OpenMed-NER-GenomeDetect-ElectraMed-560M

What it does

This model detects mentions of genes and proteins in English clinical notes, research papers, and healthcare documents. It reports an F1 score of 0.868 on the BC2GM benchmark and is based on a 559M-parameter XLM-RoBERTa architecture.

Best for

• Gene and protein mention extraction
• Literature mining for drug discovery
• Biomedical knowledge graph construction

What it detects (1 labels)

• Gene or protein

Quality

Gene/Protein Entity Recognition - Gene and protein mentions

Use it

pip install transformers torch

from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-NER-GenomeDetect-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-GenomeDetect-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-GenomeDetect-ElectraMed-560M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)