Datasets:
metadata
annotations_creators:
- derived
language:
- amh
- arq
- ary
- hau
- ibo
- kin
- pcm
- por
- swa
- tso
- twi
- yor
license: cc-by-4.0
multilinguality: multilingual
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: 1143080
num_examples: 5984
- name: validation
num_bytes: 284108
num_examples: 1497
- name: test
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num_examples: 1999
download_size: 1029065
dataset_size: 1835561
- config_name: arq
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
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num_examples: 1651
- name: validation
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num_examples: 414
- name: test
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num_examples: 958
download_size: 192507
dataset_size: 373833
- config_name: ary
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
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num_examples: 5583
- name: validation
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num_examples: 494
- name: test
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num_examples: 2048
download_size: 641623
dataset_size: 993391
- config_name: hau
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
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num_examples: 14172
- name: validation
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num_examples: 2677
- name: test
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num_examples: 2048
download_size: 1115786
dataset_size: 1810921
- config_name: ibo
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
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num_examples: 10192
- name: validation
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num_examples: 1841
- name: test
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num_examples: 2048
download_size: 725832
dataset_size: 1183129
- config_name: kin
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
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num_examples: 3302
- name: validation
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num_examples: 827
- name: test
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num_examples: 1026
download_size: 419177
dataset_size: 644377
- config_name: pcm
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
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num_examples: 5121
- name: validation
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num_examples: 1281
- name: test
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num_examples: 2048
download_size: 635281
dataset_size: 1017514
- config_name: por
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
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num_examples: 3063
- name: validation
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num_examples: 767
- name: test
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num_examples: 2048
download_size: 445458
dataset_size: 677214
- config_name: swa
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
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num_examples: 1810
- name: validation
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num_examples: 453
- name: test
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num_examples: 748
download_size: 244103
dataset_size: 362726
- config_name: tso
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 58473
num_examples: 804
- name: validation
num_bytes: 14564
num_examples: 203
- name: test
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num_examples: 254
download_size: 60441
dataset_size: 91155
- config_name: twi
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 215571
num_examples: 3481
- name: validation
num_bytes: 23552
num_examples: 388
- name: test
num_bytes: 57232
num_examples: 949
download_size: 185305
dataset_size: 296355
- config_name: yor
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 1341405
num_examples: 8522
- name: validation
num_bytes: 329966
num_examples: 2090
- name: test
num_bytes: 248982
num_examples: 2048
download_size: 1235782
dataset_size: 1920353
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: pcm
data_files:
- split: train
path: pcm/train-*
- split: validation
path: pcm/validation-*
- split: test
path: pcm/test-*
- config_name: por
data_files:
- split: train
path: por/train-*
- split: validation
path: por/validation-*
- split: test
path: por/test-*
- config_name: swa
data_files:
- split: train
path: swa/train-*
- split: validation
path: swa/validation-*
- split: test
path: swa/test-*
- config_name: tso
data_files:
- split: train
path: tso/train-*
- split: validation
path: tso/validation-*
- split: test
path: tso/test-*
- config_name: twi
data_files:
- split: train
path: twi/train-*
- split: validation
path: twi/validation-*
- split: test
path: twi/test-*
- config_name: yor
data_files:
- split: train
path: yor/train-*
- split: validation
path: yor/validation-*
- split: test
path: yor/test-*
tags:
- mteb
- text
AfriSenti is the largest sentiment analysis dataset for under-represented African languages.
| Task category | t2c |
| Domains | Social, Written |
| Reference | https://arxiv.org/abs/2302.08956 |
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["AfriSentiClassification"])
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 repitory.
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{Muhammad2023AfriSentiAT,
author = {Shamsuddeen Hassan Muhammad and Idris Abdulmumin and Abinew Ali Ayele and Nedjma Ousidhoum and David Ifeoluwa Adelani and Seid Muhie Yimam and Ibrahim Sa'id Ahmad and Meriem Beloucif and Saif Mohammad and Sebastian Ruder and Oumaima Hourrane and Pavel Brazdil and Felermino D'ario M'ario Ant'onio Ali and Davis Davis and Salomey Osei and Bello Shehu Bello and Falalu Ibrahim and Tajuddeen Gwadabe and Samuel Rutunda and Tadesse Belay and Wendimu Baye Messelle and Hailu Beshada Balcha and Sisay Adugna Chala and Hagos Tesfahun Gebremichael and Bernard Opoku and Steven Arthur},
title = {AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages},
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
Dataset Statistics
The following code contains the descriptive statistics from the task. These can also be obtained using:
import mteb
task = mteb.get_task("AfriSentiClassification")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 18222,
"number_of_characters": 1378570,
"number_texts_intersect_with_train": 595,
"min_text_length": 6,
"average_text_length": 75.65415431895511,
"max_text_length": 414,
"unique_text": 18222,
"unique_labels": 3,
"labels": {
"0": {
"count": 9206
},
"2": {
"count": 3876
},
"1": {
"count": 5140
}
}
},
"train": {
"num_samples": 63685,
"number_of_characters": 5446582,
"number_texts_intersect_with_train": null,
"min_text_length": 1,
"average_text_length": 85.52378111015153,
"max_text_length": 771,
"unique_text": 62635,
"unique_labels": 3,
"labels": {
"2": {
"count": 20108
},
"1": {
"count": 22794
},
"0": {
"count": 20783
}
}
}
}
This dataset card was automatically generated using MTEB