Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
parquet
Sub-tasks:
multiple-choice-qa
Size:
10K - 100K
ArXiv:
License:
metadata
annotations_creators:
- derived
language:
- ara
- deu
- fra
- hin
- ita
- jpn
- por
- spa
license: cc-by-4.0
multilinguality: translated
source_datasets:
- jinaai/mintakaqa
task_categories:
- text-retrieval
task_ids:
- multiple-choice-qa
dataset_info:
- config_name: ar-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 59219
num_examples: 1491
download_size: 35162
dataset_size: 59219
- config_name: ar-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 54567
num_examples: 2203
download_size: 24666
dataset_size: 54567
- config_name: ar-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 245632
num_examples: 2203
download_size: 117692
dataset_size: 245632
- config_name: de-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 51068
num_examples: 1655
download_size: 36909
dataset_size: 51068
- config_name: de-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 59047
num_examples: 2374
download_size: 26771
dataset_size: 59047
- config_name: de-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 187724
num_examples: 2374
download_size: 104325
dataset_size: 187724
- config_name: es-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 52100
num_examples: 1693
download_size: 37479
dataset_size: 52100
- config_name: es-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 60331
num_examples: 2424
download_size: 27256
dataset_size: 60331
- config_name: es-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 195740
num_examples: 2424
download_size: 105150
dataset_size: 195740
- config_name: fr-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 52994
num_examples: 1714
download_size: 38059
dataset_size: 52994
- config_name: fr-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 60803
num_examples: 2442
download_size: 27556
dataset_size: 60803
- config_name: fr-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 206591
num_examples: 2442
download_size: 110167
dataset_size: 206591
- config_name: hi-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 38005
num_examples: 770
download_size: 20066
dataset_size: 38005
- config_name: hi-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 32136
num_examples: 1337
download_size: 14220
dataset_size: 32136
- config_name: hi-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 213666
num_examples: 1337
download_size: 83801
dataset_size: 213666
- config_name: it-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 51135
num_examples: 1664
download_size: 36845
dataset_size: 51135
- config_name: it-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 59615
num_examples: 2396
download_size: 26981
dataset_size: 59615
- config_name: it-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 186497
num_examples: 2395
download_size: 103718
dataset_size: 186497
- config_name: ja-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 65908
num_examples: 1592
download_size: 38696
dataset_size: 65908
- config_name: ja-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 57373
num_examples: 2312
download_size: 26055
dataset_size: 57373
- config_name: ja-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 224887
num_examples: 2312
download_size: 114370
dataset_size: 224887
- config_name: pt-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 49999
num_examples: 1628
download_size: 36198
dataset_size: 49999
- config_name: pt-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 58537
num_examples: 2355
download_size: 26506
dataset_size: 58537
- config_name: pt-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 174935
num_examples: 2354
download_size: 98167
dataset_size: 174935
configs:
- config_name: ar-corpus
data_files:
- split: test
path: ar-corpus/test-*
- config_name: ar-qrels
data_files:
- split: test
path: ar-qrels/test-*
- config_name: ar-queries
data_files:
- split: test
path: ar-queries/test-*
- config_name: de-corpus
data_files:
- split: test
path: de-corpus/test-*
- config_name: de-qrels
data_files:
- split: test
path: de-qrels/test-*
- config_name: de-queries
data_files:
- split: test
path: de-queries/test-*
- config_name: es-corpus
data_files:
- split: test
path: es-corpus/test-*
- config_name: es-qrels
data_files:
- split: test
path: es-qrels/test-*
- config_name: es-queries
data_files:
- split: test
path: es-queries/test-*
- config_name: fr-corpus
data_files:
- split: test
path: fr-corpus/test-*
- config_name: fr-qrels
data_files:
- split: test
path: fr-qrels/test-*
- config_name: fr-queries
data_files:
- split: test
path: fr-queries/test-*
- config_name: hi-corpus
data_files:
- split: test
path: hi-corpus/test-*
- config_name: hi-qrels
data_files:
- split: test
path: hi-qrels/test-*
- config_name: hi-queries
data_files:
- split: test
path: hi-queries/test-*
- config_name: it-corpus
data_files:
- split: test
path: it-corpus/test-*
- config_name: it-qrels
data_files:
- split: test
path: it-qrels/test-*
- config_name: it-queries
data_files:
- split: test
path: it-queries/test-*
- config_name: ja-corpus
data_files:
- split: test
path: ja-corpus/test-*
- config_name: ja-qrels
data_files:
- split: test
path: ja-qrels/test-*
- config_name: ja-queries
data_files:
- split: test
path: ja-queries/test-*
- config_name: pt-corpus
data_files:
- split: test
path: pt-corpus/test-*
- config_name: pt-qrels
data_files:
- split: test
path: pt-qrels/test-*
- config_name: pt-queries
data_files:
- split: test
path: pt-queries/test-*
tags:
- mteb
- text
We introduce Mintaka, a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models. Mintaka is composed of 20,000 question-answer pairs collected in English, annotated with Wikidata entities, and translated into Arabic, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish for a total of 180,000 samples. Mintaka includes 8 types of complex questions, including superlative, intersection, and multi-hop questions, which were naturally elicited from crowd workers.
| Task category | t2t |
| Domains | Encyclopaedic, Written |
| Reference |
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("MintakaRetrieval")
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{sen-etal-2022-mintaka,
address = {Gyeongju, Republic of Korea},
author = {Sen, Priyanka and
Aji, Alham Fikri and
Saffari, Amir},
booktitle = {Proceedings of the 29th International Conference on Computational Linguistics},
month = oct,
pages = {1604--1619},
publisher = {International Committee on Computational Linguistics},
title = {Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering},
url = {https://aclanthology.org/2022.coling-1.138},
year = {2022},
}
@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("MintakaRetrieval")
desc_stats = task.metadata.descriptive_stats
{}
This dataset card was automatically generated using MTEB