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Transformers for Natural Language Processing and Computer Vision

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

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781805128724
Length 730 pages
Edition 3rd Edition
Languages
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Toc

Table of Contents (24) Chapters Close

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

From task-specific SRL to emergence with ChatGPT

We have seen that OpenAI’s ChatGPT with GPT-4 has taken LLMs further for various tasks, including SRL. Thus, general-purpose LLMs do not necessarily need to learn syntax explicitly. They don’t need to learn the rules and principles of syntax that explain how to form phrases, clauses, and sentences. They can explain sentences with and beyond SRL.

We have gone through the main aspects of SRL in this chapter with several examples. This section will focus on running GPT-4 through the API to explore its ability to perform SRL without explicitly being trained for this task.

Open Semantic_Role_Labeling_GPT-4.ipynb in the directory of this chapter in the GitHub repository.

We will first install and import OpenAI.

1. Installing OpenAI

The program updates pip and installs OpenAI:

!pip install --upgrade pip
#Importing openai
try:
  import openai
except:
  !pip install openai -qq
  import openai
from openai...
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