Transformers for Natural Language Processing

Transformers for Natural Language Processing
  • eBook:
    Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch. BERT, RoBERTa, T5, GPT-2, architecture of GPT-3, and much more
  • Author:
    Denis Rothman
  • Edition:
    1 edition
  • Categories:
  • Data:
    February 9, 2021
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    355 pages
  • Format:
    PDF, ePUB

Book Description
Become an AI language understanding expert by mastering the quantum leap of Transformer neural network model

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 in context with the Transformers.
The book takes you through Natural language processing with Python and examines various eminent models and datasets in the transformer technology created by internet giants such as Google, Facebook, Microsoft, OpenAI, Hugging Face, and other contributors.
The book trains you in three stages. The first stage introduces you to Transformer architectures, including RoBERTa, BERT, and DistilBERT Transformers with Hugging Face. 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 Generation. 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 pre-trained transformer models by tech giants to various datasets.

What you will learn

  • Use the latest pre-trained 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 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.


1. Getting Started with the Model Architecture of the Transformer
2. Fine-Tuning BERT Models
3. Pretraining a RoBERTa Model from Scratch
4. Downstream NLP Tasks with Transformers
5. Machine Translation with the Transformer
6. Text Generation with OpenAI GPT-2 and GPT-3 Models
7. Applying Transformers to Legal and Financial Documents for AI Text Summarization
8. Matching Tokenizers and Datasets
9. Semantic Role Labeling with BERT-Based Transformers
10. Let Your Data Do the Talking: Story, Questions, and Answers
11. Detecting Customer Emotions to Make Predictions
12. Analyzing Fake News with Transformers

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