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---
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](https://huggingface.co/google-bert/bert-base-multilingual-cased) that was fine-tuned on binary toxicity classification task based on our updated (2025) dataset [textdetox/multilingual_toxicity_dataset](https://huggingface.co/datasets/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
```python
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](https://pan.webis.de/clef25/pan25-web/text-detoxification.html) evaluation.
```
@inproceedings{dementieva2025overview,
title={Overview of the Multilingual Text Detoxification Task at PAN 2025},
author={Dementieva, Daryna and
Protasov, Vitaly and
Babakov, Nikolay and
Rizwan, Naquee and
Alimova, Ilseyar and
Brune, Caroline and
Konovalov, Vasily and
Muti, Arianna and
Liebeskind, Chaya and
Litvak, Marina and
Nozza, Debora, and
Shah Khan, Shehryaar and
Takeshita, Sotaro and
Vanetik, Natalia and
Ayele, Abinew Ali and
Schneider, Frolian and
Wang, Xintog and
Yimam, Seid Muhie and
Elnagar, Ashraf and
Mukherjee, Animesh and
Panchenko, Alexander},
booktitle={Working Notes of CLEF 2025 -- Conference and Labs of the Evaluation Forum},
editor={Guglielmo Faggioli and Nicola Ferro and Paolo Rosso and Damiano Spina},
month = sep,
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
site = {Vienna, Austria},
url = {https://ceur-ws.org/Vol-4038/paper_278.pdf},
year = 2025
}
``` |