add genderbench

1 files changed, 316 insertions, 0 deletions

diff --git a/gender_debias_utils.py b/gender_debias_utils.py
new file mode 100644
index 0000000..db0f9fc
--- /dev/null
+++ b/gender_debias_utils.py
@@ -0,0 +1,316 @@
+#!/usr/bin/env python3
+"""
+Gender Debias Utilities
+基于GenderBench数据实现性别去偏见训练的工具函数
+核心思想：生成除性别外完全一致的回答对
+"""
+
+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 GenderWordProcessor:
+    """性别词汇处理器"""
+    
+    def __init__(self):
+        # 性别词汇映射表
+        self.gender_pairs = {
+            # 基础代词
+            'he': 'she', 'she': 'he',
+            'him': 'her', 'her': 'him', 
+            'his': 'her', 'hers': 'his',
+            'himself': 'herself', 'herself': 'himself',
+            
+            # 群体名词
+            'men': 'women', 'women': 'men',
+            'man': 'woman', 'woman': 'man',
+            'male': 'female', 'female': 'male',
+            'males': 'females', 'females': 'males',
+            'gentleman': 'lady', 'lady': 'gentleman',
+            'gentlemen': 'ladies', 'ladies': 'gentlemen',
+            
+            # 家庭关系
+            'father': 'mother', 'mother': 'father',
+            'dad': 'mom', 'mom': 'dad',
+            'son': 'daughter', 'daughter': 'son',
+            'brother': 'sister', 'sister': 'brother',
+            'uncle': 'aunt', 'aunt': 'uncle',
+            'grandfather': 'grandmother', 'grandmother': 'grandfather',
+            'grandson': 'granddaughter', 'granddaughter': 'grandson',
+            
+            # 职业相关（一些有性别特指的）
+            'businessman': 'businesswoman', 'businesswoman': 'businessman',
+            'policeman': 'policewoman', 'policewoman': 'policeman',
+            'fireman': 'firewoman', 'firewoman': 'fireman',
+        }
+        
+        # 非二元性别词汇（保持不变或特殊处理）
+        self.neutral_words = {
+            'person', 'people', 'individual', 'human', 'adult',
+            'parent', 'child', 'sibling', 'spouse', 'partner'
+        }
+    
+    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 extract_gender_words(self, text: str) -> List[str]:
+        """提取文本中的性别词汇"""
+        words = re.findall(r'\b\w+\b', text.lower())
+        gender_words = []
+        for word in words:
+            if word in self.gender_pairs or word in self.neutral_words:
+                gender_words.append(word)
+        return gender_words
+
+class StereotypeConverter:
+    """刻板印象转换器：将stereotype转换为训练格式"""
+    
+    def __init__(self):
+        self.gender_processor = GenderWordProcessor()
+    
+    def convert_to_masked_format(self, stereotype: str) -> Dict:
+        """将stereotype转换为masked格式"""
+        # 检测性别词汇
+        gender_words = self.gender_processor.extract_gender_words(stereotype)
+        
+        if not gender_words:
+            return None
+        
+        # 创建masked版本
+        masked_text = stereotype
+        gender_positions = []
+        
+        for word in gender_words:
+            if word in self.gender_processor.gender_pairs:
+                # 找到词汇位置并替换为[MASK]
+                pattern = r'\b' + re.escape(word) + r'\b'
+                matches = list(re.finditer(pattern, masked_text, re.IGNORECASE))
+                for match in matches:
+                    gender_positions.append((match.start(), match.end(), word))
+                masked_text = re.sub(pattern, '[MASK]', masked_text, flags=re.IGNORECASE)
+        
+        return {
+            'original': stereotype,
+            'masked': masked_text,
+            'gender_words': gender_words,
+            'gender_positions': gender_positions
+        }
+    
+    def create_gender_pairs(self, masked_data: Dict) -> Tuple[str, str]:
+        """基于masked数据创建性别对比对"""
+        if not masked_data:
+            return None, None
+        
+        masked_text = masked_data['masked']
+        gender_words = masked_data['gender_words']
+        
+        # 创建男性版本
+        male_text = masked_text
+        female_text = masked_text
+        
+        for word in gender_words:
+            if word in self.gender_processor.gender_pairs:
+                opposite = self.gender_processor.get_gender_opposite(word)
+                
+                # 替换为对应性别
+                if word in ['men', 'man', 'he', 'him', 'his', 'male', 'father', 'son', 'brother']:
+                    # 这是男性词汇，保持male_text不变，female_text替换
+                    male_text = male_text.replace('[MASK]', word, 1)
+                    female_text = female_text.replace('[MASK]', opposite, 1)
+                else:
+                    # 这是女性词汇，保持female_text不变，male_text替换
+                    female_text = female_text.replace('[MASK]', word, 1)
+                    male_text = male_text.replace('[MASK]', opposite, 1)
+        
+        return male_text, female_text
+
+class JobsConverter:
+    """职业偏见转换器"""
+    
+    def __init__(self):
+        self.gender_processor = GenderWordProcessor()
+    
+    def create_job_prompts(self, job_name: str) -> Dict:
+        """为职业创建性别对比prompt"""
+        base_template = f"这个{job_name}是"
+        
+        return {
+            'base': base_template,
+            'male': f"这个{job_name}是他",
+            'female': f"这个{job_name}是她",
+            'neutral': f"这个{job_name}是一个人",
+            'job': job_name
+        }
+
+class DebiasDataLoader:
+    """去偏见数据加载器"""
+    
+    def __init__(self):
+        self.stereotype_converter = StereotypeConverter()
+        self.jobs_converter = JobsConverter()
+    
+    def load_direct_probe_data(self) -> List[Dict]:
+        """加载DirectProbe数据并转换"""
+        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:
+                # 提取原始stereotype
+                prompt_text = item.prompts[0].text
+                # 从"Do you agree with the following statement? "XXX". Please answer..."中提取XXX
+                match = re.search(r'"([^"]+)"', prompt_text)
+                if match:
+                    stereotype = match.group(1)
+                    
+                    # 转换为masked格式
+                    masked_data = self.stereotype_converter.convert_to_masked_format(stereotype)
+                    if masked_data:
+                        male_text, female_text = self.stereotype_converter.create_gender_pairs(masked_data)
+                        if male_text and female_text:
+                            converted_data.append({
+                                'type': 'stereotype',
+                                'original': stereotype,
+                                'masked': masked_data['masked'],
+                                'male_version': male_text,
+                                'female_version': female_text,
+                                'source': item.metadata.get('source', 'unknown')
+                            })
+        
+        return converted_data
+    
+    def load_jobs_probe_data(self) -> List[Dict]:
+        """加载JobsProbe数据并转换"""
+        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
+                    # 从"I am writing a novel and I need a character who works as a/an XXX"中提取职业
+                    match = re.search(r'works as a/an (.+?)\. Please', prompt_text)
+                    if match:
+                        job = match.group(1)
+                        job_prompts = self.jobs_converter.create_job_prompts(job)
+                        converted_data.append({
+                            'type': 'job',
+                            'job': job,
+                            'prompts': job_prompts
+                        })
+            
+            return converted_data
+        except Exception as e:
+            print(f"加载Jobs数据失败: {e}")
+            return []
+
+def content_similarity_loss(text1: str, text2: str, tokenizer) -> torch.Tensor:
+    """计算两个文本除性别词外的相似度损失"""
+    processor = GenderWordProcessor()
+    
+    # 移除性别词汇
+    words1 = text1.split()
+    words2 = text2.split()
+    
+    filtered_words1 = [word for word in words1 
+                      if word.lower() not in processor.gender_pairs 
+                      and word.lower() not in processor.neutral_words]
+    filtered_words2 = [word for word in words2 
+                      if word.lower() not in processor.gender_pairs 
+                      and word.lower() not in processor.neutral_words]
+    
+    # 计算编辑距离或余弦相似度
+    # 这里简化为长度差异惩罚
+    length_diff = abs(len(filtered_words1) - len(filtered_words2))
+    
+    # 计算词汇重叠度
+    set1, set2 = set(filtered_words1), set(filtered_words2)
+    overlap = len(set1.intersection(set2))
+    total = len(set1.union(set2))
+    
+    similarity = overlap / total if total > 0 else 0
+    consistency_loss = 1.0 - similarity + 0.1 * length_diff
+    
+    return torch.tensor(consistency_loss, dtype=torch.float32)
+
+def create_training_batch(converted_data: List[Dict], batch_size: int = 4) -> List[Dict]:
+    """创建训练批次"""
+    import random
+    
+    # 随机选择数据
+    selected = random.sample(converted_data, min(batch_size, len(converted_data)))
+    
+    batch = []
+    for item in selected:
+        if item['type'] == 'stereotype':
+            batch.append({
+                'male_prompt': f"请对这个陈述表态: '{item['male_version']}'",
+                'female_prompt': f"请对这个陈述表态: '{item['female_version']}'",
+                'bias_type': 'stereotype'
+            })
+        elif item['type'] == 'job':
+            batch.append({
+                'male_prompt': f"请描述：{item['prompts']['male']}",
+                'female_prompt': f"请描述：{item['prompts']['female']}",
+                'bias_type': 'occupation'
+            })
+    
+    return batch
+
+# 示例使用函数
+def demonstrate_conversion():
+    """演示转换功能"""
+    print("🔄 === 数据转换演示 ===")
+    
+    # 加载数据
+    loader = DebiasDataLoader()
+    
+    # 加载stereotype数据
+    stereotype_data = loader.load_direct_probe_data()
+    print(f"📊 加载了 {len(stereotype_data)} 个stereotype")
+    
+    # 显示转换示例
+    print("\n🎯 转换示例:")
+    for i, item in enumerate(stereotype_data[:3]):
+        print(f"  {i+1}. 原始: {item['original']}")
+        print(f"     男性版本: {item['male_version']}")
+        print(f"     女性版本: {item['female_version']}")
+        print()
+    
+    # 加载职业数据
+    jobs_data = loader.load_jobs_probe_data()
+    print(f"📊 加载了 {len(jobs_data)} 个职业")
+    
+    # 显示职业示例
+    print("\n💼 职业示例:")
+    for i, item in enumerate(jobs_data[:3]):
+        print(f"  {i+1}. 职业: {item['job']}")
+        print(f"     男性: {item['prompts']['male']}")
+        print(f"     女性: {item['prompts']['female']}")
+        print()
+
+if __name__ == "__main__":
+    demonstrate_conversion() 
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