This repo contains an implementation of the Automatic Rule Induction (ARI) framework as presented in "Automatic Rule Induction for Efficient Semi-Supervised Learning "

This repo builds off of the Wrench weak supervision benchmark.

Train a default model on the sms dataset:

python3 -m venv venv
source venv/bin/activate
pip install -U pip
pip install -r requirements.txt
python -c "import nltk; nltk.download('punkt'); nltk.download('wordnet'); nltk.download('omw-1.4')"
python main.py

To see all available arguments, run python main.py --help.

Other datasets can be obtained here (datasets.zip). After downloading, point the --data_root flag at the unzipped directory.

• main.py: main entrypoint for code
• utils.py: utility functions for main.py
• autorule_generator.py: generating the rules.
• autorule_model.py: integrating the rules with pretrained a pretrained model.
• wrench/: a lightly modified clone of the the wrench framework repo.

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