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set -e
# train text2sql-t5-base model
python -u text2sql_inputgrande.py \
--batch_size 8 \
--gradient_descent_step 2 \
--device "0" \
--learning_rate 1e-4 \
--epochs 128 \
--seed 42 \
--save_path "./models/text2sql-t5-amr" \
--tensorboard_save_path "./tensorboard_log/text2sql-t5-amr" \
--model_name_or_path "t5-base" \
--use_adafactor \
--mode train \
--train_filepath "./data/preprocessed_data/resdsql_train_spider_amr.json"
# select the best text2sql-t5-base ckpt
python -u evaluate_text2sql_ckpts_inputgrande.py \
--batch_size 8 \
--device "0" \
--seed 42 \
--save_path "./models/text2sql-t5-amr" \
--eval_results_path "./eval_results/text2sql-t5-amr" \
--mode eval \
--dev_filepath "./data/preprocessed_data/resdsql_dev_amr.json" \
--original_dev_filepath "./data/spider_amr/dev.json" \
--db_path "./database" \
--num_beams 8 \
--num_return_sequences 8 \
--target_type "sql"
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