Datasets:
Tasks:
Visual Question Answering
Formats:
parquet
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
1K - 10K
ArXiv:
License:
metadata
annotations_creators:
- human-annotated
language:
- eng
license: cc-by-4.0
multilinguality: monolingual
source_datasets:
- yale-nlp/MMVU
task_categories:
- visual-question-answering
task_ids:
- multiple-choice-qa
dataset_info:
- config_name: corpus
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 206757
num_examples: 3125
download_size: 111874
dataset_size: 206757
- config_name: qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 16655
num_examples: 625
download_size: 8368
dataset_size: 16655
- config_name: queries
features:
- name: id
dtype: string
- name: text
dtype: string
- name: video
dtype: video
splits:
- name: test
num_bytes: 1027024257
num_examples: 625
download_size: 1027035200
dataset_size: 1027024257
- config_name: top_ranked
features:
- name: query-id
dtype: string
- name: corpus-ids
list: string
splits:
- name: test
num_bytes: 41215
num_examples: 625
download_size: 38956
dataset_size: 41215
configs:
- config_name: corpus
data_files:
- split: test
path: corpus/test-*
- config_name: qrels
data_files:
- split: test
path: qrels/test-*
- config_name: queries
data_files:
- split: test
path: queries/test-*
- config_name: top_ranked
data_files:
- split: test
path: top_ranked/test-*
tags:
- mteb
- video
- text
MMVU is an expert-level, multi-discipline video understanding benchmark with questions spanning 27 subjects across Science, Healthcare, Humanities & Social Sciences, and Engineering. Each multiple-choice example pairs a specialized-domain video with a question and 5 candidate answers. The task is formulated as multiple-choice retrieval: given the (video, question) pair, retrieve the correct candidate. Used the public validation multiple-choice subset (~625 examples).
| Task category | VideoCentricQA (video+text-to-text) |
| Domains | Academic, Medical, Engineering, Web |
| Reference | MMVU: Measuring expert-level multi-discipline video understanding |
Source datasets:
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("MMVUVideoCentricQA")
model = mteb.get_model(YOUR_MODEL)
mteb.evaluate(model, task)
To learn more about how to run models on mteb task check out the GitHub repository.
Citation
If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.
@inproceedings{zhao2025mmvu,
title={MMVU: Measuring expert-level multi-discipline video understanding},
author={Zhao, Yilun and Zhang, Haowei and Xie, Lujing and Hu, Tongyan and Gan, Guo and Long, Yitao and Hu, Zhiyuan and Chen, Weiyuan and Li, Chuhan and Xu, Zhijian and others},
booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
pages={8475--8489},
year={2025}
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Loïc and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
Dataset Statistics
Dataset Statistics
The following code contains the descriptive statistics from the task. These can also be obtained using:
import mteb
task = mteb.get_task("MMVUVideoCentricQA")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 3750,
"num_queries": 625,
"num_documents": 3125,
"number_of_characters": 223931,
"documents_text_statistics": {
"total_text_length": 160329,
"min_text_length": 1,
"average_text_length": 51.30528,
"max_text_length": 248,
"unique_texts": 2860
},
"documents_image_statistics": null,
"documents_audio_statistics": null,
"documents_video_statistics": null,
"queries_text_statistics": {
"total_text_length": 63602,
"min_text_length": 26,
"average_text_length": 101.7632,
"max_text_length": 432,
"unique_texts": 618
},
"queries_image_statistics": null,
"queries_audio_statistics": null,
"queries_video_statistics": {
"total_duration_seconds": 32429.824763369106,
"total_frames": 900135,
"min_width": 202,
"average_width": 605.968,
"max_width": 640,
"min_height": 240,
"average_height": 359.0592,
"max_height": 640,
"min_duration_seconds": 9,
"average_duration_seconds": 51.88771962139057,
"max_duration_seconds": 195,
"unique_videos": 338,
"average_fps": 27.68,
"fps": {
"30": 406,
"25": 124,
"24": 72,
"10": 8,
"15": 10,
"22": 2,
"6": 3
},
"min_resolution": [
202,
360
],
"average_resolution": [
605.968,
359.0592
],
"max_resolution": [
640,
360
],
"resolutions": {
"480x360": 61,
"640x360": 450,
"640x352": 44,
"480x328": 3,
"320x240": 6,
"352x288": 2,
"558x360": 5,
"360x360": 2,
"364x360": 3,
"620x360": 1,
"600x360": 2,
"202x360": 3,
"640x354": 2,
"490x360": 12,
"640x358": 4,
"492x360": 4,
"540x360": 2,
"528x360": 1,
"454x360": 3,
"482x360": 4,
"640x332": 2,
"320x320": 1,
"602x360": 2,
"576x360": 3,
"360x640": 3
}
},
"relevant_docs_statistics": {
"num_relevant_docs": 625,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 625
},
"top_ranked_statistics": {
"num_top_ranked": 3125,
"min_top_ranked_per_query": 5,
"average_top_ranked_per_query": 5.0,
"max_top_ranked_per_query": 5
}
}
}
This dataset card was automatically generated using MTEB