The Transformer is a groundbreaking model architecture introduced for processing sequential data, especially in natural language processing (NLP). Unlike traditional models that process data sequentially, Transformers use self-attention mechanisms to weigh the importance of different parts of the input sequence simultaneously. This approach allows for parallel processing and better capture of long-range dependencies in data. Transformers have revolutionized NLP by enabling more efficient and effective training of models for tasks such as machine translation, text summarization, and question answering. Their versatility has made them a cornerstone in modern deep learning applications.