OpenMed-NER-DNADetect-MultiMed-568M

What it does

This model detects DNA, RNA, cell line, cell type, and protein names in English biomedical literature and clinical text. It reports an F1 score of 0.816 on the JNLPBA benchmark test set.

Best for

• Entity extraction from research papers
• Mining clinical notes for cell types
• Identifying proteins in drug discovery documents

What it detects (5 labels)

• DNA
• RNA
• Cell line
• Cell type
• Protein

Quality

Biomedical Entity Recognition - Proteins, DNA, RNA, cell lines, and cell types

Use it

pip install transformers torch

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