sts14-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;">STS14</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 2014 dataset. Currently only the English dataset

| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Blog, Web, Spoken |
| Reference | https://www.aclweb.org/anthology/S14-1002 |

## 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(["STS14"])
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{bandhakavi-etal-2014-generating,
address = {Dublin, Ireland},
author = {Bandhakavi, Anil and
Wiratunga, Nirmalie and
P, Deepak and
Massie, Stewart},
booktitle = {Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*{SEM} 2014)},
doi = {10.3115/v1/S14-1002},
editor = {Bos, Johan and
Frank, Anette and
Navigli, Roberto},
month = aug,
pages = {12--21},
publisher = {Association for Computational Linguistics and Dublin City University},
title = {Generating a Word-Emotion Lexicon from {\#}Emotional Tweets},
url = {https://aclanthology.org/S14-1002},
year = {2014},
}

@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("STS14")

desc_stats = task.metadata.descriptive_stats
```

```json
{
"test": {
"num_samples": 3750,
"number_of_characters": 407185,
"unique_pairs": 3682,
"min_sentence1_length": 20,
"average_sentence1_len": 56.6216,
"max_sentence1_length": 372,
"unique_sentence1": 3408,
"min_sentence2_length": 18,
"average_sentence2_len": 51.96106666666667,
"max_sentence2_length": 314,
"unique_sentence2": 3164,
"min_score": 0.0,
"avg_score": 2.8114334391534355,
"max_score": 5.0
}
}
```

</details>

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

<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;">STS14</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">一款<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'">大规模文本嵌入基准（Massive Text Embedding Benchmark，简称MTEB）</a>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 2014语义文本相似度任务数据集，目前仅提供英文版本。

| | |
|---------------|---------------------------------------------|
| 任务类别 | 文本到文本（text-to-text，简称t2t） |
| 领域 | 博客文本、网页文本、口语文本 |
| 参考来源 | https://www.aclweb.org/anthology/S14-1002 |

## 任务评估方法
你可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(["STS14"])
evaluator = mteb.MTEB(task)

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

<!-- 数据集需在自述文件中添加arXiv链接以自动关联数据集与相关论文 -->
若想了解如何在MTEB任务中运行模型，请参阅其[GitHub仓库](https://github.com/embeddings-benchmark/mteb)。

## 引用规范
若你使用本数据集，请同时引用该数据集与[MTEB](https://github.com/embeddings-benchmark/mteb)，因为本数据集在作为[大规模多语言文本嵌入基准（Massive Multilingual Text Embedding Benchmark，简称MMTEB）贡献项](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb)时经过了额外的处理流程。

bibtex

@inproceedings{bandhakavi-etal-2014-generating,
address = {Dublin, Ireland},
author = {Bandhakavi, Anil and
Wiratunga, Nirmalie and
P, Deepak and
Massie, Stewart},
booktitle = {Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*{SEM} 2014)},
doi = {10.3115/v1/S14-1002},
editor = {Bos, Johan and
Frank, Anette and
Navigli, Roberto},
month = aug,
pages = {12--21},
publisher = {Association for Computational Linguistics and Dublin City University},
title = {Generating a Word-Emotion Lexicon from {#}Emotional Tweets},
url = {https://aclanthology.org/S14-1002},
year = {2014},
}

@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 Lo{"i}c 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},
}

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

以下为本任务的描述性统计数据。你也可通过以下代码获取此类统计信息：

python
import mteb

task = mteb.get_task("STS14")

desc_stats = task.metadata.descriptive_stats

json
{
"test": {
"num_samples": 3750,
"number_of_characters": 407185,
"unique_pairs": 3682,
"min_sentence1_length": 20,
"average_sentence1_len": 56.6216,
"max_sentence1_length": 372,
"unique_sentence1": 3408,
"min_sentence2_length": 18,
"average_sentence2_len": 51.96106666666667,
"max_sentence2_length": 314,
"unique_sentence2": 3164,
"min_score": 0.0,
"avg_score": 2.8114334391534355,
"max_score": 5.0
}
}

</details>

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