remove unused envs

src/envs.py

@@ -35,10 +35,6 @@ API = HfApi(token=H4_TOKEN)
 
 # useless env
 EVAL_REQUESTS_PATH = os.path.join(HF_HOME, "data/eval-queue")
-DYNAMIC_INFO_PATH = os.path.join(HF_HOME, "dynamic-info")
-DYNAMIC_INFO_FILE_PATH = os.path.join(DYNAMIC_INFO_PATH, "model_infos.json")
-EVAL_REQUESTS_PATH_PRIVATE = "eval-queue-private"
-EVAL_RESULTS_PATH_PRIVATE = "eval-results-private"
 PATH_TO_COLLECTION = "open-llm-leaderboard/llm-leaderboard-best-models-652d6c7965a4619fb5c27a03"
 
 # Rate limit variables

src/leaderboard/build_leaderboard.py

@@ -7,7 +7,7 @@ import time
 import pandas as pd
 from huggingface_hub import snapshot_download
 
-from src.envs import DATA_ARENA_PATH, EVAL_RESULTS_PATH, HF_HOME
+from src.envs import DATA_ARENA_PATH, HF_HOME
 
 # Configure logging
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
@@ -67,17 +67,6 @@ def download_openbench():
     print("\nJudgement in openbench-eval")
     subprocess.run(["ls", f"{DATA_ARENA_PATH}/model_judgment/gpt-4-1106-preview"], check=False)
 
-    # copy the model answers / judgements to data folder
-    subprocess.run(
-        [
-            "rsync",
-            "-azPvh",
-            f"{EVAL_RESULTS_PATH}/*",
-            f"{DATA_ARENA_PATH}/",
-        ],
-        check=True,
-    )
-
 
 def build_leadearboard_df():
     # Retrieve the leaderboard DataFrame