OpenMed-ZeroShot-NER-Genome-Large-459M

What it does

This zero-shot model detects gene or protein mentions, including synonyms and symbol variants, in English biomedical text, with entity labels supplied at inference time. It reports an F1 score of 0.862 on the BC2GM test set, using a GLiNER large backbone with 459M parameters.

Best for

• Gene-centric literature curation
• Variant and association mining
• Biomedical network construction

What it detects (1 labels)

Trained on the label below; you can pass any other label names at inference time.

• Gene or protein

Quality

Gene/Protein Entity Recognition - Gene and protein mentions

Use it

pip install gliner

from gliner import GLiNER
model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Genome-Large-459M")
print(model.predict_entities("Patient with type 2 diabetes and hypertension.", ["disease"]))

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-ZeroShot-NER-Genome-Large-459M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)