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 and generates a representation, while the decoder uses this representation to generate an output sequence. Both the encoder and decoder are composed of multiple layers, each containing a multi-head self-attention mechanism and a position-wise feed-forward network. Positional encodings are added to the input embeddings to account for the order of tokens, as the self-attention mechanism itself is order-invariant.

The Transformer's ability to capture long-range dependencies effectively and its suitability for parallelization have led to its widespread adoption in various natural language processing tasks, including machine translation, text summarization, question answering, and text generation. Large language models like GPT and BERT are based on the Transformer architecture.