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Phrasebook eval harness + accent/filler-tolerant matching
ous-sow +1: Claude Opus 5.5- configs/eval/phrasebook_eval.json: 85 held-out inputs (typos, code-switching, filler words, open-ended questions that must reach the LLM), written to differ from the curated aliases. - scripts/eval_phrasebook.py: scores correct / wrong / false_hit / miss per language. - _normalize now folds Latin accents (ça -> ca), drops politeness filler (please, svp, s'il vous plaît) and leading articles (du, une, some). Applied to both sides; Bambara/Pular letters (ɛ ɔ ɓ ŋ) are untouched. Hit rate on the eval set: before aliases bam 43% ful 46% + aliases bam 80% ful 77% + normaliser bam 88% ful 86% wrong=0 and false_hit=0 at every step. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>ea5dc26