/
run.sh
55 lines (54 loc) · 1.81 KB
/
run.sh
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#! /bin/bash
for dataset in agnews yahoo dbpedia tweet clickbait
#for dataset in tweet
do
#for model in entailment nsp rnsp qa xclass lotclass
for model in entailment
do
for confidence in 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
do
data_file=data/$dataset/preds_$model.json
for label_type in normal soft
do
if [[ $label_type == "soft" ]]
then
name=${dataset}_${model}_${confidence}_soft
CUDA_VISIBLE_DEVICES=0,1,2,3 python scripts/emnlp22/train.py \
-t \
-sl \
-bs 64 \
-df $data_file \
-c $confidence \
-n $name;
else
name=${dataset}_${model}_${confidence}
CUDA_VISIBLE_DEVICES=0,1,2,3 python scripts/emnlp22/train.py \
-t \
-bs 64 \
-df $data_file \
-c $confidence \
-n $name;
fi
CUDA_VISIBLE_DEVICES=0,1,2,3 python scripts/emnlp22/benchmark.py \
-w models/$name.bin \
-df $data_file \
-bs 1024 \
-n $name
done
done
done
name=${dataset}_supervised
test_file=data/$dataset/data.json
train_file=data/$dataset/data_train.json
CUDA_VISIBLE_DEVICES=0,1,2,3 python scripts/emnlp22/train.py \
-t \
-bs 64 \
-df $train_file \
-c 0 \
-n $name;
CUDA_VISIBLE_DEVICES=0,1,2,3 python scripts/emnlp22/benchmark.py \
-w models/$name.bin \
-df $test_file \
-bs 1024 \
-n $name
done