This paper describes FluENS (Flu ENsemble System), our submission to the Social Media Mining for Health (SMM4H) 2026 Shared Task 3, which targets fine-grained classification of flu vaccination and flu testing statuses from tweets. FluENS builds on the microsoft/deberta-v2-xlarge pre-trained language model and employs a multi-seed ensemble strategy in which five models, each initialized with a different random seed and trained on the full training set, are aggregated through soft-voting over averaged softmax probabilities. We additionally incorporate balanced class weights to mitigate severe label imbalance and apply a two-stage learning rate schedule that separately controls the encoder and classification head. On the development set, FluENS achieves a macro F1 of 79.64% and micro F1 of 85.56% on the flu vaccination sub-task, and a macro F1 of 96.35% and micro F1 of 97.04% on the flu testing sub-task, substantially outperforming a roberta-base baseline across all metrics.