This repository houses tooling used to create the models on the leaderboard of WAT-Tasks. We provide wrappers to models which are trained via pytorch/fairseq to translate. Installation and usage intructions are provided below.

• Training: We use a separate fork of pytorch/fairseq at jerinphilip/fairseq-ilmt for training to optimize for our cluster and to plug and play data easily.

• Pretrained Models and Other Resources: preon.iiit.ac.in/~jerin/bhasha

# --user is optional
python3 -m pip install -r requirements.txt --user  
python3 setup.py install --user 

Downloading Models: The script examples/download-and-setup-models.sh downloads the model and dictionary files required for running examples/mm_all.py. Which models to download can be configured in the script.

from ilmulti.translator import from_pretrained

translator = from_pretrained(tag='mm-all')
sample = translator("The quick brown fox jumps over the lazy dog", tgt_lang='hi')