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Transformers for Natural Language Processing

You're reading from   Transformers for Natural Language Processing Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more

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Product type Paperback
Published in Jan 2021
Publisher Packt
ISBN-13 9781800565791
Length 384 pages
Edition 1st Edition
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Table of Contents (16) Chapters Close

Preface 1. Getting Started with the Model Architecture of the Transformer 2. Fine-Tuning BERT Models FREE CHAPTER 3. Pretraining a RoBERTa Model from Scratch 4. Downstream NLP Tasks with Transformers 5. Machine Translation with the Transformer 6. Text Generation with OpenAI GPT-2 and GPT-3 Models 7. Applying Transformers to Legal and Financial Documents for AI Text Summarization 8. Matching Tokenizers and Datasets 9. Semantic Role Labeling with BERT-Based Transformers 10. Let Your Data Do the Talking: Story, Questions, and Answers 11. Detecting Customer Emotions to Make Predictions 12. Analyzing Fake News with Transformers 13. Other Books You May Enjoy
14. Index
Appendix: Answers to the Questions

Standard NLP tasks with specific vocabulary

This section focuses on Case 3: Rare words and Case 4: Replacing rare words from the Word2Vec tokenization section of this chapter.

We will use Training_OpenAI_GPT_2_CH08.ipynb, a renamed version of the notebook we used to train a dataset in Chapter 6, Text Generation with OpenAI GPT-2 and GPT-3 Models.

Two changes were made to the notebook:

  • dset, the dataset, was renamed mdset and contains medical content
  • A Python function was added to control the text that was tokenized using byte-level BPE

We will not describe Training_OpenAI_GPT_2_CH08.ipynb in detail. If necessary, take some time to go back through Chapter 6, Text Generation with OpenAI GPT-2 and GPT-3 Models. Make sure you upload the necessary files before beginning, as explained in Chapter 6. The files are on GitHub in the gpt-2-train_files directory of Chapter08. Although we are using the same notebook as in Chapter 6, note that the dataset, dset...

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