OpenMed-NER-DNADetect-ModernMed-395M

What it does

This model detects DNA, RNA, cell lines, cell types, and proteins in English clinical notes and research papers. It reports an F1 score of 0.776 on the JNLPBA benchmark and supports sequences up to 8192 tokens.

Best for

• Gene and protein mentions in literature
• Cell type extraction from research papers
• Biomedical entity annotation in clinical notes

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-ModernMed-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-ModernMed-395M-v1-onnx-android")
for entity in model("The patient started imatinib after the consultation."):
    print(entity.to_dict())