Part 6/8:
Transformers represent a significant leap in the way language models operate. The first step in a transformer involves encoding each word as a list of numbers, essential for processing language mathematically. This encoding allows the model to handle the training process using continuous values.
A key feature of the transformer model is its "attention" mechanism. This process allows the numerical representations of words to communicate and adjust their meanings based on surrounding context. For example, the meaning of the word "bank" could be refined to represent a "riverbank" depending on adjacent words in a sentence. Additionally, transformers typically utilize feed-forward neural networks to enhance the model's ability to store information about language patterns gleaned during training.