Using t-SNE to track progress of a word vector embedding model. Pitfalls?
T-SNE is extremely useful for visualizing high-dimensional data in lower-dimensional space. However, t-SNE can have several gotchas, including comparing cluster sizes. The t-sne algorithm tries to even out cluster sizes by expanding dense clusters and contracting sparse clusters. Thus, it is not straightforward to directly compare clusters across different runs.

"How to Use t-SNE Effectively"  goes into greater detail about common pitfalls of using the technique.