Yes, makes sense. Even for cases when multi_class= True, the probability of each classes are calculated independently on just entailment vs contradiction (neutral is ignored, if I’m right).
For the questions

Well one definite way to make it faster is to move to a smaller model. To maintain the exact accuracy and still making it faster is tricky. Couple of options which could be explored are - parallelism, quantization of the layers etc
I’m assuming you’ll need pre-processing as otherwise certain symbols in conjunction with words might get categorised as a different token. But you’ll have to ensure you use a pre-processing steps which take into account the nature of the tweet, cause the hastags, tagging etc are core parts of the tweet. You wouldn’t wanna miss out on that.
But on performance side I guess you could benchmark by experimenting on a smaller data (pre-processed vs raw) and check it out.

Let me know if this looks correct.