spaCy is an open-source software library for advanced Natural Language Processing, written in the programming languages Python and Cython.
spaCy is designed to help you do real work — to build real products, or gather real insights.
spaCy comes with pre-trained statistical models and word vectors.
Key Features
- Models for spaCy can be installed as Python packages.
- Neural models for tagging, parsing and entity recognition.
- Non-destructive tokenization.
- Named entity recognition.
- “Alpha tokenization” support for over 31 languages.
- 13 Statistical models models for 8 languages.
- Pre-trained word vectors.
- Part-of-speech tagging.
- Labelled dependency parsing.
- Syntax-driven sentence segmentation.
- Text classification.
- Built in visualizers for syntax and NER.
- Convenient string-to-hash mapping.
- Export to numpy data arrays.
- Efficient binary serialization.
- Easy model packaging and deployment.
- State-of-the-art speed.
- Robust, rigorously evaluated accuracy.
- Deep learning integration. It interoperates seamlessly with TensorFlow, PyTorch, scikit-learn, Gensim and the rest of Python’s AI ecosystem.
- Comes with an extensive test suite.
- Cross-platform support – macOS / OS X, Linux, Windows (Cygwin, MinGW, Visual Studio).
Website: spacy.io
Support: GitHub code repository
Developer: Explosion AI and many contributors
License: MIT License
spaCy is written in Python. Learn Python with our recommended free books and free tutorials.
Related Software
| Natural Language Processing | |
|---|---|
| PyTorch-Transformers | Library of state-of-the-art pre-trained models |
| Natural Language Toolkit | Suite of open source Python modules, data sets and tutorials |
| Stanford CoreNLP | Extensible annotation-based NLP pipeline |
| spaCy | Industrial strength natural language processing |
| scikit-learn | Machine learning library for Python |
| Gensim | Python-based vector space modeling and topic modeling toolkit |
| flair | Simple framework for state-of-the-art NLP |
| Apache OpenNLP | Machine learning based toolkit |
| DL4J | Deploy and train deep learning models |
| Apache Lucene | Full-featured information retrieval software library |
| UIMA | Implementation of the UIMA specification |
| tidytext | Text mining using dplyr, ggplot2, and other tidy tools |
| text2vec | Framework with API for text analysis and NLP |
| quanteda | R package for Quantitative Analysis of Textual Data |
| Moses | Statistical machine translation system |
Read our verdict in the software roundup.
| Python Natural Language Processing Tools | |
|---|---|
| PyTorch-Transformers | Library of state-of-the-art pre-trained models for NLP |
| NLTK | Natural Language Toolkit |
| spaCy | Industrial strength natural language processing |
| scikit-learn | Machine learning library |
| Gensim | Vector space modeling and topic modeling toolkit |
| flair | Simple framework for state-of-the-art NLP |
| TextBlob | Python (2 and 3) library for processing textual data |
| textacy | Python library for performing NLP tasks |
| polyglot | Multilingual text (NLP) processing toolkit |
| AllenNLP | Apache 2.0 NLP research library |
| Snips NLU | Natural Language Understanding Python library |
| PyNLPI | Various modules useful for common, and less common, NLP tasks |
| nlpnet | Natural Language Processing with neural networks |
| Pattern | Web mining module |
| GluonNLP | Deep Learning for NLP |
| PyTorch-NLP | Neural network layers, text processing modules and datasets |
| NLP Architect | Deep Learning NLP/NLU library |
Read our verdict in the software roundup.
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