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edd0ffdf70
* 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
43 lines
1.8 KiB
Python
43 lines
1.8 KiB
Python
import re
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def check_standalization(model_response, prompt_style, type):
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if any (match in type for match in ['NIE','NDE','ETT','CDE','ATE']):
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if model_response.startswith(("{\"answer\":")) and model_response.endswith(("}")):
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return 0
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else:
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return 1
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elif any (match in type for match in ['PN','PS']):
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if model_response.startswith(("{\"prob\":")) and model_response.endswith(("}")):
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return 0
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else:
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return 1
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def check_empty(model_response):
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if model_response == "":
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return 1
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else:
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return 0
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def check_repetition(model_response):
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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）"]):
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return 1
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else:
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return 0
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def contains_chinese(text):
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chinese_pattern = re.compile(r'[\u4e00-\u9fff]+')
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result = 1 if chinese_pattern.search(text) is not None else 0
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return result
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def contains_english(text):
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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.
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result = 1 if english_pattern.search(text) is not None else 0
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return result
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def check_abnormality(preds):
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affect_num = sum(1 for pred in preds if pred == 0.1234) # 0.1234 is the example value in prompt for probability computation
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affected = affect_num / len(preds)
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abnormalities = "All Yes" if affected == 1 else \
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"All No" if all(pred == 0 for pred in preds) else 0
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return abnormalities |