OpenMed/OpenMed-ZeroShot-NER-Pharma-Multi-209M

Specialized model for Species Entity Recognition - Species and organism names

This model is a high-performance Named Entity Recognition (NER) model designed specifically for medical text. It identifies entities such as diseases, symptoms, procedures, medications, and healthcare providers with high precision and recall, making it ideal for clinical and healthcare applications.

项目地址：https://github.com/iioSnail/chinesemedicalner

Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature

Specialized model for Chemical Entity Recognition - Chemical entities from the BC5CDR dataset

SERAPH is a deep learning model designed for 3-state (Q3) protein secondary structure prediction. It processes raw single amino acid sequences and predicts residue-level secondary structure states: Alpha Helix (H), Beta Sheet (E), or Coil/Loop (C).