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spaCy

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About

spaCy is an industrial-strength, blazing-fast NLP library in Python, written in memory-managed Cython, making it ideal for processing entire web dumps and large-scale information extraction. It provides support for over 75 languages and 84 trained pipelines for 25 languages. Key features include multi-task learning with pretrained transformers like BERT, advanced components for named entity recognition, dependency parsing, text classification, and a production-ready training system. The library is highly extensible with custom components and attributes, and offers built-in visualizers. Additionally, the spacy-llm package allows seamless integration of Large Language Models into structured NLP pipelines for fast prototyping.

Platform
Web
Task
language processing

Features

production-ready training system

llm integration

84 trained pipelines for 25 languages

support for 75+ languages

built-in visualizers for syntax and ner

easily extensible with custom components

named entity recognition, pos tagging, dependency parsing

multi-task learning with pretrained transformers

FAQs

What type of software is spaCy?

spaCy is a free, industrial-strength open-source library for Natural Language Processing (NLP) written in Python and Cython.

How many languages are supported?

spaCy supports over 75 languages and offers 84 trained pipelines for 25 languages.

Can I integrate Large Language Models (LLMs)?

Yes, the spacy-llm package provides a modular system for integrating LLMs into structured NLP workflows for fast prototyping.

Is spaCy production ready?

Yes, spaCy is designed for real work and features a production-ready training system, model packaging, and workflow management.

Pricing Plans

Open Source
Free Plan

Support for 75+ languages

84 trained pipelines for 25 languages

Multi-task learning with pretrained transformers

Production-ready training system

Named entity recognition and PoS tagging

Dependency parsing and text classification

Built-in visualizers for syntax and NER

LLM integration via spacy-llm

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