Boosting Model Performance

Up to this point, we have solved many tasks using common approaches and achieved some success. However, we can increase our task performance by utilizing specific techniques. There are several approaches to improving the performance of Transformer models in the literature. In this chapter, we will explore some of these techniques and demonstrate how to boost the model beyond the vanilla training pipeline, such as with data augmentation or domain adaptation. Data augmentation is a powerful technique and is widely used for improving the accuracy of deep learning models. By augmenting the data points, the deep learning model can capture the underlying patterns and relationships in the data more effectively. Another method to improve model performance is domain adaptation. Since large language models are trained on general-purpose and diverse texts, there may be a discrepancy when applied to a specific domain. We may need to adjust these language models according...