In this paper, we describe systems for two #SMM4H-HeaRD 2026 shared tasks. Task 6 asks for per-axis TNM cancer staging from free-text TCGA pathology reports under severe label imbalance and long-document constraints. We fine-tune GatorTron-base separately on each axis using Focal loss with class weights and a pooled [CLS]–mean representation, reaching macro F1 of 0.700 (T), 0.774 (N), and 0.640 (M) on test set 2 against a baseline of 0.454, 0.591, and 0.554 respectively. Task 7 asks for span-level detection of opioid-related ClinicalImpacts and SocialImpacts in first-person Reddit posts. We combine DeBERTa-large and PubMedBERT (two seeds each) in a uniform-weight ensemble with boundary-aware loss, entity-replacement augmentation, and a first-person post filter, achieving strict F1 of 0.51 and relaxed F1 of 0.60, above both the task mean (0.46 / 0.55) and median (0.48 / 0.58).

Distinguishing fact from opinion in text is a nuanced but essential task, particularly in news articles where subjectivity can influence interpretation and reception. Identifying whether content is subjective or objective is critical for sentiment analysis, media bias detection, and content moderation. However, progress in this area has been limited for low-resource languages such as Bengali due to a lack of benchmark datasets and tools. To address these constraints, this work presents BeNSD (Bengali News Subjectivity Detection), a novel dataset of 8,655 Bengali news article texts, along with an enhanced transformer-based architecture (POS-Aware-MuRIL) that integrates parts-of-speech (POS) features with MuRIL embeddings at the input level to provide richer contextual representation for subjectivity detection. A range of baseline models is evaluated, and the proposed architecture achieves a macro F1-score of 93.35% in subjectivity detection for the Bengali language.