import os
from huggingface_hub import CommitOperationAdd, create_commit, RepoUrl
from huggingface_hub import EvalResult, ModelCard
from huggingface_hub.repocard_data import eval_results_to_model_index
import time
from pytablewriter import MarkdownTableWriter
import gradio as gr
import pandas as pd
from datasets import load_dataset
def get_datas():
return pd.read_parquet("https://huggingface.co/datasets/open-llm-leaderboard/contents/resolve/main/data/train-00000-of-00001.parquet").sort_values(by="Average ⬆️", ascending=False)
BOT_HF_TOKEN = os.getenv('BOT_HF_TOKEN')
df = get_datas()
desc = """
This is an automated PR created with https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr
The purpose of this PR is to add evaluation results from the Open LLM Leaderboard to your model card.
If you encounter any issues, please report them to https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr/discussions
"""
def search(df, value):
result_df = df[df["fullname"] == value]
return result_df.iloc[0].to_dict() if not result_df.empty else None
def get_details_url(repo):
author, model = repo.split("/")
return f"https://huggingface.co/datasets/open-llm-leaderboard/{author}__{model}-details"
def get_query_url(repo):
return f"https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query={repo}"
def get_task_summary(results):
return {
"IFEval":
{"dataset_type":"HuggingFaceH4/ifeval",
"dataset_name":"IFEval (0-Shot)",
"metric_type": "inst_level_strict_acc and prompt_level_strict_acc",
"metric_value": round(results["IFEval"], 2),
"dataset_config": None, # don't know
"dataset_split": None, # don't know
"dataset_revision":None,
"dataset_args":{"num_few_shot": 0},
"metric_name":"strict accuracy"
},
"BBH":
{"dataset_type":"BBH",
"dataset_name":"BBH (3-Shot)",
"metric_type":"acc_norm",
"metric_value": round(results["BBH"], 2),
"dataset_config": None, # don't know
"dataset_split": None, # don't know
"dataset_revision":None,
"dataset_args":{"num_few_shot": 3},
"metric_name":"normalized accuracy"
},
"MATH Lvl 5":
{
"dataset_type":"hendrycks/competition_math",
"dataset_name":"MATH Lvl 5 (4-Shot)",
"metric_type":"exact_match",
"metric_value": round(results["MATH Lvl 5"], 2),
"dataset_config": None, # don't know
"dataset_split": None, # don't know
"dataset_revision":None,
"dataset_args":{"num_few_shot": 4},
"metric_name":"exact match"
},
"GPQA":
{
"dataset_type":"Idavidrein/gpqa",
"dataset_name":"GPQA (0-shot)",
"metric_type":"acc_norm",
"metric_value": round(results["GPQA"], 2),
"dataset_config": None, # don't know
"dataset_split": None, # don't know
"dataset_revision":None,
"dataset_args":{"num_few_shot": 0},
"metric_name":"acc_norm"
},
"MuSR":
{
"dataset_type":"TAUR-Lab/MuSR",
"dataset_name":"MuSR (0-shot)",
"metric_type":"acc_norm",
"metric_value": round(results["MUSR"], 2),
"dataset_config": None, # don't know
"dataset_split": None, # don't know
"dataset_args":{"num_few_shot": 0},
"metric_name":"acc_norm"
},
"MMLU-PRO":
{
"dataset_type":"TIGER-Lab/MMLU-Pro",
"dataset_name":"MMLU-PRO (5-shot)",
"metric_type":"acc",
"metric_value": round(results["MMLU-PRO"], 2),
"dataset_config":"main",
"dataset_split":"test",
"dataset_args":{"num_few_shot": 5},
"metric_name":"accuracy"
}
}
def get_eval_results(repo):
results = search(df, repo)
task_summary = get_task_summary(results)
md_writer = MarkdownTableWriter()
md_writer.headers = ["Metric", "Value"]
md_writer.value_matrix = [["Avg.", round(results['Average ⬆️'], 2)]] + [[v["dataset_name"], v["metric_value"]] for v in task_summary.values()]
text = f"""
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here]({get_details_url(repo)})
{md_writer.dumps()}
"""
return text
def get_edited_yaml_readme(repo, token: str | None):
card = ModelCard.load(repo, token=token)
results = search(df, repo)
common = {"task_type": 'text-generation', "task_name": 'Text Generation', "source_name": "Open LLM Leaderboard", "source_url": f"https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query={repo}"}
tasks_results = get_task_summary(results)
if not card.data['eval_results']: # No results reported yet, we initialize the metadata
card.data["model-index"] = eval_results_to_model_index(repo.split('/')[1], [EvalResult(**task, **common) for task in tasks_results.values()])
else: # We add the new evaluations
for task in tasks_results.values():
cur_result = EvalResult(**task, **common)
if any(result.is_equal_except_value(cur_result) for result in card.data['eval_results']):
continue
card.data['eval_results'].append(cur_result)
return str(card)
def commit(repo, pr_number=None, message="Adding Evaluation Results", oauth_token: gr.OAuthToken | None = None): # specify pr number if you want to edit it, don't if you don't want
global df
finished_models = get_datas()
df = pd.DataFrame(finished_models)
if not oauth_token:
raise gr.Warning("You are not logged in. Click on 'Sign in with Huggingface' to log in.")
else:
token = oauth_token
if repo.startswith("https://huggingface.co/"):
try:
repo = RepoUrl(repo).repo_id
except Exception:
raise gr.Error(f"Not a valid repo id: {str(repo)}")
edited = {"revision": f"refs/pr/{pr_number}"} if pr_number else {"create_pr": True}
try:
try: # check if there is a readme already
readme_text = get_edited_yaml_readme(repo, token=token) + get_eval_results(repo)
except Exception as e:
if "Repo card metadata block was not found." in str(e): # There is no readme
readme_text = get_edited_yaml_readme(repo, token=token)
else:
print(f"Something went wrong: {e}")
liste = [CommitOperationAdd(path_in_repo="README.md", path_or_fileobj=readme_text.encode())]
commit = (create_commit(repo_id=repo, token=token, operations=liste, commit_message=message, commit_description=desc, repo_type="model", **edited).pr_url)
print(f"Success: {repo}")
return commit
except Exception as e:
print(f"Error: {repo}")
if "Discussions are disabled for this repo" in str(e):
return "Discussions disabled"
elif "Cannot access gated repo" in str(e):
return "Gated repo"
elif "Repository Not Found" in str(e):
return "Repository Not Found"
else:
return e