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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 the pre-trained BERT model

In Chapter 2, Understanding the BERT Model, we learned how to pre-train BERT using masked language modeling and next-sentence prediction tasks. But pre-training BERT from scratch is computationally expensive. So, we can download the pre-trained BERT model and use it. Google has open sourced the pre-trained BERT model and we can download it from Google Research's GitHub repository – https://github.com/google-research/bert. They have released the pre-trained BERT model with various configurations, shown in the following figure. denotes the number of encoder layers and denotes the size of the hidden unit (representation size):

Figure 3.1 – Different configurations of pre-trained BERT as provided by Google (https://github.com/google-research/bert)

The pre-trained model is also available in the BERT-uncased and BERT-cased formats. In BERT-uncased, all the tokens are lowercased, but in BERT-cased, the tokens are not lowercased and...

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