What is transformer architecture?
Transformer architecture is a type of neural network architecture that was first introduced in 2017 in the paper "Attention Is All You Need" by Vaswani et al. The transformer was designed specifically for sequence-to-sequence learning problems, such as machine translation and language modeling.
The transformer architecture is based on the self-attention mechanism, which allows the model to weigh different parts of the input sequence differently based on their relevance to the output. Unlike traditional recurrent neural networks (RNNs) which process sequences one element at a time, the transformer can process the entire sequence in parallel, making it more efficient and effective for many natural language processing tasks.
The transformer architecture consists of an encoder and a decoder, both of which are composed of multiple layers of self-attention and feedforward neural networks. The encoder takes in the input sequence and produces a sequence of hidden representations, while the decoder takes in the encoder output and produces the final output sequence.
