Hello there!
I LOVE the zero-shot classification API. When you think about it, it could also be used as search engine (find the documents that are related to a particular topic).
My question is: conceptually, how does it work? I saw the original post New pipeline for zero-shot text classification 11 but I cannot find anything that explains the model under the hood.
My understanding is that

the API gets the embedding for the input sequence
the API computes the embedding for the possible labels
a similarity measure is computed between the input sequence and each of the labels and - via a softmax - a probability over the topics is then given

Is that correct?
Thanks and keep up the good work!