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Mastering Transformers

You're reading from   Mastering Transformers The Journey from BERT to Large Language Models and Stable Diffusion

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Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781837633784
Length 462 pages
Edition 2nd Edition
Languages
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Concepts
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Authors (2):
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Savaş Yıldırım Savaş Yıldırım
Author Profile Icon Savaş Yıldırım
Savaş Yıldırım
Meysam Asgari- Chenaghlu Meysam Asgari- Chenaghlu
Author Profile Icon Meysam Asgari- Chenaghlu
Meysam Asgari- Chenaghlu
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Toc

Table of Contents (25) Chapters Close

Preface 1. Part 1: Recent Developments in the Field, Installations, and Hello World Applications
2. Chapter 1: From Bag-of-Words to the Transformers FREE CHAPTER 3. Chapter 2: A Hands-On Introduction to the Subject 4. Part 2: Transformer Models: From Autoencoders to Autoregressive Models
5. Chapter 3: Autoencoding Language Models 6. Chapter 4: From Generative Models to Large Language Models 7. Chapter 5: Fine-Tuning Language Models for Text Classification 8. Chapter 6: Fine-Tuning Language Models for Token Classification 9. Chapter 7: Text Representation 10. Chapter 8: Boosting Model Performance 11. Chapter 9: Parameter Efficient Fine-Tuning 12. Part 3: Advanced Topics
13. Chapter 10: Large Language Models 14. Chapter 11: Explainable AI (XAI) in NLP 15. Chapter 12: Working with Efficient Transformers 16. Chapter 13: Cross-Lingual and Multilingual Language Modeling 17. Chapter 14: Serving Transformer Models 18. Chapter 15: Model Tracking and Monitoring 19. Part 4: Transformers beyond NLP
20. Chapter 16: Vision Transformers 21. Chapter 17: Multimodal Generative Transformers 22. Chapter 18: Revisiting Transformers Architecture for Time Series 23. Index 24. Other Books You May Enjoy

From Generative Models to Large Language Models

In this chapter, you will learn about Generative Language Models (GLMs) and Large Language Model (LLMs). After this, you will learn how to pre-train any language model, such as Generated Pre-trained Transformer 2 (GPT-2), on your own text and use it for tasks such as Natural Language Generation (NLG). You will learn the basics of Text-to-Text Transfer Transformer (T5) models, conduct a hands-on multitask learning experiment with T5, and train a Multilingual T5 (mT5) model on your own Machine Translation (MT) data. After finishing this chapter, you will have an overview of GLMs and their various use cases in text2text applications, such as summarization, paraphrasing, multitask learning, zero-shot learning, and MT.

The following topics will be covered in this chapter:

  • Working with GLMs
  • Working with text-to-text models
  • AR language model training
  • GLM training
  • NLG using AR models
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