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Mastering Transformers
Mastering Transformers

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

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Profile Icon Savaş Yıldırım Profile Icon Meysam Asgari- Chenaghlu
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Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (9 Ratings)
Paperback Sep 2021 374 pages 1st Edition
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Arrow left icon
Profile Icon Savaş Yıldırım Profile Icon Meysam Asgari- Chenaghlu
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€41.99
Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (9 Ratings)
Paperback Sep 2021 374 pages 1st Edition
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€28.99 €32.99
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Mastering Transformers

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

In this chapter, we will discuss what has changed in Natural Language Processing (NLP) over two decades. We experienced different paradigms and finally entered the era of Transformer architectures. All the paradigms help us to gain a better representation of words and documents for problem-solving. Distributional semantics describes the meaning of a word or a document with vectorial representation, looking at distributional evidence in a collection of articles. Vectors are used to solve many problems in both supervised and unsupervised pipelines. For language-generation problems, n-gram language models have been leveraged as a traditional approach for years. However, these traditional approaches have many weaknesses that we will discuss throughout the chapter.

We will further discuss classical Deep Learning (DL) architectures such as Recurrent Neural Networks (RNNs), Feed-Forward Neural Networks (FFNNs), and Convolutional Neural Networks (CNNs). These have improved the performance of the problems in the field and have overcome the limitation of traditional approaches. However, these models have had their own problems too. Recently, Transformer models have gained immense interest because of their effectiveness in all NLP tasks, from text classification to text generation. However, the main success has been that Transformers effectively improve the performance of multilingual and multi-task NLP problems, as well as monolingual and single tasks. These contributions have made Transfer Learning (TL) more possible in NLP, which aims to make models reusable for different tasks or different languages.

Starting with the attention mechanism, we will briefly discuss the Transformer architecture and the differences between previous NLP models. In parallel with theoretical discussions, we will show practical examples with the popular NLP framework. For the sake of simplicity, we will choose introductory code examples that are as short as possible.

In this chapter, we will cover the following topics:

  • Evolution of NLP toward Transformers
  • Understanding distributional semantics
  • Leveraging DL
  • Overview of the Transformer architecture
  • Using TL with Transformers
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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.

Who is this book for?

This book is for deep learning researchers, hands-on NLP practitioners, as well as ML/NLP educators and students who want to start their journey with Transformers. Beginner-level machine learning knowledge and a good command of Python will help you get the best out of this book.

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
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Table of Contents

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

Customer reviews

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rockstar82 Nov 14, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The first part of the book is a long introduction (around 80 pages). The book starts with a review and some code related to the early concepts that led to Transformers (e.g., PCA, bag-of-words, embeddings, RNNs, LSTMs) and jumps right into the attention mechanism and the Transformer architecture in several pages. The architecture is explained through various pictures and visualizations in order to help people understand why this model works so well. The few references included at the end of various chapters can always provide the additional details. While the first chapter is mostly theoretical, the following chapter does contain the gritty details related to progamming - from installing the various libraries to various tricks needed in order to make certain models work for solving a particular problem. The author's habits of adding good images everywhere helps a lot in this endeavour.The second part of the book is focused on two big issues: a) understanding the architectural differences between autoencoding models (e.g., BERT) and autoregressive models (e.g., BERT); and b) applying such models for classification tasks (e.g., text or token classification). This is the part in which the authors teach you how to fine-tune such models.The last part of the book is dedicated to advanced topics like efficient Transforms, multilingual models or production-related issues (e.g., serving Transformers, visualizing their attention layers or outputs, etc.). This is the part that links directly to advanced research, not unlike the kind of research that was submitted to top conferences in the field a couple of years ago (e.g., up to 2019, there were hardly any Transformer visualizations!).I particularly liked the ease with which the authors jumped straight into a difficult subject and teached it up to research level. There are very few books able to do this (e.g., thinking of the Dive into Deep Learning books or Grokking Deep Learning). It is great to see publishers introducing such books extremely fast (e.g., it took 5 years to have the first D3.js book published, but only 2 years to have a book about BERT or GPT-2/3). Make no mistake, while the title is still about Transformers, the real stars are the newer models discussed here. I consider this a five star book and I would recommend you to read it along the various examples of Transformer / BERT / GPT-2 explained source code that are available online. You will thank yourself later!
Amazon Verified review Amazon
Kelvin D. Meeks Sep 15, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A few other key words that come to mind to describe this book: Foundational, Hands-on, Practical, Crisp, Concise, Depth & Breadth, Tremendous Value.With the continued accelerating explosion in the growth of unstructured data collected by enterprises in texts and documents – the need to be able to analyze and derive meaningful information is more critical than ever – and will be the competitive advantage that distinguishes future winners from losers in the marketplace of solutions. This book is an investment in expanding your awareness of the techniques and capabilities that will help you navigate those challenges.From the book: “Transformer models have gained immense interest because of their effectiveness in all NLP tasks, from text classification to text generation….[and] effectively improve the performance of multilingual and multi-task NLP problems, as well as monolingual and single tasks.”This book is a practical guide to leveraging (and applying) some of the leading-edge concepts, algorithms, and libraries from the fields of Deep Learning (DL) and Natural Language Processing (NLP) to solve real-world problems – ranging from summarization to question-answering.In particular, this book will serve as a gentle guided tour of some of the important advances that have occurred (and continue to evolve) as the transformer architecture gradually evolved into an attention-based encoder-decoder architecture.What I particularly liked:The deep subject-matter experience and credentials of the authors (“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.”, “Meysam Asgari-Chenaghlu is an AI manager at Carbon Consulting and is also a Ph.D. candidate at the University of Tabriz.”)The companion “Code In Action” YouTube channel playlist for the book, and the GitHub repository with code examples.The excellent quality/conciseness/crispness of the writing.The extensive citation of relevant research papers – and references at the end of chapters.The authors’ deep practical knowledge – and discussions – of the advantages and disadvantages of different approaches.The exquisitely balanced need for technical depth in the details covered by a given chapter – with the need to maintain a steady pace of educating & keeping the reader engaged. Some books go too deep, and some stay too shallow. This book is exceptionally well balanced at just the right depth.The exceptional variety of examples covered.The quality of the illustrations used to convey complex concepts – Figures 1.19, 3.2, 3.3, 7.8, 9.3 are just a few examples of the many good diagrams.Chapter-1’s focus on getting the reader immediately involved in executing a hello-world example with Transformers. The overview of RNNs, FFNNs, LSTMs, and CNNs. An excellent overview of the developments in NLP over the last 10 years that led to the Tranformer architecture.Chapter-2’s guidance on installing the required software – and the suggestion of Google Colab as an alternative to Anaconda.Chapter-2’s coverage of community-provided models, benchmarks, TensorFlow, PyTorch, and Transformer - and running a simple Transformer from scratch.Chapter-3’s coverage of BERT – as well as ALBERT, RoBERTa, and ELECTRA.Chapter-4’s coverage of AR, GPT, BART, and NLG.Chapter-5’s coverage of fine-tuning language models for text classification (e.g., for sentiment analysis, or multi-class classification).Chapter-6’s coverage of NER and POS was of particular interest – given the effort that I had to expend last year doing my own deep-dive to prepare some recommendations for a client – I wish I had had this book then.Chapter-7’s coverage of USE and SBERT, zero-shot learning with BART, and FLAIR.Chapter-8’s discussion of efficient sparse parsers (Linformer, and BigBird) – as well as the techniques of distillation, pruning, and quantization – to make efficient models out of trained models. Chapter-8 may well be worth the price of the book, itself.Chapter-9’s coverage of multilingual and cross-lingual language model training (and pretraining). I found the discussion of “Cross-lingual similarity tasks” (see p-278) to be particularly interesting.Chapter-10’s coverage of Locust for load testing, fastAPI, and TensorFlow Extended (TFX) – as well as the serving of solutions in environments where CPU/GPU is available.Chapter-11’s coverage of visualization with exBERT and BertViz – as well as the discussion on tracking model training with TensorBoard and W&BThe ”Other Books You May Enjoy” section at the end of the book (“Getting Started with Google BERT”, and “Mastering spaCy”)Suggestions for the next edition:The fonts used for the text in some figures (e.g., 3.8, 3.10, 3.12, 3.13, 3.14, 4.5, 4.6, 6.2, 6.7, 8.4, 8.6, 9.4, 9.5 ) appear to be a bit fuzzy in the PDF version of the book. Compare those with the clarity of figure 6.6.
Amazon Verified review Amazon
Colbert Philippe Jun 29, 2022
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I love that book! It gives me the introduction and understanding on the very important topic that is Transformers. The book gives a bit of history and the current standard implementation of transformers. The book also delves in the advanced topic of Attention algorithms. This book is a "must have" for the seasoned AI partitioner of Language Model Transformers.
Amazon Verified review Amazon
Daniel Eduardo Portugal Revilla Sep 15, 2021
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Currently, I have more experience as a data engineer but Machine Learning(ML) and Deep Learning(DL) are not some of my strangers. However, I have an attraction for NLP so that I had the opportunity to read the Mastering Transformers.This is a fantastic book all in one about Transformers. Transformers are the principal NLP state of the art. Very futuristic. The principal features of the book are to give good examples step by step of which I can mention BERT the most popular Transformer model by Google, another nice feature is that you can learn the origins of NLP, the traditional techniques, ML and DL evolution.You can find information about the principal BERT variants like ALBERT, ELECTRA, RoBERTa. I missed maybe some examples or information about BioBERT and ClinicalBERT for medical domains and medical apps.Finally, there is one of my favorites topics about ML. How to deploy and serving your model to production for real or experimental APPs.
Amazon Verified review Amazon
Imokhai Sep 23, 2021
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The book is quite explanatory and has some good concept. I will recommend for those in the data space
Amazon Verified review Amazon
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