Datasets:
metadata
annotations_creators:
- expert-annotated
language:
- afr
- amh
- arb
- aze
- bak
- bel
- bem
- ben
- bod
- bos
- bul
- cat
- ces
- ckb
- cym
- dan
- deu
- div
- dzo
- ell
- eng
- eus
- ewe
- fao
- fas
- fij
- fil
- fin
- fra
- fuc
- gle
- glg
- guj
- hau
- heb
- hin
- hmn
- hrv
- hun
- hye
- ibo
- ind
- isl
- ita
- jpn
- kan
- kat
- kaz
- khm
- kin
- kir
- kmr
- kor
- lao
- lav
- lit
- ltz
- mal
- mar
- mey
- mkd
- mlg
- mlt
- mon
- mri
- msa
- mya
- nde
- nep
- nld
- nno
- nob
- nso
- nya
- orm
- pan
- pol
- por
- prs
- pus
- ron
- rus
- shi
- sin
- slk
- slv
- smo
- sna
- snd
- som
- spa
- sqi
- srp
- ssw
- swa
- swe
- tah
- tam
- tat
- tel
- tgk
- tha
- tir
- ton
- tsn
- tuk
- tur
- uig
- ukr
- urd
- uzb
- ven
- vie
- wol
- xho
- yor
- yue
- zho
- zul
license: cc-by-sa-4.0
multilinguality: translated
source_datasets:
- mteb/NTREX
task_categories:
- translation
task_ids: []
dataset_info:
features:
- name: afr_Latn
dtype: string
- name: dan_Latn
dtype: string
- name: deu_Latn
dtype: string
- name: eng_Latn
dtype: string
- name: fao_Latn
dtype: string
- name: isl_Latn
dtype: string
- name: ltz_Latn
dtype: string
- name: nld_Latn
dtype: string
- name: nno_Latn
dtype: string
- name: nob_Latn
dtype: string
- name: swe_Latn
dtype: string
- name: amh_Ethi
dtype: string
- name: hau_Latn
dtype: string
- name: ibo_Latn
dtype: string
- name: nso_Latn
dtype: string
- name: orm_Ethi
dtype: string
- name: som_Latn
dtype: string
- name: ssw_Latn
dtype: string
- name: swa_Latn
dtype: string
- name: tir_Ethi
dtype: string
- name: tsn_Latn
dtype: string
- name: wol_Latn
dtype: string
- name: xho_Latn
dtype: string
- name: yor_Latn
dtype: string
- name: zul_Latn
dtype: string
- name: arb_Arab
dtype: string
- name: ben_Beng
dtype: string
- name: ckb_Arab
dtype: string
- name: ell_Grek
dtype: string
- name: fas_Arab
dtype: string
- name: fin_Latn
dtype: string
- name: fra_Latn
dtype: string
- name: heb_Hebr
dtype: string
- name: hin_Deva
dtype: string
- name: hun_Latn
dtype: string
- name: ind_Latn
dtype: string
- name: jpn_Jpan
dtype: string
- name: kmr_Latn
dtype: string
- name: kor_Hang
dtype: string
- name: lit_Latn
dtype: string
- name: mey_Arab
dtype: string
- name: pol_Latn
dtype: string
- name: por_Latn
dtype: string
- name: prs_Arab
dtype: string
- name: pus_Arab
dtype: string
- name: rus_Cyrl
dtype: string
- name: shi_Arab
dtype: string
- name: spa_Latn
dtype: string
- name: tam_Taml
dtype: string
- name: tgk_Cyrl
dtype: string
- name: tur_Latn
dtype: string
- name: vie_Latn
dtype: string
- name: zho_Hant
dtype: string
- name: aze_Latn
dtype: string
- name: bak_Cyrl
dtype: string
- name: kaz_Cyrl
dtype: string
- name: kir_Cyrl
dtype: string
- name: tat_Cyrl
dtype: string
- name: tuk_Latn
dtype: string
- name: uig_Arab
dtype: string
- name: uzb_Latn
dtype: string
- name: bel_Cyrl
dtype: string
- name: bos_Latn
dtype: string
- name: bul_Cyrl
dtype: string
- name: ces_Latn
dtype: string
- name: hrv_Latn
dtype: string
- name: mkd_Cyrl
dtype: string
- name: slk_Latn
dtype: string
- name: slv_Latn
dtype: string
- name: srp_Cyrl
dtype: string
- name: srp_Latn
dtype: string
- name: ukr_Cyrl
dtype: string
- name: bem_Latn
dtype: string
- name: ewe_Latn
dtype: string
- name: fuc_Latn
dtype: string
- name: kin_Latn
dtype: string
- name: nde_Latn
dtype: string
- name: nya_Latn
dtype: string
- name: sna_Latn
dtype: string
- name: ven_Latn
dtype: string
- name: div_Thaa
dtype: string
- name: eus_Latn
dtype: string
- name: guj_Gujr
dtype: string
- name: kan_Knda
dtype: string
- name: mar_Deva
dtype: string
- name: nep_Deva
dtype: string
- name: pan_Guru
dtype: string
- name: sin_Sinh
dtype: string
- name: snd_Arab
dtype: string
- name: tel_Telu
dtype: string
- name: urd_Arab
dtype: string
- name: bod_Tibt
dtype: string
- name: dzo_Tibt
dtype: string
- name: khm_Khmr
dtype: string
- name: lao_Laoo
dtype: string
- name: mon_Mong
dtype: string
- name: mya_Mymr
dtype: string
- name: tha_Thai
dtype: string
- name: cat_Latn
dtype: string
- name: glg_Latn
dtype: string
- name: ita_Latn
dtype: string
- name: mlt_Latn
dtype: string
- name: ron_Latn
dtype: string
- name: cym_Latn
dtype: string
- name: gle_Latn
dtype: string
- name: hye_Armn
dtype: string
- name: kat_Geor
dtype: string
- name: sqi_Latn
dtype: string
- name: fij_Latn
dtype: string
- name: fil_Latn
dtype: string
- name: hmn_Latn
dtype: string
- name: lav_Latn
dtype: string
- name: mal_Mlym
dtype: string
- name: mlg_Latn
dtype: string
- name: mri_Latn
dtype: string
- name: msa_Latn
dtype: string
- name: smo_Latn
dtype: string
- name: tah_Latn
dtype: string
- name: ton_Latn
dtype: string
- name: yue_Hant
dtype: string
- name: zho_Hans
dtype: string
splits:
- name: test
num_bytes: 48469088
num_examples: 1997
download_size: 25260237
dataset_size: 48469088
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
tags:
- mteb
- text
NTREX is a News Test References dataset for Machine Translation Evaluation, covering translation from English into 128 languages. We select language pairs according to the M2M-100 language grouping strategy, resulting in 1916 directions.
| Task category | t2t |
| Domains | News, Written |
| Reference | https://huggingface.co/datasets/davidstap/NTREX |
Source datasets:
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("NTREXBitextMining")
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 repository.
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{federmann-etal-2022-ntrex,
address = {Online},
author = {Federmann, Christian and Kocmi, Tom and Xin, Ying},
booktitle = {Proceedings of the First Workshop on Scaling Up Multilingual Evaluation},
month = {nov},
pages = {21--24},
publisher = {Association for Computational Linguistics},
title = {{NTREX}-128 {--} News Test References for {MT} Evaluation of 128 Languages},
url = {https://aclanthology.org/2022.sumeval-1.4},
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, Loïc 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("NTREXBitextMining")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 3826252,
"number_of_characters": 988355274,
"unique_pairs": 3820263,
"min_sentence1_length": 1,
"average_sentence1_length": 129.15449296073547,
"max_sentence1_length": 773,
"unique_sentence1": 241259,
"min_sentence2_length": 1,
"average_sentence2_length": 129.15449296073547,
"max_sentence2_length": 773,
"unique_sentence2": 241259
}
}
This dataset card was automatically generated using MTEB