MMTEB: Massive Multilingual Text Embedding Benchmark
Paper • 2502.13595 • Published • 50
id string | text string |
|---|---|
0 | a photo of banded texture. |
1 | a photo of blotchy texture. |
2 | a photo of braided texture. |
3 | a photo of bubbly texture. |
4 | a photo of bumpy texture. |
5 | a photo of chequered texture. |
6 | a photo of cobwebbed texture. |
7 | a photo of cracked texture. |
8 | a photo of crosshatched texture. |
9 | a photo of crystalline texture. |
10 | a photo of dotted texture. |
11 | a photo of fibrous texture. |
12 | a photo of flecked texture. |
13 | a photo of freckled texture. |
14 | a photo of frilly texture. |
15 | a photo of gauzy texture. |
16 | a photo of grid texture. |
17 | a photo of grooved texture. |
18 | a photo of honeycombed texture. |
19 | a photo of interlaced texture. |
20 | a photo of knitted texture. |
21 | a photo of lacelike texture. |
22 | a photo of lined texture. |
23 | a photo of marbled texture. |
24 | a photo of matted texture. |
25 | a photo of meshed texture. |
26 | a photo of paisley texture. |
27 | a photo of perforated texture. |
28 | a photo of pitted texture. |
29 | a photo of pleated texture. |
30 | a photo of polka-dotted texture. |
31 | a photo of porous texture. |
32 | a photo of potholed texture. |
33 | a photo of scaly texture. |
34 | a photo of smeared texture. |
35 | a photo of spiralled texture. |
36 | a photo of sprinkled texture. |
37 | a photo of stained texture. |
38 | a photo of stratified texture. |
39 | a photo of striped texture. |
40 | a photo of studded texture. |
41 | a photo of swirly texture. |
42 | a photo of veined texture. |
43 | a photo of waffled texture. |
44 | a photo of woven texture. |
45 | a photo of wrinkled texture. |
46 | a photo of zigzagged texture. |
Describable Textures Dataset in 47 categories.
| Task category | ZeroShotClassification (image-to-text) |
| Domains | Encyclopaedic |
| Reference | Proceedings of the {IEEE |
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("DTDZeroShot")
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{cimpoi14describing,
author = {M. Cimpoi and S. Maji and I. Kokkinos and S. Mohamed and and A. Vedaldi},
booktitle = {Proceedings of the {IEEE} Conf. on Computer Vision and Pattern Recognition ({CVPR})},
title = {Describing Textures in the Wild},
year = {2014},
}
@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("DTDZeroShot")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 1880,
"number_of_characters": null,
"text_statistics": null,
"image_statistics": {
"min_image_width": 300,
"average_image_width": 488.9760638297872,
"max_image_width": 900,
"min_image_height": 300,
"average_image_height": 447.4962765957447,
"max_image_height": 778,
"unique_images": 1877
},
"label_statistics": {
"min_labels_per_text": 1,
"average_label_per_text": 1.0,
"max_labels_per_text": 1,
"unique_labels": 47,
"labels": {
"0": {
"count": 40
},
"1": {
"count": 40
},
"10": {
"count": 40
},
"11": {
"count": 40
},
"12": {
"count": 40
},
"13": {
"count": 40
},
"14": {
"count": 40
},
"15": {
"count": 40
},
"16": {
"count": 40
},
"17": {
"count": 40
},
"18": {
"count": 40
},
"19": {
"count": 40
},
"2": {
"count": 40
},
"20": {
"count": 40
},
"21": {
"count": 40
},
"22": {
"count": 40
},
"23": {
"count": 40
},
"24": {
"count": 40
},
"25": {
"count": 40
},
"26": {
"count": 40
},
"27": {
"count": 40
},
"28": {
"count": 40
},
"29": {
"count": 40
},
"3": {
"count": 40
},
"30": {
"count": 40
},
"31": {
"count": 40
},
"32": {
"count": 40
},
"33": {
"count": 40
},
"34": {
"count": 40
},
"35": {
"count": 40
},
"36": {
"count": 40
},
"37": {
"count": 40
},
"38": {
"count": 40
},
"39": {
"count": 40
},
"4": {
"count": 40
},
"40": {
"count": 40
},
"41": {
"count": 40
},
"42": {
"count": 40
},
"43": {
"count": 40
},
"44": {
"count": 40
},
"45": {
"count": 40
},
"46": {
"count": 40
},
"5": {
"count": 40
},
"6": {
"count": 40
},
"7": {
"count": 40
},
"8": {
"count": 40
},
"9": {
"count": 40
}
}
},
"candidates_labels_text_statistics": {
"total_text_length": 1287,
"min_text_length": 24,
"average_text_length": 27.382978723404257,
"max_text_length": 32,
"unique_texts": 47
}
}
}
This dataset card was automatically generated using MTEB
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