I have a database table with 79,512 rows, each of which describes a category. Each row has a title and a description, and can even have a supercategory. Often, supercategories have categories.
I'm trying to train a model to predict each category's supercategory, to be used in a later model that will categorize other data. However, I never get more than a 20% accuracy on this model. My target is 84% accuracy.
These are multilingual datasets I am working with, so the pretrained model I use is also multi-lingual. However, it is also case-sensitive.
By my count, there are an average 9.86 supercategories per category.
How do I improve the accuracy?
EDIT: I suppose I had better clarify: There is a wide variety of languages here, none of them comprising more than 50% of the whole dataset. Perhaps I should translate the data?