readme: ---
  language: 
  - ru
  
  tags:
  - toxic comments classification
  ---
  
  ## RuBERT-Toxic
  RuBERT-Toxic is a [RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased) model fine-tuned on [Kaggle Russian Language Toxic Comments Dataset](https://www.kaggle.com/blackmoon/russian-language-toxic-comments). You can find a detailed description of the data used and the fine-tuning process in [this article](http://doi.org/10.28995/2075-7182-2020-19-1149-1159). You can also find this information at [GitHub](https://github.com/sismetanin/toxic-comments-detection-in-russian).
  
  | System  | P | R | F<sub>1</sub> | 
  | ------------- | ------------- | ------------- | ------------- | 
  | MNB-Toxic | 87.01% | 81.22% | 83.21% |
  | M-BERT<sub>Base</sub>-Toxic | 91.19% | 91.10% | 91.15% |
  | <b>RuBERT-Toxic</b> | <b>91.91%</b> | <b>92.51%</b> | <b>92.20%</b> |
  | M-USE<sub>CNN</sub>-Toxic | 89.69% | 90.14% | 89.91% |
  | M-USE<sub>Trans</sub>-Toxic | 90.85% | 91.92% | 91.35% |
  
  We fine-tuned two versions of Multilingual Universal Sentence Encoder (M-USE), Multilingual Bidirectional Encoder Representations from Transformers (M-BERT) and RuBERT for toxic comments detection in Russian. Fine-tuned RuBERT-Toxic achieved F<sub>1</sub> = 92.20%, demonstrating the best classification score.
  
  
  ## Toxic Comments Dataset
  [Kaggle Russian Language Toxic Comments Dataset](https://www.kaggle.com/blackmoon/russian-language-toxic-comments) is the collection of Russian-language annotated comments from [2ch](https://2ch.hk/) and [Pikabu](https://pikabu.ru/), which was published on Kaggle in 2019. It consists of 14412 comments, where 4826 texts were labelled as toxic, and 9586 were labelled as non-toxic. The average length of comments is ~175 characters; the minimum length is 21, and the maximum is 7403. 
  
  ## Citation
  If you find this repository helpful, feel free to cite our publication:
  
  ```
  @INPROCEEDINGS{Smetanin2020Toxic,
    author={Sergey Smetanin},
    booktitle={Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2020”},
    title={Toxic Comments Detection in Russian},
    year={2020},
    doi={10.28995/2075-7182-2020-19-1149-1159}
  } 
  ```