Implementing text classification
Let's recap on this chapter so far. We started by implementing an RNN using only numpy. Then, we continued with an LSTM implementation using primitive PyTorch operations. We'll conclude this arc by training the default PyTorch 1.3.1 LSTM implementation for a text classification problem. This example also requires the torchtext 0.4.0 package. Text classification (or categorization) refers to the task of assigning categories (or labels) depending on its contents. Text classification tasks include spam detection, topic labeling, and sentiment analysis. This type of problem is an example of a many-to-one relationship, which we defined in the Introduction to RNNs section.
In this section, we'll implement a sentiment ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access