OpenMed-NER-OncologyDetect-SuperClinical-434M

What it does

This named-entity recognition model detects 16 cancer-related entity types such as Cancer, Gene or gene product, and Tissue in English clinical and research documents. It is based on DeBERTa-v3-large with 434M parameters and reports an F1 score of 0.868 on the BioNLP 2013 Cancer Genetics test set.

Best for

• Cancer entity extraction from clinical notes
• Literature mining for genetic research
• Oncology knowledge graph construction

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-SuperClinical-434M", 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-SuperClinical-434M-v1-onnx-android")
for entity in model("The patient started imatinib after the consultation."):
    print(entity.to_dict())

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-NER-OncologyDetect-SuperClinical-434M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)