(This repository is still under construction. Please let us know if you find any issues :-) )

The Ovation Framework is a new framework for developing your own Conversational Intelligence model. The framework was constructed by the MindGarage in collaboration with Insiders Technologies.

The framework contains several utility classes to help you to easily build new Conversational Intelligence architectures. The idea is to give you a head start in your NLP/Deep Learning project, removing from you the burden of thinking about how to get the data, or how to develop a simple Deep Learning model. You can start from the examples here and build your own model based on them.

This repository was constructed with a set of NLP/Deep Learning tasks in mind. The datasets and models that we chose to initially support reflect this set of tasks. We intend to keep expanding it to more and more datasets, tasks and models.

This code expects Python 3.4+. We recommend you to use a virtual environment. You can create a new virtual environment with:

# Change directory to the place where you'd like to create a new virtual
# environment
cd /path/to/where/you/want/to/put/your/environment

# Create the virtual environment with Python 3
virtualenv -p 3 name_of_your_enviroment
# Activate the virtual environmnent
source name_of_your_enviroment/bin/activate

This will create a new folder with the name name_of_your_enviroment. With the environment set, you will now need to install some dependencies in the repository. For convenience, we provide a requirements.txt file that you can use directly. Just run:

# Clone this repository:
git clone https://github.com/mindgarage/Ovation.git

# Enter the new folder
cd Ovation

# Install all the requirements
pip install -r requirements.txt
sh setup_packages.sh

# Tell python where to find the modules
export PYTHONPATH=$PWD

Now you are all set to use the code!

Below is a small summary of what you can find in this repository. This repository was created with the following pipeline in mind:

• datasets: To build any Deep Learning model, you need data. Datasets that can be found in the internet come in any format, and it may take hours for one to reorganize them into the format that is convenient for him. In the datasets folder, you will find a set of utility classes that simply load the data for you and allow it to be accessed in several ways that we deemed useful for performing the Deep Learning tasks we had in mind.
• models: Now that we have access to the data, we need to write models that receive the data (in a suitable format) and output some result. In the models folder you will find some example model classes that perform tasks such as Named Entity Recognition, Sentiment Classification and Intent Classification.
• templates: Examples of how to use the classes are written in the templates folder.

Additionally, the following folders have some other useful code:

• tools: Some standalone scripts.
• utils: Utility functions used by the rest of the code.
• tests: Some code used for testing the functionalities above