Transformer Model Architecture

To convert words into a numeric representation

To provide a higher-order vector representation of each token

To provide a vector representation of the position of the word in the input

<p>It encodes each input token into a vector by learning self-attention weights and passing them through a feed-forward network (FFN)</p> Signup and view all the answers

<p>To generate new tokens based on the input sequence and learned attention weights</p> Signup and view all the answers

<p>A mechanism that learns multiple sets of attention weights in parallel to represent different aspects of the input sequence</p> Signup and view all the answers

<p>By accepting an input token, passing it through the learned attention and FFN, and generating a new token based on the probability distribution</p> Signup and view all the answers

<p>To learn the relative importance of different tokens in the input sequence</p> Signup and view all the answers

<p>Through the use of positional encodings and self-attention mechanisms</p> Signup and view all the answers

<p>It allows the model to capture multiple aspects of the input sequence simultaneously</p> Signup and view all the answers