Prepare for experiments with different inverse prompt weights

prompt-learning/prompt_utils.py

@@ -17,7 +17,7 @@ PROMPT_TEMPLATES = {
         "generate": "belief states: value = $value, slot ="
     },
     "inverse-prompt": {
-        "training": INVERSE_PROMPTS["i2"],
+        "training": INVERSE_PROMPTS["i1"],
     },
     "prompt-ensemble": {
         "training": {

prompt-learning/test_prompting.sh

@@ -51,5 +51,4 @@ mkdir -p "${OUTPUTS_DIR}"
 python prompt_decode.py \
 --output_dir="${OUTPUTS_DIR}" \
 --tuned_model_path="${FINE_TUNED_MODEL_PATH}" \
---test_data_file="${TEST_DATA_FILE}" \
+--test_data_file="${TEST_DATA_FILE}"
---with_prompt_ensemble

prompt-learning/train_prompting.sh

@@ -50,11 +50,7 @@ echo "Trained Models (epochs) will be saved in ${SAVE_DIR}"
 
 # different number of epoch for different training sets
 # when using prompt ensemble for training, preferably use more number of epochs.
-if [ "$data_split" = "5-dpd" ] || [ "$data_split" = "10-dpd" ]; then
 epochs=5
-else
-  epochs=8
-fi
 
 python prompt_train.py \
 --save_model_dir="${SAVE_DIR}" \
@@ -63,6 +59,5 @@ python prompt_train.py \
 --validation_file=../data/prompt-learning/valid/valid.soloist.json \
 --num_epochs $epochs \
 --learning_rate 5e-5 \
---with_prompt_ensemble \
 --with_inverse_prompt \
---inverse_prompt_weight 0.1
+--inverse_prompt_weight 0.3