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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?

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  • $\begingroup$ Do you have a validation dataset? Are you measuring the accuracy on it? Are you evaluating the overfitting? Are you applying early stopping on worsening of the validation accuracy? $\endgroup$
    – noe
    Feb 24 at 16:45
  • $\begingroup$ @noe: At this point, both training and validation accuracy are very low. $\endgroup$
    – moonman239
    Feb 24 at 17:06
  • $\begingroup$ One idea I have is to NOT freeze the pretrained model, so that the model gets exposure to my dataset's domain. $\endgroup$
    – moonman239
    Feb 24 at 17:15
  • $\begingroup$ When fine-tuning, don't you use a very low learning rate? $\endgroup$
    – noe
    Feb 24 at 17:36
  • $\begingroup$ It doesn't make much sense that the training accuracy is low. Have you checked that your model overfits properly training with a single batch? (this is a typical sanity check for deep learning training) $\endgroup$
    – noe
    Feb 24 at 18:29

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