OpenMed-NER-AnatomyDetect-TinyMed-135M

What it does

This model detects the entity label Anatomy, covering anatomical structures and body parts, in English clinical notes, research papers, and healthcare documents. It is a 135M-parameter DistilBERT-based model with a reported F1 score of 0.87 on the AnatEM benchmark.

Best for

• Extracting anatomy mentions from clinical notes
• Mining anatomical structures in biomedical literature
• Building knowledge graphs of body parts

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-TinyMed-135M", 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-TinyMed-135M-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-TinyMed-135M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)