spacy

💫 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

👩‍🏫 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

💫 Models for the spaCy Natural Language Processing (NLP) library

The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.

🛸 Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy

the open-source virtual assistant for Ubuntu based Linux distributions

🦙 Integrating LLMs into structured NLP pipelines

📚 Process PDFs, Word documents and more with spaCy

SpanMarker for Named Entity Recognition

📽️ Code from tutorials presented on the "Code AI with Rok" YouTube channel

This package features data-science related tasks for developing new recognizers for Presidio. It is used for the evaluation of the entire system, as well as for evaluating specific PII recognizers or PII detection models.

Technical and sentiment analysis to predict the stock market with machine learning models based on historical time series data and news article sentiment collected using APIs and web scraping.

Conversational text Analysis using various NLP techniques

Extensive tutorials for the Advanced NLP Workshop in Open Data Science Conference Europe 2020. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and Topic Models.