Natural language text exhibits implicit hierarchical structure in a variety of respects. Ideally we could incorporate our prior knowledge of the existence of some sort of hierarchy into unsupervised learning algorithms that work on text data. Recent work by Nickel and Kiela (2017) proposed using hyperbolic instead of Euclidean embedding spaces to represent hierarchical data and demonstrated encouraging results on supervised embedding tasks. In this work, apply their approach to unsupervised learning of word and sentence embeddings. Although we obtain mildly positive results, we describe the challenges we faced in using the hyperbolic metric for these problems both in terms of improving performance in downstream tasks and in understanding the learned hierarchical structures.