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| import os | |
| from huggingface_hub import Repository | |
| H4_TOKEN = os.environ.get("H4_TOKEN", None) | |
| def get_all_requested_models(requested_models_dir): | |
| depth = 1 | |
| file_names = [] | |
| for root, dirs, files in os.walk(requested_models_dir): | |
| current_depth = root.count(os.sep) - requested_models_dir.count(os.sep) | |
| if current_depth == depth: | |
| file_names.extend([os.path.join(root, file) for file in files]) | |
| return set([file_name.lower().split("eval_requests/")[1] for file_name in file_names]) | |
| def load_all_info_from_hub(LMEH_REPO, HUMAN_EVAL_REPO, GPT_4_EVAL_REPO): | |
| auto_eval_repo = None | |
| requested_models = None | |
| if H4_TOKEN: | |
| print("Pulling evaluation requests and results.") | |
| # try: | |
| # shutil.rmtree("./auto_evals/") | |
| # except: | |
| # pass | |
| auto_eval_repo = Repository( | |
| local_dir="./auto_evals/", | |
| clone_from=LMEH_REPO, | |
| use_auth_token=H4_TOKEN, | |
| repo_type="dataset", | |
| ) | |
| auto_eval_repo.git_pull() | |
| requested_models_dir = "./auto_evals/eval_requests" | |
| requested_models = get_all_requested_models(requested_models_dir) | |
| human_eval_repo = None | |
| if H4_TOKEN and not os.path.isdir("./human_evals"): | |
| print("Pulling human evaluation repo") | |
| human_eval_repo = Repository( | |
| local_dir="./human_evals/", | |
| clone_from=HUMAN_EVAL_REPO, | |
| use_auth_token=H4_TOKEN, | |
| repo_type="dataset", | |
| ) | |
| human_eval_repo.git_pull() | |
| gpt_4_eval_repo = None | |
| if H4_TOKEN and not os.path.isdir("./gpt_4_evals"): | |
| print("Pulling GPT-4 evaluation repo") | |
| gpt_4_eval_repo = Repository( | |
| local_dir="./gpt_4_evals/", | |
| clone_from=GPT_4_EVAL_REPO, | |
| use_auth_token=H4_TOKEN, | |
| repo_type="dataset", | |
| ) | |
| gpt_4_eval_repo.git_pull() | |
| return auto_eval_repo, human_eval_repo, gpt_4_eval_repo, requested_models | |
| #def load_results(model, benchmark, metric): | |
| # file_path = os.path.join("autoevals", model, f"{model}-eval_{benchmark}.json") | |
| # if not os.path.exists(file_path): | |
| # return 0.0, None | |
| # with open(file_path) as fp: | |
| # data = json.load(fp) | |
| # accs = np.array([v[metric] for k, v in data["results"].items()]) | |
| # mean_acc = np.mean(accs) | |
| # return mean_acc, data["config"]["model_args"] | |