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Build error
Build error
Fix lb
Browse files- app.py +29 -26
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
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@@ -31,7 +31,7 @@ def get_leaderboard_df(merge_values: bool = True):
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path_parts = Path(filepath).parts
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date = filepath.stem.split("_")[-1][:-3].split("T")[0]
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model_revision = "_".join(path_parts[1:4]) + "_" + date
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task = path_parts[4].capitalize()
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df.loc[model_revision, "Date"] = date
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with open(filepath, "r") as file:
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@@ -82,15 +82,25 @@ def get_leaderboard_df(merge_values: bool = True):
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for k, v in data["results"].items():
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if k != "all" and "_average" not in k:
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version = k.split("|")[1].split(":")[-1]
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value = v["qem"]
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df.loc[model_revision, f"{task}_{version}"] = value
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# For kaggle_pot we report N metrics, one for each prompt and store each one as a separate row in the dataframe
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elif task.lower() in ["aimo_kaggle_hard_pot"]:
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for k, v in data["results"].items():
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if k != "all" and "_average" not in k:
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version = k.split("|")[1].split(":")[-1]
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value = v["qem"]
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df.loc[model_revision, f"{task}_{version}"] = value
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# For AlpacaEval we report base winrate and lenght corrected one
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elif task.lower() == "alpaca_eval":
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value = data["results"][first_result_key]["win_rate"]
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@@ -98,27 +108,24 @@ def get_leaderboard_df(merge_values: bool = True):
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value = data["results"][first_result_key]["length_controlled_winrate"]
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df.loc[model_revision, "Alpaca_eval_lc"] = value / 100.0
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else:
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df.loc[model_revision, task] = value
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# Put IFEval / BBH / AGIEval / AlpacaEval in first columns
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alpaca_col = df.pop("Alpaca_eval")
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df.insert(1, "Alpaca_eval", alpaca_col)
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alpaca_col = df.pop("Alpaca_eval_lc")
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df.insert(2, "Alpaca_eval_lc", alpaca_col)
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ifeval_col = df.pop("Ifeval")
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df.insert(3, "Ifeval", ifeval_col)
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bbh_col = df.pop("Bbh")
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df.insert(4, "Bbh", bbh_col)
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agieval_col = df.pop("Agieval")
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df.insert(5, "Agieval", agieval_col)
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gsm8k_col = df.pop("Gsm8k")
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df.insert(6, "Gsm8k", gsm8k_col)
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mmlu_col = df.pop("Mmlu")
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df.insert(7, "Mmlu", mmlu_col)
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# Drop rows where every entry is NaN
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df = df.dropna(how="all", axis=0, subset=[c for c in df.columns if c != "Date"])
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df.insert(loc=1, column="Average", value=df.mean(axis=1, numeric_only=True))
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# Convert all values to percentage
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df[df.select_dtypes(include=["number"]).columns] *= 100.0
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df = df.sort_values(by=["Average"], ascending=False)
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@@ -132,12 +139,6 @@ def get_leaderboard_df(merge_values: bool = True):
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df = df[["Model", "Date"]].merge(merged_df, on="Model", how="left")
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df.drop_duplicates(subset=["Model"], inplace=True)
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df = df.sort_values(by=["Average"], ascending=False).round(2)
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# Trim minimath column names
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df.columns = [c.replace("_level_", "_l") for c in df.columns]
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# Trim AIMO column names
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df.columns = [c.replace("Aimo_", "") for c in df.columns]
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return df
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@@ -153,6 +154,8 @@ def update_table(search_query):
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search_terms = [term.strip().lower() for term in search_terms]
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pattern = "|".join(search_terms)
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df = df[df["Model"].str.lower().str.contains(pattern, regex=True)]
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return df
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path_parts = Path(filepath).parts
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date = filepath.stem.split("_")[-1][:-3].split("T")[0]
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model_revision = "_".join(path_parts[1:4]) + "_" + date
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task = path_parts[4] # .capitalize()
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df.loc[model_revision, "Date"] = date
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with open(filepath, "r") as file:
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for k, v in data["results"].items():
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if k != "all" and "_average" not in k:
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version = k.split("|")[1].split(":")[-1]
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value = v["qem"] if "qem" in v else v["score"]
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df.loc[model_revision, f"{task}_{version}"] = value
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# For kaggle_pot we report N metrics, one for each prompt and store each one as a separate row in the dataframe
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elif task.lower() in ["aimo_kaggle_hard_pot"]:
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for k, v in data["results"].items():
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if k != "all" and "_average" not in k:
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version = k.split("|")[1].split(":")[-1]
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value = v["qem"] if "qem" in v else v["score"]
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df.loc[model_revision, f"{task}_{version}"] = value
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# For kaggle_tora we report accuracy, so need to divide by 100
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elif task.lower() in [
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"aimo_tora_eval_kaggle_medium",
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"aimo_kaggle_fast_eval_hard",
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"aimo_kaggle_tora_medium",
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"aimo_kaggle_tora_hard",
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]:
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for k, v in data["results"].items():
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value = float(v["qem"]) / 100.0
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df.loc[model_revision, f"{task}"] = value
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# For AlpacaEval we report base winrate and lenght corrected one
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elif task.lower() == "alpaca_eval":
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value = data["results"][first_result_key]["win_rate"]
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value = data["results"][first_result_key]["length_controlled_winrate"]
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df.loc[model_revision, "Alpaca_eval_lc"] = value / 100.0
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else:
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df.loc[model_revision, task] = float(value)
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# Drop rows where every entry is NaN
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df = df.dropna(how="all", axis=0, subset=[c for c in df.columns if c != "Date"])
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# Trim minimath column names
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df.columns = [c.replace("_level_", "_l") for c in df.columns]
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# Trim AIMO column names
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df.columns = [c.replace("aimo_", "") for c in df.columns]
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# Rename old AIMO columns
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df.rename(
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columns={"tora_eval_kaggle_medium": "kaggle_tora_medium", "kaggle_fast_eval_hard": "kaggle_tora_hard"},
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inplace=True,
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)
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df.insert(loc=1, column="Average", value=df.mean(axis=1, numeric_only=True))
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# Convert all values to percentage
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df[df.select_dtypes(include=["number"]).columns] *= 100.0
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df = df.sort_values(by=["Average"], ascending=False)
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df = df[["Model", "Date"]].merge(merged_df, on="Model", how="left")
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df.drop_duplicates(subset=["Model"], inplace=True)
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df = df.sort_values(by=["Average"], ascending=False).round(2)
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return df
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search_terms = [term.strip().lower() for term in search_terms]
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pattern = "|".join(search_terms)
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df = df[df["Model"].str.lower().str.contains(pattern, regex=True)]
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# Drop any columns which are all NaN
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df = df.dropna(how="all", axis=1)
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return df
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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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@@ -1,187 +0,0 @@
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{
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"config_general": {
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"lighteval_sha": "?",
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"num_fewshot_seeds": 1,
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"override_batch_size": 4,
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"max_samples": null,
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"job_id": "",
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"start_time": 158474.057687477,
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"end_time": 159098.894280397,
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"total_evaluation_time_secondes": "624.8365929200081",
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"model_name": "data_deepseek-math-7b-kto-v00.00_checkpoint-1100",
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"model_sha": "",
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"model_dtype": "torch.bfloat16",
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"model_size": "12.93 GB",
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"config": null
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},
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"results": {
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"custom|aimo_kaggle_hard_pot:v0|0": {
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"qem": 0.02,
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"qem_stderr": 0.02
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},
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"custom|aimo_kaggle_hard_pot:v1|0": {
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"qem": 0.04,
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"qem_stderr": 0.02799416848895062
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},
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"custom|aimo_kaggle_hard_pot:v2|0": {
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"qem": 0.06,
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"qem_stderr": 0.0339266916772512
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},
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"custom|aimo_kaggle_hard_pot:_average|0": {
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"qem": 0.04,
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"qem_stderr": 0.02730695338873394
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},
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"all": {
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"qem": 0.04,
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"qem_stderr": 0.02730695338873394
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}
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},
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"versions": {
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"name": "aimo_kaggle_hard_pot:v0",
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"prompt_function": "kaggle_hard_pot_prompt_fn_v0",
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"hf_repo": "AI-MO/kaggle-validation-set-hard",
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"hf_subset": "v0",
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"metric": [
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"quasi_exact_match_code_and_math"
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],
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"hf_avail_splits": [
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"train"
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"evaluation_splits": [
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"train"
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"few_shots_split": null,
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"generation_size": 2048,
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"name": "aimo_kaggle_hard_pot:v1",
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| 146 |
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| 147 |
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| 148 |
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| 150 |
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| 151 |
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| 152 |
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| 153 |
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| 154 |
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| 155 |
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| 156 |
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| 157 |
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| 158 |
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},
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| 159 |
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"custom|aimo_kaggle_medium_pot:v2|0": {
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| 160 |
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| 161 |
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| 162 |
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| 164 |
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| 165 |
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| 166 |
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| 167 |
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| 168 |
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| 169 |
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| 170 |
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| 171 |
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| 172 |
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| 173 |
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| 174 |
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| 175 |
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| 176 |
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| 177 |
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| 180 |
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| 181 |
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| 186 |
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| 187 |
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