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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
Authors (2):
Savaş Yıldırım Savaş Yıldırım
Profile icon Savaş Yıldırım
Meysam Asgari- Chenaghlu Meysam Asgari- Chenaghlu
Profile icon Meysam Asgari- Chenaghlu
View More author details
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 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 similarity tasks

Cross-lingual models are capable of representing text in a unified form, where sentences are from different languages but those with close meaning are mapped to similar vectors in vector space. XLM-R, as was detailed in the previous section, is one of the successful models in this scope. Now, let's look at some applications on this.

Cross-lingual text similarity

In the following example, you will see how it is possible to use a cross-lingual language model pre-trained on the XNLI dataset to find similar texts from different languages. A use-case scenario is where a plagiarism detection system is required for this task. We will use sentences from the Azerbaijani language and see whether XLM-R finds similar sentences from English—if there are any. The sentences from both languages are identical. Here are the steps to take:

  1. First, you need to load a model for this task, as follows:
    from sentence_transformers import SentenceTransformer...
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