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 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!