What is a Transformer in AI?

The Transformer is a type of deep learning model introduced in the paper "Attention is All You Need" by Vaswani et al. It is primarily used in the field of natural language processing (NLP) and has achieved state-of-the-art results in a variety of NLP tasks.

How does a Transformer work?

The Transformer model is based on the self-attention mechanism, which allows it to weigh the importance of different words in an input sequence when generating an output sequence. Unlike previous sequence-to-sequence models like RNNs and LSTMs, the Transformer does not require the input data to be processed in a sequential manner, which makes it more efficient for processing long sequences.

The Transformer model consists of an encoder, which processes the input sequence, and a decoder, which generates the output sequence. Both the encoder and decoder are composed of multiple layers of self-attention and feed-forward neural networks.

What are the applications of Transformers?

Transformers have been used in a wide range of NLP tasks, including machine translation, text summarization, and sentiment analysis. They are also the basis for many state-of-the-art NLP models.

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