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Transformers for Natural Language Processing - Second Edition

You're reading from  Transformers for Natural Language Processing - Second Edition

Product type Book
Published in Mar 2022
Publisher Packt
ISBN-13 9781803247335
Pages 602 pages
Edition 2nd Edition
Languages
Author (1):
Denis Rothman Denis Rothman
Profile icon Denis Rothman

Table of Contents (25) Chapters

Preface 1. What are Transformers? 2. Getting Started with the Architecture of the Transformer Model 3. Fine-Tuning BERT Models 4. Pretraining a RoBERTa Model from Scratch 5. Downstream NLP Tasks with Transformers 6. Machine Translation with the Transformer 7. The Rise of Suprahuman Transformers with GPT-3 Engines 8. Applying Transformers to Legal and Financial Documents for AI Text Summarization 9. Matching Tokenizers and Datasets 10. Semantic Role Labeling with BERT-Based Transformers 11. Let Your Data Do the Talking: Story, Questions, and Answers 12. Detecting Customer Emotions to Make Predictions 13. Analyzing Fake News with Transformers 14. Interpreting Black Box Transformer Models 15. From NLP to Task-Agnostic Transformer Models 16. The Emergence of Transformer-Driven Copilots 17. The Consolidation of Suprahuman Transformers with OpenAI’s ChatGPT and GPT-4 18. Other Books You May Enjoy
19. Index
Appendix I — Terminology of Transformer Models 1. Appendix II — Hardware Constraints for Transformer Models 2. Appendix III — Generic Text Completion with GPT-2 3. Appendix IV — Custom Text Completion with GPT-2 4. Appendix V — Answers to the Questions

Training a custom GPT-2 language model

We will continue our top-to-bottom approach in this section by exploring an example with a GPT-2 custom model that we will train on a specific dataset. The goal remains to determine the level of abstract reasoning a GPT model can attain.

This section describes the interaction with a GPT-2 model for text completion trained on a specific dataset. We will focus on Step 12 of the Training_OpenAI_GPT_2.ipynb notebook described in detail in Appendix IV, Custom Text Completion with GPT-2.

You can read this section first to see how an example with a custom GPT-2 model will improve responses. Then read Appendix IV, Custom Text Completion with GPT-2, to understand how to train a GPT-2 to obtain specific responses.

You can also decide to read Appendix IV directly, which also contains the interaction of Step 12 described below.

First, let’s understand how the interaction with GPT-2 improved by training it.

Step 12...

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