From Text to Tokens: Your Step-by-Step Guide to BERT Tokenization
Did you know that the way you tokenize text can make or break your language model? Have you ever wanted to tokenize documents in a rare language or a specialized domain? Splitting text into tokens, it’s not a chore; it’s a gateway to transforming language into actionable intelligence. This story will teach you everything you need to know about tokenization, not only for BERT but for any LLM out there.
In my last story, we talked about BERT, explored its theoretical foundations and training mechanisms, and discussed how to fine-tune it and create a questing-answering system. Now, as we go further into the intricacies of this groundbreaking model, it’s time to spotlight one of the unsung heroes: tokenization.
I get it; tokenization might seem like the last boring obstacle between you and the thrilling process of training your model. Believe me, I used to think the same. But I’m here to tell you that tokenization is not just a “necessary evil”— it’s an art form in its own right.
In this story, we’ll examine every part of the tokenization pipeline. Some steps are trivial (like normalization and pre-processing), while others, like the modeling part, are what make each tokenizer unique.
By the time you finish reading this article, you’ll not only understand the ins and outs of the BERT tokenizer, but you’ll also be equipped to train it on your own data. And if you’re feeling adventurous, you’ll even have the tools to customize this crucial step when training your very own BERT model from scratch.
Splitting text into tokens, it’s not a chore; it’s a gateway to transforming language into actionable…
This post originally appeared on TechToday.