Key Features
- Build end-to-end NLP workflows, from local development to production, using Weasel and FastAPI
- Master no-training NLP development with spacy-llm, covering everything from prompt engineering to custom tasks
- Create advanced NLP solutions, including custom components and neural coreference resolution
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
Mastering spaCy, Second Edition is your comprehensive guide to building sophisticated NLP applications using the spaCy ecosystem. This revised edition builds on the expertise of Duygu Altinok, a seasoned NLP engineer and spaCy contributor, and introduces new chapters by Déborah Mesquita, a data science educator and consultant known for making complex concepts accessible. This edition embraces the latest advancements in NLP, featuring chapters on large language models with spacy-llm, transformer integration, and end-to-end workflow management with Weasel. You'll learn how to enhance NLP tasks using LLMs, streamline workflows using Weasel, and integrate spaCy with third-party libraries like Streamlit, FastAPI, and DVC. From training custom Named Entity Recognition (NER) pipelines to categorizing emotions in Reddit posts, this book covers advanced topics such as text classification and coreference resolution. Starting with the fundamentals—tokenization, NER, and dependency parsing—you'll explore more advanced topics like creating custom components, training domain-specific models, and building scalable NLP workflows. Through practical examples, clear explanations, tips, and tricks, this book will equip you to build robust NLP pipelines and seamlessly integrate them into web applications for end-to-end solutions.What you will learn
- Apply transformer models and fine-tune them for specialized NLP tasks
- Master spaCy core functionalities including data structures and processing pipelines
- Develop custom pipeline components and semantic extractors for domain-specific needs
- Build scalable applications by integrating spaCy with FastAPI, Streamlit, and DVC
- Master advanced spaCy features including coreference resolution and neural pipeline components
- Train domain-specific models, including NER and coreference resolution
- Prototype rapidly with spacy-llm and develop custom LLM tasks
Who this book is for
This book is for NLP engineers, machine learning developers, and LLM engineers looking to build production-grade language processing solutions. Not just professionals working with language models and NLP pipelines but software engineers transitioning into NLP development will also find this book valuable. Basic Python programming knowledge and familiarity with NLP concepts is recommended to leverage spaCy's latest capabilities.
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