tune_enc2dec_layers.sh

MODEL_NAME='t5-base'
BATCH_SIZE=16
DATASET='uklex-l1'
USE_LWAN=true
GEN_MAX_LENGTH=32
TRAINING_MODE='enc2dec'
OPTIMIZER='adamw_torch'
LEARNING_RATE=3e-5
export PYTHONPATH=.
export CUDA_VISIBLE_DEVICES=3
export TOKENIZERS_PARALLELISM=false

for DEC_LAYERS in 1 4 6
do
  for SEED in 21 32 84
  do
    python experiments/train_classifier.py \
    --model_name_or_path ${MODEL_NAME} \
    --t5_enc2dec true \
    --n_dec_layers ${DEC_LAYERS} \
    --dataset_name ${DATASET} \
    --output_dir data/logs/${OPTIMIZER}/${DATASET}/${MODEL_NAME}-${TRAINING_MODE}-dec-${DEC_LAYERS}/fp32/seed_${SEED} \
    --max_seq_length 512 \
    --do_train \
    --do_eval \
    --do_pred \
    --overwrite_output_dir \
    --load_best_model_at_end \
    --metric_for_best_model micro-f1 \
    --greater_is_better True \
    --evaluation_strategy epoch \
    --save_strategy epoch \
    --save_total_limit 5 \
    --num_train_epochs 20 \
    --learning_rate ${LEARNING_RATE} \
    --per_device_train_batch_size ${BATCH_SIZE} \
    --per_device_eval_batch_size ${BATCH_SIZE} \
    --seed ${SEED} \
    --optim ${OPTIMIZER} \
    --warmup_ratio 0.05 \
    --lr_scheduler_type cosine
  done
done


python report_enc2dec_results.py