Not sure, could be the large number of Spanish dialects represented in the dataset, label noise, or something else. There may just be too much diversity in the class to fit neatly in a cluster.
Also, the training dataset is highly imbalanced and Spanish is the most common class, so the model predicts it as a sort of default when it isn't confident -- this could lead to artifacts in the reduced 3d space.
Also, the training dataset is highly imbalanced and Spanish is the most common class, so the model predicts it as a sort of default when it isn't confident -- this could lead to artifacts in the reduced 3d space.