Text classification is one of the most common tasks in NLP; it can be
used for a broad range of applications, such as tagging customer
feedback into categories or routing support tickets according to their
language. Chances are that your email program’s spam filter
is using text classification to protect your inbox from a deluge of
unwanted junk!
Another common type of text classification is sentiment analysis, which
(as we saw in Chapter 1) aims to identify the
polarity of a given text. For example, a company like Tesla might
analyze Twitter posts like the one in Figure 2-1 to
determine whether people like its new car roofs or not.
Figure 2-1. Analyzing Twitter content can yield useful feedback from customers (courtesy of Aditya Veluri)
Now imagine that you are a data scientist who needs to build a system
that can automatically identify emotional states such as “anger” or
“joy” that people express about your company’s product on
Twitter. In this chapter, we’ll tackle this task using a
variant of BERT called DistilBERT.1 The main
advantage of this model is that it achieves comparable performance to
BERT, while being significantly smaller and more efficient. This enables
us to train a classifier in a few minutes, and if you want to train a
larger BERT model you can simply change the checkpoint of the pretrained
model. A checkpoint corresponds to ...
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