Datasets:

Modalities:
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Languages:
Vietnamese
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Auto-converted to Parquet
query-id
stringlengths
5
8
corpus-id
stringlengths
4
9
score
float64
1
1
test1
doc6
1
test2
doc10
1
test4
doc42
1
test5
doc50
1
test8
doc86
1
test9
doc91
1
test10
doc118
1
test11
doc136
1
test13
doc172
1
test14
doc293
1
test15
doc302
1
test16
doc305
1
test18
doc514
1
test19
doc565
1
test19
doc579
1
test21
doc635
1
test22
doc649
1
test24
doc658
1
test26
doc724
1
test27
doc763
1
test29
doc807
1
test31
doc897
1
test33
doc921
1
test36
doc1010
1
test39
doc1042
1
test41
doc1100
1
test42
doc1118
1
test43
doc1154
1
test44
doc1164
1
test46
doc1193
1
test47
doc1215
1
test48
doc1229
1
test48
doc1239
1
test49
doc1260
1
test50
doc1404
1
test50
doc1405
1
test50
doc1407
1
test52
doc1432
1
test53
doc1448
1
test54
doc1468
1
test54
doc1474
1
test57
doc1580
1
test59
doc1617
1
test60
doc1631
1
test67
doc1927
1
test68
doc2000
1
test69
doc2030
1
test70
doc2107
1
test71
doc2127
1
test72
doc2134
1
test74
doc2254
1
test77
doc2319
1
test78
doc2339
1
test80
doc2404
1
test81
doc2479
1
test82
doc2493
1
test85
doc2735
1
test86
doc2746
1
test91
doc2994
1
test94
doc3057
1
test95
doc3068
1
test96
doc3097
1
test97
doc3100
1
test98
doc3123
1
test99
doc3210
1
test100
doc3257
1
test102
doc3294
1
test103
doc3528
1
test103
doc3559
1
test104
doc3821
1
test105
doc3828
1
test106
doc3852
1
test109
doc3968
1
test111
doc3986
1
test112
doc4014
1
test116
doc4163
1
test122
doc4602
1
test123
doc4611
1
test124
doc4632
1
test125
doc4644
1
test126
doc4714
1
test131
doc4958
1
test132
doc5046
1
test134
doc5120
1
test136
doc5159
1
test137
doc5160
1
test138
doc5165
1
test141
doc5217
1
test141
doc5218
1
test142
doc5259
1
test143
doc5288
1
test144
doc5295
1
test145
doc5395
1
test146
doc5402
1
test148
doc5423
1
test150
doc5495
1
test150
doc5499
1
test152
doc5548
1
test153
doc5633
1
test153
doc5636
1
End of preview. Expand in Data Studio

How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

import mteb

task = mteb.get_tasks(["NQ-VN"])
evaluator = mteb.MTEB(task)

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)

To learn more about how to run models on mteb task check out the GitHub repitory.

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{pham2025vnmtebvietnamesemassivetext,
    title={VN-MTEB: Vietnamese Massive Text Embedding Benchmark},
    author={Loc Pham and Tung Luu and Thu Vo and Minh Nguyen and Viet Hoang},
    year={2025},
    eprint={2507.21500},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2507.21500}
}

@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, Lo{"\i}c 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},
}
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