OpenMed-NER-AnatomyDetect-MultiMed-335M

What it does

This model performs named-entity recognition to identify mentions of anatomical structures and body parts in English clinical texts, research papers, and healthcare documents. It is a BERT-based model with 334 million parameters and reports an F1 score of 0.895 on the AnatEM benchmark.

Best for

• Extracting anatomy mentions from clinical notes
• Mining anatomical entities in biomedical literature
• Supporting 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-MultiMed-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-AnatomyDetect-MultiMed-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-AnatomyDetect-MultiMed-335M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)