sts13-sts

<!-- 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;">STS13</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>

SemEval STS 2013 dataset.

| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Web, News, Non-fiction, Written |
| Reference | https://www.aclweb.org/anthology/S13-1004/ |

## 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(["STS13"])
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{Agirre2013SEM2S,
author = {Eneko Agirre and Daniel Matthew Cer and Mona T. Diab and Aitor Gonzalez-Agirre and Weiwei Guo},
booktitle = {International Workshop on Semantic Evaluation},
title = {*SEM 2013 shared task: Semantic Textual Similarity},
url = {https://api.semanticscholar.org/CorpusID:10241043},
year = {2013},
}

@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("STS13")

desc_stats = task.metadata.descriptive_stats
```

```json
{
"test": {
"num_samples": 1500,
"number_of_characters": 161949,
"unique_pairs": 1500,
"min_sentence1_length": 19,
"average_sentence1_len": 62.06733333333333,
"max_sentence1_length": 415,
"unique_sentence1": 1314,
"min_sentence2_length": 19,
"average_sentence2_len": 45.898666666666664,
"max_sentence2_length": 185,
"unique_sentence2": 1355,
"min_score": 0.0,
"avg_score": 2.3361888888888864,
"max_score": 5.0
}
}
```

</details>

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

<!-- 改编自 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;">STS13</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">**一个MTEB（Massive Text Embedding Benchmark）数据集**</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">大规模文本嵌入基准</div>
</div>

SemEval STS 2013数据集。

| 属性 | 取值 |
|--------|--------|
| 任务类别 | t2t |
| 领域 | 网络、新闻、非虚构类、书面文本 |
| 参考文献 | [https://www.aclweb.org/anthology/S13-1004/](https://www.aclweb.org/anthology/S13-1004/) |

## 本任务评估方法

你可以通过以下代码在该数据集上评估嵌入模型：

python
import mteb

task = mteb.get_tasks(["STS13"])
evaluator = mteb.MTEB(task)

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

<!-- 数据集需在README中添加arxiv链接以自动关联数据集与对应论文 -->
若想了解如何在MTEB任务上运行模型，请访问其[GitHub仓库](https://github.com/embeddings-benchmark/mteb)。

## 引用说明

若你使用本数据集，请同时引用该数据集以及MTEB，因为本数据集作为MMTEB（Massive Multilingual Text Embedding Benchmark）贡献项的一部分经过了额外处理。

bibtex

@inproceedings{Agirre2013SEM2S,
author = {Eneko Agirre and Daniel Matthew Cer and Mona T. Diab and Aitor Gonzalez-Agirre and Weiwei Guo},
booktitle = {International Workshop on Semantic Evaluation},
title = {*SEM 2013 shared task: Semantic Textual Similarity},
url = {https://api.semcholar.org/CorpusID:10241043},
year = {2013},
}

@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},
}

## 数据集统计信息
<details>
<summary> 数据集统计信息</summary>

以下代码包含该任务的描述性统计数据。你也可以通过以下代码获取：

python
import mteb

task = mteb.get_task("STS13")

desc_stats = task.metadata.descriptive_stats

json
{
"测试集": {
"样本数量": 1500,
"总字符数": 161949,
"唯一句对数量": 1500,
"句子1最小长度": 19,
"句子1平均长度": 62.06733333333333,
"句子1最大长度": 415,
"句子1唯一数量": 1314,
"句子2最小长度": 19,
"句子2平均长度": 45.898666666666664,
"句子2最大长度": 185,
"句子2唯一数量": 1355,
"最小得分": 0.0,
"平均得分": 2.3361888888888864,
"最大得分": 5.0
}
}

</details>

---
*本数据集卡片由[MTEB](https://github.com/embeddings-benchmark/mteb)自动生成*