OpenCompass/opencompass/datasets/calm/evaluation/error/basic_adversarial/Natural.py
Peng Bo edd0ffdf70
Calm dataset (#1287)
* add calm dataset

* modify config max_out_len

* update README

* Modify README

* update README

* update README

* update README

* update README

* update README

* add summarizer and modify readme

* delete summarizer config comment

* update summarizer

* modify same response to all questions

* update README
2024-07-26 11:48:16 +08:00

35 lines
1.5 KiB
Python

import re
def check_standalization(model_response, prompt_style, type):
if model_response.startswith(("{\"answer\":")) and model_response.endswith(("}")):
return 0
else:
return 1
def check_empty(model_response):
if model_response == "":
return 1
else:
return 0
def check_repetition(model_response):
if any(response in model_response for response in ["input info: imagine a self-contained","provide the calculation result to four decimal places and a final \"yes\" or \"no\" answer in json format", "输入信息:设想一个", "请根据上述信息,给出计算结果(答案保留四位小数)"]):
return 1
else:
return 0
def contains_chinese(text):
chinese_pattern = re.compile(r'[\u4e00-\u9fff]+')
result = 1 if chinese_pattern.search(text) is not None else 0
return result
def contains_english(text):
english_pattern = re.compile(r'[A-Za-z]{7,}') # Taking into account 'fake' and 'random' modes, and considering that the shortest occurrence of English characters in an 'answer' is of length 6, therefore detecting lengths of 7 or more.
result = 1 if english_pattern.search(text) is not None else 0
return result
def check_abnormality(preds):
abnormalities = "All Yes" if all(pred == "yes" or pred == "" for pred in preds) else \
"All No" if all(pred == "no" or pred == "" for pred in preds) else 0
return abnormalities