def keywords(self, top=10, normalized=True):
        self.df(None) # Populate document frequency cache.
        n = normalized and sum(self._df.values()) or 1.0
        v = ((f/n, w) for w, f in self._df.items())
        v = heapq.nsmallest(top, v, key=lambda v: (-v[0], v[1]))
        return v
Returns a sorted list of (relevance, word)-tuples that are top keywords in the model.
dataset/ETHPy150Open clips/pattern/pattern/vector/__init__.py/Model.keywords