| Résumé : |
Master advanced spaCy techniques, including custom pipelines, LLM integration, and model training, to build NLP solutions efficientlyKey FeaturesBuild end-to-end NLP workflows, from local development to production, using Weasel and FastAPIMaster no-training NLP development with spacy-llm, covering everything from prompt engineering to custom tasksCreate advanced NLP solutions, including custom components and neural coreference resolutionPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMastering 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 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 forThis 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. |
Mastering spaCy : Build structured NLP solutions with custom components and models powered by spacy-llm / PACKT PUBLISHING (2025)
