🧬 OpenMed ZeroShot NER Genome Base 220M Specialized model for Gene/Protein Entity Recognition Gene and protein mentions 📋 Model Overview Accurate gene/protein mention recognition , including synonyms and symbol variants from biomedical literature.Enables gene centric curation , variant/association mining , and network construction . OpenMed ZeroShot NER is an advanced, domain adapted Named Entity Recognition (NER) model designed specifically for medical, biomedical, and clinical text mining. Leveraging state of the art zero shot learning, this model empowers researchers, clinicians, and data scientists to extract expert level biomedical entities—such as diseases, chemicals, genes, species, and clinical findings—directly from unstructured text, without the need for task specific retraining. Built on the robust GLiNER architecture and fine tuned on curated biomedical corpora, OpenMed ZeroShot NER delivers high precision entity recognition for critical healthcare and life sciences applications. Its zero shot capability means you can flexibly define and extract any entity type relevant to your workflow, from standard biomedical categories to custom clinical concepts, supporting rapid…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy