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Mastering Transformers
Mastering Transformers

Mastering Transformers: Build state-of-the-art models from scratch with advanced natural language processing techniques

By Meysam Asgari- Chenaghlu , Savaş Yıldırım
$39.99 $27.98
Book Sep 2021 374 pages 1st Edition
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eBook
$39.99 $27.98
Print
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Renews at $15.99p/m

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Mastering Transformers

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

In this section, you will learn about all aspects of Transformers at an introductory level. You will write your first hello-world program with Transformers by loading community-provided pre-trained language models and running the related code with or without a GPU. Installing and utilizing the tensorflow, pytorch, conda, transformers, and sentenceTransformers libraries will also be explained in detail in this section.

This section comprises the following chapters:

  • Chapter 1, From Bag-of-Words to the Transformers
  • Chapter 2, A Hands-On Introduction to the Subject
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Key benefits

  • Explore quick prototyping with up-to-date Python libraries to create effective solutions to industrial problems
  • Solve advanced NLP problems such as named-entity recognition, information extraction, language generation, and conversational AI
  • Monitor your model's performance with the help of BertViz, exBERT, and TensorBoard

Description

Transformer-based language models have dominated natural language processing (NLP) studies and have now become a new paradigm. With this book, you'll learn how to build various transformer-based NLP applications using the Python Transformers library. The book gives you an introduction to Transformers by showing you how to write your first hello-world program. You'll then learn how a tokenizer works and how to train your own tokenizer. As you advance, you'll explore the architecture of autoencoding models, such as BERT, and autoregressive models, such as GPT. You'll see how to train and fine-tune models for a variety of natural language understanding (NLU) and natural language generation (NLG) problems, including text classification, token classification, and text representation. This book also helps you to learn efficient models for challenging problems, such as long-context NLP tasks with limited computational capacity. You'll also work with multilingual and cross-lingual problems, optimize models by monitoring their performance, and discover how to deconstruct these models for interpretability and explainability. Finally, you'll be able to deploy your transformer models in a production environment. By the end of this NLP book, you'll have learned how to use Transformers to solve advanced NLP problems using advanced models.

What you will learn

Explore state-of-the-art NLP solutions with the Transformers library Train a language model in any language with any transformer architecture Fine-tune a pre-trained language model to perform several downstream tasks Select the right framework for the training, evaluation, and production of an end-to-end solution Get hands-on experience in using TensorBoard and Weights & Biases Visualize the internal representation of transformer models for interpretability

Product Details

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Publication date : Sep 15, 2021
Length 374 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781801077651
Category :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
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Product feature icon Access this title in our online reader with advanced features
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Product Details


Publication date : Sep 15, 2021
Length 374 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781801077651
Category :

Table of Contents

16 Chapters
Preface Chevron down icon Chevron up icon
1. Section 1: Introduction – Recent Developments in the Field, Installations, and Hello World Applications Chevron down icon Chevron up icon
2. Chapter 1: From Bag-of-Words to the Transformer Chevron down icon Chevron up icon
3. Chapter 2: A Hands-On Introduction to the Subject Chevron down icon Chevron up icon
4. Section 2: Transformer Models – From Autoencoding to Autoregressive Models Chevron down icon Chevron up icon
5. Chapter 3: Autoencoding Language Models Chevron down icon Chevron up icon
6. Chapter 4:Autoregressive and Other Language Models Chevron down icon Chevron up icon
7. Chapter 5: Fine-Tuning Language Models for Text Classification Chevron down icon Chevron up icon
8. Chapter 6: Fine-Tuning Language Models for Token Classification Chevron down icon Chevron up icon
9. Chapter 7: Text Representation Chevron down icon Chevron up icon
10. Section 3: Advanced Topics Chevron down icon Chevron up icon
11. Chapter 8: Working with Efficient Transformers Chevron down icon Chevron up icon
12. Chapter 9:Cross-Lingual and Multilingual Language Modeling Chevron down icon Chevron up icon
13. Chapter 10: Serving Transformer Models Chevron down icon Chevron up icon
14. Chapter 11: Attention Visualization and Experiment Tracking Chevron down icon Chevron up icon
15. Other Books You May Enjoy Chevron down icon Chevron up icon

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