README.md

---
library_name: transformers
tags:
  - text-classification
  - customer-support
---

# Model Card for Ticket Classifier

A fine-tuned DistilBERT model that automatically classifies customer support tickets into four categories: Billing Question, Feature Request, General Inquiry, and Technical Issue.

## Model Details

### Model Description

This model is a fine-tuned version of `distilbert-base-uncased` that has been trained to classify customer support tickets into predefined categories. It can help support teams automatically route tickets to the appropriate department.

- **Developed by:** [Your Name/Organization]
- **Model type:** Text Classification (DistilBERT)
- **Language(s):** English
- **License:** [Your License]
- **Finetuned from model:** `distilbert-base-uncased`

## Uses

### Direct Use

This model can be directly used to classify incoming customer support tickets. It takes a text description of the customer's issue and classifies it into one of four categories:
- Billing Question (0)
- Feature Request (1)
- General Inquiry (2)
- Technical Issue (3)

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Define class mapping
id_to_label = {0: 'Billing Question', 1: 'Feature Request', 2: 'General Inquiry', 3: 'Technical Issue'}

# Load model and tokenizer
YOUR_MODEL_PATH = 'Dragneel/Ticket-classification-model'
tokenizer = AutoTokenizer.from_pretrained("YOUR_MODEL_PATH")
model = AutoModelForSequenceClassification.from_pretrained("YOUR_MODEL_PATH")

# Prepare input
text = "I was charged twice for my subscription this month"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)

# Run inference
with torch.no_grad():
    outputs = model(**inputs)
    prediction = torch.argmax(outputs.logits, dim=1).item()
    
print(f"Predicted class: {id_to_label[prediction]}")