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pllm2-full-dump

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pllm2-full-dump
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import json
import random
import os

def process_data(input_path, output_path, sample_ratio=0.1):
    """处理数据并只保存指定比例的数据"""
    print(f"处理文件: {input_path}")
    
    # 读取原始数据
    with open(input_path, 'r') as f:
        data = [json.loads(line) for line in f if line.strip()]
    
    print(f"原始数据量: {len(data)} 条")
    
    # 随机采样10%的数据
    sample_size = int(len(data) * sample_ratio)
    sampled_data = random.sample(data, sample_size)
    
    print(f"采样数据量: {len(sampled_data)} 条 ({sample_ratio*100}%)")
    
    # 保存处理后的数据
    with open(output_path, 'w') as f:
        json.dump(sampled_data, f, ensure_ascii=False, indent=2)
    
    print(f"数据已保存到: {output_path}")
    print("-" * 50)

# 设置随机种子确保结果可重现
random.seed(42)

# 创建processed_data文件夹
processed_data_dir = '/opt/tiger/PLLM1.0/dataset/processed_data'
if not os.path.exists(processed_data_dir):
    os.makedirs(processed_data_dir)
    print(f"✅ 创建文件夹: {processed_data_dir}")
else:
    print(f"📁 文件夹已存在: {processed_data_dir}")

# 处理训练数据
train_input_path = '/opt/tiger/PLLM1.0/dataset/abstract_train.json'
train_output_path = '/opt/tiger/PLLM1.0/dataset/processed_data/abstract_train.json'
process_data(train_input_path, train_output_path, sample_ratio=0.1)

# 处理测试数据
test_input_path = '/opt/tiger/PLLM1.0/dataset/abstract_test.json'
test_output_path = '/opt/tiger/PLLM1.0/dataset/processed_data/abstract_test.json'
process_data(test_input_path, test_output_path, sample_ratio=0.1)

print("✅ 数据处理完成！")