sennatitcomb/sarcasm-detector-json-joshi-gutenberg-final

# RoBERTa-Contextual-Sarcasm-Hybrid

## Model Description This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-irony optimized for detecting sarcasm in modern narrative dialogue. Unlike standard sentiment-based irony detectors, this model utilizes a Relational Attention mechanism enabled by a [PREVIOUS_CONTEXT] [SEP] [DIALOGUE] input schema.

## Training Data & Methodology The model was trained on a balanced hybrid corpus designed to minimize "classifier paranoia" in modern conversational agents:

• —Contextual JSON (152 samples): Primary high-quality dialogue with situational context.
• —Joshi Snippets (50 samples): Targeted sarcastic signals for Class 1 (Sarcastic) expansion.
• —Gutenberg Anchoring (100 samples): Formal Victorian prose used for Class 0 (Sincere) stabilization.

## Performance & Calibration The model achieves high statistical recall but demonstrates specific behavioral biases:

• —Modern Narrative: High calibration; successfully distinguishes between sincere frustration and ironic punchlines.
• —Literary Irony: Exhibits a "Politeness Bias" where formal syntax is strongly correlated with sincerity (Class 0), leading to potential false negatives in classical irony.

## Intended Use This model is intended for use in hybrid LLM systems and conversational agents where distinguishing between sincere user complaints and situational irony is critical for deterministic routing.