OpenMed-NER-DNADetect-ModernClinical-395M

What it does

This model detects five entity types—DNA, RNA, cell line, cell type, and protein—in English clinical notes and research papers. It reports an F1 score of 0.781 on the JNLPBA benchmark, with 396M parameters and a maximum sequence length of 8192 tokens.

Best for

• Entity extraction from clinical notes
• Literature mining for biomedical research
• Knowledge graph construction from papers

What it detects (5 labels)

• DNA
• RNA
• Cell line
• Cell type
• Protein

Quality

Biomedical Entity Recognition - Proteins, DNA, RNA, cell lines, and cell types

Use it

pip install transformers torch

from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-NER-DNADetect-ModernClinical-395M", aggregation_strategy="simple")
print(ner("Patient prescribed 10 mg aspirin for hypertension."))

pip install --upgrade "openmed[onnx-runtime]"

from openmed import OnnxModel
model = OnnxModel.from_pretrained("OpenMed/OpenMed-NER-DNADetect-ModernClinical-395M-v1-onnx-android")
for entity in model("The patient started imatinib after the consultation."):
    print(entity.to_dict())