Update README.md

README.md

@@ -9,6 +9,11 @@ language:
 - zh
 - ar
 - hi
+- it
+- fr
+- he
+- ja
+- tt
 license: openrail++
 size_categories:
 - 10K<n<100K
@@ -103,24 +108,36 @@ configs:
     path: data/ja-*
 ---
 
-For the shared task [CLEF TextDetox 2024](https://pan.webis.de/clef24/pan24-web/text-detoxification.html), we provide a compilation of binary toxicity classification datasets for each language.
+# 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: our labeling based on [Ukrainian Twitter texts](https://github.com/saganoren/ukr-twi-corpus)
+* 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 toxic words lists.
+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},