Datasets:
metadata
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
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:
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.
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{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:
import mteb
task = mteb.get_task("IWSLT2017BitextMining")
desc_stats = task.metadata.descriptive_stats
{
"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