OpenMed-NER-DNADetect-ElectraMed-560M

What it does

This model detects five entity types—DNA, RNA, cell line, cell type, and protein—in English biomedical literature and clinical documents. It reports an F1 score of 0.794 on the JNLPBA benchmark, with a precision of 0.75 and recall of 0.845.

Best for

• Entity extraction from research papers
• Mining clinical notes for entities
• Building biomedical knowledge graphs

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-ElectraMed-560M", 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-ElectraMed-560M-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-ElectraMed-560M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)