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)
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]}")
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