🧬 OpenMed ZeroShot NER Pathology XLarge 770M Specialized model for Disease Entity Recognition Disease entities from the NCBI dataset 📋 Model Overview High precision disease NER tuned for research literature, capturing disease mentions suitable for normalization to MeSH/OMIM.Useful for clinical NLP , cohort discovery , and knowledge graph construction , and pairs well with concept normalization modules. 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 stand…
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