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
pip install transformers torch
Usage
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
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 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 (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
- 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
- Base model: DistilBERT by Hugging Face
- Built with Transformers
📫 Contact
Jeremias Davison
- HuggingFace: @jeremiasdavison
- LinkedIn: 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.