| import pandas as pd | |
| import gradio as gr | |
| import csv | |
| import json | |
| import os | |
| import shutil | |
| from huggingface_hub import Repository | |
| HF_TOKEN = os.environ.get("HF_TOKEN") | |
| SUBJECTS = ["Biology", "Business", "Chemistry", "Computer Science", "Economics", "Engineering", | |
| "Health", "History", "Law", "Math", "Philosophy", "Physics", "Psychology", "Other"] | |
| MODEL_INFO = [ | |
| "Models", | |
| "Overall", | |
| "Biology", "Business", "Chemistry", "Computer Science", "Economics", "Engineering", | |
| "Health", "History", "Law", "Math", "Philosophy", "Physics", "Psychology", "Other"] | |
| DATA_TITLE_TYPE = ['markdown', 'number', 'number', 'number', 'number', 'number', 'number', | |
| 'number', 'number', 'number', 'number', 'number', 'number', 'number', | |
| 'number', 'number'] | |
| SUBMISSION_NAME = "mmlu_pro_leaderboard_submission" | |
| SUBMISSION_URL = os.path.join("https://huggingface.co/datasets/TIGER-Lab/", SUBMISSION_NAME) | |
| CSV_DIR = "./mmlu_pro_leaderboard_submission/results.csv" | |
| COLUMN_NAMES = MODEL_INFO | |
| LEADERBOARD_INTRODUCTION = """# MMLU-Pro Leaderboard | |
| Welcome to the MMLU-Pro leaderboard, showcasing the performance of various advanced language models on the MMLU-Pro dataset. The MMLU-Pro dataset is an enhanced version of the original MMLU, specifically engineered to offer a more rigorous and realistic evaluation environment.. | |
| The MMLU-Pro dataset consists of approximately 12,000 intricate questions that challenge the comprehension and reasoning abilities of LLMs. Below you can find the accuracies of different models tested on this dataset. | |
| ## 1. What's new about MMLU-Pro | |
| Compared to the original MMLU, there are three major differences: | |
| - The original MMLU dataset only contains 4 options, MMLU-Pro increases it to 10 options. The increase in options will make the evaluation more realistic and challenging. The random guessing will lead to a much lower score. | |
| - The original MMLU dataset contains mostly knowledge-driven questions without requiring much reasoning. Therefore, PPL results are normally better than CoT. In our dataset, we increase the problem difficulty and integrate more reasoning-focused problems. In MMLU-Pro, CoT can be 20% higher than PPL. | |
| - Due to the increase of options, we found that the model performance becomes more robust. For example, Llama-2-7B performance variance on MMLU-Pro is within 1% with several different prompts. In contrast, the performance variance on original MMLU can be as huge as 4-5%. | |
| ## 2. Dataset Summary | |
| - **Questions and Options:** Each question within the dataset typically has **ten** multiple-choice options, except for some that were reduced during the manual review process to remove unreasonable choices. This increase from the original **four** options per question is designed to enhance complexity and robustness, necessitating deeper reasoning to discern the correct answer among a larger pool of potential distractors. | |
| - **Sources:** The dataset consolidates questions from several sources: | |
| - **Original MMLU Questions:** Part of the dataset is coming from the original MMLU dataset. We remove the trivial and ambiguous questions. | |
| - **STEM Website:** Hand picking high-quality STEM problems from the Internet. | |
| - **TheoremQA:** High-quality human-annotated questions requiring theorems to solve. | |
| - **Scibench:** Science questions from college exams. | |
| For detailed information about the dataset, visit our page on Hugging Face: MMLU-Pro at Hugging Face. If you are interested in replicating these results or wish to evaluate your models using our dataset, access our evaluation scripts available on GitHub: TIGER-AI-Lab/MMLU-Pro. | |
| """ | |
| TABLE_INTRODUCTION = """ | |
| """ | |
| LEADERBOARD_INFO = """ | |
| We list the information of the used datasets as follows:<br> | |
| """ | |
| CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results" | |
| CITATION_BUTTON_TEXT = r"""""" | |
| SUBMIT_INTRODUCTION = """# Submit on Science Leaderboard Introduction | |
| ## ⚠ Please note that you need to submit the json file with following format: | |
| ```json | |
| { | |
| "Model": "[MODEL_NAME]", | |
| "Overall": 0.5678, | |
| "Biology": 0.1234, | |
| "Business": 0.4567, | |
| ..., | |
| "Other: 0.3456" | |
| } | |
| ``` | |
| After submitting, you can click the "Refresh" button to see the updated leaderboard (it may takes few seconds). | |
| """ | |
| def get_df(): | |
| repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN) | |
| repo.git_pull() | |
| df = pd.read_csv(CSV_DIR) | |
| df = df.sort_values(by=['Overall'], ascending=False) | |
| return df[COLUMN_NAMES] | |
| def add_new_eval( | |
| input_file, | |
| ): | |
| if input_file is None: | |
| return "Error! Empty file!" | |
| upload_data = json.loads(input_file) | |
| print("upload_data:\n", upload_data) | |
| data_row = [f'{upload_data["Model"]}', upload_data['Overall']] | |
| for subject in SUBJECTS: | |
| data_row += [upload_data[subject]] | |
| print("data_row:\n", data_row) | |
| submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, | |
| use_auth_token=HF_TOKEN, repo_type="dataset") | |
| submission_repo.git_pull() | |
| already_submitted = [] | |
| with open(CSV_DIR, mode='r') as file: | |
| reader = csv.reader(file, delimiter=',') | |
| for row in reader: | |
| already_submitted.append(row[0]) | |
| if data_row[0] not in already_submitted: | |
| with open(CSV_DIR, mode='a', newline='') as file: | |
| writer = csv.writer(file) | |
| writer.writerow(data_row) | |
| submission_repo.push_to_hub() | |
| print('Submission Successful') | |
| else: | |
| print('The entry already exists') | |
| def refresh_data(): | |
| return get_df() | |