The transformer architecture was invented by a team of Google researchers in 2017. The team consisted of Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. The transformer architecture is a neural network architecture that is used for natural language processing tasks. It is based on the attention mechanism, which allows the model to learn long-range dependencies between different parts of the input sequence. The transformer architecture has been shown to be very effective for a variety of NLP tasks, including machine translation, text summarization, and question answering.
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