Datasets:
metadata
annotations_creators:
- expert-annotated
language:
- ace
- ban
- bbc
- bjn
- bug
- eng
- ind
- jav
- mad
- min
- nij
- sun
license: cc-by-sa-4.0
multilinguality: multilingual
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-scoring
- sentiment-classification
- hate-speech-detection
dataset_info:
- config_name: ace
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 79892
num_examples: 500
- name: validation
num_bytes: 15898
num_examples: 100
- name: test
num_bytes: 64387
num_examples: 400
download_size: 102862
dataset_size: 160177
- config_name: ban
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 82550
num_examples: 500
- name: validation
num_bytes: 16354
num_examples: 100
- name: test
num_bytes: 66148
num_examples: 400
download_size: 107512
dataset_size: 165052
- config_name: bbc
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 84354
num_examples: 500
- name: validation
num_bytes: 16639
num_examples: 100
- name: test
num_bytes: 67308
num_examples: 400
download_size: 106190
dataset_size: 168301
- config_name: bjn
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 82498
num_examples: 500
- name: validation
num_bytes: 16248
num_examples: 100
- name: test
num_bytes: 65842
num_examples: 400
download_size: 103423
dataset_size: 164588
- config_name: bug
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 86722
num_examples: 500
- name: validation
num_bytes: 16970
num_examples: 100
- name: test
num_bytes: 69938
num_examples: 400
download_size: 109724
dataset_size: 173630
- config_name: eng
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 86830
num_examples: 500
- name: validation
num_bytes: 16802
num_examples: 100
- name: test
num_bytes: 68603
num_examples: 400
download_size: 111966
dataset_size: 172235
- config_name: ind
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 82650
num_examples: 500
- name: validation
num_bytes: 16305
num_examples: 100
- name: test
num_bytes: 66480
num_examples: 400
download_size: 103010
dataset_size: 165435
- config_name: jav
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 80441
num_examples: 500
- name: validation
num_bytes: 15855
num_examples: 100
- name: test
num_bytes: 64639
num_examples: 400
download_size: 103581
dataset_size: 160935
- config_name: mad
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 83112
num_examples: 500
- name: validation
num_bytes: 16477
num_examples: 100
- name: test
num_bytes: 66898
num_examples: 400
download_size: 106671
dataset_size: 166487
- config_name: min
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 80082
num_examples: 500
- name: validation
num_bytes: 15866
num_examples: 100
- name: test
num_bytes: 64608
num_examples: 400
download_size: 103757
dataset_size: 160556
- config_name: nij
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 80970
num_examples: 500
- name: validation
num_bytes: 16300
num_examples: 100
- name: test
num_bytes: 65765
num_examples: 400
download_size: 102158
dataset_size: 163035
- config_name: sun
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 82195
num_examples: 500
- name: validation
num_bytes: 16167
num_examples: 100
- name: test
num_bytes: 66012
num_examples: 400
download_size: 105004
dataset_size: 164374
configs:
- config_name: ace
data_files:
- split: train
path: ace/train-*
- split: validation
path: ace/validation-*
- split: test
path: ace/test-*
- config_name: ban
data_files:
- split: train
path: ban/train-*
- split: validation
path: ban/validation-*
- split: test
path: ban/test-*
- config_name: bbc
data_files:
- split: train
path: bbc/train-*
- split: validation
path: bbc/validation-*
- split: test
path: bbc/test-*
- config_name: bjn
data_files:
- split: train
path: bjn/train-*
- split: validation
path: bjn/validation-*
- split: test
path: bjn/test-*
- config_name: bug
data_files:
- split: train
path: bug/train-*
- split: validation
path: bug/validation-*
- split: test
path: bug/test-*
- config_name: eng
data_files:
- split: train
path: eng/train-*
- split: validation
path: eng/validation-*
- split: test
path: eng/test-*
- config_name: ind
data_files:
- split: train
path: ind/train-*
- split: validation
path: ind/validation-*
- split: test
path: ind/test-*
- config_name: jav
data_files:
- split: train
path: jav/train-*
- split: validation
path: jav/validation-*
- split: test
path: jav/test-*
- config_name: mad
data_files:
- split: train
path: mad/train-*
- split: validation
path: mad/validation-*
- split: test
path: mad/test-*
- config_name: min
data_files:
- split: train
path: min/train-*
- split: validation
path: min/validation-*
- split: test
path: min/test-*
- config_name: nij
data_files:
- split: train
path: nij/train-*
- split: validation
path: nij/validation-*
- split: test
path: nij/test-*
- config_name: sun
data_files:
- split: train
path: sun/train-*
- split: validation
path: sun/validation-*
- split: test
path: sun/test-*
tags:
- mteb
- text
NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak. NusaX-Senti is a 3-labels (positive, neutral, negative) sentiment analysis dataset for 10 Indonesian local languages + Indonesian and English.
| Task category | t2c |
| Domains | Reviews, Web, Social, Constructed, Written |
| Reference | https://arxiv.org/abs/2205.15960 |
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(["NusaX-senti"])
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.
@misc{winata2022nusax,
archiveprefix = {arXiv},
author = {Winata, Genta Indra and Aji, Alham Fikri and Cahyawijaya,
Samuel and Mahendra, Rahmad and Koto, Fajri and Romadhony,
Ade and Kurniawan, Kemal and Moeljadi, David and Prasojo,
Radityo Eko and Fung, Pascale and Baldwin, Timothy and Lau,
Jey Han and Sennrich, Rico and Ruder, Sebastian},
eprint = {2205.15960},
primaryclass = {cs.CL},
title = {NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages},
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("NusaX-senti")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 4800,
"number_of_characters": 739028,
"number_texts_intersect_with_train": 0,
"min_text_length": 5,
"average_text_length": 153.96416666666667,
"max_text_length": 539,
"unique_text": 4800,
"unique_labels": 3,
"labels": {
"2": {
"count": 1812
},
"1": {
"count": 1152
},
"0": {
"count": 1836
}
}
},
"train": {
"num_samples": 6000,
"number_of_characters": 920296,
"number_texts_intersect_with_train": null,
"min_text_length": 7,
"average_text_length": 153.38266666666667,
"max_text_length": 562,
"unique_text": 5998,
"unique_labels": 3,
"labels": {
"1": {
"count": 1428
},
"2": {
"count": 2268
},
"0": {
"count": 2304
}
}
}
}
This dataset card was automatically generated using MTEB