English | 2021 | ISBN: 1801077657 | 374 pages | True PDF EPUB | 28.55 MB
Take a problem-solving approach to learning all about transformers and get up and running in no time by implementing methodologies that will build the future of NLP
Key Features
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
Book 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
Who this book is 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.
Table of Contents
From Bag-of-Words to the Transformers
A Hands-On Introduction to the Subject
Autoencoding Language Models
Autoregressive and Other Language Models
Fine-Tuning Language Models for Text Classification
Fine-Tuning Language Models for Token Classification
Text Representation
Working with Efficient Transformers
Cross-Lingual and Multilingual Language Modeling
Serving Transformer Models
Attention Visualization and Experiment Tracking
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