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

A comprehensive guide for Natural Language Processing (NLP) professionals, focusing on advanced techniques and applications using the spaCy library. The text covers various topics, including linguistic feature extraction, rule-based matching, utilizing transformer models like BERT and RoBERTa within spaCy, and integrating Large Language Models (LLMs) with spacy-llm for tasks like summarization and custom applications. Additionally, it details how to train custom Named Entity Recognition (NER) components with user-provided data and how to build end-to-end NLP workflows using Weasel and DVC, highlighting best practices for data annotation and corpus creation. The book aims to equip readers with the skills to develop robust and efficient NLP solutions, offering practical examples and code snippets for hands-on learning.

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