Duplicate from DemoTest0122/ScienceMetaBench

.gitignore

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+.idea

README.md

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+---
+license: cc-by-4.0
+language:
+- en
+- zh
+viewer: true
+configs:
+- config_name: default
+  data_files:
+  - split: val
+    path: data/**/*.jsonl
+---
+# ScienceMetaBench
+
+[English](README.md) | [中文](README_ZH.md)
+
+🤗 [HuggingFace Dataset](https://huggingface.co/datasets/opendatalab/ScienceMetaBench) | 🔍 [Dingo](https://github.com/MigoXLab/dingo)
+
+ScienceMetaBench is a benchmark dataset for evaluating the accuracy of metadata extraction from scientific literature PDF files. The dataset covers three major categories: academic papers, textbooks, and ebooks, and can be used to assess the performance of Vision Language Models (VLMs) or other information extraction systems.
+
+## 📊 Dataset Overview
+
+### Data Types
+
+This benchmark includes three types of scientific literature:
+
+1. **Papers**
+   - Mainly from academic journals and conferences
+   - Contains academic metadata such as DOI, keywords, etc.
+
+2. **Textbooks**
+   - Formally published textbooks
+   - Includes ISBN, publisher, and other publication information
+
+3. **Ebooks**
+   - Digitized historical documents and books
+   - Covers multiple languages and disciplines
+
+### Data Batches
+
+This benchmark has undergone two rounds of data expansion, with each round adding new sample data:
+
+```
+data/
+├── 20250806/          # First batch (August 6, 2024)
+│   ├── ebook_0806.jsonl
+│   ├── paper_0806.jsonl
+│   └── textbook_0806.jsonl
+└── 20251022/          # Second batch (October 22, 2024)
+    ├── ebook_1022.jsonl
+    ├── paper_1022.jsonl
+    └── textbook_1022.jsonl
+```
+
+**Note**: The two batches of data complement each other to form a complete benchmark dataset. You can choose to use a single batch or merge them as needed.
+
+### PDF Files
+
+The `pdf/` directory contains the original PDF files corresponding to the benchmark data, with a directory structure consistent with the `data/` directory.
+
+**File Naming Convention**: All PDF files are named using their SHA256 hash values, in the format `{sha256}.pdf`. This naming scheme ensures file uniqueness and traceability, making it easy to locate the corresponding source file using the `sha256` field in the JSONL data.
+
+## 📝 Data Format
+
+All data files are in JSONL format (one JSON object per line).
+
+### Academic Paper Fields
+
+```json
+{
+  "sha256": "SHA256 hash of the file",
+  "doi": "Digital Object Identifier",
+  "title": "Paper title",
+  "author": "Author name",
+  "keyword": "Keywords (comma-separated)",
+  "abstract": "Abstract content",
+  "pub_time": "Publication year"
+}
+```
+
+### Textbook/Ebook Fields
+
+```json
+{
+  "sha256": "SHA256 hash of the file",
+  "isbn": "International Standard Book Number",
+  "title": "Book title",
+  "author": "Author name",
+  "abstract": "Introduction/abstract",
+  "category": "Classification number (e.g., Chinese Library Classification)",
+  "pub_time": "Publication year",
+  "publisher": "Publisher"
+}
+```
+
+## 📖 Data Examples
+
+### Academic Paper Example
+
+The following image shows an example of metadata fields extracted from an academic paper PDF:
+
+![Academic Paper Example](images/paper_example.png)
+
+As shown in the image, the following key information needs to be extracted from the paper's first page:
+- **DOI**: Digital Object Identifier (e.g., `10.1186/s41038-017-0090-z`)
+- **Title**: Paper title
+- **Author**: Author name
+- **Keyword**: List of keywords
+- **Abstract**: Paper abstract
+- **pub_time**: Publication time (usually the year)
+
+### Textbook/Ebook Example
+
+The following image shows an example of metadata fields extracted from the copyright page of a Chinese ebook PDF:
+
+![Textbook Example](images/ebook_example.png)
+
+As shown in the image, the following key information needs to be extracted from the book's copyright page:
+- **ISBN**: International Standard Book Number (e.g., `978-7-5385-8594-0`)
+- **Title**: Book title
+- **Author**: Author/editor name
+- **Publisher**: Publisher name
+- **pub_time**: Publication time (year)
+- **Category**: Book classification number
+- **Abstract**: Content introduction (if available)
+
+These examples demonstrate the core task of the benchmark test: accurately extracting structured metadata information from PDF documents in various formats and languages.
+
+## 📊 Evaluation Metrics
+
+### Core Evaluation Metrics
+
+This benchmark uses a string similarity-based evaluation method, providing two core metrics:
+
+### Similarity Calculation Rules
+
+This benchmark uses a string similarity algorithm based on `SequenceMatcher`, with the following specific rules:
+
+1. **Empty Value Handling**: One is empty and the other is not → similarity is 0
+2. **Complete Match**: Both are identical (including both being empty) → similarity is 1
+3. **Case Insensitive**: Convert to lowercase before comparison
+4. **Sequence Matching**: Use longest common subsequence algorithm to calculate similarity (range: 0-1)
+
+**Similarity Score Interpretation**:
+- `1.0`: Perfect match
+- `0.8-0.99`: Highly similar (may have minor formatting differences)
+- `0.5-0.79`: Partial match (extracted main information but incomplete)
+- `0.0-0.49`: Low similarity (extraction result differs significantly from ground truth)
+
+#### 1. Field-level Accuracy
+
+**Definition**: The average similarity score for each metadata field.
+
+**Calculation Method**:
+```
+Field-level Accuracy = Σ(similarity of that field across all samples) / total number of samples
+```
+
+**Example**: Suppose evaluating the `title` field on 100 samples, the sum of title similarity for each sample divided by 100 gives the accuracy for that field.
+
+**Use Cases**:
+- Identify which fields the model performs well or poorly on
+- Optimize extraction capabilities for specific fields
+- For example: If `doi` accuracy is 0.95 and `abstract` accuracy is 0.75, the model needs improvement in extracting abstracts
+
+#### 2. Overall Accuracy
+
+**Definition**: The average of all evaluated field accuracies, reflecting the model's overall performance.
+
+**Calculation Method**:
+```
+Overall Accuracy = Σ(field-level accuracies) / total number of fields
+```
+
+**Example**: Evaluating 7 fields (isbn, title, author, abstract, category, pub_time, publisher), sum these 7 field accuracies and divide by 7.
+
+**Use Cases**:
+- Provide a single quantitative metric for overall model performance
+- Facilitate horizontal comparison between different models or methods
+- Serve as an overall objective for model optimization
+
+### Using the Evaluation Script
+
+`compare.py` provides a convenient evaluation interface:
+
+```python
+from compare import main, write_similarity_data_to_excel
+
+# Define file paths and fields to compare
+file_llm = 'data/llm-label_textbook.jsonl'      # LLM extraction results
+file_bench = 'data/benchmark_textbook.jsonl'     # Benchmark data
+
+# For textbooks/ebooks
+key_list = ['isbn', 'title', 'author', 'abstract', 'category', 'pub_time', 'publisher']
+
+# For academic papers
+# key_list = ['doi', 'title', 'author', 'keyword', 'abstract', 'pub_time']
+
+# Run evaluation and get metrics
+accuracy, key_accuracy, detail_data = main(file_llm, file_bench, key_list)
+
+# Output results to Excel (optional)
+write_similarity_data_to_excel(key_list, detail_data, "similarity_analysis.xlsx")
+
+# View evaluation metrics
+print("Field-level Accuracy:", key_accuracy)
+print("Overall Accuracy:", accuracy)
+```
+
+### Output Files
+
+The script generates an Excel file containing detailed sample-by-sample analysis:
+
+- `sha256`: File identifier
+- For each field (e.g., `title`):
+  - `llm_title`: LLM extraction result
+  - `benchmark_title`: Benchmark data
+  - `similarity_title`: Similarity score (0-1)
+
+## 📈 Statistics
+
+### Data Scale
+
+**First Batch (20250806)**:
+- **Ebooks**: 70 records
+- **Academic Papers**: 70 records
+- **Textbooks**: 71 records
+- **Subtotal**: 211 records
+
+**Second Batch (20251022)**:
+- **Ebooks**: 354 records
+- **Academic Papers**: 399 records
+- **Textbooks**: 46 records
+- **Subtotal**: 799 records
+
+**Total**: 1010 benchmark test records
+
+The data covers multiple languages (English, Chinese, German, Greek, etc.) and multiple disciplines, with both batches together providing a rich and diverse set of test samples.
+
+## 🎯 Application Scenarios
+
+1. **LLM Performance Evaluation**: Assess the ability of large language models to extract metadata from PDFs
+2. **Information Extraction System Testing**: Test the accuracy of OCR, document parsing, and other systems
+3. **Model Fine-tuning**: Use as training or fine-tuning data to improve model information extraction capabilities
+4. **Cross-lingual Capability Evaluation**: Evaluate the model's ability to process multilingual literature
+
+## 🔬 Data Characteristics
+
+- ✅ **Real Data**: Real metadata extracted from actual PDF files
+- ✅ **Diversity**: Covers literature from different eras, languages, and disciplines
+- ✅ **Challenging**: Includes ancient texts, non-English literature, complex layouts, and other difficult cases
+- ✅ **Traceable**: Each record includes SHA256 hash and original path
+
+## 📋 Dependencies
+
+```python
+pandas>=1.3.0
+openpyxl>=3.0.0
+```
+
+Install dependencies:
+
+```bash
+pip install pandas openpyxl
+```
+
+## 🤝 Contributing
+
+If you would like to:
+- Report data errors
+- Add new evaluation dimensions
+- Expand the dataset
+
+Please submit an Issue or Pull Request.
+
+## 📧 Contact
+
+If you have questions or suggestions, please contact us through Issues.
+
+---
+
+**Last Updated**: December 26, 2025
+
+

README_ZH.md

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+# ScienceMetaBench
+
+[English](README.md) | [中文](README_ZH.md)
+
+🤗 [HuggingFace Dataset](https://huggingface.co/datasets/opendatalab/ScienceMetaBench) | 🔍 [Dingo](https://github.com/MigoXLab/dingo)
+
+ScienceMetaBench 是一个用于评估从 PDF 文件中提取科学文献元数据准确性的基准测试数据集。该数据集涵盖学术论文、教材和电子书三大类别,可用于评估视觉模型（VLM）或其他信息提取系统的性能。
+
+## 📊 数据集概览
+
+### 数据类型
+
+本基准测试包含三种类型的科学文献：
+
+1. **学术论文 (Paper)**
+   - 主要来自学术期刊和会议
+   - 包含 DOI、关键词等学术元数据
+
+2. **教材 (Textbook)**
+   - 正式出版的教科书
+   - 包含 ISBN、出版社等出版信息
+
+3. **电子书 (Ebook)**
+   - 数字化的历史文献和图书
+   - 覆盖多语言、多学科领域
+
+### 数据批次
+
+本基准测试经过两次数据扩充，每次扩充都增加了新的样本数据：
+
+```
+data/
+├── 20250806/          # 第一批数据（2024年8月6日）
+│   ├── ebook_0806.jsonl
+│   ├── paper_0806.jsonl
+│   └── textbook_0806.jsonl
+└── 20251022/          # 第二批数据（2024年10月22日）
+    ├── ebook_1022.jsonl
+    ├── paper_1022.jsonl
+    └── textbook_1022.jsonl
+```
+
+**注**：两批数据相互补充，共同构成完整的基准测试数据集，可根据需要选择使用单批或合并使用。
+
+### PDF 文件
+
+`pdf/` 目录包含与基准数据对应的原始 PDF 文件，目录结构与 `data/` 目录保持一致。
+
+**文件命名规则**：所有 PDF 文件以其 SHA256 哈希值命名，格式为 `{sha256}.pdf`。这种命名方式确保了文件的唯一性和可追溯性，便于通过 JSONL 数据中的 `sha256` 字段定位对应的源文件。
+
+## 📝 数据格式
+
+所有数据文件采用 JSONL 格式（每行一个 JSON 对象）。
+
+### 学术论文字段
+
+```json
+{
+  "sha256": "文件的 SHA256 哈希值",
+  "doi": "数字对象标识符",
+  "title": "论文标题",
+  "author": "作者姓名",
+  "keyword": "关键词（逗号分隔）",
+  "abstract": "摘要内容",
+  "pub_time": "发表年份"
+}
+```
+
+### 教材/电子书字段
+
+```json
+{
+  "sha256": "文件的 SHA256 哈希值",
+  "isbn": "国际标准书号",
+  "title": "书名",
+  "author": "作者姓名",
+  "abstract": "简介/摘要",
+  "category": "分类号（如中图分类法）",
+  "pub_time": "出版年份",
+  "publisher": "出版社"
+}
+```
+
+## 📖 数据示例
+
+### 学术论文示例
+
+下图展示了从学术论文 PDF 中提取的元数据字段示例：
+
+![学术论文示例](images/paper_example.png)
+
+如图所示，需要从论文首页提取以下关键信息：
+- **DOI**：数字对象标识符（如 `10.1186/s41038-017-0090-z`）
+- **Title**：论文标题
+- **Author**：作者姓名
+- **Keyword**：关键词列表
+- **Abstract**：论文摘要
+- **pub_time**：发表时间（通常为年份）
+
+### 教材/电子书示例
+
+下图展示了从中文电子书 PDF 版权页中提取的元数据字段示例：
+
+![教材示例](images/ebook_example.png)
+
+如图所示，需要从图书版权页提取以下关键信息：
+- **ISBN**：国际标准书号（如 `978-7-5385-8594-0`）
+- **Title**：书名
+- **Author**：作者/编者姓名
+- **Publisher**：出版社名称
+- **pub_time**：出版时间（年份）
+- **Category**：图书分类号
+- **Abstract**：内容简介（如有）
+
+这些示例展示了 benchmark 测试的核心任务：从各种格式和语言的 PDF 文档中准确提取结构化的元数据信息。
+
+## 📊 评估指标
+
+### 核心评估指标
+
+本基准测试采用基于字符串相似度的评估方法，提供两个核心指标：
+
+### 相似度计算规则
+
+本基准测试使用基于 `SequenceMatcher` 的字符串相似度算法，具体规则如下：
+
+1. **空值处理**：一个为空另一个不为空 → 相似度为 0
+2. **完全匹配**：两者完全相同（包括都为空）→ 相似度为 1
+3. **忽略大小写**：比较前统一转换为小写
+4. **序列匹配**：使用最长公共子序列算法计算相似度（范围：0-1）
+
+**相似度分数解读**：
+- `1.0`：完全匹配
+- `0.8-0.99`：高度相似（可能存在小的格式差异）
+- `0.5-0.79`：部分匹配（提取了主要信息但不完整）
+- `0.0-0.49`：低相似度（提取结果与标准答案差异较大）
+
+#### 1. 各字段准确率（Field-level Accuracy）
+
+**定义**：每个元数据字段的平均相似度分数。
+
+**计算方式**：
+```
+各字段准确率 = Σ(该字段在所有样本上的相似度) / 样本总数
+```
+
+**示例**：假设在 100 个样本上评估 `title` 字段，每个样本的 title 相似度累加后除以 100，得到该字段的准确率。
+
+**用途**：
+- 识别模型在哪些字段上表现较好或较差
+- 针对性地优化特定字段的提取能力
+- 例如：如果 `doi` 准确率为 0.95，`abstract` 准确率为 0.75，说明模型在提取摘要时需要改进
+
+#### 2. 总体准确率（Overall Accuracy）
+
+**定义**：所有评估字段准确率的平均值，反映模型的整体性能。
+
+**计算方式**：
+```
+总体准确率 = Σ(各字段准确率) / 字段总数
+```
+
+**示例**：评估 7 个字段（isbn, title, author, abstract, category, pub_time, publisher），将这 7 个字段的准确率相加后除以 7。
+
+**用途**：
+- 提供模型整体性能的单一量化指标
+- 方便不同模型或方法之间的横向比较
+- 作为模型优化的总体目标
+
+### 使用评估脚本
+
+`compare.py` 提供了便捷的评估接口：
+
+```python
+from compare import main, write_similarity_data_to_excel
+
+# 定义文件路径和要比较的字段
+file_llm = 'data/llm-label_textbook.jsonl'      # LLM 提取结果
+file_bench = 'data/benchmark_textbook.jsonl'     # 基准数据
+
+# 对于教材/电子书
+key_list = ['isbn', 'title', 'author', 'abstract', 'category', 'pub_time', 'publisher']
+
+# 对于学术论文
+# key_list = ['doi', 'title', 'author', 'keyword', 'abstract', 'pub_time']
+
+# 运行评估，获取指标
+accuracy, key_accuracy, detail_data = main(file_llm, file_bench, key_list)
+
+# 输出结果到 Excel（可选）
+write_similarity_data_to_excel(key_list, detail_data, "similarity_analysis.xlsx")
+
+# 查看评估指标
+print("各字段准确率：", key_accuracy)
+print("总体准确率：", accuracy)
+```
+
+### 输出文件
+
+脚本会生成一个 Excel 文件，包含详细的逐样本分析：
+
+- `sha256`：文件标识
+- 对于每个字段（如 `title`）：
+  - `llm_title`：LLM 提取的结果
+  - `benchmark_title`：基准数据
+  - `similarity_title`：相似度分数（0-1）
+
+## 📈 统计信息
+
+### 数据规模
+
+**第一批数据（20250806）**：
+- **电子书**：70 条记录
+- **学术论文**：70 条记录
+- **教材**：71 条记录
+- **小计**：211 条记录
+
+**第二批数据（20251022）**：
+- **电子书**：354 条记录
+- **学术论文**：399 条记录
+- **教材**：46 条记录
+- **小计**：799 条记录
+
+**总计**：1010 条基准测试记录
+
+数据涵盖多语言（英语、中文、德语、希腊语等）和多学科领域，两批数据共同提供了丰富多样的测试样本。
+
+## 🎯 应用场景
+
+1. **LLM 性能评估**：评估大语言模型从 PDF 提取元数据的能力
+2. **信息提取系统测试**：测试 OCR、文档解析等系统的准确性
+3. **模型微调**：作为训练或微调数据，提升模型的信息提取能力
+4. **跨语言能力评估**：评估模型处理多语言文献的能力
+
+## 🔬 数据特点
+
+- ✅ **真实数据**：从实际 PDF 文件中提取的真实元数据
+- ✅ **多样性**：覆盖不同年代、语言、学科的文献
+- ✅ **挑战性**：包含古籍、非英语文献、复杂排版等难例
+- ✅ **可追溯**：每条记录包含 SHA256 哈希值和原始路径
+
+## 📋 依赖项
+
+```python
+pandas>=1.3.0
+openpyxl>=3.0.0
+```
+
+安装依赖：
+
+```bash
+pip install pandas openpyxl
+```
+
+## 🤝 贡献指南
+
+如果您希望：
+- 报告数据错误
+- 添加新的评估维度
+- 扩展数据集
+
+请提交 Issue 或 Pull Request。
+
+## 📧 联系方式
+
+如有问题或建议，请通过 Issue 与我们联系。
+
+---
+
+**最后更新**：2025年12月26日
+

compare.py

@@ -0,0 +1,142 @@
+import json
+import datetime
+import pandas as pd
+from difflib import SequenceMatcher
+
+def string_similarity(str1, str2):
+    # 规则1: 一个为空另一个不为空，相似度为0
+    if (str1 is None or str1 == "") and (str2 is not None and str2 != ""):
+        return 0.0
+    if (str2 is None or str2 == "") and (str1 is not None and str1 != ""):
+        return 0.0
+
+    # 规则2: 二者完全相同（包括全为空），相似度为1
+    if (str1 or "") == (str2 or ""):
+        return 1.0
+
+
+    # 规则3: 忽略大小写进行比较
+    s1_lower = str1.lower()
+    s2_lower = str2.lower()
+
+    # 如果忽略大小写后相同，直接返回1
+    if s1_lower == s2_lower:
+        return 1.0
+
+    # 使用SequenceMatcher计算相似度
+    matcher = SequenceMatcher(None, s1_lower, s2_lower)
+    similarity = matcher.ratio()
+
+    return similarity
+
+def main(file_llm = '', file_bench = '', key_list = []):
+    all_data = {}
+    with open(file_llm, 'r', encoding='utf-8') as f:
+        for line in f:
+            j = json.loads(line).get('llm_response_dict')
+            # print(j)
+            all_data[j['sha256']] = {}
+            all_data[j['sha256']]['llm_response_dict'] = j
+
+    with open(file_bench, 'r', encoding='utf-8') as f:
+        for line in f:
+            j = json.loads(line)
+            if j['sha256'] not in all_data:
+                all_data[j['sha256']] = {}
+            all_data[j['sha256']]['benchmark_dict'] = j
+
+    sha256_to_remove = []
+    for sha256, value in all_data.items():
+        # 检查是否同时包含这两个键
+        if 'llm_response_dict' not in value or 'benchmark_dict' not in value:
+            sha256_to_remove.append(sha256)
+    for sha256 in sha256_to_remove:
+        all_data.pop(sha256)
+
+    for sha256, value in all_data.items():
+        all_data[sha256]['similarity'] = {}
+        for key in key_list:
+            # print(key)
+            all_data[sha256]['similarity'][key] = string_similarity(all_data[sha256]['llm_response_dict'].get(key), all_data[sha256]['benchmark_dict'][key])
+
+    # print(all_data)
+    key_accuracy_tmp = {key: 0 for key in key_list}
+    for sha256, value in all_data.items():
+        for key in key_list:
+            key_accuracy_tmp[key] += value['similarity'][key]
+
+    # print(key_accuracy_tmp)
+    key_accuracy = {k: v / len(all_data) for k,v in key_accuracy_tmp.items()}
+    # print(key_accuracy)
+    accuracy = sum(list(key_accuracy.values())) / len(list(key_accuracy.values()))
+    return accuracy, key_accuracy, all_data
+
+def write_similarity_data_to_excel(key_list, data_dict, output_file="similarity_analysis.xlsx"):
+    """
+    将相似度分析数据写入Excel文件
+
+    Args:
+        data_dict: 包含相似度分析数据的字典
+        output_file: 输出Excel文件名
+    """
+
+    # 准备数据列表
+    rows = []
+
+    for sha256, data in data_dict.items():
+        row = {
+            'sha256': sha256
+        }
+
+        for field in key_list:
+            # llm_response_dict 字段
+            row[f'llm_{field}'] = data['llm_response_dict'].get(field)
+            # benchmark_dict 字段
+            row[f'benchmark_{field}'] = data['benchmark_dict'].get(field)
+            # similarity 字段
+            row[f'similarity_{field}'] = data['similarity'].get(field)
+        rows.append(row)
+
+    # 创建DataFrame
+    df = pd.DataFrame(rows)
+
+    # 定义列的顺序（可选，让Excel更易读）
+    column_order = ['sha256']
+    for field in key_list:
+        column_order.extend([f'llm_{field}', f'benchmark_{field}', f'similarity_{field}'])
+
+    # 重新排列列顺序
+    df = df[column_order]
+
+    # 写入Excel文件
+    with pd.ExcelWriter(output_file, engine='openpyxl') as writer:
+        df.to_excel(writer, sheet_name='相似度分析', index=False)
+
+        # # 获取工作表并调整列宽
+        # worksheet = writer.sheets['相似度分析']
+        # worksheet.column_dimensions['A'].width = 70  # sha256列
+
+
+    print(f"数据已成功写入 {output_file}")
+    print(f"总共处理了 {len(rows)} 条记录")
+
+    return df
+
+if __name__ == '__main__':
+    file_llm = 'data/llm-label_textbook.jsonl'
+    file_bench = 'data/benchmark_textbook.jsonl'
+    # key_list = ['doi', 'title', 'author', 'keyword', 'abstract', 'pub_time']
+    key_list = ['isbn', 'title', 'author', 'abstract', 'category', 'pub_time', 'publisher']
+
+    accuracy, key_accuracy, detail_data = main(file_llm, file_bench, key_list)
+    # print(detail_data)
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    output_filename = f"similarity_analysis_{timestamp}.xlsx"
+    write_similarity_data_to_excel(key_list, detail_data, output_filename)
+
+    print(key_accuracy)
+    print(accuracy)
+
+
+
+

data/20251022/textbook_1022.jsonl

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test

@@ -0,0 +1 @@
+hello