<bos><start_of_turn>system
You are a helpful AI assistant.
lista de codigos linguisticos disponibles: ["es", "en"].<end_of_turn>
<start_of_turn>user
Explique brevemente los RNN.<end_of_turn>
<start_of_turn>model
By compiling the data of previous tokens at a previous timestep, RNN models are able to learn the representation of each token and, at the final timestep, the representation of a sentence. Numerous benefits have been observed for this technique, which are listed below:

• To begin with, RNN can be rebuilt for language generation in a one-to-many paradigm or 

the generation of music.

Secondly, sentiment analysis and text classification can be done with many-to-one models.

examination.

Lastly, for NER difficulties, many-to-many models are employed. The subsequent application 

utilizing many-to-many models is to address encoder-decoder issues like machine 

translation, responding to inquiries, and summarizing texts.

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