A lightweight binary toxicity classifier for Russian-language text, fine-tuned from cointegrated/rubert-tiny on the AlexSham/Toxic_Russian_Comments dataset.

The model outputs a single binary label — toxic (1) or non-toxic (0) — and is designed to be fast and small enough for real-time moderation pipelines (chat filters, comment sections, forums).

Training Approach

The full dataset was used as-is — no downsampling of the majority class.
Class imbalance was handled via class weights in CrossEntropyLoss (computed with sklearn.utils.class_weight.compute_class_weight(class_weight="balanced")), which upweights the toxic class roughly ~4.5x relative to the non-toxic class.
Trained with Hugging Face Trainer (4 epochs, batch size 64, lr 3e-5, warmup ratio 0.06), selecting the best checkpoint by F1 on the toxic class (not accuracy, which is misleading under this imbalance).

Evaluation

Evaluated on the held-out test split of AlexSham/Toxic_Russian_Comments:

Model	Toxic F1
BazarFiltr-0.1	0.907
cointegrated/rubert-tiny-toxicity	0.73

Note: cointegrated/rubert-tiny-toxicity was trained on a different dataset and under a different problem formulation (multilabel), so this comparison is indicative rather than a strictly controlled benchmark — but it reflects real-world performance on this specific data distribution.

Known Limitations

The model catches explicit profanity, insults, and threats.
It can miss disguised or "politely phrased" toxicity — including racist statements that don't use profanity or obvious slurs. Use with caution in contexts where subtle hate speech is a concern, and consider pairing with human review or additional filters for sensitive applications.

Usage

Inputs are truncated to a maximum of 128 tokens.

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "akanemind/BazarFiltr-0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "Ты вообще нормальный человек или нет?"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)

with torch.no_grad():
    logits = model(**inputs).logits
    probs = torch.softmax(logits, dim=-1)
    pred = torch.argmax(probs, dim=-1).item()

label = "toxic" if pred == 1 else "non-toxic"
print(f"{label} (p_toxic={probs[0][1]:.3f})")

Intended Use

Filtering/flagging toxic comments in Russian-language chats, forums, and social platforms
Pre-moderation triage (flag-for-review, not fully automated takedown, given the limitations above)
Research on Russian toxicity detection

Out of Scope

Non-Russian text
Fine-grained toxicity categories (this is binary, not multilabel like insult/threat/obscenity breakdowns)
Standalone moderation without human oversight for high-stakes decisions

Training Data

AlexSham/Toxic_Russian_Comments — Russian comments labeled toxic/non-toxic, with a natural class imbalance of roughly 82% non-toxic to 18% toxic.

Full evaluation details (precision/recall, confusion matrices)

Metric	BazarFiltr-0.1	rubert-tiny-toxicity
Accuracy	0.965	0.866
Toxic precision	0.86	0.57
Toxic recall	0.96	0.99
Toxic F1	0.907	0.73
Non-toxic F1	0.978	0.91

The baseline's high recall comes at the cost of precision — it flags far more non-toxic text as toxic (699 vs. 3285 false positives), which drags its F1 down despite catching nearly all toxic examples.