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Transformers for Natural Language Processing and Computer Vision - Third Edition

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
Author (1):
Denis Rothman Denis Rothman
Profile icon 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

Training models with AutoTrain

When we activate the training process, Hugging Face first downloads our data from the Hugging Face Hub, where we uploaded our dataset.

Hugging Face’s AutoTrain interface provides a rapid, seamless, and intuitive approach to training text and vision models. Just click on Start Training when you are ready, as shown in Figure 18.8:

A blue rectangle with white text  Description automatically generated

Figure 18.8: AutoTrain: an intuitive approach

Hugging Face AutoTrain’s module acts as an overall controller for your training process. For more, read Hugging Face’s documentation, which evolves constantly as the platform makes progress: https://huggingface.co/docs/autotrain/main/en/index#who-should-use-autotrain.

Once a model is trained, we can train other ones to create a trained model set of potential candidates for our project.

Reminder: Evaluate the cost of training before running a training process. Budget management remains a critical factor in training AI models.

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