Datasets:
metadata
annotations_creators:
- human-annotated
language:
- cmn
- deu
- eng
- fra
- jpn
- kor
- spa
license: other
multilinguality: translated
source_datasets:
- google-research-datasets/paws-x
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
- natural-language-inference
dataset_info:
- config_name: de
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: labels
dtype: int64
splits:
- name: test
num_bytes: 516206
num_examples: 2000
- name: validation
num_bytes: 506001
num_examples: 2000
download_size: 670801
dataset_size: 1022207
- config_name: en
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: labels
dtype: int64
splits:
- name: test
num_bytes: 486726
num_examples: 2000
- name: validation
num_bytes: 484279
num_examples: 2000
download_size: 591624
dataset_size: 971005
- config_name: es
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: labels
dtype: int64
splits:
- name: test
num_bytes: 511103
num_examples: 2000
- name: validation
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num_examples: 2000
download_size: 661323
dataset_size: 1016983
- config_name: fr
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: labels
dtype: int64
splits:
- name: test
num_bytes: 527093
num_examples: 2000
- name: validation
num_bytes: 525023
num_examples: 2000
download_size: 673584
dataset_size: 1052116
- config_name: ja
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: labels
dtype: int64
splits:
- name: test
num_bytes: 660628
num_examples: 2000
- name: validation
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num_examples: 2000
download_size: 784977
dataset_size: 1314398
- config_name: ko
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: labels
dtype: int64
splits:
- name: test
num_bytes: 554292
num_examples: 2000
- name: validation
num_bytes: 546867
num_examples: 2000
download_size: 709466
dataset_size: 1101159
- config_name: zh
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: labels
dtype: int64
splits:
- name: test
num_bytes: 466636
num_examples: 2000
- name: validation
num_bytes: 465110
num_examples: 2000
download_size: 666473
dataset_size: 931746
configs:
- config_name: de
data_files:
- split: test
path: de/test-*
- split: validation
path: de/validation-*
- config_name: en
data_files:
- split: test
path: en/test-*
- split: validation
path: en/validation-*
- config_name: es
data_files:
- split: test
path: es/test-*
- split: validation
path: es/validation-*
- config_name: fr
data_files:
- split: test
path: fr/test-*
- split: validation
path: fr/validation-*
- config_name: ja
data_files:
- split: test
path: ja/test-*
- split: validation
path: ja/validation-*
- config_name: ko
data_files:
- split: test
path: ko/test-*
- split: validation
path: ko/validation-*
- config_name: zh
data_files:
- split: test
path: zh/test-*
- split: validation
path: zh/validation-*
tags:
- mteb
- text
{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification
| Task category | t2t |
| Domains | Web, Encyclopaedic, Written |
| Reference | https://arxiv.org/abs/1908.11828 |
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("PawsXPairClassification")
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.
@misc{yang2019pawsx,
archiveprefix = {arXiv},
author = {Yinfei Yang and Yuan Zhang and Chris Tar and Jason Baldridge},
eprint = {1908.11828},
primaryclass = {cs.CL},
title = {PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification},
year = {2019},
}
@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("PawsXPairClassification")
desc_stats = task.metadata.descriptive_stats
{}
This dataset card was automatically generated using MTEB