In this final post of this series, we’ll look at training our learning model on our Emotions dataset. This post is the culmination of everything we’ve learned in the first three posts in this series and then implemented in the previous two posts in this series. So let’s dig in for the final stretch!
This post, and the following, will bring together everything we’ve learned in the previous four posts in this text classification series. Here we’re going to use the Emotions dataset we looked at in the last post and feed it to a model.
In this post, we’re going to look at the Emotions dataset that we briefly investigated in the previous post. Here we’re going to consider the basis of that dataset. Then we’ll load it up and see if what we have to do in order to feed the data to a training model.
In this post, we’ll explore some particular datasets. The focus here is just to get a feel for what can be presented to you and what’s available for you to use. We’ll do a little bit of code in this post to get you used to how to load a dataset.
Here we’ll continue on directly from the first post where we were learning the fundamentals of dealing with text that we plan to send to a learning model. Our focus was on the tokenization and encoding of the text. These are fundamentals that I’ll reinforce further in this post.
Let’s start this “Thinking About AI” series by thinking about the idea of classifying text. Classifying, broadly speaking, relates to testing quite well. This is because, at its core, the idea of classification focuses on categorization of data and decision making based on data. More broadly, as humans, we tend to classify everything by some categories.