import json import csv def convert_to_alpaca_format(input_file, output_file): """ 读取csv文件,提取其中的question和answer列的数据,并转换为 Alpaca 格式。 输入csv格式: question,A,B,C,D,E,F,G,H,I,J,answer 输出格式 (swift): { "system": "你是个优秀的论文分类师", "conversation": [ { "human": "Based on the title...", "assistant": "D" } ] } """ print(f"转换数据: {input_file} -> {output_file}") converted_data = [] with open(input_file, "r", encoding="utf-8") as f: csv_reader = csv.DictReader(f) for row in csv_reader: try: # 检查必要的列是否存在 if "question" not in row or "answer" not in row: print(f"警告: 数据缺少必要列: {row}") continue # 创建新的 swift 格式数据 new_data = { "messages": [ { "role": "assistant", "content": "This is a paper titled " + row["question"][19:] #"assistant": row["answer"] } ] } converted_data.append(new_data) except Exception as e: print(f"处理行时发生错误: {str(e)}") # 写入输出文件 with open(output_file, "w", encoding="utf-8") as f: for item in converted_data: f.write(json.dumps(item, ensure_ascii=False) + "\n") print(f"转换完成! 共转换 {len(converted_data)} 条数据") if __name__ == "__main__": # parser = argparse.ArgumentParser(description="转换数据到Alpaca格式") # parser.add_argument( # "--input", # type=str, # required=True, # help="输入文件路径 (swift_formatted_sft_train_data.jsonl)", # ) # parser.add_argument("--output", type=str, required=True, help="输出文件路径") # args = parser.parse_args() #input_file = "arxiv-metadata-oai-snapshot--random.json" # 20000条原始数据文件路径 input_file = "newformat_sft_test_data.csv" output_file = "newformat_sft_test_data--swift-pretrain.jsonl" # 输出文件路径 convert_to_alpaca_format(input_file, output_file)