Best approach to create a text classification model with two inputs?
All current pre-trained Transformers (BERT, RoBERTa, ...) can take two sentences as an input, simply by concatenating the sentences and separating them by a special token. You can then finetune the model on the paraphrase dataset.
The Microsoft Research Parallel Corpus is part of the GLUE benchmark for sentence representation evaluation. It has a public leaderboard, so you can have a look and get an idea, how the best models look like.