Text Classification
Transformers
PyTorch
English
medicoder_complete
medical
healthcare
clinical-notes
medical-coding
few-shot-learning
prototypical-networks
deployment-ready
self-contained
Instructions to use sshan95/medicoder-ai-v4-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshan95/medicoder-ai-v4-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sshan95/medicoder-ai-v4-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sshan95/medicoder-ai-v4-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## 🎯 Performance
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- **Top-1 Accuracy**: 46.3%
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- **Top-5 Accuracy**: ~54% (estimated)
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- **Medical Codes**: 57,768 supported codes
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- **Prototypes**: 57,768 embedded prototype vectors
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- **Deployment**: Fully self-contained
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## 🎯 Performance
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- **Medical Codes**: 57,768 supported codes
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- **Prototypes**: 57,768 embedded prototype vectors
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- **Deployment**: Fully self-contained
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