Best Approach for Text Taxonomy Classification

Beginners
1

I’m trying to build a model that given a text field (e.g. product description), examples are classified according to the taxonomy.

Consider an excerpt of the taxonomy below

Category Item Brand Model
Home and Kitchen Sofa
Fashion Shoe Nike airforce 1
Fashion Shoe Nike airmax
Fashion Shoe
Fashion Purse

I’m interested in using a fine-tuning BERT approach to tackle this problem, but am unsure how to address the following characteristics

  1. The taxonomy has variable depth
  2. Examples can apply to multiple rows of the taxonomy. (i.e. the problem is multilabel)
  3. Class imbalance will play a huge challenge

I’m not sure if I should be using a single model to predict on all, a model per level (Category, item, brand, model, etc.), a nested model approach.

Any advice is useful!