Coder Social home page Coder Social logo

dsrt's Introduction

Neural DSRT: Neural dialogue systems for humans

license

Newborn from the University of Maryland, Neural DSRT (pronounced dessert) is a high-level neural dialogue systems API, written in Python and running on top of familiar deep-learning (DL) and machine-learning (ML) libraries like Keras, TensorFlow, and scikit-learn. It focuses on allowing for the easy construction, training, and testing of neural dialogue models.

Its key purpose is to enable rapid development and experimentation in a burgeoning field otherwise bereft of high-level libraries with busy researchers in mind.

What is it for?

Neural DSRT is all about building end-to-end dialogue systems using state-of-the-art neural dialogue models. It is a new project (born at the University of Maryland in the waining days and weeks of March, 2018), and it still has a lot of growing to do.

In order to help that growth along, we adopt a few guiding principles liberally from Keras:

  • User-friendliness. Ease-of-use should be front and center, the library should expose consistent & simple APIs, and should minimize the amount of work involved in getting common use-cases up and running. The focus should be on enabling rapid, hassle-free experimentation with neural dialog models.

  • Modularity. Dialogue experiments, and their constituent components -- dataset wrappers, data preprocessors, neural dialogue models, conversation objects -- should alike be implemented as fully-configurable modules that can be plugged together with as few restrictions as possible. Experiments, and their components, should be richly configurable -- but components should fall back on sensible defaults, so that configuration should never be necessary

  • Extensibility. New modules should be simple to add (as new classes and functions), and existing modules and scripts should provide ample and liberally documented examples.

How do I use it? 90 seconds to Neural DSRT

This quickstart guide has yet to be written -- but it should be coming soon.

How can I install it?

Before attempting to install Neural DSRT, you'll need to install Keras (which it's built on):

In installing Keras, you'll of course need to install a neural-network backend. We recommend TensorFlow:

Once you've done this, you're ready to install DSRT. Currently, the only way to do this is from the GitHub source. Thankfully, this is pretty easy, so long as you have pip installed on your machine (did we mention you'll need Python? you'll need Python -- we recommend the latest version of Python 3).

Just in case, instructions for installing pip may be found here:

Assuming you've followed up until this point, proceed to clone DSRT using git:

git clone https://github.com/sbarham/dsrt.git

Now, cd to the Neural DSRT folder and install using pip:

cd dsrt
sudo pip install .

It's as easy as that. Now you're ready to use Neural DSRT!

How can I help?

As we mentioned above, DSRT is very young -- in fact, it's only a few weeks old at the moment. If you're a developer (and especially if you're confident with deep learning, machine learning, or neural dialogue systems) and you'd like to help, please contact the original authors directly at [email protected]. We'd love to collaborate with you.

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.