Finds genes, proteins, and cells in English biomedical text
Zero-shot NER (your own labels)Genes, proteins and cellsEnglishMedium · 220Mapache-2.0
What it does
This zero-shot model detects genes, proteins, and cells in English biomedical literature and clinical text, with labels supplied by the user at inference. It reports an F1 score of 0.717 on the GELLUS cell-line benchmark, with a recall of 0.961.
Best for
- Extracting genes from research articles
- Identifying cell lines in clinical notes
- Custom entity detection in genomics
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 | 0.717 |
| Precision | 0.571 |
| Recall | 0.961 |
| Accuracy | 0.942 |
| 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-Base-220M")
print(model.predict_entities("Patient with type 2 diabetes and hypertension.", ["disease"]))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.