OpenMed-NER-ProteinDetect-MultiMed-335M

What it does

This model identifies protein, protein complex, protein enumeration, protein family or group, and protein variant entities in English clinical and research text. It is a 334M-parameter BERT model with a reported F1 score of 0.917 on the FSU-PRGE test set.

Best for

• Clinical notes entity extraction
• Biomedical literature mining
• Protein annotation in research papers

What it detects (5 labels)

• Protein
• Protein complex
• Protein (enumeration)
• Protein family or group
• Protein variant

Quality

Biomedical Entity Recognition - Various biomedical entities

Use it

pip install transformers torch

from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-NER-ProteinDetect-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-ProteinDetect-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-ProteinDetect-MultiMed-335M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)