In this paper, we describe our participation in the Shared Task on Multimodal Identification of Vaccine Critical Content on Social Media (VaxMeme) of EEUCA 2026, a satellite of ACL 2026. We tackle the classification of Twitter-based vaccine memes into anti-vaccine, neutral, and pro-vaccine categories using the VaxMeme dataset with 8,195 train, 1,024 val, and 1,025 test samples. We experiment with two different architecture families: (i) Multimodal hybrids: CLIP ViT-B/32 for images + BERT-based models for texts (BERT-base-uncased, ModernBERT) with late fusion strategy based on concatenation of L2-normalized feature vectors and (ii) Text-only: pre-trained models for texts (BERT-base-uncased, RoBERTa-base, ModernBERT-base, DistilBERT-base, Deberta-v3-base) for post_text. In both cases, we use a three-layer feed-forward network with GELU activation for classification. We use class-weighted cross-entropy loss, differential learning rates, AdamW optimizer, gradient accumulation, OneCycleLR scheduler, and early stopping on the val set for optimization. Data augmentation is applied for the multimodal CLIP-based approach only. The winning approach among those tested is the text-only BERT-base-uncased with a macro-F1 of 0.8102 which is ahead of the performance of the CLIP + BERT-base hybrid model, which achieves a test macro-F1 of 0.7603.

This paper presents a comparative analysis of FastText and BERT-based approaches for Natural Language Understanding (NLU) tasks in Devanagari script languages. We evaluate these models on three critical tasks: language identification, hate speech detection, and target identification across five languages: Nepali, Marathi, Sanskrit, Bhojpuri, and Hindi. Our experiments, although with raw tweet dataset but extracting only devanagari script, demonstrate that while both models achieve exceptional performance in language identification (F1 scores > 0.99), they show varying effectiveness in hate speech detection and target identification tasks. FastText with augmented data outperforms BERT in hate speech detection (F1 score: 0.8552 vs 0.5763), while BERT shows superior performance in target identification (F1 score: 0.5785 vs 0.4898). These findings contribute to the growing body of research on NLU for low-resource languages and provide insights into model selection for specific tasks in Devanagari script processing.