Basically, they’re like a supercharged version of your regular old neural networks that can handle longer sequences of text and understand the context better.

Here’s how it works: first, you feed in some input text (like “I love pizza”) into the transformer model. The model then breaks down this text into smaller pieces called tokens (in this case, “I”, “love”, “pizza”). These tokens are fed through a series of layers that learn to understand their relationships with each other and the overall context of the sentence.

For example, if you have another input like “I hate pizza” (which is obviously just as delicious but for some reason people still prefer it), the transformer model will recognize that “hate” has a different meaning than “love”, even though they both involve feelings towards food. This is because the model can understand the context of the sentence and how each word fits into it.

So basically, these transformers are like superheroes for NLP! They can handle longer sequences of text (up to thousands of words), learn from their mistakes, and even remember what they’ve learned in previous sentences. And best of all, they don’t get tired or need coffee breaks!

Now let me give you an example: imagine you have a piece of text that says “The quick brown fox jumps over the lazy dog”. If you feed this into a transformer model, it will understand that “quick” and “brown” are both adjectives describing the same noun (“fox”), while “jumps” is a verb indicating an action. The model can also recognize that “lazy” is another adjective describing a different noun (“dog”) and that “over” connects these two actions (the fox jumping over the dog).

Transformers for NLP are like superheroes for text analysis. They can handle longer sequences of text, understand context better than regular neural networks, and remember what they’ve learned in previous sentences. And best of all, they don’t get tired or need coffee breaks!