Update src/about.py

src/about.py

@@ -93,6 +93,7 @@ And here find all the translated benchmarks provided by the Language evaluation
 
 
 To ensure a fair and unbiased assessment of the models' true capabilities, all evaluations are conducted in zero-shot settings `0-shots`. This approach eliminates any potential advantage from task-specific fine-tuning, providing a clear indication of how well the models can generalize to new tasks.
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 Also, given the nature of the tasks, which include multiple-choice and yes/no questions, the leaderboard primarily uses normalized log likelihood accuracy `loglikelihood_acc_norm` for all tasks. This metric was chosen for its ability to provide a clear and fair measurement of model performance across different types of questions.