title: Access spaCy Masked Language Model
body: <p>As of v2.1, spaCy has a BERT-style language model (LM). It predicts word-vectors instead of words, so I am going to use "words" and "word vectors" interchangeably here.</p>
  
  <p>I need to take a sentence with a word masked, and a list of words, and rank the words by how likely they are to appear in the masked slot. Currently I am using BERT for this (similar to <a href="https://github.com/yoavg/bert-syntax/blob/master/eval_bert.py#L14" rel="nofollow noreferrer">bert-syntax</a>). I would like to see if spaCy's performance on this task is acceptable. Between <a href="https://github.com/explosion/spaCy/blob/master/spacy/_ml.py" rel="nofollow noreferrer">this file</a> and <a href="https://github.com/explosion/spaCy/blob/master/spacy/cli/pretrain.py" rel="nofollow noreferrer">this one</a> I'm pretty sure it's possible to build something. However, it feels like reaching deeper into the internals of the library than I'd like.</p>
  
  <p>Is there a straightforward way to interact with spaCy's masked language model? </p>