add leaderboard tag to increase visibility

.gitattributes

@@ -25,6 +25,7 @@
 *.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
 *.wasm filter=lfs diff=lfs merge=lfs -text
@@ -32,4 +33,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-scale-hf-logo.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,22 +1,3 @@
-auto_evals/
-venv/
-__pycache__/
-.env
-.ipynb_checkpoints
-*ipynb
-.vscode/
-
-eval-queue/
-eval-results/
-eval-queue-bk/
-eval-results-bk/
-logs/
 evals/
-
-.gradio/
-.evals/
 __pycache__/*
-*.pyc
-
-# saved data automatically
-leaderboard/current-rbv2-data.csv
+*.pyc

Makefile

@@ -1,13 +0,0 @@
-.PHONY: style format
-
-
-style:
-	python -m black --line-length 119 .
-	python -m isort .
-	ruff check --fix .
-
-
-quality:
-	python -m black --check --line-length 119 .
-	python -m isort --check-only .
-	ruff check .

README.md

@@ -4,10 +4,19 @@ emoji: 📐
 colorFrom: pink
 colorTo: blue
 sdk: gradio
-sdk_version: 4.36.0 # for colors + sorting, newer cannot
+sdk_version: 4.12.0
 app_file: app.py
 pinned: true
 license: apache-2.0
 tags:
-  - leaderboard
+- leaderboard
 ---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+To develop this app, it can be run with:
+```
+gradio app.py
+```
+
+Paper: https://arxiv.org/abs/2403.13787

app.py

@@ -1,65 +1,37 @@
-import os
-from pathlib import Path
-
 import gradio as gr
-import numpy as np
-import pandas as pd
-from datasets import load_dataset
+import os
 from huggingface_hub import HfApi, snapshot_download
+from apscheduler.schedulers.background import BackgroundScheduler
+from datasets import load_dataset
+from src.utils import load_all_data
+from src.md import ABOUT_TEXT, TOP_TEXT
+from src.plt import plot_avg_correlation
+from src.constants import subset_mapping, length_categories, example_counts
+from src.css import custom_css
+import numpy as np
 
-from leaderboard.constants import example_counts, subset_mapping
-from leaderboard.css import custom_css
-from leaderboard.md import *
-from leaderboard.utils import load_all_data
-
-#######################################################
-#                     Setup                           #
-#######################################################
 api = HfApi()
 
 COLLAB_TOKEN = os.environ.get("COLLAB_TOKEN")
-evals_repo = "allenai/reward-bench-2-results"
-
-eval_set_repo = "allenai/reward-bench-2"
-eval_set_repo_v1 = "allenai/reward-bench"
+evals_repo = "allenai/reward-bench-results"
 
+eval_set_repo = "allenai/reward-bench"
 repo_dir_rewardbench = "./evals/rewardbench/"
 
+def restart_space():
+    api.restart_space(repo_id="allenai/reward-bench", token=COLLAB_TOKEN)
+
 print("Pulling evaluation results")
 repo = snapshot_download(
     local_dir=repo_dir_rewardbench,
-    ignore_patterns=["pref-sets-scores/*", "eval-set-scores/*", "eval-set/allenai/open_instruct_dev*"],
+    ignore_patterns=["pref-sets-scores/*", "eval-set-scores/*"],
     repo_id=evals_repo,
     use_auth_token=COLLAB_TOKEN,
-    tqdm_class=None,
+    tqdm_class=None, 
     etag_timeout=30,
     repo_type="dataset",
 )
 
-###########################################
-#                 Load Data               #
-###########################################
-
-
-def avg_over_rewardbench_v2(dataframe_core):
-    domain_cols = ["Factuality", "Precise IF", "Math", "Safety", "Focus", "Ties"]
-    domain_weights = [1, 1, 1, 1, 1, 1]
-    new_df = dataframe_core.copy()
-
-    # for main subsets, keys in subset_mapping, take the weighted avg by example_counts and store for the models
-    # Get the domain data and handle missing values
-    domain_data = new_df[domain_cols].values
-    masked_data = np.ma.masked_array(domain_data, np.isnan(domain_data))
-
-    # Calculate weighted average
-    average = np.ma.average(masked_data, axis=1, weights=domain_weights)
-    new_df["average"] = average.filled(np.nan)
-
-    # Rearrange columns for consistent output
-    keep_columns = ["model", "model_type", "average"] + domain_cols
-    new_df = new_df[keep_columns]
-    return new_df
-
 
 def avg_over_rewardbench(dataframe_core, dataframe_prefs):
     """
@@ -78,19 +50,13 @@ def avg_over_rewardbench(dataframe_core, dataframe_prefs):
     # for main subsets, keys in subset_mapping, take the weighted avg by example_counts and store for the models
     for subset, sub_subsets in subset_mapping.items():
         subset_cols = [col for col in new_df.columns if col in sub_subsets]
-        sub_data = new_df[subset_cols].values  # take the relevant column values
-        sub_counts = [example_counts[s] for s in subset_cols]  # take the example counts
-        new_df[subset] = np.average(sub_data, axis=1, weights=sub_counts)  # take the weighted average
+        sub_data = new_df[subset_cols].values # take the relevant column values
+        sub_counts = [example_counts[s] for s in subset_cols] # take the example counts
+        new_df[subset] = np.average(sub_data, axis=1, weights=sub_counts) # take the weighted average
         # new_df[subset] = np.round(np.nanmean(new_df[subset_cols].values, axis=1), 2)
 
     data_cols = list(subset_mapping.keys())
-    keep_columns = (
-        [
-            "model",
-        ]
-        + ["model_type"]
-        + data_cols
-    )
+    keep_columns = ["model",] + ["model_type"] + data_cols
     # keep_columns = ["model", "average"] + subsets
     new_df = new_df[keep_columns]
 
@@ -112,7 +78,7 @@ def avg_over_rewardbench(dataframe_core, dataframe_prefs):
             # new_df.at[i, "Prior Sets (0.5 weight)"] = dataframe_prefs[dataframe_prefs["model"] == model]["Prior Sets (0.5 weight)"].values[0]
         else:
             values.append(np.nan)
-
+    
     new_df["Prior Sets (0.5 weight)"] = values
 
     # add total average
@@ -129,90 +95,83 @@ def avg_over_rewardbench(dataframe_core, dataframe_prefs):
     new_df = new_df[keep_columns]
     return new_df
 
+def expand_subsets(dataframe):
+    # TODO need to modify data/ script to do this
+    pass
 
-def prep_df(df):
-    # add column to 0th entry with count (column name itself empty)
-    df.insert(0, "", range(1, 1 + len(df)))
 
-    # replace "model" with "Model" and "model_type" with "Model Type" and "average" with "Average"
-    df = df.rename(columns={"model": "Model", "model_type": "Model Type", "average": "Average"})
-
-    # if "Model Type" in columns
-    if "Model Type" in df.columns:
-        # get model_types that have generative in them
-        mask = df["Model Type"].str.contains("generative", case=False, na=False)
-
-        # set these values to "Generative"
-        df.loc[mask, "Model Type"] = "Generative"
-
-    return df
-
-
-# get v1 data
-orig_data_path = "leaderboard/final-rbv1-data.csv"
-rb_orig_snapshot = pd.read_csv(orig_data_path)
-# rename column "Unnamed: 0" to ""
-rb_orig_snapshot = rb_orig_snapshot.rename(columns={"Unnamed: 0": ""})
-# rb_orig_snapshot = rb_orig_snapshot.drop(columns=["Unnamed: 0", ''])
-rb_orig_snapshot.reset_index(drop=True, inplace=True)
-
-rewardbench_data = load_all_data(repo_dir_rewardbench, subdir="eval-set").sort_values(by="average", ascending=False)
-rewardbench_data_avg_intermediate = avg_over_rewardbench_v2(rewardbench_data.copy())
-
-# Prepare RBv1 scores for merging
-rb_v1_scores_to_merge = rb_orig_snapshot[["Model", "Score"]].copy()
+def length_bias_check(dataframe):
+    """
+    Takes the raw rewardbench dataframe and splits the data into new buckets according to length_categories.
+    Then, take the average of the three buckets as "average"
+    """
+    new_df = dataframe.copy()
+    existing_subsets = new_df.columns[3:] # model, model_type, average
+    final_subsets = ["Length Bias", "Neutral", "Terse Bias"]
+    # new data is empty list dict for each final subset
+    new_data = {s: [] for s in final_subsets}
+
+    # now, subsets correspond to those with True, Nuetral, and False length bias
+    # check if length_categories[subset] == "True" or "False" or "Neutral"
+    for subset in existing_subsets:
+        subset_data = new_df[subset].values
+        subset_length = length_categories[subset]
+        # route to the correct bucket
+        if subset_length == "True":
+            new_data["Length Bias"].append(subset_data)
+        elif subset_length == "Neutral":
+            new_data["Neutral"].append(subset_data)
+        elif subset_length == "False":
+            new_data["Terse Bias"].append(subset_data)
+
+    # take average of new_data and add to new_df (removing other columns than model)
+    for subset in final_subsets:
+        new_df[subset] = np.nanmean(new_data[subset], axis=0)
+    keep_columns = ["model"] + final_subsets
+    new_df = new_df[keep_columns]
+    # recompute average
+    # new_df["average"] = np.round(np.nanmean(new_df[final_subsets].values, axis=1), 2)
 
-# if " ⚠️" in rb_v1_scores_to_merge["Model"].values, shorten the model name without it
-rb_v1_scores_to_merge["Model"] = rb_v1_scores_to_merge["Model"].str.replace(" ⚠️", "", regex=False)
+    return new_df
 
-rb_v1_scores_to_merge.rename(columns={"Score": "RBv1"}, inplace=True)
-# rename rb_v1 "Model" to "model"
-rb_v1_scores_to_merge.rename(columns={"Model": "model"}, inplace=True)
 
-# Merge RBv1 scores into the v2 data
-rewardbench_data_avg = pd.merge(rewardbench_data_avg_intermediate, rb_v1_scores_to_merge, on="model", how="left")
+    
+rewardbench_data = load_all_data(repo_dir_rewardbench, subdir="eval-set").sort_values(by='average', ascending=False)
+rewardbench_data_length = length_bias_check(rewardbench_data).sort_values(by='Terse Bias', ascending=False)
+prefs_data = load_all_data(repo_dir_rewardbench, subdir="pref-sets").sort_values(by='average', ascending=False)
+# prefs_data_sub = expand_subsets(prefs_data).sort_values(by='average', ascending=False)
 
-# Drop any models with only RBv1 scores and no v2 scores
-rewardbench_data_avg = rewardbench_data_avg.dropna(subset=["average"])
+rewardbench_data_avg = avg_over_rewardbench(rewardbench_data, prefs_data).sort_values(by='average', ascending=False)
 
-# Sort by the v2 average
-rewardbench_data_avg = rewardbench_data_avg.sort_values(by="average", ascending=False)
+def prep_df(df):
+    # add column to 0th entry with count (column name itself empty)
+    df.insert(0, '', range(1, 1 + len(df)))
 
+    # replace "model" with "Model" and "model_type" with "Model Type" and "average" with "Average"
+    df = df.rename(columns={"model": "Model", "model_type": "Model Type", "average": "Average"})
+    return df
 
 # add count column to all dataframes
 rewardbench_data = prep_df(rewardbench_data)
 rewardbench_data_avg = prep_df(rewardbench_data_avg).rename(columns={"Average": "Score"})
+# adjust weight of this average to 50% for Prior Sets (0.5 weight), 1 for others
 
-# Ensure RBv1 is the last column if it's not already (merge usually places it at the end of non-key columns)
-# If 'RBv1' is present and not last, move it to be the last column.
-if "RBv1" in rewardbench_data_avg.columns:
-    rbv1_col = rewardbench_data_avg.pop("RBv1")
-    rewardbench_data_avg["RBv1"] = rbv1_col
+rewardbench_data_length = prep_df(rewardbench_data_length)
+prefs_data = prep_df(prefs_data)
 
-# save rewardbench_data_avg as csv to src/current-rbv2-data.csv
-v2_data_path = "leaderboard/current-rbv2-data.csv"
-rewardbench_data_avg.to_csv(v2_data_path, index=False)
-
-col_types_rewardbench = ["number"] + ["markdown"] + ["str"] + ["number"] * (len(rewardbench_data_avg.columns) - 1)
-col_types_rewardbench_v1 = ["number"] + ["markdown"] + ["str"] + ["number"] * (len(rb_orig_snapshot.columns) - 1)
-
-# import ipdb; ipdb.set_trace()
-
-###########################################
-# Leaderboard Helpers & Setting           #
-###########################################
+col_types_rewardbench = ["number"] + ["markdown"] + ["str"] + ["number"] * (len(rewardbench_data.columns) - 1)
+col_types_rewardbench_avg = ["number"] + ["markdown"]+ ["str"] + ["number"] * (len(rewardbench_data_avg.columns) - 1)
+cols_rewardbench_data_length = ["markdown"] + ["number"] * (len(rewardbench_data_length.columns) - 1)
+col_types_prefs = ["number"] + ["markdown"] + ["number"] * (len(prefs_data.columns) - 1)
+# col_types_prefs_sub = ["markdown"] + ["number"] * (len(prefs_data_sub.columns) - 1)
 
 # for showing random samples
-eval_set = load_dataset(eval_set_repo, use_auth_token=COLLAB_TOKEN, split="test")
-eval_set_v1 = load_dataset(eval_set_repo_v1, use_auth_token=COLLAB_TOKEN, split="filtered")
-subsets = eval_set.unique("subset")
-subsets_v1 = eval_set_v1.unique("subset")
-
+eval_set = load_dataset(eval_set_repo, use_auth_token=COLLAB_TOKEN, split="filtered")
 def random_sample(r: gr.Request, subset):
     if subset is None or subset == []:
         sample_index = np.random.randint(0, len(eval_set) - 1)
         sample = eval_set[sample_index]
-    else:  # filter by subsets (can be list)
+    else: # filter by subsets (can be list)
         if isinstance(subset, str):
             subset = [subset]
         # filter down dataset to only include the subset(s)
@@ -220,82 +179,24 @@ def random_sample(r: gr.Request, subset):
         sample_index = np.random.randint(0, len(eval_set_filtered) - 1)
         sample = eval_set_filtered[sample_index]
 
-    markdown_text = "\n\n".join([f"**{key}**:\n\n{value}" for key, value in sample.items()])
+    markdown_text = '\n\n'.join([f"**{key}**:\n\n{value}" for key, value in sample.items()])
     return markdown_text
 
+subsets = eval_set.unique("subset")
 
-# Duplicating because they use global variables with gradio setup
-def random_sample_v1(r: gr.Request, subset):
-    if subset is None or subset == []:
-        sample_index = np.random.randint(0, len(eval_set) - 1)
-        sample = eval_set[sample_index]
-    else:  # filter by subsets (can be list)
-        if isinstance(subset, str):
-            subset = [subset]
-        # filter down dataset to only include the subset(s)
-        eval_set_filtered = eval_set.filter(lambda x: x["subset"] in subset)
-        sample_index = np.random.randint(0, len(eval_set_filtered) - 1)
-        sample = eval_set_filtered[sample_index]
-
-    markdown_text = "\n\n".join([f"**{key}**:\n\n{value}" for key, value in sample.items()])
-    return markdown_text
-
-
-color_map = {
-    "Generative": "#7497db",
-    "Custom Classifier": "#E8ECF2",
-    "Seq. Classifier": "#ffcd75",
-    "DPO": "#75809c",
-}
-
-
-def color_model_type_column(df, color_map):
-    """
-    Apply color to the 'Model Type' column of the DataFrame based on a given color mapping.
-
-    Parameters:
-    df (pd.DataFrame): The DataFrame containing the 'Model Type' column.
-    color_map (dict): A dictionary mapping model types to colors.
-
-    Returns:
-    pd.Styler: The styled DataFrame.
-    """
-
-    # Function to apply color based on the model type
-    def apply_color(val):
-        color = color_map.get(val, "default")  # Default color if not specified in color_map
-        return f"background-color: {color}"
-
-    # Format for different columns
-    format_dict = {col: "{:.1f}" for col in df.columns if col not in ["Average", "Model", "Model Type"]}
-    format_dict["Average"] = "{:.2f}"
-    format_dict[""] = "{:d}"
-
-    return df.style.applymap(apply_color, subset=["Model Type"]).format(format_dict, na_rep="")
-
-
-def regex_table(dataframe, regex, filter_button, style=True):
+def regex_table(dataframe, regex, filter_button):
     """
     Takes a model name as a regex, then returns only the rows that has that in it.
     """
     # Split regex statement by comma and trim whitespace around regexes
     regex_list = [x.strip() for x in regex.split(",")]
     # Join the list into a single regex pattern with '|' acting as OR
-    combined_regex = "|".join(regex_list)
-
-    # remove internal ai2 data
-    dataframe = dataframe[~dataframe["Model"].str.contains("ai2", case=False, na=False)]
+    combined_regex = '|'.join(regex_list)
 
     # if filter_button, remove all rows with "ai2" in the model name
-    update_scores = False
     if isinstance(filter_button, list) or isinstance(filter_button, str):
-        if "Prior Sets" not in filter_button and "Prior Sets (0.5 weight)" in dataframe.columns:
-            update_scores = True
-            # remove the column "Prior Sets (0.5 weight)" from the outputted table
-            dataframe = dataframe.drop(columns=["Prior Sets (0.5 weight)"])
-        if "RBv1" not in filter_button and "RBv1" in dataframe.columns:
-            # remove the column "Prior Sets (0.5 weight)" from the outputted table
-            dataframe = dataframe.drop(columns=["RBv1"])
+        if "AI2 Experiments" not in filter_button and ("ai2" not in regex):
+            dataframe = dataframe[~dataframe["Model"].str.contains("ai2", case=False, na=False)]
         if "Seq. Classifiers" not in filter_button:
             dataframe = dataframe[~dataframe["Model Type"].str.contains("Seq. Classifier", case=False, na=False)]
         if "DPO" not in filter_button:
@@ -307,18 +208,8 @@ def regex_table(dataframe, regex, filter_button, style=True):
     # Filter the dataframe such that 'model' contains any of the regex patterns
     data = dataframe[dataframe["Model"].str.contains(combined_regex, case=False, na=False)]
 
-    # if update the score to not use prior sets, do so
-    if update_scores:
-        data["Score"] = (data["Chat"] + data["Chat Hard"] + data["Safety"] + data["Reasoning"]) / 4
-        # if "Prior Sets (0.5 weight)" in data.columns:
-        # data["Prior Sets (0.5 weight)"] = np.nan
-        # sort array by Score column
-        data = data.sort_values(by="Score", ascending=False)
-
-    data.reset_index(drop=True, inplace=True)
-
     # replace column '' with count/rank
-    data[""] = np.arange(1, 1 + len(data))
+    data[''] = np.arange(1, 1 + len(data))
 
     # if Score exists, round to 2 decimals
     if "Score" in data.columns:
@@ -328,206 +219,161 @@ def regex_table(dataframe, regex, filter_button, style=True):
     # round all others to 1 decimal
     for col in data.columns:
         if col not in ["", "Model", "Model Type", "Score", "Average"]:
-            # replace any data[col].values == '' with np.nan
-            data[col] = data[col].replace("", np.nan)
+            # replace any data[col].values == '' with np.NaN
+            data[col] = data[col].replace('', np.NaN)
             data[col] = np.round(np.array(data[col].values).astype(float), 1)
-    if style:
-        # apply color
-        data = color_model_type_column(data, color_map)
-
     return data
 
-
 # import ipdb; ipdb.set_trace()
 
-total_models = len(
-    regex_table(
-        rewardbench_data_avg.copy(), "", ["Seq. Classifiers", "DPO", "Custom Classifiers", "Generative"], style=False
-    ).values
-)
-total_models_v1 = len(
-    regex_table(
-        rb_orig_snapshot.copy(), "", ["Seq. Classifiers", "DPO", "Custom Classifiers", "Generative"], style=False
-    ).values
-)
-assets = Path("leaderboard").resolve()  # absolute dir with the image
-
-# Using a string for a predefined color
-theme = gr.themes.Default(primary_hue="blue")
+total_models = len(regex_table(rewardbench_data_avg.copy(), "", ["Seq. Classifiers", "DPO", "Custom Classifiers", "Generative"]).values)
 
-#############################################
-#                 Gradio App                #
-#############################################
-
-with gr.Blocks(theme=theme, css=custom_css) as app:
+with gr.Blocks(css=custom_css) as app:
     # create tabs for the app, moving the current table to one titled "rewardbench" and the benchmark_text to a tab called "About"
     with gr.Row():
         with gr.Column(scale=6):
-            gr.Markdown(TOP_TEXT)
-        # with gr.Column(scale=4):
-        #     # search = gr.Textbox(label="Model Search (delimit with , )", placeholder="Regex search for a model")
-        #     # filter_button = gr.Checkbox(label="Include AI2 training runs (or type ai2 above).", interactive=True)
-        #     # img = gr.Image(value="https://private-user-images.githubusercontent.com/10695622/310698241-24ed272a-0844-451f-b414-fde57478703e.png", width=500)
-        #     gr.Markdown("""
-        #                 ![](/gradio_api/file=leaderboard/logo.png)
-        #                 """)
-
-    with gr.Tabs(elem_id="outer-tabs", elem_classes="tabs-big") as tabs_big:
-        with gr.TabItem("🏆 RewardBench 2"):
+            gr.Markdown(TOP_TEXT.format(str(total_models)))
+        with gr.Column(scale=4):
+            # search = gr.Textbox(label="Model Search (delimit with , )", placeholder="Regex search for a model")
+            # filter_button = gr.Checkbox(label="Include AI2 training runs (or type ai2 above).", interactive=True)
+            # img = gr.Image(value="https://private-user-images.githubusercontent.com/10695622/310698241-24ed272a-0844-451f-b414-fde57478703e.png", width=500)
+            gr.Markdown("""
+                        ![](file/src/logo.png)
+                        """)
+    with gr.Tabs(elem_classes="tab-buttons") as tabs:
+        with gr.TabItem("🏆 RewardBench Leaderboard"):
+            with gr.Row():
+                search_1 = gr.Textbox(label="Model Search (delimit with , )", 
+                                      placeholder="Model Search (delimit with , )",
+                                      show_label=False)
+                model_types_1 = gr.CheckboxGroup(["Seq. Classifiers", "DPO", "Custom Classifiers", "Generative", "AI2 Experiments"], 
+                                                 value=["Seq. Classifiers", "DPO", "Custom Classifiers"], 
+                                                 label="Model Types", 
+                                                 show_label=False,
+                                                #  info="Which model types to include.",
+                                                 )
+            with gr.Row():
+                # reference data
+                rewardbench_table_hidden = gr.Dataframe(
+                    rewardbench_data_avg.values,
+                    datatype=col_types_rewardbench_avg,
+                    headers=rewardbench_data_avg.columns.tolist(),
+                    visible=False,
+                )
+                rewardbench_table = gr.Dataframe(
+                    regex_table(rewardbench_data_avg.copy(), "", ["Seq. Classifiers", "DPO", "Custom Classifiers"]).values,
+                    datatype=col_types_rewardbench_avg,
+                    headers=rewardbench_data_avg.columns.tolist(),
+                    elem_id="rewardbench_dataframe_avg",
+                    height=1000,
+                )
+                
+        with gr.TabItem("🔍 RewardBench - Detailed"):
             with gr.Row():
-                with gr.Column(scale=7):
-                    gr.Markdown(CAPTION_V2.format(str(total_models)))
-                with gr.Column(scale=3):
-                    # search = gr.Textbox(label="Model Search (delimit with , )", placeholder="Regex search for a model")
-                    # filter_button = gr.Checkbox(label="Include AI2 training runs (or type ai2 above).", interactive=True)
-                    # img = gr.Image(value="https://private-user-images.githubusercontent.com/10695622/310698241-24ed272a-0844-451f-b414-fde57478703e.png", width=500)
-                    gr.Markdown(
-                        """
-                                ![](/gradio_api/file=leaderboard/logo.png)
-                                """
-                    )
-            with gr.Tabs(elem_id="inner-tabs", elem_classes="tabs-small") as tabs:
-                with gr.TabItem("Leaderboard"):
-                    with gr.Row():
-                        search_1 = gr.Textbox(
-                            label="Model Search (delimit with , )",
-                            placeholder="Model Search (delimit with , )",
-                            show_label=False,
-                            scale=8,
-                        )
-                        model_types_1 = gr.CheckboxGroup(
-                            ["Seq. Classifiers", "Custom Classifiers", "Generative", "RBv1"],
-                            value=["Seq. Classifiers", "Custom Classifiers", "Generative"],
-                            show_label=False,
-                            scale=8,
-                        )
-                        # narrow, non-expanding download button
-                        gr.DownloadButton(
-                            label="Download CSV",
-                            value=v2_data_path,
-                            size="sm",         # shorter height / padding
-                            scale=0,           # ← **width stays just big enough for the text**
-                            min_width=140,     # (optional) guarantee it doesn’t collapse
-                        )
-                    with gr.Row():
-                        # reference data
-                        rewardbench_table_hidden = gr.Dataframe(
-                            rewardbench_data_avg.values,
-                            datatype=col_types_rewardbench_v1,
-                            headers=rewardbench_data_avg.columns.tolist(),
-                            visible=False,
-                        )
-                        rewardbench_table = gr.Dataframe(
-                            regex_table(
-                                rewardbench_data_avg.copy(),
-                                "",
-                                ["Seq. Classifiers", "Custom Classifiers", "Generative"],
-                            ),
-                            datatype=col_types_rewardbench_v1,
-                            headers=rewardbench_data_avg.columns.tolist(),
-                            elem_id="rewardbench_dataframe_avg",
-                            height=800,  # 800 px ≈ ~25 rows on default row-height
-                        )
-
-                with gr.TabItem("About"):
-                    with gr.Row():
-                        gr.Markdown(ABOUT_TEXT_V2)
-
-                with gr.TabItem("Dataset Viewer"):
-                    with gr.Row():
-                        # loads one sample
-                        gr.Markdown("""## Random Dataset Sample Viewer""")
-                        subset_selector = gr.Dropdown(subsets, label="Subset", value=None, multiselect=True)
-                        button_data = gr.Button("Show Random Sample")
-
-                    with gr.Row():
-                        sample_display = gr.Markdown("{sampled data loads here}")
-
-                    button_data.click(fn=random_sample, inputs=[subset_selector], outputs=[sample_display])
-        with gr.TabItem("RewardBench"):
+                search_2 = gr.Textbox(label="Model Search (delimit with , )", show_label=False, placeholder="Model Search (delimit with , )")
+                model_types_2 = gr.CheckboxGroup(["Seq. Classifiers", "DPO", "Custom Classifiers", "Generative", "AI2 Experiments"], 
+                                                 value=["Seq. Classifiers", "DPO", "Custom Classifiers"], 
+                                                 label="Model Types", 
+                                                 show_label=False,
+                                                #  info="Which model types to include."
+                                                 )
             with gr.Row():
-                gr.Markdown(CAPTION_V1.format(str(total_models_v1)))
-            with gr.Tabs(elem_id="inner-tabs", elem_classes="tabs-small") as tabs:
-                with gr.TabItem("Leaderboard"):
-                    with gr.Row():
-                        search_1_v1 = gr.Textbox(
-                            label="Model Search (delimit with , )",
-                            placeholder="Model Search (delimit with , )",
-                            show_label=False,
-                        )
-                        model_types_1_v1 = gr.CheckboxGroup(
-                            ["Seq. Classifiers", "DPO", "Custom Classifiers", "Generative"],
-                            value=["Seq. Classifiers", "Custom Classifiers", "Generative"],
-                            label="Model Types",
-                            show_label=False,
-                            #  info="Which model types to include.",
-                        )
-                        # narrow, non-expanding download button
-                        gr.DownloadButton(
-                            label="Download CSV",
-                            value=orig_data_path,
-                            size="sm",         # shorter height / padding
-                            scale=0,           # ← **width stays just big enough for the text**
-                            min_width=140,     # (optional) guarantee it doesn’t collapse
-                        )
-                    with gr.Row():
-                        # reference data
-                        rewardbench_table_hidden_v1 = gr.Dataframe(
-                            rb_orig_snapshot.values,
-                            datatype=col_types_rewardbench,
-                            headers=rb_orig_snapshot.columns.tolist(),
-                            visible=False,
-                        )
-                        rewardbench_table_v1 = gr.Dataframe(
-                            regex_table(
-                                rb_orig_snapshot.copy(),
-                                "",
-                                ["Seq. Classifiers", "Custom Classifiers", "Generative"],
-                            ),
-                            datatype=col_types_rewardbench,
-                            headers=rb_orig_snapshot.columns.tolist(),
-                            elem_id="rewardbench_dataframe_avg_v1",
-                            height=800,  # 800 px ≈ ~25 rows on default row-height
-                        )
-                with gr.TabItem("About"):
-                    with gr.Row():
-                        gr.Markdown(ABOUT_TEXT_V1)
-
-                with gr.TabItem("Dataset Viewer"):
-                    with gr.Row():
-                        # loads one sample
-                        gr.Markdown("""## Random Dataset Sample Viewer""")
-                        subset_selector_v1 = gr.Dropdown(subsets_v1, label="Subset", value=None, multiselect=True)
-                        button_data_v1 = gr.Button("Show Random Sample")
-
-                    with gr.Row():
-                        sample_display_v1 = gr.Markdown("{sampled data loads here}")
-
-                    button_data_v1.click(fn=random_sample_v1, inputs=[subset_selector_v1], outputs=[sample_display_v1])
+                # ref data
+                rewardbench_table_detailed_hidden = gr.Dataframe(
+                    rewardbench_data.values,
+                    datatype=col_types_rewardbench,
+                    headers=rewardbench_data.columns.tolist(),
+                    visible=False,
+                )
+                rewardbench_table_detailed = gr.Dataframe(
+                    regex_table(rewardbench_data.copy(), "", ["Seq. Classifiers", "DPO", "Custom Classifiers"]).values,
+                    datatype=col_types_rewardbench,
+                    headers=rewardbench_data.columns.tolist(),
+                    elem_id="rewardbench_dataframe",
+                    height=1000,
+                )
+        # with gr.TabItem("rewardbench Eval Set - Length Bias"):
+        #     with gr.Row():
+        #         # backup
+        #         rewardbench_table_len_hidden = gr.Dataframe(
+        #             rewardbench_data_length.values,
+        #             datatype=cols_rewardbench_data_length,
+        #             headers=rewardbench_data_length.columns.tolist(),
+        #             visible=False,
+        #         )
+        #         rewardbench_table_len = gr.Dataframe(
+        #             regex_table(rewardbench_data_length.copy(), "", False).values,
+        #             datatype=cols_rewardbench_data_length,
+        #             headers=rewardbench_data_length.columns.tolist(),
+        #             elem_id="rewardbench_dataframe_length",
+        #             height=1000,
+        #         )
+        with gr.TabItem("Prior Test Sets"):
+            with gr.Row():
+                search_3 = gr.Textbox(label="Model Search (delimit with , )", show_label=False, placeholder="Model Search (delimit with , )")
+                model_types_3 = gr.CheckboxGroup(["Seq. Classifiers", "DPO", "Custom Classifiers", "Generative", "AI2 Experiments"],
+                                                 value=["Seq. Classifiers", "DPO", "Custom Classifiers"], 
+                                                 label="Model Types",
+                                                 show_label=False, 
+                                                #  info="Which model types to include.",
+                                                 )
+            with gr.Row():
+                PREF_SET_TEXT = """
+                For more information, see the [dataset](https://huggingface.co/datasets/allenai/pref-test-sets). Only the subsets Anthropic Helpful, Anthropic HHH, Stanford SHP, and OpenAI's Summarize data are used in the leaderboard ranking.
+                """
+                gr.Markdown(PREF_SET_TEXT)
+            with gr.Row():
+                # backup
+                pref_sets_table_hidden = gr.Dataframe(
+                    prefs_data.values,
+                    datatype=col_types_prefs,
+                    headers=prefs_data.columns.tolist(),
+                    visible=False,
+                )
+                pref_sets_table = gr.Dataframe(
+                    regex_table(prefs_data.copy(), "", ["Seq. Classifiers", "DPO", "Custom Classifiers"]).values,
+                    datatype=col_types_prefs,
+                    headers=prefs_data.columns.tolist(),
+                    elem_id="prefs_dataframe",
+                    height=1000,
+                )
+
+
+        with gr.TabItem("About"):
+            with gr.Row():
+                gr.Markdown(ABOUT_TEXT)
 
-    search_1.change(regex_table, inputs=[rewardbench_table_hidden, search_1, model_types_1], outputs=rewardbench_table)
-    search_1_v1.change(
-        regex_table, inputs=[rewardbench_table_hidden_v1, search_1_v1, model_types_1_v1], outputs=rewardbench_table_v1
-    )
+        with gr.TabItem("Dataset Viewer"):
+            with gr.Row():
+                # loads one sample
+                gr.Markdown("""## Random Dataset Sample Viewer
+Warning, refusals, XSTest, and donotanswer datasets have sensitive content.""")
+                subset_selector = gr.Dropdown(subsets, label="Subset", value=None, multiselect=True)
+                button = gr.Button("Show Random Sample")
 
-    model_types_1.change(
-        regex_table, inputs=[rewardbench_table_hidden, search_1, model_types_1], outputs=rewardbench_table
-    )
-    model_types_1_v1.change(
-        regex_table, inputs=[rewardbench_table_hidden_v1, search_1_v1, model_types_1_v1], outputs=rewardbench_table_v1
-    )
+            with gr.Row():
+                sample_display = gr.Markdown("{sampled data loads here}")
+
+            button.click(fn=random_sample, inputs=[subset_selector], outputs=[sample_display])
+        # removed plot because not pretty enough
+        # with gr.TabItem("Model Correlation"):
+        #     with gr.Row():
+        #         plot = plot_avg_correlation(rewardbench_data_avg, prefs_data)
+        #         gr.Plot(plot)
+
+    search_1.change(regex_table, inputs=[rewardbench_table_hidden, search_1, model_types_1], outputs=rewardbench_table)
+    search_2.change(regex_table, inputs=[rewardbench_table_detailed_hidden, search_2, model_types_2], outputs=rewardbench_table_detailed)
+    # search.change(regex_table, inputs=[rewardbench_table_len_hidden, search, filter_button], outputs=rewardbench_table_len)
+    search_3.change(regex_table, inputs=[pref_sets_table_hidden, search_3, model_types_3], outputs=pref_sets_table)    
+    
+    model_types_1.change(regex_table, inputs=[rewardbench_table_hidden, search_1, model_types_1], outputs=rewardbench_table)
+    model_types_2.change(regex_table, inputs=[rewardbench_table_detailed_hidden, search_2, model_types_2], outputs=rewardbench_table_detailed)
+    model_types_3.change(regex_table, inputs=[pref_sets_table_hidden, search_3, model_types_3], outputs=pref_sets_table)
 
     with gr.Row():
         with gr.Accordion("📚 Citation", open=False):
             citation_button = gr.Textbox(
-                value=r"""@misc{RewardBench2,
-    title={RewardBench 2: Advancing Reward Model Evaluation},
-    author={Malik, Saumya and Pyatkin, Valentina and Land, Sander and Morrison, Jacob and Smith, Noah A. and Hajishirzi, Hannaneh and Lambert, Nathan},
-    year={2025},
-    howpublished={\url{https://huggingface.co/spaces/allenai/reward-bench}},
-}
-
-@misc{RewardBench,
+                value=r"""@misc{RewardBench,
     title={RewardBench: Evaluating Reward Models for Language Modeling},
     author={Lambert, Nathan and Pyatkin, Valentina and Morrison, Jacob and Miranda, LJ and Lin, Bill Yuchen and Chandu, Khyathi and Dziri, Nouha and Kumar, Sachin and Zick, Tom and Choi, Yejin and Smith, Noah A. and Hajishirzi, Hannaneh},
     year={2024},
@@ -538,5 +384,18 @@ with gr.Blocks(theme=theme, css=custom_css) as app:
                 elem_id="citation-button",
                 show_copy_button=True,
             )
+# Load data when app starts, TODO make this used somewhere...
+# def load_data_on_start():
+#     data_rewardbench = load_all_data(repo_dir_rewardbench)
+#     rewardbench_table.update(data_rewardbench)
+
+#     data_rewardbench_avg = avg_over_rewardbench(repo_dir_rewardbench)
+#     rewardbench_table.update(data_rewardbench_avg)
+
+#     data_prefs = load_all_data(repo_dir_prefs)
+#     pref_sets_table.update(data_prefs)
 
-app.launch(allowed_paths=[str(assets)])  # had .queue() before launch before... not sure if that's necessary
+scheduler = BackgroundScheduler()
+scheduler.add_job(restart_space, "interval", seconds=10800) # restarted every 3h
+scheduler.start()
+app.launch(allowed_paths=['src/']) # had .queue() before launch before... not sure if that's necessary

leaderboard/final-rbv1-data.csv

@@ -1,193 +0,0 @@
-,Model,Model Type,Score,Chat,Chat Hard,Safety,Reasoning,Prior Sets (0.5 weight)
-1,"<a target=""_blank"" href=""https://huggingface.co/infly/INF-ORM-Llama3.1-70B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">infly/INF-ORM-Llama3.1-70B</a>",Seq. Classifier,95.10529562974679,96.64804469273743,91.00877192982456,93.64864864864865,99.1157172477765,
-2,"<a target=""_blank"" href=""https://huggingface.co/ShikaiChen/LDL-Reward-Gemma-2-27B-v0.1"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ShikaiChen/LDL-Reward-Gemma-2-27B-v0.1</a>",Seq. Classifier,94.99413134933042,96.36871508379889,90.78947368421052,93.78378378378379,99.03455284552845,
-3,"<a target=""_blank"" href=""https://huggingface.co/nicolinho/QRM-Gemma-2-27B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">nicolinho/QRM-Gemma-2-27B</a>",Seq. Classifier,94.43611331484493,96.64804469273743,90.13157894736842,92.70270270270271,98.26212691657118,
-4,"<a target=""_blank"" href=""https://huggingface.co/Skywork/Skywork-Reward-Gemma-2-27B-v0.2"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Skywork/Skywork-Reward-Gemma-2-27B-v0.2</a>",Seq. Classifier,94.26093621016115,96.08938547486034,89.91228070175438,92.97297297297297,98.0691056910569,
-5,"<a target=""_blank"" href=""https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Reward"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">nvidia/Llama-3.1-Nemotron-70B-Reward</a> *",Custom Classifier,94.10897209520822,97.48603351955308,85.74561403508773,95.13513513513513,98.0691056910569,
-6,"<a target=""_blank"" href=""https://huggingface.co/Skywork/Skywork-Reward-Gemma-2-27B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Skywork/Skywork-Reward-Gemma-2-27B</a> ⚠️",Seq. Classifier,93.80116450605776,95.81005586592178,91.44736842105263,91.89189189189189,96.05534184536477,
-7,"<a target=""_blank"" href=""https://huggingface.co/SF-Foundation/TextEval-Llama3.1-70B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">SF-Foundation/TextEval-Llama3.1-70B</a> * ⚠️",Generative,93.48032435319458,94.1340782122905,90.13157894736842,93.24324324324324,96.41239700987613,
-8,"<a target=""_blank"" href=""https://huggingface.co/meta-metrics/MetaMetrics-RM-v1.0"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">meta-metrics/MetaMetrics-RM-v1.0</a>",Custom Classifier,93.42462545063005,98.32402234636872,86.40350877192982,90.8108108108108,98.16015987341082,
-9,"<a target=""_blank"" href=""https://huggingface.co/Skywork/Skywork-Critic-Llama-3.1-70B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Skywork/Skywork-Critic-Llama-3.1-70B</a> ⚠️",Generative,93.30801781900792,96.64804469273743,87.93859649122807,93.10810810810811,95.5373219839581,
-10,"<a target=""_blank"" href=""https://huggingface.co/nicolinho/QRM-Llama3.1-8B-v2"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">nicolinho/QRM-Llama3.1-8B-v2</a>",Seq. Classifier,93.13653373860271,96.36871508379889,86.84210526315789,92.56756756756756,96.76774703988652,
-11,"<a target=""_blank"" href=""https://huggingface.co/Skywork/Skywork-Reward-Llama-3.1-8B-v0.2"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Skywork/Skywork-Reward-Llama-3.1-8B-v0.2</a>",Seq. Classifier,93.12997963530022,94.6927374301676,88.37719298245614,92.70270270270271,96.7472854258744,
-12,"<a target=""_blank"" href=""https://huggingface.co/nicolinho/QRM-Llama3.1-8B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">nicolinho/QRM-Llama3.1-8B</a> ⚠️",Seq. Classifier,93.05891420009982,94.41340782122904,89.69298245614036,92.29729729729729,95.83196922573254,
-13,"<a target=""_blank"" href=""https://huggingface.co/LxzGordon/URM-LLaMa-3.1-8B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">LxzGordon/URM-LLaMa-3.1-8B</a> ⚠️",Seq. Classifier,92.93773298857982,95.53072625698324,88.15789473684211,91.08108108108108,96.98122987941288,
-14,"<a target=""_blank"" href=""https://huggingface.co/Salesforce/SFR-LLaMa-3.1-70B-Judge-r"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Salesforce/SFR-LLaMa-3.1-70B-Judge-r</a> *",Generative,92.71833683150776,96.92737430167598,84.75877192982456,91.62162162162163,97.56557947290882,
-15,"<a target=""_blank"" href=""https://huggingface.co/R-I-S-E/RISE-Judge-Qwen2.5-32B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">R-I-S-E/RISE-Judge-Qwen2.5-32B</a>",Generative,92.66088172895866,96.64804469273743,83.33333333333333,91.89189189189189,98.77025699787198,
-16,"<a target=""_blank"" href=""https://huggingface.co/Skywork/Skywork-Reward-Llama-3.1-8B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Skywork/Skywork-Reward-Llama-3.1-8B</a> ⚠️",Seq. Classifier,92.52495013691698,95.81005586592178,87.28070175438596,90.8108108108108,96.19823211654936,
-17,"<a target=""_blank"" href=""https://huggingface.co/AtlaAI/Selene-1"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">AtlaAI/Selene-1</a>",Generative,92.41086740661206,97.76536312849161,83.99122807017544,92.16216216216216,95.72471626561904,
-18,"<a target=""_blank"" href=""https://huggingface.co/general-preference/GPM-Llama-3.1-8B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">general-preference/GPM-Llama-3.1-8B</a> ⚠️",Custom Classifier,92.23713029788581,93.29608938547486,88.59649122807018,91.08108108108108,95.97485949691712,
-19,"<a target=""_blank"" href=""https://huggingface.co/nvidia/Nemotron-4-340B-Reward"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">nvidia/Nemotron-4-340B-Reward</a> *",Custom Classifier,91.9958677606516,95.81005586592178,87.06140350877193,91.48648648648648,93.6255251814263,
-20,"<a target=""_blank"" href=""https://huggingface.co/Ray2333/GRM-Llama3-8B-rewardmodel-ft"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Ray2333/GRM-Llama3-8B-rewardmodel-ft</a> ⚠️",Seq. Classifier,91.53526049213252,95.53072625698324,86.1842105263158,90.8108108108108,93.61529437442026,
-21,"<a target=""_blank"" href=""https://huggingface.co/nicolinho/QRM-Llama3-8B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">nicolinho/QRM-Llama3-8B</a> ⚠️",Seq. Classifier,91.0990919512119,95.81005586592178,81.14035087719299,89.86486486486487,97.581096196868,
-22,"<a target=""_blank"" href=""https://huggingface.co/SF-Foundation/TextEval-OffsetBias-12B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">SF-Foundation/TextEval-OffsetBias-12B</a> *",Generative,91.04924182882311,91.89944134078212,86.62280701754386,92.02702702702703,93.64769192993944,
-23,"<a target=""_blank"" href=""https://huggingface.co/Ray2333/GRM-llama3.2-3B-rewardmodel-ft"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Ray2333/GRM-llama3.2-3B-rewardmodel-ft</a>",Seq. Classifier,90.92295892363056,91.62011173184358,84.86842105263158,92.70270270270271,94.50060020734435,
-24,"<a target=""_blank"" href=""https://huggingface.co/Salesforce/SFR-nemo-12B-Judge-r"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Salesforce/SFR-nemo-12B-Judge-r</a> *",Generative,90.26551100385808,97.20670391061452,82.23684210526316,86.48648648648648,95.13201151306815,
-25,"<a target=""_blank"" href=""https://huggingface.co/internlm/internlm2-20b-reward"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">internlm/internlm2-20b-reward</a>",Seq. Classifier,90.15948083664846,98.88268156424581,76.53508771929825,89.45945945945945,95.76069460359032,
-26,"<a target=""_blank"" href=""https://huggingface.co/Skywork/Skywork-VL-Reward-7B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Skywork/Skywork-VL-Reward-7B</a>",Seq. Classifier,90.07022246172819,89.94413407821229,87.5,91.08108108108108,91.75567468761938,
-27,"<a target=""_blank"" href=""https://huggingface.co/facebook/Self-taught-evaluator-llama3.1-70B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">facebook/Self-taught-evaluator-llama3.1-70B</a> *",Generative,90.01358317701886,96.92737430167598,85.08771929824562,89.5945945945946,88.44464451355923,
-28,"<a target=""_blank"" href=""https://huggingface.co/LxzGordon/URM-LLaMa-3-8B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">LxzGordon/URM-LLaMa-3-8B</a>",Seq. Classifier,89.90981543420907,96.92737430167598,78.7280701754386,88.24324324324324,95.74057401647842,
-29,"<a target=""_blank"" href=""https://huggingface.co/NCSOFT/Llama-3-OffsetBias-RM-8B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">NCSOFT/Llama-3-OffsetBias-RM-8B</a>",Seq. Classifier,89.41975692993036,97.20670391061452,81.79824561403508,86.75675675675676,91.91732143831506,
-30,"<a target=""_blank"" href=""https://huggingface.co/AtlaAI/Selene-1-Mini-Llama-3.1-8B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">AtlaAI/Selene-1-Mini-Llama-3.1-8B</a>",Generative,89.12784912886812,93.57541899441341,79.3859649122807,89.25675675675676,94.29325585202162,
-31,"<a target=""_blank"" href=""https://huggingface.co/Skywork/Skywork-Critic-Llama-3.1-8B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Skywork/Skywork-Critic-Llama-3.1-8B</a>",Generative,88.95511699074142,93.57541899441341,81.35964912280701,91.08108108108108,89.80431876466416,
-32,"<a target=""_blank"" href=""https://huggingface.co/nvidia/Llama3-70B-SteerLM-RM"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">nvidia/Llama3-70B-SteerLM-RM</a> *",Custom Classifier,88.76963582088416,91.34078212290503,80.26315789473684,92.83783783783784,90.63676542805698,
-33,"<a target=""_blank"" href=""https://huggingface.co/Salesforce/SFR-LLaMa-3.1-8B-Judge-r"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Salesforce/SFR-LLaMa-3.1-8B-Judge-r</a> *",Generative,88.65372403487248,95.53072625698324,77.74122807017544,86.21621621621621,95.12672559611501,
-34,"<a target=""_blank"" href=""https://huggingface.co/facebook/Self-taught-Llama-3-70B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">facebook/Self-taught-Llama-3-70B</a> *",Generative,88.62795600264494,96.92737430167598,83.99122807017544,91.08108108108108,82.5121405576472,
-35,"<a target=""_blank"" href=""https://huggingface.co/RLHFlow/ArmoRM-Llama3-8B-v0.1"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">RLHFlow/ArmoRM-Llama3-8B-v0.1</a>",Custom Classifier,88.60367185781917,96.92737430167598,76.75438596491227,90.54054054054055,97.34715174332952,74.29414161945574
-36,"<a target=""_blank"" href=""https://huggingface.co/Ray2333/GRM-gemma2-2B-rewardmodel-ft"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Ray2333/GRM-gemma2-2B-rewardmodel-ft</a>",Seq. Classifier,88.39250002515702,93.01675977653632,77.19298245614036,92.16216216216216,91.19809570578929,
-37,"<a target=""_blank"" href=""https://huggingface.co/google/gemini-1.5-pro-0514"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">google/gemini-1.5-pro-0514</a> *",Generative,88.20069001791948,92.31843575418995,80.59210526315789,87.9054054054054,91.98681364892467,
-38,"<a target=""_blank"" href=""https://huggingface.co/R-I-S-E/RISE-Judge-Qwen2.5-7B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">R-I-S-E/RISE-Judge-Qwen2.5-7B</a>",Generative,88.19099980224239,92.17877094972067,76.53508771929825,87.97297297297297,96.07716756697768,
-39,"<a target=""_blank"" href=""https://huggingface.co/Cohere May 2024"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Cohere May 2024</a> *",Custom Classifier,88.16038708182192,96.36871508379889,71.2719298245614,92.29729729729729,97.68272221312816,78.20215489882585
-40,"<a target=""_blank"" href=""https://huggingface.co/google/flame-1.0-24B-july-2024"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">google/flame-1.0-24B-july-2024</a> *",Generative,87.80801832232187,92.17877094972067,75.65789473684211,89.5945945945946,93.80081300813008,
-41,"<a target=""_blank"" href=""https://huggingface.co/internlm/internlm2-7b-reward"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">internlm/internlm2-7b-reward</a>",Seq. Classifier,87.59316719911449,99.16201117318435,69.51754385964912,87.16216216216216,94.53095160146232,
-42,"<a target=""_blank"" href=""https://huggingface.co/ZiyiYe/Con-J-Qwen2-7B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ZiyiYe/Con-J-Qwen2-7B</a> ⚠️",Generative,87.12028871485069,91.89944134078212,80.26315789473684,88.24324324324324,88.0753123806406,
-43,"<a target=""_blank"" href=""https://huggingface.co/google/gemini-1.5-pro-0924"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">google/gemini-1.5-pro-0924</a>",Generative,86.78430992050927,94.1340782122905,76.97368421052632,85.8108108108108,90.21866644840945,
-44,"<a target=""_blank"" href=""https://huggingface.co/openai/gpt-4o-2024-08-06"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">openai/gpt-4o-2024-08-06</a>",Generative,86.72554986675267,96.08938547486034,76.09649122807018,88.10810810810811,86.60821465597208,
-45,"<a target=""_blank"" href=""https://huggingface.co/RLHFlow/pair-preference-model-LLaMA3-8B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">RLHFlow/pair-preference-model-LLaMA3-8B</a>",Custom Classifier,85.74792972712865,98.32402234636872,65.78947368421052,89.72972972972973,94.73420363398264,74.57650875557454
-46,"<a target=""_blank"" href=""https://huggingface.co/Ray2333/GRM-llama3-8B-sftreg"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Ray2333/GRM-llama3-8B-sftreg</a>",Seq. Classifier,85.42084389305319,98.60335195530726,67.76315789473684,89.1891891891892,92.29347410923774,73.08924874053665
-47,"<a target=""_blank"" href=""https://huggingface.co/opencompass/CompassJudger-1-32B-Instruct"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">opencompass/CompassJudger-1-32B-Instruct</a>",Generative,85.22047081369766,98.04469273743017,65.13157894736842,85.27027027027027,92.43534129972173,
-48,"<a target=""_blank"" href=""https://huggingface.co/Cohere March 2024"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Cohere March 2024</a> *",Custom Classifier,85.10802881361649,94.6927374301676,65.13157894736842,87.70270270270271,98.17073170731707,74.57675774743672
-49,"<a target=""_blank"" href=""https://huggingface.co/Ray2333/GRM-llama3-8B-distill"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Ray2333/GRM-llama3-8B-distill</a>",Seq. Classifier,84.63918882385776,98.32402234636872,68.42105263157895,86.75675675675676,91.3273449009658,72.09434614337957
-50,"<a target=""_blank"" href=""https://huggingface.co/Ray2333/GRM-Gemma-2B-rewardmodel-ft"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Ray2333/GRM-Gemma-2B-rewardmodel-ft</a> ⚠️",Seq. Classifier,84.46827345209587,89.3854748603352,75.21929824561404,84.45945945945945,88.80886124297484,
-51,"<a target=""_blank"" href=""https://huggingface.co/openai/gpt-4-0125-preview"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">openai/gpt-4-0125-preview</a>",Generative,84.33564801010327,95.25139664804469,74.34210526315789,87.56756756756756,86.9236645386588,70.85136405607162
-52,"<a target=""_blank"" href=""https://huggingface.co/mattshumer/Reflection-70B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">mattshumer/Reflection-70B</a>",Generative,84.22327632009588,97.48603351955308,70.6140350877193,83.17567567567568,85.61736099743548,
-53,"<a target=""_blank"" href=""https://huggingface.co/Anthropic/claude-3-5-sonnet-20240620"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Anthropic/claude-3-5-sonnet-20240620</a>",Generative,84.17242041164789,96.36871508379889,74.01315789473684,81.62162162162163,84.68618704643423,
-54,"<a target=""_blank"" href=""https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo</a>",Generative,84.12067803631126,97.20670391061452,74.56140350877193,77.56756756756756,87.14703715829104,
-55,"<a target=""_blank"" href=""https://huggingface.co/opencompass/CompassJudger-1-14B-Instruct"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">opencompass/CompassJudger-1-14B-Instruct</a>",Generative,84.09022697921793,97.48603351955308,62.280701754385966,83.91891891891892,92.67525372401374,
-56,"<a target=""_blank"" href=""https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">meta-llama/Meta-Llama-3.1-70B-Instruct</a>",Generative,84.05217990917473,97.20670391061452,70.17543859649123,82.83783783783784,85.98873929175534,
-57,"<a target=""_blank"" href=""https://huggingface.co/NCSOFT/Llama-3-OffsetBias-8B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">NCSOFT/Llama-3-OffsetBias-8B</a>",Generative,83.96777752436938,92.45810055865921,80.26315789473684,86.75675675675676,76.39309488732471,
-58,"<a target=""_blank"" href=""https://huggingface.co/openai/gpt-4-turbo-2024-04-09"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">openai/gpt-4-turbo-2024-04-09</a>",Generative,83.95011678629895,95.25139664804469,75.43859649122807,87.56756756756756,82.70345664866045,73.629016365689
-59,"<a target=""_blank"" href=""https://huggingface.co/sfairXC/FsfairX-LLaMA3-RM-v0.1"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">sfairXC/FsfairX-LLaMA3-RM-v0.1</a>",Seq. Classifier,83.38339965331156,99.44134078212291,65.13157894736842,86.75675675675676,86.43633709827031,74.91856971076719
-60,"<a target=""_blank"" href=""https://huggingface.co/openai/gpt-4o-2024-05-13"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">openai/gpt-4o-2024-05-13</a>",Generative,83.2681071132992,96.64804469273743,70.39473684210526,86.48648648648648,84.86965951874285,72.61510893954863
-61,"<a target=""_blank"" href=""https://huggingface.co/opencompass/CompassJudger-1-7B-Instruct"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">opencompass/CompassJudger-1-7B-Instruct</a>",Generative,83.16709323590604,97.76536312849161,60.96491228070175,84.45945945945945,89.47863807497134,
-62,"<a target=""_blank"" href=""https://huggingface.co/internlm/internlm2-1_8b-reward"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">internlm/internlm2-1_8b-reward</a>",Seq. Classifier,82.16733515408055,93.57541899441341,66.2280701754386,81.62162162162163,87.24422982484859,
-63,"<a target=""_blank"" href=""https://huggingface.co/CIR-AMS/BTRM_Qwen2_7b_0613"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">CIR-AMS/BTRM_Qwen2_7b_0613</a>",Seq. Classifier,81.72269085246006,97.48603351955308,57.23684210526316,90.13513513513513,87.74894963714738,70.2902968779431
-64,"<a target=""_blank"" href=""https://huggingface.co/openbmb/Eurus-RM-7b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">openbmb/Eurus-RM-7b</a>",Seq. Classifier,81.58895090730017,98.04469273743017,65.5701754385965,81.35135135135135,86.3251623288045,71.71779445333651
-65,"<a target=""_blank"" href=""https://huggingface.co/Nexusflow/Starling-RM-34B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Nexusflow/Starling-RM-34B</a>",Seq. Classifier,81.33351263768401,96.92737430167598,57.23684210526316,87.70270270270271,88.45078299776287,71.36620952434669
-66,"<a target=""_blank"" href=""https://huggingface.co/google/gemma-2-27b-it"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">google/gemma-2-27b-it</a>",Generative,80.89669003773389,94.83240223463687,59.10087719298246,86.35135135135135,83.30212937196487,
-67,"<a target=""_blank"" href=""https://huggingface.co/google/gemini-1.5-flash-001"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">google/gemini-1.5-flash-001</a>",Generative,80.5391103484727,92.17877094972067,63.48684210526316,86.95945945945945,85.1162219675888,69.36940417219024
-68,"<a target=""_blank"" href=""https://huggingface.co/Ray2333/Gemma-2B-rewardmodel-ft"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Ray2333/Gemma-2B-rewardmodel-ft</a> ⚠️",Seq. Classifier,80.47843057507436,77.93296089385476,74.78070175438596,85.27027027027027,83.92978938178643,
-69,"<a target=""_blank"" href=""https://huggingface.co/allenai/tulu-v2.5-13b-preference-mix-rm"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/tulu-v2.5-13b-preference-mix-rm</a>",Seq. Classifier,80.26558812003782,93.57541899441341,68.20175438596492,77.29729729729729,88.50261908659355,67.23611355180205
-70,"<a target=""_blank"" href=""https://huggingface.co/Anthropic/claude-3-opus-20240229"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Anthropic/claude-3-opus-20240229</a>",Generative,80.0759036376447,94.6927374301676,60.30701754385965,86.62162162162163,78.68223795492989,
-71,"<a target=""_blank"" href=""https://huggingface.co/openai/gpt-4o-mini-2024-07-18"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">openai/gpt-4o-mini-2024-07-18</a>",Generative,80.06759386119498,94.97206703910615,60.74561403508772,80.8108108108108,83.7418835597752,
-72,"<a target=""_blank"" href=""https://huggingface.co/weqweasdas/RM-Mistral-7B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">weqweasdas/RM-Mistral-7B</a>",Seq. Classifier,79.8233742639417,96.64804469273743,60.526315789473685,87.02702702702703,77.35615485349484,75.29528365000934
-73,"<a target=""_blank"" href=""https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-70B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">NousResearch/Hermes-3-Llama-3.1-70B</a>",Generative,78.47084260833167,96.22905027932961,56.68859649122807,82.29729729729729,78.6684263654717,
-74,"<a target=""_blank"" href=""https://huggingface.co/hendrydong/Mistral-RM-for-RAFT-GSHF-v0"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">hendrydong/Mistral-RM-for-RAFT-GSHF-v0</a>",Seq. Classifier,78.46503174091394,98.32402234636872,57.89473684210526,85.0,74.33602062530693,75.07572604066365
-75,"<a target=""_blank"" href=""https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo</a>",Generative,78.08002309698713,87.56983240223464,66.8859649122807,75.06756756756756,82.79672750586566,
-76,"<a target=""_blank"" href=""https://huggingface.co/Ray2333/reward-model-Mistral-7B-instruct-Unified-Feedback"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Ray2333/reward-model-Mistral-7B-instruct-Unifie...</a>",Seq. Classifier,76.61192139206588,97.76536312849161,50.6578947368421,85.27027027027027,73.88893435914224,74.3423675391006
-77,"<a target=""_blank"" href=""https://huggingface.co/Ahjeong/MMPO_Gemma_7b_gamma1.1_epoch3"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Ahjeong/MMPO_Gemma_7b_gamma1.1_epoch3</a>",DPO,76.52088102568138,97.20670391061452,63.37719298245614,76.35135135135135,72.84129972172205,69.13483329884433
-78,"<a target=""_blank"" href=""https://huggingface.co/stabilityai/stablelm-2-12b-chat"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">stabilityai/stablelm-2-12b-chat</a>",DPO,76.41872322421631,96.64804469273743,55.48245614035088,78.10810810810811,89.44862770775359,48.39403572004667
-79,"<a target=""_blank"" href=""https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">meta-llama/Meta-Llama-3-70B-Instruct</a>",Generative,76.26515082171642,97.62569832402235,58.88157894736842,72.97297297297297,78.53644895509358,70.3529589965331
-80,"<a target=""_blank"" href=""https://huggingface.co/allenai/tulu-2-dpo-70b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/tulu-2-dpo-70b</a>",DPO,76.20735542607979,97.48603351955308,60.526315789473685,84.45945945945945,74.07206580455066,52.778449688644265
-81,"<a target=""_blank"" href=""https://huggingface.co/gemini-1.5-flash-8b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">gemini-1.5-flash-8b</a>",Generative,76.00524043227317,94.41340782122904,59.86842105263158,73.98648648648648,75.75264636874557,
-82,"<a target=""_blank"" href=""https://huggingface.co/Ahjeong/MMPO_Gemma_7b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Ahjeong/MMPO_Gemma_7b</a>",DPO,75.8660587247668,96.92737430167598,61.40350877192982,71.35135135135135,77.55872483221475,68.31261000855747
-83,"<a target=""_blank"" href=""https://huggingface.co/PoLL/gpt-3.5-turbo-0125_claude-3-sonnet-20240229_meta-llama/Llama-3-70b-chat-hf"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">PoLL/gpt-3.5-turbo-0125_claude-3-sonnet-2024022...</a>",Generative,75.77705517745792,95.25139664804469,54.05701754385965,80.33783783783784,73.46196868008948,
-84,"<a target=""_blank"" href=""https://huggingface.co/allenai/llama-3-tulu-2-dpo-70b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/llama-3-tulu-2-dpo-70b</a>",DPO,74.9612075859509,96.36871508379889,57.45614035087719,74.86486486486487,80.2023653625798,56.86669694931664
-85,"<a target=""_blank"" href=""https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">NousResearch/Nous-Hermes-2-Mistral-7B-DPO</a>",DPO,74.80880493527766,92.17877094972067,60.526315789473685,82.43243243243244,73.75184154526109,55.500522983723165
-86,"<a target=""_blank"" href=""https://huggingface.co/Anthropic/claude-3-sonnet-20240229"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Anthropic/claude-3-sonnet-20240229</a>",Generative,74.57545943180953,93.43575418994413,56.578947368421055,81.6891891891892,69.07005374583947,69.63124589949818
-87,"<a target=""_blank"" href=""https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">mistralai/Mixtral-8x7B-Instruct-v0.1</a>",DPO,74.54632435829336,94.97206703910615,64.03508771929825,72.56756756756756,78.71855731980139,50.330359933093675
-88,"<a target=""_blank"" href=""https://huggingface.co/prometheus-eval/prometheus-8x7b-v2.0"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">prometheus-eval/prometheus-8x7b-v2.0</a>",Generative,74.5095375782243,93.01675977653632,47.14912280701754,80.47297297297297,77.39929475637038,
-89,"<a target=""_blank"" href=""https://huggingface.co/Ray2333/GRM-Gemma-2B-sftreg"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Ray2333/GRM-Gemma-2B-sftreg</a>",Seq. Classifier,74.50927082674883,95.53072625698324,48.68421052631579,79.32432432432432,76.83949909968898,69.82591702611495
-90,"<a target=""_blank"" href=""https://huggingface.co/general-preference/GPM-Gemma-2B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">general-preference/GPM-Gemma-2B</a>",Custom Classifier,74.49128373533642,71.50837988826815,69.73684210526316,81.21621621621621,75.50369673159818,
-91,"<a target=""_blank"" href=""https://huggingface.co/0-hero/Matter-0.1-7B-boost-DPO-preview"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">0-hero/Matter-0.1-7B-boost-DPO-preview</a>",DPO,74.47914014376505,91.06145251396649,60.96491228070175,71.35135135135135,83.94718175369673,55.6624654944527
-92,"<a target=""_blank"" href=""https://huggingface.co/allenai/tulu-v2.5-70b-uf-rm"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/tulu-v2.5-70b-uf-rm</a>",Seq. Classifier,73.98314832639727,86.59217877094972,71.71052631578948,70.13513513513513,75.70046925301467,57.571715987797305
-93,"<a target=""_blank"" href=""https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">HuggingFaceH4/zephyr-7b-alpha</a>",DPO,73.92192687696839,91.62011173184358,62.5,76.62162162162163,75.13982102908277,53.534233127619544
-94,"<a target=""_blank"" href=""https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">upstage/SOLAR-10.7B-Instruct-v1.0</a>",DPO,73.91132026830088,81.56424581005587,68.64035087719299,85.13513513513513,72.51596005892944,49.49049865208112
-95,"<a target=""_blank"" href=""https://huggingface.co/allenai/tulu-2-dpo-13b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/tulu-2-dpo-13b</a>",DPO,73.68126195691116,95.81005586592178,58.333333333333336,79.45945945945945,73.22972936105201,49.46620157266727
-96,"<a target=""_blank"" href=""https://huggingface.co/opencompass/CompassJudger-1-1.5B-Instruct"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">opencompass/CompassJudger-1-1.5B-Instruct</a>",Generative,73.44238723104029,96.36871508379889,49.23245614035088,78.17567567567568,69.99270202433568,
-97,"<a target=""_blank"" href=""https://huggingface.co/allenai/llama-3-tulu-2-8b-uf-mean-rm"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/llama-3-tulu-2-8b-uf-mean-rm</a>",Seq. Classifier,73.41574916848018,95.25139664804469,59.21052631578947,61.62162162162162,82.1155262727124,64.3436007999852
-98,"<a target=""_blank"" href=""https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">HuggingFaceH4/starchat2-15b-v0.1</a>",DPO,73.22060109644468,93.85474860335195,55.48245614035088,70.94594594594595,81.58522944289845,55.248649602907626
-99,"<a target=""_blank"" href=""https://huggingface.co/Ray2333/Gemma-2B-rewardmodel-baseline"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Ray2333/Gemma-2B-rewardmodel-baseline</a>",Seq. Classifier,72.89758740021966,94.1340782122905,46.92982456140351,78.64864864864865,73.84050853931359,68.97216667866445
-100,"<a target=""_blank"" href=""https://huggingface.co/Anthropic/claude-3-haiku-20240307"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Anthropic/claude-3-haiku-20240307</a>",Generative,72.89194286431167,92.73743016759776,51.973684210526315,79.52702702702703,70.60194658154636,66.34730980541012
-101,"<a target=""_blank"" href=""https://huggingface.co/HuggingFaceH4/zephyr-7b-beta"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">HuggingFaceH4/zephyr-7b-beta</a>",DPO,72.80507814531524,95.25139664804469,62.719298245614034,65.67567567567568,77.89497735581382,52.16300745754066
-102,"<a target=""_blank"" href=""https://huggingface.co/allenai/llama-3-tulu-2-dpo-8b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/llama-3-tulu-2-dpo-8b</a>",DPO,72.74751270450155,95.25139664804469,53.50877192982456,66.48648648648648,86.63038140448519,50.973541402832126
-103,"<a target=""_blank"" href=""https://huggingface.co/0-hero/Matter-0.1-7B-DPO-preview"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">0-hero/Matter-0.1-7B-DPO-preview</a>",DPO,72.47264404067178,89.3854748603352,57.675438596491226,63.78378378378378,88.54320128771758,53.477999309390405
-104,"<a target=""_blank"" href=""https://huggingface.co/jondurbin/bagel-dpo-34b-v0.5"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">jondurbin/bagel-dpo-34b-v0.5</a>",DPO,72.15167952196515,93.85474860335195,55.04385964912281,64.45945945945945,88.8907076990233,44.867564875771365
-105,"<a target=""_blank"" href=""https://huggingface.co/allenai/tulu-2-dpo-7b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/tulu-2-dpo-7b</a>",DPO,72.11611434356087,97.48603351955308,56.14035087719298,75.27027027027027,71.75717520598025,47.737369346054734
-106,"<a target=""_blank"" href=""https://huggingface.co/prometheus-eval/prometheus-7b-v2.0"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">prometheus-eval/prometheus-7b-v2.0</a>",Generative,72.04295178846496,85.47486033519553,49.12280701754386,77.0945945945946,76.4795452065259,
-107,"<a target=""_blank"" href=""https://huggingface.co/stabilityai/stablelm-zephyr-3b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">stabilityai/stablelm-zephyr-3b</a>",DPO,71.45809212918405,86.31284916201118,60.08771929824562,74.05405405405405,75.73184372783325,50.74989667836822
-108,"<a target=""_blank"" href=""https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO</a>",DPO,71.38329552978793,91.62011173184358,60.526315789473685,81.48648648648648,61.26104927156654,52.66173320935087
-109,"<a target=""_blank"" href=""https://huggingface.co/ai2/tulu-2-7b-rm-v0-nectar-binarized-700k.json"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ai2/tulu-2-7b-rm-v0-nectar-binarized-700k.json</a>",Seq. Classifier,71.27478404602779,93.57541899441341,40.78947368421053,79.45945945945945,,
-110,"<a target=""_blank"" href=""https://huggingface.co/berkeley-nest/Starling-RM-7B-alpha"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">berkeley-nest/Starling-RM-7B-alpha</a>",Seq. Classifier,71.13020256724107,98.04469273743017,45.6140350877193,84.45945945945945,57.998444917335085,67.93855870128164
-111,"<a target=""_blank"" href=""https://huggingface.co/ai2/tulu-2-7b-rm-v0-nectar-binarized-3.8m-checkpoint-380k.json"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ai2/tulu-2-7b-rm-v0-nectar-binarized-3.8m-check...</a>",Seq. Classifier,70.58403596186601,95.25139664804469,39.473684210526315,77.02702702702703,,
-112,"<a target=""_blank"" href=""https://huggingface.co/CohereForAI/c4ai-command-r-plus"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">CohereForAI/c4ai-command-r-plus</a>",Generative,70.56998248762835,95.11173184357541,57.56578947368421,59.86486486486486,70.40312789872866,69.23881422694875
-113,"<a target=""_blank"" href=""https://huggingface.co/ai2/tulu-2-7b-rm-v0-nectar-binarized-3.8m-checkpoint-2660k.json"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ai2/tulu-2-7b-rm-v0-nectar-binarized-3.8m-check...</a>",Seq. Classifier,70.19339171573809,94.97206703910615,37.5,78.10810810810811,,
-114,"<a target=""_blank"" href=""https://huggingface.co/allenai/llama-3-tulu-2-70b-uf-mean-rm"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/llama-3-tulu-2-70b-uf-mean-rm</a>",Seq. Classifier,70.19307792664753,86.31284916201118,56.14035087719298,60.945945945945944,82.68367708844875,59.57205519263016
-115,"<a target=""_blank"" href=""https://huggingface.co/ai2/tulu-2-7b-rm-v0-nectar-binarized-3.8m-checkpoint-3420k.json"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ai2/tulu-2-7b-rm-v0-nectar-binarized-3.8m-check...</a>",Seq. Classifier,70.07936854820123,93.85474860335195,38.81578947368421,77.56756756756756,,
-116,"<a target=""_blank"" href=""https://huggingface.co/ai2/tulu-2-7b-rm-v0-nectar-binarized-3.8m-checkpoint-3.8m.json"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ai2/tulu-2-7b-rm-v0-nectar-binarized-3.8m-check...</a>",Seq. Classifier,70.03734328271229,94.1340782122905,38.81578947368421,77.16216216216216,,
-117,"<a target=""_blank"" href=""https://huggingface.co/weqweasdas/RM-Gemma-7B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">weqweasdas/RM-Gemma-7B</a>",Seq. Classifier,69.66957334431098,96.92737430167598,49.780701754385966,57.83783783783784,73.62395645768537,70.68641939562845
-118,"<a target=""_blank"" href=""https://huggingface.co/ai2/tulu-2-7b-rm-v0-nectar-binarized-3.8m-checkpoint-3040k.json"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ai2/tulu-2-7b-rm-v0-nectar-binarized-3.8m-check...</a>",Seq. Classifier,69.44952818151877,93.85474860335195,37.06140350877193,77.43243243243244,,
-119,"<a target=""_blank"" href=""https://huggingface.co/ai2/tulu-2-7b-rm-v0-nectar-binarized-3.8m-checkpoint-1900k.json"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ai2/tulu-2-7b-rm-v0-nectar-binarized-3.8m-check...</a>",Seq. Classifier,69.2421964746281,94.41340782122904,35.74561403508772,77.56756756756756,,
-120,"<a target=""_blank"" href=""https://huggingface.co/weqweasdas/RM-Gemma-7B-4096"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">weqweasdas/RM-Gemma-7B-4096</a>",Seq. Classifier,69.22303170109127,94.97206703910615,50.219298245614034,56.08108108108108,75.10912860806461,70.24413536208964
-121,"<a target=""_blank"" href=""https://huggingface.co/ai2/tulu-2-7b-rm-v0-nectar-binarized-3.8m-checkpoint-760k.json"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ai2/tulu-2-7b-rm-v0-nectar-binarized-3.8m-check...</a>",Seq. Classifier,69.04502561956252,94.41340782122904,35.96491228070175,76.75675675675676,,
-122,"<a target=""_blank"" href=""https://huggingface.co/openbmb/UltraRM-13b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">openbmb/UltraRM-13b</a>",Seq. Classifier,69.02867919901104,96.36871508379889,55.48245614035088,59.86486486486486,62.44270748076608,72.94062565153789
-123,"<a target=""_blank"" href=""https://huggingface.co/OpenAssistant/oasst-rm-2.1-pythia-1.4b-epoch-2.5"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">OpenAssistant/oasst-rm-2.1-pythia-1.4b-epoch-2.5</a>",Seq. Classifier,69.00517292135855,88.54748603351955,48.68421052631579,63.108108108108105,77.51882468489114,65.32929758655776
-124,"<a target=""_blank"" href=""https://huggingface.co/openbmb/Eurus-7b-kto"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">openbmb/Eurus-7b-kto</a>",DPO,68.99912142883106,95.25139664804469,53.728070175438596,60.54054054054054,74.67261417580619,52.606849779819356
-125,"<a target=""_blank"" href=""https://huggingface.co/ai2/tulu-2-7b-rm-v0-nectar-binarized-3.8m-checkpoint-2280k.json"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ai2/tulu-2-7b-rm-v0-nectar-binarized-3.8m-check...</a>",Seq. Classifier,68.95403268602327,93.85474860335195,37.06140350877193,75.94594594594595,,
-126,"<a target=""_blank"" href=""https://huggingface.co/Qwen/Qwen1.5-14B-Chat"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Qwen/Qwen1.5-14B-Chat</a>",DPO,68.64045386840729,57.262569832402235,70.17543859649123,71.21621621621621,89.61129753914987,41.23304044714641
-127,"<a target=""_blank"" href=""https://huggingface.co/ai2/tulu-2-7b-rm-v0-nectar-binarized-3.8m-checkpoint-1140k.json"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ai2/tulu-2-7b-rm-v0-nectar-binarized-3.8m-check...</a>",Seq. Classifier,68.08398077583611,93.01675977653632,35.96491228070175,75.27027027027027,,
-128,"<a target=""_blank"" href=""https://huggingface.co/RLHFlow/LLaMA3-iterative-DPO-final"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">RLHFlow/LLaMA3-iterative-DPO-final</a>",DPO,67.82774529803461,83.79888268156425,59.21052631578947,78.64864864864865,61.60650952147105,43.920573347364794
-129,"<a target=""_blank"" href=""https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">HuggingFaceH4/zephyr-7b-gemma-v0.1</a>",DPO,67.57835885153328,95.81005586592178,49.56140350877193,58.24324324324324,74.63476018988378,51.70630404815817
-130,"<a target=""_blank"" href=""https://huggingface.co/ai2/tulu-2-7b-rm-v0-nectar-binarized.json"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ai2/tulu-2-7b-rm-v0-nectar-binarized.json</a>",Seq. Classifier,67.55772237983352,91.34078212290503,39.03508771929825,72.29729729729729,,
-131,"<a target=""_blank"" href=""https://huggingface.co/Qwen/Qwen1.5-7B-Chat"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Qwen/Qwen1.5-7B-Chat</a>",DPO,67.50138253417825,53.63128491620112,69.07894736842105,69.1891891891892,90.41475691602555,42.884086027930344
-132,"<a target=""_blank"" href=""https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp32"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">openbmb/MiniCPM-2B-dpo-fp32</a>",DPO,67.304776500488,89.10614525139665,49.3421052631579,57.2972972972973,82.33378348884159,49.58432590300511
-133,"<a target=""_blank"" href=""https://huggingface.co/mightbe/Better-PairRM"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">mightbe/Better-PairRM</a>",Custom Classifier,67.29754324103595,95.53072625698324,39.25438596491228,82.02702702702703,49.826076280897034,72.40145810968448
-134,"<a target=""_blank"" href=""https://huggingface.co/allenai/OLMo-7B-Instruct"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/OLMo-7B-Instruct</a>",DPO,67.27282652187517,89.66480446927375,50.6578947368421,64.86486486486487,71.6763518306324,51.72760689365022
-135,"<a target=""_blank"" href=""https://huggingface.co/Qwen/Qwen1.5-72B-Chat"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Qwen/Qwen1.5-72B-Chat</a>",DPO,67.23151527906012,62.29050279329609,66.00877192982456,67.56756756756756,85.54352867354177,42.26289558308108
-136,"<a target=""_blank"" href=""https://huggingface.co/ai2/tulu-2-7b-rm-v0.json"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ai2/tulu-2-7b-rm-v0.json</a>",Seq. Classifier,66.54559072450868,93.29608938547486,45.39473684210526,60.945945945945944,,
-137,"<a target=""_blank"" href=""https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B-Chat"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Qwen/Qwen1.5-MoE-A2.7B-Chat</a>",DPO,66.4408456376338,72.90502793296089,63.1578947368421,62.83783783783784,77.40082937742129,45.364430968579995
-138,"<a target=""_blank"" href=""https://huggingface.co/RLHFlow/RewardModel-Mistral-7B-for-DPA-v1"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">RLHFlow/RewardModel-Mistral-7B-for-DPA-v1</a>",Seq. Classifier,66.33145463112653,87.98882681564245,49.780701754385966,70.67567567567568,59.70835379494734,60.675975598835954
-139,"<a target=""_blank"" href=""https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">stabilityai/stablelm-2-zephyr-1_6b</a>",DPO,65.73535970393974,96.64804469273743,46.71052631578947,60.270270270270274,67.84218639166257,48.67618199453821
-140,"<a target=""_blank"" href=""https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo</a>",Generative,65.65164437199641,80.72625698324022,49.780701754385966,63.986486486486484,68.11313226387297,
-141,"<a target=""_blank"" href=""https://huggingface.co/weqweasdas/RM-Gemma-2B"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">weqweasdas/RM-Gemma-2B</a>",Seq. Classifier,65.48909618129333,94.41340782122904,40.78947368421053,49.86486486486486,76.37399738091341,66.51837812920436
-142,"<a target=""_blank"" href=""https://huggingface.co/openai/gpt-3.5-turbo-0125"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">openai/gpt-3.5-turbo-0125</a>",Generative,65.34011575979856,92.17877094972067,44.51754385964912,65.47297297297297,59.12315163420091,65.4761630050997
-143,"<a target=""_blank"" href=""https://huggingface.co/allenai/tulu-v2.5-70b-preference-mix-rm"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/tulu-v2.5-70b-preference-mix-rm</a>",Seq. Classifier,65.15941759094567,77.37430167597765,59.21052631578947,84.86486486486487,41.37508866699405,60.785195271258935
-144,"<a target=""_blank"" href=""https://huggingface.co/wenbopan/Faro-Yi-9B-DPO"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">wenbopan/Faro-Yi-9B-DPO</a>",DPO,64.61094996096162,92.17877094972067,53.07017543859649,55.13513513513514,58.392672013968465,63.945042573813076
-145,"<a target=""_blank"" href=""https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">meta-llama/Meta-Llama-3-8B-Instruct</a>",Generative,64.49786646478918,85.47486033519553,41.55701754385965,67.97297297297297,64.82341627107546,60.82426393689548
-146,"<a target=""_blank"" href=""https://huggingface.co/ai2/llama-2-chat-ultrafeedback-60k.jsonl"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ai2/llama-2-chat-ultrafeedback-60k.jsonl</a>",Seq. Classifier,64.3955076805709,94.41340782122904,45.39473684210526,53.37837837837838,,
-147,"<a target=""_blank"" href=""https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-7B-Reward"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">IDEA-CCNL/Ziya-LLaMA-7B-Reward</a>",Seq. Classifier,63.784551529691385,86.87150837988827,46.05263157894737,64.05405405405405,57.74540295738528,64.61376982667257
-148,"<a target=""_blank"" href=""https://huggingface.co/PKU-Alignment/beaver-7b-v2.0-reward"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">PKU-Alignment/beaver-7b-v2.0-reward</a>",Seq. Classifier,63.66172878401215,89.94413407821229,36.40350877192982,60.4054054054054,68.87004146887108,61.70937960727216
-149,"<a target=""_blank"" href=""https://huggingface.co/stabilityai/stable-code-instruct-3b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">stabilityai/stable-code-instruct-3b</a>",DPO,62.1618132126384,57.82122905027933,58.55263157894737,65.54054054054055,75.28271130026737,45.06209397367635
-150,"<a target=""_blank"" href=""https://huggingface.co/OpenAssistant/oasst-rm-2-pythia-6.9b-epoch-1"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">OpenAssistant/oasst-rm-2-pythia-6.9b-epoch-1</a>",Seq. Classifier,61.501047673154666,92.45810055865921,37.280701754385966,54.45945945945946,58.55022644186174,68.01245262965921
-151,"<a target=""_blank"" href=""https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large-v2"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">OpenAssistant/reward-model-deberta-v3-large-v2</a>",Seq. Classifier,61.25988488574668,89.3854748603352,45.175438596491226,73.37837837837837,38.54968079882141,58.361018703667625
-152,"<a target=""_blank"" href=""https://huggingface.co/llm-blender/PairRM-hf"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">llm-blender/PairRM-hf</a>",Custom Classifier,60.868838250756006,90.22346368715084,52.19298245614035,47.7027027027027,48.983739837398375,69.61376689001952
-153,"<a target=""_blank"" href=""https://huggingface.co/PKU-Alignment/beaver-7b-v2.0-cost"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">PKU-Alignment/beaver-7b-v2.0-cost</a>",Seq. Classifier,59.56778097839703,57.262569832402235,45.6140350877193,76.08108108108108,62.111570360670044,53.97151608182796
-154,"<a target=""_blank"" href=""https://huggingface.co/ContextualAI/archangel_sft-kto_llama13b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ContextualAI/archangel_sft-kto_llama13b</a>",DPO,59.52205456101889,84.07821229050279,37.719298245614034,46.486486486486484,70.76683308779397,57.5968308283755
-155,"<a target=""_blank"" href=""https://huggingface.co/ContextualAI/archangel_sft-kto_llama30b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ContextualAI/archangel_sft-kto_llama30b</a>",DPO,59.00687538053444,84.35754189944134,40.57017543859649,60.54054054054054,50.75435150324658,58.616659661160035
-156,"<a target=""_blank"" href=""https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Qwen/Qwen1.5-1.8B-Chat</a>",DPO,58.89567615638699,56.14525139664804,60.30701754385965,48.37837837837838,77.93283134173623,44.53412808623833
-157,"<a target=""_blank"" href=""https://huggingface.co/ai2/llama-2-chat-7b-nectar-3.8m.json"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ai2/llama-2-chat-7b-nectar-3.8m.json</a>",Seq. Classifier,58.426789771247286,86.31284916201118,26.535087719298247,62.432432432432435,,
-158,"<a target=""_blank"" href=""https://huggingface.co/PKU-Alignment/beaver-7b-v1.0-cost"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">PKU-Alignment/beaver-7b-v1.0-cost</a>",Seq. Classifier,57.97567401900532,61.73184357541899,42.324561403508774,73.51351351351352,54.82109728815409,56.999034609857176
-159,"<a target=""_blank"" href=""https://huggingface.co/ContextualAI/archangel_sft-dpo_llama30b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ContextualAI/archangel_sft-dpo_llama30b</a>",DPO,56.18285201407361,69.27374301675978,44.73684210526316,62.83783783783784,47.449118786489876,57.0505846339612
-160,"<a target=""_blank"" href=""https://huggingface.co/ContextualAI/archangel_sft-kto_pythia1-4b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ContextualAI/archangel_sft-kto_pythia1-4b</a>",DPO,55.809930200702766,68.43575418994413,37.93859649122807,52.567567567567565,64.47488677906914,55.455761750707126
-161,"<a target=""_blank"" href=""https://huggingface.co/ContextualAI/archangel_sft-kto_pythia6-9b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ContextualAI/archangel_sft-kto_pythia6-9b</a>",DPO,55.6117865296703,77.6536312849162,36.18421052631579,53.648648648648646,54.153707644459004,57.22568255835343
-162,"<a target=""_blank"" href=""https://huggingface.co/ContextualAI/archangel_sft-kto_pythia2-8b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ContextualAI/archangel_sft-kto_pythia2-8b</a>",DPO,54.96592159422631,75.69832402234637,34.21052631578947,47.432432432432435,62.1572679652971,55.69619287630597
-163,"<a target=""_blank"" href=""https://huggingface.co/Qwen/Qwen1.5-4B-Chat"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Qwen/Qwen1.5-4B-Chat</a>",DPO,54.77003940637828,38.8268156424581,62.719298245614034,55.67567567567568,66.89344955530092,44.69987641930703
-164,"<a target=""_blank"" href=""https://huggingface.co/ContextualAI/archangel_sft-dpo_llama13b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ContextualAI/archangel_sft-dpo_llama13b</a>",DPO,53.99846978252061,71.22905027932961,42.98245614035088,56.486486486486484,44.013272766955865,56.56369669643977
-165,"<a target=""_blank"" href=""https://huggingface.co/ContextualAI/archangel_sft-kto_llama7b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ContextualAI/archangel_sft-kto_llama7b</a>",DPO,53.883046644273705,55.865921787709496,43.64035087719298,45.67567567567568,69.41432040159329,55.754882314120465
-166,"<a target=""_blank"" href=""https://huggingface.co/ContextualAI/archangel_sft-dpo_llama7b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ContextualAI/archangel_sft-dpo_llama7b</a>",DPO,53.036829672694374,57.82122905027933,44.51754385964912,52.027027027027025,56.58147814699623,55.43691088634592
-167,"<a target=""_blank"" href=""https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">Qwen/Qwen1.5-0.5B-Chat</a>",DPO,52.982802188122534,35.47486033519553,62.93859649122807,57.027027027027025,59.83862607082447,46.28699984455265
-168,"<a target=""_blank"" href=""https://huggingface.co/ContextualAI/archangel_sft-dpo_pythia2-8b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ContextualAI/archangel_sft-dpo_pythia2-8b</a>",DPO,52.857927047782155,80.72625698324022,33.55263157894737,44.729729729729726,51.34671522889725,55.0106763884103
-169,"<a target=""_blank"" href=""https://huggingface.co/my_model/"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">my_model/</a>",Seq. Classifier,52.672491797862534,45.53072625698324,55.921052631578945,43.91891891891892,65.319269383969,
-170,"<a target=""_blank"" href=""https://huggingface.co/ContextualAI/archangel_sft-dpo_pythia6-9b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ContextualAI/archangel_sft-dpo_pythia6-9b</a>",DPO,52.6326255248281,74.86033519553072,34.21052631578947,51.75675675675676,48.470153325694326,55.09808653591037
-171,"<a target=""_blank"" href=""https://huggingface.co/ai2/llama-2-chat-nectar-180k.json"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ai2/llama-2-chat-nectar-180k.json</a>",Seq. Classifier,52.34906620822528,88.26815642458101,28.50877192982456,40.270270270270274,,
-172,"<a target=""_blank"" href=""https://huggingface.co/ContextualAI/archangel_sft-dpo_pythia1-4b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ContextualAI/archangel_sft-dpo_pythia1-4b</a>",DPO,52.334628884533196,63.96648044692738,37.280701754385966,50.4054054054054,56.71652479947619,54.27343514840888
-173,"<a target=""_blank"" href=""https://huggingface.co/stanfordnlp/SteamSHP-flan-t5-xl"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">stanfordnlp/SteamSHP-flan-t5-xl</a>",Custom Classifier,51.34535042343637,85.47486033519553,36.8421052631579,37.83783783783784,38.41156490423965,64.97541713006551
-174,"<a target=""_blank"" href=""https://huggingface.co/SultanR/SmolTulu-1.7b-RM"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">SultanR/SmolTulu-1.7b-RM</a>",Seq. Classifier,50.93872947030961,74.30167597765363,44.078947368421055,57.16216216216216,28.212132373001584,
-175,"<a target=""_blank"" href=""https://huggingface.co/ContextualAI/archangel_sft-kto_pythia12-0b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ContextualAI/archangel_sft-kto_pythia12-0b</a>",DPO,50.52988550561952,74.86033519553072,36.18421052631579,47.567567567567565,41.27175751623288,55.001227939281776
-176,"<a target=""_blank"" href=""https://huggingface.co/weqweasdas/hh_rlhf_rm_open_llama_3b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">weqweasdas/hh_rlhf_rm_open_llama_3b</a>",Seq. Classifier,50.274817067272814,81.84357541899442,37.280701754385966,41.486486486486484,32.80815190702243,65.63552247167672
-177,"<a target=""_blank"" href=""https://huggingface.co/ContextualAI/archangel_sft-dpo_pythia12-0b"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">ContextualAI/archangel_sft-dpo_pythia12-0b</a>",DPO,50.08791349970499,66.75977653631286,36.40350877192982,54.32432432432432,41.39384514650516,53.02831193920059
-178,"<a target=""_blank"" href=""https://huggingface.co/random"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">random</a>",,50.0,50.0,50.0,50.0,50.0,50.0
-179,"<a target=""_blank"" href=""https://huggingface.co/stanfordnlp/SteamSHP-flan-t5-large"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">stanfordnlp/SteamSHP-flan-t5-large</a>",Custom Classifier,49.62050475651485,85.75418994413408,33.1140350877193,37.432432432432435,35.62673923719103,62.72974940567991
-180,"<a target=""_blank"" href=""https://huggingface.co/allenai/tulu-v2.5-13b-uf-rm"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/tulu-v2.5-13b-uf-rm</a>",Seq. Classifier,48.05551076423311,39.385474860335194,42.324561403508774,55.54054054054054,47.36897746494243,63.26048833944414
-181,"<a target=""_blank"" href=""https://huggingface.co/PKU-Alignment/beaver-7b-v1.0-reward"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">PKU-Alignment/beaver-7b-v1.0-reward</a>",Seq. Classifier,47.26664990676508,81.84357541899442,28.728070175438596,37.567567567567565,34.596155944780925,59.929110947322734
-182,"<a target=""_blank"" href=""https://huggingface.co/allenai/Llama-3.1-70B-Instruct-RM-RB2"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/Llama-3.1-70B-Instruct-RM-RB2</a>",Seq. Classifier,90.20891847250666,96.64804469273743,83.55263157894737,90.94594594594595,89.68905167239592,0.0
-183,"<a target=""_blank"" href=""https://huggingface.co/allenai/Llama-3.1-8B-Instruct-RM-RB2"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/Llama-3.1-8B-Instruct-RM-RB2</a>",Seq. Classifier,88.85411761564486,95.81005586592178,81.57894736842105,89.32432432432432,88.70314290391227,0.0
-184,"<a target=""_blank"" href=""https://huggingface.co/allenai/Llama-3.1-8B-Base-RM-RB2"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/Llama-3.1-8B-Base-RM-RB2</a>",Seq. Classifier,84.63022615056406,93.29608938547486,77.85087719298247,88.51351351351352,78.86042451028537,0.0
-185,"<a target=""_blank"" href=""https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-SFT-RM-RB2"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/Llama-3.1-Tulu-3-8B-SFT-RM-RB2</a>",Seq. Classifier,85.5067097271751,94.97206703910615,79.16666666666666,87.83783783783784,80.05026736508975,0.0
-186,"<a target=""_blank"" href=""https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-DPO-RM-RB2"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/Llama-3.1-Tulu-3-8B-DPO-RM-RB2</a>",Seq. Classifier,84.30783781180817,95.53072625698324,76.09649122807018,86.62162162162163,78.98251214055765,0.0
-187,"<a target=""_blank"" href=""https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-RL-RM-RB2"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/Llama-3.1-Tulu-3-8B-RL-RM-RB2</a>",Seq. Classifier,83.68729455170623,94.6927374301676,75.87719298245614,87.02702702702703,77.15222076717411,0.0
-188,"<a target=""_blank"" href=""https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-SFT-RM-RB2"" style=""color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;"">allenai/Llama-3.1-Tulu-3-70B-SFT-RM-RB2</a>",Seq. Classifier,88.9245750153865,96.92737430167597,82.67543859649122,90.27027027027027,85.82521689310852,0.0
-
-
-
-

leaderboard/retired-app.py

@@ -1,518 +0,0 @@
-import os
-
-import gradio as gr
-import numpy as np
-from apscheduler.schedulers.background import BackgroundScheduler
-from datasets import load_dataset
-from huggingface_hub import HfApi, snapshot_download
-from src.constants import example_counts, length_categories, subset_mapping
-from src.css import custom_css
-from src.md import ABOUT_TEXT, TOP_TEXT
-from src.utils import load_all_data
-
-api = HfApi()
-
-COLLAB_TOKEN = os.environ.get("COLLAB_TOKEN")
-evals_repo = "allenai/reward-bench-results"
-
-eval_set_repo = "allenai/reward-bench"
-repo_dir_rewardbench = "./evals/rewardbench/"
-
-
-def restart_space():
-    api.restart_space(repo_id="allenai/reward-bench", token=COLLAB_TOKEN)
-
-
-print("Pulling evaluation results")
-repo = snapshot_download(
-    local_dir=repo_dir_rewardbench,
-    ignore_patterns=["pref-sets-scores/*", "eval-set-scores/*"],
-    repo_id=evals_repo,
-    use_auth_token=COLLAB_TOKEN,
-    tqdm_class=None,
-    etag_timeout=30,
-    repo_type="dataset",
-)
-
-
-def avg_over_rewardbench(dataframe_core, dataframe_prefs):
-    """
-    Averages over the subsets alpacaeval, mt-bench, llmbar, refusals, hep and returns dataframe with only these columns.
-
-    We average over 4 core sections (per prompt weighting):
-    1. Chat: Includes the easy chat subsets (alpacaeval-easy, alpacaeval-length, alpacaeval-hard, mt-bench-easy, mt-bench-medium)
-    2. Chat Hard: Includes the hard chat subsets (mt-bench-hard, llmbar-natural, llmbar-adver-neighbor, llmbar-adver-GPTInst, llmbar-adver-GPTOut, llmbar-adver-manual)
-    3. Safety: Includes the safety subsets (refusals-dangerous, refusals-offensive, xstest-should-refuse, xstest-should-respond, do not answer)
-    4. Reasoning: Includes the code and math subsets (math-prm, hep-cpp, hep-go, hep-java, hep-js, hep-python, hep-rust)
-    5. Prior Sets (0.5 weight): Includes the test sets (anthropic_helpful, mtbench_human, shp, summarize)
-    """
-    new_df = dataframe_core.copy()
-    dataframe_prefs = dataframe_prefs.copy()
-
-    # for main subsets, keys in subset_mapping, take the weighted avg by example_counts and store for the models
-    for subset, sub_subsets in subset_mapping.items():
-        subset_cols = [col for col in new_df.columns if col in sub_subsets]
-        sub_data = new_df[subset_cols].values  # take the relevant column values
-        sub_counts = [example_counts[s] for s in subset_cols]  # take the example counts
-        new_df[subset] = np.average(sub_data, axis=1, weights=sub_counts)  # take the weighted average
-        # new_df[subset] = np.round(np.nanmean(new_df[subset_cols].values, axis=1), 2)
-
-    data_cols = list(subset_mapping.keys())
-    keep_columns = (
-        [
-            "model",
-        ]
-        + ["model_type"]
-        + data_cols
-    )
-    # keep_columns = ["model", "average"] + subsets
-    new_df = new_df[keep_columns]
-
-    # selected average from pref_sets
-    pref_columns = ["anthropic_helpful", "anthropic_hhh", "shp", "summarize"]
-    pref_data = dataframe_prefs[pref_columns].values
-
-    # add column test sets knowing the rows are not identical, take superset
-    dataframe_prefs["Prior Sets (0.5 weight)"] = np.nanmean(pref_data, axis=1)
-
-    # add column Test Sets empty to new_df
-    new_df["Prior Sets (0.5 weight)"] = np.nan
-    # per row in new_df if model is in dataframe_prefs, add the value to new_df["Prior Sets (0.5 weight)"]
-    values = []
-    for i, row in new_df.iterrows():
-        model = row["model"]
-        if model in dataframe_prefs["model"].values:
-            values.append(dataframe_prefs[dataframe_prefs["model"] == model]["Prior Sets (0.5 weight)"].values[0])
-            # new_df.at[i, "Prior Sets (0.5 weight)"] = dataframe_prefs[dataframe_prefs["model"] == model]["Prior Sets (0.5 weight)"].values[0]
-        else:
-            values.append(np.nan)
-
-    new_df["Prior Sets (0.5 weight)"] = values
-
-    # add total average
-    data_cols += ["Prior Sets (0.5 weight)"]
-    final_data = new_df[data_cols].values
-    masked_data = np.ma.masked_array(final_data, np.isnan(final_data))
-    weights = [2, 2, 2, 2, 1]
-    average = np.ma.average(masked_data, axis=1, weights=weights)
-    new_df["average"] = average.filled(np.nan)
-    # new_df["average"] = np.nanmean(new_df[data_cols].values, axis=1)
-
-    # make average third column
-    keep_columns = ["model", "model_type", "average"] + data_cols
-    new_df = new_df[keep_columns]
-    return new_df
-
-
-def expand_subsets(dataframe):
-    # TODO need to modify data/ script to do this
-    pass
-
-
-def length_bias_check(dataframe):
-    """
-    Takes the raw rewardbench dataframe and splits the data into new buckets according to length_categories.
-    Then, take the average of the three buckets as "average"
-    """
-    new_df = dataframe.copy()
-    existing_subsets = new_df.columns[3:]  # model, model_type, average
-    final_subsets = ["Length Bias", "Neutral", "Terse Bias"]
-    # new data is empty list dict for each final subset
-    new_data = {s: [] for s in final_subsets}
-
-    # now, subsets correspond to those with True, Nuetral, and False length bias
-    # check if length_categories[subset] == "True" or "False" or "Neutral"
-    for subset in existing_subsets:
-        subset_data = new_df[subset].values
-        subset_length = length_categories[subset]
-        # route to the correct bucket
-        if subset_length == "True":
-            new_data["Length Bias"].append(subset_data)
-        elif subset_length == "Neutral":
-            new_data["Neutral"].append(subset_data)
-        elif subset_length == "False":
-            new_data["Terse Bias"].append(subset_data)
-
-    # take average of new_data and add to new_df (removing other columns than model)
-    for subset in final_subsets:
-        new_df[subset] = np.nanmean(new_data[subset], axis=0)
-    keep_columns = ["model"] + final_subsets
-    new_df = new_df[keep_columns]
-    # recompute average
-    # new_df["average"] = np.round(np.nanmean(new_df[final_subsets].values, axis=1), 2)
-
-    return new_df
-
-
-rewardbench_data = load_all_data(repo_dir_rewardbench, subdir="eval-set").sort_values(by="average", ascending=False)
-rewardbench_data_length = length_bias_check(rewardbench_data).sort_values(by="Terse Bias", ascending=False)
-prefs_data = load_all_data(repo_dir_rewardbench, subdir="pref-sets").sort_values(by="average", ascending=False)
-# prefs_data_sub = expand_subsets(prefs_data).sort_values(by='average', ascending=False)
-
-rewardbench_data_avg = avg_over_rewardbench(rewardbench_data, prefs_data).sort_values(by="average", ascending=False)
-
-
-def prep_df(df):
-    # add column to 0th entry with count (column name itself empty)
-    df.insert(0, "", range(1, 1 + len(df)))
-
-    # replace "model" with "Model" and "model_type" with "Model Type" and "average" with "Average"
-    df = df.rename(columns={"model": "Model", "model_type": "Model Type", "average": "Average"})
-
-    # if "Model Type" in columns
-    if "Model Type" in df.columns:
-        # get model_types that have generative in them
-        mask = df["Model Type"].str.contains("generative", case=False, na=False)
-
-        # set these values to "Generative"
-        df.loc[mask, "Model Type"] = "Generative"
-
-    return df
-
-
-# add count column to all dataframes
-rewardbench_data = prep_df(rewardbench_data)
-rewardbench_data_avg = prep_df(rewardbench_data_avg).rename(columns={"Average": "Score"})
-# adjust weight of this average to 50% for Prior Sets (0.5 weight), 1 for others
-
-# save rewardbench_data_avg to csv or json
-rewardbench_data_avg.to_csv("rewardbench_data_avg.csv", index=False)
-
-rewardbench_data_length = prep_df(rewardbench_data_length)
-prefs_data = prep_df(prefs_data)
-
-col_types_rewardbench = ["number"] + ["markdown"] + ["str"] + ["number"] * (len(rewardbench_data.columns) - 1)
-col_types_rewardbench_avg = ["number"] + ["markdown"] + ["str"] + ["number"] * (len(rewardbench_data_avg.columns) - 1)
-cols_rewardbench_data_length = ["markdown"] + ["number"] * (len(rewardbench_data_length.columns) - 1)
-col_types_prefs = ["number"] + ["markdown"] + ["number"] * (len(prefs_data.columns) - 1)
-# col_types_prefs_sub = ["markdown"] + ["number"] * (len(prefs_data_sub.columns) - 1)
-
-# for showing random samples
-eval_set = load_dataset(eval_set_repo, use_auth_token=COLLAB_TOKEN, split="filtered")
-
-
-def random_sample(r: gr.Request, subset):
-    if subset is None or subset == []:
-        sample_index = np.random.randint(0, len(eval_set) - 1)
-        sample = eval_set[sample_index]
-    else:  # filter by subsets (can be list)
-        if isinstance(subset, str):
-            subset = [subset]
-        # filter down dataset to only include the subset(s)
-        eval_set_filtered = eval_set.filter(lambda x: x["subset"] in subset)
-        sample_index = np.random.randint(0, len(eval_set_filtered) - 1)
-        sample = eval_set_filtered[sample_index]
-
-    markdown_text = "\n\n".join([f"**{key}**:\n\n{value}" for key, value in sample.items()])
-    return markdown_text
-
-
-subsets = eval_set.unique("subset")
-
-color_map = {
-    "Generative": "#7497db",
-    "Custom Classifier": "#E8ECF2",
-    "Seq. Classifier": "#ffcd75",
-    "DPO": "#75809c",
-}
-
-
-def color_model_type_column(df, color_map):
-    """
-    Apply color to the 'Model Type' column of the DataFrame based on a given color mapping.
-
-    Parameters:
-    df (pd.DataFrame): The DataFrame containing the 'Model Type' column.
-    color_map (dict): A dictionary mapping model types to colors.
-
-    Returns:
-    pd.Styler: The styled DataFrame.
-    """
-
-    # Function to apply color based on the model type
-    def apply_color(val):
-        color = color_map.get(val, "default")  # Default color if not specified in color_map
-        return f"background-color: {color}"
-
-    # Format for different columns
-    format_dict = {col: "{:.1f}" for col in df.columns if col not in ["Average", "Model", "Model Type"]}
-    format_dict["Average"] = "{:.2f}"
-    format_dict[""] = "{:d}"
-
-    return df.style.applymap(apply_color, subset=["Model Type"]).format(format_dict, na_rep="")
-
-
-def regex_table(dataframe, regex, filter_button, style=True):
-    """
-    Takes a model name as a regex, then returns only the rows that has that in it.
-    """
-    # Split regex statement by comma and trim whitespace around regexes
-    regex_list = [x.strip() for x in regex.split(",")]
-    # Join the list into a single regex pattern with '|' acting as OR
-    combined_regex = "|".join(regex_list)
-
-    # remove internal ai2 data
-    dataframe = dataframe[~dataframe["Model"].str.contains("ai2", case=False, na=False)]
-
-    # if filter_button, remove all rows with "ai2" in the model name
-    update_scores = False
-    if isinstance(filter_button, list) or isinstance(filter_button, str):
-        if "Prior Sets" not in filter_button and "Prior Sets (0.5 weight)" in dataframe.columns:
-            update_scores = True
-            # remove the column "Prior Sets (0.5 weight)" from the outputted table
-            dataframe = dataframe.drop(columns=["Prior Sets (0.5 weight)"])
-        if "Seq. Classifiers" not in filter_button:
-            dataframe = dataframe[~dataframe["Model Type"].str.contains("Seq. Classifier", case=False, na=False)]
-        if "DPO" not in filter_button:
-            dataframe = dataframe[~dataframe["Model Type"].str.contains("DPO", case=False, na=False)]
-        if "Custom Classifiers" not in filter_button:
-            dataframe = dataframe[~dataframe["Model Type"].str.contains("Custom Classifier", case=False, na=False)]
-        if "Generative" not in filter_button:
-            dataframe = dataframe[~dataframe["Model Type"].str.contains("generative", case=False, na=False)]
-    # Filter the dataframe such that 'model' contains any of the regex patterns
-    data = dataframe[dataframe["Model"].str.contains(combined_regex, case=False, na=False)]
-
-    # if update the score to not use prior sets, do so
-    if update_scores:
-        data["Score"] = (data["Chat"] + data["Chat Hard"] + data["Safety"] + data["Reasoning"]) / 4
-        # if "Prior Sets (0.5 weight)" in data.columns:
-        # data["Prior Sets (0.5 weight)"] = np.nan
-        # sort array by Score column
-        data = data.sort_values(by="Score", ascending=False)
-
-    data.reset_index(drop=True, inplace=True)
-
-    # replace column '' with count/rank
-    data[""] = np.arange(1, 1 + len(data))
-
-    # if Score exists, round to 2 decimals
-    if "Score" in data.columns:
-        data["Score"] = np.round(np.array(data["Score"].values).astype(float), 2)
-    if "Average" in data.columns:
-        data["Average"] = np.round(np.array(data["Average"].values).astype(float), 1)
-    # round all others to 1 decimal
-    for col in data.columns:
-        if col not in ["", "Model", "Model Type", "Score", "Average"]:
-            # replace any data[col].values == '' with np.nan
-            data[col] = data[col].replace("", np.nan)
-            data[col] = np.round(np.array(data[col].values).astype(float), 1)
-    if style:
-        # apply color
-        data = color_model_type_column(data, color_map)
-
-    return data
-
-
-# import ipdb; ipdb.set_trace()
-
-total_models = len(
-    regex_table(
-        rewardbench_data_avg.copy(), "", ["Seq. Classifiers", "DPO", "Custom Classifiers", "Generative"], style=False
-    ).values
-)
-
-with gr.Blocks(css=custom_css) as app:
-    # create tabs for the app, moving the current table to one titled "rewardbench" and the benchmark_text to a tab called "About"
-    with gr.Row():
-        with gr.Column(scale=6):
-            gr.Markdown(TOP_TEXT.format(str(total_models)))
-        with gr.Column(scale=4):
-            # search = gr.Textbox(label="Model Search (delimit with , )", placeholder="Regex search for a model")
-            # filter_button = gr.Checkbox(label="Include AI2 training runs (or type ai2 above).", interactive=True)
-            # img = gr.Image(value="https://private-user-images.githubusercontent.com/10695622/310698241-24ed272a-0844-451f-b414-fde57478703e.png", width=500)
-            gr.Markdown(
-                """
-                        ![](file/src/logo.png)
-                        """
-            )
-    with gr.Tabs(elem_classes="tab-buttons") as tabs:
-        with gr.TabItem("🏆 RewardBench Leaderboard"):
-            with gr.Row():
-                search_1 = gr.Textbox(
-                    label="Model Search (delimit with , )",
-                    placeholder="Model Search (delimit with , )",
-                    show_label=False,
-                )
-                model_types_1 = gr.CheckboxGroup(
-                    ["Seq. Classifiers", "DPO", "Custom Classifiers", "Generative", "Prior Sets"],
-                    value=["Seq. Classifiers", "DPO", "Custom Classifiers", "Generative"],
-                    label="Model Types",
-                    show_label=False,
-                    #  info="Which model types to include.",
-                )
-            with gr.Row():
-                # reference data
-                rewardbench_table_hidden = gr.Dataframe(
-                    rewardbench_data_avg.values,
-                    datatype=col_types_rewardbench_avg,
-                    headers=rewardbench_data_avg.columns.tolist(),
-                    visible=False,
-                )
-                rewardbench_table = gr.Dataframe(
-                    regex_table(
-                        rewardbench_data_avg.copy(),
-                        "",
-                        ["Seq. Classifiers", "DPO", "Custom Classifiers", "Generative"],
-                    ),
-                    datatype=col_types_rewardbench_avg,
-                    headers=rewardbench_data_avg.columns.tolist(),
-                    elem_id="rewardbench_dataframe_avg",
-                    height=1000,
-                )
-
-        with gr.TabItem("🔍 RewardBench - Detailed"):
-            with gr.Row():
-                search_2 = gr.Textbox(
-                    label="Model Search (delimit with , )",
-                    show_label=False,
-                    placeholder="Model Search (delimit with , )",
-                )
-                model_types_2 = gr.CheckboxGroup(
-                    ["Seq. Classifiers", "DPO", "Custom Classifiers", "Generative"],
-                    value=["Seq. Classifiers", "DPO", "Generative", "Custom Classifiers"],
-                    label="Model Types",
-                    show_label=False,
-                    #  info="Which model types to include."
-                )
-            with gr.Row():
-                # ref data
-                rewardbench_table_detailed_hidden = gr.Dataframe(
-                    rewardbench_data.values,
-                    datatype=col_types_rewardbench,
-                    headers=rewardbench_data.columns.tolist(),
-                    visible=False,
-                )
-                rewardbench_table_detailed = gr.Dataframe(
-                    regex_table(
-                        rewardbench_data.copy(), "", ["Seq. Classifiers", "DPO", "Generative", "Custom Classifiers"]
-                    ),
-                    datatype=col_types_rewardbench,
-                    headers=rewardbench_data.columns.tolist(),
-                    elem_id="rewardbench_dataframe",
-                    height=1000,
-                )
-        # with gr.TabItem("rewardbench Eval Set - Length Bias"):
-        #     with gr.Row():
-        #         # backup
-        #         rewardbench_table_len_hidden = gr.Dataframe(
-        #             rewardbench_data_length.values,
-        #             datatype=cols_rewardbench_data_length,
-        #             headers=rewardbench_data_length.columns.tolist(),
-        #             visible=False,
-        #         )
-        #         rewardbench_table_len = gr.Dataframe(
-        #             regex_table(rewardbench_data_length.copy(), "", False).values,
-        #             datatype=cols_rewardbench_data_length,
-        #             headers=rewardbench_data_length.columns.tolist(),
-        #             elem_id="rewardbench_dataframe_length",
-        #             height=1000,
-        #         )
-        with gr.TabItem("Prior Test Sets"):
-            with gr.Row():
-                search_3 = gr.Textbox(
-                    label="Model Search (delimit with , )",
-                    show_label=False,
-                    placeholder="Model Search (delimit with , )",
-                )
-                model_types_3 = gr.CheckboxGroup(
-                    ["Seq. Classifiers", "DPO", "Custom Classifiers", "Generative"],
-                    value=["Seq. Classifiers", "DPO", "Custom Classifiers"],
-                    label="Model Types",
-                    show_label=False,
-                    #  info="Which model types to include.",
-                )
-            with gr.Row():
-                PREF_SET_TEXT = """
-                For more information, see the [dataset](https://huggingface.co/datasets/allenai/pref-test-sets). Only the subsets Anthropic Helpful, Anthropic HHH, Stanford SHP, and OpenAI's Summarize data are used in the leaderboard ranking.
-                """
-                gr.Markdown(PREF_SET_TEXT)
-            with gr.Row():
-                # backup
-                pref_sets_table_hidden = gr.Dataframe(
-                    prefs_data.values,
-                    datatype=col_types_prefs,
-                    headers=prefs_data.columns.tolist(),
-                    visible=False,
-                )
-                pref_sets_table = gr.Dataframe(
-                    regex_table(prefs_data.copy(), "", ["Seq. Classifiers", "DPO", "Custom Classifiers"]),
-                    datatype=col_types_prefs,
-                    headers=prefs_data.columns.tolist(),
-                    elem_id="prefs_dataframe",
-                    height=1000,
-                )
-
-        with gr.TabItem("About"):
-            with gr.Row():
-                gr.Markdown(ABOUT_TEXT)
-
-        with gr.TabItem("Dataset Viewer"):
-            with gr.Row():
-                # loads one sample
-                gr.Markdown(
-                    """## Random Dataset Sample Viewer
-Warning, refusals, XSTest, and donotanswer datasets have sensitive content."""
-                )
-                subset_selector = gr.Dropdown(subsets, label="Subset", value=None, multiselect=True)
-                button = gr.Button("Show Random Sample")
-
-            with gr.Row():
-                sample_display = gr.Markdown("{sampled data loads here}")
-
-            button.click(fn=random_sample, inputs=[subset_selector], outputs=[sample_display])
-        # removed plot because not pretty enough
-        # with gr.TabItem("Model Correlation"):
-        #     with gr.Row():
-        #         plot = plot_avg_correlation(rewardbench_data_avg, prefs_data)
-        #         gr.Plot(plot)
-
-    search_1.change(regex_table, inputs=[rewardbench_table_hidden, search_1, model_types_1], outputs=rewardbench_table)
-    search_2.change(
-        regex_table,
-        inputs=[rewardbench_table_detailed_hidden, search_2, model_types_2],
-        outputs=rewardbench_table_detailed,
-    )
-    # search.change(regex_table, inputs=[rewardbench_table_len_hidden, search, filter_button], outputs=rewardbench_table_len)
-    search_3.change(regex_table, inputs=[pref_sets_table_hidden, search_3, model_types_3], outputs=pref_sets_table)
-
-    model_types_1.change(
-        regex_table, inputs=[rewardbench_table_hidden, search_1, model_types_1], outputs=rewardbench_table
-    )
-    model_types_2.change(
-        regex_table,
-        inputs=[rewardbench_table_detailed_hidden, search_2, model_types_2],
-        outputs=rewardbench_table_detailed,
-    )
-    model_types_3.change(
-        regex_table, inputs=[pref_sets_table_hidden, search_3, model_types_3], outputs=pref_sets_table
-    )
-
-    with gr.Row():
-        with gr.Accordion("📚 Citation", open=False):
-            citation_button = gr.Textbox(
-                value=r"""@misc{RewardBench,
-    title={RewardBench: Evaluating Reward Models for Language Modeling},
-    author={Lambert, Nathan and Pyatkin, Valentina and Morrison, Jacob and Miranda, LJ and Lin, Bill Yuchen and Chandu, Khyathi and Dziri, Nouha and Kumar, Sachin and Zick, Tom and Choi, Yejin and Smith, Noah A. and Hajishirzi, Hannaneh},
-    year={2024},
-    howpublished={\url{https://huggingface.co/spaces/allenai/reward-bench}
-}""",
-                lines=7,
-                label="Copy the following to cite these results.",
-                elem_id="citation-button",
-                show_copy_button=True,
-            )
-# Load data when app starts, TODO make this used somewhere...
-# def load_data_on_start():
-#     data_rewardbench = load_all_data(repo_dir_rewardbench)
-#     rewardbench_table.update(data_rewardbench)
-
-#     data_rewardbench_avg = avg_over_rewardbench(repo_dir_rewardbench)
-#     rewardbench_table.update(data_rewardbench_avg)
-
-#     data_prefs = load_all_data(repo_dir_prefs)
-#     pref_sets_table.update(data_prefs)
-
-scheduler = BackgroundScheduler()
-scheduler.add_job(restart_space, "interval", seconds=10800)  # restarted every 3h
-scheduler.start()
-app.launch(allowed_paths=["src/"])  # had .queue() before launch before... not sure if that's necessary

pyproject.toml

@@ -1,13 +0,0 @@
-[tool.ruff]
-# Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default.
-select = ["E", "F"]
-ignore = ["E501"] # line too long (black is taking care of this)
-line-length = 119
-fixable = ["A", "B", "C", "D", "E", "F", "G", "I", "N", "Q", "S", "T", "W", "ANN", "ARG", "BLE", "COM", "DJ", "DTZ", "EM", "ERA", "EXE", "FBT", "ICN", "INP", "ISC", "NPY", "PD", "PGH", "PIE", "PL", "PT", "PTH", "PYI", "RET", "RSE", "RUF", "SIM", "SLF", "TCH", "TID", "TRY", "UP", "YTT"]
-
-[tool.isort]
-profile = "black"
-line_length = 119
-
-[tool.black]
-line-length = 119

requirements.txt

@@ -1,4 +1,3 @@
 apscheduler
-pandas>=1.5
-datasets==2.21.0
-fastapi==0.109.2
+pandas
+datasets

{leaderboard → src}/constants.py

@@ -1,28 +1,28 @@
 # reference for length bias categories
 length_categories = {
-    "alpacaeval-easy": "True",
-    "alpacaeval-hard": "True",
-    "alpacaeval-length": "Neutral",
-    "donotanswer": "False",
-    "hep-cpp": "Neutral",
-    "hep-go": "Neutral",
-    "hep-java": "Neutral",
-    "hep-js": "Neutral",
-    "hep-python": "Neutral",
-    "hep-rust": "Neutral",
-    "llmbar-adver-GPTInst": "False",
-    "llmbar-adver-GPTOut": "Neutral",
-    "llmbar-adver-manual": "False",
-    "llmbar-adver-neighbor": "False",
-    "llmbar-natural": "Neutral",
-    "math-prm": "Neutral",
-    "mt-bench-easy": "False",
-    "mt-bench-hard": "False",
-    "mt-bench-med": "Neutral",
-    "refusals-dangerous": "False",
-    "refusals-offensive": "False",
-    "xstest-should-refuse": "False",
-    "xstest-should-respond": "True",
+    'alpacaeval-easy': 'True',
+    'alpacaeval-hard': 'True',
+    'alpacaeval-length': 'Neutral',
+    'donotanswer': 'False',
+    'hep-cpp': 'Neutral',
+    'hep-go': 'Neutral',
+    'hep-java': 'Neutral',
+    'hep-js': 'Neutral',
+    'hep-python': 'Neutral',
+    'hep-rust': 'Neutral',
+    'llmbar-adver-GPTInst': 'False',
+    'llmbar-adver-GPTOut': 'Neutral',
+    'llmbar-adver-manual': 'False',
+    'llmbar-adver-neighbor': 'False',
+    'llmbar-natural': 'Neutral',
+    'math-prm': 'Neutral',
+    'mt-bench-easy': 'False',
+    'mt-bench-hard': 'False',
+    'mt-bench-med': 'Neutral',
+    'refusals-dangerous': 'False',
+    'refusals-offensive': 'False',
+    'xstest-should-refuse': 'False',
+    'xstest-should-respond': 'True'
 }
 
 example_counts = {
@@ -32,7 +32,7 @@ example_counts = {
     "mt-bench-easy": 28,
     "mt-bench-med": 40,
     "mt-bench-hard": 37,
-    "math-prm": 984,  # actual length 447, upweighting to be equal to code
+    "math-prm": 984, # actual length 447, upweighting to be equal to code
     "refusals-dangerous": 100,
     "refusals-offensive": 100,
     "llmbar-natural": 100,
@@ -40,34 +40,21 @@ example_counts = {
     "llmbar-adver-GPTInst": 92,
     "llmbar-adver-GPTOut": 47,
     "llmbar-adver-manual": 46,
-    "xstest-should-refuse": 154,
-    "xstest-should-respond": 250,  # Note, refuse and respond were accidentally swapped until 9 Sept 2024
+    "xstest-should-refuse": 250,
+    "xstest-should-respond": 154,
     "donotanswer": 136,
     "hep-cpp": 164,
     "hep-go": 164,
     "hep-java": 164,
     "hep-js": 164,
     "hep-python": 164,
-    "hep-rust": 164,
+    "hep-rust": 164
 }
 
 # note, this order should match the dataframe.
 subset_mapping = {
-    "Chat": ["alpacaeval-easy", "alpacaeval-hard", "alpacaeval-length", "mt-bench-easy", "mt-bench-med"],
-    "Chat Hard": [
-        "llmbar-adver-GPTInst",
-        "llmbar-adver-GPTOut",
-        "llmbar-adver-manual",
-        "llmbar-adver-neighbor",
-        "llmbar-natural",
-        "mt-bench-hard",
-    ],
-    "Safety": [
-        "donotanswer",
-        "refusals-dangerous",
-        "refusals-offensive",
-        "xstest-should-refuse",
-        "xstest-should-respond",
-    ],
-    "Reasoning": ["hep-cpp", "hep-go", "hep-java", "hep-js", "hep-python", "hep-rust", "math-prm"],
+    "Chat": ['alpacaeval-easy', 'alpacaeval-hard', 'alpacaeval-length', 'mt-bench-easy', 'mt-bench-med'],
+    "Chat Hard": ['llmbar-adver-GPTInst', 'llmbar-adver-GPTOut', 'llmbar-adver-manual', 'llmbar-adver-neighbor', 'llmbar-natural', 'mt-bench-hard'],
+    "Safety": ['donotanswer', 'refusals-dangerous', 'refusals-offensive', 'xstest-should-refuse', 'xstest-should-respond'],
+    "Reasoning": ["hep-cpp", "hep-go", "hep-java", "hep-js", "hep-python", "hep-rust", "math-prm"]
 }

{leaderboard → src}/css.py

@@ -1,4 +1,3 @@
-ACCENT = "#245ED4"  # OLMo Blue. Not currently used.
 custom_css = """
 
 /* Full width space */
@@ -12,11 +11,12 @@ custom_css = """
 }
 
 .tab-buttons button {
-  font-size: 30px;
+  font-size: 20px;
 }
 
 h1 {
   font-size: 32px !important;
   margin-top: 0px !important;
 }
-"""
+
+"""

{leaderboard → src}/md.py

@@ -1,87 +1,7 @@
-from datetime import datetime
-
-import pytz
-
-ABOUT_TEXT_V2 = """
-The RewardBench 2 evaluation dataset is the new version of RewardBench that is based on unseen human data and designed to be substantially more difficult! RewardBench 2 evaluates capabilities of reward models over the following categories:
-1. **Factuality** (*NEW!*): Tests the ability of RMs to detect hallucinations and other basic errors in completions.
-2. **Precise Instruction Following** (*NEW!*): Tests the ability of RMs to judge whether text follows precise instructions, such as "Answer without the letter u".
-3. **Math**: Tests RMs' abilities at math, on open-ended human prompts ranging from middle school physics and geometry to college-level chemistry, calculus, combinatorics, and more.
-4. **Safety**: Tests RMs' abilities to correctly comply with or refuse prompts related to harmful use cases as well as general compliance behaviors.
-5. **Focus**: Tests RMs' ability to detect high-quality, on-topic answers to general user queries.
-6. **Ties** (*NEW*!): This new type of subset tests the robustness of RMs in domains with many possible similar answers. For example, the question "Name a color of the rainbow" has seven possible correct answers and infinitely many incorrect ones.
-
-The RewardBench 2 leaderboard averages over these six subsets.
-For the first five categories, the scoring for RewardBench 2 evaluates success as whether the score of a prompt-chosen pair is greater than the score of *three* prompt-rejected pairs. 
-The "Ties" score is a weighted score of accuracy (as measured by *all* valid correct answers being scored higher than *all* incorrect answers) and whether the reward margin between correct and incorrect answers exceeds that of the highest and lowest-scored correct responses. This metric rewards not only correctness, but also a model's ability to prioritize correct answers over incorrect ones more strongly than it distinguishes between equally valid correct responses.
-
-<img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/reward-bench/main-fig-hor.png" alt="RewardBench 2 Flow" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
-
-## Dataset Construction Summary
-| Domain | Count | Prompt Source | Method of generating completions | Completion Filtering |
-|--------|-------|---------------|----------------------------------|---------------------|
-| Factuality | 475 | Human | Both | Multi-LM-as-a-judge |
-| Precise IF | 160 | Human | Natural | Verifier functions |
-| Math | 183 | Human | Natural | Majority voting |
-| Safety | 450 | CoCoNot | Both | LM-as-a-judge & rubrics |
-| Focus | 495 | Human | System Prompt Variation | N/A |
-| Ties | 102 | Manual | System Prompt Variation | Manual verification |
-
-## Dataset Details
-
-Each sample in the dataset has the following items.
-Note, the dataset is single-turn:
-* `prompt` (`str`): the instruction given in the various test sets.
-* `chosen` (`list[str]`): the chosen response(s) (1 chosen response for all subsets but ties)
-* `rejected` (`list[str]`): the rejected responses (3 chosen responses for all subsets but ties)
-* `num_correct` (`int`): the number of chosen responses
-* `num_rejected` (`int`): the number of rejected responses
-* `total_completions` (`int`): the total number of responses
-* `models` (`list[str]`): a list of models that the chosen and rejected responses are generated from, respectively
-* `subset` (`str`): the subset the datapoint is part of.
-* `id` (`int`): an incremented id for every prompt in the benchmark.
-
-To select a specific subset use HuggingFace Datasets `.filter` functionality.
-```
-dataset = dataset.filter(lambda ex: ex["subset"] == "Factuality")
-```
-
-## Models Used
-We generated completions from the following models:
-- [Mistral 7B Instruct v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) (Apache 2.0)
-- [Tulu 3 8B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B) (Llama 3.1 Community License Agreement)
-- [Tulu 3 70B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B) (Llama 3.1 Community License Agreement)
-- [Llama 3.1 8B Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) (Llama 3.1 Community License Agreement)
-- [Llama 3.1 70B Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) (Llama 3.1 Community License Agreement)
-- [Llama 3.2 1B Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) (Llama 3.2 Community License Agreement)
-- [Llama 2 7B Chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) (Llama 2 Community License Agreement)
-- [Tulu 2 70B](https://huggingface.co/allenai/tulu-2-dpo-70b) (Ai2 ImpACT Low Risk License)
-- [Qwen2.5 72B Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) (Qwen License Agreement)
-- [Qwen2.5 7B Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) (Apache 2.0)
-- [Qwen2.5 14B Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) (Apache 2.0)
-- [Qwen2.5 0.5B Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) (Apache 2.0)
-- [Qwen2.5 Math 72B Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-72B-Instruct) (Qwen License Agreement)
-- [Qwen2.5 Math 7B Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) (Apache 2.0)
-- [Deepseek Math 7B RL](https://huggingface.co/deepseek-ai/deepseek-math-7b-rl) (This model is licensed under the Deepseek License. Any use of the outputs from this model must be in accordance with the use restrictions in the [Deepseek License](https://github.com/deepseek-ai/DeepSeek-Math/blob/main/LICENSE-MODEL).)
-- [OLMoE 1B 7B 0924 Instruct](https://huggingface.co/allenai/OLMoE-1B-7B-0924) (Apache 2.0)
-- [Dolphin 2.0 Mistral 7b](https://huggingface.co/cognitivecomputations/dolphin-2.0-mistral-7b) (Apache 2.0)
-- [Zephyr 7b Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) (MIT License)
-- GPT-4o (Outputs produced by GPT-4 are subject to OpenAI's [terms of use](https://openai.com/policies/row-terms-of-use/))
-- Claude 3.5 Sonnet (Outputs produced by Claude are subject to Anthropic [terms of service](https://www.anthropic.com/legal/consumer-terms) and [usage policy](https://www.anthropic.com/legal/aup))
-
-## License
-This dataset is licensed under ODC-BY. It is intended for research and educational use in accordance with Ai2's [Responsible Use Guidelines](https://allenai.org/responsible-use). This dataset includes output data generated from third party models that are subject to separate terms governing their use.
-
-## Trained Reward Models
-We also trained and released several reward models— check out the [RewardBench 2 Collection](https://huggingface.co/collections/allenai/reward-bench-2-683d2612a4b3e38a3e53bb51) to use them!
-"""
-
-ABOUT_TEXT_V1 = """
+ABOUT_TEXT = """
 We compute the win percentage for a reward model on hand curated chosen-rejected pairs for each prompt.
 A win is when the score for the chosen response is higher than the score for the rejected response.
 
-Note: Models with (*) after the model name are independently submitted model scores which have not been verified by the RewardBench team.
-
 ## Overview
 
 We average over 4 core sections (per prompt weighting):
@@ -100,15 +20,21 @@ Once all subsets weighted averages are achieved, the final RewardBench score is
 We include multiple types of reward models in this evaluation:
 1. **Sequence Classifiers** (Seq. Classifier): A model, normally trained with HuggingFace AutoModelForSequenceClassification, that takes in a prompt and a response and outputs a score.
 2. **Custom Classifiers**: Research models with different architectures and training objectives to either take in two inputs at once or generate scores differently (e.g. PairRM and Stanford SteamSHP).
-3. **DPO**: Models trained with Direct Preference Optimization (DPO), with modifiers such as `-ref-free` or `-norm` changing how scores are computed. *Note*: This also includes other models trained with implicit rewards, such as those trained with [KTO](https://arxiv.org/abs/2402.01306). 
+3. **DPO**: Models trained with Direct Preference Optimization (DPO), with modifiers such as `-ref-free` or `-norm` changing how scores are computed.
 4. **Random**: Random choice baseline.
 4. **Generative**: Prompting fine-tuned models to choose between two answers, similar to MT Bench and AlpacaEval.
 
 All models are evaluated in fp16 expect for Starling-7B, which is evaluated in fp32.
-*Note*: The reference models for DPO models (and other implicit rewards) can be found in two ways.
-* Click on a specific model in results and you'll see a key `ref_model`, e.g. [Qwen](https://huggingface.co/datasets/allenai/reward-bench-results/blob/main/eval-set/Qwen/Qwen1.5-72B-Chat.json).
-* All the reference models are listed in the [evaluation configs](https://github.com/allenai/reward-bench/blob/main/scripts/configs/eval_configs.yaml).
+Others, such as **Generative Judge** are coming soon.
 
+### Model Types
+
+Currently, we evaluate the following model types:
+1. **Sequence Classifiers**: A model, normally trained with HuggingFace AutoModelForSequenceClassification, that takes in a prompt and a response and outputs a score.
+2. **Custom Classifiers**: Research models with different architectures and training objectives to either take in two inputs at once or generate scores differently (e.g. PairRM and Stanford SteamSHP).
+3. **DPO**: Models trained with Direct Preference Optimization (DPO) with a reference model being either the base or supervised fine-tuning checkpoint.
+
+Support of DPO models without a reference model is coming soon.
 
 ### Subset Details
 
@@ -129,8 +55,8 @@ Total number of the prompts is: 2985, filtered from 5123.
 | llmbar-adver-GPTInst | 92          | (See [paper](https://arxiv.org/abs/2310.07641)) Instruction response vs. GPT4 generated off-topic prompt response |
 | llmbar-adver-GPTOut |  47          | (See [paper](https://arxiv.org/abs/2310.07641)) Instruction response vs. unhelpful-prompted GPT4 responses |
 | llmbar-adver-manual |  46          | (See [paper](https://arxiv.org/abs/2310.07641)) Challenge set chosen vs. rejected |
-| xstest-should-refuse | 450, 154         | False response dataset (see [paper](https://arxiv.org/abs/2308.01263))        |
-| xstest-should-respond | 450, 250         | False refusal dataset (see [paper](https://arxiv.org/abs/2308.01263))        |
+| xstest-should-refuse | 450, 250         | False response dataset (see [paper](https://arxiv.org/abs/2308.01263))        |
+| xstest-should-respond | 450, 154         | False refusal dataset (see [paper](https://arxiv.org/abs/2308.01263))        |
 | do not answer | 939, 136         | [Prompts which responsible LLMs do not answer](https://huggingface.co/datasets/LibrAI/do-not-answer)        |
 | math-prm | 447         | Human references vs. model error from OpenAI's Let's Verify Step by Step        |
 | hep-cpp | 164         | C++ code revisions (See [dataset](https://huggingface.co/datasets/bigcode/humanevalpack) or [paper](https://arxiv.org/abs/2308.07124))        |
@@ -170,21 +96,8 @@ Lengths (mean, std. dev.) include the prompt
 For more details, see the [dataset](https://huggingface.co/datasets/allenai/reward-bench).
 """
 
-# Get Pacific time zone (handles PST/PDT automatically)
-pacific_tz = pytz.timezone("America/Los_Angeles")
-current_time = datetime.now(pacific_tz).strftime("%H:%M %Z, %d %b %Y")
-
-TOP_TEXT = """# RewardBench: Evaluating Reward Models"""
-
-CAPTION_V2 = f"""The *new version* of RewardBench that is based on unseen human data and designed to be substantially more difficult!
-
-[Code](https://github.com/allenai/reward-bench) |  [Eval. Dataset v2](https://huggingface.co/datasets/allenai/reward-bench-2) | [Results v2](https://huggingface.co/datasets/allenai/reward-bench-2-results) | [Paper](https://arxiv.org/abs/2506.01937) | Total models: {{}} |  Last restart (PST): {current_time}"""
-
-CAPTION_V1 = f"""The original RewardBench -- the first reward model evaluation.
-
-[Code](https://github.com/allenai/reward-bench) |  [Eval. Dataset v1](https://huggingface.co/datasets/allenai/reward-bench) | [Prior Test Sets](https://huggingface.co/datasets/allenai/pref-test-sets) | [Results v1](https://huggingface.co/datasets/allenai/reward-bench-results) | [Paper v1](https://arxiv.org/abs/2403.13787) | Total models: {{}} | * Unverified models | ⚠️ Dataset Contamination |  Last restart (PST): {current_time}
-
-**Note**: This leaderboard is frozen and will not be updated. The final version of the evaluation results are available in the source for this application.
-
-⚠️ Many of the top models were trained on unintentionally contaminated, AI-generated data, for more information, see this [gist](https://gist.github.com/natolambert/1aed306000c13e0e8c5bc17c1a5dd300).
-"""
+TOP_TEXT = """
+# RewardBench: Evaluating Reward Models
+### Evaluating the capabilities, safety, and pitfalls of reward models
+[Code](https://github.com/allenai/reward-bench) | [Eval. Dataset](https://huggingface.co/datasets/allenai/reward-bench) | [Prior Test Sets](https://huggingface.co/datasets/allenai/pref-test-sets) | [Results](https://huggingface.co/datasets/allenai/reward-bench-results) | [Paper](https://arxiv.org/abs/2403.13787) | Total models: {}
+"""

{leaderboard → src}/plt.py

@@ -1,55 +1,53 @@
 import matplotlib.pyplot as plt
 import pandas as pd
-
 from .utils import undo_hyperlink
 
-
 def plot_avg_correlation(df1, df2):
     """
     Plots the "average" column for each unique model that appears in both dataframes.
-
+    
     Parameters:
     - df1: pandas DataFrame containing columns "model" and "average".
     - df2: pandas DataFrame containing columns "model" and "average".
     """
     # Identify the unique models that appear in both DataFrames
-    common_models = pd.Series(list(set(df1["model"]) & set(df2["model"])))
-
+    common_models = pd.Series(list(set(df1['model']) & set(df2['model'])))
+    
     # Set up the plot
     plt.figure(figsize=(13, 6), constrained_layout=True)
 
-    # axes from 0 to 1 for x and y
+    # axes from 0 to 1 for x and y 
     plt.xlim(0.475, 0.8)
     plt.ylim(0.475, 0.8)
 
     # larger font (16)
-    plt.rcParams.update({"font.size": 12, "axes.labelsize": 14, "axes.titlesize": 14})
+    plt.rcParams.update({'font.size': 12, 'axes.labelsize': 14,'axes.titlesize': 14})
     # plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1)
     # plt.tight_layout()
     # plt.margins(0,0)
 
     for model in common_models:
         # Filter data for the current model
-        df1_model_data = df1[df1["model"] == model]["average"].values
-        df2_model_data = df2[df2["model"] == model]["average"].values
-
+        df1_model_data = df1[df1['model'] == model]['average'].values
+        df2_model_data = df2[df2['model'] == model]['average'].values
+        
         # Plotting
         plt.scatter(df1_model_data, df2_model_data, label=model)
         m_name = undo_hyperlink(model)
         if m_name == "No text found":
             m_name = "Random"
-        # Add text above each point like
+        # Add text above each point like 
         # plt.text(x[i] + 0.1, y[i] + 0.1, label, ha='left', va='bottom')
-        plt.text(
-            df1_model_data - 0.005, df2_model_data, m_name, horizontalalignment="right", verticalalignment="center"
-        )
+        plt.text(df1_model_data - .005, df2_model_data, m_name, horizontalalignment='right', verticalalignment='center')
 
     # add correlation line to scatter plot
     # first, compute correlation
-    corr = df1["average"].corr(df2["average"])
+    corr = df1['average'].corr(df2['average'])
     # add correlation line based on corr
+    
+
 
-    plt.xlabel("HERM Eval. Set Avg.", fontsize=16)
-    plt.ylabel("Pref. Test Sets Avg.", fontsize=16)
+    plt.xlabel('HERM Eval. Set Avg.', fontsize=16)
+    plt.ylabel('Pref. Test Sets Avg.', fontsize=16)
     # plt.legend(title='Model', bbox_to_anchor=(1.05, 1), loc='upper left')
-    return plt
+    return plt

{leaderboard → src}/utils.py

@@ -1,78 +1,25 @@
-import os
-import re
-from pathlib import Path
-
-import numpy as np
 import pandas as pd
+from pathlib import Path
 from datasets import load_dataset
-
-UNVERIFIED_MODELS = []
-CONTAMINATED_MODELS = []
-
-UNVERIFIED_MODELS_V1 = [
-    "nvidia/Nemotron-4-340B-Reward",
-    "nvidia/Llama3-70B-SteerLM-RM",
-    "Cohere May 2024",
-    "google/gemini-1.5-pro-0514",
-    "google/flame-24b-july-2024",
-    "Cohere March 2024",
-    "facebook/Self-taught-Llama-3-70B",
-    "facebook/Self-taught-evaluator-llama3.1-70B",
-    "google/flame-1.0-24B-july-2024",
-    "Salesforce/SFR-LLaMa-3.1-70B-Judge-r",
-    "Salesforce/SFR-nemo-12B-Judge-r",
-    "Salesforce/SFR-LLaMa-3.1-8B-Judge-r",
-    "SF-Foundation/TextEval-OffsetBias-12B",
-    "SF-Foundation/TextEval-Llama3.1-70B",
-    "nvidia/Llama-3.1-Nemotron-70B-Reward",
-]
-
-# No longer used
-CONTAMINATED_MODELS_V1 = [
-    "Skywork/Skywork-Reward-Gemma-2-27B",
-    "Skywork/Skywork-Critic-Llama-3.1-70B",
-    "LxzGordon/URM-LLaMa-3.1-8B",
-    "Skywork/Skywork-Reward-Llama-3.1-8B",
-    "Ray2333/GRM-Llama3-8B-rewardmodel-ft",
-    "nicolinho/QRM-Llama3.1-8B",
-    "nicolinho/QRM-Llama3-8B",
-    "general-preference/GPM-Llama-3.1-8B",
-    "SF-Foundation/TextEval-Llama3.1-70B",
-    "ZiyiYe/Con-J-Qwen2-7B",
-    "Ray2333/Gemma-2B-rewardmodel-ft",
-    "Ray2333/GRM-Gemma-2B-rewardmodel-ft",
-]
-
+import numpy as np
+import os
+import re
 
 # From Open LLM Leaderboard
 def model_hyperlink(link, model_name):
-    # if model_name is above 50 characters, return first 47 characters and "..."
-    if len(model_name) > 50:
-        model_name = model_name[:47] + "..."
     if model_name == "random":
-        output = "random"
+        return "random"
     elif model_name == "Cohere March 2024":
-        output = f'<a target="_blank" href="https://huggingface.co/Cohere" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
+        return f'<a target="_blank" href="https://huggingface.co/Cohere" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
     elif "openai" == model_name.split("/")[0]:
-        output = f'<a target="_blank" href="https://huggingface.co/openai" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
+        return f'<a target="_blank" href="https://huggingface.co/openai" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
     elif "Anthropic" == model_name.split("/")[0]:
-        output = f'<a target="_blank" href="https://huggingface.co/Anthropic" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
-    elif "google" == model_name.split("/")[0]:
-        output = f'<a target="_blank" href="https://huggingface.co/google" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
-    elif "PoLL" == model_name.split("/")[0]:
-        output = model_name
-    output = f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
-
-    if model_name in UNVERIFIED_MODELS:
-        output += " *"
-    if model_name in CONTAMINATED_MODELS:
-        output += " ⚠️"
-    return output
-
+        return f'<a target="_blank" href="https://huggingface.co/Anthropic" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
+    return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
 
 def undo_hyperlink(html_string):
     # Regex pattern to match content inside > and <
-    pattern = r">[^<]+<"
+    pattern = r'>[^<]+<'
     match = re.search(pattern, html_string)
     if match:
         # Extract the matched text and remove leading '>' and trailing '<'
@@ -82,7 +29,7 @@ def undo_hyperlink(html_string):
 
 
 # Define a function to fetch and process data
-def load_all_data(data_repo, subdir: str, subsubsets=False):  # use HF api to pull the git repo
+def load_all_data(data_repo, subdir:str, subsubsets=False):    # use HF api to pull the git repo
     dir = Path(data_repo)
     data_dir = dir / subdir
     orgs = [d for d in os.listdir(data_dir) if os.path.isdir(os.path.join(data_dir, d))]
@@ -100,20 +47,21 @@ def load_all_data(data_repo, subdir: str, subsubsets=False):  # use HF api to pu
 
     # load all json data in the list models_results one by one to avoid not having the same entries
     for model in models_results:
-        model_data = load_dataset("json", data_files=data_repo + subdir + "/" + model, split="train")
+        model_data = load_dataset("json", data_files=data_repo + subdir+ "/" + model, split="train")
         df2 = pd.DataFrame(model_data)
         # add to df
         df = pd.concat([df2, df])
 
+
     # remove chat_template comlumn
     df = df.drop(columns=["chat_template"])
 
     # sort columns alphabetically
     df = df.reindex(sorted(df.columns), axis=1)
-
+    
     # move column "model" to the front
     cols = list(df.columns)
-    cols.insert(0, cols.pop(cols.index("model")))
+    cols.insert(0, cols.pop(cols.index('model')))
     df = df.loc[:, cols]
 
     # select all columns except "model"
@@ -129,7 +77,7 @@ def load_all_data(data_repo, subdir: str, subsubsets=False):  # use HF api to pu
     if "model_beaker" in cols:
         cols.remove("model_beaker")
         df = df.drop(columns=["model_beaker"])
-
+    
     # remove column xstest (outdated data)
     # if xstest is a column
     if "xstest" in cols:
@@ -154,24 +102,24 @@ def load_all_data(data_repo, subdir: str, subsubsets=False):  # use HF api to pu
         df = df.drop(columns=["pku_safer"])
         cols.remove("pku_safer")
 
-    # convert to score
-    df[cols] = df[cols] * 100
-    avg = np.nanmean(df[cols].values, axis=1)
+    # convert to score 
+    df[cols] = (df[cols]*100)
+    avg = np.nanmean(df[cols].values,axis=1)
     # add average column
     df["average"] = avg
-
+    
     # apply model_hyperlink function to column "model"
     df["model"] = df["model"].apply(lambda x: model_hyperlink(f"https://huggingface.co/{x}", x))
 
     # move average column to the second
     cols = list(df.columns)
-    cols.insert(1, cols.pop(cols.index("average")))
+    cols.insert(1, cols.pop(cols.index('average')))
     df = df.loc[:, cols]
 
     # move model_type column to first
     if "model_type" in cols:
         cols = list(df.columns)
-        cols.insert(1, cols.pop(cols.index("model_type")))
+        cols.insert(1, cols.pop(cols.index('model_type')))
         df = df.loc[:, cols]
 
     # remove models with DPO Ref. Free as type (future work)