textdetox/bert-multilingual-toxicity-classifier

Multilingual Toxicity Classifier for 15 Languages (2025)

This is an instance of bert-base-multilingual-cased that was fine-tuned on binary toxicity classification task based on our updated (2025) dataset textdetox/multilingual_toxicity_dataset.

Now, the models covers 15 languages from various language families:

Language	Code	F1 Score
English	en	0.9035
Russian	ru	0.9224
Ukrainian	uk	0.9461
German	de	0.5181
Spanish	es	0.7291
Arabic	ar	0.5139
Amharic	am	0.6316
Hindi	hi	0.7268
Chinese	zh	0.6703
Italian	it	0.6485
French	fr	0.9125
Hinglish	hin	0.6850
Hebrew	he	0.8686
Japanese	ja	0.8644
Tatar	tt	0.6170

How to use

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained('textdetox/bert-multilingual-toxicity-classifier')
model = AutoModelForSequenceClassification.from_pretrained('textdetox/bert-multilingual-toxicity-classifier')

batch = tokenizer.encode("You are amazing!", return_tensors="pt")

output = model(batch)
# idx 0 for neutral, idx 1 for toxic

Citation

The model is prepared for TextDetox 2025 Shared Task evaluation.

Citation TBD soon.