Natural Language Processing: NLP With Transformers in Python

Here we'll start with our first NLP preprocessing technique, how we can use stopwords.

In the first part of exploration of tokens in NLP, we'll look at word, character, punctuation, part-of-word tokens and more.

In the second part of tokens in NLP, we'll look at model-specific special tokens.

We take a look and the Porter and Lancaster stemmers.

Here we take a look at reducing words to their lemma roots.

Here we will introduce Unicode Normalization and the two forms of equivalence, canonical and compatibility.

Here we take a look at the two different directions in Unicode Normalization, composition, and decomposition.

We'll move onto applying Unicode Normalization in Python with both NFD and NFC forms.

In the final Unicode Normalization session, we'll learn about and implement NFKD and NFKC forms.

In this session we'll work through an example of using windows to calculate sentiment for longer pieces of text.

An introduction to the two modes of Q&A, open domain (OD) and reading comprehension (RC).

An introduction to the three key model types we will be using in Q&A, retrievers, readers, and generators.

We introduce the SQuAD Q&A dataset.

How we process the SQuAD data to be more friendly for our use-case.

We take a look at refactoring our SQuAD processing data using pattern matching syntax introduced in Python 3.10.

We put together our first Q&A model.

We introduce the ROUGE metrics.

Learn how to implement ROUGE scores using Python.

We take a look at applying ROUGE performance metrics to our first Q&A model.

We work through the intuition and mathematics behind ROUGE-N.

We work through the intuition and mathematics behind ROUGE-L.