This study seeks to evaluate the effectiveness of transfer learning in improving model performance for specific tasks in natural language processing by comparing the accuracy and efficiency of fine-tuning pre-trained language models versus training models from scratch. Transfer learning is an advanced machine learning technique that capitalizes on pre-trained models to tackle new, related tasks. This approach is particularly effective when there is a scarcity of data or computational resources for training models from scratch. By leveraging models that have been trained on large and diverse datasets, transfer learning enables the adaptation of existing knowledge to address specific problems with limited data.