# 扩充技能的语料
# rephrase_for_skill.sh: 在rephrase.sh基础上改的
# predict_for_skill.py: 在 predict_main.py基础上改的
# score_for_skill.txt 结果对比

# set gpu id to use
export CUDA_VISIBLE_DEVICES=""

start_tm=`date +%s%N`;

export HOST_NAME="wzk"
### Optional parameters ###

# If you train multiple models on the same data, change this label.
EXPERIMENT=wikisplit_experiment
# To quickly test that model training works, set the number of epochs to a
# smaller value (e.g. 0.01).
NUM_EPOCHS=10.0
export TRAIN_BATCH_SIZE=256
export PHRASE_VOCAB_SIZE=500
export MAX_INPUT_EXAMPLES=1000000
export SAVE_CHECKPOINT_STEPS=200
export enable_swap_tag=false
export output_arbitrary_targets_for_infeasible_examples=false
export WIKISPLIT_DIR="/home/${HOST_NAME}/Mywork/corpus/rephrase_corpus"
export OUTPUT_DIR="${WIKISPLIT_DIR}/output"



export max_seq_length=40 # TODO
export BERT_BASE_DIR="/home/${HOST_NAME}/Mywork/model/RoBERTa-tiny-clue" # chinese_L-12_H-768_A-12"




# Check these numbers from the "*.num_examples" files created in step 2.
export CONFIG_FILE=configs/lasertagger_config.json
export EXPERIMENT=wikisplit_experiment_name



### 4. Prediction

# Export the model.
python run_lasertagger.py \
  --label_map_file=${OUTPUT_DIR}/label_map.txt \
  --model_config_file=${CONFIG_FILE} \
  --output_dir=${OUTPUT_DIR}/models/${EXPERIMENT} \
  --do_export=true \
  --export_path=${OUTPUT_DIR}/models/${EXPERIMENT}/export

## Get the most recently exported model directory.
TIMESTAMP=$(ls "${OUTPUT_DIR}/models/${EXPERIMENT}/export/" | \
            grep -v "temp-" | sort -r | head -1)
SAVED_MODEL_DIR=${OUTPUT_DIR}/models/${EXPERIMENT}/export/${TIMESTAMP}
PREDICTION_FILE=${OUTPUT_DIR}/models/${EXPERIMENT}/pred.tsv
export domain_name=times
python skill_rephrase/predict_for_skill.py \
  --input_file=/home/${HOST_NAME}/Mywork/corpus/ner_corpus/times_corpus/slot_times.txt \
  --input_format=wikisplit \
  --output_file=/home/${HOST_NAME}/Mywork/corpus/ner_corpus/times_corpus/slot_times_expandexpand.json \
  --label_map_file=${OUTPUT_DIR}/label_map.txt \
  --vocab_file=${BERT_BASE_DIR}/vocab.txt \
  --max_seq_length=${max_seq_length} \
  --saved_model=${SAVED_MODEL_DIR}

#### 5. Evaluation
#python score_main.py --prediction_file=${PREDICTION_FILE}


end_tm=`date +%s%N`;
use_tm=`echo $end_tm $start_tm | awk '{ print ($1 - $2) / 1000000000 /3600}'`
echo "cost time" $use_tm "h"