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---
language: en
license: mit
tags:
- text-classification
- gpt2
- transformers
- pytorch
- custom-architecture
- tiktoken
library_name: transformers
---
# 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)
```python
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
| Class ID | Category |
|----------|--------------------------|
| 0 | Hardware |
| 1 | HR Support |
| 2 | Access |
| 3 | Miscellaneous |
| 4 | Storage |
| 5 | Purchase |
| 6 | Internal Project |
| 7 | Administrative Rights |
### 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
| Dataset Split | Accuracy |
|--------------|----------|
| 🏋️ Training | **76.54%** |
| 🧪 Validation | **75.67%** |
| 🧠 Test | **73.83%** |
---
## 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](https://huggingface.co/FarhanAK128) |