Transformer

A Transformer is a type of neural network architecture that revolutionized natural language processing by leveraging self-attention mechanisms to capture contextual relationships between words or tokens in a sequence.

In AI, the Transformer architecture introduced a groundbreaking approach for processing sequential data, particularly in natural language processing tasks. Unlike traditional recurrent neural networks (RNNs) that process sequences sequentially, Transformers utilize self-attention mechanisms to capture the interdependencies between words or tokens in a sequence all at once. This allows the model to consider the context and relationships of each element in the sequence simultaneously, enabling more effective language understanding and generation. Transformers have been widely adopted in various applications, including machine translation, text summarization, sentiment analysis, and question-answering systems. They have shown exceptional performance by capturing long-range dependencies and leveraging parallel computation, making them highly efficient and capable of handling large-scale language data.

In summary, a Transformer in the context of AI is a neural network architecture that revolutionized natural language processing by utilizing self-attention mechanisms to capture contextual relationships between words or tokens in a sequence, leading to significant advancements in language understanding and generation tasks.