/README.md

## Requirements
- Python 3
- gurobi optimizer (academic license can be requested)
- gurobipy (follow gurobi instructions, can be installed from package)
- `pip3 install -r requirements.txt`

## Documentation: [Coming-Soon](https://gitlab.com/purdueNlp/DRaiL/-/wikis/home)

## Running a simple example
State and Agreement prediction on gun control debates (using bert-tiny)
- Add your DRaiL directory to `PYTHONPATH`
- `cd` to `examples/4forms`
- Local learning: `python3 run_debates_4forums.py --dir data/ --config config/config_bert_tiny.json --rule rules/rule_guncontrol.dr --savedir [dir-to-save-models] --bert_tiny -m local -f feats -n neuro  --issue gun_control --logging_config ../../logging_conf.json --gpu_index [index-of-gpu:defaults to 0]`
- Global learning: `python3 run_debates_4forums.py --dir data/ --config config/config_bert_tiny.json --rule rules/rule_guncontrol.dr --savedir [dir-to-save-models] --bert_tiny -m global --loss hinge --lrate 2e-5 -f examples/4forums/ -n examples/4forums/  --issue gun_control --logging_config ../../logging_conf.json --gpu_index [index-of-gpu:defaults to 0]`

## Quick tips
- If you are using soft rules (NNet rules) that have unknown predicates
  on the left hand side, you have to use `hinge_latent` loss instead of
`hinge` (even if you have supervision for that predicate), and you
have to generate features for the unknown predicate (otherwise all
groundings will have the same input at learning time)