🧬 OpenMed ZeroShot NER Genomic Small 166M Specialized model for Gene Entity Recognition Gene related entities 📋 Model Overview Targets gene and genetics entities , handling symbol/name variants commonly found in genomics literature.Useful for genetic association studies , variant curation , and genomics informatics . 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 adaptation to n…
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