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

Building KantaiBERT from scratch

We will build KantaiBERT in 15 steps from scratch and run it on an MLM example.

Open Google Colaboratory (you need a Gmail account). Then upload KantaiBERT.ipynb, which is on GitHub in this chapter’s directory.

The titles of the 15 steps of this section are similar to the titles of the notebook cells, which makes them easy to follow.

Let’s start by loading the dataset.

Step 1: Loading the dataset

Ready-to-use datasets provide an objective way to train and compare transformers. In Chapter 5, Downstream NLP Tasks with Transformers, we will explore several datasets. However, this chapter aims to understand the training process of a transformer with notebook cells that can be run in real time without waiting for hours to obtain a result.

I chose to use the works of Immanuel Kant (1724-1804), the German philosopher who was the epitome of the Age of Enlightenment. The idea is to introduce human-like logic and pretrained...

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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

Building KantaiBERT from scratch

Step 1: Loading the dataset

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Authors (1)