Finds gene and protein mentions in English biomedical text
Zero-shot NER (your own labels)Genes, proteins and cellsEnglishMedium · 209Mapache-2.0
What it does
This model detects gene or protein mentions in English biomedical literature, including synonyms and symbol variants. It is a 209M-parameter GLiNER-based model that reports an F1 score of 0.749 on the BC2GM benchmark.
Best for
- Gene-centric curation from literature
- Variant and association mining
- Biomedical network construction
What it detects (1 labels)
Trained on the label below; you can pass any other label names at inference time.
- Gene or protein
Quality
| Benchmark | BC2GM · all models on this benchmark |
|---|---|
| F1 | 0.749 |
| Precision | 0.699 |
| Recall | 0.808 |
| Accuracy | 0.934 |
| Source | Reported by the authors (test_results.json in the repository) |
Gene/Protein Entity Recognition - Gene and protein mentions
Use it
pip install gliner
from gliner import GLiNER
model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Genome-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-Genome-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.