| #!/usr/bin/env bash |
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| echo `date`, Setup the environment ... |
| set -e |
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| exp_path=exp_tuning |
| data_path=exp_main/data |
| res_path=$exp_path/results |
| mkdir -p $exp_path $res_path |
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| datasets="xsum squad writing" |
| source_models="gpt2-xl opt-2.7b gpt-neo-2.7B" |
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| echo `date`, Evaluate tuning parameter n_base: |
| n_bases=( 4 8 16 32 64 ) |
| for N in "${n_bases[@]}"; do |
| for D in $datasets; do |
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| train_parts=() |
| for d in $datasets; do |
| if [[ ${d} != ${D} ]]; then |
| train_parts+=("$d") |
| fi |
| done |
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| for M in $source_models; do |
| train_dataset="${data_path}/${train_parts[0]}_${M}&${data_path}/${train_parts[1]}_${M}" |
| echo `date`, Evaluating StatsDetectGPT on ${D}_${M} with "$N" bases ... |
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| python scripts/detect_gpt_ada.py --sampling_model_name $M --scoring_model_name $M --dataset $D --train_dataset "$train_dataset" --dataset_file $data_path/${D}_${M} --output_file $res_path/${D}_${M}_base"$N" --config "{\"start\": -32, \"end\": 0, \"n_bases\": $N, \"spline_order\": 2}" |
| done |
| done |
| done |
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| echo `date`, Evaluate tuning parameter spline_order: |
| spline_orders=( 1 2 3 4 ) |
| for N in "${spline_orders[@]}"; do |
| for D in $datasets; do |
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| train_parts=() |
| for d in $datasets; do |
| if [[ ${d} != ${D} ]]; then |
| train_parts+=("$d") |
| fi |
| done |
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| for M in $source_models; do |
| train_dataset="${data_path}/${train_parts[0]}_${M}&${data_path}/${train_parts[1]}_${M}" |
| echo `date`, Evaluating StatsDetectGPT on ${D}_${M} with "$N" bases ... |
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| python scripts/detect_gpt_ada.py --sampling_model_name $M --scoring_model_name $M --dataset $D --train_dataset "$train_dataset" --dataset_file $data_path/${D}_${M} --output_file $res_path/${D}_${M}_order"$N" --config "{\"start\": -32, \"end\": 0, \"n_bases\": 16, \"spline_order\": $N}" |
| done |
| done |
| done |
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