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Update src/about.py

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@@ -42,7 +42,7 @@ TITLE = """<img src="https://raw.githubusercontent.com/BobTsang1995/Multilingual
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  INTRODUCTION_TEXT = """
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  🌍 **Multilingual MMLU Benchmark Leaderboard:** This leaderboard is dedicated to evaluating and comparing the multilingual capabilities of large language models across different languages and cultures.
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- πŸ”¬ MMMLU Dataset: The dataset used for evaluation is (OpenAI MMMLU Benchmark)[https://huggingface.co/datasets/openai/MMMLU], which covers a broad range of topics from 57 different categories, covering elementary-level knowledge up to advanced professional subjects like law, physics, history, and computer science. MMMLU contains 14 languages: AR_XY (Arabic), BN_BD (Bengali), DE_DE (German), ES_LA (Spanish), FR_FR (French), HI_IN (Hindi), ID_ID (Indonesian), IT_IT (Italian), JA_JP (Japanese), KO_KR (Korean), PT_BR (Brazilian Portuguese), SW_KE (Swahili), YO_NG (Yoruba), ZH_CN (Simplified Chinese).
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  🎯 Our Goal is to raise awareness about the importance of improving the performance of LLMs across various languages, with a particular focus on cultural contexts. We strive to make LLM more inclusive and effective for users worldwide.
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  """
 
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  INTRODUCTION_TEXT = """
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  🌍 **Multilingual MMLU Benchmark Leaderboard:** This leaderboard is dedicated to evaluating and comparing the multilingual capabilities of large language models across different languages and cultures.
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+ πŸ”¬ MMMLU Dataset: The dataset used for evaluation is [OpenAI MMMLU Benchmark](https://huggingface.co/datasets/openai/MMMLU), which covers a broad range of topics from 57 different categories, covering elementary-level knowledge up to advanced professional subjects like law, physics, history, and computer science. MMMLU contains 14 languages: AR_XY (Arabic), BN_BD (Bengali), DE_DE (German), ES_LA (Spanish), FR_FR (French), HI_IN (Hindi), ID_ID (Indonesian), IT_IT (Italian), JA_JP (Japanese), KO_KR (Korean), PT_BR (Brazilian Portuguese), SW_KE (Swahili), YO_NG (Yoruba), ZH_CN (Simplified Chinese).
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  🎯 Our Goal is to raise awareness about the importance of improving the performance of LLMs across various languages, with a particular focus on cultural contexts. We strive to make LLM more inclusive and effective for users worldwide.
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  """