  Recent advancements in NLP have given us models like mBERT and XLMR that can
serve over 100 languages. The languages that these models are evaluated on,
however, are very few in number, and it is unlikely that evaluation datasets
will cover all the languages that these models support. Potential solutions to
the costly problem of dataset creation are to translate datasets to new
languages or use template-filling based techniques for creation. This paper
proposes an alternate solution for evaluating a model across languages which
make use of the existing performance scores of the model on languages that a
particular task has test sets for. We train a predictor on these performance
scores and use this predictor to predict the model's performance in different
evaluation settings. Our results show that our method is effective in filling
the gaps in the evaluation for an existing set of languages, but might require
additional improvements if we want it to generalize to unseen languages.
