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- **Model uncertainty quantification on GoEmotions test set**
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  The distribution demonstrates strong calibration, as the highest error density correlates with increased epistemic uncertainty. While most high-probability predictions are correct, a small fragment of overconfident incorrects remains likely due to dataset bias or linguistic nuances like sarcasm. These outliers identify a clear opportunity for further refinement using **temperature scaling**.
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  ![epistemic_unc](outputs/epistemic_unc_scatter.png)
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+ **Model uncertainty quantification on GoEmotions test set**
 
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  The distribution demonstrates strong calibration, as the highest error density correlates with increased epistemic uncertainty. While most high-probability predictions are correct, a small fragment of overconfident incorrects remains likely due to dataset bias or linguistic nuances like sarcasm. These outliers identify a clear opportunity for further refinement using **temperature scaling**.
 
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  ![epistemic_unc](outputs/epistemic_unc_scatter.png)
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