Update README.md

README.md

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 PhyloGPN is a convolutional neural network that takes encoded DNA sequences as input and outputs rate matrix parameters for [Felsenstein's 1981 model](https://en.wikipedia.org/wiki/Models_of_DNA_evolution#F81_model_(Felsenstein_1981)) (the F81 model, for short). It was trained to maximize the likelihood of columns in the [Zoonomia alignment](https://cglgenomics.ucsc.edu/november-2023-nature-zoonomia-with-expanded-primates-alignment/) given a phylogenetic tree. The stationary distribution of the substitution process described by the F81 model indicates the relative viability of each allele at any given locus. As a result, PhyloGPN is formally a genomic language model. It can be used for transfer learning.
 
-The following Python snippet shows how to obtain embeddings and log rate parameters from PhyloGPN for each site in a batch of sequences. Note that PhyloGPN is designed as a sliding window function: it takes a batch of \\(b\\) sequences of any given length \\(\ell >= 481\\) as input and yields outputs for the \\(b \times (\ell - 480)\\) central positions.
+The following Python snippet shows how to obtain embeddings and log rate parameters from PhyloGPN for each site in a batch of sequences. Note that PhyloGPN is designed as a sliding window function: it takes a batch of \\(b\\) encoded sequences of any given length \\(\ell >= 481\\) as input and yields outputs for the \\(b \times (\ell - 480)\\) central positions.
 
 ```python
 import torch