Transformer

Transformer

A Transformer is a type of neural network architecture used in natural language processing and other machine learning tasks. It was introduced in a 2017 paper titled "Attention is All You Need" by Vaswani et al.

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 making predictions. This mechanism enables the Transformer to capture long-range dependencies in data and has proven highly effective in handling sequential data, such as language.

The Transformer model consists of an encoder and a decoder, each composed of multiple layers. The encoder processes the input data, such as a sentence, while the decoder generates the output, which could be a translation, summary, or any other relevant task. The attention mechanism allows the model to focus on different parts of the input sequence, enabling parallelization and making it more efficient compared to traditional sequential models like recurrent neural networks (RNNs).

Transformers have become a foundational architecture in natural language processing and are widely used in various applications, including machine translation, text summarization, question answering, and more. Notable Transformer-based models include BERT, GPT (Generative Pre-trained Transformer), and T5 (Text-To-Text Transfer Transformer).