更新数据转换功能，支持从新格式提取信息并生成多种问题模板，优化输入输出文件路径
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@@ -23,10 +23,11 @@ def convert_to_alpaca_format(input_file, output_file):
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]
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}
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"""
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choice_text=", A. quant-ph\nB. physics.chem-ph\nC. physics.atom-ph\nD. cond-mat.soft\nE. cs.RO\nF. cs.CL\nG. cs.SE\nH. cs.IR\nI. hep-th\nJ. hep-ph\nK. physics.optics\nL. cs.AI\nM. cs.CV\nN. nucl-th\nO. astro-ph\nP. math.PR\nQ. cs.OS\nR. eess.SP\nS. math.OC\nT. math.DS\nU. math.DG\nV. math.MP\nW. cs.MM\nX. stat.ME\nY. math.CO\nZ. cs.NE"
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print(f"转换数据: {input_file} -> {output_file}")
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converted_data = []
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with open(input_file, "r", encoding="utf-8") as f:
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with open(input_file, "r", encoding="utf-8-sig") as f:
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csv_reader = csv.DictReader(f)
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for row in csv_reader:
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try:
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@@ -44,7 +45,7 @@ def convert_to_alpaca_format(input_file, output_file):
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"system": "你是个优秀的论文分类师",
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"conversation": [
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{
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"human": row["question"],
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"human": row["question"]+choice_text,
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"assistant": row["answer"]
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}
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]
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@@ -62,19 +63,8 @@ def convert_to_alpaca_format(input_file, output_file):
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print(f"转换完成! 共转换 {len(converted_data)} 条数据")
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if __name__ == "__main__":
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# parser = argparse.ArgumentParser(description="转换数据到Alpaca格式")
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# parser.add_argument(
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# "--input",
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# type=str,
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# required=True,
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# help="输入文件路径 (swift_formatted_sft_train_data.jsonl)",
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# )
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# parser.add_argument("--output", type=str, required=True, help="输出文件路径")
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# args = parser.parse_args()
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#input_file = "arxiv-metadata-oai-snapshot--random.json" # 20000条原始数据文件路径
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input_file = "newformat_sft_test_data.csv"
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output_file = "newformat_sft_test_data--swift-sft.jsonl" # 输出文件路径
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input_file = "G:\\11\\data-prepare\\eval_oc_data-26gai.csv"
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output_file = "G:\\11\\data-prepare\\newformat_sft_test_data--swift-sft-26.jsonl" # 输出文件路径
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convert_to_alpaca_format(input_file, output_file)
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@@ -2,6 +2,7 @@
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import json
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import os
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import argparse
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import random
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@@ -99,7 +100,7 @@ def convert_to_alpaca_format(input_file, output_file):
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def convert_onedata2multi_type(input_file, output_file):
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def convert_onedata2multi_type(input_file, output_file, num_templates):
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"""
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读取input_file，将Swift格式的1条数据按20种问题模板格式转换为20条数据，
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并保存为output_file
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@@ -108,6 +109,7 @@ def convert_onedata2multi_type(input_file, output_file):
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input_file: 输入文件路径
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output_file: 输出文件路径
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"""
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print(f"开始转换数据...每条数据生成{num_templates}条变体")
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print(f"开始转换数据: {input_file} -> {output_file}")
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category_text=" A. quant-ph\nB. physics.chem-ph\nC. physics.atom-ph\nD. cond-mat.soft\nE. cs.RO\nF. cs.CL\nG. cs.SE\nH. cs.IR\nI. hep-th\nJ. hep-ph\nK. physics.optics\nL. cs.AI\nM. cs.CV\nN. nucl-th\nO. astro-ph\nP. math.PR\nQ. cs.OS\nR. eess.SP\nS. math.OC\nT. math.DS\nU. math.DG\nV. math.MP\nW. cs.MM\nX. stat.ME\nY. math.CO\nZ. cs.NE\n"
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@@ -183,44 +185,83 @@ def convert_onedata2multi_type(input_file, output_file):
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try:
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data = json.loads(line.strip())
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# 检查数据结构
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if "system" not in data or "conversation" not in data or not data["conversation"]:
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print(f"警告: 数据缺少必要字段: {data}")
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continue
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# 获取系统指令
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system_instruction = data.get("system", "根据论文的标题、作者和摘要，确定该论文的科学类别。")
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# 处理对话
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for turn in data["conversation"]:
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if "human" in turn and "assistant" in turn:
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# 提取标题、作者和摘要
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extracted = extract_title_author_and_abstract(turn["human"])
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title = extracted.get("title", "")
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authors = extracted.get("authors", "")
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abstract = extracted.get("abstract", "")
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# 检查新格式的数据结构
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if "messages" in data and isinstance(data["messages"], list) and len(data["messages"]) >= 3:
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# 提取系统指令
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system_instruction = ""
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human_content = ""
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assistant_content = ""
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for msg in data["messages"]:
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if msg["role"] == "system":
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system_instruction = msg["content"]
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elif msg["role"] == "user":
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human_content = msg["content"]
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elif msg["role"] == "assistant":
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assistant_content = msg["content"]
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# 提取标题、作者和摘要
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extracted = extract_title_author_and_abstract(human_content)
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title = extracted.get("title", "")
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authors = extracted.get("authors", "")
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abstract = extracted.get("abstract", "")
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n = min(num_templates, len(question_templates))
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selected_templates = random.sample(question_templates, n)
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# 为每个问题模板创建新数据
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for template in selected_templates:
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formatted_question = template.format(
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title=title,
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authors=authors,
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abstract=abstract,
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category_text=category_text
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)
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# 为每个问题模板创建新数据
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for template in question_templates:
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# 格式化问题
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formatted_question = template.format(
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title=title,
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authors=authors,
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abstract=abstract,
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category_text=category_text
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)
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# 创建新的数据条目（保持新格式）
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new_data = {
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"messages": [
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{"role": "system", "content": system_instruction},
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{"role": "user", "content": formatted_question},
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{"role": "assistant", "content": assistant_content}
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]
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}
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multi_type_data.append(new_data)
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# 检查旧格式的数据结构
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elif "system" in data and "conversation" in data and data["conversation"]:
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system_instruction = data.get("system", "根据论文的标题、作者和摘要，确定该论文的科学类别。")
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for turn in data["conversation"]:
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if "human" in turn and "assistant" in turn:
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extracted = extract_title_author_and_abstract(turn["human"])
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title = extracted.get("title", "")
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authors = extracted.get("authors", "")
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abstract = extracted.get("abstract", "")
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n = min(num_templates, len(question_templates))
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selected_templates = random.sample(question_templates, n)
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# 创建新的数据条目
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new_data = {
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"system": system_instruction,
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"conversation": [
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{
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"human": formatted_question,
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"assistant": turn["assistant"]
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}
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]
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}
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multi_type_data.append(new_data)
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for template in selected_templates:
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formatted_question = template.format(
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title=title,
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authors=authors,
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abstract=abstract,
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category_text=category_text
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)
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new_data = {
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"system": system_instruction,
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"conversation": [
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{
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"human": formatted_question,
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"assistant": turn["assistant"]
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}
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]
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}
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multi_type_data.append(new_data)
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else:
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print(f"警告: 数据格式不识别: {data}")
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continue
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except json.JSONDecodeError:
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print(f"警告: 无法解析JSON行: {line}")
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@@ -248,10 +289,12 @@ if __name__ == "__main__":
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content_text="Based on the title 'The Quantum Primordial Black Holes, Dimensionless Small Parameter, Inflationary Cosmology and Non-Gaussianity', authors 'Alexander Shalyt-Margolin', and abstract 'In the present work consideration is given to the primordial black holes ({\\bf pbhs}) in the Schwarzschild-de Sitter Metric with small mass (ultralight) in the preinflationary epoch. Within the scope of natural assumptions, it has been shown that the quantum-gravitational corrections ({\\bf qgcs}) to the characteristics of such black holes can contribute to all the cosmological parameters, shifting them compared with the semiclassical consideration. These contributions are determined by a series expansion in terms of a small parameter dependent on the hole mass (radius). For this pattern different cases have been considered (stationary, black hole evaporation...). It has been demonstrated that involvement of ({\\bf qgcs}) leads to a higher probability for the occurrence of such {\\bf pbhs}. Besides, high-energy deformations of Friedmann Equations created on the basis of these corrections have been derived for different patterns. In the last section of this work it is introduced a study into the contributions generated by the above-mentioned {\\bf qgcs} in inflationary cosmological perturbations. Besides, it has been shown that non-Gaussianity of these perturbations is higher as compared to the semi-classical pattern.', please determine the scientific category of this paper. Additional info: 35 pages, Latex , A. quant-ph\nB. physics.chem-ph\nC. physics.atom-ph\nD. cond-mat.soft\nE. cs.RO\nF. cs.CL\nG. cs.SE\nH. cs.IR\nI. hep-th\nJ. hep-ph\nK. physics.optics\nL. cs.AI\nM. cs.CV\nN. nucl-th\nO. astro-ph\nP. math.PR\nQ. cs.OS\nR. eess.SP\nS. math.OC\nT. math.DS\nU. math.DG\nV. math.MP\nW. cs.MM\nX. stat.ME\nY. math.CO\nZ. cs.NE"
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extract_title_author_and_abstract(content_text)
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input_file = "G:\\11\\data-prepare\\val_dataset.jsonl"
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output_file = "G:\\11\\data-prepare\\val_dataset-m.jsonl" # 输出文件路径
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# input_file = "G:\\11\\data-prepare\\val_dataset.jsonl"
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# output_file = "G:\\11\\data-prepare\\val_dataset-m2.jsonl" # 输出文件路径
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input_file = "G:\\11\\data-prepare\\newformat_sft_test_data--swift-sft-26.jsonl"
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output_file = "G:\\11\\data-prepare\\newformat_sft_test_data--swift-sft-26-m2.jsonl" # 输出文件路径
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convert_onedata2multi_type(input_file, output_file)
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convert_onedata2multi_type(input_file, output_file, num_templates=2)
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