README.md

---
language:
- ru
license: apache-2.0
tags:
- text-classification
- toxicity
- russian
- rubert-tiny2
- pytorch
- transformers
metrics:
- f1
- precision
- recall
- accuracy
model-index:
- name: rubert-tiny2-toxic-detection
  results:
  - task:
      type: text-classification
      name: Text Classification
    metrics:
    - type: f1
      value: 0.9535
      name: Toxic F1
    - type: precision
      value: 0.9647
      name: Toxic Precision
    - type: recall
      value: 0.9425
      name: Toxic Recall
---

# rubert-tiny2-toxic-detector

A lightweight and high-performance model for detecting toxic comments and messages in Russian. Fine-tuned on top of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) using threshold optimization.

## Test Set Metrics

Evaluated on an independent test set of **102,308** samples (**80/10/10 split**).

* **Optimal Threshold**: `0.70` (determined on the validation set).

| Class        | Precision | Recall | F1-Score | Support |
|:-------------|:---------:|:------:|:--------:|:-------:|
| Normal (0)   |  0.9997   | 0.9998 |  0.9998  | 101,786 |
| Toxic (1)    |  0.9647   | 0.9425 |  0.9535  |   522   |
|              |           |        |          |         |
| Accuracy     |           |        |  0.9995  | 102,308 |
| Macro Avg    |  0.9822   | 0.9712 |  0.9766  | 102,308 |
| Weighted Avg |  0.9995   | 0.9995 |  0.9995  | 102,308 |

## Quick Start / Usage

```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

MODEL_NAME = "KvaytG/rubert-tiny2-toxic-detector"
THRESHOLD = 0.70

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
model.eval()

texts = [
    "Привет! Как твои дела?"
]

inputs = tokenizer(texts, padding=True, truncation=True, max_length=128, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.sigmoid(outputs.logits.squeeze(-1)).cpu().numpy()

for text, prob in zip(texts, probs):
    is_toxic = bool(prob > THRESHOLD)
    print(f"Text: '{text}'")
    print(f"  Toxicity probability: {prob:.4f} | Is toxic: {is_toxic}\n")
```

## Training Details & Hyperparameters

* **Base Model**: [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2)
* **Dataset Size**: 1,023,075 rows (80% Train / 10% Val / 10% Test)
* **Loss Function**: Binary Focal Loss (`alpha=0.75`, `gamma=2.0`)
* **Optimizer**: AdamW (`learning_rate=3e-5`, `weight_decay=0.01`)
* **LR Scheduler**: Linear Schedule with Warmup (10% warmup steps)
* **Batch Size**: 64
* **Max Sequence Length**: 128
* **Epochs**: 3
* **Mixed Precision**: PyTorch AMP (Automatic Mixed Precision)

## License

This model is released under the **Apache License 2.0**.

## Citation

```bibtex
@misc{kvaytg_rubert_tiny2_toxic_detector,
  author       = {KvaytG},
  title        = {RuBERT-tiny2 Toxic Text Detector},
  year         = {2026},
  publisher    = {Hugging Face},
  journal      = {Hugging Face Models},
  url          = {https://huggingface.co/KvaytG/rubert-tiny2-toxic-detector},
  note         = {High-performance lightweight toxic text detector for Russian language}
}
```