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summarize_results.py
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import argparse
import json
import os
from collections import defaultdict
from CMIRB import *
from mteb import MTEB
med_tasks = ['MedExamRetrieval', "DuBaikeRetrieval", "DXYDiseaseRetrieval",
'MedicalRetrieval', "CmedqaRetrieval", "DXYConsultRetrieval",
"CovidRetrieval", "IIYIPostRetrieval", "CSLCiteRetrieval", "CSLRelatedRetrieval"]
med_models = ['bge-large-zh-v1.5']
def read_results(task_types, except_tasks, args):
tasks_results = {}
model_dirs = {}
for t_type in task_types:
tasks_results[t_type] = {}
for t in MTEB(task_types=[t_type], task_langs=args.lang).tasks:
task_name = t.description["name"]
if task_name not in med_tasks:
continue
if task_name in except_tasks: continue
metric = t.description["main_score"]
tasks_results[t_type][task_name] = defaultdict(None)
for model_name in os.listdir(args.results_dir):
if model_name not in med_models:
continue
model_dir = os.path.join(args.results_dir, model_name)
if not os.path.isdir(model_dir): continue
model_dirs[model_name] = model_dir
if os.path.exists(os.path.join(model_dir, task_name + '.json')):
data = json.load(open(os.path.join(model_dir, task_name + '.json')))
for s in ['test', 'dev', 'validation']:
if s in data:
split = s
break
if 'en' in args.lang:
if 'en-en' in data[split]:
temp_data = data[split]['en-en']
elif 'en' in data[split]:
temp_data = data[split]['en']
else:
temp_data = data[split]
elif 'zh' in args.lang:
if 'zh' in data[split]:
temp_data = data[split]['zh']
elif 'zh-CN' in data[split]:
temp_data = data[split]['zh-CN']
else:
temp_data = data[split]
if metric == 'ap':
tasks_results[t_type][task_name][model_name] = round(temp_data['cos_sim']['ap'] * 100, 2)
elif metric == 'cosine_spearman':
tasks_results[t_type][task_name][model_name] = round(temp_data['cos_sim']['spearman'] * 100, 2)
else:
tasks_results[t_type][task_name][model_name] = round(temp_data[metric] * 100, 2)
return tasks_results, model_dirs
def output_markdown(tasks_results, model_names, save_file):
task_type_res = {}
with open(save_file, 'w') as f:
for t_type, type_results in tasks_results.items():
has_CQADupstack = False
task_cnt = 0
task_type_res[t_type] = defaultdict()
f.write(f'Task Type: {t_type} \n')
first_line = "| Model |"
second_line = "|:-------------------------------|"
for task_name in med_tasks:
# for task_name in type_results.keys():
if "CQADupstack" in task_name:
has_CQADupstack = True
continue
first_line += f" {task_name} |"
second_line += ":--------:|"
task_cnt += 1
if has_CQADupstack:
first_line += f" CQADupstack |"
second_line += ":--------:|"
task_cnt += 1
f.write(first_line + '\n')
f.write(second_line + '\n')
for model in model_names:
write_line = f"| {model} |"
all_res = []
cqa_res = []
for task_name in med_tasks:
if task_name not in type_results:
continue
results = type_results[task_name]
# for task_name, results in type_results.items():
if "CQADupstack" in task_name:
if model in results:
cqa_res.append(results[model])
continue
if model in results:
write_line += f" {results[model]},"
all_res.append(results[model])
else:
write_line += f" ,"
if len(cqa_res) > 0:
write_line += f" {round(sum(cqa_res) / len(cqa_res), 2)} \t"
all_res.append(round(sum(cqa_res) / len(cqa_res), 2))
# if len(all_res) == task_cnt:
# write_line += f" {round(sum(all_res) / len(all_res), 2)} \t"
# task_type_res[t_type][model] = all_res
# else:
write_line += f" \t"
f.write(write_line + ' \n')
# f.write(f'Overall \n')
# first_line = "| Model |"
# second_line = "|:-------------------------------|"
# for t_type in task_type_res.keys():
# first_line += f" {t_type} |"
# second_line += ":--------:|"
# f.write(first_line + ' Avg | \n')
# f.write(second_line + ':--------:| \n')
# for model in model_names:
# write_line = f"| {model} |"
# all_res = []
# for type_name, results in task_type_res.items():
# if model in results:
# write_line += f" {round(sum(results[model]) / len(results[model]), 2)} |"
# all_res.extend(results[model])
# else:
# write_line += f" |"
#
# if len(all_res) > 0:
# write_line += f" {round(sum(all_res) / len(all_res), 2)} |"
#
# f.write(write_line + ' \n')
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--results_dir', default="./zh_results", type=str)
parser.add_argument('--lang', default="zh", type=str)
return parser.parse_args()
if __name__ == '__main__':
args = get_args()
if args.lang == 'zh':
task_types = ["Retrieval"]
except_tasks = []
args.lang = ['zh', 'zh-CN']
elif args.lang == 'en':
task_types = ["Retrieval", "Clustering", "PairClassification", "Reranking", "STS", "Summarization",
"Classification"]
except_tasks = ['MSMARCOv2']
args.lang = ['en']
else:
raise NotImplementedError(f"args.lang must be zh or en, but{args.lang}")
task_results, model_dirs = read_results(task_types, except_tasks, args=args)
output_markdown(task_results, model_dirs.keys(),
save_file=os.path.join(args.results_dir, f'{args.lang[0]}_results.md'))