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

Building KantaiBERT from scratch

We will build KantaiBERT in 15 steps from scratch and run it on an MLM example.Open Google Colaboratory (you need a Gmail account). Then upload KantaiBERT.ipynb, which is on GitHub in this chapter’s directory.The titles of the 15 steps of this section are similar to the titles of the notebook cells, which makes them easy to follow.Let’s start by loading the dataset.

Step 1: Loading the dataset

Ready-to-use datasets provide an objective way to train and compare transformers. This chapter aims to understand the training process of a transformer with notebook cells that can be run in real-time without waiting for hours to obtain a result.I chose to use the works of Immanuel Kant (1724-1804), the German philosopher who was the epitome of the Age of Enlightenment. The idea is to introduce human-like logic and pretrained reasoning for downstream reasoning tasks.Project Gutenberg, https://www.gutenberg.org, offers a wide range of free eBooks that...