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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

Defining SRL

Shi and Lin (2019) advanced and proved that we can find who did what and where without depending on lexical or syntactic features. This chapter is based on Peng Shi and Jimmy Lin’s research at the University of Waterloo, California. They showed how transformers learn language structures better with attention layers.

SRL labels the semantic role as the role a word or group of words plays in a sentence and the relationship established with the predicate.

A semantic role is the role a noun or noun phrase plays in relation to the main verb in a sentence. For example, in the sentence Marvin walked in the park, Marvin is the agent of the event occurring in the sentence. The agent is the doer of the event. The main verb, or governing verb, is walked.

The predicate describes something about the subject or agent. The predicate could be anything that provides information on the features or actions of a subject. In our approach, we will refer to the predicate...

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