Displaying 1 to 20 from 49 results

sense2vec - ๐Ÿฆ† 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 - ๐Ÿ’ซ 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. ๐Ÿ’ซ Version 2.0 out now! Check out the new features here.

thinc - ๐Ÿ”ฎ 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 - โœจFast 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.

Rasa - Create chatbots and voice assistants

  •    Python

Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build chatbots on Facebook, Slack, Microsoft Bot Framework, Rocket.Chat, Mattermost, Telegram etc. Rasa's primary purpose is to help you build contextual, layered conversations with lots of back-and-forth. To have a real conversation, you need to have some memory and build on things that were said earlier. Rasa lets you do that in a scalable way.

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.


spacy-transformers - ๐Ÿ›ธ Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy

  •    Python

This package provides spaCy components and architectures to use transformer models via Hugging Face's transformers in spaCy. The result is convenient access to state-of-the-art transformer architectures, such as BERT, GPT-2, XLNet, etc. This release requires spaCy v3. For the previous version of this library, see the v0.6.x branch.

projects - ๐Ÿช End-to-end NLP workflows from prototype to production

  •    Python

spaCy projects let you manage and share end-to-end spaCy workflows for different use cases and domains, and orchestrate training, packaging and serving your custom pipelines. You can start off by cloning a pre-defined project template, adjust it to fit your needs, load in your data, train a pipeline, export it as a Python package, upload your outputs to a remote storage and share your results with your team. โš ๏ธ spaCy project templates require spaCy v3. You can install it from pip with pip install spacy or conda with conda install spacy -c conda-forge. Make sure to use a fresh virtual environment.

spacy-stanza - ๐Ÿ’ฅ Use the latest Stanza (StanfordNLP) research models directly in spaCy

  •    Python

This package wraps the Stanza (formerly StanfordNLP) library, so you can use Stanford's models in a spaCy pipeline. The Stanford models achieved top accuracy in the CoNLL 2017 and 2018 shared task, which involves tokenization, part-of-speech tagging, morphological analysis, lemmatization and labeled dependency parsing in 68 languages. As of v1.0, Stanza also supports named entity recognition for selected languages. โš ๏ธ Previous version of this package were available as spacy-stanfordnlp.

spacy-streamlit - ๐Ÿ‘‘ spaCy building blocks and visualizers for Streamlit apps

  •    Python

This package contains utilities for visualizing spaCy models and building interactive spaCy-powered apps with Streamlit. It includes various building blocks you can use in your own Streamlit app, like visualizers for syntactic dependencies, named entities, text classification, semantic similarity via word vectors, token attributes, and more. The package includes building blocks that call into Streamlit and set up all the required elements for you. You can either use the individual components directly and combine them with other elements in your app, or call the visualize function to embed the whole visualizer.

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

  •    Javascript

โš ๏ธ 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.

prodigy-recipes - ๐Ÿณ Recipes for the Prodigy, our fully scriptable annotation tool

  •    Python

This repository contains a collection of recipes for Prodigy, our scriptable annotation tool for text, images and other data. In order to use this repo, you'll need a license for Prodigy – see this page for more details. For questions and bug reports, please use the Prodigy Support Forum. If you've found a mistake or bug, feel free to submit a pull request. โœจ Important note: The recipes in this repository aren't 100% identical to the built-in recipes shipped with Prodigy. They've been edited to include comments and more information, and some of them have been simplified to make it easier to follow what's going on, and to use them as the basis for a custom recipe.

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

โš ๏ธ 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.






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