Final Project for Trustworthy ML
Example for execution without perturbations/collocations of any kind with the RTE dataset:
python run_glue.py \
--model_name_or_path microsoft/deberta-v3-base \
--task_name rte \
--do_train \
--do_eval \
--max_seq_length 256 \
--per_device_train_batch_size 8 \
--learning_rate 2e-5 \
--num_train_epochs 1 \
--output_dir /tmp/rte/ \
--overwrite_output_dir \
--seed 124 \
Use the run_glue.sh script to run the perturbations.
- https://github.com/sjmeis/CLMLDP
- https://github.com/sjmeis/MLDP
- https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification
- https://www.youtube.com/watch?v=fDzCBKArdYg
- https://huggingface.co/docs/transformers/installation#install-from-source
- https://github.com/protocolbuffers/protobuf/tree/main/python#installation
To run CollocationExtractor and DatasetPerturbation you will need to have a local copy of their models and also the data.zip file that contains bigrams and trigrams from their repository. Not included in this repo since they're large files. You can get both the data and the vectors from https://github.com/sjmeis/CLMLDP