Datasets:
metadata
annotations_creators:
- human-annotated
language:
- amh
- arq
- ary
- gaz
- hau
- ibo
- kin
- pcm
- som
- swh
- tir
- twi
- xho
- yor
- zul
license: cc-by-4.0
multilinguality: multilingual
source_datasets:
- afrihate/afrihate
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-scoring
- sentiment-classification
- hate-speech-detection
dataset_info:
- config_name: amh
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 770967
num_examples: 3460
- name: validation
num_bytes: 165243
num_examples: 743
- name: test
num_bytes: 166726
num_examples: 744
download_size: 606981
dataset_size: 1102936
- config_name: arq
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 133423
num_examples: 713
- name: validation
num_bytes: 37782
num_examples: 210
- name: test
num_bytes: 69681
num_examples: 321
download_size: 135225
dataset_size: 240886
- config_name: ary
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 602078
num_examples: 3153
- name: validation
num_bytes: 127992
num_examples: 654
- name: test
num_bytes: 130279
num_examples: 666
download_size: 539429
dataset_size: 860349
- config_name: hau
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 551505
num_examples: 4564
- name: validation
num_bytes: 121655
num_examples: 1029
- name: test
num_bytes: 127181
num_examples: 1049
download_size: 531604
dataset_size: 800341
- config_name: ibo
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 318458
num_examples: 3095
- name: validation
num_bytes: 64091
num_examples: 626
- name: test
num_bytes: 68978
num_examples: 668
download_size: 277727
dataset_size: 451527
- config_name: kin
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 500856
num_examples: 3265
- name: validation
num_bytes: 105321
num_examples: 684
- name: test
num_bytes: 104965
num_examples: 692
download_size: 485541
dataset_size: 711142
- config_name: orm
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 678918
num_examples: 3517
- name: validation
num_bytes: 145911
num_examples: 756
- name: test
num_bytes: 146154
num_examples: 759
download_size: 643951
dataset_size: 970983
- config_name: pcm
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 961921
num_examples: 6996
- name: validation
num_bytes: 194646
num_examples: 1398
- name: test
num_bytes: 193826
num_examples: 1394
download_size: 895620
dataset_size: 1350393
- config_name: som
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 684987
num_examples: 3174
- name: validation
num_bytes: 159470
num_examples: 741
- name: test
num_bytes: 161957
num_examples: 745
download_size: 690194
dataset_size: 1006414
- config_name: swa
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 1363382
num_examples: 13889
- name: validation
num_bytes: 276567
num_examples: 2778
- name: test
num_bytes: 272387
num_examples: 2735
download_size: 1210099
dataset_size: 1912336
- config_name: tir
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 766926
num_examples: 3545
- name: validation
num_bytes: 162544
num_examples: 759
- name: test
num_bytes: 165855
num_examples: 765
download_size: 606532
dataset_size: 1095325
- config_name: twi
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 249258
num_examples: 2537
- name: validation
num_bytes: 60528
num_examples: 629
- name: test
num_bytes: 66562
num_examples: 690
download_size: 265544
dataset_size: 376348
- config_name: xho
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 218906
num_examples: 2454
- name: validation
num_bytes: 47842
num_examples: 543
- name: test
num_bytes: 53007
num_examples: 594
download_size: 212117
dataset_size: 319755
- config_name: yor
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 461005
num_examples: 3318
- name: validation
num_bytes: 99593
num_examples: 716
- name: test
num_bytes: 115686
num_examples: 815
download_size: 456300
dataset_size: 676284
- config_name: zul
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 226674
num_examples: 2473
- name: validation
num_bytes: 35381
num_examples: 410
- name: test
num_bytes: 44828
num_examples: 498
download_size: 198240
dataset_size: 306883
configs:
- config_name: amh
data_files:
- split: train
path: amh/train-*
- split: validation
path: amh/validation-*
- split: test
path: amh/test-*
- config_name: arq
data_files:
- split: train
path: arq/train-*
- split: validation
path: arq/validation-*
- split: test
path: arq/test-*
- config_name: ary
data_files:
- split: train
path: ary/train-*
- split: validation
path: ary/validation-*
- split: test
path: ary/test-*
- config_name: hau
data_files:
- split: train
path: hau/train-*
- split: validation
path: hau/validation-*
- split: test
path: hau/test-*
- config_name: ibo
data_files:
- split: train
path: ibo/train-*
- split: validation
path: ibo/validation-*
- split: test
path: ibo/test-*
- config_name: kin
data_files:
- split: train
path: kin/train-*
- split: validation
path: kin/validation-*
- split: test
path: kin/test-*
- config_name: orm
data_files:
- split: train
path: orm/train-*
- split: validation
path: orm/validation-*
- split: test
path: orm/test-*
- config_name: pcm
data_files:
- split: train
path: pcm/train-*
- split: validation
path: pcm/validation-*
- split: test
path: pcm/test-*
- config_name: som
data_files:
- split: train
path: som/train-*
- split: validation
path: som/validation-*
- split: test
path: som/test-*
- config_name: swa
data_files:
- split: train
path: swa/train-*
- split: validation
path: swa/validation-*
- split: test
path: swa/test-*
- config_name: tir
data_files:
- split: train
path: tir/train-*
- split: validation
path: tir/validation-*
- split: test
path: tir/test-*
- config_name: twi
data_files:
- split: train
path: twi/train-*
- split: validation
path: twi/validation-*
- split: test
path: twi/test-*
- config_name: xho
data_files:
- split: train
path: xho/train-*
- split: validation
path: xho/validation-*
- split: test
path: xho/test-*
- config_name: yor
data_files:
- split: train
path: yor/train-*
- split: validation
path: yor/validation-*
- split: test
path: yor/test-*
- config_name: zul
data_files:
- split: train
path: zul/train-*
- split: validation
path: zul/validation-*
- split: test
path: zul/test-*
tags:
- mteb
- text
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:
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
tweettotext - 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: Normal1: Abuse2: 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:
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.
Citation
If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.
@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
Dataset Statistics
The following code contains the descriptive statistics from the task. These can also be obtained using:
import mteb
task = mteb.get_task("AfriHateClassification")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 14250,
"number_texts_intersect_with_train": 1026,
"text_statistics": {
"total_text_length": 1532087,
"min_text_length": 8,
"average_text_length": 107.51487719298245,
"max_text_length": 617,
"unique_texts": 14129
},
"image_statistics": null,
"audio_statistics": null,
"label_statistics": {
"min_labels_per_text": 1,
"average_label_per_text": 1.0,
"max_labels_per_text": 1,
"unique_labels": 3,
"labels": {
"2": {
"count": 3017
},
"0": {
"count": 5699
},
"1": {
"count": 5534
}
}
}
},
"train": {
"num_samples": 62466,
"number_texts_intersect_with_train": null,
"text_statistics": {
"total_text_length": 6707920,
"min_text_length": 4,
"average_text_length": 107.38513751480805,
"max_text_length": 764,
"unique_texts": 60182
},
"image_statistics": null,
"audio_statistics": null,
"label_statistics": {
"min_labels_per_text": 1,
"average_label_per_text": 1.0,
"max_labels_per_text": 1,
"unique_labels": 3,
"labels": {
"0": {
"count": 25703
},
"1": {
"count": 25750
},
"2": {
"count": 11013
}
}
}
}
}
This dataset card was automatically generated using MTEB