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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

Steps 6-7: Intermediate instructions

In this section, we will go through Steps 6, 7, and 7a, which are intermediate steps leading to Step 8, in which we will define and activate the model.

We want to print UTF-encoded text to the console when we are interacting with the model:

#@title Step 6: Printing UTF encoded text to the console
!export PYTHONIOENCODING=UTF-8

We want to make sure we are in the src directory:

#@title Step 7: Project Source Code
import os # import after runtime is restarted
os.chdir("/content/gpt-2/src")

We are ready to interact with the GPT-2 model. We could run it directly with a command, as we will do in the Training a GPT-2 language model section of Appendix IV, Custom Text Completion with GPT-2. However, in this section, we will go through the main aspects of the code.

interactive_conditional_samples.py first imports the necessary modules required to interact with the model:

#@title Step 7a: Interactive Conditional Samples...
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