Create README.md


README.md

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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–based text classification model  
+- **Base architecture:** GPT-2 (OpenAI)  
+- **Framework:** PyTorch  
+- **Task:** Text Classification  
+- **Number of classes:** 8  
+- **Language:** English  
+- **License:** MIT  
+- **Finetuned from model:** OpenAI GPT-2  
+
+### Classification Labels
+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 Loss](https://cdn-uploads.huggingface.co/production/uploads/65bc1af7ce846f8aa908a978/IDm_z1ud6mP35T_8eMHNl.png)
+
+
+#### Training and Validation Accuracy
+![Training and Validation Accuracy](https://cdn-uploads.huggingface.co/production/uploads/65bc1af7ce846f8aa908a978/WwprMh8Ohj3fy0tjnb0Sd.png)
+
+ ### 📊 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)