Displaying 1 to 20 from 33 results

sense2vec - 🦆 Use NLP to go beyond vanilla word2vec

  •    C++

sense2vec (Trask et. al, 2015) is a nice twist on word2vec that lets you learn more interesting, detailed and context-sensitive word vectors. For an interactive example of the technology, see our sense2vec demo that lets you explore semantic similarities across all Reddit comments of 2015. This library is a simple Python/Cython implementation for loading and querying sense2vec models. While it's best used in combination with spaCy, the sense2vec library itself is very lightweight and can also be used as a standalone module. See below for usage details.

spaCy - 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython

  •    Python

spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. spaCy comes with pre-trained statistical models and word vectors, and currently supports tokenization for 20+ languages. It features the fastest syntactic parser in the world, convolutional neural network models for tagging, parsing and named entity recognition and easy deep learning integration. It's commercial open-source software, released under the MIT license. 💫 Version 2.0 out now! Check out the new features here.

thinc - 🔮 spaCy's Machine Learning library for NLP in Python

  •    Assembly

Thinc is the machine learning library powering spaCy. It features a battle-tested linear model designed for large sparse learning problems, and a flexible neural network model under development for spaCy v2.0. Thinc is a practical toolkit for implementing models that follow the "Embed, encode, attend, predict" architecture. It's designed to be easy to install, efficient for CPU usage and optimised for NLP and deep learning with text – in particular, hierarchically structured input and variable-length sequences.

practical-machine-learning-with-python - 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

  •    Jupyter

"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web. "Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.




text-analytics-with-python - 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

  •    Python

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. A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems.

neuralcoref - ✨Fast Coreference Resolution in spaCy with Neural Networks

  •    Python

NeuralCoref is a pipeline extension for spaCy 2.0 that annotates and resolves coreference clusters using a neural network. NeuralCoref is production-ready, integrated in spaCy's NLP pipeline and easily extensible to new training datasets. For a brief introduction to coreference resolution and NeuralCoref, please refer to our blog post. NeuralCoref is written in Python/Cython and comes with pre-trained statistical models for English. It can be trained in other languages. NeuralCoref is accompanied by a visualization client NeuralCoref-Viz, a web interface powered by a REST server that can be tried online. NeuralCoref is released under the MIT license.

textacy - NLP, before and after spaCy

  •    Python

textacy is a Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance spacy library. With the fundamentals --- tokenization, part-of-speech tagging, dependency parsing, etc. --- delegated to another library, textacy focuses on the tasks that come before and follow after. Note: Docs used to be hosted on ReadTheDocs, but the builds stopped working many months ago, and now those docs are out-of-date. This is unfortunate, especially since ReadTheDocs allows for multiple versions while GitHub Pages does not. I'll keep trying on ReadTheDocs; if the build issues ever get resolved, I'll switch the docs back.

displacy - :boom: displaCy.js: An open-source NLP visualiser for the modern web

  •    Javascript

⚠️ As of v2.0.0, the displaCy visualizers are now integrated into the core library. See here for more details on how to visualize a Doc object from within spaCy. We're also working on a new suite of tools for serving and testing spaCy models. The code of the standalone visualizers will still be available on GitHub, just not actively maintained. displaCy.js is a modern and service-independent visualisation library. We hope this makes it easy to compare different services, and explore your own in-house models. If you're using spaCy's syntactic parser, displaCy should be part of your regular workflow. Because spaCy's parser is statistical, it's often hard to predict how it will analyse a given sentence. Using displaCy, you can simply try and see. You can also share the page for discussion with your team, or save the SVG to use elsewhere. If you're developing your own model, you can run the service yourself — it's 100% open source.


gsoc2018-spacy - Greek language support for spacy

  •    Python

Welcome to the home repository of Greek language integration for spaCy. This project is developed for Google Summer of Code 2018, under the auspices of GFOSS - Open Technologies Alliance.

spacy_api - Server/Client around Spacy to load spacy only once

  •    Python

Helps with loading models in a separate, dedicated process. Caching happens on unique arguments.

Arch-Data-Science - Archlinux PKGBUILDs for Data Science, Machine Learning, Deep Learning, NLP and Computer Vision

  •    Shell

Welcome to my repo to build Data Science, Machine Learning, Computer Vision, Natural language Processing and Deep Learning packages from source. My Data Science environment is running from a LXC container so Tensorflow build system, bazel, must be build with its auto-sandboxing disabled.

spacyr - R wrapper to spaCy NLP

  •    R

This package is an R wrapper to the spaCy “industrial strength natural language processing” Python library from http://spacy.io. The easiest way to install spaCy and spacyr is through an auto-installation function in spacyr package. This function utilizes a conda environment and therefore, some version of conda has to be installed in the system. You can install miniconda from https://conda.io/miniconda.html (Choose 64-bit version for your system).

displacy-ent - :boom: displaCy-ent.js: An open-source named entity visualiser for the modern web

  •    CSS

⚠️ As of v2.0.0, the displaCy visualizers are now integrated into the core library. See here for more details on how to visualize a Doc object from within spaCy. We're also working on a new suite of tools for serving and testing spaCy models. The code of the standalone visualizers will still be available on GitHub, just not actively maintained. Data exploration is an important part of effective named entity recognition because systems often make common unexpected errors that are easily fixed once identified. Despite the apparent simplicity of the task, automatic named entity recognition systems still make many errors, unless trained on examples closely tailored to the use-case. Check out the demo to visualise spaCy's guess at the named entities in the document. You can filter the displayed types, to only show the annotations you're interested in.

spacy-benchmarks - 💫 Runtime performance comparison of spaCy against other NLP libraries

  •    Python

The speed test expects to read documents from a simple SQLite table. More corpus injestors need to be written. So far there's one to create the table from the Gigaword corpus. This should download and install spaCy and other NLP libraries.

spacy-dev-resources - 💫 Scripts, tools and resources for developing spaCy

  •    Python

This repository is a collection of community resources and contains scripts, tools and helpers for developing spaCy, adding new languages and training new models. Feel free to submit a pull request to contribute. We always appreciate pull requests! 🙌 For more info on how to contribute to the project, see our contribution guidelines.

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

  •    Python

This repository contains releases of models for the spaCy NLP library. For more info on how to download, install and use the models, see the models documentation. ⚠️ Important note: Because the models can be very large and consist mostly of binary data, we can't simply provide them as files in a GitHub repository. Instead, we've opted for adding them to releases as .tar.gz files. This allows us to still maintain a public release history.

spacy-notebooks - 💫 Jupyter notebooks for spaCy examples and tutorials

  •    Jupyter

Please keep in mind that this repository is MIT-licensed, so we'll only be able to publish notebooks that are available under MIT or a more permissive license.

spacy-services - 💫 REST microservices for various spaCy-related tasks

  •    Python

This repository includes REST microservices for various spaCy-related tasks. The services power our interactive demos and can be used as examples of exposing spaCy's capabilities as a microservice. All APIs are built with hug and require Python 3. The following services are available – for more details, see the API docs in the respective directories.

talks - 💥 Browser-based slides or PDFs of our talks and presentations

  •    Javascript

Ongoing collection of talks about spaCy and other AI-related topics. Most slides are available as PDFs. HTML slides are implemented in Jade (aka Pug) with help of reveal.js. The site is built or served by Harp. To view the slides, simply install Harp, clone this repository and run the server from the new directory. Then navigate your browser to http://localhost:9000.





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