import re def check_standalization(model_response, prompt_style, type): if any (match in type for match in ['NIE','NDE','ETT','CDE','ATE']): if model_response.startswith(("{\"answer\":")) and model_response.endswith(("}")): return 0 else: return 1 elif any (match in type for match in ['PN','PS']): if model_response.startswith(("{\"prob\":")) 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", "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): affect_num = sum(1 for pred in preds if pred == 0.1234) # 0.1234 is the example value in prompt for probability computation affected = affect_num / len(preds) abnormalities = "All Yes" if affected == 1 else \ "All No" if all(pred == 0 for pred in preds) else 0 return abnormalities