How do attention mechanisms work in transformer models?

Transformer models are based on attention mechanisms, which revolutionize the way machines understand and process language. Transformers, unlike earlier models which processed words in a sequential manner, rely on attention for handling entire sequences simultaneously. This innovation allows the model to focus on the most important parts of a sequence input when making predictions. This improves performance for tasks such as translation, summarization and question answering. https://www.sevenmentor.com/da....ta-science-course-in

In a Transformer model, each word can consider the other words in a sentence, regardless of where they are located. The "self-attention" component is used to achieve this. Self-attention assigns each word a score based on its importance in relation to the other words. In the sentence, "The cat sat upon the mat", the word "cat", for example, might pay more attention to the words "sat" or "mat" rather than "the" to help the model better understand the meaning of the sentence.