Sometimes We Want Ungrammatical Translations

Using the NMT models

If running in compute-canada clusters, download the models below from the launch machine.

config = AutoConfig.from_pretrained("Helsinki-NLP/opus-mt-en-zh")
tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-zh")
model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-zh")

tokenizer.save_pretrained("/home/pparth2/scratch/UMT/UMT/Results/cached/Helsinki-NLP-opus-en-zh")
config.save_pretrained("/home/pparth2/scratch/UMT/UMT/Results/cached/Helsinki-NLP-opus-en-zh")
model.save_pretrained("/home/pparth2/scratch/UMT/UMT/Results/cached/Helsinki-NLP-opus-en-zh-model")

Create the files:

python datasets_preprocess.py

For sanity check, open Data/<model>/<lang>/stats.txt and that should show the fraction of samples used by the different perturbations. If none of them are zero, then the create files works well.

Run the translate:

Set the model and lang to generate the translations. If the GPU memory is < 32G, opt for a lower batch-size.

python translate.py should get the results.

Compute Metrics:

Set the model and lang to generate the translations. If the GPU memory is < 32G, opt for a lower batch-size.

python compute_metrics.py should get the results in Metrics\ in root.