Detects disease names in English biomedical text with zero-shot labels
What it does
This model identifies disease and condition entities in English biomedical text, including clinical records and scientific literature. It is a 209M-parameter GLiNER model fine-tuned on the BC5CDR dataset, reporting an F1 of 0.797 on that benchmark's test set.
Best for
- Disease mentions in clinical notes
- Disease indexing in research papers
- Phenotyping from patient records
What it detects (1 labels)
Trained on the label below; you can pass any other label names at inference time.
- Disease
Quality
| Benchmark | BC5CDR (diseases) · all models on this benchmark |
|---|---|
| F1 | 0.797 |
| Precision | 0.749 |
| Recall | 0.851 |
| Accuracy | 0.965 |
| Source | Reported by the authors (test_results.json in the repository) |
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-Multi-209M")
print(model.predict_entities("Patient with type 2 diabetes and hypertension.", ["disease"]))Apple Silicon (MLX) build
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-Multi-209M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
print(e.label, e.text)All model information shown here is public and belongs to its authors: use and download it freely under each model’s license. The selection, organization, search design and presentation of this catalog are © 2026 Clinical Corpus. Please link to these pages rather than copy them.