Create README.md

README.md

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+---
+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 | 0.937     | 0.966  | 0.951    | 0.1       |
+| threat    | 0.957     | 0.957  | 0.957    | 0.2       |
+| illegal   | 1.000     | 0.800  | 0.889    | 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%}")