Book Image

Transformers for Natural Language Processing - Second Edition

By : Denis Rothman
5 (1)
Book Image

Transformers for Natural Language Processing - Second Edition

5 (1)
By: Denis Rothman

Overview of this book

Transformers are...well...transforming the world of AI. There are many platforms and models out there, but which ones best suit your needs? Transformers for Natural Language Processing, 2nd Edition, guides you through the world of transformers, highlighting the strengths of different models and platforms, while teaching you the problem-solving skills you need to tackle model weaknesses. You'll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model. If you're looking to fine-tune a pretrained model, including GPT-3, then Transformers for Natural Language Processing, 2nd Edition, shows you how with step-by-step guides. The book investigates machine translations, speech-to-text, text-to-speech, question-answering, and many more NLP tasks. It provides techniques to solve hard language problems and may even help with fake news anxiety (read chapter 13 for more details). You'll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using DALL-E 2, ChatGPT, and GPT-4. By the end of this book, you'll know how transformers work and how to implement them and resolve issues like an AI detective.
Table of Contents (25 chapters)
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Appendix I — Terminology of Transformer Models

What are Transformers?

Transformers are industrialized, homogenized post-deep learning models designed for parallel computing on supercomputers. Through homogenization, one transformer model can carry out a wide range of tasks with no fine-tuning. Transformers can perform self-supervised learning on billions of records of raw unlabeled data with billions of parameters.

These particular architectures of post-deep learning are called foundation models. Foundation model transformers represent the epitome of the Fourth Industrial Revolution that began in 2015 with machine-to-machine automation that will connect everything to everything. Artificial intelligence in general and specifically Natural Language Processing (NLP) for Industry 4.0 (I4.0) has gone far beyond the software practices of the past.

In less than five years, AI has become an effective cloud service with seamless APIs. The former paradigm of downloading libraries and developing is becoming an educational exercise in many cases.

An Industry 4.0 project manager can go to OpenAI’s cloud platform, sign up, obtain an API key, and get to work in a few minutes. A user can then enter a text, specify the NLP task, and obtain a response sent by a GPT-3 transformer engine. Finally, a user can go to OpenAI and create applications with no knowledge of programming. Prompt engineering is a new skill that emerged from these models.

However, sometimes a GPT-3 model might not fit a specific task. For example, a project manager, consultant, or developer might want to use another system provided by Google AI, Amazon Web Services (AWS), the Allen Institute for AI, or Hugging Face.

Should a project manager choose to work locally? Or should the implementation be done directly on Google Cloud, Microsoft Azure, or AWS? Should a development team select Hugging Face, Google Trax, OpenAI, or AllenNLP? Should an artificial intelligence specialist or a data scientist use an API with practically no AI development?

The answer is all the above. You do not know what a future employer, customer, or user may want or specify. Therefore, you must be ready to adapt to any need that comes up. This book does not describe all the offers that exist on the market. However, this book provides the reader with enough solutions to adapt to Industry 4.0 AI-driven NLP challenges.

This chapter first explains what transformers are at a high level. Then the chapter explains the importance of acquiring a flexible understanding of all types of methods to implement transformers. The definition of platforms, frameworks, libraries, and languages is blurred by the number of APIs and automation available on the market.

Finally, this chapter introduces the role of an Industry 4.0 AI specialist with advances in embedded transformers.

We need to address these critical notions before starting our journey to explore the variety of transformer model implementations described in this book.

This chapter covers the following topics:

  • The emergence of the Fourth Industrial Revolution, Industry 4.0
  • The paradigm change of foundation models
  • Introducing prompt engineering, a new skill
  • The background of transformers
  • The challenges of implementing transformers
  • The game-changing transformer model APIs
  • The difficulty of choosing a transformer library
  • The difficulty of choosing a transformer model
  • The new role of an Industry 4.0 artificial intelligence specialist
  • Embedded transformers

Our first step will be to explore the ecosystem of transformers.