run_train_progression_multiseed.sh

seeds=(
    60 87 224 371 488 549 610 769 831 975 1082 1167 1372 1516 1704 1711 1842 1870 
    2087 2140 2150 2172 2202 2229 2263 2358 2383 2413 2419 2537 2594 2716 3284
)
supervised_models=('supervised_base' 'supervised_large' 'supervised_large_adapted')
supervised_bases=('roberta-base' 'roberta-large' 'LACAI/roberta-large-dialog-narrative')
for seed in ${seeds[@]}
do
    python dialogue-progression/train_progression.py \
        --run-name=unsupervised_$seed \
        --recency-weight=0.3 \
        --kmeans-n-clusters=21 \
        --normalize-embeddings \
        --kmeans-progression-inv-dist \
        --append-results \
        --model-random-state=$seed

    python dialogue-progression/progression_model_eval_manual.py \
        --progression-modelpath=models/progression/persuasion_for_good/unsupervised_${seed} \
        --append-results

    for ((i = 0; i < ${#supervised_models[@]}; i++))
    do
        python dialogue-progression/train_progression.py \
            --run-name=${supervised_models[i]}_${seed} \
            --embedding-level=dialog \
            --normalize-embeddings \
            --batch-size=16 \
            --transformers-modelpath=${supervised_bases[i]} \
            --append-results \
            --model-random-state=$seed

        python dialogue-progression/progression_model_eval_manual.py \
            --progression-modelpath=models/progression/persuasion_for_good/${supervised_models[i]}_${seed} \
            --append-results
    done
done