What is Transformer Architecture?
The Transformer architecture provides an innovative way to process and generate sequences of data, such as natural language, by leveraging self-attention mechanisms. Traditionally, recurrent neural networks (RNNs) were extensively used for sequence modeling tasks. However, RNNs suffer from inherent limitations like sequential computation, making it difficult to parallelize and capturing long-range dependencies effectively. The Transformer architecture, on the other hand, eliminates the sequential computation bottleneck and achieves parallelization by introducing self-attention.Self-attention is the key mechanism that sets the Transformer apart from other models. It allows the model to focus on different parts of the input sequence to determine the importance and relationships among its elements. The input sequence is divided into queries, keys, and values, and attention weights are computed between them. These attention weights represent the relevance of each element in the sequence to every other element.Related terms
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