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Transformers for Natural Language Processing and Computer Vision - Third Edition

You're reading from  Transformers for Natural Language Processing and Computer Vision - Third Edition

Product type Book
Published in Feb 2024
Publisher Packt
ISBN-13 9781805128724
Pages 728 pages
Edition 3rd Edition
Languages
Author (1):
Denis Rothman Denis Rothman
Profile icon Denis Rothman

Table of Contents (24) Chapters

Preface 1. What Are Transformers? 2. Getting Started with the Architecture of the Transformer Model 3. Emergent vs Downstream Tasks: The Unseen Depths of Transformers 4. Advancements in Translations with Google Trax, Google Translate, and Gemini 5. Diving into Fine-Tuning through BERT 6. Pretraining a Transformer from Scratch through RoBERTa 7. The Generative AI Revolution with ChatGPT 8. Fine-Tuning OpenAI GPT Models 9. Shattering the Black Box with Interpretable Tools 10. Investigating the Role of Tokenizers in Shaping Transformer Models 11. Leveraging LLM Embeddings as an Alternative to Fine-Tuning 12. Toward Syntax-Free Semantic Role Labeling with ChatGPT and GPT-4 13. Summarization with T5 and ChatGPT 14. Exploring Cutting-Edge LLMs with Vertex AI and PaLM 2 15. Guarding the Giants: Mitigating Risks in Large Language Models 16. Beyond Text: Vision Transformers in the Dawn of Revolutionary AI 17. Transcending the Image-Text Boundary with Stable Diffusion 18. Hugging Face AutoTrain: Training Vision Models without Coding 19. On the Road to Functional AGI with HuggingGPT and its Peers 20. Beyond Human-Designed Prompts with Generative Ideation 21. Other Books You May Enjoy
22. Index
Appendix: Answers to the Questions

Summarization with T5 and ChatGPT

During the first seven chapters, we explored the architecture training, fine-tuning, and usage of several transformer ecosystems. In Chapter 7, The Generative AI Revolution with ChatGPT, we discovered that OpenAI has begun experimenting with zero-shot models that require no fine-tuning or development and can be implemented in a few lines.

The underlying concept of such an evolution relies on how transformers strive to teach a machine how to understand a language and express itself in a human-like manner. Thus, we have gone from training a model to teaching languages to machines.

ChatGPT, New Bing, Gemini, and other end-user software can summarize, so why bother with T5? Because Hugging Face T5 might be the right solution for your project, as we will see. It has unique qualities, such as task-specific parameters for summarizing.

Raffel et al. (2019) designed a transformer meta-model based on a simple assertion: every NLP problem can be...

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