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Full Description
Discover how to master advanced spaCy techniques, including custom pipelines, LLM integration, and model training, to build NLP solutions efficiently and effectively
Key Features
Build End-to-End NLP Workflows, From Local Development to Production with Weasel and FastAPI
Master No-Training NLP Development with spaCy-LLM, From Prompt Engineering to Custom Tasks
Create Advanced NLP Solutions, From Custom Components to Neural Coreference Resolution
Book DescriptionMastering spaCy, Second Edition is your comprehensive guide to building sophisticated NLP applications using the spaCy ecosystem. This revised edition embraces the latest advancements in NLP, featuring new chapters on Large Language Models with spaCy-LLM, transformers integration, and end-to-end workflow management with Weasel.
With this new edition you'll learn to enhance NLP tasks using LLMs with spaCy-llm, manage end-to-end workflows using Weasel and integrating spaCy with third-party libraries like Streamlit, FastAPI, and DVC. From training custom named entity recognition (NER) pipelines to categorizing emotions in Reddit posts, readers will explore advanced topics like text classification and coreference resolution. This book takes you on a journey through spaCy's capabilities, starting with the fundamentals of NLP, such as tokenization, named entity recognition, and dependency parsing. As you progress, you'll delve into advanced topics like creating custom components, training domain-specific models, and building scalable NLP workflows.
By end of the book, through practical examples, clear explanations, tips and tricks you will be empowered to build robust NLP pipelines and integrate them with web applications to build 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 forThis book is tailored for NLP engineers, machine learning developers, and LLM engineers looking to build production-grade language processing solutions. While primarily targeting professionals working with language models and NLP pipelines, it's also valuable for software engineers transitioning into NLP development. Basic Python programming knowledge and familiarity with NLP concepts is recommended to leverage spaCy's latest capabilities.
Contents
Table of Contents
Getting started with spaCy
Exploring spaCy Core Operations
Extracting Linguistic Features
Mastering Rule-Based Matching
Extracting Semantic Representations with spaCy Pipelines
Utilizing spaCy with Transformers
Enhancing NLP tasks using LLMs with spacy-llm
Training a NER pipeline component with spaCy
Creating End-to-End spaCy Workflows with Weasel
Training a Coreference Resolution pipeline
Integrating spaCy with third-party libraries
