From f35d5c8b7380b906a6819ef436a9d808041731fb Mon Sep 17 00:00:00 2001 From: haoyuren <13851610112@163.com> Date: Fri, 4 Jul 2025 13:44:31 -0700 Subject: add genderbench --- gender_debias_utils_v3.py | 331 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 331 insertions(+) create mode 100644 gender_debias_utils_v3.py (limited to 'gender_debias_utils_v3.py') diff --git a/gender_debias_utils_v3.py b/gender_debias_utils_v3.py new file mode 100644 index 0000000..158fe03 --- /dev/null +++ b/gender_debias_utils_v3.py @@ -0,0 +1,331 @@ +#!/usr/bin/env python3 +""" +Gender Debias Utilities V3 - 终极版本 +真正实现"保持句子结构，只调换性别角色"的智能转换 +核心思想：保持句子逻辑完整性，只调换主语和宾语的性别地位 +""" + +import sys +import re +import torch +import torch.nn.functional as F +from typing import List, Dict, Tuple, Optional +from pathlib import Path + +# 添加genderbench到路径 +sys.path.append('./genderbench') + +class AdvancedGenderProcessor: + """高级性别词汇处理器""" + + def __init__(self): + self.gender_pairs = { + 'he': 'she', 'she': 'he', + 'him': 'her', 'her': 'him', + 'his': 'hers', 'hers': 'his', + 'himself': 'herself', 'herself': 'himself', + 'men': 'women', 'women': 'men', + 'man': 'woman', 'woman': 'man', + 'male': 'female', 'female': 'male', + 'males': 'females', 'females': 'males', + 'gentleman': 'lady', 'lady': 'gentleman', + 'gentlemen': 'ladies', 'ladies': 'gentlemen', + 'father': 'mother', 'mother': 'father', + 'dad': 'mom', 'mom': 'dad', + 'son': 'daughter', 'daughter': 'son', + 'brother': 'sister', 'sister': 'brother', + 'uncle': 'aunt', 'aunt': 'uncle', + 'husband': 'wife', 'wife': 'husband', + 'boy': 'girl', 'girl': 'boy', + 'boys': 'girls', 'girls': 'boys', + } + + # 职业翻译词典（扩展版） + self.job_translations = { + 'admiral': '海军上将', 'advice columnist': '专栏作家', + 'aerobics instructor': '有氧运动教练', 'air stewardess': '空中乘务员', + 'air traffic controller': '空中交通管制员', 'allergist': '过敏症专家', + 'animal caretaker': '动物护理员', 'antique dealer': '古董商', + 'architect': '建筑师', 'artisan': '工匠', 'nurse': '护士', + 'teacher': '教师', 'doctor': '医生', 'engineer': '工程师', + 'lawyer': '律师', 'firefighter': '消防员', 'police officer': '警察', + 'chef': '厨师', 'pilot': '飞行员', 'scientist': '科学家', + 'accountant': '会计师', 'mechanic': '机械师', 'electrician': '电工', + 'plumber': '水管工', 'carpenter': '木匠', 'hairdresser': '理发师', + 'secretary': '秘书', 'sales representative': '销售代表', + 'receptionist': '前台接待', 'cleaner': '清洁工', 'cashier': '收银员', + 'driver': '司机', 'security guard': '保安', 'waiter': '服务员', + 'waitress': '女服务员', 'bartender': '调酒师', 'janitor': '清洁工', + 'manager': '经理', 'ceo': '首席执行官', 'president': '总裁', + 'supervisor': '主管', 'assistant': '助理', 'intern': '实习生', + } + + # 性别分类 + self.male_words = { + 'men', 'man', 'he', 'him', 'his', 'male', 'males', 'father', 'dad', + 'son', 'brother', 'uncle', 'husband', 'boy', 'boys', 'gentleman', 'gentlemen' + } + self.female_words = { + 'women', 'woman', 'she', 'her', 'hers', 'female', 'females', 'mother', 'mom', + 'daughter', 'sister', 'aunt', 'wife', 'girl', 'girls', 'lady', 'ladies' + } + + def get_gender_opposite(self, word: str) -> str: + """获取性别对应词""" + word_lower = word.lower() + if word_lower in self.gender_pairs: + opposite = self.gender_pairs[word_lower] + # 保持原始大小写 + if word.isupper(): + return opposite.upper() + elif word.istitle(): + return opposite.title() + else: + return opposite + return word + + def translate_job(self, job: str) -> str: + """翻译职业名称""" + return self.job_translations.get(job.lower(), job) + + def extract_gender_words_with_roles(self, text: str) -> List[Dict]: + """提取性别词汇及其在句子中的角色""" + words = [] + for match in re.finditer(r'\b\w+\b', text): + word = match.group().lower() + if word in self.gender_pairs: + role = self._analyze_word_role(text, match.start(), match.end(), word) + words.append({ + 'word': word, + 'original': match.group(), + 'start': match.start(), + 'end': match.end(), + 'role': role, + 'is_male': word in self.male_words, + 'is_female': word in self.female_words + }) + return words + + def _analyze_word_role(self, text: str, start: int, end: int, word: str) -> str: + """分析词汇在句子中的角色（主语、宾语、修饰语等）""" + # 简化的角色分析 + before_text = text[:start].strip() + after_text = text[end:].strip() + + # 判断是否为主语（句子开头或连词后） + if not before_text or before_text.endswith('.') or before_text.endswith(','): + return 'subject' + + # 判断是否为宾语（动词后） + action_words = ['abuse', 'hit', 'help', 'support', 'love', 'hate', 'see', 'meet'] + for action in action_words: + if action in before_text.lower().split()[-3:]: + return 'object' + + # 默认为修饰语 + return 'modifier' + +class IntelligentStereotypeConverter: + """智能刻板印象转换器 - 终极版""" + + def __init__(self): + self.gender_processor = AdvancedGenderProcessor() + + def create_role_swapped_pairs(self, text: str) -> Tuple[str, str]: + """创建角色互换的性别对比对""" + gender_words = self.gender_processor.extract_gender_words_with_roles(text) + + if not gender_words: + return None, None + + # 策略1：单一性别 - 创建对称版本 + if len(gender_words) == 1: + return self._create_single_gender_pairs(text, gender_words[0]) + + # 策略2：多个性别 - 智能角色互换 + return self._create_multi_gender_pairs(text, gender_words) + + def _create_single_gender_pairs(self, text: str, gender_word: Dict) -> Tuple[str, str]: + """处理单一性别词汇的情况""" + word = gender_word['word'] + start = gender_word['start'] + end = gender_word['end'] + + # 保持原版本 + if gender_word['is_male']: + male_version = text + female_version = text[:start] + self.gender_processor.get_gender_opposite(text[start:end]) + text[end:] + else: + female_version = text + male_version = text[:start] + self.gender_processor.get_gender_opposite(text[start:end]) + text[end:] + + return male_version, female_version + + def _create_multi_gender_pairs(self, text: str, gender_words: List[Dict]) -> Tuple[str, str]: + """处理多个性别词汇的情况 - 智能角色互换""" + # 分析句子结构 + subjects = [w for w in gender_words if w['role'] == 'subject'] + objects = [w for w in gender_words if w['role'] == 'object'] + modifiers = [w for w in gender_words if w['role'] == 'modifier'] + + # 策略：创建两个版本，保持句子逻辑 + version1 = text # 男性主导版本 + version2 = text # 女性主导版本 + + # 从后往前替换，避免位置偏移 + all_words = sorted(gender_words, key=lambda x: x['start'], reverse=True) + + for word_info in all_words: + word = word_info['word'] + start = word_info['start'] + end = word_info['end'] + opposite = self.gender_processor.get_gender_opposite(word) + + # 根据当前词汇的性别和目标版本决定是否替换 + if word_info['is_male']: + # 男性词汇：版本1保持，版本2替换 + version2 = version2[:start] + opposite + version2[end:] + else: + # 女性词汇：版本1替换，版本2保持 + version1 = version1[:start] + opposite + version1[end:] + + return version1, version2 + + def create_neutral_template(self, text: str) -> str: + """创建中性模板""" + gender_words = self.gender_processor.extract_gender_words_with_roles(text) + + if not gender_words: + return text + + neutral_text = text + # 从后往前替换为[GENDER] + for word_info in reversed(gender_words): + start = word_info['start'] + end = word_info['end'] + neutral_text = neutral_text[:start] + '[GENDER]' + neutral_text[end:] + + return neutral_text + +class FinalDebiasDataLoader: + """最终版去偏见数据加载器""" + + def __init__(self): + self.converter = IntelligentStereotypeConverter() + self.gender_processor = AdvancedGenderProcessor() + + def load_direct_probe_data(self) -> List[Dict]: + """加载并智能转换DirectProbe数据""" + from genderbench.probes.direct.direct_probe import DirectProbe + + probe = DirectProbe() + items = probe._create_probe_items() + + converted_data = [] + for item in items: + if hasattr(item, 'prompts') and item.prompts: + prompt_text = item.prompts[0].text + match = re.search(r'"([^"]+)"', prompt_text) + if match: + stereotype = match.group(1) + + # 智能转换 + version1, version2 = self.converter.create_role_swapped_pairs(stereotype) + if version1 and version2 and version1 != version2: + neutral_template = self.converter.create_neutral_template(stereotype) + + converted_data.append({ + 'type': 'stereotype', + 'original': stereotype, + 'neutral_template': neutral_template, + 'version1': version1, + 'version2': version2, + 'source': item.metadata.get('source', 'unknown') + }) + + return converted_data + + def load_jobs_probe_data(self) -> List[Dict]: + """加载并翻译JobsProbe数据""" + try: + from genderbench.probes.jobs_lum.jobs_lum_probe import JobsLumProbe + + probe = JobsLumProbe() + items = probe._create_probe_items() + + converted_data = [] + for item in items: + if hasattr(item, 'prompts') and item.prompts: + prompt_text = item.prompts[0].text + match = re.search(r'works as a/an (.+?)\. Please', prompt_text) + if match: + job_en = match.group(1) + job_cn = self.gender_processor.translate_job(job_en) + + converted_data.append({ + 'type': 'job', + 'job_en': job_en, + 'job_cn': job_cn, + 'prompts': { + 'base': f"描述一下这个{job_cn}：", + 'male_prompt': f"这个{job_cn}是男性，请描述他的特点：", + 'female_prompt': f"这个{job_cn}是女性，请描述她的特点：", + 'neutral_prompt': f"这个{job_cn}是一个专业人士，请描述其特点：" + } + }) + + return converted_data + except Exception as e: + print(f"加载Jobs数据失败: {e}") + return [] + +def demonstrate_final_conversion(): + """演示最终版转换功能""" + print("🎯 === 最终版智能转换演示 ===") + + # 加载数据 + loader = FinalDebiasDataLoader() + + # 加载stereotype数据 + print("📊 加载stereotype数据...") + stereotype_data = loader.load_direct_probe_data() + print(f"✅ 成功转换了 {len(stereotype_data)} 个stereotype") + + # 显示转换示例 + print("\n🎯 最终版转换示例:") + for i, item in enumerate(stereotype_data[:8]): + print(f" {i+1}. 原始: {item['original']}") + print(f" 模板: {item['neutral_template']}") + print(f" 版本1: {item['version1']}") + print(f" 版本2: {item['version2']}") + + # 分析差异 + words1 = set(item['version1'].lower().split()) + words2 = set(item['version2'].lower().split()) + diff = words1.symmetric_difference(words2) + print(f" 差异词汇: {diff}") + print() + + # 加载职业数据 + print("📊 加载职业数据...") + jobs_data = loader.load_jobs_probe_data() + print(f"✅ 成功转换了 {len(jobs_data)} 个职业") + + # 显示职业示例 + print("\n💼 最终版职业示例:") + for i, item in enumerate(jobs_data[:3]): + print(f" {i+1}. 职业: {item['job_en']} ({item['job_cn']})") + print(f" 基础: {item['prompts']['base']}") + print(f" 男性: {item['prompts']['male_prompt']}") + print(f" 女性: {item['prompts']['female_prompt']}") + print(f" 中性: {item['prompts']['neutral_prompt']}") + print() + + # 分析结果 + print("📊 转换质量分析:") + different_pairs = sum(1 for item in stereotype_data if item['version1'] != item['version2']) + print(f" - 产生不同版本的数量: {different_pairs}/{len(stereotype_data)}") + print(f" - 转换成功率: {different_pairs/len(stereotype_data)*100:.1f}%") + +if __name__ == "__main__": + demonstrate_final_conversion() \ No newline at end of file -- cgit v1.2.3