Decoders are used in various natural language processing tasks such as machine translation, text summarization, and question answering. They are also used in transformer-based models.

Decoders take an input sequence and produce an output sequence. In machine translation, the input is a source sentence and the output is a target sentence. In text summarization, the input is a document and the output is a summary. In question answering, the input is a question and the output is an answer.

Decoders are usually recurrent neural networks (RNNs). They can be unidirectional or bidirectional. Unidirectional decoders only have access to the input sequence. Bidirectional decoders have access to both the input and output sequences.The main difference between a decoder and an encoder is that a decoder generates sequences while an encoder consumes sequences. Transformers are a type of neural network that can be used for various NLP tasks.

They are especially well-suited for machine translation. BERT is a transformer-based model that is trained on a large amount of text data. It can be used for various NLP tasks such as text classification, question answering, and text generation.

Today's post suggests the following video which put yesterday and today's post in complete perspective.

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