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 built KantaiBERT, a RoBERTa-like model transformer, from scratch using the building blocks provided by Hugging Face.

We first started by loading a customized dataset on a specific topic related to the works of Immanuel Kant. Depending on your goals, you can load an existing dataset or create your own. We saw that using a customized dataset provides insights into how a transformer model thinks. However, this experimental approach has its limits. Training a model beyond educational purposes would take a much larger dataset.

The KantaiBERT project was used to train a tokenizer on the kant.txt dataset. The trained merges.txt and vocab.json files were saved. A tokenizer was recreated with our pretrained files. KantaiBERT built the customized dataset and defined a data collator to process the training batches for backpropagation. The trainer was initialized, and we explored the parameters of the RoBERTa model in detail. The model was trained and saved...