OpenMed-NER-OncologyDetect-ElectraMed-109M

What it does

This model performs named-entity recognition for cancer genetics, identifying entities such as Cancer, Gene or gene product, and Organism substance in English clinical and research documents. It reports an F1 score of 0.732 on the BioNLP 2013 Cancer Genetics test set, with 109M parameters and a 512-token input limit.

Best for

• Entity extraction from cancer research papers
• Mining clinical notes for oncology terms
• Building biomedical knowledge graphs

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-ElectraMed-109M", 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-ElectraMed-109M-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-ElectraMed-109M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)