README.md

---
annotations_creators:
- derived
language:
- eng
license: unknown
multilinguality: monolingual
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
tags:
- mteb
- text
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->

<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
  <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">SprintDuplicateQuestions</h1>
  <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
  <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>

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:

```python
import mteb

task = mteb.get_tasks(["SprintDuplicateQuestions"])
evaluator = mteb.MTEB(task)

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

<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). 

## Citation

If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).

```bibtex

@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
<details>
  <summary> Dataset Statistics</summary>

The following code contains the descriptive statistics from the task. These can also be obtained using:

```python
import mteb

task = mteb.get_task("SprintDuplicateQuestions")

desc_stats = task.metadata.descriptive_stats
```

```json
{
    "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
            }
        }
    }
}
```

</details>

---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*