{"url":"/dataset/biored","name":"BioRED","full_name":null,"description_markdown":"BioRED is a first-of-its-kind biomedical relation extraction dataset with multiple entity types (e.g. gene/protein, disease, chemical) and relation pairs (e.g. gene–disease; chemical–chemical) at the document level, on a set of600 PubMed abstracts. Furthermore, BioRED label each relation as describing either a novel finding or previously known background knowledge, enabling automated algorithms to differentiate between novel and background information.","description_withheld":null,"homepage":"https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbac282/6645993","introduced_date":"2022-04-08","introduced_date_note":null,"introduced_by":{"paper":"/paper/biored-a-comprehensive-biomedical-relation","title":"BioRED: A Rich Biomedical Relation Extraction Dataset","first_author":"Ling Luo","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Text Classification","url":"/task/text-classification","datasets_with_task":"/datasets/task/text-classification"},{"name":"Named Entity Recognition (NER)","url":"/task/named-entity-recognition-ner","datasets_with_task":"/datasets/task/named-entity-recognition-ner"},{"name":"Relation Extraction","url":"/task/relation-extraction","datasets_with_task":"/datasets/task/relation-extraction"},{"name":"Entity Linking","url":"/task/entity-linking","datasets_with_task":"/datasets/task/entity-linking"},{"name":"Binary Relation Extraction","url":"/task/binary-relation-extraction","datasets_with_task":"/datasets/task/binary-relation-extraction"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["BioRED"],"data_loaders":[],"num_papers_in_archive":25,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/named-entity-recognition-on-biored","task":"Named Entity Recognition (NER)","dataset_variant":"BioRED","rows":3,"metrics":["F1"],"first_row_in_archive_order":{"model":"PubMedBERT-CRF","paper":"/paper/biored-a-comprehensive-biomedical-relation","metrics":{"F1":"89.3"},"code_links":[{"title":"ncbi/BioRED","url":"https://github.com/ncbi/BioRED"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/binary-relation-extraction-on-biored","task":"Binary Relation Extraction","dataset_variant":"BioRED","rows":2,"metrics":["F1"],"first_row_in_archive_order":{"model":"PubMedBERT","paper":"/paper/biored-a-comprehensive-biomedical-relation","metrics":{"F1":"72.9"},"code_links":[{"title":"ncbi/BioRED","url":"https://github.com/ncbi/BioRED"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/relation-extraction-on-biored","task":"Relation Extraction","dataset_variant":"BioRED","rows":2,"metrics":["F1"],"first_row_in_archive_order":{"model":"PubMedBERT","paper":"/paper/biored-a-comprehensive-biomedical-relation","metrics":{"F1":"58.9"},"code_links":[{"title":"ncbi/BioRED","url":"https://github.com/ncbi/BioRED"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/biored-a-comprehensive-biomedical-relation","title":"BioRED: A Rich Biomedical Relation Extraction Dataset","date":"2022-04-08","rows_on_this_dataset":5,"code_links":1,"syntology":null},{"paper":"/paper/bert-gt-cross-sentence-n-ary-relation","title":"BERT-GT: Cross-sentence n-ary relation extraction with BERT and Graph Transformer","date":"2021-01-11","rows_on_this_dataset":2,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}