---
license: mit
datasets:
- sennatitcomb/sarcasm-sincere-sentences
language:
- en
metrics:
- f1
- accuracy
library_name: transformers
tags:
- sarcasm-detection
- nlu
- roberta
- contextual-analysis
model-index:
- name: RoBERTa-Contextual-Sarcasm-Hybrid
  results:
  - task:
      type: text-classification
      name: Sarcasm Detection
    metrics:
    - type: f1
      value: 0.8889
      name: Golden Set F1
    - type: f1
      value: 0.8000
      name: Human Set F1
---

  # 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.

  | Metric | Score |
  | :--- | :--- |
  | **Golden Set F1** | 0.8889 |
  | **Human Set F1** | 0.8000 |
  | **Threshold (Optimal)** | 0.60 - 0.75 |

  ## 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.