 The introduction of the Transformer brought to light the fact that attention mechanisms were powerful in themselves, and that sequential recurrent processing of data was not necessary for achieving the performance gains of RNNs with attention.The Transformer uses an attention mechanism without being an RNN, processing all tokens at the same time and calculating attention weights between them.The fact that Transformers do not rely on sequential processing, and lend themselves very easily to parallelization, allows Transformers to be trained more efficiently on larger amounts of data.