Token Classification
Transformers
Safetensors
English
deberta-v2
named-entity-recognition
biomedical-nlp
disease-entity-recognition
medical-diagnosis
pathology
biocuration
disease
Instructions to use OpenMed/OpenMed-NER-DiseaseDetect-SuperClinical-141M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-DiseaseDetect-SuperClinical-141M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-DiseaseDetect-SuperClinical-141M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-DiseaseDetect-SuperClinical-141M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-DiseaseDetect-SuperClinical-141M") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-DiseaseDetect-SuperClinical-141M
53e68b8 verified - Xet hash:
- 056d8ad0f300bda9eb0ad74c07b9354902124043655052f4a82dc33f6b18389c
- Size of remote file:
- 283 MB
- SHA256:
- 5df10b1ff0e5da052fb45b573439ae9d5ed662698af8a0720a1885a11edb8530
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