Transformers are a revolutionary neural network architecture introduced in 2017, primarily designed for processing sequential data, such as text. They operate by breaking down sentences into smaller units called tokens, which are then converted into numerical representations. The architecture consists of two main components: an encoder and a decoder. The encoder transforms the input tokens into abstract representations stored in a memory bank, while the decoder generates output tokens one at a time, utilizing attention mechanisms to focus on relevant parts of the input. This innovative approach has significantly advanced natural language processing tasks, including translation and text generation.