Transformer architectures have revolutionized the field of natural language processing (NLP) due to their remarkable ability to capture long-range dependencies within text. Unlike traditional recurrent neural networks (RNNs), which process information sequentially, transformers leverage a mechanism called self-attention to weigh the relevance of ev

Transformer architectures utilize revolutionized the field of natural language processing (NLP) due to their robust ability to model long-range dependencies within text. These models are characterized by their global attention mechanism, which allows them to seamlessly weigh the relevance of different copyright in a sentence, regardless of their p