Yep, this works fine as long as we have few sentences to process, but in my case, with about 20,000 of them, I soon run out of memory if I try to pass all sentence encodings to model() at once. I guess I could write a for loop around the forward pass to process one sentence at a time but it doesn’t look very performant. The “right” way, I guess, is to run inference on mid-sized batches, which is what Trainer.predict() does under the hoods - so I was being lazy and tried to make advantage of that, rather than writing the batching process myself