", "bigbio_pubmed": false, "bigbio_public": true, "bigbio_tasks": ["TEXT_PAIRS_CLASSIFICATION"]}
TAGS
#multilinguality-monolingual #language-English #license-cc-by-nc-2.0 #region-us 



# Dataset Card for SciFact

## Dataset Description

- Homepage: URL
- Pubmed: False
- Public: True
- Tasks: TXT2CLASS


### Scifact Corpus Source

        SciFact is a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales.
     This config has abstracts and document ids.
    

### Scifact Claims Source

    {_DESCRIPTION_BASE} This config connects the claims to the evidence and doc ids.
    

### Scifact Rationale Bigbio Pairs

    {_DESCRIPTION_BASE} This task is the following: given a claim and a text span composed of one or more sentences from an abstract, predict a label from ("rationale", "not_rationale") indicating if the span is evidence (can be supporting or refuting) for the claim. This roughly corresponds to the second task outlined in Section 5 of the paper."
    

### Scifact Labelprediction Bigbio Pairs

    {_DESCRIPTION_BASE} This task is the following: given a claim and a text span composed of one or more sentences from an abstract, predict a label from ("SUPPORT", "NOINFO", "CONTRADICT") indicating if the span supports, provides no info, or contradicts the claim. This roughly corresponds to the thrid task outlined in Section 5 of the paper.
    








# Dataset Card for SciFact ## Dataset Description

- Homepage: URL
- Pubmed: False
- Public: True
- Tasks: TXT2CLASS ### Scifact Corpus Source

        SciFact is a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales.
     This config has abstracts and document ids. ### Scifact Claims Source

    {_DESCRIPTION_BASE} This config connects the claims to the evidence and doc ids. ### Scifact Rationale Bigbio Pairs

    {_DESCRIPTION_BASE} This task is the following: given a claim and a text span composed of one or more sentences from an abstract, predict a label from ("rationale", "not_rationale") indicating if the span is evidence (can be supporting or refuting) for the claim. This roughly corresponds to the second task outlined in Section 5 of the paper." ### Scifact Labelprediction Bigbio Pairs

    {_DESCRIPTION_BASE} This task is the following: given a claim and a text span composed of one or more sentences from an abstract, predict a label from ("SUPPORT", "NOINFO", "CONTRADICT") indicating if the span supports, provides no info, or contradicts the claim. This roughly corresponds to the thrid task outlined in Section 5 of the paper.
TAGS
#multilinguality-monolingual #language-English #license-cc-by-nc-2.0 #region-us 
 # Dataset Card for SciFact ## Dataset Description

- Homepage: URL
- Pubmed: False
- Public: True
- Tasks: TXT2CLASS ### Scifact Corpus Source

        SciFact is a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales.
     This config has abstracts and document ids. ### Scifact Claims Source

    {_DESCRIPTION_BASE} This config connects the claims to the evidence and doc ids. ### Scifact Rationale Bigbio Pairs

    {_DESCRIPTION_BASE} This task is the following: given a claim and a text span composed of one or more sentences from an abstract, predict a label from ("rationale", "not_rationale") indicating if the span is evidence (can be supporting or refuting) for the claim. This roughly corresponds to the second task outlined in Section 5 of the paper." ### Scifact Labelprediction Bigbio Pairs

    {_DESCRIPTION_BASE} This task is the following: given a claim and a text span composed of one or more sentences from an abstract, predict a label from ("SUPPORT", "NOINFO", "CONTRADICT") indicating if the span supports, provides no info, or contradicts the claim. This roughly corresponds to the thrid task outlined in Section 5 of the paper.