spaCy
spaCy provides Python natural language processing tools for entity recognition, tagging, parsing, and word vectors.
About spaCy
spaCy is a free Python library for natural language processing tasks including named entity recognition, part-of-speech tagging, dependency parsing, and use of word vectors. It also covers tokenization, sentence segmentation, text classification, lemmatization, morphological analysis, and entity linking, with support for 75+ languages and 84 trained pipelines for 25 languages.
It provides a configurable training system and project templates for preprocessing and training steps, and works with pretrained transformers and custom PyTorch or TensorFlow models. The separate spacy-llm package connects large language models to spaCy pipelines without training data, and built-in visualizers cover syntax and named entities.
Key features
- Named entity recognition, tagging, parsing and entity linking
- Sentence segmentation, text classification, lemmatization and morphological analysis
- Support for 75+ languages; 84 trained pipelines for 25 languages
- Multi-task learning with pretrained transformers such as BERT
- Custom components and custom models in PyTorch or TensorFlow
- Built-in visualizers for syntax and named entity recognition
Best for: Python developers building products or analyses that extract information from large text collections.
Good for
- API described as simple and productive for routine work
- Written in memory-managed Cython for large-scale information extraction
- Ecosystem with plugins and machine-learning stack integrations
Watch out for
- Trained pipelines cover 25 languages, fewer than the 75+ supported languages
spaCy at a glance
- Website
- spacy.io
- Category
- Development › Developer Tools
- Type
- Open-source project
- Pricing
- Free
- Platforms
- Web
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
Guides for Developer Tools
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spaCy FAQ
Which languages does spaCy support?
Support for 75+ languages, with 84 trained pipelines for 25 languages.
Can spaCy work with large language models without training data?
Yes, the spacy-llm package integrates LLMs into pipelines for prototyping and prompting, turning unstructured responses into outputs for NLP tasks with no training data required.
How do I start a custom training run?
Use the quickstart widget or the init config command, or clone a project template such as pipelines/tagger_parser_ud.
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