Difference between context-sensitive tensors and word vectors
Word vectors are stored in a big table in the model and when you look up cat, you always get the same vector from this table.
The context-sensitive tensors are dense feature vectors computed by the models in the pipeline while analyzing the text. You will get different vectors for cat in different texts. If you use en_core_web_sm, the token cat in I have a cat will not have the same vector as in The cat is black. Having the context-sensitive tensors available when the model doesn't include word vectors lets the similarity functions work to some degree, but the results are very different than with word vectors.
For most purposes, you probably want to use the _md or _lg model with word vectors.
I am currently working in python with spacy and there are different pre-trained models like the en_core_web_sm or the en_core_web_md. One of them is using words vectors to find word similarity and the other one is using context-sensitive tensors. 
What is the difference between using context-sensitive tensors and using word vectors? And what is context-senstiive tensors exactly?