spaCy is a powerful open-source library for Natural Language Processing (NLP) in Python. It provides a wide range of NLP tools and features, including Named Entity Recognition (NER), Part-of-Speech (POS) tagging, dependency parsing, word vectors, and more. With its user-friendly API and efficient performance, spaCy is designed to help users build real products and gather insights from text data. It supports over 75 languages and offers pre-trained models and transformers for various NLP tasks. Additionally, spaCy has a large ecosystem, with plugins and components that can be easily integrated into your machine learning stack. Whether you're a beginner or an experienced developer, spaCy provides the tools and resources you need for efficient and accurate NLP processing.
Features
- Support for 75+ languages
- 84 trained pipelines for 25 languages
- Multi-task learning with pretrained transformers like BERT
- Pretrained word vectors
- State-of-the-art speed
- Production-ready training system
- Linguistically-motivated tokenization
- Components for named entity recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking and more
- Easily extensible with custom components and attributes
- Support for custom models in PyTorch, TensorFlow and other frameworks
- Built-in visualizers for syntax and NER
- Easy model packaging, deployment and workflow management
- Robust, rigorously evaluated accuracy
Use Cases
- Information extraction
- Named Entity Recognition
- Part-of-Speech tagging
- Dependency parsing
- Text classification
- Lemmatization
- Morphological analysis
- Entity linking
- Building NLP pipelines
Suited For
- Data scientists and NLP researchers
- Machine learning engineers
- Software developers
- NLP enthusiasts