Overview
The Transformer model has transformed the landscape of natural language processing by introducing a novel architecture that relies heavily on self-attention mechanisms. This allows the model to weigh the importance of different words in a sentence, leading to improved understanding and generation of...
Key Terms
Example: In the sentence 'The cat sat on the mat', self-attention helps determine the relationship between 'cat' and 'sat'.
Example: Positional encoding helps distinguish 'The cat' from 'cat The' in a sentence.
Example: Multi-head attention can analyze different aspects of a sentence simultaneously.
Example: The encoder transforms the input sentence into a format the model can understand.
Example: The decoder takes the encoded representation and produces a translated sentence.
Example: Feed-forward networks help refine the information before passing it to the next layer.