On this page, we'll be featuring demos, libraries and products from the spaCy community. Have you done something cool with spaCy? Let us know!
Server/client to load models in a separate, dedicated process.
by Pascal van Kooten
Expose spaCy NLP text parsing to Node.js (and other languages) via Socket.IO.
by Wah Loon Keng
spaCy accessed by a REST API, wrapped in a Docker container.
by Johannes Gontrum
Docker image exposing spaCy with ZeroMQ bindings.
by Phaninder Pasupula
Higher-level NLP built on spaCy.
by Burton DeWilde (Chartbeat)
Keras-based LSTM/CNN models for Visual Question Answering.
by Avi Singh
High level APIs for building your own language parser using existing NLP and ML libraries.
An R wrapper for spaCy.
by Kenneth Benoit
displaCyby Ines Montani
- An open-source NLP visualiser for the modern web.
displaCy ENTby Ines Montani
- An open-source named entity visualiser for the modern web.
Built with spaCy
sense2vecby Matthew Honnibal and Ines Montani
- Semantic analysis of the Reddit hivemind.
TruthBotby Team Truthbot
- The world's first artificially intelligent fact checking robot.
Laiceby Kendrick Tan
- Train your own Natural Language Processor from a browser.
- Smart tools for writers.
- An AI chat assistant for group shopping.
- Text and image analysis powered by Machine Learning.
- Online tool for spaCy's tokenizer, parser, NER and more.
We're excited to see books featuring spaCy already start to appear.
- Introduction to Machine Learning with Python: A Guide for Data Scientists
Andreas is a lead developer of Scikit-Learn, and Sarah is a lead data scientist at Mashable. We're proud to get a mention.
by Andreas C. Müller and Sarah Guido (O'Reilly, 2016)
- Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data
Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem.
by Dipanjan Sarkar (Apress / Springer, 2016)
Researchers are using spaCy to build ambitious, next-generation text processing technologies. spaCy is particularly popular amongst the biomedical NLP community, who are working on extracting knowledge from the huge volume of literature in their field. For an up-to-date list of the papers citing spaCy, see Semantic Scholar.
- Distributional semantics for understanding spoken meal descriptions
by Mandy Korpusik et al. (2016)
- Refactoring the Genia Event Extraction Shared Task Toward a General Framework for IE-Driven KB Development
by Jin-Dong Kim et al. (2016)
- Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec
by Christopher E. Moody (2016)
- Predicting Pre-click Quality for Native Advertisements
by Ke Zhou et al. (2016)
- Threat detection in online discussions
by Aksel Wester et al. (2016)
- The language of mental health problems in social media
by George Gkotsis et al. (2016)