Did you recognize that the way in which you tokenize text could make or break your language model? Have you ever ever desired 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 remodeling language into actionable intelligence. This story will teach you the whole lot it is advisable to find out about tokenization, not just for BERT but for any LLM on the market.
In my last story, we talked about BERT, explored its theoretical foundations and training mechanisms, and discussed 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 highlight considered one of the unsung heroes: tokenization.
I get it; tokenization might look like the last boring obstacle between you and the thrilling strategy of training your model. Consider me, I used to think the identical. But I’m here to let you know that tokenization just isn’t only a “vital evil”— it’s an art form in its own right.
On this story, we’ll examine every a part of the tokenization pipeline. Some steps are trivial (like normalization and pre-processing), while others, just like the modeling part, are what make each tokenizer unique.
By the point you finish reading this text, you’ll not only understand the ins and outs of the BERT tokenizer, but you’ll even be equipped to coach it on your individual data. And for those who’re feeling adventurous, you’ll even have the tools to customize this important step when training your very own BERT model from scratch.
Splitting text into tokens, it’s not a chore; it’s a gateway to remodeling language into actionable…