Word Level Text Generation using Neural and Statistical Language Models

Synopsis

The goal of this project is to develop a word-level text generator, built using on top of a neural language modeler or a statistical language modeler.

The user can either select one of the saved models, or train one on his/her own on the Brown Corpus, so as to use xwordgen to generate words that follow a given input sequence of words.

Getting Started

This section describes the preqrequisites, and contains instructions, to get the project up and running.

Setup

This project can easily be set up with all the prerequisite packages by following these instructions:

1. Install Conda using the conda_install.sh file, with the command: $ bash conda_install.sh

2. Create a conda environment from the included environment.yml file using the following command:

   $ conda env create -f environment.yml

3. Activate the environment

   $ conda activate xwordgen

Usage

The user can get a description of the options by using the command: $ xwordgen --help.

Furthermore, using either the saved models or the trained ones, the user will be prompted to enter an input sequence, for which the word that follows will be generated.

Built With

• Python

Contributing Guidelines

There are no specific guidelines for contributing, apart from a few general guidelines we tried to follow, such as:

• Code should follow PEP8 standards as closely as possible
• We use Google-Style docstrings to document the Python modules in this project.

If you see something that could be improved, send a pull request!
I am always happy to look at improvements, to ensure that xwordgen, as a project, is the best version of itself.

If you think something should be done differently (or is just-plain-broken), please create an issue.

License

See the LICENSE file for more details.