Datasets:
metadata
annotations_creators:
- human-annotated
language:
- abs
- bbc
- bew
- bhp
- ind
- jav
- mad
- mak
- min
- mui
- rej
- sun
license: cc-by-sa-4.0
multilinguality: multilingual
task_categories:
- translation
task_ids: []
dataset_info:
- config_name: ind-abs
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 303680
num_examples: 1000
download_size: 210436
dataset_size: 303680
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- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
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num_examples: 6600
download_size: 1444897
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dtype: string
- name: sentence2
dtype: string
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num_examples: 1000
download_size: 193136
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- config_name: ind-btk
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- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
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num_examples: 6600
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- config_name: ind-jav
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- name: sentence2
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- config_name: ind-min
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- config_name: ind-mui
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- name: sentence2
dtype: string
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- name: train
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- config_name: ind-sun
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- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
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num_examples: 6600
download_size: 1420271
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configs:
- config_name: ind-abs
data_files:
- split: train
path: ind-abs/train-*
- config_name: ind-bew
data_files:
- split: train
path: ind-bew/train-*
- config_name: ind-bhp
data_files:
- split: train
path: ind-bhp/train-*
- config_name: ind-btk
data_files:
- split: train
path: ind-btk/train-*
- config_name: ind-jav
data_files:
- split: train
path: ind-jav/train-*
- config_name: ind-mad
data_files:
- split: train
path: ind-mad/train-*
- config_name: ind-mak
data_files:
- split: train
path: ind-mak/train-*
- config_name: ind-min
data_files:
- split: train
path: ind-min/train-*
- config_name: ind-mui
data_files:
- split: train
path: ind-mui/train-*
- config_name: ind-rej
data_files:
- split: train
path: ind-rej/train-*
- config_name: ind-sun
data_files:
- split: train
path: ind-sun/train-*
tags:
- mteb
- text
NusaTranslation is a parallel dataset for machine translation on 11 Indonesia languages and English.
| Task category | t2t |
| Domains | Social, Written |
| Reference | https://huggingface.co/datasets/indonlp/nusatranslation_mt |
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(["NusaTranslationBitextMining"])
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{cahyawijaya2023nusawrites,
author = {Cahyawijaya, Samuel and Lovenia, Holy and Koto, Fajri and Adhista, Dea and Dave, Emmanuel and Oktavianti, Sarah and Akbar, Salsabil and Lee, Jhonson and Shadieq, Nuur and Cenggoro, Tjeng Wawan and others},
booktitle = {Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages = {921--945},
title = {NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource 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("NusaTranslationBitextMining")
desc_stats = task.metadata.descriptive_stats
{
"train": {
"num_samples": 50200,
"number_of_characters": 14759870,
"unique_pairs": 50140,
"min_sentence1_length": 5,
"average_sentence1_length": 145.4552390438247,
"max_sentence1_length": 873,
"unique_sentence1": 8258,
"min_sentence2_length": 5,
"average_sentence2_length": 148.56607569721115,
"max_sentence2_length": 980,
"unique_sentence2": 50102
}
}
This dataset card was automatically generated using MTEB