train_pseudo_label.sh

OUTPUT_DIR=outputs/pseudo_label/roberta-base/

echo "OUTPUT DIRECTORY $OUTPUT_DIR"

mkdir -p $OUTPUT_DIR

cp qs_kpa/pseudo_label/models.py  $OUTPUT_DIR

for fold_id in 1 2 3 4 5 6 7
do
        echo "TRAINING ON FOLD $fold_id"
        python scripts/main.py \
                --experiment "pseudolabel" \
                --output_dir "$OUTPUT_DIR/fold_$fold_id" \
                --model_name_or_path roberta-base \
                --tokenizer roberta-base \
                --distance "cosine" \
                --directory "kpm_k_folds/fold_$fold_id" \
                --test_directory "kpm_k_folds/test/" \
                --logging_dir "$OUTPUT_DIR/fold_$fold_id" \
                --logging_steps 20 \
                --max_pos 30 \
                --max_neg 90\
                --max_unknown 15 \
                --overwrite_output_dir \
                --num_train_epochs 5 \
                --early_stop 10 \
                --train_batch_size 1 \
                --val_batch_size 128 \
                --do_train \
                --evaluate_during_training \
                --warmup_steps 0 \
                --gradient_accumulation_steps 1 \
                --learning_rate 0.00003 \
                --margin 0.3 \
                --drop_rate 0.1 \
                --n_hiddens 4 \
                --max_len 30 \
                --statement_max_len 50 \
                --stance_dim 32 \
                --text_dim 256 \
                --num_workers 4 \
                --seed 0 \
                --stance_free

done