add code.

app.py

@@ -129,6 +129,10 @@ INTRO_TEXT = f"""
 
 We introduce Mexa, a method for assessing the multilingual capabilities of English-centric large language models (LLMs). Mexa builds on the observation that English-centric LLMs semantically use English as a kind of pivot language in their intermediate layers. Mexa computes the alignment between non-English languages and English using parallel sentences, estimating the transfer of language understanding capabilities from English to other languages through this alignment. This metric can be useful in estimating task performance, provided we know the English performance in the task and the alignment score between languages derived from a parallel dataset.
 
+## Code
+
+https://github.com/kargaranamir
+
 ## Details
 We use parallel datasets from FLORES and the Bible. In the ARC style, we use mean pooling over layers, and the English score achieved by each LLM in the ARC benchmark is used to adjust the multilingual scores. In the Belebele style, we use max pooling over layers, and the English score achieved by each LLM in Belebele is used to adjust the multilingual scores.
 """