PhillyMac/Giving_and_Receiving_Feedback_Content_1

---
license: cc0-1.0
task_categories:
- text-generation
- feature-extraction
language:
- en
tags:
- corpus
- leadership
- historical
- deku-corpus-builder
size_categories:
- 1K<n<10K
---

# Giving and Receiving Feedback Content 1

This corpus was automatically generated by the **Deku Corpus Builder** for use in RAG-based AI applications.

## Dataset Description

- **Subject**: Giving and Receiving Feedback Leadership
- **Subject Type**: topic
- **Total Items**: 1,749
- **Items Requiring Attribution**: 0
- **Has Embeddings**: Yes (all-MiniLM-L6-v2)
- **Created**: 2026-04-01

## 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 1,749 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/Giving_and_Receiving_Feedback_Content_1")

# 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.

---
license: cc0-1.0
task_categories:
- text-generation
- feature-extraction
language:
- en
tags:
- corpus
- leadership
- historical
- Deku Corpus Builder
size_categories:
- 1K<n<10K
---

# 给予与接收反馈内容1

本语料库由**Deku语料库构建器（Deku Corpus Builder）**自动生成，专为基于检索增强生成（Retrieval-Augmented Generation，简称RAG）的人工智能应用打造。

## 数据集说明

- **主题**：给予与接收反馈的领导力实践
- **主题类型**：话题
- **总条目数**：1749
- **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**：2026-04-01

## 数据集结构

每条数据记录包含以下字段：
- `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`public_domain`（公有领域）、`cc_by`（知识共享署名许可）、`cc_by_sa`（知识共享署名-相同方式共享许可））
- `attribution_required`：布尔值——对于CC BY、CC BY-SA等需标注来源的许可证，该值为`True`
- `attribution_text`：格式化后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 deed 页面的网址（无需标注时为空）
- `relevance_score`：与主题的相关度评分（取值范围0-1）
- `quality_score`：内容质量评分（取值范围0-1）
- `topics`：检测到的主题的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"personality"（人物）或"topic"（话题）
- `extraction_date`：内容提取时间
- `embedding`：预计算得到的384维词嵌入向量

## 来源标注

本语料库的1749个文本块中，无任何条目需根据其来源许可证标注来源。
当基于这些文本块构建教学内容时，需按照《Legend领导力归因跟踪规范》（Legend Leadership Attribution Tracking Spec）在教学输出中展示`attribution_text`字段的内容。

## 使用方法

python
from datasets import load_dataset

dataset = load_dataset("PhillyMac/Giving_and_Receiving_Feedback_Content_1")

# Access attribution-required chunks
for item in dataset["train"]:
if item["attribution_required"]:
print(item["attribution_text"])

## 与RAG系统集成

本数据集专为与现有嵌入语料库集成而设计。其词嵌入由`sentence-transformers/all-MiniLM-L6-v2`模型生成，兼容FAISS索引。

## 许可证

本数据集内容源自公有领域及知识共享许可协议授权的素材。各文本块的具体许可证信息请参见`license_type`字段。

## 生成方

[Deku语料库构建器（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。