MMTEB: Massive Multilingual Text Embedding Benchmark
Paper • 2502.13595 • Published • 50
id string | text string |
|---|---|
0 | satellite imagery of airplane. |
1 | satellite imagery of airport. |
2 | satellite imagery of baseball_diamond. |
3 | satellite imagery of basketball_court. |
4 | satellite imagery of beach. |
5 | satellite imagery of bridge. |
6 | satellite imagery of chaparral. |
7 | satellite imagery of church. |
8 | satellite imagery of circular_farmland. |
9 | satellite imagery of cloud. |
10 | satellite imagery of commercial_area. |
11 | satellite imagery of dense_residential. |
12 | satellite imagery of desert. |
13 | satellite imagery of forest. |
14 | satellite imagery of freeway. |
15 | satellite imagery of golf_course. |
16 | satellite imagery of ground_track_field. |
17 | satellite imagery of harbor. |
18 | satellite imagery of industrial_area. |
19 | satellite imagery of intersection. |
20 | satellite imagery of island. |
21 | satellite imagery of lake. |
22 | satellite imagery of meadow. |
23 | satellite imagery of medium_residential. |
24 | satellite imagery of mobile_home_park. |
25 | satellite imagery of mountain. |
26 | satellite imagery of overpass. |
27 | satellite imagery of palace. |
28 | satellite imagery of parking_lot. |
29 | satellite imagery of railway. |
30 | satellite imagery of railway_station. |
31 | satellite imagery of rectangular_farmland. |
32 | satellite imagery of river. |
33 | satellite imagery of roundabout. |
34 | satellite imagery of runway. |
35 | satellite imagery of sea_ice. |
36 | satellite imagery of ship. |
37 | satellite imagery of snowberg. |
38 | satellite imagery of sparse_residential. |
39 | satellite imagery of stadium. |
40 | satellite imagery of storage_tank. |
41 | satellite imagery of tennis_court. |
42 | satellite imagery of terrace. |
43 | satellite imagery of thermal_power_station. |
44 | satellite imagery of wetland. |
Remote Sensing Image Scene Classification by Northwestern Polytechnical University (NWPU).
| Task category | ZeroShotClassification (image-to-text) |
| Domains | Encyclopaedic |
| Reference | Remote Sensing Image Scene Classification: Benchmark and State of the Art |
Source datasets:
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("RESISC45ZeroShot")
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.
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{7891544,
author = {Cheng, Gong and Han, Junwei and Lu, Xiaoqiang},
doi = {10.1109/JPROC.2017.2675998},
journal = {Proceedings of the IEEE},
keywords = {Remote sensing;Benchmark testing;Spatial resolution;Social network services;Satellites;Image analysis;Machine learning;Unsupervised learning;Classification;Benchmark data set;deep learning;handcrafted features;remote sensing image;scene classification;unsupervised feature learning},
number = {10},
pages = {1865-1883},
title = {Remote Sensing Image Scene Classification: Benchmark and State of the Art},
volume = {105},
year = {2017},
}
@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},
}
The following code contains the descriptive statistics from the task. These can also be obtained using:
import mteb
task = mteb.get_task("RESISC45ZeroShot")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 6300,
"number_of_characters": null,
"text_statistics": null,
"image_statistics": {
"min_image_width": 256,
"average_image_width": 256.0,
"max_image_width": 256,
"min_image_height": 256,
"average_image_height": 256.0,
"max_image_height": 256,
"unique_images": 6300
},
"label_statistics": {
"min_labels_per_text": 1,
"average_label_per_text": 1.0,
"max_labels_per_text": 1,
"unique_labels": 45,
"labels": {
"31": {
"count": 135
},
"11": {
"count": 144
},
"28": {
"count": 135
},
"43": {
"count": 154
},
"41": {
"count": 144
},
"33": {
"count": 134
},
"19": {
"count": 130
},
"16": {
"count": 127
},
"22": {
"count": 130
},
"34": {
"count": 143
},
"24": {
"count": 164
},
"0": {
"count": 169
},
"13": {
"count": 146
},
"25": {
"count": 115
},
"6": {
"count": 132
},
"36": {
"count": 135
},
"39": {
"count": 142
},
"18": {
"count": 140
},
"23": {
"count": 147
},
"37": {
"count": 159
},
"15": {
"count": 122
},
"29": {
"count": 140
},
"9": {
"count": 159
},
"27": {
"count": 140
},
"21": {
"count": 131
},
"3": {
"count": 134
},
"1": {
"count": 162
},
"32": {
"count": 153
},
"26": {
"count": 150
},
"35": {
"count": 151
},
"44": {
"count": 118
},
"30": {
"count": 154
},
"20": {
"count": 139
},
"4": {
"count": 130
},
"42": {
"count": 127
},
"40": {
"count": 137
},
"5": {
"count": 140
},
"17": {
"count": 142
},
"2": {
"count": 123
},
"38": {
"count": 130
},
"10": {
"count": 140
},
"12": {
"count": 146
},
"8": {
"count": 146
},
"7": {
"count": 143
},
"14": {
"count": 118
}
}
},
"candidates_labels_text_statistics": {
"total_text_length": 1447,
"min_text_length": 26,
"average_text_length": 32.15555555555556,
"max_text_length": 43,
"unique_texts": 45
}
}
}
This dataset card was automatically generated using MTEB