Add results and blog post link to HF

README.md

@@ -15,6 +15,13 @@ Helix-mRNA emerges as a hybrid state-space and transformer based model, leveragi
 
 We tokenize mRNA sequences at single-nucleotide resolution by mapping each nucleotide (A, C, U, G) and ambiguous base (N) to a unique integer. A further special character E is incorporated into the sequence, denoting the start of each codon. This fine-grained approach maximizes the model's ability to extract patterns from the sequences. Unlike coarser tokenization methods that might group nucleotides together or use k-mer based approaches, our single-nucleotide resolution preserves the full sequential information of the mRNA molecule. This simple yet effective encoding scheme ensures that no information is lost during the preprocessing stage, allowing the downstream model to learn directly from the raw sequence composition.
 
+<p align="center">
+<img src="assets/results_graph.png" alt="bar_charts" width="750"/>
+<figcaption align="center">Helix-mRNA benchmark comparison against Transformer HELM, Transformer XE and CodonBERT.</figcaption>
+</p>
+
+Read more about it in our [blog post](https://www.helical-ai.com/blog/helix-mrna-v0)!
+
 # Helical<a name="helical"></a>
 
 #### Install the package