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README.md

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+# Email Classification Model (Simple Version)
+
+A dual-head transformer model for classifying healthcare emails into categories and subcategories.
+
+## 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