MMTEB: Massive Multilingual Text Embedding Benchmark
Paper • 2502.13595 • Published • 50
Datasets:
id stringlengths 1 3 | text stringlengths 37 69 |
|---|---|
0 | a photo showing the country of Andorra
. |
1 | a photo showing the country of United Arab Emirates
. |
2 | a photo showing the country of Afghanistan
. |
3 | a photo showing the country of Antigua and Barbuda
. |
4 | a photo showing the country of Anguilla
. |
5 | a photo showing the country of Albania
. |
6 | a photo showing the country of Armenia
. |
7 | a photo showing the country of Angola
. |
8 | a photo showing the country of Antarctica
. |
9 | a photo showing the country of Argentina
. |
10 | a photo showing the country of Austria
. |
11 | a photo showing the country of Australia
. |
12 | a photo showing the country of Aruba
. |
13 | a photo showing the country of Aland Islands
. |
14 | a photo showing the country of Azerbaijan
. |
15 | a photo showing the country of Bosnia and Herzegovina
. |
16 | a photo showing the country of Barbados
. |
17 | a photo showing the country of Bangladesh
. |
18 | a photo showing the country of Belgium
. |
19 | a photo showing the country of Burkina Faso
. |
20 | a photo showing the country of Bulgaria
. |
21 | a photo showing the country of Bahrain
. |
22 | a photo showing the country of Benin
. |
23 | a photo showing the country of Bermuda
. |
24 | a photo showing the country of Brunei Darussalam
. |
25 | a photo showing the country of Bolivia
. |
26 | a photo showing the country of Bonaire, Saint Eustatius and Saba
. |
27 | a photo showing the country of Brazil
. |
28 | a photo showing the country of Bahamas
. |
29 | a photo showing the country of Bhutan
. |
30 | a photo showing the country of Botswana
. |
31 | a photo showing the country of Belarus
. |
32 | a photo showing the country of Belize
. |
33 | a photo showing the country of Canada
. |
34 | a photo showing the country of DR Congo
. |
35 | a photo showing the country of Central African Republic
. |
36 | a photo showing the country of Switzerland
. |
37 | a photo showing the country of Cote d'Ivoire
. |
38 | a photo showing the country of Cook Islands
. |
39 | a photo showing the country of Chile
. |
40 | a photo showing the country of Cameroon
. |
41 | a photo showing the country of China
. |
42 | a photo showing the country of Colombia
. |
43 | a photo showing the country of Costa Rica
. |
44 | a photo showing the country of Cuba
. |
45 | a photo showing the country of Cabo Verde
. |
46 | a photo showing the country of Curacao
. |
47 | a photo showing the country of Cyprus
. |
48 | a photo showing the country of Czech Republic
. |
49 | a photo showing the country of Germany
. |
50 | a photo showing the country of Denmark
. |
51 | a photo showing the country of Dominica
. |
52 | a photo showing the country of Dominican Republic
. |
53 | a photo showing the country of Algeria
. |
54 | a photo showing the country of Ecuador
. |
55 | a photo showing the country of Estonia
. |
56 | a photo showing the country of Egypt
. |
57 | a photo showing the country of Spain
. |
58 | a photo showing the country of Ethiopia
. |
59 | a photo showing the country of Finland
. |
60 | a photo showing the country of Fiji
. |
61 | a photo showing the country of Falkland Islands
. |
62 | a photo showing the country of Faeroe Islands
. |
63 | a photo showing the country of France
. |
64 | a photo showing the country of Gabon
. |
65 | a photo showing the country of United Kingdom
. |
66 | a photo showing the country of Grenada
. |
67 | a photo showing the country of Georgia
. |
68 | a photo showing the country of French Guiana
. |
69 | a photo showing the country of Guernsey
. |
70 | a photo showing the country of Ghana
. |
71 | a photo showing the country of Gibraltar
. |
72 | a photo showing the country of Greenland
. |
73 | a photo showing the country of Gambia
. |
74 | a photo showing the country of Guadeloupe
. |
75 | a photo showing the country of Greece
. |
76 | a photo showing the country of South Georgia and South Sandwich Is.
. |
77 | a photo showing the country of Guatemala
. |
78 | a photo showing the country of Guam
. |
79 | a photo showing the country of Guyana
. |
80 | a photo showing the country of Hong Kong
. |
81 | a photo showing the country of Honduras
. |
82 | a photo showing the country of Croatia
. |
83 | a photo showing the country of Haiti
. |
84 | a photo showing the country of Hungary
. |
85 | a photo showing the country of Indonesia
. |
86 | a photo showing the country of Ireland
. |
87 | a photo showing the country of Israel
. |
88 | a photo showing the country of Isle of Man
. |
89 | a photo showing the country of India
. |
90 | a photo showing the country of Iraq
. |
91 | a photo showing the country of Iran
. |
92 | a photo showing the country of Iceland
. |
93 | a photo showing the country of Italy
. |
94 | a photo showing the country of Jersey
. |
95 | a photo showing the country of Jamaica
. |
96 | a photo showing the country of Jordan
. |
97 | a photo showing the country of Japan
. |
98 | a photo showing the country of Kenya
. |
99 | a photo showing the country of Kyrgyz Republic
. |
End of preview. Expand in Data Studio
Classifying images of 211 countries.
| Task category | ZeroShotClassification (image-to-text) |
| Domains | Scene |
| Reference | Learning Transferable Visual Models From Natural Language Supervision |
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("Country211ZeroShot")
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{radford2021learning,
author = {Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and others},
journal = {arXiv preprint arXiv:2103.00020},
title = {Learning Transferable Visual Models From Natural Language Supervision},
year = {2021},
}
@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("Country211ZeroShot")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 21100,
"number_of_characters": null,
"text_statistics": null,
"image_statistics": {
"min_image_width": 32,
"average_image_width": 468.5923222748815,
"max_image_width": 500,
"min_image_height": 37,
"average_image_height": 381.726682464455,
"max_image_height": 500,
"unique_images": 21100
},
"label_statistics": {
"min_labels_per_text": 1,
"average_label_per_text": 1.0,
"max_labels_per_text": 1,
"unique_labels": 211,
"labels": {
"0": {
"count": 100
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},
"candidates_labels_text_statistics": {
"total_text_length": 8853,
"min_text_length": 37,
"average_text_length": 41.95734597156398,
"max_text_length": 69,
"unique_texts": 211
}
}
}
This dataset card was automatically generated using MTEB
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