423 lines
16 KiB
Python
423 lines
16 KiB
Python
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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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# 科学类别文本常量
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CATEGORY_TEXT = """ A. quant-ph
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B. physics.chem-ph
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C. physics.atom-ph
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D. cond-mat.soft
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E. cs.RO
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F. cs.CL
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G. cs.SE
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H. cs.IR
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I. hep-th
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J. hep-ph
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K. physics.optics
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L. cs.AI
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M. cs.CV
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N. nucl-th
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O. astro-ph
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P. math.PR
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Q. cs.OS
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R. eess.SP
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S. math.OC
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T. math.DS
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U. math.DG
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V. math.MP
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W. cs.MM
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X. stat.ME
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Y. math.CO
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Z. cs.NE
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"""
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# 问题模板常量
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QUESTION_TEMPLATES = [
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# 直接提问式
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"{category_text}What is the scientific category for a paper titled '{title}', authored by {authors}, with abstract '{abstract}'?",
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# 命令式
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"Classify this paper into its scientific category based on title '{title}', authors '{authors}', and abstract '{abstract}'.{category_text}",
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# 描述性引导
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"{category_text}Given a research paper with title '{title}', authors {authors}, and abstract '{abstract}', identify the appropriate discipline.",
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# 正式请求
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"Please assign the scientific category for the paper: title '{title}', authors '{authors}', abstract '{abstract}'.{category_text}",
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# 摘要优先
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"Using the abstract '{abstract}', title '{title}', and authors '{authors}', determine the paper's category.{category_text}",
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# 作者强调
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"{category_text}From authors '{authors}', title '{title}', and abstract '{abstract}', what category does this paper fall into?",
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# 问题链式
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"Here's a paper: title '{title}', authors {authors}, abstract '{abstract}'. What is its scientific category?{category_text}",
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# 简洁版
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"Category for: title '{title}', authors '{authors}', abstract '{abstract}'?{category_text}",
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# 上下文嵌入
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"Considering the title '{title}', the authors '{authors}', and the abstract content '{abstract}', please specify the paper's field.{category_text}",
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# 非正式口语
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"Hey, what category is this paper? Title '{title}', by {authors}, abstract '{abstract}'.{category_text}",
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# 元素罗列
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"{category_text}Title: '{title}'. Authors: '{authors}'. Abstract: '{abstract}'. Now, what's the scientific category?",
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# 假设场景
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"If a paper has title '{title}', authors '{authors}', and abstract '{abstract}', which scientific category best fits it?{category_text}",
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# 强调关键信息
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"Based solely on the title '{title}', authors list '{authors}', and abstract text '{abstract}', categorize this paper.{category_text}",
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# 间接询问
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"For the paper '{title}' by {authors}, with abstract '{abstract}', could you indicate its scientific discipline?{category_text}",
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# 完整句子整合
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"Determine the category of the research paper entitled '{title}', written by {authors}, and summarized as '{abstract}'.{category_text}",
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# 问题聚焦摘要
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"The abstract '{abstract}' describes a paper titled '{title}' by authors '{authors}'. What category is it?{category_text}",
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# 标题驱动
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"{category_text}Starting from the title '{title}', and considering authors '{authors}' and abstract '{abstract}', what is the paper's category?",
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# 多部分查询
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"Part 1: Title is '{title}'. Part 2: Authors are '{authors}'. Part 3: Abstract is '{abstract}'. Based on this, classify the paper.{category_text}",
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# 比较式
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"Given the details: title '{title}', authors '{authors}', abstract '{abstract}', how would you categorize this paper scientifically?{category_text}",
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# 行动导向
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"Using the provided title '{title}', authors '{authors}', and abstract '{abstract}', output the scientific category for this paper.{category_text}"
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]
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def extract_title_author_and_abstract(content_text):
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"""
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content_text: 格式示例"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 ,
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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", "assistant": "I"}]}}
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"""
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try:
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# 针对可以直接解析的JSON格式数据进行处理
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if content_text.strip().startswith('{') and '"title"' in content_text and '"author_names"' in content_text:
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try:
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# 尝试解析为JSON对象
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paper_data = json.loads(content_text)
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title = paper_data.get("title", "")
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authors = ", ".join(paper_data.get("author_names", []))
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abstract = paper_data.get("summary", paper_data.get("abstract", ""))
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return {"title": title, "authors": authors, "abstract": abstract}
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except:
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pass
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#content_text.split("',")
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parts = content_text.split("',")
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if len(parts) < 3:
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# 如果分割后的部分少于3个，返回默认值
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return {"title": "", "authors": "", "abstract": ""}
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# 安全地提取标题
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title_parts = parts[0].split("'")
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if len(title_parts) >= 2:
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title = title_parts[1].strip()
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else:
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title = ""
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# 安全地提取作者
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authors_parts = parts[1].split("'")
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if len(authors_parts) >= 2:
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authors = authors_parts[1].strip()
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else:
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authors = ""
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# 安全地提取摘要
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abstract_parts = parts[2].split("'")
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if len(abstract_parts) >= 2:
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abstract = abstract_parts[1].strip()
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else:
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abstract = ""
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return {"title": title, "authors": authors, "abstract": abstract}
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except Exception as e:
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# 如果出现任何异常，返回默认值
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print(f"解析内容时出错: {e}")
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return {"title": "", "authors": "", "abstract": ""}
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def parse_new_format_data(data):
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"""
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解析新格式的数据
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Args:
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data: 新格式的JSON数据
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Returns:
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tuple: (system_instruction, human_content, assistant_content) 或 (None, None, None)
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"""
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if "messages" not in data or not isinstance(data["messages"], list) or len(data["messages"]) < 3:
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return None, None, None
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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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return system_instruction, human_content, assistant_content
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def parse_old_format_data(data):
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"""
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解析旧格式的数据
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Args:
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data: 旧格式的JSON数据
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Returns:
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tuple: (system_instruction, conversation_data) 或 (None, None)
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"""
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if "system" not in data or "conversation" not in data or not data["conversation"]:
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return None, None
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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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return system_instruction, data["conversation"]
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def generate_multi_type_samples(title, authors, abstract, system_instruction, assistant_content, num_templates):
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"""
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根据模板生成多种类型的样本
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Args:
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title: 论文标题
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authors: 作者
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abstract: 摘要
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system_instruction: 系统指令
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assistant_content: 助手回复
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num_templates: 使用的模板数量
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Returns:
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list: 生成的多种类型数据列表
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"""
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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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samples = []
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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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"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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samples.append(new_data)
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return samples
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def process_new_format_data(data, num_templates):
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"""
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处理新格式数据
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Args:
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data: 新格式数据
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num_templates: 模板数量
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Returns:
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list: 处理后的数据列表
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"""
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system_instruction, human_content, assistant_content = parse_new_format_data(data)
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if not human_content:
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return []
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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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return generate_multi_type_samples(title, authors, abstract, system_instruction, assistant_content, num_templates)
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def process_old_format_data(data, num_templates):
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"""
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处理旧格式数据
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Args:
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data: 旧格式数据
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num_templates: 模板数量
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Returns:
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list: 处理后的数据列表
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"""
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system_instruction, conversation_data = parse_old_format_data(data)
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if not conversation_data:
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return []
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samples = []
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for turn in conversation_data:
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if "human" not in turn or "assistant" not in turn:
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continue
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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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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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samples.append(new_data)
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return samples
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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条数据按多种问题模板格式转换为多条数据，
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并保存为output_file
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参数:
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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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num_templates: 每条数据生成的模板数量
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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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multi_type_data = []
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# 检查是否为JSON文件格式
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if input_file.endswith('.json'):
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# 处理JSON格式文件
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with open(input_file, "r", encoding="utf-8") as f:
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json_data = json.load(f)
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for item in json_data:
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title = item.get("title", "")
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authors = ", ".join(item.get("author_names", item.get("authors", [])))
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abstract = item.get("summary", item.get("abstract", ""))
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category = item.get("category", "Unknown")
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# 生成系统指令
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system_instruction = "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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n = min(num_templates, len(QUESTION_TEMPLATES))
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selected_templates = random.sample(QUESTION_TEMPLATES, n)
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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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"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": category}
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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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# 原有的处理逻辑
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with open(input_file, "r", encoding="utf-8") as f:
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for line_num, line in enumerate(f, 1):
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try:
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data = json.loads(line.strip())
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# 处理新格式数据
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if "messages" in data:
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samples = process_new_format_data(data, num_templates)
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multi_type_data.extend(samples)
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# 处理旧格式数据
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elif "system" in data and "conversation" in data:
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samples = process_old_format_data(data, num_templates)
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multi_type_data.extend(samples)
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else:
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print(f"警告: 第{line_num}行数据格式不识别: {data}")
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continue
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except json.JSONDecodeError:
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print(f"警告: 第{line_num}行无法解析JSON: {line}")
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except Exception as e:
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print(f"处理第{line_num}行时发生错误: {str(e)}")
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# 写入输出文件
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with open(output_file, "w", encoding="utf-8") as f:
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for item in multi_type_data:
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f.write(json.dumps(item, ensure_ascii=False) + "\n")
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print(f"转换完成! 共转换 {len(multi_type_data)} 条数据")
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if __name__ == "__main__":
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# 示例用法
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input_file = "G:\\11\\data-prepare\\arxiv-metadata-oai-snapshot--swift-26-500.jsonl"
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output_file = "G:\\11\\data-prepare\\arxiv-metadata-oai-snapshot--swift-26-500-m+.jsonl"
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convert_onedata2multi_type(input_file, output_file, num_templates=1)
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