AI is happening. The future is updating.OpenMed-ZeroShot-NER-Pharma-XLarge-770M
**Specialized model for Chemical Entity Recognition - Chemical entities from the BC5CDR dataset** Focused on **chemical mentions** in the BC5CDR domain, capturing pharmaceutical compounds and therapeutic agents in context with diseases.Enables **pharmacovigilance**, **adverse event mining**, and **chemical–disease relation pipelines** when paired with downstream relation extraction. 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.
**Specialized model for Chemical Entity Recognition - Chemical entities from the BC5CDR dataset** Focused on **chemical mentions** in the BC5CDR domain, capturing pharmaceutical compounds and therapeutic agents in context with diseases.Enables **pharmacovigilance**, **adverse event mining**, and **chemical–disease relation pipelines** when paired with downstream relation extraction. 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.