Deep Learning
Transformer Architecture
A deep learning model architecture based on the attention mechanism, primarily used for natural language processing tasks.
Explanation
Introduced in the 2017 paper "Attention Is All You Need," the transformer architecture replaced recurrent neural networks (RNNs) and long short-term memory (LSTM) networks in many applications. It relies on self-attention mechanisms to weigh the significance of different parts of the input data. Unlike RNNs, transformers process data in parallel rather than sequentially, allowing for significantly faster training times and better handling of long-range dependencies in text. It consists of an encoder and a decoder, though many modern variants like BERT or GPT use only one of these components.