The paper introduces the Transformer, a novel network architecture that utilizes attention mechanisms exclusively, eliminating the need for recurrence and convolutions, thereby enabling more parallelization and achieving superior translation quality with reduced training time. It delineates the benefits of self-attention over traditional models and provides a detailed description of the Transformer's architecture, which includes stacked self-attention and point-wise, fully connected layers within an encoder-decoder structure. The Transformer has demonstrated success in various applications, such as reading comprehension and textual entailment, due to its innovative use of attention functions like Scaled Dot-Product Attention and Multi-Head Attention.