• Extracted: 24GB across 668 files
• Target: 67GB total corpus
• Status: Actively extracting and processing

• C4 Dataset: 3.9GB - Web crawl data for language modeling
• Wikipedia: 10.5GB - Complete English Wikipedia dump
• CommonCrawl: 5.4GB - Web crawl archives
• StackOverflow: 2.2GB - Programming Q&A data
• OpenWebText: 14MB (partial) - Reddit-sourced web text
• Entrepreneur Content: 8.1GB - Startup/business focused content

1. Extraction: Decompress archives → /data/extracted/
2. Cleaning: Remove noise, normalize → /data/cleaned/
3. Categorization: Topic classification → /data/categorized/
4. Versioning: Track changes → Git LFS

data/
├── extracted/     # Raw extracted files (24GB)
├── cleaned/       # Cleaned text (in progress)
├── categorized/   # Topic-sorted (pending)
└── processed/     # Training-ready (pending)

# Check corpus status
python scripts/check_status.py

# Extract remaining archives
python pipeline/extractors/extract_all.py

# Clean extracted data
python pipeline/cleaners/clean_corpus.py

The corpus is being prepared for Nova NLM training. Current focus:

• Entrepreneur/startup content
• Innovation/disruption topics
• Resource-constrained solutions
• Anti-corporate language patterns

This pipeline integrates with other Nova agents via DragonflyDB streams:

• nova.meridian.status - Pipeline status updates
• nova.vertex.requests - Training data requests
• nova.collaboration - Inter-agent coordination