OpenMed-NER-GenomeDetect-TinyMed-82M

What it does

This model detects mentions of genes or proteins in English clinical notes and research literature. It reports an F1 score of 0.845 on the BC2GM test set, using a RoBERTa backbone with 82 million parameters.

Best for

• Gene mentions in 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-82M", 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-82M-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-82M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)