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--- |
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license: openrail++ |
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dataset_info: |
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features: |
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- name: text |
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dtype: string |
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- name: tags |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 2105604 |
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num_examples: 12682 |
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- name: validation |
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num_bytes: 705759 |
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num_examples: 4227 |
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- name: test |
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num_bytes: 710408 |
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num_examples: 4214 |
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download_size: 2073133 |
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dataset_size: 3521771 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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- split: test |
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path: data/test-* |
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--- |
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## Ukrainian Toxicity Dataset |
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This is the first of its kind toxicity classification dataset for the Ukrainian language. |
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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)) |
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## Dataset formation: |
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1. Filtering Ukrainian tweets so that only tweets containing toxic language remain with toxic keywords. Source data: https://github.com/saganoren/ukr-twi-corpus |
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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 |
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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. |
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Labels: 0 - non-toxic, 1 - toxic. |
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## Load dataset: |
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``` |
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from datasets import load_dataset |
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dataset = load_dataset("ukr-detect/ukr-toxicity-dataset") |
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``` |
