Home Artificial Intelligence The Ultimate Guide to Training BERT from Scratch: The Tokenizer

The Ultimate Guide to Training BERT from Scratch: The Tokenizer

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The Ultimate Guide to Training BERT from Scratch: The Tokenizer

From Text to Tokens: Your Step-by-Step Guide to BERT Tokenization

Towards Data Science
Photo by Glen Carrie on Unsplash

Did you realize 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 reworking language into actionable intelligence. This story will teach you every thing you should learn 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 easy methods 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 highlight one among the unsung heroes: tokenization.

I get it; tokenization might appear to be the last boring obstacle between you and the thrilling means of training your model. Imagine me, I used to think the identical. But I’m here to let you know that tokenization is just not only a “mandatory 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.

Tokenization pipeline — Image by Creator

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 personal data. And in case you’re feeling adventurous, you’ll even have the tools to customize this significant step when training your very own BERT model from scratch.

Splitting text into tokens, it’s not a chore; it’s a gateway to reworking language into actionable

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