OpenMed-NER-DNADetect-TinyMed-66M

What it does

This model detects five entity types—DNA, RNA, cell line, cell type, and protein—in English clinical notes and research literature. It is a 66M-parameter DistilBERT model with a reported F1 score of 0.792 on the JNLPBA benchmark.

Best for

• Entity extraction from clinical notes
• Mining biomedical research papers
• Identifying cell and protein mentions

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-TinyMed-66M", 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-TinyMed-66M-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-TinyMed-66M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)