Removing -- is in place in https://huggingface.co/spaces/Bias-Leaderboard/leaderboard-backend

main_backend.py

@@ -1,84 +0,0 @@
-import logging
-import pprint
-
-from huggingface_hub import snapshot_download
-from datasets import load_dataset
-
-logging.getLogger("openai").setLevel(logging.WARNING)
-
-from src.backend.run_eval_suite import run_evaluation
-from src.backend.manage_requests import check_completed_evals, get_eval_requests, set_eval_request
-from src.backend.sort_queue import sort_models_by_priority
-
-from src.envs import TOKEN, QUEUE_REPO, EVAL_REQUESTS_PATH_BACKEND, RESULTS_REPO, EVAL_RESULTS_PATH_BACKEND, DEVICE, API, LIMIT
-from src.about import Tasks, NUM_FEWSHOT
-TASKS_HARNESS = [task.value.benchmark for task in Tasks]
-
-logging.basicConfig(level=logging.ERROR)
-pp = pprint.PrettyPrinter(width=80)
-
-PENDING_STATUS = "PENDING"
-RUNNING_STATUS = "RUNNING"
-FINISHED_STATUS = "FINISHED"
-FAILED_STATUS = "FAILED"
-
-print("JUST trying toxigen access...")
-load_dataset("skg/toxigen-data", token=TOKEN)
-print("Done.")
-
-print("Downloading snapshot from %s to %s" % (RESULTS_REPO, EVAL_RESULTS_PATH_BACKEND))
-snapshot_download(repo_id=RESULTS_REPO, revision="main", local_dir=EVAL_RESULTS_PATH_BACKEND, repo_type="dataset", token=TOKEN, max_workers=60)
-snapshot_download(repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", token=TOKEN, max_workers=60)
-
-def run_auto_eval():
-    current_pending_status = [PENDING_STATUS]
-
-    # pull the eval dataset from the hub and parse any eval requests
-    # check completed evals and set them to finished
-    check_completed_evals(
-        api=API,
-        checked_status=RUNNING_STATUS,
-        completed_status=FINISHED_STATUS,
-        failed_status=FAILED_STATUS,
-        hf_repo=QUEUE_REPO,
-        local_dir=EVAL_REQUESTS_PATH_BACKEND,
-        hf_repo_results=RESULTS_REPO,
-        local_dir_results=EVAL_RESULTS_PATH_BACKEND
-    )
-
-    # Get all eval request that are PENDING, if you want to run other evals, change this parameter
-    eval_requests = get_eval_requests(job_status=current_pending_status, hf_repo=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH_BACKEND)
-    # Sort the evals by priority (first submitted first run)
-    eval_requests = sort_models_by_priority(api=API, models=eval_requests)
-
-    print(f"Found {len(eval_requests)} {','.join(current_pending_status)} eval requests")
-
-    if len(eval_requests) == 0:
-        return
-
-    eval_request = eval_requests[0]
-    pp.pprint(eval_request)
-
-    set_eval_request(
-        api=API,
-        eval_request=eval_request,
-        set_to_status=RUNNING_STATUS,
-        hf_repo=QUEUE_REPO,
-        local_dir=EVAL_REQUESTS_PATH_BACKEND,
-    )
-
-    run_evaluation(
-        eval_request=eval_request, 
-        task_names=TASKS_HARNESS, 
-        num_fewshot=NUM_FEWSHOT, 
-        local_dir=EVAL_RESULTS_PATH_BACKEND,
-        results_repo=RESULTS_REPO,
-        batch_size=1, 
-        device=DEVICE, 
-        no_cache=True, 
-        limit=LIMIT
-        )
-
-
-if __name__ == "__main__":
-    run_auto_eval()