--- annotations_creators: - expert-annotated language: - ara - cmn - deu - eng - fra - ita - jpn - kor - nld - ron license: cc-by-nc-nd-4.0 multilinguality: multilingual task_categories: - translation task_ids: [] dataset_info: - config_name: ar-en features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 241206 num_examples: 888 download_size: 140326 dataset_size: 241206 - config_name: de-en features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 210975 num_examples: 888 download_size: 132175 dataset_size: 210975 - config_name: en-ar features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 241206 num_examples: 888 download_size: 140326 dataset_size: 241206 - config_name: en-de features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 210975 num_examples: 888 download_size: 132175 dataset_size: 210975 - config_name: en-fr features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 207579 num_examples: 890 download_size: 129502 dataset_size: 207579 - config_name: en-it features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 200023 num_examples: 929 download_size: 127687 dataset_size: 200023 - config_name: en-ja features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 208124 num_examples: 871 download_size: 128549 dataset_size: 208124 - config_name: en-ko features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 219295 num_examples: 879 download_size: 134436 dataset_size: 219295 - config_name: en-nl features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 197814 num_examples: 1003 download_size: 128499 dataset_size: 197814 - config_name: en-ro features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 205028 num_examples: 914 download_size: 131395 dataset_size: 205028 - config_name: en-zh features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 202537 num_examples: 879 download_size: 128895 dataset_size: 202537 - config_name: fr-en features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 207579 num_examples: 890 download_size: 129502 dataset_size: 207579 - config_name: it-en features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 200023 num_examples: 929 download_size: 127687 dataset_size: 200023 - config_name: it-nl features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 197774 num_examples: 1001 download_size: 129302 dataset_size: 197774 - config_name: it-ro features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 204989 num_examples: 914 download_size: 133564 dataset_size: 204989 - config_name: ja-en features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 208124 num_examples: 871 download_size: 128549 dataset_size: 208124 - config_name: ko-en features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 219295 num_examples: 879 download_size: 134436 dataset_size: 219295 - config_name: nl-en features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 197814 num_examples: 1003 download_size: 128499 dataset_size: 197814 - config_name: nl-it features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 197774 num_examples: 1001 download_size: 129302 dataset_size: 197774 - config_name: nl-ro features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 202380 num_examples: 913 download_size: 131434 dataset_size: 202380 - config_name: ro-en features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 205028 num_examples: 914 download_size: 131395 dataset_size: 205028 - config_name: ro-it features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 204989 num_examples: 914 download_size: 133564 dataset_size: 204989 - config_name: ro-nl features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 202380 num_examples: 913 download_size: 131434 dataset_size: 202380 - config_name: zh-en features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: validation num_bytes: 202537 num_examples: 879 download_size: 128895 dataset_size: 202537 configs: - config_name: ar-en data_files: - split: validation path: ar-en/validation-* - config_name: de-en data_files: - split: validation path: de-en/validation-* - config_name: en-ar data_files: - split: validation path: en-ar/validation-* - config_name: en-de data_files: - split: validation path: en-de/validation-* - config_name: en-fr data_files: - split: validation path: en-fr/validation-* - config_name: en-it data_files: - split: validation path: en-it/validation-* - config_name: en-ja data_files: - split: validation path: en-ja/validation-* - config_name: en-ko data_files: - split: validation path: en-ko/validation-* - config_name: en-nl data_files: - split: validation path: en-nl/validation-* - config_name: en-ro data_files: - split: validation path: en-ro/validation-* - config_name: en-zh data_files: - split: validation path: en-zh/validation-* - config_name: fr-en data_files: - split: validation path: fr-en/validation-* - config_name: it-en data_files: - split: validation path: it-en/validation-* - config_name: it-nl data_files: - split: validation path: it-nl/validation-* - config_name: it-ro data_files: - split: validation path: it-ro/validation-* - config_name: ja-en data_files: - split: validation path: ja-en/validation-* - config_name: ko-en data_files: - split: validation path: ko-en/validation-* - config_name: nl-en data_files: - split: validation path: nl-en/validation-* - config_name: nl-it data_files: - split: validation path: nl-it/validation-* - config_name: nl-ro data_files: - split: validation path: nl-ro/validation-* - config_name: ro-en data_files: - split: validation path: ro-en/validation-* - config_name: ro-it data_files: - split: validation path: ro-it/validation-* - config_name: ro-nl data_files: - split: validation path: ro-nl/validation-* - config_name: zh-en data_files: - split: validation path: zh-en/validation-* tags: - mteb - text ---

IWSLT2017BitextMining

An MTEB dataset
Massive Text Embedding Benchmark
The IWSLT 2017 Multilingual Task addresses text translation, including zero-shot translation, with a single MT system across all directions including English, German, Dutch, Italian and Romanian. | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | Non-fiction, Fiction, Written | | Reference | https://aclanthology.org/2017.iwslt-1.1/ | ## 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_tasks(["IWSLT2017BitextMining"]) 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](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{cettolo-etal-2017-overview, address = {Tokyo, Japan}, author = {Cettolo, Mauro and Federico, Marcello and Bentivogli, Luisa and Niehues, Jan and St{\"u}ker, Sebastian and Sudoh, Katsuhito and Yoshino, Koichiro and Federmann, Christian}, booktitle = {Proceedings of the 14th International Conference on Spoken Language Translation}, editor = {Sakti, Sakriani and Utiyama, Masao}, month = dec # { 14-15}, pages = {2--14}, publisher = {International Workshop on Spoken Language Translation}, title = {Overview of the {IWSLT} 2017 Evaluation Campaign}, url = {https://aclanthology.org/2017.iwslt-1.1}, year = {2017}, } @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: ```python import mteb task = mteb.get_task("IWSLT2017BitextMining") desc_stats = task.metadata.descriptive_stats ``` ```json { "validation": { "num_samples": 21938, "number_of_characters": 4256244, "unique_pairs": 21840, "min_sentence1_length": 2, "average_sentence1_length": 97.0061992889051, "max_sentence1_length": 521, "unique_sentence1": 11563, "min_sentence2_length": 2, "average_sentence2_length": 97.0061992889051, "max_sentence2_length": 521, "unique_sentence2": 11563 } } ```
--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*