Transformer Architecture

About

The Transformer architecture is a revolutionary neural network design that has significantly advanced the field of natural language processing (NLP). Introduced in 2017, it replaced traditional Recurrent Neural Networks (RNNs) by leveraging self-attention mechanisms to process input sequences in parallel. This allows Transformers to efficiently capture long-range dependencies and complex relationships within text, making them highly effective for tasks like machine translation, text summarization, and question answering. Transformers consist of two primary components: an encoder and a decoder. The encoder transforms input sequences into vector representations, while the decoder generates output sequences based on these representations. The self-attention mechanism is central to this process, enabling the model to focus on relevant parts of the input when generating output. This architecture has led to the development of powerful models like BERT and GPT, revolutionizing language modeling and processing capabilities.