OpenMed-NER-GenomicDetect-PubMed-335M

What it does

This model identifies cell line names in English biomedical text, including clinical notes and research papers, using a BERT-based architecture. It reports an F1 score of 0.996 on the Gelius benchmark, processes sequences up to 512 tokens, and has 334 million parameters.

Best for

• Cell line mentions in research articles
• Entity extraction from clinical notes
• Biomedical literature mining for cell lines

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