Materials for "Advanced Techniques of Machine Translation". Please refer to the assignment sheet for instructions on how to use the toolkit.
The toolkit is based on this implementation.
In case you prefer to run the code locally, we suggest creating a Python environment to prevent library clashes with future projects, using either Conda or virtualenv (Conda is suggested). For other options, see supplementary material
# ensure that you have conda (or miniconda) installed (https://conda.io/projects/conda/en/latest/user-guide/install/index.html) and that it is activated
# create clean environment
conda create --name atmt311 python=3.11
# activate the environment
conda activate atmt311
# intall required packages
conda install pytorch=2.0.1 numpy tqdm sacrebleu
# ensure that you have python > 3.6 downloaded and installed (https://www.python.org/downloads/)
# install virtualenv
pip install virtualenv # for both powershell and WSL
# create a virtual environment named "atmt311"
virtualenv --python=python3.11 atmt311 # on WSL terminal
python -m venv atmt311 # on powershell
# launch the newly created environment
source atmt311/bin/activate
.\atmt311\Scripts\Activate.ps1 # on powershell
# intall required packages
pip install torch==2.0.1 numpy tqdm sacrebleu # for both powershell and WSL
python train.py \
--data path/to/prepared/data \
--source-lang en \
--target-lang sv \
--save-dir path/to/model/checkpoints \
--train-on-tiny # for testing purposes only
Notes:
path/to/prepared/dataandpath/to/model/checkpointsare placholders, not true paths. Replace these arguments with the correct paths for your system.- only use
--train-on-tinyfor testing. This will train a dummy model on thetiny_trainsplit. - add the
--cudaflag if you want to train on a GPU, e.g. using Google Colab
Run inference on test set
python translate.py \
--data path/to/prepared/data \
--dicts path/to/prepared/data \
--checkpoint-path path/to/model/checkpoint/file/for/loading \
--output path/to/output/file/model/translations
Postprocess model translations
bash scripts/postprocess.sh path/to/output/file/model/translations path/to/postprocessed/model/translations/file en
Score with SacreBLEU
cat path/to/postprocessed/model/translations/file | sacrebleu path/to/raw/target/test/file
Assignments must be submitted on OLAT by 14:00 on their respective due dates.