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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

Training the BERTSUM model

The code for training the BERTSUM model is open-sourced by the researchers of BERTSUM and it is available at https://github.com/nlpyang/BertSum. In this section, let's explore this and learn how to train the BERTSUM model. We will train the BERTSUM model on the CNN/DailyMail news dataset. We can also access the complete code from the GitHub repository of the book. In order to run the code smoothly, clone the GitHub repository of the book and run the code using Google Colab.

First, let's install the necessary libraries:

!pip install pytorch-pre-trained-bert
!pip install torch==1.1.0 pytorch_transformers tensorboardX multiprocess pyrouge
!pip install googleDriveFileDownloader

If you are working with Google Colab, switch to the content directory with the following code:

cd /content/

Clone the BERTSUM repository:

!git clone https://github.com/nlpyang/BertSum.git

Now switch to the bert_data directory:

cd /content/BertSum/bert_data/

The researchers have...

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