Did you know that the way you mark up your text can make or break your language model? Have you ever wanted to mark up documents with a rare language or specialized domain? Splitting text into markup isn't a chore; it's the gateway to turning language into actionable intelligence. This story will teach you everything you need to know about tokenization, not just for BERT but for any LL.M.
In my last article, we discussed BERT, explored its theoretical foundations and training mechanisms, and discussed how to fine-tune it and create a question-answering system. Now, as we further explore the complexities of this groundbreaking model, it’s time to focus on one of the unsung heroes: tokenization.
I get it; tokenization seems like the last boring obstacle between you and the exciting process of model training. Believe me, I used to think so. But I’m here to tell you that tokenization isn’t just a “necessary evil” — it’s an art form in its own right.
In this story, we will examine each part of the tokenization pipeline. Some steps are trivial (like normalization and preprocessing), while others (like the modeling part) make each tokenizer unique.
By the time you finish reading this article, you will not only know the details of the BERT tokenizer, but you will also be able to train it on your own data. If you're feeling adventurous, you can even use tools to customize this crucial step when training your own BERT model from scratch.
Splitting text into markup isn't a chore; it's the gateway to turning language into actionable intelligence.
So, why is tokenization so important? Essentially, tokenization is a translator; it takes human language and translates it into a language that machines can understand: numbers. But there's a catch: during this translation process, the tokenizer must maintain the critical balance of finding meaning and calculating