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language: ru
tags:
- toxicity
- binary-classification
- russian
- RoBERTa
license: mit
---
# Toxicity Classifier for Russian Texts
## Model description
This model is a fine-tuned version of **[ai-forever/ru-en-RoSBERTa](https://huggingface.co/ai-forever/ru-en-RoSBERTa)** for binary classification of Russian texts into **toxic** (1) and **non-toxic** (0).
It was trained on a balanced dataset of ~74k examples (split 80/10/10) derived from several public sources:
- Toxic: ok.ru comments, inappropriate messages, multilingual toxicity data.
- Non-toxic: voice assistant commands, intent datasets, QA pairs.
Only the classification head was trained; the encoder weights were frozen.
## Metrics (on test set)
| Metric | Value |
|------------|--------|
| Accuracy | 0.9992 |
| Precision | 0.9992 |
| Recall | 0.9992 |
| F1 | 0.9992 |
| MCC | 0.9984 |
| ROC AUC | 1.0000 |
Confusion matrix:
[[3713 3]
[ 3 3713]]
## How to use
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
model_name = "RooLeX/Homework2-llm-toxicity"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def predict_toxicity(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=64)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.softmax(outputs.logits, dim=-1)
return int(torch.argmax(probs)), probs[0, 1].item()
# Пример
print(predict_toxicity("Ты идиот!")) # (1, ~0.9998)
print(predict_toxicity("Здравствуйте, чем могу помочь?")) # (0, ~0.0000)
```
## Training details
- Base model: ai-forever/ru-en-RoSBERTa
- Max sequence length: 64 tokens
- Batch size: 64
- Learning rate: 2e-4
- Optimizer: AdamW
- Scheduler: CosineAnnealingWarmRestarts
- Early stopping with patience=3 on validation loss
## Limitations
- The model was trained on a limited set of domains (social media, customer support, QA). Performance may degrade on very different styles.
- It works only for Russian language.
- May misinterpret sarcasm or cultural references.
## Authors
RooLeX
## Contact
Hugging Face: RooLeX
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