Best Approach for Text Taxonomy Classification
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 belowCategoryItemBrandModelHome and KitchenSofaFashionShoeNikeairforce 1FashionShoeNikeairmaxFashionShoeFashionPurseI’m interested in using a fine-tuning BERT approach to tackle this problem, but am unsure how to address the following characteristicsThe taxonomy has variable depthExamples can apply to multiple rows of the taxonomy. (i.e. the problem is multilabel)Class imbalance will play a huge challengeI’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!