The Self-Attention Mechanism is a key component of the Transformer Architecture that allows the model to weigh the importance of different words or tokens in a sequence relative to each other, regardless of their distance. This capability enables the model to capture “long-range dependencies” effectively. It allows the model to decide “which word should be given more attention when you predict the next word”.
