What is transformer?

A transformer is a type of neural network architecture that was introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017. Transformers have become a widely used architecture for natural language processing (NLP) tasks, such as machine translation, text classification, and language generation, due to their ability to capture long-range dependencies and handle sequential data efficiently.

The key innovation of the transformer architecture is the self-attention mechanism, which allows the model to weigh the importance of different words in a sentence when processing each word, rather than relying on fixed positional encodings as in traditional recurrent neural networks (RNNs). Self-attention allows the model to attend to different words in a sentence with different levels of importance, depending on the context, which makes it more flexible and capable of capturing both local and global dependencies.

The transformer architecture consists of an encoder and a decoder. The encoder takes an input sequence and generates a sequence of hidden representations, while the decoder takes the encoder's output and generates an output sequence. Both the encoder and decoder consist of multiple layers of self-attention and feed-forward neural networks, with residual connections and layer normalization applied to each layer to improve training stability.

Transformers have several advantages over traditional RNN-based architectures. They can handle long sequences more efficiently due to their parallelizable nature, and they are not limited by the vanishing gradient problem that can affect RNNs. Transformers have achieved state-of-the-art performance on a wide range of NLP tasks and have become the foundation for many popular models, such as the BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) models, among others.