run_pipeline.sh

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

TASK="$1"

if [ "$TASK" = "insurance" ]
then
  MODEL_TYPE="bert"
  USE_CRF=true
  MODEL_DIR="results/${TASK}-${MODEL_TYPE}-CRF${USE_CRF}"
  IS_BIO=false
elif [ "$TASK" = "fastfood" ]
then
  MODEL_TYPE="bert"
  USE_CRF=true
  MODEL_DIR="results/${TASK}-${MODEL_TYPE}-CRF${USE_CRF}"
  IS_BIO=true
else
  exit 1
fi

echo "Model path = ${MODEL_DIR}" 

# ----------------
# Step 1: IC/SL
# 1.1 Train the JointBERT model
cd JointBERT

if [ "$USE_CRF" = true ]
then
   python3 main.py --task $TASK --model_type $MODEL_TYPE --model_dir "../${MODEL_DIR}" --do_train --do_eval --use_crf
else
   python3 main.py --task $TASK --model_type $MODEL_TYPE --model_dir "../${MODEL_DIR}" --do_train --do_eval
fi


# 1.2 Use the trained model to do IC/SL on the test data
python3 predict.py --input_file "../data/${TASK}/test/seq.in" --output_file "../${MODEL_DIR}/test_seq_out.txt" --model_dir "../${MODEL_DIR}"
cd ..
# ----------------
# Step 2: Run the rest of the pipeline
for PIPELINE in 'deterministic' 'probabilistic'
do
    if [ "$IS_BIO" = true ]
    then
       python3 main.py --task $TASK --nlu_result_path "${MODEL_DIR}/test_seq_out.txt" --pipeline $PIPELINE --result_folder $MODEL_DIR --is_bio
    else
       python3 main.py --task $TASK --nlu_result_path "${MODEL_DIR}/test_seq_out.txt" --pipeline $PIPELINE --result_folder $MODEL_DIR
    fi
done