OpenMed-NER-GenomeDetect-TinyMed-65M

What it does

This model detects mentions of genes and proteins in English clinical notes and research literature. It is a 65M-parameter DistilBERT model that reports an F1 score of 0.869 on the BC2GM benchmark.

Best for

• Gene mentions from research papers
• Protein names in clinical notes
• Biomedical literature mining

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-TinyMed-65M", 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-TinyMed-65M-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-TinyMed-65M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)