Modern NLP with Hugging Face Transformers: A Hands-On Introduction to Transformers, Tokenization, Text Classification, Pretrained Models, and NLP with Python
Learn modern natural language processing by building real Transformer-powered applications with Python and Hugging Face.
Natural language processing has become far more accessible, but getting started can still feel overwhelming. Transformers, tokenizers, pretrained models, pipelines, datasets, fine-tuning, and evaluation can quickly become confusing when they are introduced all at once.
Modern NLP with Hugging Face Transformers provides a clear, hands-on path for beginners who want to understand how modern NLP actually works and use it in practical Python projects.
Rather than burying you in advanced mathematics or disconnected examples, this book takes a progressive approach. You will begin with the foundations of NLP and Transformer models, then gradually learn how to load pretrained models, prepare text, run predictions, work with datasets, fine-tune a model, evaluate its performance, and turn it into a usable application.
Throughout the book, you will build a customer feedback classification project that develops alongside your skills, giving every new concept a practical purpose.
Inside, you will learn how to:Understand modern NLP and why Transformers changed language processing
Set up Python, PyTorch, and the Hugging Face ecosystem
Find and select suitable pretrained models from the Hugging Face Hub
Load models and tokenizers with Hugging Face Auto Classes
Understand tokenization, token IDs, special tokens, padding, truncation, and attention masks
Use Hugging Face pipelines for sentiment analysis, text classification, named entity recognition, and question answering
Run Transformer inference directly with PyTorch
Build a practical text classification workflow
Load, prepare, split, and tokenize datasets
Fine-tune a pretrained Transformer using Trainer and TrainingArguments
Evaluate models using accuracy, precision, recall, F1 score, and confusion matrices
Examine incorrect predictions and improve model performance
Save, reload, and reuse fine-tuned models
Build a complete Python application for classifying new customer feedback
The book also includes a Python refresher, troubleshooting guide, Transformers and Datasets quick reference, and recommended next steps for continuing your NLP journey.
You do not need previous experience with NLP, deep learning, or Transformer models. Basic Python familiarity is enough.
If you want to move beyond simply hearing about Transformers and start using pretrained language models, understanding their outputs, fine-tuning them on real data, and building practical NLP applications, this book gives you a structured place to begin.
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