🧬 OpenMed ZeroShot NER Organism Tiny 60M Specialized model for Species Entity Recognition Species names from the Species 800 dataset 📋 Model Overview Optimized for species identification in scientific text, covering a wide range of taxa and naming variants.Useful for ecology studies , organism tagging , and biocuration . 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…
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