Skip to content

Latest commit

 

History

History
102 lines (74 loc) · 3.08 KB

README.md

File metadata and controls

102 lines (74 loc) · 3.08 KB
Raw

atmt code base

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.

Environment Setup

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

conda

# 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

virtualenv

# 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

Training a model

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/data and path/to/model/checkpoints are placholders, not true paths. Replace these arguments with the correct paths for your system.
  • only use --train-on-tiny for testing. This will train a dummy model on the tiny_train split.
  • add the --cuda flag if you want to train on a GPU, e.g. using Google Colab

Evaluating a trained model

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
scripts/postprocess.sh  assignments/01/baseline/infopankki_translations.txt assignments/01/baseline/postprocess.txt

Score with SacreBLEU

cat path/to/postprocessed/model/translations/file | sacrebleu path/to/raw/target/test/file

Assignments

Assignments must be submitted on OLAT by 14:00 on their respective due dates.