OpenMed-ZeroShot-NER-BloodCancer-Medium-209M

What it does

This model performs zero-shot named-entity recognition for cell line names in English clinical text, with labels supplied at inference. It reports an F1 of 0.913 on the CLL corpus (2,156 documents), though this score is reported by the author and not independently reported.

Best for

• Cell line mentions in clinical notes
• Hematology research text mining
• Custom entity extraction in clinical NLP

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

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-Medium-209M")
print(model.predict_entities("Patient with type 2 diabetes and hypertension.", ["disease"]))

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-BloodCancer-Medium-209M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)