I would recommend considering the following strategies to handle imbalanced labels in your dataset:

Oversampling: You can oversample the minority classes by generating synthetic examples or by sampling with replacement from the minority classes. This can help to balance the class distribution and improve the model's performance on the minority classes.

Undersampling: You can undersample the majority classes by randomly sampling a smaller number of examples from the majority classes. This can help to balance the class distribution and prevent the model from being biased towards the majority classes.

Weighted loss: You can assign higher weights to the minority classes in the loss function to give them more influence on the model's learning. This can help to balance the class distribution and improve the model's performance on the minority classes.

Class-specific metrics: You can use metrics that are specifically designed to evaluate the model's performance on imbalanced datasets, such as the F1 score or the AUC (Area Under the Curve) of a precision-recall curve.

In your particular case, you may want to consider oversampling or using weighted loss, since you have only one example for some of the minority classes. It may also be helpful to combine these strategies to achieve the best results.