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‎scripts/10/0.sh‎

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device="cuda:0"
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for i in 0 1 2; do
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for model_name in studio-ousia/luke-japanese-large-lite studio-ousia/luke-japanese-base-lite; do
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for lr in 1e-5 3e-5 5e-5; do
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for batch_size in 512; do
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for model_name in nlp-waseda/roberta-large-japanese ku-nlp/deberta-v2-large-japanese; do
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for lr in 1e-5 3e-5 5e-5; do
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for batch_size in 64 128 256 512; do
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for dataset_name in nu-snli nu-mnli nu-snli+mnli; do
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poetry run python src/train_sup.py \
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--dataset_name jsnli+nu-snli \
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--dataset_name $dataset_name \
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--model_name $model_name \
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--batch_size $batch_size \
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--lr $lr \
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--use_jumanpp \
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--gradient_checkpointing \
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--device $device
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done
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for batch_size in 256 128 64; do
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poetry run python src/train_sup.py \
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--dataset_name jsnli+nu-snli \
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--model_name $model_name \
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--batch_size $batch_size \
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--lr $lr \
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--device $device
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done
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done
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done
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done

‎scripts/10/1.sh‎

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device="cuda:1"
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for i in 0 1 2; do
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for model_name in cl-tohoku/bert-large-japanese ku-nlp/roberta-large-japanese-char-wwm; do
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for lr in 1e-5 3e-5 5e-5; do
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for batch_size in 512; do
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for model_name in studio-ousia/mluke-large-lite xlm-roberta-large; do
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for lr in 1e-5 3e-5 5e-5; do
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for batch_size in 64 128 256 512; do
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for dataset_name in nu-snli nu-mnli nu-snli+mnli; do
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poetry run python src/train_sup.py \
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--dataset_name jsnli+nu-snli \
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--dataset_name $dataset_name \
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--model_name $model_name \
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--batch_size $batch_size \
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--lr $lr \
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--gradient_checkpointing \
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--device $device
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done
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for batch_size in 256 128 64; do
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poetry run python src/train_sup.py \
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--dataset_name jsnli+nu-snli \
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--model_name $model_name \
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--batch_size $batch_size \
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--lr $lr \
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--device $device
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done
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done
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done
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done

‎scripts/10/2.sh‎

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device="cuda:2"
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for i in 0 1 2; do
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for model_name in nlp-waseda/roberta-large-japanese ku-nlp/deberta-v2-large-japanese; do
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for lr in 1e-5 3e-5 5e-5; do
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for batch_size in 512; do
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for model_name in cl-tohoku/bert-large-japanese studio-ousia/luke-japanese-large-lite; do
4+
for lr in 1e-5 3e-5 5e-5; do
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for batch_size in 64 128 256 512; do
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for dataset_name in nu-snli nu-mnli nu-snli+mnli; do
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poetry run python src/train_sup.py \
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--dataset_name jsnli+nu-snli \
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--dataset_name $dataset_name \
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--model_name $model_name \
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--batch_size $batch_size \
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--lr $lr \
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--use_jumanpp \
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--gradient_checkpointing \
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--device $device
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done
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for batch_size in 256 128 64; do
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poetry run python src/train_sup.py \
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--dataset_name jsnli+nu-snli \
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--model_name $model_name \
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--batch_size $batch_size \
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--lr $lr \
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--use_jumanpp \
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--device $device
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done
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done
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done
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done

‎scripts/10/3.sh‎

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device="cuda:3"
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3-
for i in 0 1 2; do
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for model_name in xlm-roberta-large studio-ousia/mluke-large-lite; do
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for lr in 1e-5 3e-5 5e-5; do
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for batch_size in 512; do
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for model_name in ku-nlp/roberta-large-japanese-char-wwm cl-tohoku/bert-base-japanese-char-v2; do
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for lr in 1e-5 3e-5 5e-5; do
5+
for batch_size in 64 128 256 512; do
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for dataset_name in nu-snli nu-mnli nu-snli+mnli; do
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poetry run python src/train_sup.py \
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--dataset_name jsnli+nu-snli \
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--dataset_name $dataset_name \
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--model_name $model_name \
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--batch_size $batch_size \
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--lr $lr \
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--gradient_checkpointing \
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--device $device
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done
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for batch_size in 256 128 64; do
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poetry run python src/train_sup.py \
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--dataset_name jsnli+nu-snli \
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--model_name $model_name \
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--batch_size $batch_size \
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--lr $lr \
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--device $device
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done
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done
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done
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done

‎scripts/10/prev/2023-03-21/0.sh‎

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device="cuda:0"
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for i in 0 1 2; do
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for model_name in studio-ousia/luke-japanese-large-lite studio-ousia/luke-japanese-base-lite; do
5+
for lr in 1e-5 3e-5 5e-5; do
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for batch_size in 512; do
7+
poetry run python src/train_sup.py \
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--dataset_name jsnli+nu-snli \
9+
--model_name $model_name \
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--batch_size $batch_size \
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--lr $lr \
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--gradient_checkpointing \
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--device $device
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done
15+
for batch_size in 256 128 64; do
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poetry run python src/train_sup.py \
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--dataset_name jsnli+nu-snli \
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--model_name $model_name \
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--batch_size $batch_size \
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--lr $lr \
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--device $device
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done
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done
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done
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done

‎scripts/10/prev/2023-03-21/1.sh‎

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device="cuda:1"
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for i in 0 1 2; do
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for model_name in cl-tohoku/bert-large-japanese ku-nlp/roberta-large-japanese-char-wwm; do
5+
for lr in 1e-5 3e-5 5e-5; do
6+
for batch_size in 512; do
7+
poetry run python src/train_sup.py \
8+
--dataset_name jsnli+nu-snli \
9+
--model_name $model_name \
10+
--batch_size $batch_size \
11+
--lr $lr \
12+
--gradient_checkpointing \
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--device $device
14+
done
15+
for batch_size in 256 128 64; do
16+
poetry run python src/train_sup.py \
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--dataset_name jsnli+nu-snli \
18+
--model_name $model_name \
19+
--batch_size $batch_size \
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--lr $lr \
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--device $device
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done
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done
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done
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done

‎scripts/10/prev/2023-03-21/2.sh‎

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device="cuda:2"
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for i in 0 1 2; do
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for model_name in nlp-waseda/roberta-large-japanese ku-nlp/deberta-v2-large-japanese; do
5+
for lr in 1e-5 3e-5 5e-5; do
6+
for batch_size in 512; do
7+
poetry run python src/train_sup.py \
8+
--dataset_name jsnli+nu-snli \
9+
--model_name $model_name \
10+
--batch_size $batch_size \
11+
--lr $lr \
12+
--use_jumanpp \
13+
--gradient_checkpointing \
14+
--device $device
15+
done
16+
for batch_size in 256 128 64; do
17+
poetry run python src/train_sup.py \
18+
--dataset_name jsnli+nu-snli \
19+
--model_name $model_name \
20+
--batch_size $batch_size \
21+
--lr $lr \
22+
--use_jumanpp \
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--device $device
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done
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done
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done
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done

‎scripts/10/prev/2023-03-21/3.sh‎

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device="cuda:3"
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for i in 0 1 2; do
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for model_name in xlm-roberta-large studio-ousia/mluke-large-lite; do
5+
for lr in 1e-5 3e-5 5e-5; do
6+
for batch_size in 512; do
7+
poetry run python src/train_sup.py \
8+
--dataset_name jsnli+nu-snli \
9+
--model_name $model_name \
10+
--batch_size $batch_size \
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--lr $lr \
12+
--gradient_checkpointing \
13+
--device $device
14+
done
15+
for batch_size in 256 128 64; do
16+
poetry run python src/train_sup.py \
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--dataset_name jsnli+nu-snli \
18+
--model_name $model_name \
19+
--batch_size $batch_size \
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--lr $lr \
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--device $device
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done
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done
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done
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done

‎scripts/11/0.sh‎

Lines changed: 6 additions & 21 deletions
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device="cuda:0"
22

3-
for i in 0 1 2; do
4-
for model_name in studio-ousia/luke-japanese-large-lite studio-ousia/luke-japanese-base-lite; do
5-
for batch_size in 512; do
6-
for lr in 5e-5 3e-5 1e-5; do
3+
for model_name in nlp-waseda/roberta-large-japanese ku-nlp/deberta-v2-large-japanese; do
4+
for lr in 1e-5 3e-5 5e-5; do
5+
for batch_size in 64 128 256 512; do
6+
for dataset_name in nu-snli nu-mnli nu-snli+mnli; do
77
poetry run python src/train_sup.py \
8-
--dataset_name jsnli \
9-
--model_name $model_name \
10-
--batch_size $batch_size \
11-
--lr $lr \
12-
--gradient_checkpointing \
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--device $device
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15-
poetry run python src/train_sup.py \
16-
--dataset_name nu-snli \
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--model_name $model_name \
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--batch_size $batch_size \
19-
--lr $lr \
20-
--gradient_checkpointing \
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--device $device
22-
23-
poetry run python src/train_sup.py \
24-
--dataset_name nu-snli+mnli \
8+
--dataset_name $dataset_name \
259
--model_name $model_name \
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--batch_size $batch_size \
2711
--lr $lr \
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--use_jumanpp \
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--gradient_checkpointing \
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--device $device
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done

‎scripts/11/1.sh‎

Lines changed: 5 additions & 21 deletions
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@@ -1,27 +1,11 @@
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device="cuda:1"
22

3-
for i in 0 1 2; do
4-
for model_name in cl-tohoku/bert-large-japanese ku-nlp/roberta-large-japanese-char-wwm; do
5-
for batch_size in 512; do
6-
for lr in 5e-5 3e-5 1e-5; do
3+
for model_name in studio-ousia/mluke-large-lite xlm-roberta-large; do
4+
for lr in 1e-5 3e-5 5e-5; do
5+
for batch_size in 64 128 256 512; do
6+
for dataset_name in nu-snli nu-mnli nu-snli+mnli; do
77
poetry run python src/train_sup.py \
8-
--dataset_name jsnli \
9-
--model_name $model_name \
10-
--batch_size $batch_size \
11-
--lr $lr \
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--gradient_checkpointing \
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--device $device
14-
15-
poetry run python src/train_sup.py \
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--dataset_name nu-snli \
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--model_name $model_name \
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--batch_size $batch_size \
19-
--lr $lr \
20-
--gradient_checkpointing \
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--device $device
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poetry run python src/train_sup.py \
24-
--dataset_name nu-snli+mnli \
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--dataset_name $dataset_name \
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--model_name $model_name \
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--batch_size $batch_size \
2711
--lr $lr \

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