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@@ -38,6 +38,8 @@ In designing the benchmark, our objective was to create challenges that would be
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  demanding for models without prior training on Hungarian data. As such, we anticipate that models trained on Hungarian datasets will perform well on the benchmark, whereas those lacking this experience
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  may encounter difficulties. Notwithstanding, a model's strong performance on the benchmark does not imply expertise in a specific task; rather, it indicates a proficiency in understanding Hungarian
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  language and its structures.
 
 
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  """
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  # Which evaluations are you running? how can people reproduce what you have?
 
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  demanding for models without prior training on Hungarian data. As such, we anticipate that models trained on Hungarian datasets will perform well on the benchmark, whereas those lacking this experience
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  may encounter difficulties. Notwithstanding, a model's strong performance on the benchmark does not imply expertise in a specific task; rather, it indicates a proficiency in understanding Hungarian
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  language and its structures.
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+
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+ Note that the benchmark is a work in progress, and we are continuously refining and expanding it to ensure that it remains a robust evaluation tool for Hungarian language models. Your feedback and suggestions are welcome as we strive to enhance the benchmark's quality and utility.
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  """
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  # Which evaluations are you running? how can people reproduce what you have?