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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

Chapter 6: Fine-Tuning Language Models for Token Classification

In this chapter, we will learn about fine-tuning language models for token classification. Tasks such as Named Entity Recognition (NER), Part-of-Speech (POS) tagging, and Question Answering (QA) are explored in this chapter. We will learn how a specific language model can be fine-tuned on such tasks. We will focus on BERT more than other language models. You will learn how to apply POS, NER, and QA using BERT. You will get familiar with the theoretical details of these tasks such as their respective datasets and how to perform them. After finishing this chapter, you will be able to perform any token classification using Transformers.

In this chapter, we will fine-tune BERT for the following tasks: fine-tuning BERT for token classification problems such as NER and POS, fine-tuning a language model for an NER problem, and thinking of the QA problem as a start/stop token classification.

The following topics will be...

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