biorxiv-clustering-s2s

<!-- 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;">BiorxivClusteringS2S.v2</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>

Clustering of titles from biorxiv across 26 categories.

| | |
|---------------|---------------------------------------------|
| Task category | t2c |
| Domains | Academic, Written |
| Reference | https://api.biorxiv.org/ |

## 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(["BiorxivClusteringS2S.v2"])
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{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("BiorxivClusteringS2S.v2")

desc_stats = task.metadata.descriptive_stats
```

```json
{
"test": {
"num_samples": 53787,
"number_of_characters": 5471373,
"min_text_length": 13,
"average_text_length": 101.72296279770205,
"max_text_length": 378,
"unique_texts": 246,
"min_labels_per_text": 4,
"average_labels_per_text": 1.0,
"max_labels_per_text": 9821,
"unique_labels": 26,
"labels": {
"bioinformatics": {
"count": 4324
},
"evolutionary biology": {
"count": 2548
},
"synthetic biology": {
"count": 480
},
"genetics": {
"count": 1668
},
"plant biology": {
"count": 2005
},
"neuroscience": {
"count": 9821
},
"zoology": {
"count": 297
},
"biophysics": {
"count": 2700
},
"developmental biology": {
"count": 1720
},
"cell biology": {
"count": 3179
},
"bioengineering": {
"count": 1626
},
"microbiology": {
"count": 5368
},
"ecology": {
"count": 2467
},
"biochemistry": {
"count": 2167
},
"genomics": {
"count": 2423
},
"animal behavior and cognition": {
"count": 816
},
"cancer biology": {
"count": 2105
},
"immunology": {
"count": 2632
},
"scientific communication and education": {
"count": 245
},
"systems biology": {
"count": 1078
},
"molecular biology": {
"count": 2094
},
"physiology": {
"count": 936
},
"epidemiology": {
"count": 4
},
"pharmacology and toxicology": {
"count": 634
},
"pathology": {
"count": 364
},
"paleontology": {
"count": 86
}
}
}
}
```

</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;">BiorxivClusteringS2S.v2</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，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（Massive Text Embedding Benchmark）</div>
</div>

针对BioRxiv平台26个分类下的论文标题开展聚类任务。

| 项目 | 内容 |
|---------------|---------------------------------------------|
| 任务类别 | 文本到分类（text-to-category，t2c） |
| 领域 | 学术领域、书面文本 |
| 参考链接 | https://api.biorxiv.org/ |

## 该任务的评估方法

您可通过以下代码在本数据集上评估嵌入模型：

python
import mteb

task = mteb.get_tasks(["BiorxivClusteringS2S.v2"])
evaluator = mteb.MTEB(task)

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

若需了解如何在MTEB任务上运行模型，请查阅[该GitHub仓库](https://github.com/embeddings-benchmark/mteb)。

## 引用说明

若您使用本数据集，请同时引用本数据集与[MTEB](https://github.com/embeddings-benchmark/mteb)相关文献，因本数据集的额外处理工作隶属于[大规模多语言文本嵌入基准（Massive Multilingual Text Embedding Benchmark，MMTEB）贡献项目](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb)。

bibtex

@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>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，您也可以通过如下代码获取：

python
import mteb

task = mteb.get_task("BiorxivClusteringS2S.v2")

desc_stats = task.metadata.descriptive_stats

json
{
"测试集": {
"样本数量": 53787,
"总字符数": 5471373,
"文本最小长度": 13,
"文本平均长度": 101.72296279770205,
"文本最大长度": 378,
"唯一文本数": 246,
"单文本最小标签数": 4,
"单文本平均标签数": 1.0,
"单文本最大标签数": 9821,
"唯一标签数": 26,
"标签分布": {
"生物信息学": {"数量": 4324},
"进化生物学": {"数量": 2548},
"合成生物学": {"数量": 480},
"遗传学": {"数量": 1668},
"植物学": {"数量": 2005},
"神经科学": {"数量": 9821},
"动物学": {"数量": 297},
"生物物理学": {"数量": 2700},
"发育生物学": {"数量": 1720},
"细胞生物学": {"数量": 3179},
"生物工程学": {"数量": 1626},
"微生物学": {"数量": 5368},
"生态学": {"数量": 2467},
"生物化学": {"数量": 2167},
"基因组学": {"数量": 2423},
"动物行为与认知学": {"数量": 816},
"癌症生物学": {"数量": 2105},
"免疫学": {"数量": 2632},
"科学传播与教育学": {"数量": 245},
"系统生物学": {"数量": 1078},
"分子生物学": {"数量": 2094},
"生理学": {"数量": 936},
"流行病学": {"数量": 4},
"药理学与毒理学": {"数量": 634},
"病理学": {"数量": 364},
"古生物学": {"数量": 86}
}
}
}

</details>

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