OpenMed-NER-AnatomyDetect-EuroMed-212M

What it does

This model performs named-entity recognition to identify anatomical structures and body parts in English clinical notes and biomedical literature. It reports an F1 score of 0.839 on the AnatEM benchmark and supports sequences up to 8192 tokens.

Best for

• Clinical text mining for anatomy mentions
• Biomedical literature entity extraction
• Knowledge graph construction from research papers

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-EuroMed-212M", 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-EuroMed-212M-v1-onnx-android")
for entity in model("The patient started imatinib after the consultation."):
    print(entity.to_dict())