sib200

<!-- 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;">SIB200ClusteringS2S</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>

SIB-200 is the largest publicly available topic classification
dataset based on Flores-200 covering 205 languages and dialects annotated. The dataset is
annotated in English for the topics, science/technology, travel, politics, sports,
health, entertainment, and geography. The labels are then transferred to the other languages
in Flores-200 which are human-translated.

| | |
|---------------|---------------------------------------------|
| Task category | t2c |
| Domains | News, Written |
| Reference | https://arxiv.org/abs/2309.07445 |

## 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(["SIB200ClusteringS2S"])
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

@article{adelani2023sib,
author = {Adelani, David Ifeoluwa and Liu, Hannah and Shen, Xiaoyu and Vassilyev, Nikita and Alabi, Jesujoba O and Mao, Yanke and Gao, Haonan and Lee, Annie En-Shiun},
journal = {arXiv preprint arXiv:2309.07445},
title = {SIB-200: A simple, inclusive, and big evaluation dataset for topic classification in 200+ languages and dialects},
year = {2023},
}

@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("SIB200ClusteringS2S")

desc_stats = task.metadata.descriptive_stats
```

```json
{
"test": {
"num_samples": 197788,
"number_of_characters": 26633239,
"min_text_length": 10,
"average_text_length": 134.6554846603434,
"max_text_length": 597,
"unique_texts": 448,
"min_labels_per_text": 16351,
"average_labels_per_text": 1.0,
"max_labels_per_text": 49644,
"unique_labels": 7,
"labels": {
"1": {
"count": 16351
},
"4": {
"count": 49644
},
"0": {
"count": 18321
},
"3": {
"count": 28762
},
"2": {
"count": 21670
},
"6": {
"count": 39006
},
"5": {
"count": 24034
}
}
}
}
```

</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;">SIB200ClusteringS2S</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'">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测试（Massive Text Embedding Benchmark）</div>
</div>

SIB-200是当前基于Flores-200构建的规模最大的公开主题分类数据集，覆盖205种语言与方言并完成全量标注。该数据集的主题标签以英语进行标注，涵盖科技、旅游、政治、体育、健康、娱乐及地理七大类别，随后将这些标签映射至Flores-200中其余经人工翻译的语言版本。

| 任务类别 | t2c |
|---------------|---------------------------------------------|
| 应用领域 | 新闻、书面文本 |
| 参考来源 | https://arxiv.org/abs/2309.07445 |

## 如何在该任务上开展评估
研究者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(["SIB200ClusteringS2S"])
evaluator = mteb.MTEB(task)

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

若需了解如何在MTEB任务中运行模型，请访问其[GitHub仓库](https://github.com/embeddings-benchmark/mteb)。

## 引用规范
若您使用本数据集，请同时引用该数据集与MTEB相关成果，因为本数据集在[多语言大规模文本嵌入基准测试（Massive Multilingual Text Embedding Benchmark，MMTEB）贡献项目](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb)中经过了额外的后处理。

bibtex

@article{adelani2023sib,
author = {Adelani, David Ifeoluwa and Liu, Hannah and Shen, Xiaoyu and Vassilyev, Nikita and Alabi, Jesujoba O and Mao, Yanke and Gao, Haonan and Lee, Annie En-Shiun},
journal = {arXiv preprint arXiv:2309.07445},
title = {SIB-200: A simple, inclusive, and big evaluation dataset for topic classification in 200+ languages and dialects},
year = {2023},
}

@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},
}

## 数据集统计信息
<details>
<summary> 展开查看数据集统计信息</summary>

以下代码展示了本任务的描述性统计数据，您也可通过以下代码获取对应统计结果：
python
import mteb

task = mteb.get_task("SIB200ClusteringS2S")

desc_stats = task.metadata.descriptive_stats

json
{
"test": {
"num_samples": 197788,
"number_of_characters": 26633239,
"min_text_length": 10,
"average_text_length": 134.6554846603434,
"max_text_length": 597,
"unique_texts": 448,
"min_labels_per_text": 16351,
"average_labels_per_text": 1.0,
"max_labels_per_text": 49644,
"unique_labels": 7,
"labels": {
"1": {
"count": 16351
},
"4": {
"count": 49644
},
"0": {
"count": 18321
},
"3": {
"count": 28762
},
"2": {
"count": 21670
},
"6": {
"count": 39006
},
"5": {
"count": 24034
}
}
}
}

</details>

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