OpenMed-NER-ProteinDetect-ElectraMed-335M

What it does

This model detects protein, protein complex, protein enumeration, protein family or group, and protein variant entities in English clinical and research documents. It reports an F1 score of 0.912 on the FSU-PRGE benchmark, with 334 million parameters and a maximum sequence length of 512 tokens.

Best for

• Protein mentions in clinical notes
• Protein families in research papers
• Protein variants in drug discovery

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-ElectraMed-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-ElectraMed-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-ElectraMed-335M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)