OpenMed-NER-OncologyDetect-ElectraMed-335M

What it does

This model performs named-entity recognition for cancer and oncology, identifying 16 entity types such as Cancer, Gene or gene product, Cell, and Organism in English clinical and research documents. It reports an F1 score of 0.774 on the BioNLP 2013 Cancer Genetics test set, with 334 million parameters and a 512-token sequence limit.

Best for

• Extracting cancer entities from clinical notes
• Mining genetic entities in research papers
• Building oncology 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-335M", 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-335M-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-335M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)