README.md

---
annotations_creators:
- human-annotated
language:
- afr
- amh
- arb
- arq
- ary
- eng
- hau
- hin
- ind
- kin
- mar
- tel
license: unknown
multilinguality: multilingual
source_datasets:
- SemRel/SemRel2024
task_categories:
- sentence-similarity
task_ids:
- semantic-similarity-scoring
dataset_info:
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configs:
- config_name: afr
  data_files:
  - split: test
    path: afr/test-*
  - split: dev
    path: afr/dev-*
- config_name: amh
  data_files:
  - split: train
    path: amh/train-*
  - split: test
    path: amh/test-*
  - split: dev
    path: amh/dev-*
- config_name: arb
  data_files:
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    path: arb/test-*
  - split: dev
    path: arb/dev-*
- config_name: arq
  data_files:
  - split: train
    path: arq/train-*
  - split: test
    path: arq/test-*
  - split: dev
    path: arq/dev-*
- config_name: ary
  data_files:
  - split: train
    path: ary/train-*
  - split: test
    path: ary/test-*
  - split: dev
    path: ary/dev-*
- config_name: eng
  data_files:
  - split: train
    path: eng/train-*
  - split: test
    path: eng/test-*
  - split: dev
    path: eng/dev-*
- config_name: hau
  data_files:
  - split: train
    path: hau/train-*
  - split: test
    path: hau/test-*
  - split: dev
    path: hau/dev-*
- config_name: hin
  data_files:
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    path: hin/test-*
  - split: dev
    path: hin/dev-*
- config_name: ind
  data_files:
  - split: test
    path: ind/test-*
  - split: dev
    path: ind/dev-*
- config_name: kin
  data_files:
  - split: train
    path: kin/train-*
  - split: test
    path: kin/test-*
  - split: dev
    path: kin/dev-*
- config_name: mar
  data_files:
  - split: train
    path: mar/train-*
  - split: test
    path: mar/test-*
  - split: dev
    path: mar/dev-*
- config_name: tel
  data_files:
  - split: train
    path: tel/train-*
  - split: test
    path: tel/test-*
  - split: dev
    path: tel/dev-*
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;">SemRel24STS</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>

SemRel2024 is a collection of Semantic Textual Relatedness (STR) datasets for 14 languages, including African and Asian languages. The datasets are composed of sentence pairs, each assigned a relatedness score between 0 (completely) unrelated and 1 (maximally related) with a large range of expected relatedness values.

|               |                                             |
|---------------|---------------------------------------------|
| Task category | t2t                              |
| Domains       | Spoken, Written                               |
| Reference     | https://huggingface.co/datasets/SemRel/SemRel2024 |




## 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_task("SemRel24STS")
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 repository](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

@misc{ousidhoum2024semrel2024,
  archiveprefix = {arXiv},
  author = {Nedjma Ousidhoum and Shamsuddeen Hassan Muhammad and Mohamed Abdalla and Idris Abdulmumin and Ibrahim Said Ahmad and
Sanchit Ahuja and Alham Fikri Aji and Vladimir Araujo and Abinew Ali Ayele and Pavan Baswani and Meriem Beloucif and
Chris Biemann and Sofia Bourhim and Christine De Kock and Genet Shanko Dekebo and
Oumaima Hourrane and Gopichand Kanumolu and Lokesh Madasu and Samuel Rutunda and Manish Shrivastava and
Thamar Solorio and Nirmal Surange and Hailegnaw Getaneh Tilaye and Krishnapriya Vishnubhotla and Genta Winata and
Seid Muhie Yimam and Saif M. Mohammad},
  eprint = {2402.08638},
  primaryclass = {cs.CL},
  title = {SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 14 Languages},
  year = {2024},
}


@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},
}
```

# 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("SemRel24STS")

desc_stats = task.metadata.descriptive_stats
```

```json
{}
```

</details>

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