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---
license: mit
language:
- en
metrics:
- accuracy
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
---
# **BERT for IT Support Ticket Classification**
_A fine-tuned BERT model for classifying IT-related support tickets into predefined categories._
## **Model Details**
- **Model Name**: `bert-it-issue`
- **Author**: [SalomonMetre13](https://huggingface.co/SalomonMetre13)
- **Architecture**: BERT-based model fine-tuned for **IT support ticket classification**
- **Task**: Text Classification (`text-classification`)
- **Dataset**: Processed IT support tickets dataset (`all_tickets_processed_improved_v3.csv`)
- **Labels**:
- `0`: Hardware
- `1`: Access
- `2`: Miscellaneous
- `3`: HR Support
- `4`: Purchase
- `5`: Administrative rights
- `6`: Storage
- `7`: Internal Project
## **Usage**
You can use this model for **automatically classifying IT support requests** based on their content.
### **Example Usage with Transformers (Python)**
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="SalomonMetre13/bert-it-issue")
text = "I need a new laptop because mine stopped working."
prediction = classifier(text)
print(prediction) # [{'label': 'Hardware', 'score': 0.97}]
```
### **Using Hugging Face API (cURL)**
```bash
curl -X POST "https://api-inference.huggingface.co/models/SalomonMetre13/bert-it-issue" \
-H "Authorization: Bearer YOUR_HF_API_TOKEN" \
-H "Content-Type: application/json" \
-d '{"inputs": "I need access to my email account."}'
```
## **Performance**
The model was trained and evaluated on a dataset of categorized IT support tickets, achieving **high accuracy on validation and test sets**.
## **Applications**
- **Automated IT ticket classification**
- **Helpdesk support systems**
- **Chatbot integration for IT requests**
## **Limitations**
- May misclassify ambiguous requests.
- Performance depends on how well the training data represents real-world IT tickets.
- Doesn't handle multi-label classification (only assigns one category per ticket).
## **Contributions & Feedback**
Feel free to contribute by fine-tuning, reporting issues, or suggesting improvements! 🚀
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