textdetox/glot500-toxicity-classifier

Multilingual Toxicity Classifier for 15 Languages (2025)

This is an instance of Glot500 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	0.9071
Russian	ru	0.9022
Ukrainian	uk	0.9075
German	de	0.6528
Spanish	es	0.7430
Arabic	ar	0.6207
Amharic	am	0.6676
Hindi	hi	0.7171
Chinese	zh	0.6483
Italian	it	0.5975
French	fr	0.9125
Hinglish	hin	0.7051
Hebrew	he	0.8911
Japanese	ja	0.9058
Tatar	tt	0.5834

How to use

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained('textdetox/glot500-toxicity-classifier')
model = AutoModelForSequenceClassification.from_pretrained('textdetox/glot500-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.