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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
Languages
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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
Applying BERT to Other Languages

In previous chapters, we learned how BERT works and we also explored its different variants. Hitherto, however, we have only applied BERT to the English language. Can we also apply BERT to other languages? The answer to this question is yes, and that's precisely what we will learn in this chapter. We will use multilingual BERT (M-BERT) to compute the representation of different languages other than English. We will begin the chapter by understanding how M-BERT works and how to use it.

Next, we will understand how multilingual the M-BERT model is by investigating it in detail. Following this, we will learn about the XLM model. XLM stands for the cross-lingual language model, which is used to obtain cross-lingual representations. We will understand how XLM works and how it differs from M-BERT in detail.

Following on from this, we will learn...

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