Transformers are a neural network architecture built around attention mechanisms, especially self-attention. They process sequences in parallel rather than step-by-step, making them efficient and powerful. The article focuses on how self-attention works within Transformers to manage dependencies between tokens. Transformers are composed of layers of attention and feed-forward networks, with optional encoder-decoder structures. They've become the foundation of modern NLP. Understanding Transformers requires understanding self-attention, making the article's insights directly relevant. Their design eliminates the need for recurrence or convolutions.