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run_encoder_training.sh
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run_encoder_training.sh
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dataset=$1
# Max pooling.
## Dropout 0.15.
python scripts/neg/finetune.py -bs 128 -c scripts/neg/pretraining_configs/t5_vae_32_max_none_0.15_yf.yaml -d $dataset -t -pe -eo &&
python scripts/neg/finetune.py -bs 128 -c scripts/neg/pretraining_configs/t5_vae_32_max_none_0.15_nf.yaml -d $dataset -t -pe -eo &&
## No dropout.
python scripts/neg/finetune.py -bs 128 -c scripts/neg/pretraining_configs/t5_vae_32_max_none_none_yf.yaml -d $dataset -t -pe -eo &&
python scripts/neg/finetune.py -bs 128 -c scripts/neg/pretraining_configs/t5_vae_32_max_none_none_nf.yaml -d $dataset -t -pe -eo &&
## Dropout 0.4.
python scripts/neg/finetune.py -bs 128 -c scripts/neg/pretraining_configs/t5_vae_32_max_none_0.4_yf.yaml -d $dataset -t -pe -eo &&
python scripts/neg/finetune.py -bs 128 -c scripts/neg/pretraining_configs/t5_vae_32_max_none_0.4_nf.yaml -d $dataset -t -pe -eo &&
# Mean pooling.
## Dropout 0.15.
python scripts/neg/finetune.py -bs 128 -c scripts/neg/pretraining_configs/t5_vae_32_mean_none_0.15_yf.yaml -d $dataset -t -pe -eo &&
python scripts/neg/finetune.py -bs 128 -c scripts/neg/pretraining_configs/t5_vae_32_mean_none_0.15_nf.yaml -d $dataset -t -pe -eo
## No dropout.
#python scripts/neg/finetune.py -bs 128 -c scripts/neg/pretraining_configs/t5_vae_32_mean_none_none_yf.yaml -d $dataset -t -pe -eo &&
#python scripts/neg/finetune.py -bs 128 -c scripts/neg/pretraining_configs/t5_vae_32_mean_none_none_nf.yaml -d $dataset -t -pe -eo &&
## Different latents
#python scripts/neg/finetune.py -bs 128 -c scripts/neg/pretraining_configs/t5_vae_64_max_none_0.15_yf.yaml -d $dataset -t -pe -eo &&
#python scripts/neg/finetune.py -bs 128 -c scripts/neg/pretraining_configs/t5_vae_128_max_none_0.15_yf.yaml -d $dataset -t -pe -eo