{"url":"/sota/named-entity-recognition-on-biored","task":{"name":"Named Entity Recognition (NER)","url":"/task/named-entity-recognition-ner","note":null},"dataset":{"name":"BioRED","url":"/dataset/biored"},"category":"Natural Language Processing","categories":["Natural Language Processing"],"category_note":null,"description":"**Named Entity Recognition (NER)** is a task of Natural Language Processing (NLP) that involves identifying and classifying named entities in a text into predefined categories such as person names, organizations, locations, and others. The goal of NER is to extract structured information from unstructured text data and represent it in a machine-readable format. Approaches typically use BIO notation, which differentiates the beginning (B) and the inside (I) of entities. O is used for non-entity tokens.\r\n\r\nExample:\r\n\r\n| Mark | Watney | visited | Mars |\r\n| --- | ---| --- | --- |\r\n| B-PER | I-PER | O | B-LOC |\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Zalando](https://research.zalando.com/welcome/mission/research-projects/flair-nlp/) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["F1"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"F1":"higher"}},"counts":{"rows":3,"rows_with_code":3,"rows_with_paper_page":3,"rows_dated":3,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"PubMedBERT-CRF","metrics":{"F1":"89.3"},"uses_additional_data":false,"paper_date":"2022-04-08","paper":"/paper/biored-a-comprehensive-biomedical-relation","paper_url":"https://arxiv.org/abs/2204.04263v2","paper_title":"BioRED: A Rich Biomedical Relation Extraction Dataset","code":"https://github.com/ncbi/BioRED","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"BioBERT-CRF","metrics":{"F1":"88.7"},"uses_additional_data":false,"paper_date":"2022-04-08","paper":"/paper/biored-a-comprehensive-biomedical-relation","paper_url":"https://arxiv.org/abs/2204.04263v2","paper_title":"BioRED: A Rich Biomedical Relation Extraction Dataset","code":"https://github.com/ncbi/BioRED","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"BiLSTM-CRF","metrics":{"F1":"87.1"},"uses_additional_data":false,"paper_date":"2022-04-08","paper":"/paper/biored-a-comprehensive-biomedical-relation","paper_url":"https://arxiv.org/abs/2204.04263v2","paper_title":"BioRED: A Rich Biomedical Relation Extraction Dataset","code":"https://github.com/ncbi/BioRED","n_code_links":1,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}