four pictures showing different parts of a computer

A transformer model is a type of deep learning architecture designed for sequence-to-sequence tasks, particularly suited for natural language processing (NLP) tasks. It was introduced by Vaswani et al. in the paper "Attention is All You Need" in 2017. The key innovation of transformer models lies in their attention mechanism, which allows the model to focus on different parts of the input sequence when processing each output element. This attention mechanism enables the model to capture dependencies between input and output elements without relying on recurrent neural networks (RNNs) or convolutional neural networks (CNNs), which were previously commonly used in sequence processing tasks. The transformer architecture consists of an encoder and a decoder. The encoder processes the input sequence, while the decoder generates the output sequence. Both the encoder and decoder are composed of multiple layers of self-attention mechanisms and feedforward neural networks. In addition to its effectiveness in sequen