See requirement.txt. We use PyTorch 1.4 and CUDA 10.1 in our experiments.
task=gab # choices are gab, ws (stormfront), fdcl, dwmw, biasbios
base= # the root of the directory where you store models
exp_name=
# the output dir would be <base>/<exp_name>/<seed>
# explanation regularization bias mitigation on GHC
python scripts/upstream.py --lr 1e-5 --seed 0 21 42 --task gab --name ${exp_name} --do_reg --base runs/${base} --extra "##reg_strength 0.03 ##neutral_words_file datasets/identity_gab.csv" --epoch 5
# vanilla model on GHC
python scripts/upstream.py --lr 1e-5 --seed 0 21 42 --task gab --name ${exp_name} --base runs/${base} --epoch 5
# mtl on GHC, FDCL, and biasbios
base= # the root of the directory where you store models
task=gab # the "main" task. Other tasks should can specified in "mtl args"
exp_name=
python scripts/upstream.py --lr 1e-5 --seed 0 21 42 --task gab --name ${exp_name} --do_reg --extra "##reg_strength 0.03 ##neutral_words_file datasets/identity_gab.csv ##adv_lr_scale 100.0 ##logging_steps 0" --base runs-0711/three --epoch 8 --es 1000 --mtl --mtl_args "{'mtl_data_dir': 'datasets/FDCL18', 'mtl_task_name': 'fdcl', 'mtl_reg_args': {'reg_explanations': 1, 'reg_strength': 0.1, 'neutral_words_file': 'datasets/aae_words.csv'}}" "{'mtl_data_dir': 'datasets/biasbios', 'mtl_task_name': 'biasbios', 'mtl_reg_args': {'adv_debias': 1, 'adv_objective': 'adv_ce', 'adv_strength': 1.0, 'adv_grad_rev_strength': 1.0}}"
task=ws
exp_name=
source_path= # e.g. 'runs/roberta-base-vanilla/'
base=
python scripts/run_transfer.py --lr 1e-5 5e-6 --seed 0 21 42 --task ${task} --name ${exp_name} --source ${source_path} --source_name ${source_name} --base ${base} --epoch 5 --es 1000 --extra "##load_epoch best_f1"
- Gab Hate Corpus (GHC) released under CC-By Attribution 4.0 International License. version can be found in this repository.
- Stormfront (Stf.) released under Creative Commons Attribution-ShareAlike 3.0 Spain License. A preprocessed version can be found in this repository.
- DWMW released under MIT license and can be found in this directory.
- iptts77k released under Apache License 2.0.
If you use any of the datasets above, please consider citing the original work.
Similarly, if you use any of the datasets above, please consider citing the original work.