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dev_natlang.sh
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dev_natlang.sh
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data_dir=./data/NatLang
lr=1e-5
ep=50
eval_split=dev
output_dir=./output/NatLang
# model=roberta_rr # PRover
model=roberta_probr # PRobr
echo $output_dir
python3 ./run_experiment_natlang.py \
--data_dir $data_dir \
--output_dir $output_dir \
--per_gpu_eval_batch_size 8 \
--per_gpu_train_batch_size 8 \
--model_type $model \
--model_name_or_path roberta-large \
--task_name rr \
--do_eval \
--do_lower_case \
--max_seq_length 300 \
--learning_rate $lr \
--gradient_accumulation_steps 1 \
--num_train_epochs $ep \
--logging_steps 4752 \
--save_steps 4750 \
--seed 42 \
--data_cache_dir ./output/cache/ \
--evaluate_during_training \
python3 ./ilp_infer/inference_natlang.py \
--data_dir $data_dir \
--eval_split $eval_split \
--node_preds $output_dir/prediction_nodes_${eval_split}.lst \
--edge_logits $output_dir/prediction_edge_logits_${eval_split}.lst \
--edge_preds $output_dir/edge_preds_natlang_${eval_split}.lst \
--natlang_metadata $data_dir/turk-questions-${eval_split}-mappings.tsv
python3 ./evaluation/eval_natlang.py \
--data_dir $data_dir \
--eval_split $eval_split \
--qa_pred_file $output_dir/predictions_${eval_split}.lst \
--node_pred_file $output_dir/prediction_nodes_${eval_split}.lst \
--edge_pred_file $output_dir/edge_preds_natlang_${eval_split}.lst \
--natlang_metadata $data_dir/turk-questions-${eval_split}-mappings.tsv \
| tee $output_dir/${eval_split}_natlang_eval.log