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{
"cells": [
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-09-08T15:26:59.355018Z",
"start_time": "2024-09-08T15:26:57.990909Z"
}
},
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"# 定义一个函数，将 Options 字符串转换为 key-value 格式的列表\n",
"def format_options(options_str):\n",
" options_list = []\n",
" # 按行分割选项\n",
" options_lines = options_str.split(\"\\n\")\n",
" for line in options_lines:\n",
" if len(line) > 1:\n",
" key = line[0] # 第一个字符为选项字母\n",
" value = line[2:].strip() # 从第三个字符开始为选项内容\n",
" options_list.append({\"key\": key, \"value\": value})\n",
" return options_list\n",
"\n",
"# 读取 CSV 文件\n",
"train_data = pd.read_csv('./data/train.csv', encoding='utf-8')\n",
"valid_data = pd.read_csv('./data/val.csv', encoding='utf-8')\n",
"test_data = pd.read_csv('./data/test_with_annotations.csv', encoding='utf-8')\n",
"\n",
"# 将json数据只保留与train和valid一致的字段\n",
"test_data = test_data[['Question', 'Options', 'Answer', 'Explanation']]\n",
"\n",
"# 遍历数据集，将每个样本的 Options 列格式化\n",
"train_data['Options'] = train_data['Options'].apply(format_options)\n",
"valid_data['Options'] = valid_data['Options'].apply(format_options)\n",
"test_data['Options'] = test_data['Options'].apply(format_options)\n",
"\n",
"# 将修改后的 DataFrame 保存为 JSON 文件\n",
"train_data.to_json('./data/train.json', orient='records', lines=True, force_ascii=False)\n",
"valid_data.to_json('./data/valid.json', orient='records', lines=True, force_ascii=False)\n",
"test_data.to_json('./data/test.json', orient='records', lines=True, force_ascii=False)\n"
],
"id": "c003560dea95e12e",
"outputs": [],
"execution_count": 5
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": "",
"id": "8d142ed01196946"
}
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