OpenMed-ZeroShot-NER-Pathology-Small-166M

What it does

This model identifies disease entities in English clinical and research text, allowing users to supply custom entity labels at inference time. It reports an F1 score of 0.709 on the NCBI Disease benchmark, with 166 million parameters and a GLiNER backbone.

Best for

• Disease mentions from research literature
• Custom entity detection in clinical notes
• Biomedical knowledge graph construction

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 NCBI dataset

Use it

pip install gliner

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