OpenMed-NER-OncologyDetect-ModernClinical-395M

What it does

This model performs named-entity recognition for cancer genetics, identifying entity types such as Cancer, Gene or gene product, and Tissue in English clinical and research documents. It reports an F1 score of 0.847 on the BioNLP 2013 Cancer Genetics test set and is based on ModernBERT with 396M parameters.

Best for

• Entity extraction from oncology research papers
• Mining genetic entities from clinical notes
• Identifying cancer-related terms in literature

What it detects (16 labels)

• Amino acid
• Anatomical system
• Cancer
• Cell
• Cellular component
• Developing anatomical structure
• Gene or gene product
• Immaterial anatomical entity
• Multi-tissue structure
• Organ
• Organism
• Organism subdivision
• Organism substance
• Pathological formation
• Simple chemical
• Tissue

Quality

Cancer Genetics - Cancer-related genetic entities

Use it

pip install transformers torch

from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-NER-OncologyDetect-ModernClinical-395M", aggregation_strategy="simple")
print(ner("Patient prescribed 10 mg aspirin for hypertension."))

pip install --upgrade "openmed[onnx-runtime]"

from openmed import OnnxModel
model = OnnxModel.from_pretrained("OpenMed/OpenMed-NER-OncologyDetect-ModernClinical-395M-v1-onnx-android")
for entity in model("The patient started imatinib after the consultation."):
    print(entity.to_dict())