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--- |
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language: en |
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license: mit |
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tags: |
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- text-classification |
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- gpt2 |
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- transformers |
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- pytorch |
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- custom-architecture |
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- tiktoken |
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library_name: transformers |
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--- |
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# TicketClassificationGPT |
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## Model Summary |
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**TicketClassificationGPT** is a GPT-2–based text classification model designed entirely from scratct to classify IT support tickets into 8 predefined categories. |
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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. |
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This model is fully compatible with the Hugging Face `transformers` ecosystem and can be loaded using `AutoModel.from_pretrained`. |
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--- |
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## How to Get Started with the Model |
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### Inference Example (Transformers + tiktoken) |
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```python |
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from transformers import AutoModel |
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import tiktoken |
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# Load tokenizer |
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tokenizer = tiktoken.get_encoding("gpt2") |
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# Load model |
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model_id = "FarhanAK128/TicketClassificationGPT" |
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model = AutoModel.from_pretrained( |
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model_id, |
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trust_remote_code=True |
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) |
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# Example prediction |
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text = "Need extra space on Google Drive." |
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prediction = model.predict(text, tokenizer) |
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print("Predicted class:", prediction) # Predicted class: Storage |
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``` |
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**Note:** This model uses a custom `.predict()` method defined in the repository and requires `trust_remote_code=True` to function. |
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--- |
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## Model Details |
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### 📝 Model Description |
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- **Developed by:** Farhan Ali Khan |
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- **Model type:** GPT-2–based text classification model |
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- **Base architecture:** GPT-2 (OpenAI) |
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- **Framework:** PyTorch |
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- **Task:** Text Classification |
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- **Number of classes:** 8 |
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- **Language:** English |
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- **License:** MIT |
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- **Finetuned from model:** OpenAI GPT-2 |
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### 📋 Classification Labels |
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| Class ID | Category | |
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|----------|--------------------------| |
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| 0 | Hardware | |
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| 1 | HR Support | |
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| 2 | Access | |
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| 3 | Miscellaneous | |
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| 4 | Storage | |
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| 5 | Purchase | |
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| 6 | Internal Project | |
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| 7 | Administrative Rights | |
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### Model Sources |
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- **Repository:** https://huggingface.co/FarhanAK128/TicketClassificationGPT |
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- **Base model:** OpenAI GPT-2 like architecture from scratch |
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- **Paper:** https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf |
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--- |
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## Training Details |
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### Training Data |
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The model was trained on the **IT Service Ticket Classification Dataset** available on Kaggle. |
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- **Dataset name:** IT Service Ticket Classification Dataset |
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- **Source:** Kaggle |
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- **Link:** https://www.kaggle.com/datasets/adisongoh/it-service-ticket-classification-dataset |
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- **Content:** Labeled IT support ticket text data |
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- **Language:** English |
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The dataset was used for supervised multi-class classification after standard text preprocessing and tokenization. |
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### Training Procedure |
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- **Base weights:** OpenAI GPT-2 |
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- **Fine-tuning strategy:** Partial fine-tuning (classification head + final transformer layers) |
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- **Optimizer:** AdamW |
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- **Learning rate:** 1e-4 |
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- **Weight decay:** 0.1 |
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- **Epochs:** 5 |
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- **Random seed:** 123 |
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- **Loss function:** Cross-Entropy Loss |
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- **Training regime:** FP32 |
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- **Evaluation frequency:** Every 30 steps |
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- **Total training time:** ~140 minutes |
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- **Final training loss:** ~0.61 |
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- **Final validation loss:** ~0.86 |
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### 📈 Training Progress |
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#### Training and Validation Loss |
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#### Training and Validation Accuracy |
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### 📊 Model Performance |
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| Dataset Split | Accuracy | |
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|--------------|----------| |
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| 🏋️ Training | **76.54%** | |
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| 🧪 Validation | **75.67%** | |
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| 🧠 Test | **73.83%** | |
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--- |
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## Uses |
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### Direct Use |
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This model can be used directly to classify short IT support ticket texts into predefined categories. |
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Example use cases: |
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- Automated ticket routing |
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- Helpdesk prioritization |
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- Internal IT workflow automation |
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### Downstream Use |
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The model may be further fine-tuned on: |
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- Organization-specific ticket data |
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- Expanded label sets |
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- Domain-specific terminology |
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### Out-of-Scope Use |
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- Multilingual text classification |
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- Open-domain topic classification |
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- Legal, medical, or safety-critical decision-making |
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--- |
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## Bias, Risks, and Limitations |
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- Trained on a limited-domain dataset (IT support tickets) |
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- Not evaluated for demographic or social bias |
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- Predictions may be unreliable for unseen ticket categories |
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- Performance depends on input text quality and length |
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### Recommendations |
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Human validation is recommended before using predictions in production systems. |
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For best results, further fine-tuning on in-domain data is advised. |
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--- |
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## Model Card Authors |
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**Farhan Ali Khan** |
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## Model Card Contact |
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For questions or feedback, please reach out via my Hugging Face profile: |
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[FarhanAK128](https://huggingface.co/FarhanAK128) |
