README.md

---
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
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  - name: nno_Latn
    dtype: string
  - name: nob_Latn
    dtype: string
  - name: swe_Latn
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  - name: amh_Ethi
    dtype: string
  - name: hau_Latn
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  - name: ibo_Latn
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  - name: nso_Latn
    dtype: string
  - name: orm_Ethi
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  - name: som_Latn
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  - 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
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->

<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
  <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">NTREXBitextMining</h1>
  <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
  <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>

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:
- [mteb/NTREX](https://huggingface.co/datasets/mteb/NTREX)


## How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

```python
import mteb

task = mteb.get_task("NTREXBitextMining")
evaluator = mteb.MTEB([task])

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```

<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repository](https://github.com/embeddings-benchmark/mteb).

## Citation

If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).

```bibtex

@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
<details>
  <summary> Dataset Statistics</summary>

The following code contains the descriptive statistics from the task. These can also be obtained using:

```python
import mteb

task = mteb.get_task("NTREXBitextMining")

desc_stats = task.metadata.descriptive_stats
```

```json
{
    "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
    }
}
```

</details>

---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*