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

Python

Concepts

Deep Learning

Author (1):

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

Interpreting Hugging Face transformers with SHAP

In this section, we will interpret the Hugging Face transformers with SHAP. The Hugging Face platform provides an interface for an impressive list of transformer models.The section is divided into two parts:

Introducing SHAP
Explaining Hugging Face outputs with SHAP

Introducing SHAP

In Game Theory, a Shapley value expresses the distribution of the total values among "players" through their marginal contribution. In a sentence, the words are the "players." Each word will have a score. The total score is the value of the game. The value of each word is calculated over all the permutations of the sentence.The goal is to see how each word changes the meaning of a sentence.For example, there are seven words in the following sentence: "I love playing chess with my friends"The total number of permutations = !7= 7x6x5x4x3x2x1= 5040.The immediate conclusion is that SHAP will be challenging for a long text. However...