Tools+Data


Open-Source Python Packages

Preppy

This repository contains convenienve functions for preparing text data for training NLP models. It is possible to prepare batches of data that preserve the order of documents in the corpus.

Website: https://github.com/phueb/Preppy

Visualized

This repository contains convenience functions for common visualizations, built on top of matplotlib.

Website: https://github.com/UIUCLearningLanguageLab/Visualized


Natural Language Data

American-English CHILDES

To create a custom CHILDES corpus, use the Python package CreateCHILDESCorpus. The package allows you to define custom tokenization rules, among other features. The output is a text file containing line-separated transcripts, and will be ordered by the age of the child spoken to.

Website: https://github.com/UIUCLearningLanguageLab/CreateCHILDESCorpus

English Wikipedia

To create a custom Wikipedia corpus, use the Python package CreateWikiCorpus. Due to the size of English Wikipedia - 4billion words as of 2019 - corpus creation is distributed across Ludwig workers, and output files are saved to the server. A single corpus is distributed across multiple text files, each created on a separate worker.

Website: https://github.com/UIUCLearningLanguageLab/CreateWikiCorpus


Ludwig

We use a custom-built job submission system, called Ludwig, for computationally expensive simulations. Documentation about how Ludwig workers are setup is available here:

https://docs.philhuebner.com/ludwig

The system is built on top of 8 workers, and 1 shared file server. Documentation about the lab’s file server and file sharing is available here:

https://docs.philhuebner.com/file-sharing

Ludwig-Template

Used internally, to quickly get started building jobs to submit to Ludwig.

Website: https://github.com/UIUCLearningLanguageLab/Ludwig-Template

Ludwig-Viz

Used internally, to quickly visualize metrics tracked as part of one or more jobs submitted to Ludwig. For example, compare learning curves (e.g. MSE) for groups of neural networks each trained with different hyper-parameters.

Website: https://github.com/phueb/LudwigViz