How Transformer Learn From Whole Datasets
Transformer architecture by research gate This post is about a very deep question about transformer architecture. Question is: how does self-attention in transformer models handles long-range dependencies, and whether its ability extends to the entire dataset or just individual sequences. Before diving into the answer let see about Transformer. The transformer architecture is a deep learning model that has revolutionized natural language processing (NLP) and other sequence-based tasks. It relies on the attention mechanism, particularly self-attention, to weigh the importance of different parts of an input sequence when processing it. This allows transformers to capture long-range dependencies and relationships within the data, unlike traditional recurrent neural networks (RNNs) that process data ...