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60 lines
1.8 KiB
60 lines
1.8 KiB
#!/bin/bash
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# lr 1e-5 to 5e-5
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# mc_loss_efficient 0.1 to 1
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usage="$(basename "$0") [-d <data-split-name>]
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Argument -d takes (few-shot) data split names.
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Possible valid names : 50-dpd|100-dpd|125-dpd|250-dpd"
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while getopts :d: flag
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do
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case "${flag}" in
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d) data_split=${OPTARG};;
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:) printf "missing argument for -%s\n" "$OPTARG" >&2; echo "$usage" >&2; exit 1;;
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esac
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done
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# check for mandatory/required -d argument
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# mandatory arguments
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if [ ! "$data_split" ]; then
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echo "arguments -d must be provided"
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echo "$usage" >&2; exit 1
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fi
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# Check whether the required environment vars are set
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if [[ -z "${SAVED_MODELS_BASELINE}" ]] || [[ -z "${PRE_TRAINED_SOLOIST}" ]]; then
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echo "Required Environment variables not set. First run set_env.sh"
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exit 1
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fi
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datetime_now=$(date +"%Y%m%dT%H%M%S")
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experiment_folder=experiment-${datetime_now}/"${data_split}"
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SAVE_DIR="${SAVED_MODELS_BASELINE}"/"${experiment_folder}"
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# create the dirs if not exist
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mkdir -p "${SAVE_DIR}"
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echo "Trained Model checkpoints are saved in ${SAVE_DIR}"
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echo "Cleaning the directory (${experiment_folder}) to make sure it's empty!"
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# delete the contents of the dir (where model checkpoints will be saved!)
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rm -rf "${SAVE_DIR}"
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python soloist_train.py \
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--output_dir="${SAVE_DIR}" \
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--model_type=gpt2 \
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--model_name_or_path="${PRE_TRAINED_SOLOIST}" \
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--do_train \
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--train_data_file=../data/baseline/"${data_split}"/train.soloist.json \
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--eval_data_file=../data/baseline/valid/valid.soloist.json \
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--add_special_action_tokens=../data/resource/special_tokens.txt \
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--per_gpu_train_batch_size 1 \
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--num_train_epochs 50 \
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--learning_rate 5e-5 \
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--overwrite_cache \
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--save_steps 5000 \
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--max_seq 100 \
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--overwrite_output_dir \
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--max_turn 15 \
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--num_candidates 1 \
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--mc_loss_efficient 0.33 \
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--add_belief_prediction |