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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 - Second Edition

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 - Second Edition

Denis Rothman, Antonio Gulli

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Transformers are...well...transforming the world of AI. There are many platforms and models out there, but which ones best suit your needs?

Transformers for Natural Language Processing, 2nd Edition, guides you through the world of transformers, highlighting the strengths of different models and platforms, while teaching you the problem-solving skills you need to tackle model weaknesses.

You'll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model.

If you're looking to fine-tune a pretrained model, including GPT-3, then Transformers for Natural Language Processing, 2nd Edition, shows you how with step-by-step guides.

The book investigates machine translations, speech-to-text, text-to-speech, question-answering, and many more NLP tasks. It provides techniques to solve hard language problems and may even help with fake news anxiety (read chapter 13 for more details).

You'll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using DALL-E 2, ChatGPT, and GPT-4.

By the end of this book, you'll know how transformers work and how to implement them and resolve issues like an AI detective.
  • 1. What are Transformers?
  • 2. Getting Started with the Architecture of the Transformer Model
  • 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. Appendix I — Terminology of Transformer Models
  • 19. Appendix II — Hardware Constraints for Transformer Models
  • 20. Appendix III — Generic Text Completion with GPT-2
  • 21. Appendix IV — Custom Text Completion with GPT-2
  • 22. Appendix V — Answers to the Questions
  • Title: 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 - Second Edition
  • Author: Denis Rothman, Antonio Gulli
  • Original title: 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 - Second Edition
  • ISBN: 9781803243481, 9781803243481
  • Date of issue: 2022-03-25
  • Format: Ebook
  • Item ID: e_2t50
  • Publisher: Packt Publishing