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

Uploading the dataset

First, click on Upload Training File(s) as shown in Figure 18.2:

A close-up of a cloud Description automatically generated

Figure 18.2: Uploading the training dataset

The interface may evolve, but you will need to upload data. You will need to read the documentation on the Hugging Face platform carefully to prepare your data. The interface evolves constantly to follow the cutting-edge AI market. Choose your method but remain focused on the task: loading data.

You will need to follow Hugging Face’s procedures for data formatting: https://huggingface.co/docs/autotrain/image_classification.

Make sure to read the upgrades regularly. Again, it is worthwhile!

In this case, we are loading CIFAR-10 images as shown in the Figure 18.3 excerpt:

A collage of different images of airplanes and cars Description automatically generated

Figure 18.3: Excerpt of CIFAR-10 transportation images

The CIFAR-10 images in this chapter are from Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, 2009:https://www.cs.toronto.edu/~kriz/learning-features-2009-TR...

The rest of the chapter is locked

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

Table of Contents (24) Chapters

Uploading the dataset

Authors (1)

Personalised recommendations for you

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

Table of Contents (24) Chapters

Uploading the dataset

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you