:param projection_model: sklearn unsupervised model (e.g., PCA) by default the recommended model is umap.UMAP,
        which requires UMAP in to be installed

        :return: array, shape (num dimension, vocab size)

def project(self, projection_model=None):
        '''
        :param projection_model: sklearn unsupervised model (e.g., PCA) by default the recommended model is umap.UMAP,
        which requires UMAP in to be installed

        :return: array, shape (num dimension, vocab size)
        '''
        if self.embeddings_ is None:
            raise Exception("Run set_embeddings_model or set_embeddings to get embeddings")
        if projection_model is None:
            try:
                import umap
            except:
                raise Exception("Please install umap (pip install umap-learn) to use the default projection_model.")
            projection_model = umap.UMAP(min_dist=0.5, metric='cosine')
        axes = projection_model.fit_transform(self.embeddings_)
        return axes