Datasets:
metadata
annotations_creators:
- expert-annotated
language:
- eng
license: cc-by-4.0
multilinguality: monolingual
source_datasets:
- mm-bright/MM-BRIGHT
task_categories:
- other
- image-to-text
- text-to-image
- image-text-to-text
task_ids: []
dataset_info:
- config_name: corpus
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 129787768
num_examples: 64044
download_size: 74279378
dataset_size: 129787768
- config_name: image-corpus
features:
- name: id
dtype: string
- name: image
dtype:
image:
mode: RGB
splits:
- name: test
num_bytes: 42598985
num_examples: 488
download_size: 42590511
dataset_size: 42598985
- config_name: image-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 7499
num_examples: 111
download_size: 3447
dataset_size: 7499
- config_name: qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 3609
num_examples: 76
download_size: 3406
dataset_size: 3609
- config_name: queries
features:
- name: id
dtype: string
- name: text
dtype: string
- name: image
dtype:
image:
mode: RGB
splits:
- name: test
num_bytes: 4926472
num_examples: 34
download_size: 4909117
dataset_size: 4926472
- config_name: top_ranked
features:
- name: query-id
dtype: string
- name: corpus-ids
list: string
splits:
- name: test
num_bytes: 82074194
num_examples: 34
download_size: 82079566
dataset_size: 82074194
configs:
- config_name: corpus
data_files:
- split: test
path: corpus/test-*
- config_name: image-corpus
data_files:
- split: test
path: image-corpus/test-*
- config_name: image-qrels
data_files:
- split: test
path: image-qrels/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
- text
- image
MM-BRIGHT text-and-image queries retrieving reasoning-intensive technical passages in the Quantitative Finance domain.
| Task category | Any2AnyRetrieval (image+text-to-text) |
| Domains | Academic, Web, Medical, Legal, Religious |
| Reference | {MM-BRIGHT |
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("MMBrightQuantIT2TRetrieval")
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.
@article{abdallah2026mmbright,
archiveprefix = {arXiv},
author = {Abdelrahman Abdallah and Mohamed Darwish Mounis and Mahmoud Abdalla and Mahmoud SalahEldin Kasem and Mostafa Farouk Senussi and Mohamed Mahmoud and Mohammed Ali and Adam Jatowt and Hyun-Soo Kang},
eprint = {2601.09562},
primaryclass = {cs.IR},
title = {{MM-BRIGHT}: A Multi-Task Multimodal Benchmark for Reasoning-Intensive Retrieval},
url = {https://arxiv.org/abs/2601.09562},
year = {2026},
}
@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("MMBrightQuantIT2TRetrieval")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 64072,
"num_queries": 28,
"num_documents": 64044,
"number_of_characters": 126474591,
"documents_text_statistics": {
"total_text_length": 126431993,
"min_text_length": 50,
"average_text_length": 1974.1426675410655,
"max_text_length": 2554995,
"unique_texts": 59197
},
"documents_image_statistics": null,
"documents_audio_statistics": null,
"documents_video_statistics": null,
"queries_text_statistics": {
"total_text_length": 42598,
"min_text_length": 312,
"average_text_length": 1521.357142857143,
"max_text_length": 5063,
"unique_texts": 28
},
"queries_image_statistics": {
"min_image_width": 274,
"average_image_width": 817.3214285714286,
"max_image_width": 2541,
"min_image_height": 47,
"average_image_height": 625.1785714285714,
"max_image_height": 2035,
"unique_images": 28
},
"queries_audio_statistics": null,
"queries_video_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 70,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 2.5,
"max_relevant_docs_per_query": 6,
"unique_relevant_docs": 70,
"num_missing_query_ids": 0,
"num_missing_corpus_ids": 0
},
"top_ranked_statistics": {
"num_top_ranked": 1788023,
"min_top_ranked_per_query": 63460,
"average_top_ranked_per_query": 63857.96428571428,
"max_top_ranked_per_query": 64044
}
}
}
This dataset card was automatically generated using MTEB