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lewtun HF staff commited on
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43975e6
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1 Parent(s): 33a8591

Fix lb

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Files changed (9) hide show
  1. app.py +29 -26
  2. eval_results/data/deepseek-math-7b-kto-v00.00/checkpoint-1100/main/aimo_kaggle_hard_pot/results_2024-05-29T11-53-40.101219.json +0 -187
  3. eval_results/data/deepseek-math-7b-kto-v00.00/checkpoint-1100/main/aimo_kaggle_medium_pot/results_2024-05-29T11-48-26.048131.json +0 -187
  4. eval_results/data/deepseek-math-7b-kto-v01.00/checkpoint-500/main/aimo_kaggle_hard_pot/results_2024-05-30T09-30-22.587150.json +0 -187
  5. eval_results/data/deepseek-math-7b-kto-v01.00/checkpoint-500/main/aimo_kaggle_hard_pot/results_2024-05-30T11-56-47.491725.json +0 -187
  6. eval_results/data/deepseek-math-7b-kto-v01.00/checkpoint-500/main/aimo_kaggle_hard_pot/results_2024-05-30T14-47-24.379216.json +0 -187
  7. eval_results/data/deepseek-math-7b-kto-v01.00/checkpoint-500/main/aimo_kaggle_hard_pot/results_2024-05-30T15-40-17.606001.json +0 -187
  8. eval_results/data/deepseek-math-7b-kto-v01.00/checkpoint-500/main/aimo_kaggle_medium_pot/results_2024-05-30T09-22-56.144851.json +0 -187
  9. eval_results/data/deepseek-math-7b-kto-v01.00/checkpoint-500/main/aimo_kaggle_medium_pot/results_2024-05-30T15-35-30.139225.json +0 -187
app.py CHANGED
@@ -31,7 +31,7 @@ def get_leaderboard_df(merge_values: bool = True):
31
  path_parts = Path(filepath).parts
32
  date = filepath.stem.split("_")[-1][:-3].split("T")[0]
33
  model_revision = "_".join(path_parts[1:4]) + "_" + date
34
- task = path_parts[4].capitalize()
35
  df.loc[model_revision, "Date"] = date
36
 
37
  with open(filepath, "r") as file:
@@ -82,15 +82,25 @@ def get_leaderboard_df(merge_values: bool = True):
82
  for k, v in data["results"].items():
83
  if k != "all" and "_average" not in k:
84
  version = k.split("|")[1].split(":")[-1]
85
- value = v["qem"]
86
  df.loc[model_revision, f"{task}_{version}"] = value
87
  # For kaggle_pot we report N metrics, one for each prompt and store each one as a separate row in the dataframe
88
  elif task.lower() in ["aimo_kaggle_hard_pot"]:
89
  for k, v in data["results"].items():
90
  if k != "all" and "_average" not in k:
91
  version = k.split("|")[1].split(":")[-1]
92
- value = v["qem"]
93
  df.loc[model_revision, f"{task}_{version}"] = value
 
 
 
 
 
 
 
 
 
 
94
  # For AlpacaEval we report base winrate and lenght corrected one
95
  elif task.lower() == "alpaca_eval":
96
  value = data["results"][first_result_key]["win_rate"]
@@ -98,27 +108,24 @@ def get_leaderboard_df(merge_values: bool = True):
98
  value = data["results"][first_result_key]["length_controlled_winrate"]
99
  df.loc[model_revision, "Alpaca_eval_lc"] = value / 100.0
100
  else:
101
- df.loc[model_revision, task] = value
102
-
103
- # Put IFEval / BBH / AGIEval / AlpacaEval in first columns
104
- alpaca_col = df.pop("Alpaca_eval")
105
- df.insert(1, "Alpaca_eval", alpaca_col)
106
- alpaca_col = df.pop("Alpaca_eval_lc")
107
- df.insert(2, "Alpaca_eval_lc", alpaca_col)
108
- ifeval_col = df.pop("Ifeval")
109
- df.insert(3, "Ifeval", ifeval_col)
110
- bbh_col = df.pop("Bbh")
111
- df.insert(4, "Bbh", bbh_col)
112
- agieval_col = df.pop("Agieval")
113
- df.insert(5, "Agieval", agieval_col)
114
- gsm8k_col = df.pop("Gsm8k")
115
- df.insert(6, "Gsm8k", gsm8k_col)
116
- mmlu_col = df.pop("Mmlu")
117
- df.insert(7, "Mmlu", mmlu_col)
118
 
119
  # Drop rows where every entry is NaN
120
  df = df.dropna(how="all", axis=0, subset=[c for c in df.columns if c != "Date"])
 
 
 
 
 
 
 
 
 
 
 
 
121
  df.insert(loc=1, column="Average", value=df.mean(axis=1, numeric_only=True))
 
122
  # Convert all values to percentage
123
  df[df.select_dtypes(include=["number"]).columns] *= 100.0
124
  df = df.sort_values(by=["Average"], ascending=False)
@@ -132,12 +139,6 @@ def get_leaderboard_df(merge_values: bool = True):
132
  df = df[["Model", "Date"]].merge(merged_df, on="Model", how="left")
133
  df.drop_duplicates(subset=["Model"], inplace=True)
134
  df = df.sort_values(by=["Average"], ascending=False).round(2)
135
-
136
- # Trim minimath column names
137
- df.columns = [c.replace("_level_", "_l") for c in df.columns]
138
-
139
- # Trim AIMO column names
140
- df.columns = [c.replace("Aimo_", "") for c in df.columns]
141
  return df
142
 
143
 
@@ -153,6 +154,8 @@ def update_table(search_query):
153
  search_terms = [term.strip().lower() for term in search_terms]
154
  pattern = "|".join(search_terms)
155
  df = df[df["Model"].str.lower().str.contains(pattern, regex=True)]
 
 
156
  return df
157
 
158
 
 
31
  path_parts = Path(filepath).parts
32
  date = filepath.stem.split("_")[-1][:-3].split("T")[0]
33
  model_revision = "_".join(path_parts[1:4]) + "_" + date
34
+ task = path_parts[4] # .capitalize()
35
  df.loc[model_revision, "Date"] = date
36
 
37
  with open(filepath, "r") as file:
 
82
  for k, v in data["results"].items():
83
  if k != "all" and "_average" not in k:
84
  version = k.split("|")[1].split(":")[-1]
85
+ value = v["qem"] if "qem" in v else v["score"]
86
  df.loc[model_revision, f"{task}_{version}"] = value
87
  # For kaggle_pot we report N metrics, one for each prompt and store each one as a separate row in the dataframe
88
  elif task.lower() in ["aimo_kaggle_hard_pot"]:
89
  for k, v in data["results"].items():
90
  if k != "all" and "_average" not in k:
91
  version = k.split("|")[1].split(":")[-1]
92
+ value = v["qem"] if "qem" in v else v["score"]
93
  df.loc[model_revision, f"{task}_{version}"] = value
94
+ # For kaggle_tora we report accuracy, so need to divide by 100
95
+ elif task.lower() in [
96
+ "aimo_tora_eval_kaggle_medium",
97
+ "aimo_kaggle_fast_eval_hard",
98
+ "aimo_kaggle_tora_medium",
99
+ "aimo_kaggle_tora_hard",
100
+ ]:
101
+ for k, v in data["results"].items():
102
+ value = float(v["qem"]) / 100.0
103
+ df.loc[model_revision, f"{task}"] = value
104
  # For AlpacaEval we report base winrate and lenght corrected one
105
  elif task.lower() == "alpaca_eval":
106
  value = data["results"][first_result_key]["win_rate"]
 
108
  value = data["results"][first_result_key]["length_controlled_winrate"]
109
  df.loc[model_revision, "Alpaca_eval_lc"] = value / 100.0
110
  else:
111
+ df.loc[model_revision, task] = float(value)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
112
 
113
  # Drop rows where every entry is NaN
114
  df = df.dropna(how="all", axis=0, subset=[c for c in df.columns if c != "Date"])
115
+ # Trim minimath column names
116
+ df.columns = [c.replace("_level_", "_l") for c in df.columns]
117
+
118
+ # Trim AIMO column names
119
+ df.columns = [c.replace("aimo_", "") for c in df.columns]
120
+
121
+ # Rename old AIMO columns
122
+ df.rename(
123
+ columns={"tora_eval_kaggle_medium": "kaggle_tora_medium", "kaggle_fast_eval_hard": "kaggle_tora_hard"},
124
+ inplace=True,
125
+ )
126
+
127
  df.insert(loc=1, column="Average", value=df.mean(axis=1, numeric_only=True))
128
+
129
  # Convert all values to percentage
130
  df[df.select_dtypes(include=["number"]).columns] *= 100.0
131
  df = df.sort_values(by=["Average"], ascending=False)
 
139
  df = df[["Model", "Date"]].merge(merged_df, on="Model", how="left")
140
  df.drop_duplicates(subset=["Model"], inplace=True)
141
  df = df.sort_values(by=["Average"], ascending=False).round(2)
 
 
 
 
 
 
142
  return df
143
 
144
 
 
154
  search_terms = [term.strip().lower() for term in search_terms]
155
  pattern = "|".join(search_terms)
156
  df = df[df["Model"].str.lower().str.contains(pattern, regex=True)]
157
+ # Drop any columns which are all NaN
158
+ df = df.dropna(how="all", axis=1)
159
  return df
160
 
161
 
eval_results/data/deepseek-math-7b-kto-v00.00/checkpoint-1100/main/aimo_kaggle_hard_pot/results_2024-05-29T11-53-40.101219.json DELETED
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