Identifies protein-related entities in English biomedical text
What it does
This zero-shot named-entity recognition model detects protein-related entities such as protein complexes, families, and variants in English biomedical text. It is based on the GLiNER architecture with 459M parameters and reports an F1 score of 0.92 on the FSU-PRGE benchmark.
Best for
- Protein–protein interaction mining
- Pathway modeling from literature
- Custom entity detection in research
What it detects (5 labels)
Trained on the label below; you can pass any other label names at inference time.
- Protein
- Protein complex
- Protein (enumeration)
- Protein family or group
- Protein variant
Quality
| Benchmark | FSU-PRGE · all models on this benchmark |
|---|---|
| F1 | 0.920 |
| Precision | 0.916 |
| Recall | 0.925 |
| Accuracy | 0.983 |
| Source | Reported by the authors (test_results.json in the repository) |
Biomedical Entity Recognition - Various biomedical entities
Use it
pip install gliner
from gliner import GLiNER
model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Protein-Large-459M")
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-Protein-Large-459M", 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.