Datasets:
metadata
annotations_creators:
- expert-annotated
language:
- hau
- ibo
- pcm
- 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: hau
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 1377534
num_examples: 14172
- name: test
num_bytes: 420643
num_examples: 5303
download_size: 1111315
dataset_size: 1798177
- config_name: ibo
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 888336
num_examples: 10192
- name: test
num_bytes: 230041
num_examples: 3682
download_size: 687283
dataset_size: 1118377
- config_name: pcm
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 657515
num_examples: 5121
- name: test
num_bytes: 426214
num_examples: 4154
download_size: 678909
dataset_size: 1083729
- config_name: yor
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 1341405
num_examples: 8522
- name: test
num_bytes: 565182
num_examples: 4515
download_size: 1213252
dataset_size: 1906587
configs:
- config_name: hau
data_files:
- split: train
path: hau/train-*
- split: test
path: hau/test-*
- config_name: ibo
data_files:
- split: train
path: ibo/train-*
- split: test
path: ibo/test-*
- config_name: pcm
data_files:
- split: train
path: pcm/train-*
- split: test
path: pcm/test-*
- config_name: yor
data_files:
- split: train
path: yor/train-*
- split: test
path: yor/test-*
tags:
- mteb
- text
NaijaSenti is the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria — Hausa, Igbo, Nigerian-Pidgin, and Yorùbá — consisting of around 30,000 annotated tweets per language, including a significant fraction of code-mixed tweets.
| Task category | t2c |
| Domains | Social, Written |
| Reference | https://github.com/hausanlp/NaijaSenti |
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(["NaijaSenti"])
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{muhammad-etal-2022-naijasenti,
address = {Marseille, France},
author = {Muhammad, Shamsuddeen Hassan and
Adelani, David Ifeoluwa and
Ruder, Sebastian and
Ahmad, Ibrahim Sa{'}id and
Abdulmumin, Idris and
Bello, Bello Shehu and
Choudhury, Monojit and
Emezue, Chris Chinenye and
Abdullahi, Saheed Salahudeen and
Aremu, Anuoluwapo and
Jorge, Al{\'\i}pio and
Brazdil, Pavel},
booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference},
month = jun,
pages = {590--602},
publisher = {European Language Resources Association},
title = {{N}aija{S}enti: A {N}igerian {T}witter Sentiment Corpus for Multilingual Sentiment Analysis},
url = {https://aclanthology.org/2022.lrec-1.63},
year = {2022},
}
@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("NaijaSenti")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 17654,
"number_of_characters": 1295492,
"number_texts_intersect_with_train": 926,
"min_text_length": 6,
"average_text_length": 73.38234960915374,
"max_text_length": 276,
"unique_text": 17654,
"unique_labels": 3,
"labels": {
"0": {
"count": 6188
},
"1": {
"count": 5457
},
"2": {
"count": 6009
}
},
"hf_subset_descriptive_stats": {
"hau": {
"num_samples": 5303,
"number_of_characters": 355133,
"number_texts_intersect_with_train": 0,
"min_text_length": 8,
"average_text_length": 66.9683198189704,
"max_text_length": 275,
"unique_text": 5303,
"unique_labels": 3,
"labels": {
"0": {
"count": 1755
},
"1": {
"count": 1789
},
"2": {
"count": 1759
}
}
},
"ibo": {
"num_samples": 3682,
"number_of_characters": 175228,
"number_texts_intersect_with_train": 0,
"min_text_length": 9,
"average_text_length": 47.59043997827268,
"max_text_length": 269,
"unique_text": 3682,
"unique_labels": 3,
"labels": {
"0": {
"count": 1118
},
"1": {
"count": 1621
},
"2": {
"count": 943
}
}
},
"pcm": {
"num_samples": 4154,
"number_of_characters": 375268,
"number_texts_intersect_with_train": 926,
"min_text_length": 8,
"average_text_length": 90.3389504092441,
"max_text_length": 276,
"unique_text": 4154,
"unique_labels": 3,
"labels": {
"0": {
"count": 1397
},
"1": {
"count": 431
},
"2": {
"count": 2326
}
}
},
"yor": {
"num_samples": 4515,
"number_of_characters": 389863,
"number_texts_intersect_with_train": 0,
"min_text_length": 6,
"average_text_length": 86.3483942414175,
"max_text_length": 266,
"unique_text": 4515,
"unique_labels": 3,
"labels": {
"0": {
"count": 1918
},
"1": {
"count": 1616
},
"2": {
"count": 981
}
}
}
}
},
"train": {
"num_samples": 38007,
"number_of_characters": 3412356,
"number_texts_intersect_with_train": null,
"min_text_length": 9,
"average_text_length": 89.7823032599258,
"max_text_length": 354,
"unique_text": 37495,
"unique_labels": 3,
"labels": {
"2": {
"count": 12286
},
"1": {
"count": 12600
},
"0": {
"count": 13121
}
},
"hf_subset_descriptive_stats": {
"hau": {
"num_samples": 14172,
"number_of_characters": 1106209,
"number_texts_intersect_with_train": null,
"min_text_length": 17,
"average_text_length": 78.055955405024,
"max_text_length": 337,
"unique_text": 14172,
"unique_labels": 3,
"labels": {
"2": {
"count": 4573
},
"1": {
"count": 4912
},
"0": {
"count": 4687
}
}
},
"ibo": {
"num_samples": 10192,
"number_of_characters": 709705,
"number_texts_intersect_with_train": null,
"min_text_length": 11,
"average_text_length": 69.6335361067504,
"max_text_length": 354,
"unique_text": 10192,
"unique_labels": 3,
"labels": {
"2": {
"count": 2600
},
"1": {
"count": 4508
},
"0": {
"count": 3084
}
}
},
"pcm": {
"num_samples": 5121,
"number_of_characters": 594073,
"number_texts_intersect_with_train": null,
"min_text_length": 9,
"average_text_length": 116.00722515133764,
"max_text_length": 279,
"unique_text": 4609,
"unique_labels": 3,
"labels": {
"2": {
"count": 3241
},
"1": {
"count": 72
},
"0": {
"count": 1808
}
}
},
"yor": {
"num_samples": 8522,
"number_of_characters": 1002369,
"number_texts_intersect_with_train": null,
"min_text_length": 9,
"average_text_length": 117.62133302041774,
"max_text_length": 354,
"unique_text": 8522,
"unique_labels": 3,
"labels": {
"2": {
"count": 1872
},
"1": {
"count": 3108
},
"0": {
"count": 3542
}
}
}
}
}
}
This dataset card was automatically generated using MTEB