README.md

metadata

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

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.

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)

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

@article{dementieva2024toxicity,
  title={Toxicity Classification in Ukrainian},
  author={Dementieva, Daryna and Khylenko, Valeriia and Babakov, Nikolay and Groh, Georg},
  journal={arXiv preprint arXiv:2404.17841},
  year={2024}
}