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

Preprocessing a WMT dataset

Vaswani et al. (2017) present the Transformer’s achievements on the WMT 2014 English-to-German translation task and the WMT 2014 English-to-French translation task. The Transformer achieves a state-of-the-art BLEU score. BLEU will be described in the Evaluating machine translation with BLEU section of this chapter.

The 2014 WMT contained several European language datasets. One of the datasets contained data taken from version 7 of the Europarl corpus. We will be using the French-English dataset from the European Parliament Proceedings Parallel Corpus, 1996-2011 (https://www.statmt.org/europarl/v7/fr-en.tgz).

Once you have downloaded the files and have extracted them, we will preprocess the two parallel files:

europarl-v7.fr-en.en
europarl-v7.fr-en.fr

We will load, clear, and reduce the size of the corpus.

Let’s start the preprocessing.

Preprocessing the raw data

In this section, we will preprocess...

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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

Preprocessing a WMT dataset

Preprocessing the raw data

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Authors (1)