| # Using: $ bash dbap_train_and_predict.bash --lang {en|ru} --train_data {en|ru|en_aug|ru_aug} | |
| # Parsing parameters | |
| lang=${lang:-"en"} | |
| train_data=${train_data:-"en_aug"} | |
| lrb=${lrb:-0.00002} | |
| lrp=${lrp:-0.000002} | |
| while [ $# -gt 0 ]; do | |
| if [[ $1 == *"--"* ]]; then | |
| param="${1/--/}" | |
| declare $param="$2" | |
| echo $1 $2 | |
| fi | |
| shift | |
| done | |
| export LR_BERT=$lrb | |
| export LR_PARSER=$lrp | |
| export LANG=$lang | |
| export TRAIN_DATA=$train_data | |
| echo LANG $LANG | |
| echo TRAIN_DATA $TRAIN_DATA | |
| # Prepare directory | |
| export DIRECTORY=dbap_${TRAIN_DATA} | |
| # Prepare configs for K-fold validation | |
| export CONFIGFILE=configs/dbap_${LANG}_fold_0.jsonnet | |
| rm configs/dbap_${LANG}_fold* | |
| cp initial_configs/dbap_${LANG}_fold_0.jsonnet $CONFIGFILE | |
| echo CONFIGFILE $CONFIGFILE | |
| K=10 | |
| python utils/make_k_copies.py --filename $CONFIGFILE --k $K | |
| # Default training | |
| rm -r $DIRECTORY | |
| mkdir $DIRECTORY | |
| for (( FOLD=0; FOLD<$K; FOLD++ )) | |
| do | |
| allennlp train -s ${DIRECTORY}/fold_${FOLD} configs/dbap_${LANG}_fold_${FOLD}.jsonnet | |
| allennlp predict --use-dataset-reader --silent \ | |
| --output-file ${DIRECTORY}/fold_${FOLD}/predictions_test.json ${DIRECTORY}/fold_${FOLD}/model.tar.gz \ | |
| data/conll_cv_rst/${LANG}/test_fold_${FOLD}.conll \ | |
| --include-package dependency_parser | |
| python utils/evaluate_predictions.py --model_name $DIRECTORY --corpus_key ru_full --foldnum $FOLD | |
| done |