Attention is all you need: The Transformer is a new network architecture based solely on attention mechanisms that excel in sequence transduction tasks like language modelling and machine translation. Unlike traditional recurrent models, the Transformer allows for parallelization during training, leading to faster training times, especially with longer sequences. Notably, the Transformer utilizes self-attention, which computes a representation of a sequence by relating different positions within the sequence itself. This mechanism enables the model to process information from different representation subspaces and learn long-range dependencies more effectively than recurrent or convolutional layers. Empirical results demonstrate that the Transformer surpasses previous state-of-the-art models in translation quality and efficiency. Moreover, the Transformer demonstrates promising generalizability by achieving competitive results in English constituency parsing, a task that poses unique challenges due to structural constraints and length discrepancies between input and output.