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| import pandas as pd | |
| import requests | |
| from tqdm.auto import tqdm | |
| from huggingface_hub import HfApi, hf_hub_download | |
| from huggingface_hub.repocard import metadata_load | |
| # Based on Omar Sanseviero work | |
| # Make model clickable link | |
| def make_clickable_model(model_name): | |
| # remove user from model name | |
| model_name_show = ' '.join(model_name.split('/')[1:]) | |
| link = "https://huggingface.co/" + model_name | |
| return f'<a target="_blank" href="{link}">{model_name_show}</a>' | |
| # Make user clickable link | |
| def make_clickable_user(user_id): | |
| link = "https://huggingface.co/" + user_id | |
| return f'<a target="_blank" href="{link}">{user_id}</a>' | |
| def get_model_ids(rl_env): | |
| api = HfApi() | |
| models = api.list_models(filter=rl_env) | |
| model_ids = [x.modelId for x in models] | |
| return model_ids | |
| def get_metadata(model_id): | |
| try: | |
| readme_path = hf_hub_download(model_id, filename="README.md") | |
| return metadata_load(readme_path) | |
| except requests.exceptions.HTTPError: | |
| # 404 README.md not found | |
| return None | |
| def parse_metrics_accuracy(meta): | |
| if "model-index" not in meta: | |
| return None | |
| result = meta["model-index"][0]["results"] | |
| metrics = result[0]["metrics"] | |
| accuracy = metrics[0]["value"] | |
| return accuracy | |
| # We keep the worst case episode | |
| def parse_rewards(accuracy): | |
| default_std = -1000 | |
| default_reward=-1000 | |
| if accuracy != None: | |
| accuracy = str(accuracy) | |
| parsed = accuracy.split(' +/- ') | |
| if len(parsed)>1: | |
| mean_reward = float(parsed[0]) | |
| std_reward = float(parsed[1]) | |
| else: | |
| mean_reward = float(default_std) | |
| std_reward = float(default_reward) | |
| else: | |
| mean_reward = float(default_std) | |
| std_reward = float(default_reward) | |
| return mean_reward, std_reward | |