MMTEB: Massive Multilingual Text Embedding Benchmark
Paper • 2502.13595 • Published • 50
Datasets:
id stringlengths 1 3 | text stringlengths 17 41 |
|---|---|
0 | a photo of a abbey. |
1 | a photo of a airplane cabin. |
2 | a photo of a airport terminal. |
3 | a photo of a alley. |
4 | a photo of a amphitheater. |
5 | a photo of a amusement arcade. |
6 | a photo of a amusement park. |
7 | a photo of a anechoic chamber. |
8 | a photo of a apartment building outdoor. |
9 | a photo of a apse indoor. |
10 | a photo of a aquarium. |
11 | a photo of a aqueduct. |
12 | a photo of a arch. |
13 | a photo of a archive. |
14 | a photo of a arrival gate outdoor. |
15 | a photo of a art gallery. |
16 | a photo of a art school. |
17 | a photo of a art studio. |
18 | a photo of a assembly line. |
19 | a photo of a athletic field outdoor. |
20 | a photo of a atrium public. |
21 | a photo of a attic. |
22 | a photo of a auditorium. |
23 | a photo of a auto factory. |
24 | a photo of a badlands. |
25 | a photo of a badminton court indoor. |
26 | a photo of a baggage claim. |
27 | a photo of a bakery shop. |
28 | a photo of a balcony exterior. |
29 | a photo of a balcony interior. |
30 | a photo of a ball pit. |
31 | a photo of a ballroom. |
32 | a photo of a bamboo forest. |
33 | a photo of a banquet hall. |
34 | a photo of a bar. |
35 | a photo of a barn. |
36 | a photo of a barndoor. |
37 | a photo of a baseball field. |
38 | a photo of a basement. |
39 | a photo of a basilica. |
40 | a photo of a basketball court outdoor. |
41 | a photo of a bathroom. |
42 | a photo of a batters box. |
43 | a photo of a bayou. |
44 | a photo of a bazaar indoor. |
45 | a photo of a bazaar outdoor. |
46 | a photo of a beach. |
47 | a photo of a beauty salon. |
48 | a photo of a bedroom. |
49 | a photo of a berth. |
50 | a photo of a biology laboratory. |
51 | a photo of a bistro indoor. |
52 | a photo of a boardwalk. |
53 | a photo of a boat deck. |
54 | a photo of a boathouse. |
55 | a photo of a bookstore. |
56 | a photo of a booth indoor. |
57 | a photo of a botanical garden. |
58 | a photo of a bow window indoor. |
59 | a photo of a bow window outdoor. |
60 | a photo of a bowling alley. |
61 | a photo of a boxing ring. |
62 | a photo of a brewery indoor. |
63 | a photo of a bridge. |
64 | a photo of a building facade. |
65 | a photo of a bullring. |
66 | a photo of a burial chamber. |
67 | a photo of a bus interior. |
68 | a photo of a butchers shop. |
69 | a photo of a butte. |
70 | a photo of a cabin outdoor. |
71 | a photo of a cafeteria. |
72 | a photo of a campsite. |
73 | a photo of a campus. |
74 | a photo of a canal natural. |
75 | a photo of a canal urban. |
76 | a photo of a candy store. |
77 | a photo of a canyon. |
78 | a photo of a car interior backseat. |
79 | a photo of a car interior frontseat. |
80 | a photo of a carrousel. |
81 | a photo of a casino indoor. |
82 | a photo of a castle. |
83 | a photo of a catacomb. |
84 | a photo of a cathedral indoor. |
85 | a photo of a cathedral outdoor. |
86 | a photo of a cavern indoor. |
87 | a photo of a cemetery. |
88 | a photo of a chalet. |
89 | a photo of a cheese factory. |
90 | a photo of a chemistry lab. |
91 | a photo of a chicken coop indoor. |
92 | a photo of a chicken coop outdoor. |
93 | a photo of a childs room. |
94 | a photo of a church indoor. |
95 | a photo of a church outdoor. |
96 | a photo of a classroom. |
97 | a photo of a clean room. |
98 | a photo of a cliff. |
99 | a photo of a cloister indoor. |
End of preview. Expand in Data Studio
Large scale scene recognition in 397 categories.
| Task category | ZeroShotClassification (image-to-text) |
| Domains | Encyclopaedic |
| Reference | 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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("SUN397ZeroShot")
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.
@inproceedings{5539970,
author = {Xiao, Jianxiong and Hays, James and Ehinger, Krista A. and Oliva, Aude and Torralba, Antonio},
booktitle = {2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2010.5539970},
number = {},
pages = {3485-3492},
title = {SUN database: Large-scale scene recognition from abbey to zoo},
volume = {},
year = {2010},
}
@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("SUN397ZeroShot")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 21750,
"number_of_characters": null,
"text_statistics": null,
"image_statistics": {
"min_image_width": 125,
"average_image_width": 354.21843678160917,
"max_image_width": 696,
"min_image_height": 94,
"average_image_height": 291.1662988505747,
"max_image_height": 595,
"unique_images": 21750
},
"label_statistics": {
"min_labels_per_text": 1,
"average_label_per_text": 1.0,
"max_labels_per_text": 1,
"unique_labels": 397,
"labels": {
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}
},
"candidates_labels_text_statistics": {
"total_text_length": 10281,
"min_text_length": 17,
"average_text_length": 25.896725440806044,
"max_text_length": 41,
"unique_texts": 397
}
}
}
This dataset card was automatically generated using MTEB
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