Upgrade Your Beginner NLP Project with BERT

When I first started learning Data Science and looking at projects, I thought you could either do a Deep Learning or regular project. This is not the case. With powerful models becoming more and more accessible, we can easily leverage some of the power of deep learning without having to optimize a neural network or use a GPU. In this post, we are going to look at embeddings. This is the way deep learning models represent words as vectors. We can part of the model to generate embeddings and fit a regular (scikit-learn) model on top to get some really incredible results! I'm going to explain each method individually, using graphs to represent why it works and show how to implement these techniques in Python.