OpenMed/OpenMed-ZeroShot-NER-Species-Large-459M

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

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

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.

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).

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

# Model Description This model is a fine-tuned version of BioBERT on the NCBI disease dataset for named entity recognition (NER) of diseases. It can be used to extract disease mentions from unstructured text in the medical and biological domains.