Dataset Viewer
sentences
sequencelengths 4.34k
28.6k
| labels
sequencelengths 4.34k
28.6k
|
|---|---|
["'Brisbane's Banksy' Anthony Lister found guilty of graffiti","Dick Smith enters voluntary administ(...TRUNCATED) | ["australia.txt","australia.txt","australia.txt","australia.txt","australia.txt","australia.txt","au(...TRUNCATED) |
["Parrot placed in witness protection","Little cute monkey is taking shower in the kitchen this is a(...TRUNCATED) | ["Animals.txt","Animals.txt","Animals.txt","Animals.txt","Animals.txt","Animals.txt","Animals.txt","(...TRUNCATED) |
["My wife is pregnant on a Zika infected area, How can assess our risk? [Serious]","My mom used my m(...TRUNCATED) | ["Advice.txt","Advice.txt","Advice.txt","Advice.txt","Advice.txt","Advice.txt","Advice.txt","Advice.(...TRUNCATED) |
["Looking for advice on a pistol loadout","What are the first upgrades you should make to your gun?"(...TRUNCATED) | ["airsoft.txt","airsoft.txt","airsoft.txt","airsoft.txt","airsoft.txt","airsoft.txt","airsoft.txt","(...TRUNCATED) |
["A friend of mine from Bauhaus University is researching activity localizations in apartment floor (...TRUNCATED) | ["architecture.txt","architecture.txt","architecture.txt","architecture.txt","architecture.txt","arc(...TRUNCATED) |
["Could anyone tell me what breed my bicolor kitten is?","My cat wants to be a tiger when he grows u(...TRUNCATED) | ["cats.txt","cats.txt","cats.txt","cats.txt","cats.txt","cats.txt","cats.txt","cats.txt","cats.txt",(...TRUNCATED) |
["Merlin's summer vs winter coat.","Kit the Cat - my Cat","I just wanted a quite bath.","African Man(...TRUNCATED) | ["cats.txt","cats.txt","cats.txt","cats.txt","cats.txt","cats.txt","cats.txt","cats.txt","cats.txt",(...TRUNCATED) |
["PSA: If you have a DashCam with a LiPo battery, keep an eye on it.","Tim Wilson: government should(...TRUNCATED) | ["australia.txt","australia.txt","australia.txt","australia.txt","australia.txt","australia.txt","au(...TRUNCATED) |
["Never enough Mahler! Symphony Nr. 6 played by Sinfónica de Galicia conducted by Dima Slobodeniouk(...TRUNCATED) | ["classicalmusic.txt","classicalmusic.txt","classicalmusic.txt","classicalmusic.txt","classicalmusic(...TRUNCATED) |
["Прогноз гороскоп на 30 января 2016. Все знаки зодиака","\"Co(...TRUNCATED) | ["Art.txt","Art.txt","Art.txt","Art.txt","Art.txt","Art.txt","Art.txt","Art.txt","Art.txt","Art.txt"(...TRUNCATED) |
End of preview. Expand
in Data Studio
Clustering of titles from 199 subreddits. Clustering of 25 sets, each with 10-50 classes, and each class with 100 - 1000 sentences.
| Task category | t2c |
| Domains | Web, Social, Written |
| Reference | https://arxiv.org/abs/2104.07081 |
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(["RedditClustering.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 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.
@article{geigle:2021:arxiv,
archiveprefix = {arXiv},
author = {Gregor Geigle and
Nils Reimers and
Andreas R{\"u}ckl{\'e} and
Iryna Gurevych},
eprint = {2104.07081},
journal = {arXiv preprint},
title = {TWEAC: Transformer with Extendable QA Agent Classifiers},
url = {http://arxiv.org/abs/2104.07081},
volume = {abs/2104.07081},
year = {2021},
}
@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},
}
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("RedditClustering.v2")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 2048,
"number_of_characters": 134119,
"min_text_length": 18,
"average_text_length": 65.48779296875,
"max_text_length": 299,
"unique_texts": 178,
"min_labels_per_text": 23,
"average_labels_per_text": 1.0,
"max_labels_per_text": 60,
"unique_labels": 50,
"labels": {
"17": {
"count": 48
},
"43": {
"count": 32
},
"44": {
"count": 54
},
"8": {
"count": 48
},
"15": {
"count": 42
},
"29": {
"count": 32
},
"5": {
"count": 43
},
"21": {
"count": 36
},
"14": {
"count": 42
},
"24": {
"count": 36
},
"39": {
"count": 45
},
"1": {
"count": 33
},
"32": {
"count": 36
},
"16": {
"count": 52
},
"27": {
"count": 51
},
"6": {
"count": 33
},
"36": {
"count": 45
},
"31": {
"count": 46
},
"46": {
"count": 60
},
"12": {
"count": 45
},
"34": {
"count": 37
},
"41": {
"count": 41
},
"47": {
"count": 43
},
"13": {
"count": 37
},
"25": {
"count": 36
},
"10": {
"count": 34
},
"42": {
"count": 29
},
"2": {
"count": 45
},
"48": {
"count": 38
},
"35": {
"count": 33
},
"11": {
"count": 37
},
"33": {
"count": 45
},
"40": {
"count": 37
},
"30": {
"count": 33
},
"26": {
"count": 40
},
"28": {
"count": 31
},
"0": {
"count": 34
},
"4": {
"count": 45
},
"20": {
"count": 49
},
"9": {
"count": 38
},
"18": {
"count": 38
},
"37": {
"count": 50
},
"19": {
"count": 38
},
"22": {
"count": 45
},
"49": {
"count": 55
},
"7": {
"count": 44
},
"45": {
"count": 40
},
"23": {
"count": 44
},
"38": {
"count": 23
},
"3": {
"count": 50
}
}
}
}
This dataset card was automatically generated using MTEB
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