Distribution plot of word embeddings I have a list of sentences and a 10 dimensional embedding for each of the sentence.   I am trying to visualize these embeddings so that i can see if several sentence embeddings form a cluster such that these have similar embeddings which in turn mean similar context's. How can i do this in python? Also, i am wondering if there's any good metric or method using which i can form cluster's or calculate similarity between sentences that can tell me how close two sentences are?
One method to visualize high-dimensional data in lower dimensions is t-sne, which has a python implementation in scikit-learn (here)

To calculate similarity between two sentences, having their embeddings, its common to use the cosine similarity