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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: | |
if type(accuracy)!=str: # Always convert to string | |
accuracy = str(accuracy) | |
parsed = accuracy.split(' +/- ') | |
if len(parsed)>1: | |
mean_reward = float(parsed[0]) | |
std_reward = float(parsed[1]) | |
elif len(parsed)==1: #only mean reward | |
mean_reward = float(parsed[0]) | |
std_reward = float(0) | |
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 | |