--- license: openrail++ dataset_info: features: - name: text dtype: string - name: tags dtype: float64 splits: - name: train num_bytes: 2105604 num_examples: 12682 - name: validation num_bytes: 705759 num_examples: 4227 - name: test num_bytes: 710408 num_examples: 4214 download_size: 2073133 dataset_size: 3521771 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- ## Ukrainian Toxicity Dataset (Semi-natural) This is the first of its kind toxicity classification dataset for the Ukrainian language. The datasets was obtained semi-automatically by toxic keywords filtering. For manually collected datasets with crowdsourcing, please, check [textdetox/multilingual_toxicity_dataset](https://huggingface.co/datasets/textdetox/multilingual_toxicity_dataset). Due to the subjective nature of toxicity, definitions of toxic language will vary. We include items that are commonly referred to as vulgar or profane language. ([NLLB paper](https://arxiv.org/pdf/2207.04672.pdf)) ## Dataset formation: 1. Filtering Ukrainian tweets so that only tweets containing toxic language remain with toxic keywords. Source data: https://github.com/saganoren/ukr-twi-corpus 2. Non-toxic sentences were obtained from a previous dataset of tweets as well as sentences from news and fiction from UD Ukrainian IU: https://universaldependencies.org/treebanks/uk_iu/index.html 3. After that, the dataset was split into a train-test-val and all data were balanced both by the toxic/non-toxic criterion and by data source. Labels: 0 - non-toxic, 1 - toxic. ## Load dataset: ``` from datasets import load_dataset dataset = load_dataset("ukr-detect/ukr-toxicity-dataset") ``` ## Citation ``` @inproceedings{dementieva-etal-2025-cross, title = "Cross-lingual Text Classification Transfer: The Case of {U}krainian", author = "Dementieva, Daryna and Khylenko, Valeriia and Groh, Georg", editor = "Rambow, Owen and Wanner, Leo and Apidianaki, Marianna and Al-Khalifa, Hend and Eugenio, Barbara Di and Schockaert, Steven", booktitle = "Proceedings of the 31st International Conference on Computational Linguistics", month = jan, year = "2025", address = "Abu Dhabi, UAE", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2025.coling-main.97/", pages = "1451--1464", abstract = "Despite the extensive amount of labeled datasets in the NLP text classification field, the persistent imbalance in data availability across various languages remains evident. To support further fair development of NLP models, exploring the possibilities of effective knowledge transfer to new languages is crucial. Ukrainian, in particular, stands as a language that still can benefit from the continued refinement of cross-lingual methodologies. Due to our knowledge, there is a tremendous lack of Ukrainian corpora for typical text classification tasks, i.e., different types of style, or harmful speech, or texts relationships. However, the amount of resources required for such corpora collection from scratch is understandable. In this work, we leverage the state-of-the-art advances in NLP, exploring cross-lingual knowledge transfer methods avoiding manual data curation: large multilingual encoders and translation systems, LLMs, and language adapters. We test the approaches on three text classification tasks{---}toxicity classification, formality classification, and natural language inference (NLI){---}providing the {\textquotedblleft}recipe{\textquotedblright} for the optimal setups for each task." } ``` and ``` @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.", } ```