In natural language processing (NLP), fine-tuning large pre-trained language models like BERT has become the standard for achieving state-of-the-art performance on downstream tasks. However, fine-tuning the entire model can be computationally expensive. The extensive resource requirements pose significant challenges. In this project, I explore using a parameter-efficient fine-tuning (PEFT) technique called LoRA to fine-tune BERT…