PhillyMac/Transformational_Leadership_Practical

Transformational Leadership — Practical

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

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 780 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

from datasets import load_dataset

dataset = load_dataset("PhillyMac/Transformational_Leadership_Practical")

# 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 - An automated corpus building system for AI applications.