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

Python

Concepts

Deep Learning

Author (1):

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

The paradigm shift: What is an NLP task?

ChatGPT stunned the world when it suddenly became mainstream in late 2022 and early 2023. An AI could generate human-like text on practically any topic. Thousands of tasks were submitted to this incredible Generative AI transformer. ChatGPT Plus with GPT-4 seemed to be able to perform any task an end user came up with.

However, OpenAI couldn’t have possibly pretrained ChatGPT on thousands of tasks that could not be guessed beforehand. Nor could OpenAI have possibly fine-tuned its GPT models for everything the end user was coming up with.

Of course, a transformer model can be trained for specific tasks and determined downstream tasks such as summarizing. However, models such as ChatGPT can perform downstream tasks for which they were not trained.

This section takes us inside the head of a transformer model to see how the architecture described in Chapter 2, Getting Started with the Architecture of the Transformer Model, applies...