Commit
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ca1e7f4
1
Parent(s):
f2a6ef6
Initial version of AlpacaEval Visualizations
Browse files- .gitignore +2 -1
- app.py +233 -1
- data/model_win_rates.json +1 -0
- prep_data.py +92 -0
- requirements.txt +5 -0
.gitignore
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env/
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env/
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submodules/
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app.py
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import streamlit as st
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import pandas as pd
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def app():
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st.title("
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if __name__ == "__main__":
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import streamlit as st
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import pandas as pd
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import plotly.express as px
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import plotly.graph_objects as go
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import statsmodels.api as sm
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# Set the layout to wide
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st.set_page_config(layout="wide")
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def prep_rankings_table(df, y_column):
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# Create a copy of the dataframe.
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df_copy = df.copy()
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# Select the columns we care about, sort by the y column, and reset the index.
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df_copy = (
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df_copy[
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[
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"model_name",
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y_column,
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"num_words_mean",
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]
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]
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.sort_values(y_column, ascending=False)
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.reset_index()
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)
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# Create a rank column.
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df_copy["rank"] = df_copy.index + 1
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# Round the y column.
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df_copy[y_column] = df_copy[y_column].round(2)
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# Fix the order.
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df_copy = df_copy[["rank", "model_name", y_column, "num_words_mean"]]
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return df_copy
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def app():
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st.title("AlpacaEval Visualizations")
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st.markdown("## Win rate vs. overall mean length")
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# Load the data
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df = pd.read_json("data/model_win_rates.json")
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# Add a model name column for hover labels
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df["model_name"] = df.index.astype(str)
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# Define the preset groups
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presets = {
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"gpt": df[df["model_name"].str.contains("openai|gpt", case=False)][
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"model_name"
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].tolist(),
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"claude": df[df["model_name"].str.contains("claude", case=False)][
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"model_name"
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].tolist(),
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"moa": df[df["model_name"].str.contains("moa", case=False)][
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"model_name"
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].tolist(),
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"llama": df[df["model_name"].str.contains("llama", case=False)][
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"model_name"
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].tolist(),
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"custom": [],
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}
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# Add radio button for preset groups
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preset_selection = st.radio(
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"Select a preset group of models or choose 'custom' to select manually",
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options=["custom", "gpt", "claude", "moa", "llama"],
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)
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# Add multiselect for custom model selection
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if preset_selection == "custom":
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selected_models = st.multiselect(
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"Select models to highlight", options=df["model_name"].unique()
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)
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else:
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selected_models = presets[preset_selection]
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def create_scatter_plot(df, y_column, selected_models, title):
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fig = go.Figure()
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# Add scatter plots for num_words_mean and num_tokens_mean
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fig.add_trace(
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go.Scatter(
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x=df["num_words_mean"],
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y=df[y_column],
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mode="markers",
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name="words",
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text=df["model_name"],
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marker=dict(size=5, color="skyblue"),
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showlegend=True,
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visible="legendonly", # Make 'words' trace initially visible only in legend
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)
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)
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fig.add_trace(
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go.Scatter(
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x=df["num_tokens_mean"],
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y=df[y_column],
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mode="markers",
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name="tokens",
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text=df["model_name"],
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marker=dict(size=5, color="orange"),
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showlegend=True,
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)
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)
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# Highlight selected models
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if selected_models:
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selected_data = df[df["model_name"].isin(selected_models)]
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fig.add_trace(
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go.Scatter(
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x=selected_data["num_words_mean"],
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y=selected_data[y_column],
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mode="markers",
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name="selected words",
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text=selected_data["model_name"],
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marker=dict(size=10, color="blue"),
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showlegend=True,
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visible="legendonly", # Make 'selected words' trace initially visible only in legend
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)
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)
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fig.add_trace(
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go.Scatter(
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x=selected_data["num_tokens_mean"],
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y=selected_data[y_column],
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mode="markers",
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name="selected tokens",
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text=selected_data["model_name"],
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marker=dict(size=10, color="orangered"),
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showlegend=True,
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)
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)
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# Add trendlines
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def add_trendline(fig, x, y, name, color, visibility="legendonly"):
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X = sm.add_constant(df[x])
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model = sm.OLS(df[y], X).fit()
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trendline = model.predict(X)
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fig.add_trace(
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go.Scatter(
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x=df[x],
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y=trendline,
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mode="lines",
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name=f"{name} trendline",
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line=dict(color=color, width=2),
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visible=visibility, # Control the initial visibility
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)
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)
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return model.rsquared
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r_squared_words = add_trendline(
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fig, "num_words_mean", y_column, "words", "blue"
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)
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r_squared_tokens = add_trendline(
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fig, "num_tokens_mean", y_column, "tokens", "orangered", visibility=True
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)
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# Update layout with titles and labels
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fig.update_layout(
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xaxis_title="Mean length",
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yaxis_title=(
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"Win rate"
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if y_column == "win_rate"
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else (
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"LC Win Rate"
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if y_column == "length_controlled_winrate"
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else "Discrete Win Rate"
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)
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),
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title=title,
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legend_title="Legend",
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)
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return fig, r_squared_words, r_squared_tokens
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y_column1 = "length_controlled_winrate"
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y_column2 = "win_rate"
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y_column3 = "discrete_win_rate"
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fig1, r_squared_words_1, r_squared_tokens_1 = create_scatter_plot(
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df, y_column1, selected_models, "Length-Controlled Win Rate"
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)
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fig2, r_squared_words_2, r_squared_tokens_2 = create_scatter_plot(
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df, y_column2, selected_models, "Win Rate"
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)
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fig3, r_squared_words_3, r_squared_tokens_3 = create_scatter_plot(
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df, y_column3, selected_models, "Discrete Win Rate"
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)
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# Create tabs for each chart
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tab1, tab2, tab3 = st.tabs(["LC Win Rate", "Win Rate", "Discrete Win Rate"])
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with tab1:
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col1, col2 = st.columns([3, 2])
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col1.plotly_chart(fig1)
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col2.markdown("#### Rankings")
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prepped_df = prep_rankings_table(df, "length_controlled_winrate")
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col2.dataframe(
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prepped_df,
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hide_index=True,
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)
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with st.expander("Trendline R²"):
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st.markdown(
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f"- R² (Words vs {y_column1}): {r_squared_words_1:.2f} \n- R² (Tokens vs {y_column1}): {r_squared_tokens_1:.2f}"
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)
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with tab2:
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col1, col2 = st.columns([3, 2])
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col1.plotly_chart(fig2)
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col2.markdown("#### Rankings")
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prepped_df = prep_rankings_table(df, "win_rate")
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col2.dataframe(
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prepped_df,
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hide_index=True,
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)
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with st.expander("Trendline R²"):
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st.markdown(
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f"- R² (Words vs {y_column2}): {r_squared_words_2:.2f} \n- R² (Tokens vs {y_column2}): {r_squared_tokens_2:.2f}"
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)
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with tab3:
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col1, col2 = st.columns([3, 2])
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col1.plotly_chart(fig3)
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col2.markdown("#### Rankings")
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prepped_df = prep_rankings_table(df, "discrete_win_rate")
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col2.dataframe(
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prepped_df,
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hide_index=True,
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)
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with st.expander("Trendline R²"):
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st.markdown(
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f"- R² (Words vs {y_column3}): {r_squared_words_3:.2f}\n- R² (Tokens vs {y_column3}): {r_squared_tokens_3:.2f}"
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)
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with st.expander("Raw data"):
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st.dataframe(df)
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if __name__ == "__main__":
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data/model_win_rates.json
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1 |
+
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ge2-16B-Chat":214,"gpt4_0613":164,"pythia-12b-mix-sft":181,"alpaca-7b-neft":97,"Qwen1.5-14B-Chat":224,"gpt-4-0125-preview":242,"guanaco-33b":236,"oasst-sft-llama-33b":145,"gpt4_0613_verbose":166,"llama-2-chat-7b-evol70k-neft":143,"gpt35_turbo_instruct":241,"platolm-7b":187,"llama-2-13b-chat-hf":175,"Nanbeige-Plus-Chat-v0.1":216,"openchat-v2-13b":205,"mistral-orpo-beta":205,"Snorkel-Mistral-PairRM-DPO-best-of-16":255,"tulu-2-dpo-7b":738,"alpaca-7b_verbose":111,"OpenHermes-2.5-Mistral-7B":225,"claude-2.1_verbose":120,"ultralm-13b-v2.0":188,"deita-7b-v1.0":221,"minichat-1.5-3b":215,"Qwen-14B-Chat":227,"airoboros-33b":324,"alpaca-farm-ppo-sim-gpt4-20k":68,"ultralm-13b":158,"openbuddy-falcon-40b-v9":198,"openchat8192-13b":257,"wizardlm-13b":155,"vicuna-13b":153,"merlinite-7B-AOT":180,"gpt4_0314":212,"gpt4_0613_concise":110,"jina-chat":101,"Contextual-KTO-Mistral-PairRM":272,"xwinlm-13b-v0.1":268,"LMCocktail-10.7B-v1":168,"SPPO-Mistral7B-PairRM-ExPO":193,"Mixtral-8x7B-Instruct-v0.1_concise":185,"gpt4_1106_preview_concise":184,"Mistral-7B-ReMax-v0.1":188,"Llama-3-Instruct-8B-SimPO-ExPO":191,"dolphin-2.2.1-mistral-7b":218,"humpback-llama2-70b":214,"openpipe-moa-gpt-4-turbo-v1":193,"vicuna-7b-v1.5":165,"Starling-LM-7B-alpha":250,"falcon-40b-instruct":182,"Samba-CoE-v0.2-best-of-16":199,"opencoderplus-15b":272,"xwinlm-70b-v0.1":267,"wizardlm-13b-v1.2":308,"aligner-2b_qwen1.5-72b-chat":210,"internlm2-chat-7b-ExPO":232,"claude-2.1":114,"vicuna-7b-v1.3":159,"oasst-rlhf-llama-33b":257,"zephyr-7b-alpha-ExPO":204,"openchat-v3.1-13b":224,"SPPO-Llama-3-Instruct-8B-PairRM":208,"minotaur-13b":164,"tulu-2-dpo-13b-ExPO":203,"zephyr-7b-beta-ExPO":217,"tulu-2-dpo-7b-ExPO":217,"Llama-3-Instruct-8B-SimPO":200,"baize-v2-13b":149,"guanaco-7b":292,"ultralm-13b-v2.0-best-of-16":169,"claude-2.1_concise":88,"openchat-13b":241,"tulu-2-dpo-70b":281,"deepseek-llm-67b-chat":206,"humpback-llama-65b":156,"tulu-2-dpo-70b-ExPO":203,"TempNet-LLaMA2-Chat-7B-v0.1":178,"nous-hermes-13b":201,"gpt-3.5-turbo-0613":194,"alpaca-7b_concise":70,"baichuan-13b-chat":400,"claude-3-5-sonnet-20240620":200,"gpt-3.5-turbo-1106":134,"minichat-3b":156,"Storm-7B":188,"oasst-sft-pythia-12b":186,"Conifer-7B-DPO":163,"Snorkel-Mistral-PairRM-DPO":342,"internlm2-chat-20b-ExPO":283,"Samba-CoE-v0.2":196,"gemini-pro":227,"pairrm-tulu-2-70b":438,"text_davinci_003":89,"gpt4":203,"Yi-34B-Chat":235,"Starling-LM-7B-beta-ExPO":143,"pairrm-Yi-34B-Chat":276,"gpt4_1106_preview":249,"evo-7b":241,"zephyr-7b-beta":238,"guanaco-13b":402,"alpaca-7b":69,"internlm2-chat-20b-ppo":403,"gemma-2b-it":168,"pairrm-zephyr-7b-beta":250,"evo-v2-7b":235,"causallm-14b":234,"SPPO-Mistral7B-PairRM":198,"gpt-3.5-turbo-1106_concise":80,"openbuddy-llama-65b-v8":152,"claude2-alpaca-13b":176,"Starling-LM-7B-alpha-ExPO":220,"openbuddy-falcon-7b-v6":189,"gemma-7b-it":160,"phi-2-sft":164,"gpt4_gamed":81,"llama-2-70b-chat-hf":222,"openbuddy-llama2-70b-v10.1":161,"wizardlm-70b":231,"ultralm-13b-best-of-16":164}}
|
prep_data.py
ADDED
@@ -0,0 +1,92 @@
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1 |
+
import os
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2 |
+
import json
|
3 |
+
import tiktoken
|
4 |
+
from alpaca_eval import utils, metrics, annotators, constants, analyze, plotting, main
|
5 |
+
from alpaca_eval.metrics.glm_winrate import get_length_controlled_winrate
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6 |
+
import os
|
7 |
+
import pandas as pd
|
8 |
+
import json
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9 |
+
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10 |
+
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11 |
+
# Define the path to the top-level directory
|
12 |
+
TOP_LEVEL_DIRECTORY = "submodules/alpaca_eval/results"
|
13 |
+
|
14 |
+
# Initialize an empty dictionary to hold the model name to dataframe mapping
|
15 |
+
model_dataframes_outputs = {}
|
16 |
+
|
17 |
+
# Iterate through each subdirectory in the top-level directory
|
18 |
+
for model_name in os.listdir(TOP_LEVEL_DIRECTORY):
|
19 |
+
model_dir = os.path.join(TOP_LEVEL_DIRECTORY, model_name)
|
20 |
+
if os.path.isdir(model_dir):
|
21 |
+
model_output_file = os.path.join(model_dir, "model_outputs.json")
|
22 |
+
if os.path.exists(model_output_file):
|
23 |
+
model_dataframes_outputs[model_name] = pd.read_json(model_output_file)
|
24 |
+
|
25 |
+
|
26 |
+
def get_num_words(text):
|
27 |
+
return len(text.split())
|
28 |
+
|
29 |
+
|
30 |
+
ENCODING = tiktoken.get_encoding("cl100k_base")
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31 |
+
|
32 |
+
|
33 |
+
def get_num_tokens(text):
|
34 |
+
"""Uses tiktoken to get the number of tokens in the text."""
|
35 |
+
try:
|
36 |
+
return len(ENCODING.encode(str(text)))
|
37 |
+
except:
|
38 |
+
breakpoint()
|
39 |
+
|
40 |
+
|
41 |
+
model_name_to_num_words = {}
|
42 |
+
model_name_to_num_tokens = {}
|
43 |
+
for model_name, model_dataframe in model_dataframes_outputs.items():
|
44 |
+
print(f"model_name_to_num_words for {model_name}")
|
45 |
+
model_dataframe["output_num_words"] = model_dataframe["output"].apply(get_num_words)
|
46 |
+
model_dataframe["output_num_tokens"] = model_dataframe["output"].apply(
|
47 |
+
get_num_tokens
|
48 |
+
)
|
49 |
+
model_name_to_num_words[model_name] = {
|
50 |
+
"mean": int(model_dataframe["output_num_words"].mean()),
|
51 |
+
"std": int(model_dataframe["output_num_words"].std()),
|
52 |
+
}
|
53 |
+
model_name_to_num_tokens[model_name] = {
|
54 |
+
"mean": int(model_dataframe["output_num_tokens"].mean()),
|
55 |
+
"std": int(model_dataframe["output_num_tokens"].std()),
|
56 |
+
}
|
57 |
+
|
58 |
+
num_words_df = pd.DataFrame(model_name_to_num_words).T
|
59 |
+
num_tokens_df = pd.DataFrame(model_name_to_num_tokens).T
|
60 |
+
|
61 |
+
model_name_to_win_rate = {}
|
62 |
+
for model_name in os.listdir(TOP_LEVEL_DIRECTORY):
|
63 |
+
print(f"model_name_to_win_rate for {model_name}")
|
64 |
+
model_dir = os.path.join(TOP_LEVEL_DIRECTORY, model_name)
|
65 |
+
if os.path.isdir(model_dir):
|
66 |
+
model_output_file = os.path.join(
|
67 |
+
model_dir, "weighted_alpaca_eval_gpt4_turbo", "annotations.json"
|
68 |
+
)
|
69 |
+
if os.path.exists(model_output_file):
|
70 |
+
model_dataframe = pd.read_json(model_output_file)
|
71 |
+
model_name_to_win_rate[model_name] = get_length_controlled_winrate(
|
72 |
+
model_dataframe
|
73 |
+
)
|
74 |
+
|
75 |
+
win_rate_df = pd.DataFrame(model_name_to_win_rate).T
|
76 |
+
|
77 |
+
df = num_words_df.join(win_rate_df, how="inner")
|
78 |
+
df = df.rename(
|
79 |
+
columns={
|
80 |
+
"mean": "num_words_mean",
|
81 |
+
"std": "num_words_std",
|
82 |
+
}
|
83 |
+
)
|
84 |
+
df = df.join(num_tokens_df, how="inner")
|
85 |
+
df = df.rename(
|
86 |
+
columns={
|
87 |
+
"mean": "num_tokens_mean",
|
88 |
+
"std": "num_tokens_std",
|
89 |
+
}
|
90 |
+
)
|
91 |
+
|
92 |
+
df.to_json("data/model_win_rates.json")
|
requirements.txt
CHANGED
@@ -1,3 +1,8 @@
|
|
1 |
streamlit
|
2 |
plotly
|
3 |
pandas
|
|
|
|
|
|
|
|
|
|
|
|
1 |
streamlit
|
2 |
plotly
|
3 |
pandas
|
4 |
+
tiktoken
|
5 |
+
alpaca-eval
|
6 |
+
seaborn
|
7 |
+
setuptools
|
8 |
+
statsmodels
|