metadata
annotations_creators:
- derived
language:
- eng
license: cc-by-sa-4.0
multilinguality: monolingual
source_datasets:
- MRBench/mbeir_infoseek_task8
task_categories:
- visual-document-retrieval
- image-to-image
- image-to-text
- text-to-image
task_ids: []
dataset_info:
- config_name: corpus
features:
- name: id
dtype: string
- name: modality
dtype: string
- name: text
dtype: string
- name: image
dtype: image
splits:
- name: test
num_bytes: 8487586259.256
num_examples: 481782
download_size: 7721329401
dataset_size: 8487586259.256
- config_name: qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 3892284
num_examples: 131376
download_size: 955079
dataset_size: 3892284
- config_name: queries
features:
- name: id
dtype: string
- name: modality
dtype: string
- name: text
dtype: string
- name: image
dtype: image
splits:
- name: test
num_bytes: 268638897
num_examples: 17593
download_size: 266685529
dataset_size: 268638897
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-*
tags:
- mteb
- text
- image
Retrieve source text and image information to answer questions about images.
| Task category | it2it |
| Domains | Encyclopaedic |
| Reference | https://aclanthology.org/2023.emnlp-main.925 |
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("InfoSeekIT2ITRetrieval")
evaluator = mteb.MTEB([task])
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
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{chen2023can,
author = {Chen, Yang and Hu, Hexiang and Luan, Yi and Sun, Haitian and Changpinyo, Soravit and Ritter, Alan and Chang, Ming-Wei},
booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
pages = {14948--14968},
title = {Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?},
year = {2023},
}
@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("InfoSeekIT2ITRetrieval")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"number_of_characters": 291755841,
"num_samples": 499375,
"num_queries": 17593,
"num_documents": 481782,
"min_document_length": 8,
"average_document_length": 603.9548135878883,
"max_document_length": 4062,
"unique_documents": 481782,
"num_document_images": 481782,
"min_query_length": 21,
"average_query_length": 44.40874211334053,
"max_query_length": 87,
"unique_queries": 350,
"num_query_images": 17593,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 7.467515489114989,
"max_relevant_docs_per_query": 128,
"unique_relevant_docs": 7891
}
}
This dataset card was automatically generated using MTEB