OpenMed-NER-DNADetect-SuperClinical-434M

What it does

This model identifies five entity types—DNA, RNA, cell line, cell type, and protein—in English clinical notes and research literature. It reports an F1 score of 0.819 on the JNLPBA test set, using a DeBERTa-v3-large backbone with 434 million parameters.

Best for

• Entity extraction from medical records
• Literature mining for biomedical research
• Identifying proteins in scientific 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-SuperClinical-434M", 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-SuperClinical-434M-v1-onnx-android")
for entity in model("The patient started imatinib after the consultation."):
    print(entity.to_dict())

pip install "openmed[mlx]"

from openmed import analyze_text
from openmed.core.config import OpenMedConfig
result = analyze_text("Patient started imatinib in March.", model_name="OpenMed/OpenMed-NER-DNADetect-SuperClinical-434M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)