Update README.

README.md

@@ -21,10 +21,12 @@ See the project's README at https://github.com/vertaix/Vendi-Score for more info
 ## Metric Description
 The Vendi Score (VS) is a metric for evaluating diversity in machine learning.
 The input to metric is a collection of samples and a pairwise similarity function, and the output is a number, which can be interpreted as the effective number of unique elements in the sample.
-Specifically, given a positive semi-definite matrix $K \in \mathbb{R}^{n \times n}$ of similarity scores, the score is defined as:
-$$\mathrm{VS}(K) = \exp(-\mathrm{tr}(K/n \log K/n)) = \exp(-\sum_{i=1}^n \lambda_i \log \lambda_i),$$
-where $\lambda_i$ are the eigenvalues of $K/n$ and $0 \log 0 = 0$.
-That is, the Vendi Score is equal to the exponential of the von Neumann entropy of $K/n$, or the Shannon entropy of the eigenvalues, which is also known as the effective rank.
+Specifically, given an `n x n` positive semi-definite matrix `K` of similarity scores, the score is defined as:
+```
+VS(K) = exp(tr(K/n @ log(K/n))) = exp(-sum_i lambda_i log lambda_i),
+```
+where `lambda_i` are the eigenvalues of `K/n` and `0 log 0 = 0`.
+That is, the Vendi Score is equal to the exponential of the von Neumann entropy of `K/n`, or the Shannon entropy of the eigenvalues, which is also known as the effective rank.
 
 ## How to Use
 The Vendi Score is available as a Python package or in HuggingFace `evaluate`.