Transformers are a class of neural network architecture that has revolutionized sequence modeling, particularly in natural language processing. Unlike recurrent networks, transformers process sequences in parallel and rely on a mechanism called self-attention to weigh the influence of different parts of the input sequence on each other​.A transformer model typically consists of an encoder-decoder structure (in tasks like translation) or just an encoder stack (for tasks like classification or GPT-style generation), with multiple layers of self-attention and feed-forward networks. The self-attention mechanism allows the model to capture long-range dependencies efficiently by attending to all positions in the sequence when computing each output element​.Transformers achieved state-of-the-art in translation (e.g., Google’s Transformer, 2017) and have since become the foundation for large language models (BERT, GPT) and even made inroads in vision (Vision Transformers). Their ability to handle context and large input sequences has made them a cornerstone of modern deep learning models​.