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--- |
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language: |
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- en |
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- uk |
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- ru |
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- de |
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- zh |
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- am |
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- ar |
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- hi |
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- es |
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license: openrail++ |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- text-generation |
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dataset_info: |
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features: |
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- name: toxic_sentence |
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dtype: string |
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splits: |
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- name: en |
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num_bytes: 24945 |
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num_examples: 400 |
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- name: ru |
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num_bytes: 48249 |
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num_examples: 400 |
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- name: uk |
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num_bytes: 40226 |
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num_examples: 400 |
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- name: de |
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num_bytes: 44940 |
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num_examples: 400 |
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- name: es |
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num_bytes: 30159 |
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num_examples: 400 |
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- name: am |
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num_bytes: 72606 |
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num_examples: 400 |
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- name: zh |
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num_bytes: 36219 |
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num_examples: 400 |
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- name: ar |
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num_bytes: 44668 |
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num_examples: 400 |
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- name: hi |
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num_bytes: 57291 |
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num_examples: 400 |
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download_size: 257508 |
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dataset_size: 399303 |
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configs: |
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- config_name: default |
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data_files: |
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- split: en |
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path: data/en-* |
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- split: ru |
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path: data/ru-* |
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- split: uk |
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path: data/uk-* |
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- split: de |
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path: data/de-* |
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- split: es |
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path: data/es-* |
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- split: am |
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path: data/am-* |
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- split: zh |
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path: data/zh-* |
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- split: ar |
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path: data/ar-* |
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- split: hi |
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path: data/hi-* |
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--- |
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**MultiParaDetox** |
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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. |
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For each of 9 languages, we collected 1k pairs of toxic<->detoxified instances splitted into two parts: dev (400 pairs) and test (600 pairs). |
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**Now, only dev set toxic sentences are released. Dev set references and test set toxic sentences will be released later with the test phase of the competition!** |
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The list of the sources for the original toxic sentences: |
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* English: [Jigsaw](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge), [Unitary AI Toxicity Dataset](https://github.com/unitaryai/detoxify) |
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* 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) |
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* Ukrainian: [Ukrainian Twitter texts](https://github.com/saganoren/ukr-twi-corpus) |
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* 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/) |
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* German: [GemEval 2018, 2021](https://aclanthology.org/2021.germeval-1.1/) |
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* Amhairc: [Amharic Hate Speech](https://github.com/uhh-lt/AmharicHateSpeech) |
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* Arabic: [OSACT4](https://edinburghnlp.inf.ed.ac.uk/workshops/OSACT4/) |
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* 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) |
