Transfer Learning with BERT
Deep learning models really shine with large amounts of training data. Having enough labeled data is a constant challenge in the field, especially in NLP. A successful approach that has yielded great results in the last couple of years is that of transfer learning. A model is trained in an unsupervised or semi-supervised way on a large corpus and then fine-tuned for a specific application. Such models have shown excellent results. In this chapter, we will build on the IMDb movie review sentiment analysis and use transfer learning to build models using GloVe (Global Vectors for Word Representation) pre-trained embeddings and BERT (Bi-Directional Encoder Representations from Transformers) contextual models. In this chapter, we will cover the following topics:
- Overview of transfer learning and use in NLP
- Loading pre-trained GloVe embeddings in a model
- Building a sentiment analysis model using pre-trained GloVe embeddings and fine...
