Repository navigation
spaCy
Created by Explosion
- Repository
- explosion/spaCy
- Website
- spacy.io
- Wikipedia
- 维基百科
相关主题
机器学习自然语言处理Entity resolution
spaCy is a free library for advanced Natural Language Processing (NLP) in Python. It’s designed specifically for production use and helps you build applications that process and “understand” large volumes of text. It can be used to build information extraction or natural language understanding systems.
💫 Industrial-strength Natural Language Processing (NLP) in Python
💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants
An open-source framework for detecting, redacting, masking, and anonymizing sensitive data (PII) across text, images, and structured data. Supports NLP, pattern matching, and customizable pipelines.
✨Fast Coreference Resolution in spaCy with Neural Networks
🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
Python Packages as AWS Lambda Layers
👩🏫 Advanced NLP with spaCy: A free online course
Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.
Python implementation of TextRank algorithms ("textgraphs") for phrase extraction
A full spaCy pipeline and models for scientific/biomedical documents.
💫 Models for the spaCy Natural Language Processing (NLP) library
Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer.
The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
the open-source virtual assistant for Ubuntu based Linux distributions
🪐 End-to-end NLP workflows from prototype to production
🛸 Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy
🦙 Integrating LLMs into structured NLP pipelines
Precompiled packages for AWS Lambda