By default it actually pads to the length of the longest sequence in the batch, so that part is efficient. The thing to keep in mind with this method is that each sequence/label pair has to be fed through the model together. So if you are running with a large # of candidate labels, that’s going to be your bottleneck. The other thing is that the default model, bart-large-mnli, is pretty big. Theoretically, the pipeline can be used with any model that has been trained on NLI 23, which you can specify with the model parameter when you create the pipeline. So you could try out some smaller models, but you probably won’t get anything to work as well as Bart or Roberta in terms of accuracy.