AIが動く。未来が更新される。OpenMed-ZeroShot-NER-Pathology-Large-459M
**Specialized model for Disease Entity Recognition - Disease entities from the NCBI dataset** 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.
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**Specialized model for Disease Entity Recognition - Disease entities from the NCBI dataset** 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.