Detects cell line names in English biomedical and clinical text
Extract biomedical entitiesGenes, proteins and cellsEnglishMedium · 396Mapache-2.0
What it does
This model performs named-entity recognition to identify cell line names in English clinical notes, research papers, and healthcare documents. It is built on a ModernBERT backbone with 396M parameters and reports an F1 score of 0.985 on the Gelius benchmark, with a maximum sequence length of 8192 tokens.
Best for
- Cell line extraction from research papers
- Entity recognition in clinical records
- Biomedical literature mining for cell lines
What it detects (1 labels)
- Cell line
Quality
| Benchmark | Gellus (cell lines) · all models on this benchmark |
|---|---|
| F1 | 0.985 |
| Precision | 0.989 |
| Recall | 0.980 |
| Accuracy | 0.998 |
| Source | Reported by the authors (test_results.json in the repository) |
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-ModernClinical-395M", aggregation_strategy="simple")
print(ner("Patient prescribed 10 mg aspirin for hypertension."))Android, browser, CPU (ONNX) build
pip install --upgrade "openmed[onnx-runtime]"
from openmed import OnnxModel
model = OnnxModel.from_pretrained("OpenMed/OpenMed-NER-GenomicDetect-ModernClinical-395M-v1-onnx-android")
for entity in model("The patient started imatinib after the consultation."):
print(entity.to_dict())All model information shown here is public and belongs to its authors: use and download it freely under each model’s license. The selection, organization, search design and presentation of this catalog are © 2026 Clinical Corpus. Please link to these pages rather than copy them.