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Upload app.py (#7)
Browse files- Upload app.py (d99d0376d489de9ff899d60ce46d59ec04bc4621)
Co-authored-by: CultriX <CultriX@users.noreply.huggingface.co>
app.py
CHANGED
@@ -10,6 +10,7 @@ from huggingface_hub.utils import RepositoryNotFoundError, RevisionNotFoundError
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from yall import create_yall
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def convert_markdown_table_to_dataframe(md_content):
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"""
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Converts markdown table to Pandas DataFrame, handling special characters and links,
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@@ -36,10 +37,10 @@ def convert_markdown_table_to_dataframe(md_content):
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return df
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@st.cache_data
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def get_model_info(df):
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api = HfApi()
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# Initialize new columns for likes and tags
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df['Likes'] = None
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df['Tags'] = None
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@@ -58,7 +59,8 @@ def get_model_info(df):
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return df
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def create_bar_chart(df, category):
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"""Create and display a bar chart for a given category."""
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st.write(f"### {category} Scores")
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# Sort the DataFrame based on the category score
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sorted_df = df[['Model', category]].sort_values(by=category, ascending=True)
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# Create the bar chart with color gradient
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fig = go.Figure(go.Bar(
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x=sorted_df[category],
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y=sorted_df['Model'],
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orientation='h',
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marker=dict(color=sorted_df[category], colorscale='
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))
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# Update layout for better readability
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margin=dict(l=20, r=20, t=20, b=20)
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)
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-
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def main():
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st.set_page_config(page_title="YALL - Yet Another LLM Leaderboard", layout="wide")
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st.title("π YALL - Yet Another LLM Leaderboard")
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st.markdown("Leaderboard made with π§ [LLM AutoEval](https
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content = create_yall()
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tab1, tab2 = st.tabs(["π Leaderboard", "π About"])
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@@ -96,7 +101,7 @@ def main():
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if content:
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try:
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score_columns = ['Average', 'AGIEval', 'GPT4All', 'TruthfulQA', 'Bigbench']
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# Display dataframe
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full_df = convert_markdown_table_to_dataframe(content)
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for col in score_columns:
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full_df = get_model_info(full_df)
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full_df['Tags'] = full_df['Tags'].fillna('')
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df = pd.DataFrame(columns=full_df.columns)
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# Toggles
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col1, col2, col3 = st.columns(3)
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with col1:
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show_phi = st.checkbox("Phi (2.8B)", value=True)
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with col2:
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show_mistral = st.checkbox("Mistral (7B)", value=True)
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with col3:
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show_other = st.checkbox("Other", value=True)
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dfs_to_concat = []
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if show_phi:
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dfs_to_concat.append(full_df[full_df['Tags'].str.lower().str.contains('phi,|phi-msft,')])
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if show_mistral:
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if show_other:
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other_df = full_df[~full_df['Tags'].str.lower().str.contains('phi,|phi-msft,|mistral,')]
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dfs_to_concat.append(other_df)
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-
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# Concatenate the DataFrames
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if dfs_to_concat:
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df = pd.concat(dfs_to_concat, ignore_index=True)
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#
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#
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st.dataframe(
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df[['Model'] + score_columns + ['Likes', 'URL']],
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use_container_width=True,
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"URL": st.column_config.LinkColumn("URL"),
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},
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hide_index=True,
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height=len(df)*37,
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)
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# Full-width plot for the first category
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create_bar_chart(df, score_columns[0])
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# Next two plots in two columns
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col1, col2 = st.columns(2)
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with col1:
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create_bar_chart(df, score_columns[1])
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with col2:
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create_bar_chart(df, score_columns[2])
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# Last two plots in two columns
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col3, col4 = st.columns(2)
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with col3:
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create_bar_chart(df, score_columns[3])
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with col4:
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create_bar_chart(df, score_columns[4])
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-
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except Exception as e:
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st.error("An error occurred while processing the markdown table.")
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st.error(str(e))
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@@ -176,26 +197,18 @@ def main():
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st.markdown('''
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### Nous benchmark suite
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Popularized by [Teknium](https
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* [**
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* **GPT4ALL** (0-shot): `hellaswag,openbookqa,winogrande,arc_easy,arc_challenge,boolq,piqa`
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* [**TruthfulQA**](https://arxiv.org/abs/2109.07958) (0-shot): `truthfulqa_mc`
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* [**Bigbench**](https://arxiv.org/abs/2206.04615) (0-shot): `bigbench_causal_judgement,bigbench_date_understanding,bigbench_disambiguation_qa,bigbench_geometric_shapes,bigbench_logical_deduction_five_objects,bigbench_logical_deduction_seven_objects,bigbench_logical_deduction_three_objects,bigbench_movie_recommendation,bigbench_navigate,bigbench_reasoning_about_colored_objects,bigbench_ruin_names,bigbench_salient_translation_error_detection,bigbench_snarks,bigbench_sports_understanding,bigbench_temporal_sequences,bigbench_tracking_shuffled_objects_five_objects,bigbench_tracking_shuffled_objects_seven_objects,bigbench_tracking_shuffled_objects_three_objects`
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-
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### Reproducibility
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You can easily reproduce these results using π§ [LLM AutoEval](https
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### Clone this space
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You can create your own leaderboard with your LLM AutoEval results on GitHub Gist. You just need to clone this space and specify two variables:
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* Change the `gist_id` in [yall.py](https
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A special thanks to [gblazex](https://huggingface.co/gblazex) for providing many evaluations.
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''')
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if __name__ == "__main__":
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main()
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from yall import create_yall
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def convert_markdown_table_to_dataframe(md_content):
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"""
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Converts markdown table to Pandas DataFrame, handling special characters and links,
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return df
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@st.cache_data
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def get_model_info(df):
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api = HfApi()
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# Initialize new columns for likes and tags
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df['Likes'] = None
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df['Tags'] = None
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return df
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def create_bar_chart(df, category):
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"""Create and display a bar chart for a given category."""
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st.write(f"### {category} Scores")
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# Sort the DataFrame based on the category score
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sorted_df = df[['Model', category]].sort_values(by=category, ascending=True)
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# Create the bar chart with a color gradient (using 'Viridis' color scale as an example)
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fig = go.Figure(go.Bar(
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x=sorted_df[category],
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y=sorted_df['Model'],
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orientation='h',
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marker=dict(color=sorted_df[category], colorscale='Twilight') # You can change 'Viridis' to another color scale
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))
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# Update layout for better readability
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margin=dict(l=20, r=20, t=20, b=20)
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)
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# Adjust the height of the chart based on the number of rows in the DataFrame
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st.plotly_chart(fig, use_container_width=True, height=len(df) * 35)
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# Example usage:
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# create_bar_chart(your_dataframe, 'Your_Category')
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def main():
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st.set_page_config(page_title="YALL - Yet Another LLM Leaderboard", layout="wide")
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st.title("π YALL - Yet Another LLM Leaderboard")
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st.markdown("Leaderboard made with π§ [LLM AutoEval](https:
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content = create_yall()
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tab1, tab2 = st.tabs(["π Leaderboard", "π About"])
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if content:
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try:
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score_columns = ['Average', 'AGIEval', 'GPT4All', 'TruthfulQA', 'Bigbench']
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# Display dataframe
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full_df = convert_markdown_table_to_dataframe(content)
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for col in score_columns:
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full_df = get_model_info(full_df)
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full_df['Tags'] = full_df['Tags'].fillna('')
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df = pd.DataFrame(columns=full_df.columns)
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# Toggles for filtering by tags
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show_phi = st.checkbox("Phi (2.8B)", value=True)
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show_mistral = st.checkbox("Mistral (7B)", value=True)
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show_other = st.checkbox("Other", value=True)
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# Create a DataFrame based on selected filters
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dfs_to_concat = []
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if show_phi:
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dfs_to_concat.append(full_df[full_df['Tags'].str.lower().str.contains('phi,|phi-msft,')])
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if show_mistral:
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if show_other:
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other_df = full_df[~full_df['Tags'].str.lower().str.contains('phi,|phi-msft,|mistral,')]
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dfs_to_concat.append(other_df)
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# Concatenate the DataFrames
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if dfs_to_concat:
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df = pd.concat(dfs_to_concat, ignore_index=True)
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# Add a search bar
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search_query = st.text_input("Search models", "")
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# Filter the DataFrame based on the search query
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if search_query:
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df = df[df['Model'].str.contains(search_query, case=False)]
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# Display the filtered DataFrame or the entire leaderboard
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st.dataframe(
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df[['Model'] + score_columns + ['Likes', 'URL']],
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use_container_width=True,
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"URL": st.column_config.LinkColumn("URL"),
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},
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hide_index=True,
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height=len(df) * 37,
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)
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# Add a button to export data to CSV
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if st.button("Export to CSV"):
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# Export the DataFrame to CSV
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csv_data = df.to_csv(index=False)
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# Create a link to download the CSV file
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st.download_button(
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label="Download CSV",
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data=csv_data,
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file_name="leaderboard.csv",
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key="download-csv",
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help="Click to download the CSV file",
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)
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# Full-width plot for the first category
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create_bar_chart(df, score_columns[0])
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# Next two plots in two columns
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col1, col2 = st.columns(2)
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with col1:
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create_bar_chart(df, score_columns[1])
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with col2:
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create_bar_chart(df, score_columns[2])
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# Last two plots in two columns
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col3, col4 = st.columns(2)
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with col3:
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create_bar_chart(df, score_columns[3])
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with col4:
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create_bar_chart(df, score_columns[4])
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except Exception as e:
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st.error("An error occurred while processing the markdown table.")
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st.error(str(e))
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st.markdown('''
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### Nous benchmark suite
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Popularized by [Teknium](https:
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* [**AGIEval**](https: * **GPT4ALL** (0-shot): `hellaswag,openbookqa,winogrande,arc_easy,arc_challenge,boolq,piqa`
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* [**TruthfulQA**](https: * [**Bigbench**](https:
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### Reproducibility
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You can easily reproduce these results using π§ [LLM AutoEval](https:
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### Clone this space
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You can create your own leaderboard with your LLM AutoEval results on GitHub Gist. You just need to clone this space and specify two variables:
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* Change the `gist_id` in [yall.py](https: * Create "New Secret" in Settings > Variables and secrets (name: "github", value: [your GitHub token](https:
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A special thanks to [gblazex](https: ''')
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if __name__ == "__main__":
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main()
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