Token Classification
Transformers
Safetensors
English
modernbert
named-entity-recognition
biomedical-nlp
disease-entity-recognition
medical-diagnosis
pathology
biocuration
disease
Instructions to use OpenMed/OpenMed-NER-DiseaseDetect-ModernClinical-149M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-DiseaseDetect-ModernClinical-149M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-DiseaseDetect-ModernClinical-149M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-DiseaseDetect-ModernClinical-149M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-DiseaseDetect-ModernClinical-149M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-DiseaseDetect-ModernClinical-149M
aa95624 verified - Xet hash:
- 205690c6f2924713eca0b7897138b7f3d1c30d25e8b6e39f0abbbd323f62a414
- Size of remote file:
- 299 MB
- SHA256:
- 5b72b016d05488e829f60187db4fdf0aecd41b54223107941354c2c9affda837
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