Domain Matcher is a library that aims at matching a pre-defined domain to your input data. Input without domain are deemed not important and thus can be safely filtered out.
Domain Matching performs very cheap OoD detection using topic modeling and keyword extraction.
pip install domain-matcher
from datasets import load_dataset
from domain_matcher.core import DomainMatcher, DMConfig
# Custom version of `clinc-oos` where non-banking classes are assigned to oos.
ds = load_dataset("GlowstickAI/banking-clinc-oos", "plus")
config = DMConfig(text_column='text', label_column='intent', oos_class='oos')
dmatcher = DomainMatcher(config)
# Fit DM on your train data see our blog to see what's happening!
dmatcher.fit(ds['train'])
# Predict: You can predict on a string, List[str] or Dataset
dmatcher.transform("Can you cancel my credit card?")['in_domain']
# >>> True
dmatcher.transform("Can you cancel my reservation at Giorgi's?")['in_domain']
# >>> FalseFor troubleshooting, please see our wiki or submit an issue if you can't find what you're looking for.
- Install Pyenv
curl https://pyenv.run | bashpyenv install 3.9.13 && pyenv global 3.9.13
- Install Poetry
poetry install- Add precommits
poetry run pre-commit install
make format: format the code with Ruffmake test: run unit tests and mypy.