Which type word embedding ( as in BERT, word2vec, Glove etc) does spacy use by default?

I was watching a tutorial for spacy. There code (which I put below doesn't make it clear which type of word embedding is used). They were able to find word similarity between words with scores.

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nlp = spacy.load("en\_core\_web\_md") **with** open ("data/wiki\_us.txt", "r") **as** f: text = f.read() doc = nlp(text) sentence1 = list(doc.sents)\[0\]

your\_word = "dog" ms = nlp.vocab.vectors.most\_similar( np.asarray(\[nlp.vocab.vectors\[nlp.vocab.strings\[your\_word\]\]\]), n=10) words = \[nlp.vocab.strings\[w\] **for** w **in** ms\[0\]\[0\]\] distances = ms\[2\] print(words)

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*# Similarity of tokens and …

bert embedding languagetechnology spacy type word2vec