When should I use subword tokenization?

What is subword tokenization and when should I use it?

Subword tokenization is a technique that breaks down words into smaller units, or subwords. It's useful when dealing with rare words or languages with rich morphology.

Can you give me an example of when it would be beneficial?

Sure! If you're training a model on a language with many compound words, like German, subword tokenization helps the model understand and generate these words better.

Are there any downsides to using subword tokenization?

Yes, one downside is that it can increase the complexity of the model and may require more computational resources. Additionally, it might lead to less interpretable results.

How does it compare to traditional word tokenization?

Traditional word tokenization splits text into whole words, which can miss nuances in languages with complex word forms. Subword tokenization captures these nuances better.