OpenMed-NER-GenomeDetect-SnowMed-568M

What it does

This model identifies mentions of genes and proteins in English clinical notes, research papers, and healthcare documents. It reports an F1 score of 0.87 on the BC2GM benchmark, with 567 million parameters and a maximum sequence length of 8194 tokens.

Best for

• Gene mentions in clinical notes
• Protein mentions in research papers
• Entity extraction for knowledge graphs

What it detects (1 labels)

• Gene or protein

Quality

Gene/Protein Entity Recognition - Gene and protein mentions

Use it

pip install transformers torch

from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-NER-GenomeDetect-SnowMed-568M", 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-GenomeDetect-SnowMed-568M-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-GenomeDetect-SnowMed-568M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)