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Transformers for Natural Language Processing

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

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781803247335
Length 602 pages
Edition 2nd Edition
Languages
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Table of Contents (25) Chapters Close

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

Pretraining a RoBERTa Model from Scratch

In this chapter, we will build a RoBERTa model from scratch. The model will use the bricks of the transformer construction kit we need for BERT models. Also, no pretrained tokenizers or models will be used. The RoBERTa model will be built following the fifteen-step process described in this chapter.

We will use the knowledge of transformers acquired in the previous chapters to build a model that can perform language modeling on masked tokens step by step. In Chapter 2, Getting Started with the Architecture of the Transformer Model, we went through the building blocks of the original Transformer. In Chapter 3, Fine-Tuning BERT Models, we fine-tuned a pretrained BERT model.

This chapter will focus on building a pretrained transformer model from scratch using a Jupyter notebook based on Hugging Face’s seamless modules. The model is named KantaiBERT.

KantaiBERT first loads a compilation of Immanuel Kant’s books created...

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