So basically, BERT is this fancy algorithm that can read text backwards AND forwards at the same time. It’s kind of like having superhuman reading skills or something.

Here’s how it works: first, we take some input text and chop it up into tiny little pieces called “tokens”. These tokens are basically just individual words (or parts of words) that BERT can work with. Then, BERT uses a fancy algorithm to figure out which tokens are related to each other based on the context in which they appear.

For example, let’s say we have this sentence: “The quick brown fox jumps over the lazy dog.” If we feed this into BERT, it might output something like this:

– The (token) is related to quick (token), because they both appear at the beginning of the sentence.
– Brown (token) is related to quick (token), because they’re next to each other and form a compound word (“quick brown”).
– Fox (token) is related to jumps (token), because they’re separated by “lazy dog” but still appear in close proximity to each other.

And so on, until BERT has figured out all the relationships between every single token in our input text. This process of figuring out which tokens are related to each other is called “contextualization”, and it’s what makes BERT such a powerful tool for understanding natural language.

Now that we understand how BERT works, some real-world applications. One popular use case for BERT is in the field of sentiment analysis (i.e., figuring out whether a piece of text is positive or negative). For example, if we feed this sentence into BERT: “I absolutely love this product!”, it might output something like this:

– Love (token) is related to product (token), because they’re both important parts of the sentence.
– Absolutely (token) is also related to love (token), because it helps emphasize how much we love the product.
– This (token) is related to product (token), because it indicates that we’re talking about a specific item.

Based on this analysis, BERT might conclude that our sentiment towards the product is positive and that we really like it! Pretty cool, right?

Another popular use case for BERT is in the field of named entity recognition (i.e., figuring out which parts of a text are names or other important entities). For example, if we feed this sentence into BERT: “Barack Obama was born on August 4th, 1961.”, it might output something like this:

– Barack (token) is related to Obama (token), because they’re both part of the same name.
– Was (token) is also related to Obama (token), because it helps indicate that we’re talking about a specific person.
– Born (token) is related to August 4th, 1961 (token), because they appear in close proximity and are both important parts of the sentence.

Based on this analysis, BERT might conclude that “Barack Obama” is a named entity (i.e., a person’s name) and that we’re talking about his birth date! Pretty cool, right?

It’s basically like having superhuman reading skills for your computer to use on all kinds of text data. And the best part is, it can do this kind of analysis much faster and more accurately than any human could ever hope to achieve.