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| # Email Classification Model (Simple Version) |
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|
| A dual-head transformer model for classifying healthcare emails into categories and subcategories. |
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|
| ## Model Details |
| - **Base Model**: distilbert-base-uncased |
| - **Categories**: 6 |
| - **Subcategories**: 14 |
|
|
| ## Categories |
| appointments, denials, eligibility, other, patient_balance, submission |
| |
| ## Usage |
| |
| ```python |
| import torch |
| from transformers import AutoModel, AutoTokenizer |
| |
| # Download the inference script |
| # wget https://huggingface.co/commure-smislam/email-classification-simple/resolve/main/inference.py |
| |
| from inference import EmailClassifierInference |
| |
| # Load model |
| classifier = EmailClassifierInference("./") |
| |
| # Predict |
| result = classifier.predict("Patient appointment confirmation for tomorrow") |
| print(result) |
| ``` |
| |
| ## Expected Output |
| ```json |
| { |
| "text": "Patient appointment confirmation for tomorrow", |
| "category": {"label": "appointments", "confidence": 0.95}, |
| "subcategory": {"label": "appointments.confirmation", "confidence": 0.92} |
| } |
| ``` |
| |
| ## Files |
| - `backbone/`: Base transformer model |
| - `tokenizer/`: Tokenizer files |
| - `classification_heads.pt`: Classification layer weights |
| - `inference.py`: Inference script |
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