Matching datasets and tokenizers

Downloading benchmark datasets to train transformers has many advantages. The data has been prepared, and every research lab uses the same references. Also, the performance of a transformer model can be compared to another model with the same data.

However, more needs to be done to improve the performance of transformers. Furthermore, implementing a transformer model in production requires careful planning and defining best practices.

In this section, we will define some best practices to avoid critical stumbling blocks.

Then we will go through a few examples in Python using cosine similarity to measure the limits of tokenization and encoding datasets.

Let’s start with best practices.

Best practices

Raffel et al. (2019) defined a standard text-to-text T5 transformer model. They also went further. They began destroying the myth of using raw data without preprocessing it first.

Preprocessing data reduces training time...