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SpaCy

Process text, extract information, tokenize, parse, and recognize named entities with speed and accuracy.

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What is SpaCy?

SpaCy is a Python library for natural language processing (NLP) that helps you extract meaning from text. It handles core NLP tasks like breaking text into words (tokenisation), identifying grammatical structure (parsing), and spotting named entities such as people, organisations, and locations. The library is built for speed and accuracy, making it suitable for production systems that need to process large volumes of text. SpaCy works well for developers and data scientists who need to build text processing pipelines without extensive machine learning expertise. It comes with pre-trained models for multiple languages and includes tools for training custom models on your own data.

Key features

Tokenisation

splits text into individual words, punctuation, and meaningful units

Named entity recognition

identifies and labels people, places, organisations, and other entity types in text

Dependency parsing

maps grammatical relationships between words to understand sentence structure

Part-of-speech tagging

labels words with their grammatical role

Word vectors and similarity

compares words and documents based on meaning

Pre-trained models

includes ready-to-use language models for English, German, French, Portuguese, Dutch, Greek, and Norwegian

Pros & cons

Advantages

  • Fast and efficient; designed to handle large-scale text processing without excessive memory use
  • Well-documented with clear examples and straightforward API
  • Works entirely locally; no need to send data to external servers
  • Flexible training options; you can fine-tune models on your own domain-specific text
  • Active community with regular updates and a range of third-party extensions

Limitations

  • Requires Python programming knowledge; not a point-and-click tool
  • Pre-trained models work best on formal, clean text; performance drops on informal language, slang, or highly technical jargon
  • Smaller language model range compared to some commercial NLP platforms

Use cases

Extracting company names and contact details from business documents

Building search filters that understand what users mean rather than just matching keywords

Automatically categorising customer support tickets or emails

Preparing text data for machine learning projects by converting raw text into structured features

Analysing social media content to identify topics and sentiment patterns

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Pricing

Open Source (Free)

Free

Full access to SpaCy library, pre-trained models for multiple languages, training and customisation tools

Prodigy (Commercial)

Paid license required

Annotation tool and active learning platform for training custom NLP models more efficiently; complements SpaCy

Get started with SpaCy

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Try SpaCy free
  • Free plan available
  • No credit card