How are Transformers revolutionizing NLP?
— Transformers are revolutionizing NLP by outperforming recurrent neural networks in sequence problems, thanks to their attention mechanisms.
What is the role of the attention mechanism in Transformers?
— The attention mechanism in Transformers allows for an infinite window to reference from, enabling the use of the entire context of a story while generating text.
How are word embeddings used in Transformers?
— Word embeddings are mapped to continuous values and positional information is added to the input embeddings using sine and cosine functions.
What is multi-headed attention in Transformers?
— Multi-headed attention is a module in a transformer network that uses attention weights to encode information on how each word should attend to all other words in a sequence.
How do Transformers make predictions better than other networks?
— Transformers leverage the power of the attention mechanism to make better predictions than other networks, allowing the NLP industry to achieve unprecedented results.
We’ve got the additional info