spaCy · Industrial-strength Natural Language Processing in Python

Discover spaCy: the advanced NLP tool in Python for efficient info extraction, with 75+ language support and transformer integration. Harness its speed and customization for real work & insights. #NLP #Python #AI

spaCy is an industrial-strength natural language processing library in Python designed for real work, enabling the building of real products and gathering real insights.
It excels at large-scale information extraction tasks and is optimized for efficiency and accuracy, especially useful for processing entire web dumps.
Released in 2015, spaCy has developed into an industry standard with a vast ecosystem offering plugins, integration capabilities with machine learning stacks, and the possibility to build custom components and workflows.
The library supports 75+ languages, features 84 trained pipelines for 25 languages, and offers support for pretrained transformers like BERT and custom models in PyTorch, TensorFlow, and other frameworks.
With a focus on speed, spaCy provides state-of-the-art features such as linguistically-motivated tokenization, components for various NLP tasks, visualization tools, and model packaging and deployment options.
The spacy-llm package integrates Large Language Models (LLMs) into spaCy, facilitating fast prototyping and transforming unstructured responses into robust outputs for NLP tasks without requiring training data.
Prodigy, a tool by the makers of spaCy, enables efficiently annotated data for rapid machine learning model iteration in tasks like entity recognition, intent detection, and image classification.
spaCy v3.0 introduces reproducible training for custom pipelines, allowing comprehensive configuration of training runs without hidden defaults, featuring a quickstart widget and project templates for end-to-end workflows.
The new project system of spaCy facilitates smooth transitions from prototype to production, with features like source asset download, command execution, and caching, ensuring projects are ready for automation.
Users can get custom spaCy pipelines tailored to their NLP problems by spaCy's core developers, offering predictability, maintainability, and production-ready solutions with full code, data, tests, and documentation.
A free online course teaches advanced natural language understanding systems using spaCy and showcases transformer-based pipelines that enhance accuracy, bringing spaCy to the state-of-the-art NLP performance level.
Benchmarks show substantial improvements in accuracy with transformer-based pipelines in spaCy v3.0, making it competitive with current state-of-the-art NLP systems.