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{
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{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"已将Parquet文件转换为JSONL格式，保存至: /Users/wjc/Documents/学习手撕/ceval/ceval_output1.jsonl\n"
]
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" <td>3 \" ，5 \" -磷酸二酯键</td>\n",
" <td>互补碱基对之间的氢键</td>\n",
" <td>碱基堆积力</td>\n",
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" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>分离鉴定氨基酸的纸色谱属于____。</td>\n",
" <td>亲和色谱</td>\n",
" <td>吸附色谱</td>\n",
" <td>离子交换色谱</td>\n",
" <td>分配色谱</td>\n",
" <td></td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>生物膜的结构特点不包括____</td>\n",
" <td>膜的运动性</td>\n",
" <td>完全由脂质双层分子构成</td>\n",
" <td>膜的流动性与相变</td>\n",
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" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>组成蛋白质的基本结构单位是____</td>\n",
" <td>氨基酸</td>\n",
" <td>葡萄糖酸</td>\n",
" <td>脂肪酸</td>\n",
" <td>核苷酸</td>\n",
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" <td></td>\n",
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" <td>4</td>\n",
" <td>全酶是指____。</td>\n",
" <td>酶的辅助因子以外的部分</td>\n",
" <td>酶的无活性前体</td>\n",
" <td>一种酶一抑制剂复合物</td>\n",
" <td>一种需要辅助因子的酶，具备了酶蛋白、辅助因子各种成分</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
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"</table>\n",
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"text/plain": [
" id question A B \\\n",
"0 0 下列关于DNA的双螺旋二级结构稳定的因素，不正确的是____。 3 \" ，5 \" -磷酸二酯键 互补碱基对之间的氢键 \n",
"1 1 分离鉴定氨基酸的纸色谱属于____。 亲和色谱 吸附色谱 \n",
"2 2 生物膜的结构特点不包括____ 膜的运动性 完全由脂质双层分子构成 \n",
"3 3 组成蛋白质的基本结构单位是____ 氨基酸 葡萄糖酸 \n",
"4 4 全酶是指____。 酶的辅助因子以外的部分 酶的无活性前体 \n",
"\n",
" C D answer explanation \n",
"0 碱基堆积力 磷酸基团上的负电荷与介质中的阳离子之间形成的离子键 \n",
"1 离子交换色谱 分配色谱 \n",
"2 膜的流动性与相变 膜上的蛋白和脂质存在相互的作用 \n",
"3 脂肪酸 核苷酸 \n",
"4 一种酶一抑制剂复合物 一种需要辅助因子的酶，具备了酶蛋白、辅助因子各种成分 "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import json\n",
"\n",
"# 读取Parquet文件\n",
"file_path = \"/Users/wjc/Documents/学习手撕/ceval/0000 (1).parquet\"\n",
"df = pd.read_parquet(file_path)\n",
"\n",
"# 将DataFrame转换为JSONL格式\n",
"output_path = \"/Users/wjc/Documents/学习手撕/ceval/ceval_output1.jsonl\"\n",
"\n",
"# 逐行写入JSONL文件\n",
"with open(output_path, 'w', encoding='utf-8') as f:\n",
" for _, row in df.iterrows():\n",
" # 将每行数据转换为字典，然后转为JSON字符串\n",
" json_str = json.dumps(row.to_dict(), ensure_ascii=False)\n",
" f.write(json_str + '\\n')\n",
"\n",
"print(f\"已将Parquet文件转换为JSONL格式，保存至: {output_path}\")\n",
"\n",
"# 显示前几行数据以验证\n",
"df.head()\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"已将Parquet文件转换为JSONL格式，保存至: /Users/wjc/Documents/学习手撕/ceval/basic_medicine.jsonl\n"
]
},
{
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"<div>\n",
"<style scoped>\n",
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"\n",
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" vertical-align: top;\n",
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" <th>0</th>\n",
" <td>0</td>\n",
" <td>急性肝淤血的病理变化有____</td>\n",
" <td>肝细胞脂肪变性</td>\n",
" <td>肝小叶中央静脉和肝窦扩张</td>\n",
" <td>肝细胞胞质可见多个脂肪空泡</td>\n",
" <td>槟榔肝</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>下列哪种为恶性肿瘤____</td>\n",
" <td>纤维腺瘤</td>\n",
" <td>畸胎瘤</td>\n",
" <td>混合瘤</td>\n",
" <td>霍奇金病</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>不涉及第二信使的细胞信息传递途径是____</td>\n",
" <td>PKA途径</td>\n",
" <td>PKC途径</td>\n",
" <td>PKC途径</td>\n",
" <td>TPK途径</td>\n",
" <td></td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>白色血栓主要发生的组织部位是____</td>\n",
" <td>毛细血管</td>\n",
" <td>静脉瓣膜</td>\n",
" <td>动脉管壁</td>\n",
" <td>心瓣膜</td>\n",
" <td></td>\n",
" <td></td>\n",
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" <tr>\n",
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" <td>4</td>\n",
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" <td>抑癌基因</td>\n",
" <td>病毒癌基因</td>\n",
" <td>操纵子调节基因</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
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"text/plain": [
" id question A B C D \\\n",
"0 0 急性肝淤血的病理变化有____ 肝细胞脂肪变性 肝小叶中央静脉和肝窦扩张 肝细胞胞质可见多个脂肪空泡 槟榔肝 \n",
"1 1 下列哪种为恶性肿瘤____ 纤维腺瘤 畸胎瘤 混合瘤 霍奇金病 \n",
"2 2 不涉及第二信使的细胞信息传递途径是____ PKA途径 PKC途径 PKC途径 TPK途径 \n",
"3 3 白色血栓主要发生的组织部位是____ 毛细血管 静脉瓣膜 动脉管壁 心瓣膜 \n",
"4 4 Rb基因是一种____ 细胞原癌基因 抑癌基因 病毒癌基因 操纵子调节基因 \n",
"\n",
" answer explanation \n",
"0 \n",
"1 \n",
"2 \n",
"3 \n",
"4 "
]
},
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"metadata": {},
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],
"source": [
"import pandas as pd\n",
"import json\n",
"\n",
"# 读取Parquet文件\n",
"file_path = \"/Users/wjc/Documents/学习手撕/ceval/basic_medicine.parquet\"\n",
"df = pd.read_parquet(file_path)\n",
"\n",
"# 将DataFrame转换为JSONL格式\n",
"output_path = \"/Users/wjc/Documents/学习手撕/ceval/basic_medicine.jsonl\"\n",
"\n",
"# 逐行写入JSONL文件\n",
"with open(output_path, 'w', encoding='utf-8') as f:\n",
" for _, row in df.iterrows():\n",
" # 将每行数据转换为字典，然后转为JSON字符串\n",
" json_str = json.dumps(row.to_dict(), ensure_ascii=False)\n",
" f.write(json_str + '\\n')\n",
"\n",
"print(f\"已将Parquet文件转换为JSONL格式，保存至: {output_path}\")\n",
"\n",
"# 显示前几行数据以验证\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"已将Parquet文件转换为JSONL格式，保存至: /Users/wjc/Documents/学习手撕/ceval/clinical_medicine.jsonl\n"
]
},
{
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" <th></th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>梅毒的病原体是____</td>\n",
" <td>病毒</td>\n",
" <td>细菌</td>\n",
" <td>螺旋体</td>\n",
" <td>支原体</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>临床确诊支气管扩张主要根据____</td>\n",
" <td>肺功能测定</td>\n",
" <td>HRCT(葛分辨率CT)</td>\n",
" <td>胸部X线照片</td>\n",
" <td>支气管造影</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>下列哪一项不符合支气管扩张的特点____</td>\n",
" <td>多发生于叶及段等大支气管</td>\n",
" <td>支气管壁的炎症损伤是主要发病基础</td>\n",
" <td>肺脓肿为其常见并发症</td>\n",
" <td>可导致肺心病</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>非霍奇金淋巴瘤多发生于____</td>\n",
" <td>纵隔淋巴结</td>\n",
" <td>肠系膜淋巴结</td>\n",
" <td>腹膜后淋巴结</td>\n",
" <td>颈部淋巴结</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>关于急性中毒，下列哪项说法是错误的____</td>\n",
" <td>皮肤黏膜樱桃红色可见于一氧化碳中毒</td>\n",
" <td>亚硝酸盐中毒多能产生高铁血红蛋白血症而出现发绀</td>\n",
" <td>氰化物中毒呼气有蒜臭味</td>\n",
" <td>铅中毒时口中可有金属味</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id question A B \\\n",
"0 0 梅毒的病原体是____ 病毒 细菌 \n",
"1 1 临床确诊支气管扩张主要根据____ 肺功能测定 HRCT(葛分辨率CT) \n",
"2 2 下列哪一项不符合支气管扩张的特点____ 多发生于叶及段等大支气管 支气管壁的炎症损伤是主要发病基础 \n",
"3 3 非霍奇金淋巴瘤多发生于____ 纵隔淋巴结 肠系膜淋巴结 \n",
"4 4 关于急性中毒，下列哪项说法是错误的____ 皮肤黏膜樱桃红色可见于一氧化碳中毒 亚硝酸盐中毒多能产生高铁血红蛋白血症而出现发绀 \n",
"\n",
" C D answer explanation \n",
"0 螺旋体 支原体 \n",
"1 胸部X线照片 支气管造影 \n",
"2 肺脓肿为其常见并发症 可导致肺心病 \n",
"3 腹膜后淋巴结 颈部淋巴结 \n",
"4 氰化物中毒呼气有蒜臭味 铅中毒时口中可有金属味 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import json\n",
"\n",
"# 读取Parquet文件\n",
"file_path = \"/Users/wjc/Documents/学习手撕/ceval/clinical_medicine.parquet\"\n",
"df = pd.read_parquet(file_path)\n",
"\n",
"# 将DataFrame转换为JSONL格式\n",
"output_path = \"/Users/wjc/Documents/学习手撕/ceval/clinical_medicine.jsonl\"\n",
"\n",
"# 逐行写入JSONL文件\n",
"with open(output_path, 'w', encoding='utf-8') as f:\n",
" for _, row in df.iterrows():\n",
" # 将每行数据转换为字典，然后转为JSON字符串\n",
" json_str = json.dumps(row.to_dict(), ensure_ascii=False)\n",
" f.write(json_str + '\\n')\n",
"\n",
"print(f\"已将Parquet文件转换为JSONL格式，保存至: {output_path}\")\n",
"\n",
"# 显示前几行数据以验证\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"已将Parquet文件转换为JSONL格式，保存至: /Users/wjc/Documents/学习手撕/ceval/physician.jsonl\n"
]
},
{
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" <th>0</th>\n",
" <td>0</td>\n",
" <td>按照抗菌药物药代动力学和药效动力学理论(PK/PD)，下列哪类属于浓度依赖性抗菌药物____</td>\n",
" <td>青霉素类</td>\n",
" <td>大环内酯类</td>\n",
" <td>碳青霉烯类</td>\n",
" <td>氟喹诺酮类</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>男性，20岁，发热起病3天后，自行缓解，高度乏力，腹胀，黄疸进行加深，病程第九天出现躁动，神...</td>\n",
" <td>急性黄疸型肝炎</td>\n",
" <td>急性重型肝炎</td>\n",
" <td>亚急性重型肝炎</td>\n",
" <td>慢性重型肝炎</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>2岁患儿，4天前发热，流涕、咳嗽，结膜充血，畏光，今晨发现耳后及颈部有淡红色斑丘疹，体温39...</td>\n",
" <td>风疹</td>\n",
" <td>幼儿急疹</td>\n",
" <td>麻疹</td>\n",
" <td>肠道病毒感染</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>75岁老人，跌倒手掌着地，致桡骨下端骨折，应如何处理____</td>\n",
" <td>切开复位内固定</td>\n",
" <td>闭合复位外固定支架固定</td>\n",
" <td>悬吊牵引</td>\n",
" <td>骨牵引</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>一名患者，脑损伤后6小时，意识清，头痛，下列哪项处理原则不可取____</td>\n",
" <td>意识清，故回家观察</td>\n",
" <td>观察意识，瞳孔，生命体征等变化</td>\n",
" <td>作头颅CT检查</td>\n",
" <td>对症处置</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id question A \\\n",
"0 0 按照抗菌药物药代动力学和药效动力学理论(PK/PD)，下列哪类属于浓度依赖性抗菌药物____ 青霉素类 \n",
"1 1 男性，20岁，发热起病3天后，自行缓解，高度乏力，腹胀，黄疸进行加深，病程第九天出现躁动，神... 急性黄疸型肝炎 \n",
"2 2 2岁患儿，4天前发热，流涕、咳嗽，结膜充血，畏光，今晨发现耳后及颈部有淡红色斑丘疹，体温39... 风疹 \n",
"3 3 75岁老人，跌倒手掌着地，致桡骨下端骨折，应如何处理____ 切开复位内固定 \n",
"4 4 一名患者，脑损伤后6小时，意识清，头痛，下列哪项处理原则不可取____ 意识清，故回家观察 \n",
"\n",
" B C D answer explanation \n",
"0 大环内酯类 碳青霉烯类 氟喹诺酮类 \n",
"1 急性重型肝炎 亚急性重型肝炎 慢性重型肝炎 \n",
"2 幼儿急疹 麻疹 肠道病毒感染 \n",
"3 闭合复位外固定支架固定 悬吊牵引 骨牵引 \n",
"4 观察意识，瞳孔，生命体征等变化 作头颅CT检查 对症处置 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import json\n",
"\n",
"# 读取Parquet文件\n",
"file_path = \"/Users/wjc/Documents/学习手撕/ceval/physician.parquet\"\n",
"df = pd.read_parquet(file_path)\n",
"\n",
"# 将DataFrame转换为JSONL格式\n",
"output_path = \"/Users/wjc/Documents/学习手撕/ceval/physician.jsonl\"\n",
"\n",
"# 逐行写入JSONL文件\n",
"with open(output_path, 'w', encoding='utf-8') as f:\n",
" for _, row in df.iterrows():\n",
" # 将每行数据转换为字典，然后转为JSON字符串\n",
" json_str = json.dumps(row.to_dict(), ensure_ascii=False)\n",
" f.write(json_str + '\\n')\n",
"\n",
"print(f\"已将Parquet文件转换为JSONL格式，保存至: {output_path}\")\n",
"\n",
"# 显示前几行数据以验证\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"已将Parquet文件转换为JSONL格式，保存至: /Users/wjc/Documents/学习手撕/ceval/veterinary_medicine.jsonl\n"
]
},
{
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" <th>0</th>\n",
" <td>0</td>\n",
" <td>下列关于DNA的双螺旋二级结构稳定的因素，不正确的是____。</td>\n",
" <td>3 \" ，5 \" -磷酸二酯键</td>\n",
" <td>互补碱基对之间的氢键</td>\n",
" <td>碱基堆积力</td>\n",
" <td>磷酸基团上的负电荷与介质中的阳离子之间形成的离子键</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>分离鉴定氨基酸的纸色谱属于____。</td>\n",
" <td>亲和色谱</td>\n",
" <td>吸附色谱</td>\n",
" <td>离子交换色谱</td>\n",
" <td>分配色谱</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>生物膜的结构特点不包括____</td>\n",
" <td>膜的运动性</td>\n",
" <td>完全由脂质双层分子构成</td>\n",
" <td>膜的流动性与相变</td>\n",
" <td>膜上的蛋白和脂质存在相互的作用</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>组成蛋白质的基本结构单位是____</td>\n",
" <td>氨基酸</td>\n",
" <td>葡萄糖酸</td>\n",
" <td>脂肪酸</td>\n",
" <td>核苷酸</td>\n",
" <td></td>\n",
" <td></td>\n",
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" <th>4</th>\n",
" <td>4</td>\n",
" <td>全酶是指____。</td>\n",
" <td>酶的辅助因子以外的部分</td>\n",
" <td>酶的无活性前体</td>\n",
" <td>一种酶一抑制剂复合物</td>\n",
" <td>一种需要辅助因子的酶，具备了酶蛋白、辅助因子各种成分</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" id question A B \\\n",
"0 0 下列关于DNA的双螺旋二级结构稳定的因素，不正确的是____。 3 \" ，5 \" -磷酸二酯键 互补碱基对之间的氢键 \n",
"1 1 分离鉴定氨基酸的纸色谱属于____。 亲和色谱 吸附色谱 \n",
"2 2 生物膜的结构特点不包括____ 膜的运动性 完全由脂质双层分子构成 \n",
"3 3 组成蛋白质的基本结构单位是____ 氨基酸 葡萄糖酸 \n",
"4 4 全酶是指____。 酶的辅助因子以外的部分 酶的无活性前体 \n",
"\n",
" C D answer explanation \n",
"0 碱基堆积力 磷酸基团上的负电荷与介质中的阳离子之间形成的离子键 \n",
"1 离子交换色谱 分配色谱 \n",
"2 膜的流动性与相变 膜上的蛋白和脂质存在相互的作用 \n",
"3 脂肪酸 核苷酸 \n",
"4 一种酶一抑制剂复合物 一种需要辅助因子的酶，具备了酶蛋白、辅助因子各种成分 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import json\n",
"\n",
"# 读取Parquet文件\n",
"file_path = \"/Users/wjc/Documents/学习手撕/ceval/veterinary_medicine.parquet\"\n",
"df = pd.read_parquet(file_path)\n",
"\n",
"# 将DataFrame转换为JSONL格式\n",
"output_path = \"/Users/wjc/Documents/学习手撕/ceval/veterinary_medicine.jsonl\"\n",
"\n",
"# 逐行写入JSONL文件\n",
"with open(output_path, 'w', encoding='utf-8') as f:\n",
" for _, row in df.iterrows():\n",
" # 将每行数据转换为字典，然后转为JSON字符串\n",
" json_str = json.dumps(row.to_dict(), ensure_ascii=False)\n",
" f.write(json_str + '\\n')\n",
"\n",
"print(f\"已将Parquet文件转换为JSONL格式，保存至: {output_path}\")\n",
"\n",
"# 显示前几行数据以验证\n",
"df.head()"
]
}
],
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|