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---
configs:
- config_name: default
data_files:
- split: uk
path: data/uk-*
- split: hi
path: data/hi-*
- split: zh
path: data/zh-*
- split: ar
path: data/ar-*
- split: de
path: data/de-*
- split: en
path: data/en-*
- split: ru
path: data/ru-*
- split: am
path: data/am-*
- split: es
path: data/es-*
- split: it
path: data/it-*
- split: fr
path: data/fr-*
- split: he
path: data/he-*
- split: hin
path: data/hin-*
- split: tt
path: data/tt-*
- split: ja
path: data/ja-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: uk
num_bytes: 64010
num_examples: 600
- name: hi
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num_examples: 600
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- name: ar
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- name: de
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- name: en
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- name: es
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- name: it
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- name: fr
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- name: he
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- name: hin
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- name: tt
num_bytes: 69603
num_examples: 600
- name: ja
num_bytes: 68980
num_examples: 600
download_size: 620757
dataset_size: 971060
---
**Multilingual Text Detoxification with Parallel Data (Test)**
**[May, 2025]** The full [TextDetox2025](https://pan.webis.de/clef25/pan25-web/text-detoxification.html) test set is now available!
**[2025]** For the second edition of [TextDetox2025 shared](https://pan.webis.de/clef25/pan25-web/text-detoxification.html) task, we extend to more languages: Italian, French, Hebrew, Hinglish, Japanese, and Tatar!
**[2024]** This is the multilingual parallel dataset for text detoxification prepared for [CLEF TextDetox 2024](https://pan.webis.de/clef24/pan24-web/text-detoxification.html) shared task.
For each of 9 languages, we collected 1k pairs of toxic<->detoxified instances splitted into two parts: dev (400 pairs) and test (600 pairs).
### !! This is a **test** part of Multilingual Paradetox. For the **train** part please refer to [textdetox/multilingual_paradetox](https://huggingface.co/datasets/textdetox/multilingual_paradetox)
The list of the sources for the original toxic sentences:
* English: [Jigsaw](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge), [Unitary AI Toxicity Dataset](https://github.com/unitaryai/detoxify)
* Russian: [Russian Language Toxic Comments](https://www.kaggle.com/datasets/blackmoon/russian-language-toxic-comments), [Toxic Russian Comments](https://www.kaggle.com/datasets/alexandersemiletov/toxic-russian-comments)
* Ukrainian: [Ukrainian Twitter texts](https://github.com/saganoren/ukr-twi-corpus)
* Spanish: [Detecting and Monitoring Hate Speech in Twitter](https://www.mdpi.com/1424-8220/19/21/4654), [Detoxis](https://rdcu.be/dwhxH), [RoBERTuito: a pre-trained language model for social media text in Spanish](https://aclanthology.org/2022.lrec-1.785/)
* German: [GemEval 2018, 2021](https://aclanthology.org/2021.germeval-1.1/)
* Amhairc: [Amharic Hate Speech](https://github.com/uhh-lt/AmharicHateSpeech)
* Arabic: [OSACT4](https://edinburghnlp.inf.ed.ac.uk/workshops/OSACT4/)
* Hindi: [Hostility Detection Dataset in Hindi](https://competitions.codalab.org/competitions/26654#learn_the_details-dataset), [Overview of the HASOC track at FIRE 2019: Hate Speech and Offensive Content Identification in Indo-European Languages](https://dl.acm.org/doi/pdf/10.1145/3368567.3368584?download=true)
* Italian: [AMI](https://github.com/dnozza/ami2020), [HODI](https://github.com/HODI-EVALITA/HODI_2023), [Jigsaw Multilingual Toxic Comment](https://www.kaggle.com/competitions/jigsaw-multilingual-toxic-comment-classification/overview)
* French: [FrenchToxicityPrompts](https://europe.naverlabs.com/research/publications/frenchtoxicityprompts-a-large-benchmark-for-evaluating-and-mitigating-toxicity-in-french-texts/), [Jigsaw Multilingual Toxic Comment](https://www.kaggle.com/competitions/jigsaw-multilingual-toxic-comment-classification/overview)
* Hebrew: [Hebrew Offensive Language Dataset](https://github.com/NataliaVanetik/HebrewOffensiveLanguageDatasetForTheDetoxificationProject/tree/main)
* Hinglish: [Hinglish Hate Detection](https://github.com/victor7246/Hinglish_Hate_Detection/blob/main/data/raw/trac1-dataset/hindi/agr_hi_dev.csv)
* Japanese: posts from [2chan](https://huggingface.co/datasets/p1atdev/open2ch)
* Tatar: ours.
## Citation
If you would like to acknowledge our work, please, cite the following manuscripts:
```
@inproceedings{dementieva2024overview,
title={Overview of the Multilingual Text Detoxification Task at PAN 2024},
author={Dementieva, Daryna and Moskovskiy, Daniil and Babakov, Nikolay and Ayele, Abinew Ali and Rizwan, Naquee and Schneider, Frolian and Wang, Xintog and Yimam, Seid Muhie and Ustalov, Dmitry and Stakovskii, Elisei and Smirnova, Alisa and Elnagar, Ashraf and Mukherjee, Animesh and Panchenko, Alexander},
booktitle={Working Notes of CLEF 2024 - Conference and Labs of the Evaluation Forum},
editor={Guglielmo Faggioli and Nicola Ferro and Petra Galu{\v{s}}{\v{c}}{\'a}kov{\'a} and Alba Garc{\'i}a Seco de Herrera},
year={2024},
organization={CEUR-WS.org}
}
```
```
@inproceedings{DBLP:conf/ecir/BevendorffCCDEFFKMMPPRRSSSTUWZ24,
author = {Janek Bevendorff and
Xavier Bonet Casals and
Berta Chulvi and
Daryna Dementieva and
Ashaf Elnagar and
Dayne Freitag and
Maik Fr{\"{o}}be and
Damir Korencic and
Maximilian Mayerl and
Animesh Mukherjee and
Alexander Panchenko and
Martin Potthast and
Francisco Rangel and
Paolo Rosso and
Alisa Smirnova and
Efstathios Stamatatos and
Benno Stein and
Mariona Taul{\'{e}} and
Dmitry Ustalov and
Matti Wiegmann and
Eva Zangerle},
editor = {Nazli Goharian and
Nicola Tonellotto and
Yulan He and
Aldo Lipani and
Graham McDonald and
Craig Macdonald and
Iadh Ounis},
title = {Overview of {PAN} 2024: Multi-author Writing Style Analysis, Multilingual
Text Detoxification, Oppositional Thinking Analysis, and Generative
{AI} Authorship Verification - Extended Abstract},
booktitle = {Advances in Information Retrieval - 46th European Conference on Information
Retrieval, {ECIR} 2024, Glasgow, UK, March 24-28, 2024, Proceedings,
Part {VI}},
series = {Lecture Notes in Computer Science},
volume = {14613},
pages = {3--10},
publisher = {Springer},
year = {2024},
url = {https://doi.org/10.1007/978-3-031-56072-9\_1},
doi = {10.1007/978-3-031-56072-9\_1},
timestamp = {Fri, 29 Mar 2024 23:01:36 +0100},
biburl = {https://dblp.org/rec/conf/ecir/BevendorffCCDEFFKMMPPRRSSSTUWZ24.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```