MMTEB: Massive Multilingual Text Embedding Benchmark
Paper • 2502.13595 • Published • 50
Datasets:
id stringlengths 1 3 | text stringlengths 15 50 |
|---|---|
0 | a video of abseiling |
1 | a video of air drumming |
2 | a video of answering questions |
3 | a video of applauding |
4 | a video of applying cream |
5 | a video of archery |
6 | a video of arm wrestling |
7 | a video of arranging flowers |
8 | a video of assembling computer |
9 | a video of auctioning |
10 | a video of baby waking up |
11 | a video of baking cookies |
12 | a video of balloon blowing |
13 | a video of bandaging |
14 | a video of barbequing |
15 | a video of bartending |
16 | a video of beatboxing |
17 | a video of bee keeping |
18 | a video of belly dancing |
19 | a video of bench pressing |
20 | a video of bending back |
21 | a video of bending metal |
22 | a video of biking through snow |
23 | a video of blasting sand |
24 | a video of blowing glass |
25 | a video of blowing leaves |
26 | a video of blowing nose |
27 | a video of blowing out candles |
28 | a video of bobsledding |
29 | a video of bookbinding |
30 | a video of bouncing on trampoline |
31 | a video of bowling |
32 | a video of braiding hair |
33 | a video of breading or breadcrumbing |
34 | a video of breakdancing |
35 | a video of brush painting |
36 | a video of brushing hair |
37 | a video of brushing teeth |
38 | a video of building cabinet |
39 | a video of building shed |
40 | a video of bungee jumping |
41 | a video of busking |
42 | a video of canoeing or kayaking |
43 | a video of capoeira |
44 | a video of carrying baby |
45 | a video of cartwheeling |
46 | a video of carving pumpkin |
47 | a video of catching fish |
48 | a video of catching or throwing baseball |
49 | a video of catching or throwing frisbee |
50 | a video of catching or throwing softball |
51 | a video of celebrating |
52 | a video of changing oil |
53 | a video of changing wheel |
54 | a video of checking tires |
55 | a video of cheerleading |
56 | a video of chopping wood |
57 | a video of clapping |
58 | a video of clay pottery making |
59 | a video of clean and jerk |
60 | a video of cleaning floor |
61 | a video of cleaning gutters |
62 | a video of cleaning pool |
63 | a video of cleaning shoes |
64 | a video of cleaning toilet |
65 | a video of cleaning windows |
66 | a video of climbing a rope |
67 | a video of climbing ladder |
68 | a video of climbing tree |
69 | a video of contact juggling |
70 | a video of cooking chicken |
71 | a video of cooking egg |
72 | a video of cooking on campfire |
73 | a video of cooking sausages |
74 | a video of counting money |
75 | a video of country line dancing |
76 | a video of cracking neck |
77 | a video of crawling baby |
78 | a video of crossing river |
79 | a video of crying |
80 | a video of curling hair |
81 | a video of cutting nails |
82 | a video of cutting pineapple |
83 | a video of cutting watermelon |
84 | a video of dancing ballet |
85 | a video of dancing charleston |
86 | a video of dancing gangnam style |
87 | a video of dancing macarena |
88 | a video of deadlifting |
89 | a video of decorating the christmas tree |
90 | a video of digging |
91 | a video of dining |
92 | a video of disc golfing |
93 | a video of diving cliff |
94 | a video of dodgeball |
95 | a video of doing aerobics |
96 | a video of doing laundry |
97 | a video of doing nails |
98 | a video of drawing |
99 | a video of dribbling basketball |
End of preview. Expand in Data Studio
Kinetics-400 is a large-scale action recognition dataset containing 400 human action classes from YouTube videos. Each clip is approximately 10 seconds long. Sampled 25 random shard files from each official path list, capped at 10 per class across 400 classes (~3,970 train / ~4,000 test).
| Task category | VideoZeroshotClassification (video-to-text) |
| Domains | Activity, Web |
| Reference | The Kinetics Human Action Video Dataset |
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("Kinetics400ZeroShot")
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.
@misc{kay2017kineticshumanactionvideo,
archiveprefix = {arXiv},
author = {Will Kay and Joao Carreira and Karen Simonyan and Brian Zhang and Chloe Hillier and Sudheendra Vijayanarasimhan and Fabio Viola and Tim Green and Trevor Back and Paul Natsev and Mustafa Suleyman and Andrew Zisserman},
eprint = {1705.06950},
primaryclass = {cs.CV},
title = {The Kinetics Human Action Video Dataset},
url = {https://arxiv.org/abs/1705.06950},
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},
}
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("Kinetics400ZeroShot")
desc_stats = task.metadata.descriptive_stats
{
"test": {
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"image_statistics": null,
"audio_statistics": null,
"video_statistics": {
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"average_height": 483.96495619524404,
"max_height": 720,
"min_duration_seconds": 0.567233,
"average_duration_seconds": 9.560825674092527,
"max_duration_seconds": 10.129282,
"unique_videos": 3995,
"average_fps": 26.911639549436796,
"fps": {
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},
"min_resolution": [
88,
72
],
"average_resolution": [
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483.96495619524404
],
"max_resolution": [
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],
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}
},
"label_statistics": {
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"average_label_per_text": 1.0,
"max_labels_per_text": 1,
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},
"candidates_labels_text_statistics": {
"total_text_length": 9956,
"min_text_length": 15,
"average_text_length": 24.89,
"max_text_length": 50,
"unique_texts": 400
}
}
}
This dataset card was automatically generated using MTEB
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