You could say that your brain is basically taking all these words, putting them together, and figuring out what they mean based on context and previous knowledge. That’s kind of how Transformers work too!
So Let’s jump right into the details. First off, a Transformer has this thing called an encoder that takes in some input (like text or speech) and breaks it down into smaller pieces called tokens. These tokens are then fed through a series of layers where they get transformed (hence the name “Transformer”) by paying attention to each other based on their context.
For example, let’s say you have this sentence: “I love pizza.” The encoder would break it down into individual words like “I”, “love”, and “pizza”. Then those tokens would go through a series of layers where they get transformed by paying attention to each other based on their context. So the word “love” might pay more attention to the previous token (which is “I”) because it’s related in meaning, while ignoring other words that don’t have as much relevance (like “pizza”).
This process of transforming and attending to tokens continues until all the input has been processed. Then another part of the Transformer called a decoder takes over and generates an output based on what it learned from the encoder. So if you were using this Transformer for something like machine translation, the decoder would generate a translated version of your original sentence in a different language!
Now some specific examples of how Transformers are being used in real-life applications. One popular use case is for natural language processing (NLP) tasks like sentiment analysis or text classification. These models can help businesses understand customer feedback, identify trends in social media data, and more. Another application is for machine translation, which allows people to communicate across languages without the need for a human translator. And finally, Transformers are also being used for language generation tasks like writing news articles or creating product descriptions based on input data.
The Transformer architecture in a nutshell (or should we say “in a pizza slice”?) It’s basically a fancy way of saying that your brain is doing the same thing when you read and understand language, but with lots more math and computers involved.