6/6/2023 0 Comments Inetwork dony tranUnlike RNNs (like seq2seq, 2014) or convolutional neural networks (CNNs) (for example, ByteNet), Transformers are able to capture distant or long-range contexts and dependencies in the data between distant positions in the input or output sequences.Layer outputs can be computed in parallel, instead of a series like an RNN. The main reasons is that Transformers replaced recurrence with attention, and computations can happen simultaneously. This makes them efficient on hardware like GPUs and TPUs. Unlike the recurrent neural networks (RNNs), Transformers are parallelizable.
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