Mastering spaCy Build structured NLP solutions with custom components and models powered by spacy-llm

Key FeaturesBuild End-to-End NLP Workflows, From Local Development to Production with Weasel and FastAPIMaster No-Training NLP Development with spaCy-LLM, From Prompt Engineering to Custom TasksCreate Advanced NLP Solutions, From Custom Components to Neural Coreference ResolutionBook Description
Mastering 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 learnApply transformer models and fine-tune them for specialized NLP tasksMaster spaCy core functionalities including data structures and processing pipelinesDevelop custom pipeline components and semantic extractors for domain-specific needsBuild scalable applications by integrating spaCy with FastAPI, Streamlit, and DVCMaster advanced spaCy features including coreference resolution and neural pipeline componentsTrain domain-specific models, including NER and coreference resolutionPrototype rapidly with spaCy-LLM and develop custom LLM tasksWho this book is for
This 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.
Table of ContentsGetting started with spaCyExploring spaCy Core OperationsExtracting Linguistic FeaturesMastering Rule-Based MatchingExtracting Semantic Representations with spaCy PipelinesUtilizing spaCy with TransformersEnhancing NLP tasks using LLMs with spacy-llmTraining a NER pipeline component with spaCyCreating End-to-End spaCy Workflows with WeaselTraining a Coreference Resolution pipelineIntegrating spaCy with third-party libraries

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