OpenMed-NER-AnatomyDetect-SuperClinical-141M

What it does

This model detects the entity type Anatomy, covering anatomical structures and body parts, in English clinical notes and research literature. It reports an F1 score of 0.873 on the AnatEM benchmark and runs on PyTorch, ONNX, and Apple Silicon.

Best for

• Anatomy mentions in clinical notes
• Anatomical terms in research papers
• Entity extraction for knowledge graphs

What it detects (1 labels)

• Anatomy

Quality

Anatomical Entity Recognition - Anatomical structures and body parts

Use it

pip install transformers torch

from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-NER-AnatomyDetect-SuperClinical-141M", 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-AnatomyDetect-SuperClinical-141M-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-AnatomyDetect-SuperClinical-141M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)