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Getting Started with Google BERT

You're reading from   Getting Started with Google BERT Build and train state-of-the-art natural language processing models using BERT

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Product type Paperback
Published in Jan 2021
Publisher Packt
ISBN-13 9781838821593
Length 352 pages
Edition 1st Edition
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Author (1):
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Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
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Table of Contents (15) Chapters Close

Preface 1. Section 1 - Starting Off with BERT
2. A Primer on Transformers FREE CHAPTER 3. Understanding the BERT Model 4. Getting Hands-On with BERT 5. Section 2 - Exploring BERT Variants
6. BERT Variants I - ALBERT, RoBERTa, ELECTRA, and SpanBERT 7. BERT Variants II - Based on Knowledge Distillation 8. Section 3 - Applications of BERT
9. Exploring BERTSUM for Text Summarization 10. Applying BERT to Other Languages 11. Exploring Sentence and Domain-Specific BERT 12. Working with VideoBERT, BART, and More 13. Assessments 14. Other Books You May Enjoy

Exploring BERT libraries

In the previous chapter, we saw how to use Hugging Face's transformers library. In this section, let's explore the following two other popular libraries for BERT:

  • ktrain
  • bert-as-service

Understanding ktrain

ktrain 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 more easier. It is implemented using tf.keras. It includes several interesting functionalities, such as a learning rate finder, a learning rate scheduler, and others.

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...

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