Update src/about.py

src/about.py

@@ -60,13 +60,15 @@ We use our own framework to evaluate the models on the following benchmarks (TO
 ### Tasks 
 - PeKA: Persian Knowledge Assesment (0-shot) - a set of multiple-choice questions that tests the level of native knowledge in Persian language in more 15 domains and categories: From art to history and geography, cinema, tv, sports, law and medicine, and much more.
 - PersBETS: Persian Bias Ethics Toxicity and Skills (0-shot) - a test of model's capability in linguistic skills such as Grammar and Praphrasing, and also questions examining the bias, ethics, and toxicity of the model.
-- <a href="https://arxiv.org/abs/2404.06644" target="_blank">  Khayyam Challenge (Persian MMLU) </a>  (0-shot) - comprising 20,192 four-choice questions sourced from 38 diverse tasks extracted from Persian examinations, spanning a wide spectrum of subjects, complexities, and ages
+- <a href="https://arxiv.org/abs/2404.06644" target="_blank">  Khayyam Challenge (Persian MMLU) </a>  (0-shot) - comprising 20,805 four-choice questions (of which we use 20,776, removing questions that are longer than 200 words) sourced from 38 diverse tasks extracted from Persian examinations, spanning a wide spectrum of subjects, complexities, and ages
 - <a href="https://arxiv.org/abs/2012.06154" target="_blank">  ParsiNLU MCQA </a> (0-shot) - a series of multiple-choice questions in domains of *literature*, *math & logic*, and *common knowledge*.
 - <a href="https://arxiv.org/abs/2012.06154" target="_blank">  ParsiNLU NLI </a> (max[0,3,5,10]-shot) - a 3-way classification to determine whether a hypothesis sentence entails, contradicts, or is neutral with respect to a given premise sentence.
 - <a href="https://arxiv.org/abs/2012.06154" target="_blank">  ParsiNLU QQP </a> (max[0,2,5,10]-shot) - task of deciding whether a whether two given questions are paraphrases of each other or not.
 
 For all these evaluations, a higher score is a better score.
 
+We use the given *test* subset (for thsoe who also have *train* and *dev* subsets) for all these evaluations.
+
 We chose these benchmarks for now, but several other benchmarks are going to be added later to help us perform a more thorough examination of models.
 
 The last two benchmarks, ParsiNLU NLI and ParsiNLU QQP are evaluated in different few-shot settings and then the maximum score is returned as the final evaluation.