Within the realm of machine learning, the term "Transformer" denotes a neural network architecture that has garnered significant acclaim, primarily in the domain of natural language processing (NLP) tasks. Its introduction occurred in the seminal research paper titled "Attention Is All You Need," authored by Vaswani et al. in 2017. Since then, the Transformer has emerged as a fundamental framework in numerous applications within the NLP domain.
The Transformer architecture is purposefully designed to overcome the limitations encountered by conventional recurrent neural networks (RNNs) when confronted with sequential data, such as sentences or documents. Unlike RNNs, Transformers do not rely on sequential processing and possess the ability to parallelize computations, thereby facilitating enhanced efficiency and scalability.