PhillyMac/Strategic_Thinking_Content_2

--- license: cc0-1.0 task_categories: - text-generation - feature-extraction language: - en tags: - corpus - leadership - historical - deku-corpus-builder size_categories: - 1K<n<10K --- # Strategic Thinking Content 2 This corpus was automatically generated by the **Deku Corpus Builder** for use in RAG-based AI applications. ## Dataset Description - **Subject**: Strategic Thinking Leadership - **Subject Type**: topic - **Total Items**: 1,322 - **Items Requiring Attribution**: 0 - **Has Embeddings**: Yes (all-MiniLM-L6-v2) - **Created**: 2026-03-31 ## Dataset Structure Each record contains: - `text`: The content text - `source_url`: Original source URL - `source_title`: Title of the source document - `source_domain`: Domain of the source - `license_type`: License classification (e.g. `public_domain`, `cc_by`, `cc_by_sa`) - `attribution_required`: Boolean — True for CC BY / CC BY-SA and other attribution-required licenses - `attribution_text`: Formatted Creative Commons attribution string (empty if not required) - `license_url`: URL to the CC license deed (empty if not required) - `relevance_score`: Relevance to the subject (0-1) - `quality_score`: Content quality score (0-1) - `topics`: JSON array of detected topics - `character_count`: Length of the text - `subject_name`: The subject this content relates to - `subject_type`: "personality" or "topic" - `extraction_date`: When the content was extracted - `embedding`: Pre-computed 384-dimensional embedding vector ## Attribution 0 of 1,322 chunks in this corpus require attribution under their source license. When building lessons from these chunks, the `attribution_text` field must be surfaced in the lesson output per the Legend Leadership Attribution Tracking Spec. ## Usage ```python from datasets import load_dataset dataset = load_dataset("PhillyMac/Strategic_Thinking_Content_2") # Access attribution-required chunks for item in dataset["train"]: if item["attribution_required"]: print(item["attribution_text"]) ``` ## Integration with RAG This dataset is designed to be integrated with existing embedded corpuses. The embeddings use the `sentence-transformers/all-MiniLM-L6-v2` model, compatible with FAISS indexing. ## License Content is sourced from public domain and Creative Commons licensed materials. See individual `license_type` fields for per-chunk licensing details. ## Generated By [Deku Corpus Builder](https://github.com/PhillyMac/deku-corpus-builder) - An automated corpus building system for AI applications.--- 许可证：CC0-1.0 任务类别： - 文本生成 - 特征提取 语言： - 英语 标签： - 语料库 - 领导力 - 历史 - Deku Corpus Builder 规模分类： - 1000 < 样本数 < 10000 --- # 战略思维内容2 本语料库由**Deku Corpus Builder**自动生成，专为基于检索增强生成（Retrieval-Augmented Generation，RAG）的人工智能应用打造。 ## 数据集说明 - **主题**：战略思维领导力 - **主题类型**：话题 - **总条目数**：1322 - **需标注来源条目数**：0 - **已生成词嵌入**：是（采用all-MiniLM-L6-v2模型） - **创建时间**：2026-03-31 ## 数据集结构 每条记录包含以下字段： - `text`：内容文本 - `source_url`：原始来源网址 - `source_title`：来源文档标题 - `source_domain`：来源域名 - `license_type`：许可证分类（例如`公有领域`、`CC BY`、`CC BY-SA`） - `attribution_required`：布尔值——若为CC BY/CC BY-SA或其他需标注来源的许可证，则取值为真 - `attribution_text`：格式化的知识共享署名字符串（无需标注时为空） - `license_url`：知识共享许可证官方页面链接（无需标注时为空） - `relevance_score`：与主题的相关性得分（范围0-1） - `quality_score`：内容质量得分（范围0-1） - `topics`：检测到的话题的JSON数组 - `character_count`：文本字符数 - `subject_name`：该内容关联的主题名称 - `subject_type`：取值为“人物”或“话题” - `extraction_date`：内容提取时间 - `embedding`：预计算的384维词嵌入向量 ## 署名说明 本语料库的1322个文本块中，无任何条目需依据来源许可证进行署名标注。若基于这些文本块构建教学内容，需按照《Legend Leadership 署名追踪规范》在教学成果中展示`attribution_text`字段内容。 ## 使用方法 python from datasets import load_dataset dataset = load_dataset("PhillyMac/Strategic_Thinking_Content_2") # 遍历需标注来源的文本块 for item in dataset["train"]: if item["attribution_required"]: print(item["attribution_text"]) ## 与检索增强生成（Retrieval-Augmented Generation，RAG）系统集成 本数据集专为与现有嵌入语料库集成而设计。其词嵌入采用`sentence-transformers/all-MiniLM-L6-v2`模型，可兼容FAISS（Facebook人工智能研究院相似性搜索库）索引构建。 ## 许可证 本数据集内容源自公有领域及知识共享许可授权的素材。各文本块的具体许可证信息请查看`license_type`字段。 ## 生成工具 [Deku Corpus Builder](https://github.com/PhillyMac/deku-corpus-builder)——一款面向人工智能应用的自动化语料库构建系统。