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summarize_val.sh
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#!/bin/bash
# Copyright 2022 Statistics and Machine Learning Research Group at HKUST. 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.
#
# Summarizes validation results to a summary file. The output file is sorted
# by the first column in descending order.
function collect_validation_result() {
# ===== Reported results
# MRPC F1/Accuracy 88.85/84.07 2:21
# CoLA Matthews corr 56.53 3:17
# SST-2 Accuracy 92.32 26:06
# STS-B Pearson/Spearman corr. 88.64/88.48 2:13
# QQP Accuracy/F1 90.71/87.49 2:22:26
# MNLI Matched acc./Mismatched acc. 83.91/84.10 2:35:23
# QNLI Accuracy 90.66 40:57
# RTE Accuracy 65.70 57
# WNLI Accuracy 56.34 24
local task_name=$1
local log_dir=$2
local result_file=$3
if [ "${task_name}" = "mrpc" ]; then
echo "eval_f1 eval_accuracy eval_loss finetune_file" > ${result_file}
elif [ "${task_name}" = "cola" ]; then
echo "eval_matthews_correlation eval_loss finetune_file" > ${result_file}
elif [ "${task_name}" = "stsb" ]; then
echo "eval_pearson eval_spearmanr eval_loss finetune_file" > ${result_file}
elif [ "${task_name}" = "qqp" ]; then
echo "eval_f1 eval_accuracy eval_loss finetune_file" > ${result_file}
elif [ "${task_name}" = "mnli" ]; then
echo "eval_matched_accuracy eval_loss finetune_file" > ${result_file}
else
echo "eval_accuracy eval_loss finetune_file" > ${result_file}
fi
for file_err_path in ${log_dir}/*.err; do
local file_prefix=$(echo ${file_err_path} | sed 's/\.err//g')
cat ${file_prefix}.log ${file_prefix}.err > ${file_prefix}.log-err
local file_path=${file_prefix}.log-err
# Collects relevant statisitcs (can be missed if not available)
local eval_loss=$(cat ${file_path} | grep -F "***** eval metrics *****" -A 11 | grep "eval_loss " | awk '{ print $NF }' | sed -e "s/\r//")
local eval_f1=$(cat ${file_path} | grep -F "***** eval metrics *****" -A 11 | grep "eval_f1 " | awk '{ print $NF }' | sed -e "s/\r//")
local eval_accuracy=$(cat ${file_path} | grep -F "***** eval metrics *****" -A 11 | grep "eval_accuracy " | awk '{ print $NF }' | sed -e "s/\r//")
local eval_matthews_correlation=$(cat ${file_path} | grep -F "***** eval metrics *****" -A 11 | grep "eval_matthews_correlation " | awk '{ print $NF }' | sed -e "s/\r//")
local eval_pearson=$(cat ${file_path} | grep -F "***** eval metrics *****" -A 11 | grep "eval_pearson " | awk '{ print $NF }' | sed -e "s/\r//")
local eval_spearmanr=$(cat ${file_path} | grep -F "***** eval metrics *****" -A 11 | grep "eval_spearmanr " | awk '{ print $NF }' | sed -e "s/\r//")
local eval_matched_accuracy=$(cat ${file_path} | grep -F "***** eval metrics *****" -A 11 | grep "eval_accuracy " | awk '{ print $NF }' | sed -e "s/\r//")
local file=$(basename ${file_path})
if [ "${task_name}" = "mrpc" ]; then
echo "${eval_f1} ${eval_accuracy} ${eval_loss} ${file}"
elif [ "${task_name}" = "cola" ]; then
echo "${eval_matthews_correlation} ${eval_loss} ${file}"
elif [ "${task_name}" = "stsb" ]; then
echo "${eval_pearson} ${eval_spearmanr} ${eval_loss} ${file}"
elif [ "${task_name}" = "qqp" ]; then
echo "${eval_f1} ${eval_accuracy} ${eval_loss} ${file}"
elif [ "${task_name}" = "mnli" ]; then
echo "${eval_matched_accuracy} ${eval_loss} ${file}"
else
echo "${eval_accuracy} ${eval_loss} ${file}"
fi
done | sort -rn >> ${result_file}
}
function main() {
local task_name
# ===== Test setting
# local pretrain_dataset_name="bookcorpus-evenly-article-partition"
# ===== Standard finetune setting
local pretrain_dataset_name=$1
local prefix=$2
local model_set_path="saved_models/pretrain/${pretrain_dataset_name}"
for model_path in ${model_set_path}/${prefix}; do
for task_name in wnli rte mrpc stsb cola sst2 qnli qqp mnli;do
# for task_name in wnli;do
local model_name=$(basename ${model_path})
local log_dir="log/finetune/${pretrain_dataset_name}/${task_name}/${model_name}"
local result_file="${log_dir}/summary.log"
echo "${task_name}: summarize results to '${result_file}'..." >&2
collect_validation_result ${task_name} ${log_dir} ${result_file}
done
done
}
main "$@"