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Mastering Transformers

You're reading from Mastering Transformers

Product type Book

Published in Sep 2021

Publisher Packt

ISBN-13 9781801077651

Pages 374 pages

Edition 1st Edition

Languages

Concepts

Mobile Application Development

Authors (2):

Savaş Yıldırım

Meysam Asgari- Chenaghlu

View More author details

Table of Contents (16) Chapters

Preface

1. Section 1: Introduction – Recent Developments in the Field, Installations, and Hello World Applications

2. Chapter 1: From Bag-of-Words to the Transformer

3. Chapter 2: A Hands-On Introduction to the Subject

4. Section 2: Transformer Models – From Autoencoding to Autoregressive Models

5. Chapter 3: Autoencoding Language Models

6. Chapter 4:Autoregressive and Other 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. Section 3: Advanced Topics

11. Chapter 8: Working with Efficient Transformers

12. Chapter 9:Cross-Lingual and Multilingual Language Modeling

13. Chapter 10: Serving Transformer Models

14. Chapter 11: Attention Visualization and Experiment Tracking

15. Other Books You May Enjoy

Cross-lingual classification

So far, you have learned that cross-lingual models are capable of understanding different languages in semantic vector space where similar sentences, regardless of their language, are close in terms of vector distance. But how it is possible to use this capability in use cases where we have few samples available?

For example, you are trying to develop an intent classification for a chatbot in which there are few samples or no samples available for the second language; but for the first language—let's say English—you do have enough samples. In such cases, it is possible to freeze the cross-lingual model itself and just train a classifier for the task. A trained classifier can be tested on a second language instead of the language it is trained on.

In this section, you will learn how to train a cross-lingual model in English for text classification and test it in other languages. We have selected a very low-resource language known...

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Authors (2)

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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See other products by Savaş Yıldırım

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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