metadata
library_name: transformers
language:
- en
- fr
- it
- es
- ru
- uk
- tt
- ar
- hi
- ja
- zh
- he
- am
- de
license: openrail++
datasets:
- textdetox/multilingual_toxicity_dataset
metrics:
- f1
base_model:
- google-bert/bert-base-multilingual-cased
pipeline_tag: text-classification
tags:
- toxic
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.