The self-attention mechanism, a pivotal component of transformer models, has significantly enhanced the handling of long-range dependencies in natural language processing (NLP) tasks. This mechanism addresses the limitations inherent in traditional recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), which often struggle with capturing dependencies over long sequences due to their sequential nature