From Textual content to Tokens: How BERT’s tokenizer, WordPiece, works.

From Textual content to Tokens: Your Step-by-Step Information to BERT Tokenization

Do you know that the way in which you tokenize textual content could make or break your language mannequin? Have you ever ever needed to tokenize paperwork in a uncommon language or a specialised area? Splitting textual content into tokens, it’s not a chore; it’s a gateway to reworking language into actionable intelligence. This story will train you every thing you could find out about tokenization, not just for BERT however for any LLM on the market.

In my final story, we talked about BERT, explored its theoretical foundations and coaching mechanisms, and mentioned how you can fine-tune it and create a questing-answering system. Now, as we go additional into the intricacies of this groundbreaking mannequin, it’s time to highlight one of many unsung heroes: tokenization.

I get it; tokenization would possibly look like the final boring impediment between you and the thrilling course of of coaching your mannequin. Imagine me, I used to suppose the identical. However I’m right here to inform you that tokenization isn’t just a “vital evil”— it’s an artwork kind in its personal proper.

On this story, we’ll look at each a part of the tokenization pipeline. Some steps are trivial (like normalization and pre-processing), whereas others, just like the modeling half, are what make every tokenizer distinctive.

By the point you end studying this text, you’ll not solely perceive the ins and outs of the BERT tokenizer, however you’ll even be geared up to coach it by yourself knowledge. And should you’re feeling adventurous, you’ll even have the instruments to customise this important step when coaching your very personal BERT mannequin from scratch.

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