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

Über die Autorin bzw. den Autor

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. He has significantly contributed to the Turkish NLP community by developing a lot of open source software and resources. He also provides comprehensive consultancy to AI companies on their R&D projects. In his spare time, he writes and directs short films, and enjoys practicing yoga.

Meysam Asgari-Chenaghlu is an AI manager at Carbon Consulting and is also a Ph.D. candidate at the University of Tabriz. He has been a consultant for Turkey's leading telecommunication and banking companies. He has also worked on various projects, including natural language understanding and semantic search.

Von der hinteren Coverseite

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