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feat: improve ensemble voting with confidence weighting and per-class F1 scores
aviseth- Keep all 3 voting strategies display (hard/soft/weighted) - Soft voting now weights by model confidence (more confident models count more) - Weighted voting uses per-class F1 scores instead of overall accuracy - Add uncertainty metric (1 - max confidence) - Add model_agreement metric (% of models that agree) - Token merging weighted by class-specific F1 scores - Update all tests to include new metrics4cc1ba8 -
revert: restore 3 voting strategies display
avisethUser prefers seeing all 3 voting strategies (hard/soft/weighted) in the responsea6355a1 -
refactor: simplify ensemble voting to single smart strategy
aviseth- Replace 3 voting methods (hard/soft/weighted) with 1 smart_voting method - Use confidence-weighted predictions with per-class F1 scores - Add uncertainty and model_agreement metrics to results - Weight token explanations by class-specific F1 scores - Simplify API response structure (remove voting_strategies) - Update all tests to match new simplified structure - Minimalist approach for better user experiencec0e80a3 -
chore: remove documentation files
aviseth98a3c02