A transformer is a type of deep learning model architecture that was introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017. It has since become a fundamental building block for various natural language processing (NLP) tasks and other sequence-to-sequence tasks.
The transformer architecture is based on the concept of self-attention mechanisms, which allows the model to weigh the importance of different words in a sequence when processing each word. This mechanism enables the transformer to process entire sequences in parallel, making it highly efficient and scalable.
The key components of a transformer are:
Encoder: The encoder takes an input sequence (e.g., a sentence in NLP) and processes it in parallel. It consists of multiple layers, each containing a self-attention mechanism and a feedforward neural network. The self-attention mechanism allows the model to focus on important words while the feedforward network processes the information locally.
Decoder: The decoder is used in tasks where the model generates an output sequence, such as machine translation. It also consists of multiple layers, each containing self-attention, encoder-decoder attention, and feedforward networks. The encoder-decoder attention mechanism helps the decoder focus on relevant parts of the input sequence during the generation process.
Self-Attention: Self-attention is the core mechanism of the transformer. It allows the model to compute the attention score between each word/token in the input sequence with respect to all other words/tokens in the same sequence. This way, the model can weigh the importance of each word based on its relationships with other words.
The transformer's ability to process entire sequences in parallel and capture long-range dependencies makes it especially effective for tasks requiring context understanding and generating coherent sequences. It has been widely used in various NLP tasks, such as machine translation, text summarization, question-answering, language modeling, and more. Additionally, transformers have also found applications in computer vision and other domains beyond NLP.