Arabic sentiment analysis must cope with dialects that differ in vocabulary, morphology and spelling. Subtask 1 of the DialectSentEval 2026 shared task poses this problem directly: systems label hotel review sentences from five Arabic dialects as positive, negative or neutral, and one dialect (Lebanese) appears only in the test set. In this paper, we describe the Lahja submission. Since the training corpus is small (1,731 sentences) and consists of parallel translations, we treat unreliable measurement (i.e. leaky validation splits and single training runs) rather than model capacity as the main risk. We fine-tune MARBERTv2 under three random seeds, average their softmax outputs, and validate on a split grouped by review identifier so that all dialect versions of a review stay on the same side. The ensemble reached 0.790 macro-F1 on the official test set, ranking 10th of 12 teams, and lost only about two points from validation to test despite the unseen dialect. Our ablations show that the variation between seeds (about 1.5 points) exceeds the effect of orthographic normalisation or class weighting, and that the normalised ensemble, although best on validation, generalised worst on test. We therefore recommend grouped validation and seed-spread reporting as standard practice for small-data shared tasks.