You're reading from Transformers for Natural Language Processing and Computer Vision Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3

Product type Paperback

Published in Feb 2024

Publisher Packt

ISBN-13 9781805128724

Length 730 pages

Edition 3rd Edition

Languages

Processing

Tools

Processing

Concepts

Computer Vision

Author (1):

Denis Rothman

View More author details

Table of Contents (24) Chapters

Preface

1. What Are Transformers?

2. Getting Started with the Architecture of the Transformer Model FREE CHAPTER

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. Index

Appendix A: Revolutionizing AI: The Power of Optimized Time Complexity in Transformer Models

1. Appendix B: Answers to the Questions

Getting started with cutting-edge SRL

Chapter 7, The Generative AI Revolution with ChatGPT, introduced the complexity of adapting to the widening taxonomy of deep learning models in general, and LLMs, such as ChatGPT, in particular.

SRL provides enhanced information extraction on each word’s role in a sentence. The extracted information can improve translation, summarization, and other NLP tasks. A model that understands the semantic role of words will provide better-quality NLP outputs.

However, choosing a path to implement an NLP task has become challenging. SRL is no exception. We can sum up the issues to examine with four parameters that describe the resources used in this chapter: task-specific, general-purpose, development, and self-service:

A picture containing text, circle, screenshot, diagram Description automatically generated

Figure 12.1: The complexity of LLM management

Figure 12.1 shows the complexity of LLM project management. The parameters interact in various possible ways for a project. The risk of each parameter must be carefully...