metadata
language:
- ru
tags:
- russian
- pretraining
license: mit
widget:
- text: '[CLS] привет [SEP] привет! [SEP] как дела? [RESPONSE_TOKEN] норм'
example_title: Dialog example 1
- text: '[CLS] привет [SEP] привет! [SEP] как дела? [RESPONSE_TOKEN] соси вола'
example_title: Dialog example 2
- text: >-
[CLS] здравствуйте товарищ [RESPONSE_TOKEN] что это за говно на тебе
надето?))
example_title: Dialog example 3
dialog-inapropriate-messages-classifier
BERT classifier from Skoltech, finetuned on contextual data with 4 labels.
Training
Skoltech/russian-inappropriate-messages was finetuned on a multiclass data with four classes
- OK label -- the message is OK in context and does not intent to offend or somehow harm the reputation of a speaker.
- Toxic label -- the message might be seen as a offensive one in given context.
- Severe toxic label -- the message is offencive, full of anger and was written to provoke a fight or any other discomfort
- Risks label -- the message touches on sensitive topics and can harm the reputation of the speaker (i.e. religion, politics)
The model was finetuned on DATASET_LINK.
Evaluation results
Model achieves the following results:
OK - F1-score | TOXIC - F1-score | SEVERE TOXIC - F1-score | RISKS - F1-score | |
---|---|---|---|---|
DATASET_TWITTER val.csv | 0.896 | 0.348 | 0.490 | 0.591 |
DATASET_GENA val.csv | 0.940 | 0.295 | 0.729 | 0.46 |
The work was done during internship at Tinkoff by Nikita Stepanov.