README.md

metadata

license: mit
language:
  - en
task_categories:
  - text-classification
size_categories:
  - 1K<n<10K
tags:
  - scifact
  - information-retrieval
  - claim-verification
  - relevance-classification
  - scientific-text
configs:
  - config_name: title
    data_files:
      - split: train
        path: title/train-*
      - split: test
        path: title/test-*
  - config_name: abstract
    data_files:
      - split: train
        path: abstract/train-*
      - split: test
        path: abstract/test-*
  - config_name: title_abstract
    data_files:
      - split: train
        path: title_abstract/train-*
      - split: test
        path: title_abstract/test-*

SciFact Evidence Relevance Pairs

Custom (claim, document, label) pairs derived from BEIR SciFact for binary evidence relevance classification: given a scientific claim and a candidate paper field, decide whether the paper is relevant evidence for the claim.

This dataset accompanies the scifact-relevance-classifier project, built as the Lab 3 / Assignment 1 deliverable for Information Retrieval 5LN712 (Master's in Language Technology, Uppsala University, 2026).

Quick start

from datasets import load_dataset

# Three configs (one per document field variant)
ds = load_dataset("andreiaalexa/scifact-relevance-pairs", "title_abstract")
ds["train"][0]
# {
#   "claim": "...", "title": "...", "abstract": "...",
#   "document_text": "...", "label": "relevant", "label_id": 1, ...
# }

Configurations

Config	Document field used	Train	Test	Total
title	only the paper title	2,537	939	3,476
abstract	only the abstract	2,537	939	3,476
title_abstract	"{title}. {abstract}"	2,537	939	3,476

Class balance is approximately 64% not_relevant / 36% relevant in both splits.

How it was built

BEIR SciFact (Wadden et al., 2020) is originally a retrieval benchmark — each claim has one or more abstracts marked as evidence. This dataset converts that into a classification task by emitting one row per (claim, candidate-document) pair:

• Positives: every (claim, doc) pair that appears in the BEIR qrels for that claim.
• Negatives: a mix of two kinds, sampled per claim with random_state=42:
  • Random negatives (~2 per positive) — sampled uniformly from the corpus, excluding the claim's known positives. Teaches broad non-relevance.
  • TF-IDF hard negatives (~2 per positive) — top-similarity documents to the claim by TF-IDF cosine, excluding known positives. Teaches the model not to rely on lexical overlap alone. Same family of technique as the BM25 hard negatives in DPR (Karpukhin et al., 2020).

The construction script lives at scifact_dataset.py in the GitHub repo and is fully deterministic given the same seed.

Columns

Column	Type	Description
split	string	train or test (matches BEIR's split). Redundant with the HF split but kept for traceability.
field_variant	string	Which field is used for document_text in this config (title, abstract, or title_abstract).
query_id	string	BEIR query id.
doc_id	string	BEIR corpus doc id.
claim	string	The scientific claim text.
title	string	Document title (always populated for traceability).
abstract	string	Document abstract (always populated for traceability).
document_text	string	The exact text the classifier sees, depending on field_variant.
label	string	relevant or not_relevant.
label_id	int	1 for relevant, 0 for not_relevant.

Intended use

• Training and benchmarking small embedding-based classifiers for scientific evidence retrieval / re-ranking.
• Educational use illustrating: (a) converting a retrieval benchmark into a classification dataset, (b) the role of hard-negative mining, (c) ablations across input field choices.

Out-of-scope use

• This dataset is not suitable for clinical decision-making or any high-stakes scientific judgment. Labels are derived from BEIR qrels, which are themselves expert annotations on a small biomedical claim set; they do not generalise to arbitrary scientific domains.
• Predictions from models trained on this dataset should be treated as a first-pass relevance signal, not as ground truth about scientific evidence quality.

License

MIT, matching the source code repository. The underlying SciFact corpus and qrels are released under CC BY-NC 2.0 by AI2 — please cite Wadden et al. (2020) when using this dataset.

Citations

If you use this dataset, please cite both the original SciFact paper and (optionally) this project:

@inproceedings{wadden-etal-2020-fact,
    title = "Fact or Fiction: Verifying Scientific Claims",
    author = "Wadden, David  and  Lin, Shanchuan  and  Lo, Kyle  and
              Wang, Lucy Lu  and  van Zuylen, Madeleine  and
              Cohan, Arman  and  Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    year = "2020",
    url = "https://aclanthology.org/2020.emnlp-main.609/",
}

@inproceedings{thakur-etal-2021-beir,
    title = "{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models",
    author = "Thakur, Nandan and Reimers, Nils and R{\"u}ckl{\'e}, Andreas and Srivastava, Abhishek and Gurevych, Iryna",
    booktitle = "NeurIPS 2021 Datasets and Benchmarks Track",
    year = "2021",
    url = "https://arxiv.org/abs/2104.08663",
}

Acknowledgements

Built with Hugging Face Datasets and scikit-learn. Course taught by Birger Moëll at Uppsala University.