A practical guide to start applying the BERT language model to your own business problem.

BERT, Bi-directional Encoder Representation from Transformer, is a state of the art language model by Google which can be used for cutting-edge natural language processing (NLP) tasks.

After reading this article, you will have a basic understanding of BERT and will be able to utilize it for your own business applications. It would be helpful if you are familiar with Python and have a general idea of machine learning.

The BERT models I will cover in this article are:

• Binary or multi-class classification
• Regression model
• Question-answering applications

Introduction to BERT

BERT is trained on the entirety of Wikipedia (~2.5 billion words), along with a book corpus (~800 million words). In order to utilize BERT, you won’t have to repeat this compute-intensive process.

BERT brings the transfer learning approach into the natural language processing area in a way that no language model has done before.

Transfer Learning

Transfer learning is a process where a machine learning model developed for a general task can be reused as a starting point for a specific business problem.

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