AfriHateClassification
Massive Text Embedding Benchmark
AfriHate is a multilingual collection of hate speech and abusive language datasets covering 15 African languages. Each example is a tweet annotated by native speakers with sociocultural understanding of the context and language, addressing the crucial need for localized and community-driven moderation resources.
| | |
|---------------|---------------------------------------------|
| Task category | t2c |
| Domains | Social, Written |
| Reference | https://aclanthology.org/2025.naacl-long.92/ |
Source datasets:
- [afrihate/afrihate](https://huggingface.co/datasets/afrihate/afrihate)
## Dataset Preparation in MTEB
This repository is a staging copy of `afrihate/afrihate` for the `AfriHateClassification` task. The intended long-term canonical benchmark copy is `mteb/AfriHateClassification`.
### Transformations
- Renamed `tweet` to `text`
- Mapped labels to integers: `Normal -> 0`, `Abuse -> 1`, `Hate -> 2`
- Applied dataset cleaning before upload to reduce duplicates and train-test leakage in the benchmark copy
### Label Schema
- `0`: Normal
- `1`: Abuse
- `2`: Hate
### Splits and subsets
The multilingual subset structure from the source dataset is preserved. The uploaded copy contains the cleaned train/eval splits used by MTEB.
## 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("AfriHateClassification")
evaluator = mteb.MTEB([task])
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```
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
@inproceedings{muhammad-etal-2025-afrihate,
address = {Albuquerque, New Mexico},
author = {Muhammad, Shamsuddeen Hassan and
Abdulmumin, Idris and
Ayele, Abinew Ali and
Adelani, David Ifeoluwa and
Ahmad, Ibrahim Said and
Aliyu, Saminu Mohammad and
R{\"o}ttger, Paul and
Oppong, Abigail and
Bukula, Andiswa and
Chukwuneke, Chiamaka Ijeoma and
Jibril, Ebrahim Chekol and
Ismail, Elyas Abdi and
Alemneh, Esubalew and
Gebremichael, Hagos Tesfahun and
Aliyu, Lukman Jibril and
Beloucif, Meriem and
Hourrane, Oumaima and
Mabuya, Rooweither and
Osei, Salomey and
Rutunda, Samuel and
Belay, Tadesse Destaw and
Guge, Tadesse Kebede and
Asfaw, Tesfa Tegegne and
Wanzare, Lilian Diana Awuor and
Onyango, Nelson Odhiambo and
Yimam, Seid Muhie and
Ousidhoum, Nedjma},
booktitle = {Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)},
editor = {Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu},
isbn = {979-8-89176-189-6},
month = apr,
pages = {1854--1871},
publisher = {Association for Computational Linguistics},
title = {{A}fri{H}ate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for {A}frican Languages},
url = {https://aclanthology.org/2025.naacl-long.92/},
year = {2025},
}
@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