I have data with multiple descriptions of the same product, but they may be worded slightly differently, contain different product ids etc. I would like to group them together  Would an approach here be

Train a classifier on the products to predict say product category
use the description embeddings to compute cosine similarity. And then
group by some similarity threshold?

Is there a better approach to this?