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Update utils.py
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utils.py
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@@ -136,7 +136,7 @@ LLM_BENCHMARKS_ABOUT_TEXT = f"""
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> 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.
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> 4. **Collaborative Development**
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> This leaderboard represents a significant collaboration between Part AI and Professor Saeedeh Momtazi of Amirkabir University of Technology, leveraging industrial expertise and academic research to create a high-quality, open benchmarking tool. The partnership underscores a shared commitment to advancing Persian LLMs.
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> 5. **Comprehensive Evaluation Pipeline**
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> 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.
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> 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.
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> 4. **Collaborative Development**
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> 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.
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> 5. **Comprehensive Evaluation Pipeline**
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> 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.
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