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

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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

Summary

In this chapter, we saw how the T5 transformer models standardized the input of the encoder and decoder stacks of the Original Transformer. The Original Transformer architecture has an identical structure for each block (or layer) of the encoder and decoder stacks. However, the Original Transformer did not have a standardized input format for NLP tasks.

Raffel et al. (2018) designed a standard input for a wide range of NLP tasks by defining a text-to-text model. They added a prefix to an input sequence, indicating the NLP problem type to solve. This led to a standard text-to-text format. The Text-To-Text Transfer Transformer (T5) was born. This deceptively simple evolution made it possible to use the same model and hyperparameters for a wide range of NLP tasks. The invention of T5 takes the standardization process of transformer models a step further.

We then implemented a T5 model that could summarize any text. We tested the model on texts that were not part of ready...