karimouda commited on
Commit
98e8d9a
·
1 Parent(s): 7207b98
Files changed (2) hide show
  1. app.py +1 -1
  2. src/about.py +6 -2
app.py CHANGED
@@ -89,7 +89,7 @@ def init_leaderboard(dataframe):
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  demo = gr.Blocks(css=custom_css)
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  with demo:
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  gr.HTML(TITLE, elem_classes="abl_header")
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- gr.Markdown(INTRODUCTION_TEXT, elem_classes="abl_desc_text")
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  with gr.Tabs(elem_classes="tab-buttons") as tabs:
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  with gr.TabItem("🏅 Arabic LLM Leaderboard", elem_id="llm-benchmark-tab-table", id=0):
 
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  demo = gr.Blocks(css=custom_css)
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  with demo:
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  gr.HTML(TITLE, elem_classes="abl_header")
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+ gr.HTML(INTRODUCTION_TEXT, elem_classes="abl_desc_text")
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  with gr.Tabs(elem_classes="tab-buttons") as tabs:
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  with gr.TabItem("🏅 Arabic LLM Leaderboard", elem_id="llm-benchmark-tab-table", id=0):
src/about.py CHANGED
@@ -23,8 +23,12 @@ TITLE = """<div ><img class='abl_header_image' src='https://huggingface.co/space
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  # What does your leaderboard evaluate?
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  INTRODUCTION_TEXT = """
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- <h1 style='width: 100%;text-align: center;' id="space-title">Arabic Board Benchmark (ABL). The first comprehensive Leaderboard for Arabic LLMs</h1>
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- ABL is the official leaderboard of <a href='https://huggingface.co/datasets/silma-ai/arabic-broad-benchmark' target='_blank'>Arabic Board Benchmark (ABB)</a>. ABB is a compact, yet comprehensive benchmark aiming to evaluate Arabic LLMs from all angels. The benchmark consists of <b>450</b> high quality questions sampled from <b>63</b> Arabic benchmarking datasets, evaluating <b>22 categories</b> some of which are unique to Arabic language such as Diacritization and Dialects. Find more details in the about Tab.
 
 
 
 
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  """
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  # What does your leaderboard evaluate?
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  INTRODUCTION_TEXT = """
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+ <h1 style='width: 100%;text-align: center;' id="space-title">Arabic Board Leaderboard (ABL) - The first comprehensive Leaderboard for Arabic LLMs</h1>
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+ ABL is the official Leaderboard of <a href='https://huggingface.co/datasets/silma-ai/arabic-broad-benchmark' target='_blank'>Arabic Board Benchmark (ABB)</a>.
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+ With advanced features and innovative visualizations, we provide the community with a comprehensive view of the capabilities of Arabic models, showcasing their speed, diverse skills while also defending against benchmarking contamination.
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+ The benchmark consists of <b>450</b> high quality questions sampled from <b>63</b> Arabic benchmarking datasets, evaluating <b>22 categories and skills</b>.
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+ Find more details in the about Tab.
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+
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  """
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