Token Classification
Transformers
Safetensors
English
modernbert
named-entity-recognition
biomedical-nlp
gene-recognition
genetics
genomics
molecular-biology
cell-line-name
Instructions to use OpenMed/OpenMed-NER-GenomicDetect-ModernClinical-395M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-GenomicDetect-ModernClinical-395M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-GenomicDetect-ModernClinical-395M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-GenomicDetect-ModernClinical-395M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-GenomicDetect-ModernClinical-395M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-GenomicDetect-ModernClinical-395M
7246967 verified - Xet hash:
- 0acfa5927bd8008e383cd3f81e8f0f61909e1bd2d75b3fe866126af22bbe1fd4
- Size of remote file:
- 792 MB
- SHA256:
- a7290a968a185eb24a48b2d477297c4725b2bc68d74228dad768b919ab96fb68
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