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Python ML pipeline (scripts/classification/) that classifies ~800K .docx documents by document type (10 classes) and topic (9 classes) using the FineWeb-Edu pattern: LLM labels a sample → train ModernBERT → apply at scale. Pipeline steps: - sample.py: stratified sampling across languages and word count - label.py: async LLM labeling with Claude (resumable) - train.py: fine-tune two ModernBERT classifiers - classify.py: batch inference on full corpus - evaluate.py: quality metrics and distribution analysis Also adds: - LLM classification fields and methods to DbClient - CLAUDE.md / AGENTS.md at root, packages/shared, and scripts/classification - Updated README with Phase 5 (Classify) and project structure
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Python ML pipeline (scripts/classification/) that classifies ~800K .docx documents by document type (10 classes) and topic (9 classes) using the FineWeb-Edu pattern: LLM labels a sample → train ModernBERT → apply at scale.
Pipeline steps:
Also adds: