---
language: ru
license: mit
library_name: transformers
tags:
- toxicity
- russian
- text-classification
base_model: cointegrated/rubert-tiny2
metrics:
- accuracy
- f1
- precision
- recall
datasets:
- Gloomreach/ru-toxicity-int-ml
---

# Multi-Task Toxicity Classifier (Russian)

Модель для одновременного определения трёх типов токсичности в русскоязычных текстах:
- profanity — ненормативная лексика
- threat — угрозы  
- illegal — призывы к нарушению закона

## Архитектура

Модель основана на cointegrated/rubert-tiny2 с тремя независимыми классификационными головами (Multi-Task Learning).


## Обучение

**Датасет:** [Gloomreach/ru-toxicity-int-ml](https://huggingface.co/datasets/Gloomreach/ru-toxicity-int-ml)



## Метрики качества

| Класс     | Precision | Recall   | F1-Score | Threshold |
|-----------|-----------|----------|----------|-----------|
| profanity | 0.939856  | 0.961855 | 0.950728 | 0.2       |
| threat    | 0.956522  | 0.956522 | 0.956522 | 0.4       |
| illegal   | 1.000000  | 0.600000 | 0.750000 | 0.5       |


## Использование

### Через transformers

```python
import torch
import torch.nn as nn
from transformers import AutoModel, AutoTokenizer

class MultiTaskToxicityEncoder(nn.Module):
    def __init__(self, model_name="cointegrated/rubert-tiny2"):
        super().__init__()
        self.encoder = AutoModel.from_pretrained(model_name)
        hidden_size = self.encoder.config.hidden_size
        self.profanity_head = nn.Linear(hidden_size, 1)
        self.threat_head = nn.Linear(hidden_size, 1)
        self.illegal_head = nn.Linear(hidden_size, 1)
        
    def forward(self, input_ids, attention_mask):
        outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
        cls_embedding = outputs.last_hidden_state[:, 0, :]
        return (
            self.profanity_head(cls_embedding),
            self.threat_head(cls_embedding),
            self.illegal_head(cls_embedding)
        )

#загрузка модели
model = MultiTaskToxicityEncoder.from_pretrained("Gloomreach/ru-toxicity-multi-task-encoder")
tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny2")

text = "Пример текста для проверки"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)

with torch.no_grad():
    logits = model(inputs["input_ids"], inputs["attention_mask"])
    probs = torch.sigmoid(torch.cat(logits, dim=1)).numpy()[0]

print(f"Profanity: {probs[0]:.2%}")
print(f"Threat: {probs[1]:.2%}")
print(f"Illegal: {probs[2]:.2%}")