This paper presents our system submission to the Shared Task on Hope Speech Detection in Code-Mixed Tulu Language at DravidianLangTech @ ACL 2026. We introduce a transformer-based approach built on XLM RoBERTa-base for multilingual hope speechclassification. Our system addresses two sub tasks: coarse-grained classification of hope versus non-hope speech and fine-grained categorization of different hope expressions. Since hope is often expressed in subtle ways, especially in mixed-language text, our model looks at the full context of a sentence to understand its real meaning rather than just focusing on specific words. Experimental results demonstrate that multilingual transformer models effectively model supportive and encouraging expressions, underscoring their suitability for promoting constructive discourse in low-resourceand code-mixed language settings.