import pandas as pd import gradio as gr import csv import json import os import requests import io import shutil import pprint as pp from huggingface_hub import Repository from datasets import DATASETS HF_TOKEN = os.environ.get("HF_TOKEN") BASE_COLS = ["Rank", "Models", "Model Size(B)", "Data Source"] TASKS_V1 = ["V1-Overall", "I-CLS", "I-QA", "I-RET", "I-VG"] COLUMN_NAMES = BASE_COLS + TASKS_V1 DATA_TITLE_TYPE = ['number', 'markdown', 'str', 'markdown'] + \ ['number'] * len(TASKS_V1) LEADERBOARD_INTRODUCTION = """ # 📊 **MMEB LEADERBOARD (VLM2Vec)** ## Introduction We introduce a novel benchmark, **MMEB-V1 (Massive Multimodal Embedding Benchmark)**, which includes 36 datasets spanning four meta-task categories: classification, visual question answering, retrieval, and visual grounding. MMEB provides a comprehensive framework for training and evaluating embedding models across various combinations of text and image modalities. All tasks are reformulated as ranking tasks, where the model follows instructions, processes a query, and selects the correct target from a set of candidates. The query and target can be an image, text, or a combination of both. MMEB-V1 is divided into 20 in-distribution datasets, which can be used for training, and 16 out-of-distribution datasets, reserved for evaluation. Building upon on **MMEB-V1**, **MMEB-V2** expands the evaluation scope to include five new tasks: four video-based tasks — Video Retrieval, Moment Retrieval, Video Classification, and Video Question Answering — and one task focused on visual documents, Visual Document Retrieval. This comprehensive suite enables robust evaluation of multimodal embedding models across static, temporal, and structured visual data settings. | [**📈Overview**](https://tiger-ai-lab.github.io/VLM2Vec/) | [**Github**](https://github.com/TIGER-AI-Lab/VLM2Vec) | [**📖MMEB-V2/VLM2Vec-V2 Paper**](https://arxiv.org/abs/2507.04590) | [**📖MMEB-V1/VLM2Vec-V1 Paper**](https://arxiv.org/abs/2410.05160) | [**🤗Hugging Face**](https://huggingface.co/datasets/TIGER-Lab/MMEB-V2) | [**Discord**](https://discord.gg/njyKubdtry) | """ TABLE_INTRODUCTION = """***Important Notes: *** This is the MMEB-V1 leaderboard, which is now deprecated. MMEB-V1 is now the Image section of MMEB-V2, and the results on this leaderboard have been integrated into MMEB-V2 Image tab. For researchers relying on MMEB-V1, we recommend transitioning to MMEB-V2 for more comprehensive evaluation metrics and support. Thank you for your collaborations and understanding! \n""" LEADERBOARD_INFO = f""" ## Dataset Overview This is the dictionary of all datasets used in our code. Please make sure all datasets' scores are included in your submission. \n {pp.pformat(DATASETS)} """ CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results" CITATION_BUTTON_TEXT = r"""@article{jiang2024vlm2vec, title={VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks}, author={Jiang, Ziyan and Meng, Rui and Yang, Xinyi and Yavuz, Semih and Zhou, Yingbo and Chen, Wenhu}, journal={arXiv preprint arXiv:2410.05160}, year={2024} }""" SUBMIT_INTRODUCTION = """# Submit on MMEB Leaderboard Introduction ## Please refer to the [**GitHub page**](https://github.com/TIGER-AI-Lab/VLM2Vec) for detailed instructions about evaluating your model. \n ## ⚠️ Please note that you need to submit the JSON file with the following format: ```json { "metadata": { "model_name": "", "url": "" or null, "model_size": or null, "data_source": "Self-Reported", ... ... }, "metrics": { "image": { "ImageNet-1K": { "hit@1": 0.5, "ndcg@1": 0.5, ... ... }, "N24News": { ... ... }, ... ... }, "visdoc": { "ViDoRe": { "hit@1": 0.5, "ndcg@1": 0.5, ... ... }, ... ... }, "video": { "DiDeMo": { "hit@1": 0.5, "ndcg@1": 0.5, ... ... }, "MSR-VTT": { ... ... }, ... ... } } } ``` To submit, create a pull request and upload the generated JSON file to the ***scores*** folder, then inform us on [our discord server](https://discord.gg/njyKubdtry), or send us an email at m7su@uwaterloo.ca, including your model's information. \n We will review your submission and update the leaderboard accordingly. \n We highly recommend joining our [discord server](https://discord.gg/njyKubdtry), which provides a convenient way to stay informed with latest updates, or share any feedback you have for improving the leaderboard experience. We appreciate your contributions to the MMEB community! """ def create_hyperlinked_names(df): def convert_url(url, model_name): return f'{model_name}' if url is not None else model_name def add_link_to_model_name(row): row['Models'] = convert_url(row['URL'], row['Models']) return row df = df.copy() df = df.apply(add_link_to_model_name, axis=1) return df # def fetch_data(file: str) -> pd.DataFrame: # # fetch the leaderboard data from remote # if file is None: # raise ValueError("URL Not Provided") # url = f"https://huggingface.co/spaces/TIGER-Lab/MMEB/resolve/main/{file}" # print(f"Fetching data from {url}") # response = requests.get(url) # if response.status_code != 200: # raise requests.HTTPError(f"Failed to fetch data: HTTP status code {response.status_code}") # return pd.read_json(io.StringIO(response.text), orient='records', lines=True) def get_df(file="results.jsonl"): df = pd.read_json(file, orient='records', lines=True) df['Model Size(B)'] = df['Model Size(B)'].apply(process_model_size) for task in TASKS_V1: if df[task].isnull().any(): df[task] = df[task].apply(lambda score: '-' if pd.isna(score) else score) df = df.sort_values(by=['V1-Overall'], ascending=False) df = create_hyperlinked_names(df) df['Rank'] = range(1, len(df) + 1) return df def refresh_data(): df = get_df() return df[COLUMN_NAMES] def search_and_filter_models(df, query, min_size, max_size): filtered_df = df.copy() if query: filtered_df = filtered_df[filtered_df['Models'].str.contains(query, case=False, na=False)] size_mask = filtered_df['Model Size(B)'].apply(lambda x: (min_size <= 1000.0 <= max_size) if x == 'unknown' else (min_size <= x <= max_size)) filtered_df = filtered_df[size_mask] return filtered_df[COLUMN_NAMES] def search_models(df, query): if query: return df[df['Models'].str.contains(query, case=False, na=False)] return df def get_size_range(df): sizes = df['Model Size(B)'].apply(lambda x: 0.0 if x == 'unknown' else x) if (sizes == 0.0).all(): return 0.0, 1000.0 return float(sizes.min()), float(sizes.max()) def process_model_size(size): if pd.isna(size) or size == 'unk': return 'unknown' try: val = float(size) return round(val, 3) except (ValueError, TypeError): return 'unknown' def filter_columns_by_tasks(df, selected_tasks=None): if selected_tasks is None or len(selected_tasks) == 0: return df[COLUMN_NAMES] base_columns = ['Models', 'Model Size(B)', 'Data Source', 'Overall'] selected_columns = base_columns + selected_tasks available_columns = [col for col in selected_columns if col in df.columns] return df[available_columns]