March 10, 2021

764 words 4 mins read



Build and train state-of-the-art natural language processing models using BERT

repo name sudharsan13296/Getting-Started-with-Google-BERT
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created 2021-01-25

Getting started with BERT

Build and train state-of-the-art natural language processing models using BERT

About the book

BERT (bidirectional encoder representations from transformer) has revolutionized the world of natural language processing (NLP) with promising results. This book is an introductory guide that will help you get to grips with Google’s BERT architecture. With a detailed explanation of the transformer architecture, this book will help you understand how the transformer’s encoder and decoder work.

You’ll explore the BERT architecture by learning how the BERT model is pre-trained and how to use pre-trained BERT for downstream tasks by fine-tuning it for NLP tasks such as sentiment analysis and text summarization with the Hugging Face transformers library. As you advance, you’ll learn about different variants of BERT such as ALBERT, RoBERTa, and ELECTRA, and look at SpanBERT, which is used for NLP tasks like question answering. You’ll also cover simpler and faster BERT variants based on knowledge distillation such as DistilBERT and TinyBERT.

The book takes you through MBERT, XLM, and XLM-R in detail and then introduces you to sentence-BERT, which is used for obtaining sentence representation. Finally, you’ll discover domain-specific BERT models such as BioBERT and ClinicalBERT, and discover an interesting variant called VideoBERT.

Get the book

Clone the repo and run in Google Colab

1. A Primer on Transformer

2. Understanding the BERT model

3. Getting hands-on with BERT


5. BERT variants II - Based on knowledge distillation

6. Exploring BERTSUM for text summarization

7. Applying BERT for other languages

8. Exploring Sentence and Domain Specific BERT

9. Understanding VideoBERT, BART, and more

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