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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

Running downstream tasks

In this section, we will jump into some transformer cars and drive them around a bit to see what they do. There are many models and tasks. We will run a few of them in this section. We will be going through variants of these models during our journey in the book. Once you understand the process of running a few tasks, you will quickly understand all of them. After all, the human baseline for all these tasks is us!

A downstream task is a fine-tuned transformer task that inherits the model and parameters from a pretrained transformer model.

A downstream task is thus the perspective of a pretrained model running fine-tuned tasks. That means, depending on the model, a task is downstream if it was not used to fully pretrain the model. In this section, we will consider all the tasks downstream since we did not pretrain them.

Models will evolve, as will databases, benchmark methods, accuracy measurement methods, and leaderboard criteria. However, the...

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