Sign inSign up

lab41/pythia

By lab41

Updated almost 10 years ago

Novelty detection in text corpora

Image
2

484

lab41/pythia repository overview

Pythia

pythia logo

CircleCI codecov Docker Automated build

Novelty detection in text corpora

Pythia is Lab41's exploration of approaches to novel content detection. We are interested in making it easier to tell when a document coming into a corpus has something new to say. We welcome your contributions (see our contributor guidelines) and attention.

Run a quick experiment

You can get started very quickly on a system with Docker using the following commands to pull our publicly available image and train an XGBoost model on the sample data that comes with the repository:

docker pull lab41/pythia
docker run -it lab41/pythia experiments/experiments.py with XGB=True BOW_APPEND=True BOW_PRODUCT=True

Tests and building

docker build -t lab41/pythia .     # runs tests and builds project image

Prerequisites

Our code is written in Python 3. It requires a recent version of Anaconda, as well as a C/C++ compiler system, e.g. GNU gcc/g++ (available in package build-essential on Ubuntu/Debian systems).

Once these have been installed on your system, envs/make_envs.sh will install the necessary Python dependencies in an Anaconda environment called py3-pythia.

The Docker-based distribution comes prepackaged with all necessary dependencies, provided Docker itself is available.

Documentation

Prebuilt documentation available at http://lab41.github.io/pythia

Tag summary

Content type

Image

Digest

Size

1.6 GB

Last updated

almost 10 years ago

docker pull lab41/pythia