In the context of machine learning and natural language processing, the Transformer architecture is a groundbreaking model introduced in the paper "Attention Is All You Need" by Vaswani et al. in 2017. It eschews traditional recurrent layers, relying instead on self-attention mechanisms to draw global dependencies between input and output. This enables the Transformer to handle sequences more effectively and efficiently, paving the way for state-of-the-art models like BERT, GPT, and their various successors in a wide range of applications, from machine translation to text generation. The architecture consists of an encoder and a decoder, each made up of multiple layers that can attend to different parts of the input data in parallel, as opposed to sequentially. The Transformer has been instrumental in achieving remarkable progress in the field of natural language processing and has influenced the architecture of many subsequent models.