utils_v2.py

import json
import os
import pandas as pd
from utils import create_hyperlinked_names, process_model_size
from datasets import *

BASE_COLS = ['Rank', 'Models', 'Model Size(B)']
BASE_DATA_TITLE_TYPE = ['number', 'markdown', 'str', 'markdown']

COLUMN_NAMES = BASE_COLS + ["Overall", 'Image-Overall', 'Video-Overall', 'Visdoc-Overall']
DATA_TITLE_TYPE = BASE_DATA_TITLE_TYPE + \
                    ['number'] * 3

SUB_TASKS_I = ["I-CLS", "I-QA", "I-RET", "I-VG"]
TASKS_I = ['Image-Overall'] + SUB_TASKS_I + ALL_DATASETS_SPLITS['image']
COLUMN_NAMES_I = BASE_COLS + TASKS_I
DATA_TITLE_TYPE_I = BASE_DATA_TITLE_TYPE + \
                    ['number'] * len(TASKS_I + SUB_TASKS_I)

SUB_TASKS_V = ["V-CLS", "V-QA", "V-RET", "V-MRET"]
TASKS_V = ['Video-Overall'] + SUB_TASKS_V + ALL_DATASETS_SPLITS['video']
COLUMN_NAMES_V = BASE_COLS + TASKS_V
DATA_TITLE_TYPE_V = BASE_DATA_TITLE_TYPE + \
                    ['number'] * len(TASKS_V + SUB_TASKS_V)

SUB_TASKS_D = ['ViDoRe-V1', 'ViDoRe-V2', 'VisRAG', 'VisDoc-OOD']
TASKS_D = ['Visdoc-Overall'] + SUB_TASKS_D + ALL_DATASETS_SPLITS['visdoc']
COLUMN_NAMES_D = BASE_COLS + TASKS_D
DATA_TITLE_TYPE_D = BASE_DATA_TITLE_TYPE + \
                    ['number'] * len(TASKS_D + SUB_TASKS_D)

TABLE_INTRODUCTION = """**MMEB**: Massive MultiModal Embedding Benchmark \n
                        Models are ranked based on **Overall**"""
TABLE_INTRODUCTION_I = """**I-CLS**: Image Classification, **I-QA**: (Image) Visual Question Answering, **I-RET**: Image Retrieval, **I-VG**: (Image) Visual Grounding \n
                        Models are ranked based on **Image-Overall**"""
TABLE_INTRODUCTION_V = """**V-CLS**: Video Classification, **V-QA**: (Video) Visual Question Answering, **V-RET**: Video Retrieval, **V-MRET**: Video Moment Retrieval \n
                        Models are ranked based on **Video-Overall**"""
TABLE_INTRODUCTION_D = """**VisDoc**: Visual Document Understanding \n
                        Models are ranked based on **VisDoc**"""

LEADERBOARD_INFO = """
## Dataset Summary
"""

CITATION_BUTTON_TEXT = r"""@misc{meng2025vlm2vecv2advancingmultimodalembedding,
      title={VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual Documents}, 
      author={Rui Meng and Ziyan Jiang and Ye Liu and Mingyi Su and Xinyi Yang and Yuepeng Fu and Can Qin and Zeyuan Chen and Ran Xu and Caiming Xiong and Yingbo Zhou and Wenhu Chen and Semih Yavuz},
      year={2025},
      eprint={2507.04590},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2507.04590}, 
}"""

def load_single_json(file_path):
    with open(file_path, 'r') as file:
        data = json.load(file)
    return data

def load_data(base_dir=SCORE_BASE_DIR):
    all_data = []
    for file_name in os.listdir(base_dir):
        if file_name.endswith('.json'):
            file_path = os.path.join(base_dir, file_name)
            data = load_single_json(file_path)
            all_data.append(data)
    return all_data

def load_scores(raw_scores=None):
    """This function loads the raw scores from the user provided scores summary and flattens them into a single dictionary."""
    all_scores = {}
    for modality, datasets_list in DATASETS.items(): # Ex.: ('image', {'I-CLS': [...], 'I-QA': [...]})
        for sub_task, datasets in datasets_list.items(): # Ex.: ('I-CLS', ['VOC2007', 'N24News', ...])
            for dataset in datasets: # Ex.: 'VOC2007'
                score = raw_scores.get(modality, {}).get(dataset, 0.0)
                score = 0.0 if score == "FILE_N/A" else score
                metric = SPECIAL_METRICS.get(dataset, 'hit@1')
                if isinstance(score, dict):
                    if modality == 'visdoc':
                        metric = "ndcg_linear@5" if "ndcg_linear@5" in score else "ndcg@5"
                    score = score.get(metric, 0.0)
                all_scores[dataset] = round(score * 100.0, 2)
    return all_scores

def calculate_score(raw_scores=None):
    """This function calculates the overall average scores for all datasets as well as avg scores for each modality and sub-task based on the raw scores.
    """
    def get_avg(sum_score, leng):
        avg = sum_score / leng if leng > 0 else 0.0
        avg = round(avg, 2)  # Round to 2 decimal places
        return avg
    
    all_scores = load_scores(raw_scores)
    avg_scores = {}

    # Calculate overall score for all datasets
    avg_scores['Overall'] = get_avg(sum(all_scores.values()), len(ALL_DATASETS))

    # Calculate scores for each modality
    for modality in MODALITIES:
        datasets_for_each_modality = ALL_DATASETS_SPLITS[modality]
        avg_scores[f"{modality.capitalize()}-Overall"] = get_avg(
            sum(all_scores.get(dataset, 0.0) for dataset in datasets_for_each_modality),
            len(datasets_for_each_modality)
        )
    
    # Calculate scores for each sub-task
    for modality, datasets_list in DATASETS.items():
        for sub_task, datasets in datasets_list.items():
            sub_task_score = sum(all_scores.get(dataset, 0.0) for dataset in datasets)
            avg_scores[sub_task] = get_avg(sub_task_score, len(datasets))

    all_scores.update(avg_scores)
    return all_scores

def generate_model_row(data):
    metadata = data['metadata']
    row = {
        'Models': metadata.get('model_name', None), 
        'Model Size(B)': metadata.get('model_size', None),
        'URL': metadata.get('url', None), 
        'Data Source': metadata.get('data_source', 'Self-Reported'),
    }
    scores = calculate_score(data['metrics'])
    row.update(scores)
    return row

def rank_models(df, column='Overall', rank_name='Rank'):
    """Ranks the models based on the specific score."""
    df = df.sort_values(by=column, ascending=False).reset_index(drop=True)
    df[rank_name] = range(1, len(df) + 1)
    return df

def get_df():
    """Generates a DataFrame from the loaded data."""
    all_data = load_data()
    rows = [generate_model_row(data) for data in all_data]
    df = pd.DataFrame(rows)
    df['Model Size(B)'] = df['Model Size(B)'].apply(process_model_size)
    df = create_hyperlinked_names(df)
    df = rank_models(df, column='Overall')
    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]