🧬 OpenMed ZeroShot NER Species Small 166M Specialized model for Species Entity Recognition Species and organism names 📋 Model Overview Specialized in species and organism mentions with robust handling of scientific/common names and abbreviations.Applies to biodiversity mining , metagenomics reporting , and taxonomy aware 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 means you can flexibly define and extract any entity type relevant to your workflow, from standard biomedical categories to custom clinical concepts, suppor…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy