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Mastering Transformers

You're reading from   Mastering Transformers Build state-of-the-art models from scratch with advanced natural language processing techniques

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781801077651
Length 374 pages
Edition 1st Edition
Languages
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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 (16) Chapters Close

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 FREE CHAPTER 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

Translation language modeling and cross-lingual knowledge sharing

So far, you have learned about Masked Language Modeling (MLM) as a cloze task. However, language modeling using neural networks is divided into three categories based on the approach itself and its practical usage, as follows:

  • MLM
  • Causal Language Modeling (CLM)
  • Translation Language Modeling (TLM)

It is also important to note that there are other pre-training approaches such as Next Sentence Prediction (NSP) and Sentence Order Prediction (SOP) too, but we just considered token-based language modeling. These three are the main approaches that are used in the literature. MLM, described and detailed in previous chapters, is a very close concept to a cloze task in language learning.

CLM is defined by predicting the next token, which is followed by some previous tokens. For example, if you see the following context, you can easily predict the next token:

<s&gt...

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