The Transformer architecture was introduced in 2017 and is based on the idea of self-attention, which enables capturing relationships between all input elements simultaneously.
Unlike recurrent or convolutional models, a Transformer processes entire sequences in parallel, significantly improving training speed and scalability.
A Transformer consists of encoder and decoder layers, each composed of multi-head self-attention mechanisms and feedforward networks.
The self-attention mechanism calculates a weighted combination of all words in the sequence for each word to consider important contextual information.
This architecture is the foundation of many modern NLP models used for both language understanding and text generation.