Update utils.py

utils.py

@@ -136,7 +136,7 @@ LLM_BENCHMARKS_ABOUT_TEXT = f"""
 >    A sample of the evaluation dataset is hosted on [Hugging Face Datasets](https://huggingface.co/datasets/PartAI/llm-leaderboard-datasets-sample), offering the AI community a glimpse of the benchmark content and format. This sample allows developers to pre-assess their models against representative data before a full leaderboard evaluation.
 > 
 > 4. **Collaborative Development**  
->    This leaderboard represents a significant collaboration between Part AI and Professor Saeedeh Momtazi of Amirkabir University of Technology (with key contributions from [Shahriar Shariati](https://huggingface.co/shahriarshm) and [Farhan Farsi](https://huggingface.co/FarhanFarsi)), leveraging industrial expertise and academic research to create a high-quality, open benchmarking tool. The partnership underscores a shared commitment to advancing Persian LLMs.
+>    This leaderboard represents a significant collaboration between Part AI and Professor Saeedeh Momtazi of Amirkabir University of Technology (with key contributions from [Shahriar Shariati](https://huggingface.co/shahriarshm), [Farhan Farsi](https://huggingface.co/FarhanFarsi) and [Shayan Bali](https://huggingface.co/shayanbali)), leveraging industrial expertise and academic research to create a high-quality, open benchmarking tool. The partnership underscores a shared commitment to advancing Persian LLMs.
 > 
 > 5. **Comprehensive Evaluation Pipeline**  
 >    By integrating a standardized evaluation pipeline, models are assessed across a variety of data types, including text, mathematical formulas, and numerical data. This multi-faceted approach enhances the evaluation’s reliability and allows for precise, nuanced assessment of model performance across multiple dimensions.