Computational Semantics Project, ETH Zürich

For Linux system, use the command below to build up the conda environment and then activate the environment.

conda env create -f environment.yml
conda activate csnlp

We can use the pipeline.py file to process different parts of the project.

Use the file data_processing.py

method: GPT2 + top_k/top_p

metrics: bleu, perplexity, frequency

to get the final augmentation examples.

python pipeline.py --pipeline final_augmentation

run rs_mlm.py for random search for parameters for MLM.

run run_mlm.py for final mask language modellig adpater training and storing.

run rs_cls_baseline.py for random search baseline.

run cls_baseline_base.py and cls_baseline_mini.py for final baseline.

run rs_cls_enhanced.py for random search enhanced model.

run cls_enhanced_base.py and cls_enhanced_mini.py for final enhanced model.