[自然语言处理|NLP]NLP在可解释性和公平性的应用：从原理到实践

# 示例代码：注意力机制在NLP医疗诊断中的应用
import torch
from transformers import BertTokenizer, BertForSequenceClassification

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)

# 输入文本
input_text = "Patient has a history of persistent cough and shortness of breath."

# 对文本进行tokenize和padding
input_ids = tokenizer(input_text, return_tensors="pt")['input_ids']

# 获取模型的注意力权重
outputs = model(input_ids)
attention_weights = outputs.attentions[-1][0].mean(dim=0).detach().numpy()

# 可视化注意力权重
import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(10, 6))
sns.heatmap(attention_weights, xticklabels=tokenizer.convert_ids_to_tokens(input_ids[0]), cmap="viridis")
plt.title("Attention Weights in Medical Diagnosis")
plt.show()

# 示例代码：再平衡技术在招聘推荐系统中的应用
import numpy as np

def rebalance_recommendations(original_recommendations, demographic_information):
    # 根据群体信息调整推荐的职位数量
    adjusted_recommendations = original_recommendations.copy()
    
    for group, count in demographic_information.items():
        if count < len(original_recommendations) / len(demographic_information):
            # 如果某群体推荐数量过少，增加推荐数量
            additional_recommendations = np.random.choice(original_recommendations, 
                                                           len(original_recommendations) // len(demographic_information) - count,
                                                           replace=False)
            adjusted_recommendations.extend(additional_recommendations)
    
    return adjusted_recommendations

# 示例
original_recommendations = ["Software Engineer", "Data Scientist", "Product Manager", "UX Designer"]
demographic_information = {"Male": 50, "Female": 30, "Non-binary": 20}

adjusted_recommendations = rebalance_recommendations(original_recommendations, demographic_information)

print("Adjusted Recommendations:", adjusted_recommendations)