Fine-Tuning GPT-2 with LoRA

This project involved the fine-tuning of the GPT-2 language model using LoRA (Low-Rank Adaptation) for the classification of emails into spam or non-spam categories. The fine-tuning process leveraged LoRA's parameter-efficient technique to adapt the pre-trained GPT-2 model to the specific task while minimizing computational and memory overhead. A dataset of labeled emails was preprocessed to ensure the extraction of key features such as subject lines, sender details, and message content. The model was trained to identify patterns and contextual cues indicative of spam behavior, such as phishing attempts, promotional content, and suspicious links. Evaluation metrics, including accuracy, precision, recall, and F1-score, were used to assess the model's performance. The project demonstrated the effectiveness of LoRA in adapting large language models for domain-specific tasks while preserving generalization capabilities. The fine-tuned model offers potential applications in email security systems, enabling real-time spam detection and user protection against malicious content.