Transformer Architecture
Transformer Architecture is a neural network architecture that is primarily used for natural language processing tasks such as language translation, text summarization, and question answering. It was introduced in 2017 by Vaswani et al. and has since become a popular choice due to its ability to handle long-range dependencies and its parallelizable nature. The architecture consists of an encoder and a decoder, both of which are composed of multiple layers of self-attention and feedforward neural networks. Self-attention allows the model to weigh the importance of different parts of the input sequence when making predictions, while the feedforward networks provide non-linear transformations. The Transformer Architecture has achieved state-of-the-art results on a variety of natural language processing tasks and has been used in many real-world applications.
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