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--- |
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language: |
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- ru |
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tags: |
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- toxic comments classification |
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licenses: |
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- cc-by-nc-sa |
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--- |
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Bert-based classifier (finetuned from [Conversational Rubert](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational)) trained on merge of Russian Language Toxic Comments [dataset](https://www.kaggle.com/blackmoon/russian-language-toxic-comments/metadata) collected from 2ch.hk and Toxic Russian Comments [dataset](https://www.kaggle.com/alexandersemiletov/toxic-russian-comments) collected from ok.ru. |
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The datasets were merged, shuffled, and split into train, dev, test splits in 80-10-10 proportion. |
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The metrics obtained from test dataset is as follows |
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| | precision | recall | f1-score | support | |
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|:------------:|:---------:|:------:|:--------:|:-------:| |
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| 0 | 0.98 | 0.99 | 0.98 | 21384 | |
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| 1 | 0.94 | 0.92 | 0.93 | 4886 | |
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| accuracy | | | 0.97 | 26270| |
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| macro avg | 0.96 | 0.96 | 0.96 | 26270 | |
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| weighted avg | 0.97 | 0.97 | 0.97 | 26270 | |
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## How to use |
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```python |
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from transformers import BertTokenizer, BertForSequenceClassification |
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# load tokenizer and model weights |
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tokenizer = BertTokenizer.from_pretrained('SkolkovoInstitute/russian_toxicity_classifier') |
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model = BertForSequenceClassification.from_pretrained('SkolkovoInstitute/russian_toxicity_classifier') |
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# prepare the input |
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batch = tokenizer.encode('ты супер', return_tensors='pt') |
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# inference |
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model(batch) |
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``` |
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## Licensing Information |
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[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. |
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[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] |
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[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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[cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png |
