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spaCy

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description spaCy Overview

spaCy is the leading library for production NLP. Unlike many research-oriented libraries, spaCy is designed to be fast and efficient enough for industrial use cases. It provides pre-trained pipelines for Named Entity Recognition (NER), Part-of-Speech tagging, and dependency parsing.

Its 'industrial-strength' philosophy means it prioritizes accuracy and speed over providing every possible linguistic algorithm, making it perfect for building production-ready text processing systems.

help spaCy FAQ

What NLP tasks does spaCy handle out of the box?

spaCy provides production-ready pipelines for tokenization, part-of-speech tagging, dependency parsing, lemmatization, sentence segmentation, and named entity recognition. It is a Python library made by Explosion.

Why do teams choose spaCy over NLTK?

spaCy is built for fast production pipelines, while NLTK is often used for teaching and experimentation. If you need to process many documents and deploy a model in an application, spaCy is usually the more practical fit.

Does spaCy support transformer models?

Yes, spaCy 3 supports transformer-based pipelines through its modern training and config system. It can also use smaller CPU-friendly statistical pipelines when latency and deployment size matter more.

Can spaCy train a custom named entity recognizer?

Yes. Teams commonly train spaCy NER models for domain-specific entities such as product names, medical terms, legal parties, or ticket IDs, using annotated examples and spaCy's training CLI.

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