OpenMed-NER-DNADetect-TinyMed-82M

What it does

This model detects DNA, RNA, proteins, cell lines, and cell types in English biomedical text such as clinical notes and research papers. It reports an F1 score of 0.796 on the JNLPBA test set, with 82 million parameters and a maximum sequence length of 514 tokens.

Best for

• Entity extraction from clinical notes
• Mining biomedical research literature
• Identifying proteins and cell types in abstracts

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-TinyMed-82M", 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-TinyMed-82M-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-TinyMed-82M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)