Token Classification
Transformers
Safetensors
English
roberta
named-entity-recognition
biomedical-nlp
disease-entity-recognition
medical-diagnosis
pathology
biocuration
disease
Instructions to use OpenMed/OpenMed-NER-DiseaseDetect-SuperMedical-125M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-DiseaseDetect-SuperMedical-125M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-DiseaseDetect-SuperMedical-125M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-DiseaseDetect-SuperMedical-125M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-DiseaseDetect-SuperMedical-125M") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-DiseaseDetect-SuperMedical-125M
77edc87 verified - Xet hash:
- c496c52a96ad24bd31b838c59d97551895c0c7d62f8e49e914edee0c0a8c0407
- Size of remote file:
- 248 MB
- SHA256:
- 637e2bf464e245741904f5993989210181e9969dc3286a1af912fe6cdd9ffb59
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