Instead, we can just feed the model a bunch of random texts and let it figure out what words are important for understanding the overall meaning.

Here’s how it works: first, we take a big ol’ corpus (like Wikipedia) and chop it up into tiny little pieces called tokens. These could be individual words or phrases, depending on how you set things up. Then, we feed those tokens through a neural network that learns to predict the next token based on what came before. This is called “masked language modeling”.

For example: let’s say our input text is “The quick brown fox jumps over the lazy dog.” The model would see something like this: [The, quick, brown, fox, jumps, over, the, lazy, dog.] and then it would have to predict what comes next based on that context. Maybe it guesses “cat” or “house”, but either way it’s learning how words are related to each other in a sentence.

Now, here’s where things get interesting: instead of always showing the model the same input text (which would be boring and not very helpful), we can randomly mask out certain tokens and force the model to guess what they might have been based on the surrounding context. This is called “next sentence prediction” or NSP for short.

For example, let’s say our input text is: [The quick brown fox jumps over the lazy dog.] [The cat sat on the mat.] The model would see something like this: [The quick brown fox jumps over the lazy dog., The cat sat on the mat.] and then it would have to predict whether those two sentences are related or not. Maybe they’re both part of a larger story, or maybe they’re just random facts that happen to be next to each other in this corpus.

By training our model like this (with lots of different input texts and masked tokens), we can make it better at understanding the overall meaning of a sentence or paragraph without having to manually label every single one as “positive” or “negative”. This is called “pretraining”, because we’re doing all the heavy lifting upfront before actually using the model for something specific (like answering questions or generating new text).

And that, bro, is how BERT works in a nutshell. It’s not as scary as it sounds once you break it down into simpler terms!