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SPACY
Industrial-strength NLP in Python
MIT
ABOUT
Building production NLP pipelines typically requires stitching together multiple libraries for tokenization, named entity recognition, dependency parsing, and text classification — each with different APIs, data formats, and serving infrastructure. spaCy solves this with a single, unified library that handles the full NLP pipeline: it provides pretrained pipelines for 70+ languages, a streamlined training system for custom models, and production-ready model packaging and deployment out of the box.
INSTALL
pip install spacyINTEGRATION GUIDE
1. Extract named entities, parts of speech, and dependency relations from text at scale
2. Build custom text classification and multi-label categorization pipelines with transformers
3. Deploy NLP models in production with efficient Cython-optimized inference
4. Train custom pipelines on domain-specific text data with active learning support
TAGS
nlppythontext-classificationtokenizationnermachine-learningcythonnatural-language-processing