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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

SRL experiments with ChatGPT with GPT-4

We will run our SRL experiments with ChatGPT with GPT-4, beginning with a basic sample and then challenging the model with a more complex example to explore the system’s capacity and limits. You can access ChatGPT on OpenAI’s platform: https://chat.openai.com/.

ChatGPT has two revolutionary features:

  • It is syntax-free, meaning that it does not rely on syntax trees or rules at all. This approach is a paradigm shift from classical AI to generative models. Generative models detect statistical patterns in sequences but do not learn rules at all. The rules are implicit through statistical training, not explicit.
  • The responses are not pre-designed and remain stochastic, meaning that we will get a mostly reliable (like for any AI model) output but not the same word-for-word output each time. This stochastic, random behavior makes recent LLMs so human-like.

Let’s begin with a basic sample.

Basic...

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