--- 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 ---

NTREXBitextMining

An MTEB dataset
Massive Text Embedding Benchmark
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) ``` 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
Dataset Statistics 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 } } ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*