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What is a Transformer model in AI? What is encoder-decoder architecture?

What is a Transformer model in AI? What is encoder-decoder architecture?

A Transformer model is a type of artificial intelligence (AI) architecture that uses self-attention mechanisms to process sequences of data.

It was first proposed in 2017 by researchers at Google AI and has since been adopted by many researchers and industry practitioners.

Transformer models are based on the encoder-decoder architecture, which consists of two components: an encoder and a decoder.

The encoder reads in a sequence of data and then converts it into a vector representation.

The decoder then takes the vector representation and converts it back into a sequence of data.

Examples of applications where Transformer models are used include natural language processing, machine translation, time series forecasting, and image recognition.

Transformer models are popular because they are faster to train and have better performance than traditional recurrent neural networks.

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