Tokenization is a common task we have when working with text data. It consists of splitting an entire text into small units, also known as tokens. Most Natural Language Processing (NLP) projects have tokenization as the first step because it’s the foundation for developing good models and helps us better understand the text we have.
Although tokenization in Python could be as simple as writing .split(), this method might not be the most efficient in some projects. That’s why, in this article, I’ll show 5 ways that will help you tokenize small texts, a large corpus, and even text written in a language other than English.
