--- 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! 🚀 ---