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| # Importing necessary libraries | |
| import re | |
| import streamlit as st | |
| import requests | |
| import pandas as pd | |
| from io import StringIO | |
| import plotly.graph_objs as go | |
| from huggingface_hub import HfApi | |
| from huggingface_hub.utils import RepositoryNotFoundError, RevisionNotFoundError | |
| from yall import create_yall | |
| from functools import cache | |
| # Function to get model info from Hugging Face API using caching | |
| def cached_model_info(api, model): | |
| try: | |
| return api.model_info(repo_id=str(model)) | |
| except (RepositoryNotFoundError, RevisionNotFoundError): | |
| return None | |
| # Function to get model info from DataFrame and update it with likes and tags | |
| def get_model_info(df): | |
| api = HfApi() | |
| for index, row in df.iterrows(): | |
| model_info = cached_model_info(api, row['Model'].strip()) | |
| if model_info: | |
| df.loc[index, 'Likes'] = model_info.likes | |
| df.loc[index, 'Tags'] = ', '.join(model_info.tags) | |
| else: | |
| df.loc[index, 'Likes'] = -1 | |
| df.loc[index, 'Tags'] = '' | |
| return df | |
| # Function to convert markdown table to DataFrame and extract Hugging Face URLs | |
| def convert_markdown_table_to_dataframe(md_content): | |
| """ | |
| Converts markdown table to Pandas DataFrame, handling special characters and links, | |
| extracts Hugging Face URLs, and adds them to a new column. | |
| """ | |
| # Remove leading and trailing | characters | |
| cleaned_content = re.sub(r'\|\s*$', '', re.sub(r'^\|\s*', '', md_content, flags=re.MULTILINE), flags=re.MULTILINE) | |
| # Create DataFrame from cleaned content | |
| df = pd.read_csv(StringIO(cleaned_content), sep="\|", engine='python') | |
| # Remove the first row after the header | |
| df = df.drop(0, axis=0) | |
| # Strip whitespace from column names | |
| df.columns = df.columns.str.strip() | |
| # Extract Hugging Face URLs and add them to a new column | |
| model_link_pattern = r'\[(.*?)\]\((.*?)\)\s*\[.*?\]\(.*?\)' | |
| df['URL'] = df['Model'].apply(lambda x: re.search(model_link_pattern, x).group(2) if re.search(model_link_pattern, x) else None) | |
| # Clean Model column to have only the model link text | |
| df['Model'] = df['Model'].apply(lambda x: re.sub(model_link_pattern, r'\1', x)) | |
| return df | |
| def get_model_info(df): | |
| api = HfApi() | |
| # Initialize new columns for likes and tags | |
| df['Likes'] = None | |
| df['Tags'] = None | |
| # Iterate through DataFrame rows | |
| for index, row in df.iterrows(): | |
| model = row['Model'].strip() | |
| try: | |
| model_info = api.model_info(repo_id=str(model)) | |
| df.loc[index, 'Likes'] = model_info.likes | |
| df.loc[index, 'Tags'] = ', '.join(model_info.tags) | |
| except (RepositoryNotFoundError, RevisionNotFoundError): | |
| df.loc[index, 'Likes'] = -1 | |
| df.loc[index, 'Tags'] = '' | |
| return df | |
| # Function to create bar chart for a given category | |
| def create_bar_chart(df, category): | |
| """Create and display a bar chart for a given category.""" | |
| st.write(f"### {category} Scores") | |
| # Sort the DataFrame based on the category score | |
| sorted_df = df[['Model', category]].sort_values(by=category, ascending=True) | |
| # Create the bar chart with a color gradient (using 'Viridis' color scale as an example) | |
| fig = go.Figure(go.Bar( | |
| x=sorted_df[category], | |
| y=sorted_df['Model'], | |
| orientation='h', | |
| marker=dict(color=sorted_df[category], colorscale='Spectral') # You can change 'Viridis' to another color scale | |
| )) | |
| # Update layout for better readability | |
| fig.update_layout( | |
| margin=dict(l=20, r=20, t=20, b=20) | |
| ) | |
| # Adjust the height of the chart based on the number of rows in the DataFrame | |
| st.plotly_chart(fig, use_container_width=True, height=len(df) * 50) | |
| import plotly.graph_objs as go | |
| import streamlit as st | |
| def create_combined_chart(df, category): | |
| """Create and display a combined bar and line chart for a given category.""" | |
| st.write(f"### {category} Scores") | |
| # Sort the DataFrame based on the category score | |
| sorted_df = df[['Model', category]].sort_values(by=category, ascending=True) | |
| # Create a figure | |
| fig = go.Figure() | |
| # Add bar graph to the figure | |
| fig.add_trace( | |
| go.Bar( | |
| x=sorted_df['Model'], | |
| y=sorted_df[category], | |
| name='Bar Chart', | |
| marker=dict(color=sorted_df[category], colorscale='Spectral') # You can change the color | |
| ) | |
| ) | |
| # Add line graph to the figure | |
| fig.add_trace( | |
| go.Scatter( | |
| x=sorted_df['Model'], | |
| y=sorted_df[category], | |
| mode='lines', | |
| name='Line Chart', | |
| line=dict(color='red') # You can change the line color | |
| ) | |
| ) | |
| # Update layout if needed | |
| fig.update_layout( | |
| title=f"{category} Scores", | |
| xaxis_title="Model", | |
| yaxis_title=f"{category} Value", | |
| margin=dict(l=20, r=20, t=20, b=20) | |
| ) | |
| # Display the figure in Streamlit | |
| st.plotly_chart(fig, use_container_width=True, height=len(df) * 50) | |
| # Main function to run the Streamlit app | |
| def main(): | |
| # Set page configuration and title | |
| st.set_page_config(page_title="YALL - Yet Another LLM Leaderboard", layout="wide") | |
| st.title("🏆 YALL - Yet Another LLM Leaderboard") | |
| st.markdown("Leaderboard made with 🧐 [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) using [Nous](https://huggingface.co/NousResearch) benchmark suite.") | |
| # Create tabs for leaderboard and about section | |
| content = create_yall() | |
| tab1, tab2 = st.tabs(["🏆 Leaderboard", "📝 About"]) | |
| # Leaderboard tab | |
| with tab1: | |
| if content: | |
| try: | |
| score_columns = ['Average', 'AGIEval', 'GPT4All', 'TruthfulQA', 'Bigbench'] | |
| # Display dataframe | |
| full_df = convert_markdown_table_to_dataframe(content) | |
| for col in score_columns: | |
| # Corrected use of pd.to_numeric | |
| full_df[col] = pd.to_numeric(full_df[col].str.strip(), errors='coerce') | |
| full_df = get_model_info(full_df) | |
| full_df['Tags'] = full_df['Tags'].fillna('') | |
| df = pd.DataFrame(columns=full_df.columns) | |
| # Toggles for filtering by tags | |
| show_phi = st.checkbox("Phi (2.8B)", value=True) | |
| show_mistral = st.checkbox("Mistral (7B)", value=True) | |
| show_other = st.checkbox("Other", value=True) | |
| # Create a DataFrame based on selected filters | |
| dfs_to_concat = [] | |
| if show_phi: | |
| dfs_to_concat.append(full_df[full_df['Tags'].str.lower().str.contains('phi,|phi-msft,')]) | |
| if show_mistral: | |
| dfs_to_concat.append(full_df[full_df['Tags'].str.lower().str.contains('mistral,')]) | |
| if show_other: | |
| other_df = full_df[~full_df['Tags'].str.lower().str.contains('phi,|phi-msft,|mistral,')] | |
| dfs_to_concat.append(other_df) | |
| # Concatenate the DataFrames | |
| if dfs_to_concat: | |
| df = pd.concat(dfs_to_concat, ignore_index=True) | |
| # Add a search bar | |
| search_query = st.text_input("Search models", "") | |
| # Filter the DataFrame based on the search query | |
| if search_query: | |
| df = df[df['Model'].str.contains(search_query, case=False)] | |
| # Display the filtered DataFrame or the entire leaderboard | |
| st.dataframe( | |
| df[['Model'] + score_columns + ['Likes', 'URL']], | |
| use_container_width=True, | |
| column_config={ | |
| "Likes": st.column_config.NumberColumn( | |
| "Likes", | |
| help="Number of likes on Hugging Face", | |
| format="%d ❤️", | |
| ), | |
| "URL": st.column_config.LinkColumn("URL"), | |
| }, | |
| hide_index=True, | |
| height=len(df) * 37, | |
| ) | |
| selected_models = st.multiselect('Select models to compare', df['Model'].unique()) | |
| comparison_df = df[df['Model'].isin(selected_models)] | |
| st.dataframe(comparison_df) | |
| # Add a button to export data to CSV | |
| if st.button("Export to CSV"): | |
| # Export the DataFrame to CSV | |
| csv_data = df.to_csv(index=False) | |
| # Create a link to download the CSV file | |
| st.download_button( | |
| label="Download CSV", | |
| data=csv_data, | |
| file_name="leaderboard.csv", | |
| key="download-csv", | |
| help="Click to download the CSV file", | |
| ) | |
| # Horizontal barcharts | |
| # Full-width plot for the first category | |
| create_combined_chart(df, score_columns[0]) | |
| # Vertical barcharts | |
| create_bar_chart(df, score_columns[0]) | |
| # Next two plots in two columns | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| create_bar_chart(df, score_columns[1]) | |
| with col2: | |
| create_bar_chart(df, score_columns[2]) | |
| # Last two plots in two columns | |
| col3, col4 = st.columns(2) | |
| with col3: | |
| create_bar_chart(df, score_columns[3]) | |
| with col4: | |
| create_bar_chart(df, score_columns[4]) | |
| except Exception as e: | |
| st.error("An error occurred while processing the markdown table.") | |
| st.error(str(e)) | |
| else: | |
| st.error("Failed to download the content from the URL provided.") | |
| # About tab | |
| with tab2: | |
| st.markdown(''' | |
| ### Nous benchmark suite | |
| Popularized by [Teknium](https://huggingface.co/teknium) and [NousResearch](https://huggingface.co/NousResearch), this benchmark suite aggregates four benchmarks: | |
| * [**AGIEval**](https://arxiv.org/abs/2304.06364) (0-shot): `agieval_aqua_rat,agieval_logiqa_en,agieval_lsat_ar,agieval_lsat_lr,agieval_lsat_rc,agieval_sat_en,agieval_sat_en_without_passage,agieval_sat_math` | |
| * **GPT4ALL** (0-shot): `hellaswag,openbookqa,winogrande,arc_easy,arc_challenge,boolq,piqa` | |
| * [**TruthfulQA**](https://arxiv.org/abs/2109.07958) (0-shot): `truthfulqa_mc` | |
| * [**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` | |
| ### Reproducibility | |
| You can easily reproduce these results using 🧐 [LLM AutoEval](https://github.com/mlabonne/llm-autoeval/tree/master), a colab notebook that automates the evaluation process (benchmark: `nous`). This will upload the results to GitHub as gists. You can find the entire table with the links to the detailed results [here](https://gist.github.com/mlabonne/90294929a2dbcb8877f9696f28105fdf). | |
| ### Clone this space | |
| 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: | |
| * Change the `gist_id` in [yall.py](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard/blob/main/yall.py#L126). | |
| * Create "New Secret" in Settings > Variables and secrets (name: "github", value: [your GitHub token](https://github.com/settings/tokens)) | |
| A special thanks to [gblazex](https://huggingface.co/gblazex) for providing many evaluations. | |
| ''') | |
| # Run the main function if this script is run directly | |
| if __name__ == "__main__": | |
| main() |