README.md

---
language: en
license: mit
tags:
- text-classification
- it-support
- customer-service
- distilbert
datasets:
- Tobi-Bueck/customer-support-tickets
metrics:
- accuracy
- f1
model-index:
- name: it-support-ticket-classifier
  results:
  - task:
      type: text-classification
      name: IT Support Ticket Classification
    metrics:
    - type: accuracy
      value: 0.89
      name: Accuracy
---

# 🎫 IT Support Ticket Classifier

Fine-tuned DistilBERT model for automatic classification of IT support tickets into predefined categories.

## 🎯 Model Description

This model classifies IT support tickets into different categories to help route them to the appropriate support team. It's based on **DistilBERT** (distilbert-base-uncased), a lighter and faster version of BERT, making it ideal for production environments.

**Key Features:**
- ⚡ Fast inference (DistilBERT architecture)
- 🎯 89% accuracy on test set
- 🔧 Ready for production deployment
- 📦 Easy integration with Transformers pipeline

## 📊 Performance

| Metric | Score |
|--------|-------|
| **Accuracy** | **89%** |
| F1-Score | 0.88 |
| Precision | 0.87 |
| Recall | 0.89 |

## 🚀 Quick Start

### Installation

```bash
pip install transformers torch
```

### Usage

```python
from transformers import pipeline

# Load the classifier
classifier = pipeline(
    "text-classification",
    model="jeremiasdavison/it-support-ticket-classifier"
)

# Classify a ticket
ticket = "My laptop won't connect to the office WiFi network"
result = classifier(ticket)

print(result)
# Output: [{'label': 'NETWORK', 'score': 0.95}]
```

### Get All Class Probabilities

```python
classifier = pipeline(
    "text-classification",
    model="jeremiasdavison/it-support-ticket-classifier",
    return_all_scores=True
)

ticket = "I forgot my password and can't log into the system"
results = classifier(ticket)[0]

for result in results:
    print(f"{result['label']}: {result['score']:.2%}")
```

## 🏷️ Categories

The model classifies tickets into the following categories:

- **Hardware** - Physical device issues (laptop, printer, monitor, etc.)
- **Software** - Application bugs, software errors, installation problems
- **Network** - WiFi, VPN, connectivity issues
- **Account** - Login problems, password resets, permissions
- **General** - General inquiries, documentation requests

## 📚 Training Data

The model was fine-tuned on the [Tobi-Bueck/customer-support-tickets](https://huggingface.co/datasets/Tobi-Bueck/customer-support-tickets) dataset:

- **Total examples**: ~61,800 tickets
- **Filtered for**: English language only
- **Train/Val/Test split**: 80/10/10
- **Features**: Subject + Body combined as input text

## 🛠️ Training Details

### Hyperparameters

- **Base model**: distilbert-base-uncased
- **Learning rate**: 2e-5
- **Batch size**: 16
- **Epochs**: 3
- **Optimizer**: AdamW
- **Max sequence length**: 128 tokens

### Framework

- Transformers 4.x
- PyTorch
- Trained on Google Colab (T4 GPU)

## 🌐 Live Demo

Try the model in action: [HuggingFace Space](https://huggingface.co/spaces/jeremiasdavison/it-support-classifier-demo) *(coming soon)*

## 💡 Use Cases

- **Automated ticket routing** - Direct tickets to the right support team
- **Priority detection** - Identify urgent issues automatically  
- **Analytics** - Understand ticket distribution by category
- **Chatbot integration** - Pre-classify user issues in conversational interfaces

## ⚠️ Limitations

- Trained primarily on synthetic IT support data
- Best performance on English-language tickets
- May require fine-tuning for domain-specific terminology
- Performance may vary on tickets with multiple issues

## 🤝 Contributing

Feedback and contributions are welcome! If you encounter issues or have suggestions:
- Open an issue on the [model repository](https://huggingface.co/jeremiasdavison/it-support-ticket-classifier)
- Reach out via the community tab

## 📄 License

MIT License - feel free to use this model in your projects.

## 🙏 Acknowledgments

- Dataset: [Tobi-Bueck/customer-support-tickets](https://huggingface.co/datasets/Tobi-Bueck/customer-support-tickets)
- Base model: [DistilBERT](https://huggingface.co/distilbert-base-uncased) by Hugging Face
- Built with [Transformers](https://github.com/huggingface/transformers)

## 📫 Contact

**Jeremias Davison**  
- HuggingFace: [@jeremiasdavison](https://huggingface.co/jeremiasdavison)
- LinkedIn: [linkedin.com/in/jeremiasdavison](https://linkedin.com/in/jeremiasdavison)

---

*This model was created as a portfolio project to demonstrate end-to-end ML workflow: from dataset selection to model training and deployment.*