Sequence-to-Sequence Models
Sequence-to-sequence models are a type of neural network architecture that is used for tasks where the input and output are both sequences of arbitrary length. These models consist of two main components: an encoder that processes the input sequence and a decoder that generates the output sequence. The encoder and decoder are typically implemented using recurrent neural networks (RNNs) such as LSTMs or GRUs. Sequence-to-sequence models have been successfully applied to a wide range of natural language processing tasks such as machine translation, text summarization, and question answering.
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