OpenMed-NER-GenomicDetect-TinyMed-66M

What it does

This model identifies cell line names in English clinical texts, research papers, and healthcare documents. It reports an F1 score of 0.976 on the Gelius cell line benchmark, as reported by the author.

Best for

• Cell line mentions in research papers
• Entity extraction from medical records
• Literature mining for biomedical studies

What it detects (1 labels)

• Cell line

Quality

Gene Entity Recognition - Gene-related entities

Use it

pip install transformers torch

from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-NER-GenomicDetect-TinyMed-66M", 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-GenomicDetect-TinyMed-66M-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-GenomicDetect-TinyMed-66M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)