Use Spacy to find most similar sentences in doc

I'm looking for a solution to use something like most_similar() from Gensim but using Spacy. I want to find the most similar sentence in a list of sentences using NLP.

I tried to use similarity() from Spacy (e.g. https://spacy.io/api/doc#similarity) one by one in loop, but it takes a very long time.

To go deeper :

I would like to put all these sentences in a graph (like this) to find sentence clusters.

Any idea ?

This is a simple, built-in solution you could use:

import spacy

nlp = spacy.load("en_core_web_lg")
text = (
    "Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between items is based on the likeness of their meaning or semantic content as opposed to lexicographical similarity."
    " These are mathematical tools used to estimate the strength of the semantic relationship between units of language, concepts or instances, through a numerical description obtained according to the comparison of information supporting their meaning or describing their nature."
    " The term semantic similarity is often confused with semantic relatedness."
    " Semantic relatedness includes any relation between two terms, while semantic similarity only includes 'is a' relations."
    " My favorite fruit is apples."
)
doc = nlp(text)
max_similarity = 0.0
most_similar = None, None
for i, sent in enumerate(doc.sents):
    for j, other in enumerate(doc.sents):
        if j <= i:
            continue
        similarity = sent.similarity(other)
        if similarity > max_similarity:
            max_similarity = similarity
            most_similar = sent, other
print("Most similar sentences are:")
print(f"-> '{most_similar[0]}'")
print("and")
print(f"-> '{most_similar[1]}'")
print(f"with a similarity of {max_similarity}")

(text from wikipedia)

It will yield the following output:

Most similar sentences are:
-> 'Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between items is based on the likeness of their meaning or semantic content as opposed to lexicographical similarity.'
and
-> 'These are mathematical tools used to estimate the strength of the semantic relationship between units of language, concepts or instances, through a numerical description obtained according to the comparison of information supporting their meaning or describing their nature.'
with a similarity of 0.9583859443664551

Note the following information from spacy.io:

To make them compact and fast, spaCy’s small pipeline packages (all packages that end in sm) don’t ship with word vectors, and only include context-sensitive tensors. This means you can still use the similarity() methods to compare documents, spans and tokens – but the result won’t be as good, and individual tokens won’t have any vectors assigned. So in order to use real word vectors, you need to download a larger pipeline package:

- python -m spacy download en_core_web_sm
+ python -m spacy download en_core_web_lg

Also see Document similarity in Spacy vs Word2Vec for advice on how to improve the similarity scores.