text.py

import json
import random

# 文件路径
file_path = 'robot_comment.json'

# 读取JSON数据
with open(file_path, 'r', encoding='utf-8') as file:
    data = json.load(file)

# 分开存储safe和unsafe数据
safe_data = []
unsafe_data = []
safe_count = 0
for item in data:
    content = item['content']
    audit_status = item['audit_status']
    status = item['status']

    # 根据audit_status分类存储数据
    if audit_status == 1:
        if safe_count < 500:
            safe_data.append({
                "prompt": content,
                "response": "N/A",
                "violated_category_codes": [],
                "label": "safe",
                "explanation": ""
            })
            safe_count += 1
    elif audit_status == -1 or status==0:
        unsafe_data.append({
            "prompt": content,
            "response": "N/A",
            "violated_category_codes": ["S12"],
            "label": "unsafe",
            "explanation": "This text is not suitable for public display"
        })

# 随机抽取50条safe和50条unsafe作为测试集
test_safe = random.sample(safe_data, 50)
test_unsafe = random.sample(unsafe_data, 50)
test_data = test_safe + test_unsafe

# 将余下的数据作为训练集
train_safe = [item for item in safe_data if item not in test_safe]
train_unsafe = [item for item in unsafe_data if item not in test_unsafe]
train_data = train_safe + train_unsafe

# 输出测试数据和训练数据到文件
output_test_path = 'test_data.json'
output_train_path = 'transformed_data.json'

with open(output_test_path, 'w', encoding='utf-8') as test_output_file:
    json.dump(test_data, test_output_file, indent=2)

with open(output_train_path, 'w', encoding='utf-8') as train_output_file:
    json.dump(train_data, train_output_file, indent=2)