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Transformers for Natural Language Processing

You're reading from Transformers for Natural Language Processing

Product type Book

Published in Jan 2021

Publisher Packt

ISBN-13 9781800565791

Pages 384 pages

Edition 1st Edition

Languages

Concepts

Mobile Application Development

Author (1):

Denis Rothman

Table of Contents (16) Chapters

Preface

1. Getting Started with the Model Architecture of the Transformer

2. Fine-Tuning BERT Models

3. Pretraining a RoBERTa Model from Scratch

4. Downstream NLP Tasks with Transformers

5. Machine Translation with the Transformer

6. Text Generation with OpenAI GPT-2 and GPT-3 Models

7. Applying Transformers to Legal and Financial Documents for AI Text Summarization

8. Matching Tokenizers and Datasets

9. Semantic Role Labeling with BERT-Based Transformers

10. Let Your Data Do the Talking: Story, Questions, and Answers

11. Detecting Customer Emotions to Make Predictions

12. Analyzing Fake News with Transformers

13. Other Books You May Enjoy

14. Index

Appendix: Answers to the Questions

Training a GPT-2 language model

This section will train a GPT-2 model on a custom dataset that we will encode. We will then interact with our customized model. We will be using the same kant.txt dataset as in Chapter 3, Pretraining a RoBERTa Model from Scratch.

This section refers to the code of Training_OpenAI_GPT_2.ipynb, which is in this chapter's directory of the book on GitHub.

It is important to note that we are running a low-level GPT- 2 model and not a one-line call to obtain a result. We are also avoiding pre-packaged versions. We are getting our hands dirty to understand the architecture of a GPT-2 from scratch. You might get some deprecation messages. However, the effort is worthwhile.

We will open the notebook and run it cell by cell.

Step 1: Prerequisites

The files referred to in this section are available in the chapter directory of the GitHub repository of this book:

Activate the GPU in the notebook's runtime...

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Authors (1)

Denis Rothman

Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an Advanced Planning and Scheduling (APS) solution used worldwide.

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