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sarcasm-detector-json-only-final

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+{}
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+# Contextual Sarcasm Detector (Fine-Tuned RoBERTa)
+## Model Description
+This model is a fine-tuned version of `cardiffnlp/twitter-roberta-base-irony` designed to detect sarcasm in narrative and conversational contexts.
+### Key Research Improvements
+Unlike standard irony detectors trained on short-form social media (e.g., the **Joshi Dataset**), this model was fine-tuned on a consolidated pool of high-signal samples. The Joshi dataset was explicitly removed during experimentation to reduce noise and prevent "classifier paranoia" in modern conversational contexts.
+## Training Data
+The model was trained on a balanced corpus of 152 samples:
+- **Class 1 (Sarcastic):** Curated contextual JSON samples focusing on literary and modern narrative and conversational irony.
+- **Class 0 (Sincere):** Curated contextual JSON samples focusing on literary modern dialogue and sentences.
+## Evaluation Results
+The fine-tuned model demonstrated a significant architectural improvement over the baseline irony model.
+| Metric               | Base Irony Model | Fine-Tuned Model |
+|----------------------|------------------|------------------|
+| **Golden Set F1** | 0.5714           | **0.8889** |
+| **Golden Set Acc** | 0.7000           | **0.9000** |
+| **Human Set F1** | 0.5455           | **0.6667** |
+| **Human Set Acc** | 0.5000           | **0.7000** |
+## Research Findings & Limitations
+### Domain Shift and Syntax Bias
+Qualitative analysis via a sliding-window inference test revealed a **Syntax-Based Decision Boundary**.
+1. **Modern Success:** The model successfully calibrated its probabilities in modern dialogue, distinguishing between narrative setup and ironic punchlines.
+2. **Literary Regression:** A performance regression was noted in the "Mr. Collins" test case. Because the model's "Sincere" (Class 0) training data was heavily derived from Victorian prose, it developed a heuristic where formal/classical syntax is strongly correlated with sincerity. This resulted in a failure to detect biting irony when expressed in a formal style.
+## Intended Use
+This model is best suited for modern narrative text or conversational AI agents requiring context-aware sarcasm detection. It is less effective on formal literary irony from the 19th century.