How to Build a Bert WordPiece Tokenizer in Python and HuggingFace
Building a transformer model from scratch can often be the only option for many more specific use cases. Although BERT and other transformer models have been pre-trained for a vast number of languages and domains, they do not cover everything.

Often, it is these less common use cases that stand to gain the most from having someone come along and build a specific transformer model. It could be for an uncommon language or less tech-savvy domain.

BERT is the most popular transformer for a wide range of language-based machine learning - from sentiment analysis to question and answering, BERT has enabled a diverse range of innovation across many borders and industries.

The first step for many in designing a new BERT model is the tokenizer. In this article, we'll take a look at the WordPiece tokenizer used by BERT - and see how we can build our own from scratch.

📕 Medium article:
https://towardsdatascience.com/how-to-build-a-wordpiece-tokenizer-for-bert-f505d97dddbb

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https://towardsdatascience.com/how-to-build-a-wordpiece-tokenizer-for-bert-f505d97dddbb?sk=eea06e01c9faecd939e10589e9de1291