🧬 OpenMed ZeroShot NER Chemical Small 166M Specialized model for Chemical Entity Recognition Identifies chemical compounds and substances in biomedical literature 📋 Model Overview Purpose built for chemical entity recognition in biomedical literature. It robustly identifies small molecules, drugs, reagents, and chemical synonyms across abstracts and full text articles.Ideal for drug discovery workflows , compound indexing , entity linking to ChEBI/DrugBank , and pharmacology literature curation . 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…
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