Finds species and organism names in English biomedical text
What it does
This model performs zero-shot named-entity recognition, identifying species and organism names in English biomedical text, such as scientific and common names, with user-supplied entity labels at inference. It reports an F1 score of 0.955 on the Linnaeus benchmark, with 770 million parameters.
Best for
- Biodiversity literature mining
- Metagenomics reporting
- Taxonomy-aware curation
What it detects (1 labels)
Trained on the label below; you can pass any other label names at inference time.
- Species
Quality
| Benchmark | Linnaeus · all models on this benchmark |
|---|---|
| F1 | 0.955 |
| Precision | 0.931 |
| Recall | 0.981 |
| Accuracy | 0.996 |
| Source | Reported by the authors (test_results.json in the repository) |
Species Entity Recognition - Species and organism names
Use it
pip install gliner
from gliner import GLiNER
model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Species-XLarge-770M")
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.