---
language:
- en
- ru
- uk
- de
- es
- am
- zh
- ar
- hi
- it
- fr
- he
- ja
- tt
license: openrail++
size_categories:
- 10K<n<100K
task_categories:
- text-classification
dataset_info:
  features:
  - name: text
    dtype: string
  - name: toxic
    dtype: int64
  splits:
  - name: en
    num_bytes: 411178
    num_examples: 5000
  - name: ru
    num_bytes: 710001
    num_examples: 5000
  - name: uk
    num_bytes: 630595
    num_examples: 5000
  - name: de
    num_bytes: 941017
    num_examples: 5000
  - name: es
    num_bytes: 978750
    num_examples: 5000
  - name: am
    num_bytes: 1102628
    num_examples: 5000
  - name: zh
    num_bytes: 359235
    num_examples: 5000
  - name: ar
    num_bytes: 889661
    num_examples: 5000
  - name: hi
    num_bytes: 1842662
    num_examples: 5000
  - name: it
    num_bytes: 791069
    num_examples: 5000
  - name: fr
    num_bytes: 621103
    num_examples: 5000
  - name: he
    num_bytes: 243823
    num_examples: 2011
  - name: hin
    num_bytes: 836167
    num_examples: 4363
  - name: tt
    num_bytes: 764917
    num_examples: 5000
  - name: ja
    num_bytes: 714729
    num_examples: 5000
  download_size: 6802095
  dataset_size: 11837535
configs:
- config_name: default
  data_files:
  - split: en
    path: data/en-*
  - split: ru
    path: data/ru-*
  - split: uk
    path: data/uk-*
  - split: de
    path: data/de-*
  - split: es
    path: data/es-*
  - split: am
    path: data/am-*
  - split: zh
    path: data/zh-*
  - split: ar
    path: data/ar-*
  - split: hi
    path: data/hi-*
  - 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-*
---

# Multilingual Toxicity Detection Dataset

**[2025]** We extend our binary toxicity classification dataset to **more languages**! Now also covered: Italian, French, Hebrew, Hindglish, Japanese, Tatar. The data is prepared for [TextDetox 2025](https://pan.webis.de/clef25/pan25-web/text-detoxification.html) shared task.

**[2024]** For the shared task [TextDetox 2024](https://pan.webis.de/clef24/pan24-web/text-detoxification.html), we provide a compilation of binary toxicity classification datasets for each language.
Namely, for each language, we provide 5k subparts of the datasets -- 2.5k toxic and 2.5k non-toxic samples.

The list of original sources:
* 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: [ours](https://huggingface.co/datasets/ukr-detect/ukr-toxicity-dataset)
* Spanish: [CLANDESTINO, the Spanish toxic language dataset](https://github.com/microsoft/Clandestino/tree/main)
* German: [DeTox-Dataset](https://github.com/hdaSprachtechnologie/detox), [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: []
* Hebrew: [Hebrew Offensive Language Dataset](https://github.com/NataliaVanetik/HebrewOffensiveLanguageDatasetForTheDetoxificationProject/blob/main/OLaH-dataset-filtered.xlsx)
* Hinglish: []
* Japanese: [filtered](https://huggingface.co/datasets/sobamchan/ja-toxic-text-classification-open2ch) [2chan posts](https://huggingface.co/datasets/p1atdev/open2ch) by Perspective API;
* Tatar: ours. 

All credits go to the authors of the original corpora.

## Citation
If you would like to acknowledge our work, please, cite the following manuscripts:

**[2024]**

```
@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{dementieva-etal-2024-toxicity,
    title = "Toxicity Classification in {U}krainian",
    author = "Dementieva, Daryna  and
      Khylenko, Valeriia  and
      Babakov, Nikolay  and
      Groh, Georg",
    editor = {Chung, Yi-Ling  and
      Talat, Zeerak  and
      Nozza, Debora  and
      Plaza-del-Arco, Flor Miriam  and
      R{\"o}ttger, Paul  and
      Mostafazadeh Davani, Aida  and
      Calabrese, Agostina},
    booktitle = "Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.woah-1.19/",
    doi = "10.18653/v1/2024.woah-1.19",
    pages = "244--255",
    abstract = "The task of toxicity detection is still a relevant task, especially in the context of safe and fair LMs development. Nevertheless, labeled binary toxicity classification corpora are not available for all languages, which is understandable given the resource-intensive nature of the annotation process. Ukrainian, in particular, is among the languages lacking such resources. To our knowledge, there has been no existing toxicity classification corpus in Ukrainian. In this study, we aim to fill this gap by investigating cross-lingual knowledge transfer techniques and creating labeled corpora by: (i){\textasciitilde}translating from an English corpus, (ii){\textasciitilde}filtering toxic samples using keywords, and (iii){\textasciitilde}annotating with crowdsourcing. We compare LLMs prompting and other cross-lingual transfer approaches with and without fine-tuning offering insights into the most robust and efficient baselines."
}
```

```
@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}
}
```