TicketClassificationGPT

TicketClassificationGPT

Model Summary

TicketClassificationGPT is a GPT-2–based text classification model designed entirely from scratct to classify IT support tickets into 8 predefined categories.
The model uses the original OpenAI GPT-2 architecture and weights, with the language modeling head replaced by a custom classification head. Only the final layers were fine-tuned for the ticket classification task.

This model is fully compatible with the Hugging Face transformers ecosystem and can be loaded using AutoModel.from_pretrained.

---

How to Get Started with the Model

Inference Example (Transformers + tiktoken)

from transformers import AutoModel
import tiktoken

# Load tokenizer
tokenizer = tiktoken.get_encoding("gpt2")

# Load model
model_id = "FarhanAK128/TicketClassificationGPT"
model = AutoModel.from_pretrained(
    model_id,
    trust_remote_code=True
)

# Example prediction
text = "Need extra space on Google Drive."
prediction = model.predict(text, tokenizer)

print("Predicted class:", prediction) # Predicted class: Storage

Note: This model uses a custom .predict() method defined in the repository and requires trust_remote_code=True to function.

---

Model Details

📝 Model Description

• Developed by: Farhan Ali Khan
• Model type: GPT-2–like text classification model
• Framework: PyTorch
• Task: Text Classification
• Number of classes: 8
• Language: English
• License: MIT

📋 Classification Labels

Model Sources

• Repository: https://huggingface.co/FarhanAK128/TicketClassificationGPT
• Base model: OpenAI GPT-2 like architecture from scratch
• Paper: https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf

---

Training Details

Training Data

The model was trained on the IT Service Ticket Classification Dataset available on Kaggle.

• Dataset name: IT Service Ticket Classification Dataset
• Source: Kaggle
• Link: https://www.kaggle.com/datasets/adisongoh/it-service-ticket-classification-dataset
• Content: Labeled IT support ticket text data
• Language: English

The dataset was used for supervised multi-class classification after standard text preprocessing and tokenization.

Training Procedure

• Base weights: OpenAI GPT-2
• Fine-tuning strategy: Partial fine-tuning (classification head + final transformer layers)
• Optimizer: AdamW
• Learning rate: 1e-4
• Weight decay: 0.1
• Epochs: 5
• Random seed: 123
• Loss function: Cross-Entropy Loss
• Training regime: FP32
• Evaluation frequency: Every 30 steps
• Total training time: ~140 minutes
• Final training loss: ~0.61
• Final validation loss: ~0.86

📈 Training Progress

Training and Validation Loss

Training and Validation Accuracy

📊 Model Performance

---

Uses

Direct Use

This model can be used directly to classify short IT support ticket texts into predefined categories.

Example use cases:

• Automated ticket routing
• Helpdesk prioritization
• Internal IT workflow automation

Downstream Use

The model may be further fine-tuned on:

• Organization-specific ticket data
• Expanded label sets
• Domain-specific terminology

Out-of-Scope Use

• Multilingual text classification
• Open-domain topic classification
• Legal, medical, or safety-critical decision-making

---

Bias, Risks, and Limitations

• Trained on a limited-domain dataset (IT support tickets)
• Not evaluated for demographic or social bias
• Predictions may be unreliable for unseen ticket categories
• Performance depends on input text quality and length

Recommendations

Human validation is recommended before using predictions in production systems.
For best results, further fine-tuning on in-domain data is advised.

---

Model Card Authors

Farhan Ali Khan

Model Card Contact

For questions or feedback, please reach out via my Hugging Face profile: FarhanAK128