How does DeepSeek handle class imbalance during fine-tuning?

DeepSeek addresses class imbalance during fine-tuning by combining techniques like weighted loss functions, data resampling, and threshold adjustment. These methods ensure the model doesn’t overly favor majority classes while maintaining performance on underrepresented ones. The approach is practical and tailored to the specific imbalance severity and dataset characteristics.

First, DeepSeek uses weighted loss functions to adjust the importance of classes during training. For example, in a binary classification task where 95% of samples are negative, the loss for misclassifying a rare positive sample is multiplied by a weight (e.g., 19x for a 1:19 imbalance). This forces the model to prioritize correcting errors on minority classes. In frameworks like PyTorch, this is implemented by passing class weights to the loss function (e.g., nn.CrossEntropyLoss(weight=class_weights)). Developers can calculate weights inversely proportional to class frequencies or tune them empirically. This method is efficient and doesn’t require modifying the dataset, making it suitable for large-scale training.

Second, data resampling is applied to balance class distributions. For instance, oversampling minority classes by duplicating samples or generating synthetic data using techniques like SMOTE (Synthetic Minority Oversampling Technique) helps expose the model to more diverse examples. Conversely, undersampling majority classes reduces redundancy but risks losing valuable data. DeepSeek often combines both: for example, oversampling a rare class (e.g., medical anomalies) while randomly subsampling a frequent class (e.g., normal cases). Additionally, mini-batch sampling strategies ensure each training batch has a balanced mix, preventing the model from biasing toward majority classes within individual gradient updates. Tools like the imbalanced-learn library or custom data loaders streamline this process.

Finally, post-processing adjustments refine predictions. After training, DeepSeek may adjust the decision threshold (e.g., lowering it for minority classes) to improve recall. For example, in fraud detection, a threshold shift from 0.5 to 0.3 could flag more potential fraud cases. Metrics like F1-score or precision-recall curves guide these changes instead of relying solely on accuracy. Ensemble methods, such as training multiple models on resampled data subsets and averaging predictions, further stabilize performance. These steps are paired with rigorous evaluation on balanced validation sets to avoid overfitting. By systematically addressing imbalance at multiple stages, DeepSeek ensures models remain robust and generalize effectively across all classes.