Transformner
Transformer is a type of artificial neural network architecture introduced in 2017 by Google researchers in the paper "Attention Is All You Need." It has become highly influential in the field of natural language processing and has also found applications in computer vision and other domains. The core innovation of the Transformer is its reliance on the self-attention mechanism, which allows the model to weigh the importance of different words in an input sequence when processing it, regardless of their distance from each other. This contrasts with previous recurrent neural network (RNN) and convolutional neural network (CNN) architectures, which processed information sequentially or with fixed local contexts.
The Transformer architecture consists of an encoder and a decoder. The encoder processes the input sequence
The Transformer's ability to capture long-range dependencies effectively and its suitability for parallelization have led to