Datasets:
metadata
annotations_creators:
- derived
language:
- bos
- cat
- ces
- dan
- eus
- glv
- ilo
- kur
- lav
- min
- mlt
- sco
- sqi
- wln
license: cc-by-sa-3.0
multilinguality: multilingual
source_datasets:
- ryzzlestrizzle/multi-wiki-clustering-p2p
- ryzzlestrizzle/multi-wiki-clustering-p2p
task_categories:
- text-classification
task_ids: []
dataset_info:
- config_name: bs
features:
- name: sentences
dtype: string
- name: labels
dtype: int64
splits:
- name: test
num_bytes: 2188488
num_examples: 2048
download_size: 1123149
dataset_size: 2188488
- config_name: ca
features:
- name: sentences
dtype: string
- name: labels
dtype: int64
splits:
- name: test
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num_examples: 2048
download_size: 788051
dataset_size: 1282506
- config_name: cs
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- name: sentences
dtype: string
- name: labels
dtype: int64
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- name: test
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num_examples: 2048
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- config_name: da
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- name: sentences
dtype: string
- name: labels
dtype: int64
splits:
- name: test
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num_examples: 2048
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- config_name: eu
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- name: sentences
dtype: string
- name: labels
dtype: int64
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- name: test
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- name: sentences
dtype: string
- name: labels
dtype: int64
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- name: test
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num_examples: 2048
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- config_name: ilo
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- name: sentences
dtype: string
- name: labels
dtype: int64
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- name: test
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- config_name: ku
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- name: sentences
dtype: string
- name: labels
dtype: int64
splits:
- name: test
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- config_name: lv
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- name: sentences
dtype: string
- name: labels
dtype: int64
splits:
- name: test
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num_examples: 2048
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- config_name: min
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- name: sentences
dtype: string
- name: labels
dtype: int64
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- name: test
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num_examples: 2048
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- config_name: mt
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- name: sentences
dtype: string
- name: labels
dtype: int64
splits:
- name: test
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num_examples: 2048
download_size: 636171
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- config_name: sco
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- name: sentences
dtype: string
- name: labels
dtype: int64
splits:
- name: test
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num_examples: 2048
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- config_name: sq
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- name: sentences
dtype: string
- name: labels
dtype: int64
splits:
- name: test
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- config_name: wa
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- name: sentences
dtype: string
- name: labels
dtype: int64
splits:
- name: test
num_bytes: 479096
num_examples: 2048
download_size: 206105
dataset_size: 479096
configs:
- config_name: bs
data_files:
- split: test
path: bs/test-*
- config_name: ca
data_files:
- split: test
path: ca/test-*
- config_name: cs
data_files:
- split: test
path: cs/test-*
- config_name: da
data_files:
- split: test
path: da/test-*
- config_name: eu
data_files:
- split: test
path: eu/test-*
- config_name: gv
data_files:
- split: test
path: gv/test-*
- config_name: ilo
data_files:
- split: test
path: ilo/test-*
- config_name: ku
data_files:
- split: test
path: ku/test-*
- config_name: lv
data_files:
- split: test
path: lv/test-*
- config_name: min
data_files:
- split: test
path: min/test-*
- config_name: mt
data_files:
- split: test
path: mt/test-*
- config_name: sco
data_files:
- split: test
path: sco/test-*
- config_name: sq
data_files:
- split: test
path: sq/test-*
- config_name: wa
data_files:
- split: test
path: wa/test-*
tags:
- mteb
- text
Clustering of wikipedia articles inspired by BlubrbsClusteringP2P. Labels are taken from top-level categories of the respective languages (e.g., https://lv.wikipedia.org/wiki/Kategorija:Pamatkategorijas).
| Task category | t2c |
| Domains | Encyclopaedic, Written |
| Reference | https://github.com/Rysias/wiki-clustering |
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("WikiClusteringP2P.v2")
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 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{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("WikiClusteringP2P.v2")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 28672,
"text_statistics": {
"total_text_length": 18055981,
"min_text_length": 1,
"average_text_length": 629.7426409040179,
"max_text_length": 69506,
"unique_texts": 23102
},
"image_statistics": null,
"labels_statistics": {
"min_labels_per_text": 1,
"average_label_per_text": 1.0,
"max_labels_per_text": 1,
"unique_labels": 39,
"labels": {
"16": {
"count": 541
},
"3": {
"count": 1607
},
"12": {
"count": 846
},
"0": {
"count": 2410
},
"15": {
"count": 878
},
"11": {
"count": 864
},
"6": {
"count": 787
},
"9": {
"count": 654
},
"14": {
"count": 966
},
"8": {
"count": 1389
},
"2": {
"count": 2428
},
"10": {
"count": 839
},
"1": {
"count": 1370
},
"4": {
"count": 2942
},
"7": {
"count": 2514
},
"5": {
"count": 1490
},
"13": {
"count": 918
},
"19": {
"count": 315
},
"17": {
"count": 711
},
"20": {
"count": 345
},
"18": {
"count": 800
},
"24": {
"count": 467
},
"25": {
"count": 928
},
"21": {
"count": 62
},
"26": {
"count": 270
},
"22": {
"count": 186
},
"23": {
"count": 36
},
"27": {
"count": 465
},
"28": {
"count": 62
},
"36": {
"count": 139
},
"32": {
"count": 57
},
"38": {
"count": 43
},
"30": {
"count": 52
},
"34": {
"count": 80
},
"33": {
"count": 75
},
"35": {
"count": 62
},
"31": {
"count": 63
},
"37": {
"count": 8
},
"29": {
"count": 3
}
}
}
}
}
This dataset card was automatically generated using MTEB