Project 2 of the course IFT6759
Install the virtual environnement and call the evaluator.py script with the proper input file and target file.
# create and source a clean virtual env
pip install -r requirements.txt
# run the evaluator
python evaluator.py --input-file-path /project/cq-training-1/project2/data/train.lang1 --target-file-path /project/cq-training-1/project2/data/train.lang2
Example to train a transformer on the aligned data.
python train.py --model_name=transformer --batch_size=128 --epochs=100
Example to train a transformer with pre-trained embeddings.
python train.py --embedding=fasttext --embedding_dim=256 --model_name=transformer --batch_size=128 --epochs=100
Example to train a transformer using back-translation.
- First train a model target->source (fr->en)
python train.py --fr_to_en --model_name=transformer --batch_size=128 --epochs=100
- Train the model source->target (en->en)
python train.py --back_translation=True --back_translation_model=<path_to_model> --back_translation_ratio=4 --model_name=transformer --batch_size=128 --epochs=100
Model configurations can be passed with the argument --config=<configuration_dict>
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent
