README.md

---
annotations_creators:
- derived
language:
- amh
- eng
- fra
- hau
- ibo
- lin
- lug
- orm
- pcm
- run
- sna
- som
- swa
- tir
- xho
- yor
license: afl-3.0
multilinguality: multilingual
task_categories:
- text-classification
task_ids: []
dataset_info:
- config_name: amh
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  - name: labels
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configs:
- config_name: amh
  data_files:
  - split: test
    path: amh/test-*
- config_name: eng
  data_files:
  - split: test
    path: eng/test-*
- config_name: fra
  data_files:
  - split: test
    path: fra/test-*
- config_name: hau
  data_files:
  - split: test
    path: hau/test-*
- config_name: ibo
  data_files:
  - split: test
    path: ibo/test-*
- config_name: lin
  data_files:
  - split: test
    path: lin/test-*
- config_name: lug
  data_files:
  - split: test
    path: lug/test-*
- config_name: orm
  data_files:
  - split: test
    path: orm/test-*
- config_name: pcm
  data_files:
  - split: test
    path: pcm/test-*
- config_name: run
  data_files:
  - split: test
    path: run/test-*
- config_name: sna
  data_files:
  - split: test
    path: sna/test-*
- config_name: som
  data_files:
  - split: test
    path: som/test-*
- config_name: swa
  data_files:
  - split: test
    path: swa/test-*
- config_name: tir
  data_files:
  - split: test
    path: tir/test-*
- config_name: xho
  data_files:
  - split: test
    path: xho/test-*
- config_name: yor
  data_files:
  - split: test
    path: yor/test-*
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;">MasakhaNEWSClusteringP2P</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 news article headlines and texts from MasakhaNEWS dataset. Clustering of 10 sets on the news article label.

|               |                                             |
|---------------|---------------------------------------------|
| Task category | t2c                              |
| Domains       | News, Written, Non-fiction                               |
| Reference     | https://huggingface.co/datasets/masakhane/masakhanews |


## 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(["MasakhaNEWSClusteringP2P"])
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{adelani2023masakhanews,
  author = {David Ifeoluwa Adelani and  Marek Masiak and  Israel Abebe Azime and  Jesujoba Oluwadara Alabi and  Atnafu Lambebo Tonja and  Christine Mwase and  Odunayo Ogundepo and  Bonaventure F. P. Dossou and  Akintunde Oladipo and  Doreen Nixdorf and  Chris Chinenye Emezue and  Sana Sabah al-azzawi and  Blessing K. Sibanda and  Davis David and  Lolwethu Ndolela and  Jonathan Mukiibi and  Tunde Oluwaseyi Ajayi and  Tatiana Moteu Ngoli and  Brian Odhiambo and  Abraham Toluwase Owodunni and  Nnaemeka C. Obiefuna and  Shamsuddeen Hassan Muhammad and  Saheed Salahudeen Abdullahi and  Mesay Gemeda Yigezu and  Tajuddeen Gwadabe and  Idris Abdulmumin and  Mahlet Taye Bame and  Oluwabusayo Olufunke Awoyomi and  Iyanuoluwa Shode and  Tolulope Anu Adelani and  Habiba Abdulganiy Kailani and  Abdul-Hakeem Omotayo and  Adetola Adeeko and  Afolabi Abeeb and  Anuoluwapo Aremu and  Olanrewaju Samuel and  Clemencia Siro and  Wangari Kimotho and  Onyekachi Raphael Ogbu and  Chinedu E. Mbonu and  Chiamaka I. Chukwuneke and  Samuel Fanijo and  Jessica Ojo and  Oyinkansola F. Awosan and  Tadesse Kebede Guge and  Sakayo Toadoum Sari and  Pamela Nyatsine and  Freedmore Sidume and  Oreen Yousuf and  Mardiyyah Oduwole and  Ussen Kimanuka and  Kanda Patrick Tshinu and  Thina Diko and  Siyanda Nxakama and   Abdulmejid Tuni Johar and  Sinodos Gebre and  Muhidin Mohamed and  Shafie Abdi Mohamed and  Fuad Mire Hassan and  Moges Ahmed Mehamed and  Evrard Ngabire and  and Pontus Stenetorp},
  journal = {ArXiv},
  title = {MasakhaNEWS: News Topic Classification for African languages},
  volume = {},
  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("MasakhaNEWSClusteringP2P")

desc_stats = task.metadata.descriptive_stats
```

```json
{
    "test": {
        "num_samples": 80,
        "number_of_characters": 6242,
        "min_text_length": 35,
        "average_text_length": 78.025,
        "max_text_length": 190,
        "unique_texts": 6236,
        "min_labels_per_text": 286,
        "average_labels_per_text": 78.025,
        "max_labels_per_text": 1589,
        "unique_labels": 7,
        "labels": {
            "0": {
                "count": 785
            },
            "2": {
                "count": 1258
            },
            "3": {
                "count": 1589
            },
            "5": {
                "count": 1265
            },
            "1": {
                "count": 762
            },
            "6": {
                "count": 297
            },
            "4": {
                "count": 286
            }
        }
    }
}
```

</details>

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