Transformers and RNNs are both types of neural networks commonly used in natural language processing (NLP), but they differ in several key ways.

Recurrent neural networks (RNNs) are a type of neural network that is designed to handle sequential data, such as language. They work by passing information from one time step to the next, allowing them to model dependencies between words in a sentence or characters in a text. However, RNNs can be slow to train and can struggle with long-term dependencies, where information from earlier time steps may be relevant to later ones.

Transformers, on the other hand, are a more recent type of neural network that use self-attention mechanisms to process sequences of data, such as sentences or paragraphs of text. Unlike RNNs, Transformers process the entire input sequence at once, rather than passing information from one time step to the next. This allows them to capture dependencies between words that are farther apart in the sequence, and can result in more efficient training and inference.
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