You're reading from Transformers for Natural Language Processing Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4

Product type Paperback

Published in Mar 2022

Publisher Packt

ISBN-13 9781803247335

Length 602 pages

Edition 2nd Edition

Languages

Processing

Tools

Processing

Concepts

Mobile Application Development

Author (1):

Denis Rothman

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Table of Contents (25) Chapters

Preface

1. What are Transformers?

2. Getting Started with the Architecture of the Transformer Model FREE CHAPTER

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

Evaluating machine translation with BLEU

Papineni et al. (2002) came up with an efficient way to evaluate a human translation. The human baseline was difficult to define. However, they realized that we could obtain efficient results if we compared human translation with machine translation, word for word.

Papineni et al. (2002) named their method the Bilingual Evaluation Understudy Score (BLEU).

In this section, we will use the Natural Language Toolkit (NLTK) to implement BLEU:

http://www.nltk.org/api/nltk.translate.html#nltk.translate.bleu_score.sentence_bleu

We will begin with geometric evaluations.

Geometric evaluations

The BLEU method compares the parts of a candidate sentence to a reference sentence or several reference sentences.

Open BLEU.py, which is in the chapter directory of the GitHub repository of this book.

The program imports the nltk module:

from nltk.translate.bleu_score import sentence_bleu
from nltk.translate.bleu_score import...

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You're reading from Transformers for Natural Language Processing Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4

Table of Contents (25) Chapters

Evaluating machine translation with BLEU

Geometric evaluations

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Authors (1)