Transformer's architecture

The Transformer architecture is a type of neural network designed for understanding and generating language. It works by processing entire sequences of words simultaneously, using a mechanism called "attention" to identify which parts of the input are most relevant. This allows it to capture context and relationships across words, regardless of their position. Transformers consist of layers that include attention mechanisms and feedforward networks, enabling them to learn complex patterns efficiently. They are the foundation of many modern language models, making sense of language in a way that is both flexible and scalable.