Reinforcing your learning through the Python Notebook
Even though NLP ML is highly complex, the implementation for the wrapper code is deceptively simple. This is because of Pluto’s structured object-oriented approach. First, we created a base class for Pluto in Chapter 1 and used the decorator to add a new method as we learned new augmentation concepts. In Chapter 2, Pluto learned to download any of the thousands of real-world datasets from the Kaggle website. Chapters 3 and 4 introduced the wrapper functions process using powerful open source libraries under the hood. Finally, Chapter 5 put forward the text augmentation concepts and methods when using the Nlpaug library.
Therefore, building upon our previous knowledge, the wrapper functions in this chapter use the powerful NLP ML pre-trained model to perform the augmentations.
In particular, this chapter will present wrapper functions and the augmenting results for the Netflix and Twitter real-world datasets using the...