OpusCleaner

OpusCleaner is a machine translation/language model data cleaner and training scheduler. The Training scheduler has moved to empty-trainer.

Cleaner

The cleaner bit takes care of downloading and cleaning multiple different datasets and preparing them for translation.

Dependencies

(Mainly listed as shortcuts to documentation)

• FastAPI as the base for the backend part.
• Pydantic for conversion of untyped JSON to typed objects. And because FastAPI automatically supports it and gives you useful error messages if you mess up things.
• Vue for frontend

Screenshots

List and categorize the datasets you are going to use for training.

Download more datasets right from the interface.

Filter each individual dataset, showing you the results immediately.

Compare the dataset at different stages of filtering to see what the impact is of each filter.

Paths

• data/train-parts is scanned for datasets
• filters should contain filter json (but that's not implemented yet, right now it just has a hard-coded FILTERS dict in code)

Installation for development

python3 -m venv .env
bash --init-file .env/bin/activate
pip install -e .

cd frontend
npm clean-install
npm run build
cd ..

Link the frontend build folder into opuscleaner. Normally this is done during packaging but when opuscleaner is installed as an editable package, this doesn't happen.

ln -s ../frontend/dist opuscleaner/frontend

Finally you can run opuscleaner-server as normal. The --reload option will cause it to restart when any of the python files change.

opuscleaner-server --reload

Then go to http://127.0.0.1:8000/ for the "interface" or http://127.0.0.1:8000/docs for the API.

Frontend development

If you're doing frontend development, try also running:

cd frontend
npm run dev

Then go to http://127.0.0.1:5173/ for the "interface".

This will put vite in hot-reloading mode for easier Javascript dev. All API requests will be proxied to the python server running in 8000, which is why you need to run both at the same time.

Filters

If you want to use LASER, you will also need to download its assets:

python -m laserembeddings download-models

Packaging

Run pip wheel . to build & package OpusCleaner. Packaging is done through hatch, see the pyproject.toml and build_frontend.py files for details. You'll need to have a recent version of node and npm in your PATH for this to work.

Acknowledgements

This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350 and from UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10052546]