Similarly to<cite> Bolukbasi et al. (2016)</cite> , figure 1 shows that the first eigenvalue is significantly larger than the rest and that there is also a single direction describing the majority of variance in these vectors, still the difference between the percentage of variances is less in case of contextualized embeddings, which may refer that there is less bias in such embeddings. We can easily note the difference in the case of random, where there is a smooth and gradual decrease in eigenvalues, and hence the variance percentage.