metadata
annotations_creators:
- derived
language:
- eng
license: cc-by-sa-4.0
multilinguality: monolingual
source_datasets:
- MRBench/mbeir_oven_task6
task_categories:
- visual-document-retrieval
- image-to-text
- text-to-image
- image-text-to-text
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: 375331621
num_examples: 676667
download_size: 236228643
dataset_size: 375331621
- config_name: qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 14791270
num_examples: 492654
download_size: 1587519
dataset_size: 14791270
- 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: 681512224
num_examples: 50004
download_size: 676624404
dataset_size: 681512224
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
Retrieval a Wiki passage to answer query about an image.
| Task category | it2t |
| Domains | Encyclopaedic |
| Reference | https://openaccess.thecvf.com/content/ICCV2023/html/Hu_Open-domain_Visual_Entity_Recognition_Towards_Recognizing_Millions_of_Wikipedia_Entities_ICCV_2023_paper.html |
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("OVENIT2TRetrieval")
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{hu2023open,
author = {Hu, Hexiang and Luan, Yi and Chen, Yang and Khandelwal, Urvashi and Joshi, Mandar and Lee, Kenton and Toutanova, Kristina and Chang, Ming-Wei},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages = {12065--12075},
title = {Open-domain visual entity recognition: Towards recognizing millions of wikipedia entities},
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("OVENIT2TRetrieval")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"number_of_characters": 356548734,
"num_samples": 726671,
"num_queries": 50004,
"num_documents": 676667,
"min_document_length": 2,
"average_document_length": 524.4973081294048,
"max_document_length": 60546,
"unique_documents": 676667,
"num_document_images": 0,
"min_query_length": 11,
"average_query_length": 32.771658267338616,
"max_query_length": 78,
"unique_queries": 1540,
"num_query_images": 50004,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 9.852291816654668,
"max_relevant_docs_per_query": 174,
"unique_relevant_docs": 24471
}
}
This dataset card was automatically generated using MTEB