Evaluate baseline bce model performance

[d-ulker]

Fix CombinedLossTrainer bug and implement Asymmetric/Combined Loss strategies with optimized hyperparameters to address catastrophic BCE baseline failure due to class imbalance.

The BCE baseline model achieved an F1 Macro of only 0.054, learning only 3 out of 28 emotion classes, due to an extreme class imbalance ratio of 99.67. This PR introduces specialized loss functions (Asymmetric Loss, Combined Loss) and refined hyperparameters to effectively handle this imbalance and significantly improve model performance. A critical bug in CombinedLossTrainer was also fixed.

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