OpenMed-NER-AnatomyDetect-ModernClinical-395M

What it does

This model identifies anatomical entities, such as structures and body parts, in English clinical notes and biomedical literature. It reports an F1 score of 0.874 on the AnatEM benchmark, with a sequence length of up to 8192 tokens.

Best for

• Anatomy mentions in clinical notes
• Entity extraction from research papers
• Biomedical knowledge graph construction

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-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-AnatomyDetect-ModernClinical-395M-v1-onnx-android")
for entity in model("The patient started imatinib after the consultation."):
    print(entity.to_dict())