Finds gene, protein, and cell entities in English biomedical text
What it does
This model performs zero-shot named-entity recognition, identifying genes, proteins, and cells in English biomedical literature and clinical text, with users supplying entity labels at inference time. It reports an F1 score of 0.733 on the Gelius cell-line benchmark, with 209M parameters and a GLiNER backbone.
Best for
- Genetic association studies
- Variant curation
- Genomics informatics
What it detects (1 labels)
Trained on the label below; you can pass any other label names at inference time.
- Cell line
Quality
| Benchmark | Gellus (cell lines) · all models on this benchmark |
|---|---|
| F1 | 0.733 |
| Precision | 0.609 |
| Recall | 0.922 |
| Accuracy | 0.947 |
| Source | Reported by the authors (test_results.json in the repository) |
Gene Entity Recognition - Gene-related entities
Use it
pip install gliner
from gliner import GLiNER
model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Genomic-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-Genomic-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.