Finds cell line names in English clinical text
What it does
This zero-shot named-entity recognition model detects cell line names in English clinical text, with the user supplying entity labels at inference time. It reports an F1 of 0.667 and a precision of 0.917 on the CLL corpus of 2,156 documents.
Best for
- Cell line mentions in clinical notes
- Hematology research text mining
- Biomedical literature entity extraction
Note on the model card. Cell line name recognition in clinical text (CLL corpus, 2,156 documents; label CL = cell line). The model card's 'chronic lymphocytic leukemia' wording is a misreading of the corpus name; see the OpenMed paper, Table 1.
What it detects (1 labels)
Trained on the label below; you can pass any other label names at inference time.
- Cell line
Quality
| Benchmark | CLL (cell lines, clinical text) · all models on this benchmark |
|---|---|
| F1 | 0.667 |
| Precision | 0.917 |
| Recall | 0.524 |
| Accuracy | 0.960 |
| Source | Reported by the authors (test_results.json in the repository) |
Cell line name recognition in clinical text (CLL corpus, 2,156 documents; label CL = cell line). The model card's 'chronic lymphocytic leukemia' wording is a misreading of the corpus name; see the OpenMed paper, Table 1.
Use it
pip install gliner
from gliner import GLiNER
model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-BloodCancer-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.