Natural Language Processing with Transformers, Revised Edition: Building Language Applications with Hugging Face

Natural Language Processing with Transformers, Revised Edition: Building Language Applications with Hugging Face by Lewis Tunstall, Leandro von Werra, and Thomas Wolf is a practical guide for developers, data scientists, and machine learning engineers who want to build modern Natural Language Processing (NLP) applications using transformer-based models. Published by O'Reilly Media, the book combines foundational concepts with hands-on examples using the Hugging Face ecosystem.

The book explores how transformer models support a wide range of language tasks, including text classification, named entity recognition, question answering, summarization, translation, and conversational AI. Readers learn how to work with pretrained language models, prepare datasets, fine-tune models for specific tasks, and evaluate their performance using practical workflows.

Designed to connect machine learning concepts with real-world implementation, the book covers techniques for developing and applying transformer-based models in language applications. Code examples and practical guidance help readers understand the tools and processes involved in building NLP systems with Python and Hugging Face libraries.

Suitable for software developers, data scientists, AI practitioners, and students with a foundation in Python and machine learning, this book provides a hands-on introduction to modern transformer-based NLP development.

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