Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification
Assuming you have done the following preparation:
- Downloaded, processed and saved your task data files: say it is in
/Dataset
and task name ismnli
- Having the devices (gpus) ready for experiment: say you have two gpus and you can allocate 2 trails per gpu (for grid search)
- Saved your hyper-parameter settings (including the grid search) in a yml file: say it is at
/config/contrastive_roberta_mnli.yml
- Ready to name your next experiment: say
my_exp
- Going to redirect your outputs to a file: say
result.out
Then you may run your experiment in the following way:
CUDA_VISIBLE_DEVICES=0,1 nohup python3 -u parallel_main.py \
--random_seed 123 \
--model_config_path ./config/contrastive_roberta_mnli.yml \
--special_tag my_exp \
--gpus 2 \
--gpus_per_trail 0.5 \
--task_name mnli \
> result.out &
Then you will be able to check the status prints in result.out
and the logs in a newly created folder named under special_tag + task_name
Feel free to play with our script/code and enjoy your journey of research discovery! Yeah!
If you are interested in this paper, please consider cite our work:
- Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification
- IEEE Big Data 2021
- Guanyi Mou1, Yichuan Li1, Kyumin Lee
@inproceedings{mou2021reducing,
title={Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification},
author={Mou, Guanyi and Li, Yichuan and Lee, Kyumin},
booktitle={2021 IEEE International Conference on Big Data (Big Data)},
pages={876--887},
year={2021},
organization={IEEE}
}