This book provides a hands-on guide to using transformer models for natural language processing (NLP), focusing on Hugging Face’s powerful ecosystem. It covers key concepts in deep learning, model fine-tuning, and deployment, with practical examples in Python. The authors guide readers through implementing state-of-the-art NLP applications using libraries like Transformers, PyTorch, and TensorFlow.

Why Read This Book

• Learn the fundamentals of transformer architectures, including BERT, GPT, and T5.
• Gain hands-on experience with Hugging Face’s tools for training and fine-tuning models.
• Explore real-world NLP tasks such as text classification, question answering, and machine translation.
• Understand model optimization, performance tuning, and deployment strategies.
• Suitable for both beginners and experienced machine learning practitioners.

About the Authors

Lewis Tunstall is a machine learning engineer specializing in NLP and transformer-based models. He contributes to open-source AI projects and works on real-world NLP applications.

Leandro Von Werra is a data scientist and machine learning researcher at Hugging Face, focusing on large-scale language models and model optimization.

Thomas Wolf is the co-founder and chief science officer of Hugging Face, a leading company in NLP and transformer-based AI models. He is a key contributor to the open-source  Transformers library and an expert in deep learning research.