def set_embeddings_model(self, model=None, term_acceptance_re=re.compile('[a-z]{3,}')):
        '''

        :param model: gensim word2vec.Word2Vec model
        :param term_acceptance_re : SRE_Pattern, Regular expression to identify
            valid terms, default re.compile('[a-z]{3,}')
        :return: EmbeddingsResolver
        '''
        if self.embeddings_ is not None:
            raise Exception("You have already set embeddings by running set_embeddings or set_embeddings_model.")
        self.word2vec_model_ = model
        if term_acceptance_re is not None:
            acceptable_terms = set([t for t in self.corpus_.get_terms() if term_acceptance_re.match(t)])
        else:
            acceptable_terms = set(self.corpus_.get_terms())
        model = Word2VecFromParsedCorpus(self.corpus_, model).train()
        self.corpus_ = self.corpus_.remove_terms(set(self.corpus_.get_terms()) - acceptable_terms)
        weight_list = [model.wv[word] for word in model.wv.key_to_index.keys()]
        self.embeddings_ = np.stack(weight_list)
        self.vocab_ = list(model.wv.key_to_index.keys())
        return self


        :param model: gensim word2vec.Word2Vec model
        :param term_acceptance_re : SRE_Pattern, Regular expression to identify
            valid terms, default re.compile('[a-z]{3,}')
        :return: EmbeddingsResolver
        
scattertext/representations/EmbeddingsResolver.py