Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Sub-tasks:
multiple-choice-qa
Size:
10K - 100K
ArXiv:
License:
metadata
annotations_creators:
- derived
language:
- ara
- deu
- eng
- fra
- ita
- jpn
- kor
- nor
- por
- spa
- swe
license: cc-by-4.0
multilinguality: translated
source_datasets:
- zeta-alpha-ai/NanoMSMARCO
- LiquidAI/nanobeir-multilingual-extended
task_categories:
- text-retrieval
task_ids:
- multiple-choice-qa
dataset_info:
- config_name: ar-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 2558171
num_examples: 5043
download_size: 1299668
dataset_size: 2558171
- config_name: ar-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 1465
num_examples: 50
download_size: 2551
dataset_size: 1465
- config_name: ar-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 3576
num_examples: 50
download_size: 3786
dataset_size: 3576
- config_name: de-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 1936666
num_examples: 5043
download_size: 1198262
dataset_size: 1936666
- config_name: de-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 1465
num_examples: 50
download_size: 2551
dataset_size: 1465
- config_name: de-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 2788
num_examples: 50
download_size: 3561
dataset_size: 2788
- config_name: en-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 1745074
num_examples: 5043
download_size: 1087455
dataset_size: 1745074
- config_name: en-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 1465
num_examples: 50
download_size: 2551
dataset_size: 1465
- config_name: en-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 2328
num_examples: 50
download_size: 3224
dataset_size: 2328
- config_name: es-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 1926339
num_examples: 5043
download_size: 1172463
dataset_size: 1926339
- config_name: es-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 1465
num_examples: 50
download_size: 2551
dataset_size: 1465
- config_name: es-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 2933
num_examples: 50
download_size: 3678
dataset_size: 2933
- config_name: fr-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 2017755
num_examples: 5043
download_size: 1215222
dataset_size: 2017755
- config_name: fr-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 1465
num_examples: 50
download_size: 2551
dataset_size: 1465
- config_name: fr-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 3011
num_examples: 50
download_size: 3705
dataset_size: 3011
- config_name: it-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 1886669
num_examples: 5043
download_size: 1171893
dataset_size: 1886669
- config_name: it-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 1465
num_examples: 50
download_size: 2551
dataset_size: 1465
- config_name: it-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 2838
num_examples: 50
download_size: 3651
dataset_size: 2838
- config_name: ja-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 2203454
num_examples: 5043
download_size: 1281104
dataset_size: 2203454
- config_name: ja-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 1465
num_examples: 50
download_size: 2551
dataset_size: 1465
- config_name: ja-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 4492
num_examples: 50
download_size: 4614
dataset_size: 4492
- config_name: ko-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 2033768
num_examples: 5043
download_size: 1225116
dataset_size: 2033768
- config_name: ko-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 1465
num_examples: 50
download_size: 2551
dataset_size: 1465
- config_name: ko-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 3009
num_examples: 50
download_size: 3689
dataset_size: 3009
- config_name: no-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 1778066
num_examples: 5043
download_size: 1079549
dataset_size: 1778066
- config_name: no-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 1465
num_examples: 50
download_size: 2551
dataset_size: 1465
- config_name: no-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 2484
num_examples: 50
download_size: 3343
dataset_size: 2484
- config_name: pt-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 1869617
num_examples: 5043
download_size: 1160001
dataset_size: 1869617
- config_name: pt-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 1465
num_examples: 50
download_size: 2551
dataset_size: 1465
- config_name: pt-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 2815
num_examples: 50
download_size: 3536
dataset_size: 2815
- config_name: sv-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 1760376
num_examples: 5043
download_size: 1091131
dataset_size: 1760376
- config_name: sv-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 1465
num_examples: 50
download_size: 2551
dataset_size: 1465
- config_name: sv-queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 2462
num_examples: 50
download_size: 3330
dataset_size: 2462
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: en-corpus
data_files:
- split: test
path: en-corpus/test-*
- config_name: en-qrels
data_files:
- split: test
path: en-qrels/test-*
- config_name: en-queries
data_files:
- split: test
path: en-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: 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: ko-corpus
data_files:
- split: test
path: ko-corpus/test-*
- config_name: ko-qrels
data_files:
- split: test
path: ko-qrels/test-*
- config_name: ko-queries
data_files:
- split: test
path: ko-queries/test-*
- config_name: no-corpus
data_files:
- split: test
path: no-corpus/test-*
- config_name: no-qrels
data_files:
- split: test
path: no-qrels/test-*
- config_name: no-queries
data_files:
- split: test
path: no-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-*
- config_name: sv-corpus
data_files:
- split: test
path: sv-corpus/test-*
- config_name: sv-qrels
data_files:
- split: test
path: sv-qrels/test-*
- config_name: sv-queries
data_files:
- split: test
path: sv-queries/test-*
tags:
- mteb
- text
NanoMSMARCORetrieval is a smaller subset of MS MARCO, a collection of datasets focused on deep learning in search.
| Task category | Retrieval (text-to-text) |
| Domains | Web |
| Reference | {MS |
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("MultilingualNanoMSMARCORetrieval")
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{DBLP:journals/corr/NguyenRSGTMD16,
archiveprefix = {arXiv},
author = {Tri Nguyen and Mir Rosenberg and Xia Song and Jianfeng Gao and Saurabh Tiwary and Rangan Majumder and Li Deng},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/journals/corr/NguyenRSGTMD16.bib},
eprint = {1611.09268},
journal = {CoRR},
timestamp = {Mon, 13 Aug 2018 16:49:03 +0200},
title = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset},
url = {http://arxiv.org/abs/1611.09268},
volume = {abs/1611.09268},
year = {2016},
}
@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("MultilingualNanoMSMARCORetrieval")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 56023,
"num_queries": 550,
"num_documents": 55473,
"number_of_characters": 17046740,
"documents_text_statistics": {
"total_text_length": 17027360,
"min_text_length": 3,
"average_text_length": 306.94860562796316,
"max_text_length": 35139,
"unique_texts": 55465
},
"documents_image_statistics": null,
"documents_audio_statistics": null,
"documents_video_statistics": null,
"queries_text_statistics": {
"total_text_length": 19380,
"min_text_length": 6,
"average_text_length": 35.236363636363635,
"max_text_length": 263,
"unique_texts": 547
},
"queries_image_statistics": null,
"queries_audio_statistics": null,
"queries_video_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 550,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 550
},
"top_ranked_statistics": null
}
}
This dataset card was automatically generated using MTEB