The advantages of Transformer models include their superior performance on tasks involving sequences, their ability to process sequences in parallel, and their adaptability to different kinds of data. However, they are also computationally intensive and require significant resources to train and run. In addition, understanding why they make certain predictions can be difficult, a challenge common to many complex machine learning models.