The framework has revolutionized NLP, achieving state-of-the-art results in a wide variety of tasks. At its core, the transformer relies on a novel mechanism called intra-attention, which allows the model to weigh the relevance of different copyright in a string when comprehending meaning. This feature enables transformers to capture long-range dep

Transformers utilize emerged as a revolutionary paradigm in the field of natural language processing (NLP). These models leverage attention mechanisms to process and understand data in an unprecedented manner. With their capability to capture extended dependencies within sequences, transformers exhibit state-of-the-art results on a broad range of N