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Mastering Transformers

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

Product type Paperback

Published in Jun 2024

Publisher Packt

ISBN-13 9781837633784

Length 462 pages

Edition 2nd Edition

Languages

Python

Tools

BERT

Concepts

GPT/LLMs

Authors (2):

Savaş Yıldırım

Meysam Asgari- Chenaghlu

View More author details

Table of Contents (25) Chapters

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

Why subscribe?

24. Other Books You May Enjoy

Technical requirements

You will need to install the libraries and software listed next. Although having the latest version is a plus, it is mandatory to install versions that are compatible with each other. For more information about the latest version installation for the Hugging Face transformer, take a look at their official web page at https://huggingface.co/docs/transformers/installation:

Anaconda
transformers 4.25.1
pytorch 1.13.1
tensorflow 2.9.2
datasets 1.4.1

Finally, all the code shown in this chapter is available in this book’s GitHub repository at https://github.com/PacktPublishing/Mastering-Transformers-Second-Edition.

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Authors (2)

Savaş Yıldırım

Savaş Yıldırım

Savaş Yıldırım graduated from the Istanbul Technical University Department of Computer Engineering and holds a Ph.D. degree in Natural Language Processing (NLP). Currently, he is an associate professor at the Istanbul Bilgi University, Turkey, and is a visiting researcher at the Ryerson University, Canada. He is a proactive lecturer and researcher with more than 20 years of experience teaching courses on machine learning, deep learning, and NLP. He has significantly contributed to the Turkish NLP community by developing a lot of open source software and resources. He also provides comprehensive consultancy to AI companies on their R&D projects. In his spare time, he writes and directs short films, and enjoys practicing yoga.

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Meysam Asgari- Chenaghlu

Meysam Asgari- Chenaghlu

Meysam Asgari-Chenaghlu is an AI manager at Carbon Consulting and is also a Ph.D. candidate at the University of Tabriz. He has been a consultant for Turkey's leading telecommunication and banking companies. He has also worked on various projects, including natural language understanding and semantic search.

See other products by Meysam Asgari- Chenaghlu

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