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test.sh
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test.sh
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data_dir=./data/depth-5
eval_split=test
ep=50
lr=1e-5
output_dir=./output/d5
# model=roberta_rr # PRover
model=roberta_probr # PRobr
echo $output_dir
python3 ./run_experiment_probr.py \
--data_dir $data_dir \
--output_dir $output_dir \
--per_gpu_eval_batch_size 32 \
--per_gpu_train_batch_size 8 \
--model_type $model \
--model_name_or_path roberta-large \
--task_name rr \
--run_on_test \
--do_lower_case \
--max_seq_length 300 \
--logging_steps 4752 \
--save_steps 4750 \
--seed 42 \
--data_cache_dir ./output/cache/ \
--evaluate_during_training \
python3 ./ilp_infer/inference.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 $output_dir/edge_preds_${eval_split}.lst \
python3 ./evaluation/eval_proof.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 $output_dir/edge_preds_${eval_split}.lst \
| tee $output_dir/${eval_split}_eval.log