{"url":"/sota/named-entity-recognition-on-linnaeus","task":{"name":"Named Entity Recognition (NER)","url":"/task/named-entity-recognition-ner","note":null},"dataset":{"name":"LINNAEUS","url":"/dataset/linnaeus"},"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":6,"rows_with_code":5,"rows_with_paper_page":5,"rows_dated":5,"rows_using_additional_data":1},"rows":[{"rank_in_archive_order":1,"model":"BLSTM-CNN-Char (SparkNLP)","metrics":{"F1":"86.26"},"uses_additional_data":false,"paper_date":"2020-11-12","paper":"/paper/biomedical-named-entity-recognition-at-scale","paper_url":"https://arxiv.org/abs/2011.06315v1","paper_title":"Biomedical Named Entity Recognition at Scale","code":"https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/1.4.Biomedical_NER_SparkNLP_paper_reproduce.ipynb","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"Spark NLP","metrics":{"F1":"86.26"},"uses_additional_data":false,"paper_date":"2020-11-12","paper":"/paper/biomedical-named-entity-recognition-at-scale","paper_url":"https://arxiv.org/abs/2011.06315v1","paper_title":"Biomedical Named Entity Recognition at Scale","code":"https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/1.4.Biomedical_NER_SparkNLP_paper_reproduce.ipynb","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"BioFLAIR","metrics":{"F1":"87.02"},"uses_additional_data":true,"paper_date":"2019-08-13","paper":"/paper/bioflair-pretrained-pooled-contextualized","paper_url":"https://arxiv.org/abs/1908.05760v1","paper_title":"BioFLAIR: Pretrained Pooled Contextualized Embeddings for Biomedical Sequence Labeling Tasks","code":"https://github.com/shreyashub/BioFLAIR","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"BERN","metrics":{"F1":"88.0"},"uses_additional_data":false,"paper_date":"2019-06-04","paper":"/paper/a-neural-named-entity-recognition-and-multi","paper_url":"https://ieeexplore.ieee.org/document/8730332","paper_title":"A Neural Named Entity Recognition and Multi-Type Normalization Tool for Biomedical Text Mining","code":"https://github.com/dmis-lab/bern","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"aimped","metrics":{"F1":"89.72"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":6,"model":"BERN2","metrics":{"F1":"92.7"},"uses_additional_data":false,"paper_date":"2022-01-06","paper":"/paper/bern2-an-advanced-neural-biomedical-named","paper_url":"https://arxiv.org/abs/2201.02080v3","paper_title":"BERN2: an advanced neural biomedical named entity recognition and normalization tool","code":"https://github.com/dmis-lab/bern2","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":0,"n_samples":2,"n_pointer_only_licence":0}}],"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":1,"rows_with_any_sample_ran":1,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":2,"n_unverified":0,"n_samples":2,"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":2,"n_unverified":0,"n_samples":2,"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"}}}