Become an AI language understanding expert by mastering the quantum leap of Transformer neural network models
Key Features
- Build and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning models
- Go through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machine
- Learn training tips and alternative language understanding methods to illustrate important key concepts
Book Description
The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers.
The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face.
The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification.
By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets.
What you will learn
- Use the latest pretrained transformer models
- Grasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer models
- Create language understanding Python programs using concepts that outperform classical deep learning models
- Use a variety of NLP platforms, including Hugging Face, Trax, and AllenNLP
- Apply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and more
- Measure the productivity of key transformers to define their scope, potential, and limits in production
Who this book is for
Since the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers.
Readers who can benefit the most from this book include deep learning & NLP practitioners, data analysts and data scientists who want an introduction to AI language understanding to process the increasing amounts of language-driven functions.
Table of Contents
- Getting Started with the Model Architecture of the Transformer
- Fine-Tuning BERT Models
- Pretraining a RoBERTa Model from Scratch
- Downstream NLP Tasks with Transformers
- Machine Translation with the Transformer
- Text Generation with OpenAI GPT-2 and GPT-3 Models
- Applying Transformers to Legal and Financial Documents for AI Text Summarization
- Matching Tokenizers and Datasets
- Semantic Role Labeling with BERT-Based Transformers
- Let Your Data Do the Talking: Story, Questions, and Answers
- Detecting Customer Emotions to Make Predictions
- Analyzing Fake News with Transformers
- Appendix: Answers to the Questions
Denis Rothman graduated from Sorbonne University and Paris-Diderot University, patenting one of the very first word2matrix embedding solutions. Denis Rothman is the author of three cutting-edge AI solutions: one of the first AI cognitive chatbots more than 30 years ago; a profit-orientated AI resource optimizing system; and an AI APS (Advanced Planning and Scheduling) solution based on cognitive patterns used worldwide in aerospace, rail, energy, apparel, and many other fields. Designed initially as a cognitive AI bot for IBM, it then went on to become a robust APS solution used to this day.
