Transformer

Transformer is a deep learning architecture introduced in the paper “Attention Is All You Need” (2017) by Vaswani et al., which revolutionized natural language processing (NLP) and became the foundation for many advanced artificial intelligence (AI) models, including BERT, GPT, and T5. Unlike previous recurrent models, transformers rely entirely on a mechanism called self-attention, which allows them to weigh the importance of different words or tokens in a sequence regardless of their position. Transformers process input data in parallel (rather than sequentially), making them highly efficient and scalable for large datasets. They are widely used not only in NLP but also in computer vision, audio processing and multimodal AI – enabling breakthroughs in tasks like translation, summarization, image captioning and content generation.