While Keras works especially well in standalone text classification tasks, sometimes it might be useful to use Keras in tandem with spaCy, which works exceedingly well in text analysis. In Chapter 3, spaCy's Language Models, Chapter 5, POS-Tagging and Its Applications, Chapter 6, NER-Tagging and Its Applications, and Chapter 7, Dependency Parsing, we already saw how well spaCy works with textual data, and it is no exception when it comes to deep learning – its text oriented approach makes it easy to build a classifier that works well with text. There are two ways to perform text classification with spaCy – one is using its own neural network library, thinc, while the other uses Keras. Both the examples we will explain are from spaCy's documentation, and it is highly recommended that you check out the original examples!
The first...
