OpenMed-ZeroShot-NER-Disease-Small-166M

What it does

This model performs zero-shot named-entity recognition, identifying disease and condition names in English biomedical text, with labels supplied by the user at inference time. It reports an F1 score of 0.769 on the BC5CDR disease benchmark, using 166M parameters and based on the GLiNER architecture.

Best for

• Phenotyping from clinical notes
• Disease indexing in literature
• Custom entity detection without retraining

What it detects (1 labels)

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

• Disease

Quality

Disease Entity Recognition - Disease entities from the BC5CDR dataset

Use it

pip install gliner

from gliner import GLiNER
model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Disease-Small-166M")
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-Disease-Small-166M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)