Exploring BERT Libraries

In this lesson, let's explore the popular ktrain library for BERT.

Understanding ktrain

The ktrain library is a low-code library for augmented machine learning that was developed by Arun S. Maiya. It is a lightweight wrapper for Keras that makes it easier for us to build, train, and deploy deep learning models. It also includes several pre-trained models that make tasks such as text classification, summarization, question answering, translation, regression, and easier. It is implemented using tf.keras. It includes several interesting functionalities, such as a learning rate finder, a learning rate scheduler, and more.

With ktrain, you can build a model in 3-5 lines of code, which the author calls low-code machine learning. Let's see how we can use ktrain.

Before going forward, let's install the ktrain library. It can be installed via pip as shown here:

Now that we have installed ktrain, we will learn how to use the ktrain library for sentiment analysis, question-answering, and summarization.

Sentiment analysis using ktrain

Let's learn how to perform sentiment analysis using ktrain. We will use the Amazon product reviews dataset.

By following the link above, we can find the complete reviews data and also a small subset of data. In this lesson, we will use the subset of data containing the reviews of digital music. We can ...