Datasets:
metadata
annotations_creators:
- human-annotated
language:
- ace
- ban
- bbc
- bjn
- bug
- eng
- ind
- jav
- mad
- min
- nij
- sun
license: cc-by-sa-4.0
multilinguality: multilingual
task_categories:
- translation
task_ids: []
dataset_info:
- config_name: eng-ace
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 158722
num_examples: 500
download_size: 104175
dataset_size: 158722
- config_name: eng-ban
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 161380
num_examples: 500
download_size: 106223
dataset_size: 161380
- config_name: eng-bbc
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 163184
num_examples: 500
download_size: 106140
dataset_size: 163184
- config_name: eng-bjn
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 161328
num_examples: 500
download_size: 104640
dataset_size: 161328
- config_name: eng-bug
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 165552
num_examples: 500
download_size: 107833
dataset_size: 165552
- config_name: eng-ind
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 161480
num_examples: 500
download_size: 104291
dataset_size: 161480
- config_name: eng-jav
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 159271
num_examples: 500
download_size: 104827
dataset_size: 159271
- config_name: eng-mad
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 161942
num_examples: 500
download_size: 106027
dataset_size: 161942
- config_name: eng-min
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
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num_examples: 500
download_size: 104487
dataset_size: 158912
- config_name: eng-nij
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 159800
num_examples: 500
download_size: 103637
dataset_size: 159800
- config_name: eng-sun
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 161025
num_examples: 500
download_size: 105046
dataset_size: 161025
configs:
- config_name: eng-ace
data_files:
- split: train
path: eng-ace/train-*
- config_name: eng-ban
data_files:
- split: train
path: eng-ban/train-*
- config_name: eng-bbc
data_files:
- split: train
path: eng-bbc/train-*
- config_name: eng-bjn
data_files:
- split: train
path: eng-bjn/train-*
- config_name: eng-bug
data_files:
- split: train
path: eng-bug/train-*
- config_name: eng-ind
data_files:
- split: train
path: eng-ind/train-*
- config_name: eng-jav
data_files:
- split: train
path: eng-jav/train-*
- config_name: eng-mad
data_files:
- split: train
path: eng-mad/train-*
- config_name: eng-min
data_files:
- split: train
path: eng-min/train-*
- config_name: eng-nij
data_files:
- split: train
path: eng-nij/train-*
- config_name: eng-sun
data_files:
- split: train
path: eng-sun/train-*
tags:
- mteb
- text
NusaX is a parallel dataset for machine translation and sentiment analysis on 11 Indonesia languages and English.
| Task category | t2t |
| Domains | Reviews, Written |
| Reference | https://huggingface.co/datasets/indonlp/NusaX-senti/ |
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(["NusaXBitextMining"])
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{winata2023nusax,
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 others},
booktitle = {Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics},
pages = {815--834},
title = {NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages},
year = {2023},
}
@misc{winata2024miners,
archiveprefix = {arXiv},
author = {Genta Indra Winata and Ruochen Zhang and David Ifeoluwa Adelani},
eprint = {2406.07424},
primaryclass = {cs.CL},
title = {MINERS: Multilingual Language Models as Semantic Retrievers},
year = {2024},
}
@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("NusaXBitextMining")
desc_stats = task.metadata.descriptive_stats
{
"train": {
"num_samples": 5500,
"number_of_characters": 1728596,
"unique_pairs": 5499,
"min_sentence1_length": 18,
"average_sentence1_length": 161.66,
"max_sentence1_length": 562,
"unique_sentence1": 500,
"min_sentence2_length": 7,
"average_sentence2_length": 152.63018181818182,
"max_sentence2_length": 550,
"unique_sentence2": 5498
}
}
This dataset card was automatically generated using MTEB