Exploring BERT Libraries
Learn about the ktrain library and how to train a model to perform sentiment analysis using it.
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:
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