It feels weird how word embedding models have come to refer to both the underlying model, as well as the implementation. word2vec is the implementation of two models: the continuous bag-of-word and the skipgram models by Mikolov, while LexVec implements a version of the PPMI weighted count matrix as referenced in the README file. But the papers also discuss implementation details of LexVec that has no bearing on the final accuracy. I feel like we should make more effort to keep the models and reference implementations separate.