change dataset name

src/envs.py

@@ -7,13 +7,13 @@ load_dotenv()
 
 # Hugging Face configuration
 TOKEN = os.environ.get("HF_TOKEN")  # A read/write token for your org
-OWNER = os.environ.get("OWNER", "guard-bench")  # Change to your org
+OWNER = os.environ.get("OWNER", "whitecircle-ai")  # Change to your org
 SUBMITTER_TOKEN = os.environ.get("SUBMITTER_TOKEN")
 ADMIN_USERNAME = os.environ.get("ADMIN_USERNAME")
 ADMIN_PASSWORD = os.environ.get("ADMIN_PASSWORD")
 
 # Repository IDs
-REPO_ID = f"{OWNER}/guardbench"
+REPO_ID = f"{OWNER}/circle-guard-bench"
 RESULTS_DATASET_ID = os.environ.get("RESULTS_DATASET_ID", f"{OWNER}/guardbench-results")
 
 # Cache paths

src/submission/submit.py

@@ -118,7 +118,7 @@ def process_submission(file_path: str, metadata: Dict, version="v0") -> str:
         model_name = metadata.get("model_name", "unknown")
         model_name_safe = model_name.replace("/", "_").replace(" ", "_")
         guard_model_type = metadata.get("guard_model_type", "unknown")
-        target_file = os.path.join(results_dir + "/guardbench_dataset_1k_public", f"{model_name_safe}.jsonl")
+        target_file = os.path.join(results_dir + "/circleguardbench_public", f"{model_name_safe}.jsonl")
 
         # Upload raw submission file
         api = HfApi(token=TOKEN)
@@ -130,7 +130,7 @@ def process_submission(file_path: str, metadata: Dict, version="v0") -> str:
             repo_type="dataset",
             commit_message=f"Add raw submission for {model_name}"
         )
-        os.makedirs(results_dir + "/guardbench_dataset_1k_public", exist_ok=True)
+        os.makedirs(results_dir + "/circleguardbench_public", exist_ok=True)
 
         # (f"Submission path: {submission_path}")
         # print(f"Target file: {target_file}")
@@ -147,9 +147,9 @@ def process_submission(file_path: str, metadata: Dict, version="v0") -> str:
             # Set results directory
             ctx.results_dir = results_dir
             # Set bench name from the results directory
-            ctx.bench_name = "guardbench_dataset_1k_public"
+            ctx.bench_name = "circleguardbench_public"
             # Load dataset
-            ctx.load_dataset("whitecircle-ai/guardbench_dataset_1k_public")
+            ctx.load_dataset("whitecircle-ai/circleguardbench_public")
             # Mark as initialized
             ctx.is_initialized = True