The paper introduces the Transformer, a new neural network architecture that uses only attention mechanisms, eliminating the need for recurrence and convolution. This model is designed for sequence transduction tasks, such as machine translation. The Transformer is trained using attention mechanisms that allow it to process input and output sequences in parallel, significantly improving training efficiency and performance. The model achieves high BLEU scores on the WMT 2014 English-to-German and English-to-French translation tasks, outperforming previous state-of-the-art models. The Transformer is also shown to generalize well to other tasks, such as English constituency parsing, both with large and limited training data. The model uses self-attention mechanisms to capture dependencies between input and output sequences, and employs multi-head attention to allow the model to jointly attend to information from different representation subspaces. The Transformer is trained using a combination of attention and feed-forward networks, with positional encodings to account for the order of the sequence. The model is trained on large datasets and achieves high performance with relatively low computational cost. The paper also discusses the advantages of the Transformer over other sequence transduction models, including its ability to parallelize computation and its effectiveness in capturing long-range dependencies. The model is evaluated on various tasks and is shown to outperform previous models in terms of translation quality and training efficiency. The paper concludes that the Transformer is a significant advancement in sequence transduction models, offering a new approach to processing sequence data with high efficiency and performance.The paper introduces the Transformer, a new neural network architecture that uses only attention mechanisms, eliminating the need for recurrence and convolution. This model is designed for sequence transduction tasks, such as machine translation. The Transformer is trained using attention mechanisms that allow it to process input and output sequences in parallel, significantly improving training efficiency and performance. The model achieves high BLEU scores on the WMT 2014 English-to-German and English-to-French translation tasks, outperforming previous state-of-the-art models. The Transformer is also shown to generalize well to other tasks, such as English constituency parsing, both with large and limited training data. The model uses self-attention mechanisms to capture dependencies between input and output sequences, and employs multi-head attention to allow the model to jointly attend to information from different representation subspaces. The Transformer is trained using a combination of attention and feed-forward networks, with positional encodings to account for the order of the sequence. The model is trained on large datasets and achieves high performance with relatively low computational cost. The paper also discusses the advantages of the Transformer over other sequence transduction models, including its ability to parallelize computation and its effectiveness in capturing long-range dependencies. The model is evaluated on various tasks and is shown to outperform previous models in terms of translation quality and training efficiency. The paper concludes that the Transformer is a significant advancement in sequence transduction models, offering a new approach to processing sequence data with high efficiency and performance.