Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
semantic-similarity-classification
Languages:
English
Size:
< 1K
ArXiv:
License:
metadata
annotations_creators:
- derived
language:
- eng
license: unknown
multilinguality: monolingual
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
tags:
- mteb
- text
Duplicate questions from the Sprint community.
| Task category | t2t |
| Domains | Programming, Written |
| Reference | https://www.aclweb.org/anthology/D18-1131/ |
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(["SprintDuplicateQuestions"])
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.
@inproceedings{shah-etal-2018-adversarial,
abstract = {We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions. As finding and annotating such potential duplicates manually is very tedious and costly, automatic methods based on machine learning are a viable alternative. However, many forums do not have annotated data, i.e., questions labeled by experts as duplicates, and thus a promising solution is to use domain adaptation from another forum that has such annotations. Here we focus on adversarial domain adaptation, deriving important findings about when it performs well and what properties of the domains are important in this regard. Our experiments with StackExchange data show an average improvement of 5.6{\%} over the best baseline across multiple pairs of domains.},
address = {Brussels, Belgium},
author = {Shah, Darsh and
Lei, Tao and
Moschitti, Alessandro and
Romeo, Salvatore and
Nakov, Preslav},
booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
doi = {10.18653/v1/D18-1131},
editor = {Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi},
month = oct # {-} # nov,
pages = {1056--1063},
publisher = {Association for Computational Linguistics},
title = {Adversarial Domain Adaptation for Duplicate Question Detection},
url = {https://aclanthology.org/D18-1131},
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, 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("SprintDuplicateQuestions")
desc_stats = task.metadata.descriptive_stats
{
"validation": {
"num_samples": 101000,
"number_of_characters": 12006640,
"unique_pairs": 101000,
"min_sentence1_length": 22,
"avg_sentence1_length": 65.159,
"max_sentence1_length": 139,
"unique_sentence1": 1000,
"min_sentence2_length": 12,
"avg_sentence2_length": 53.71862376237624,
"max_sentence2_length": 223,
"unique_sentence2": 7932,
"unique_labels": 2,
"labels": {
"1": {
"count": 1000
},
"0": {
"count": 100000
}
}
},
"test": {
"num_samples": 101000,
"number_of_characters": 12292709,
"unique_pairs": 101000,
"min_sentence1_length": 19,
"avg_sentence1_length": 67.944,
"max_sentence1_length": 157,
"unique_sentence1": 1000,
"min_sentence2_length": 12,
"avg_sentence2_length": 53.7659900990099,
"max_sentence2_length": 223,
"unique_sentence2": 7932,
"unique_labels": 2,
"labels": {
"1": {
"count": 1000
},
"0": {
"count": 100000
}
}
}
}
This dataset card was automatically generated using MTEB