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
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
{
"text": "Patient appointment confirmation for tomorrow",
"category": {"label": "appointments", "confidence": 0.95},
"subcategory": {"label": "appointments.confirmation", "confidence": 0.92}
}
Files
backbone/: Base transformer modeltokenizer/: Tokenizer filesclassification_heads.pt: Classification layer weightsinference.py: Inference script