Transformer

The Transformer is a neural network architecture based on deep learning that is widely used in natural language processing (NLP), image recognition, and other fields. Proposed in the 2017 Google paper "Attention is All You Need," it replaced the previously dominant RNN and LSTM architectures and has since become the backbone of most modern AI models. The Transformer's defining feature is its use of a **self-attention mechanism** to process relationships between words in a sentence in parallel. This allows it to efficiently understand context even in long texts, making it capable of handling a wide variety of natural language tasks including translation, summarization, dialogue, and classification. Well-known models based on the Transformer include: • BERT (Bidirectional Encoder Representations from Transformers) • GPT (Generative Pre-trained Transformer) • T5, XLNet, RoBERTa, Vision Transformer (ViT), and more Major applications include: • Contextual understanding in search engines • Natural language text generation and translation • Multimodal AI integrating speech and image recognition • Advances in question-answering systems, dialogue systems, and recommendation engines The Transformer is one of the most important architectures driving AI progress — and its applications continue to expand across fields including healthcare, education, creative work, and research.