MMTEB: Massive Multilingual Text Embedding Benchmark
Paper • 2502.13595 • Published • 50
Datasets:
id stringlengths 1 3 | text stringlengths 15 60 |
|---|---|
0 | a video of abseiling |
1 | a video of acting in play |
2 | a video of adjusting glasses |
3 | a video of air drumming |
4 | a video of alligator wrestling |
5 | a video of answering questions |
6 | a video of applauding |
7 | a video of applying cream |
8 | a video of archaeological excavation |
9 | a video of archery |
10 | a video of arguing |
11 | a video of arm wrestling |
12 | a video of arranging flowers |
13 | a video of assembling bicycle |
14 | a video of assembling computer |
15 | a video of attending conference |
16 | a video of auctioning |
17 | a video of backflip (human) |
18 | a video of baking cookies |
19 | a video of bandaging |
20 | a video of barbequing |
21 | a video of bartending |
22 | a video of base jumping |
23 | a video of bathing dog |
24 | a video of battle rope training |
25 | a video of beatboxing |
26 | a video of bee keeping |
27 | a video of belly dancing |
28 | a video of bench pressing |
29 | a video of bending back |
30 | a video of bending metal |
31 | a video of biking through snow |
32 | a video of blasting sand |
33 | a video of blowdrying hair |
34 | a video of blowing bubble gum |
35 | a video of blowing glass |
36 | a video of blowing leaves |
37 | a video of blowing nose |
38 | a video of blowing out candles |
39 | a video of bobsledding |
40 | a video of bodysurfing |
41 | a video of bookbinding |
42 | a video of bottling |
43 | a video of bouncing on bouncy castle |
44 | a video of bouncing on trampoline |
45 | a video of bowling |
46 | a video of braiding hair |
47 | a video of breading or breadcrumbing |
48 | a video of breakdancing |
49 | a video of breaking boards |
50 | a video of breathing fire |
51 | a video of brush painting |
52 | a video of brushing hair |
53 | a video of brushing teeth |
54 | a video of building cabinet |
55 | a video of building lego |
56 | a video of building sandcastle |
57 | a video of building shed |
58 | a video of bull fighting |
59 | a video of bulldozing |
60 | a video of bungee jumping |
61 | a video of burping |
62 | a video of busking |
63 | a video of calculating |
64 | a video of calligraphy |
65 | a video of canoeing or kayaking |
66 | a video of capoeira |
67 | a video of capsizing |
68 | a video of card stacking |
69 | a video of card throwing |
70 | a video of carrying baby |
71 | a video of cartwheeling |
72 | a video of carving ice |
73 | a video of carving pumpkin |
74 | a video of casting fishing line |
75 | a video of catching fish |
76 | a video of catching or throwing baseball |
77 | a video of catching or throwing frisbee |
78 | a video of catching or throwing softball |
79 | a video of celebrating |
80 | a video of changing gear in car |
81 | a video of changing oil |
82 | a video of changing wheel (not on bike) |
83 | a video of checking tires |
84 | a video of cheerleading |
85 | a video of chewing gum |
86 | a video of chiseling stone |
87 | a video of chiseling wood |
88 | a video of chopping meat |
89 | a video of chopping vegetables |
90 | a video of chopping wood |
91 | a video of clam digging |
92 | a video of clapping |
93 | a video of clay pottery making |
94 | a video of clean and jerk |
95 | a video of cleaning gutters |
96 | a video of cleaning pool |
97 | a video of cleaning shoes |
98 | a video of cleaning toilet |
99 | a video of cleaning windows |
End of preview. Expand in Data Studio
Kinetics-600 is a large-scale action recognition dataset containing 600 human action classes from YouTube videos. Each clip is approximately 10 seconds long. This variant uses both video and audio modalities. Sampled the official test split, capped at 16 per class across 600 classes (9,576 examples).
| Task category | VideoZeroshotClassification (video+audio-to-text) |
| Domains | Activity, Web |
| Reference | A Short Note about Kinetics-600 |
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("Kinetics600VAZeroShot")
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{carreira2018short,
author = {Carreira, Joao and Noland, Eric and Banki-Horvath, Andras and Hillier, Chloe and Zisserman, Andrew},
journal = {arXiv preprint arXiv:1808.01340},
title = {A Short Note about Kinetics-600},
year = {2018},
}
@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("Kinetics600VAZeroShot")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 9576,
"text_statistics": null,
"image_statistics": null,
"audio_statistics": {
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"min_duration_seconds": 0.3413125,
"average_duration_seconds": 9.651674028822457,
"max_duration_seconds": 10.112,
"unique_audios": 9325,
"average_sampling_rate": 16000.0,
"sampling_rates": {
"16000": 9576
}
},
"video_statistics": {
"total_duration_seconds": 92401.9248019963,
"total_frames": 2523127,
"min_width": 108,
"average_width": 741.1386800334169,
"max_width": 1280,
"min_height": 96,
"average_height": 498.6019214703425,
"max_height": 720,
"min_duration_seconds": 0.5005,
"average_duration_seconds": 9.649323809732278,
"max_duration_seconds": 10.145567,
"unique_videos": 9576,
"average_fps": 27.303258145363408,
"fps": {
"30": 6578,
"8": 48,
"7": 19,
"25": 1093,
"24": 619,
"6": 39,
"15": 503,
"29": 182,
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"12": 42,
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"11": 21,
"10": 94,
"16": 31,
"13": 23,
"26": 17,
"21": 12,
"17": 22,
"22": 10,
"19": 12,
"27": 36,
"9": 10
},
"min_resolution": [
128,
96
],
"average_resolution": [
741.1386800334169,
498.6019214703425
],
"max_resolution": [
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720
],
"resolutions": {
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}
},
"label_statistics": {
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"max_labels_per_text": 1,
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}
}
}
This dataset card was automatically generated using MTEB
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