Bagratuni commited on
Commit
6b4ef20
·
1 Parent(s): 9bf8bd3
Files changed (2) hide show
  1. app.py +10 -10
  2. data_handler.py +0 -10
app.py CHANGED
@@ -17,8 +17,11 @@ def refresh_data():
17
 
18
  global_output_armenian = unified_exam_result_table(global_unified_exam_df)
19
  global_output_mmlu = mmlu_result_table(global_mmlu_df)
20
-
21
- return global_output_armenian, unified_exam_chart(global_output_armenian, 'Average')
 
 
 
22
 
23
  def main():
24
  global global_mmlu_df, global_unified_exam_df, global_output_armenian, global_output_mmlu
@@ -60,10 +63,10 @@ def main():
60
  **Creator Company:** Metric AI Research Lab, Yerevan, Armenia."""
61
  )
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  gr.Image("logo.png", width=200, show_label=False, show_download_button=False, show_fullscreen_button=False, show_share_button=False)
63
- gr.Markdown("""
64
  - [Website](https://metric.am/)
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  - [Hugging Face](https://huggingface.co/Metric-AI)
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-
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  MMLU-Pro-Hy is a massive multi-task test in MCQA format, inspired by the original MMLU benchmark, adapted for the Armenian language. The Armenian Unified Exams benchmark allows for comparison with human-level knowledge.
68
  """
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  )
@@ -72,7 +75,7 @@ def main():
72
  """
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  To submit a model for evaluation, please follow these steps:
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  1. **Evaluate your model**:
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- - Follow the evaluation script provided here: [https://github.com/Anania-AI/Arm-LLM-Benchmark](https://github.com/Anania-AI/Arm-LLM-Benchmark)
76
  2. **Format your submission file**:
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  - After evaluation, you will get a `result.json` file. Ensure the file follows this format:
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  ```json
@@ -111,12 +114,9 @@ def main():
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  refresh_button = gr.Button("Refresh Data")
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  refresh_button.click(
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  fn=refresh_data,
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- outputs=[table_output_armenian,
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- table_output_mmlu,
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- plot_output_armenian,
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- plot_output_mmlu
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- ],
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  )
 
120
  app.launch(share=True, debug=True)
121
 
122
  if __name__ == "__main__":
 
17
 
18
  global_output_armenian = unified_exam_result_table(global_unified_exam_df)
19
  global_output_mmlu = mmlu_result_table(global_mmlu_df)
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+
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+ unified_chart = unified_exam_chart(global_output_armenian, 'Average')
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+ mmlu_chart_output = mmlu_chart(global_output_mmlu, 'Average')
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+
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+ return global_output_armenian, global_output_mmlu, unified_chart, mmlu_chart_output, 'Average', 'Average'
25
 
26
  def main():
27
  global global_mmlu_df, global_unified_exam_df, global_output_armenian, global_output_mmlu
 
63
  **Creator Company:** Metric AI Research Lab, Yerevan, Armenia."""
64
  )
65
  gr.Image("logo.png", width=200, show_label=False, show_download_button=False, show_fullscreen_button=False, show_share_button=False)
66
+ gr.Markdown("""
67
  - [Website](https://metric.am/)
68
  - [Hugging Face](https://huggingface.co/Metric-AI)
69
+
70
  MMLU-Pro-Hy is a massive multi-task test in MCQA format, inspired by the original MMLU benchmark, adapted for the Armenian language. The Armenian Unified Exams benchmark allows for comparison with human-level knowledge.
71
  """
72
  )
 
75
  """
76
  To submit a model for evaluation, please follow these steps:
77
  1. **Evaluate your model**:
78
+ - Follow the evaluation script provided here: [https://github.com/Anania-AI/Arm-LLM-Benchmark](https://github.com/Anania-AI/Arm-LLM-Benchmark)
79
  2. **Format your submission file**:
80
  - After evaluation, you will get a `result.json` file. Ensure the file follows this format:
81
  ```json
 
114
  refresh_button = gr.Button("Refresh Data")
115
  refresh_button.click(
116
  fn=refresh_data,
117
+ outputs=[table_output_armenian, table_output_mmlu, plot_output_armenian, plot_output_mmlu, plot_column_dropdown_unified_exam, plot_column_dropdown_mmlu],
 
 
 
 
118
  )
119
+
120
  app.launch(share=True, debug=True)
121
 
122
  if __name__ == "__main__":
data_handler.py CHANGED
@@ -59,10 +59,6 @@ def unified_exam_chart(unified_exam_df, plot_column):
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  title=title,
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  orientation='h'
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  )
62
- # max_chart_height = 600
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-
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- # chart_height = df.shape[0] * 50
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- # chart_height = min(chart_height, max_chart_height)
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  fig.update_layout(
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  xaxis=dict(range=[0, x_range_max]),
@@ -70,7 +66,6 @@ def unified_exam_chart(unified_exam_df, plot_column):
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  xaxis_title=dict(font=dict(size=12)),
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  yaxis_title=dict(font=dict(size=12)),
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  yaxis=dict(autorange="reversed"),
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- # height=chart_height,
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  width=1400
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  )
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  return fig
@@ -93,10 +88,6 @@ def mmlu_chart(mmlu_df, plot_column):
93
  orientation='h',
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  range_color=[0,1]
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  )
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- # max_chart_height = 600
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-
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- # chart_height = df.shape[0] * 50
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- # chart_height = min(chart_height, max_chart_height)
100
 
101
  fig.update_layout(
102
  xaxis=dict(range=[0, x_range_max]),
@@ -104,7 +95,6 @@ def mmlu_chart(mmlu_df, plot_column):
104
  xaxis_title=dict(font=dict(size=12)),
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  yaxis_title=dict(font=dict(size=12)),
106
  yaxis=dict(autorange="reversed"),
107
- # height=chart_height,
108
  width=1400
109
  )
110
  return fig
 
59
  title=title,
60
  orientation='h'
61
  )
 
 
 
 
62
 
63
  fig.update_layout(
64
  xaxis=dict(range=[0, x_range_max]),
 
66
  xaxis_title=dict(font=dict(size=12)),
67
  yaxis_title=dict(font=dict(size=12)),
68
  yaxis=dict(autorange="reversed"),
 
69
  width=1400
70
  )
71
  return fig
 
88
  orientation='h',
89
  range_color=[0,1]
90
  )
 
 
 
 
91
 
92
  fig.update_layout(
93
  xaxis=dict(range=[0, x_range_max]),
 
95
  xaxis_title=dict(font=dict(size=12)),
96
  yaxis_title=dict(font=dict(size=12)),
97
  yaxis=dict(autorange="reversed"),
 
98
  width=1400
99
  )
100
  return fig