When doing sentence similarity when to use tensors?
Hi,I’m trying to do some sentence similarity to compare 2 list of items.I’m following the guides and so far it works. I was juste wondering why do we need to convert the embeddings to tensors?I run my script (which is exactly this:Semantic Textual Similarity — Sentence-Transformers documentation) with 2 arrays of about 200 sentences.The execution times are:with print: ~9swithout print: ~2.5sBut then without converting to tensors:with print : ~3swithout: ~2.5sFor both, the encode time are quite similar.The cosine similarity time are very different and it goes way faster when not using tensor embeddings:with tensor: 0.01swithout: 0.0004sI’m very new to ML so my understanding is that the tensor is higher dimension vector.So my question is why do we need tensors? Will it be faster if i put more and more sentences? Does it impact the precision of the results? And why does this impact the time so much when i print the result?Thank you very much!