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:
- cardiffnlp/twitter-xlm-roberta-large-2022
pipeline_tag: text-classification
Multilingual Toxicity Classifier for 15 Languages (2025)
This is an instance of cardiffnlp/twitter-xlm-roberta-large-2022 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 | 0.7597 |
| French | fr | 0.9114 |
| Hinglish | hin | 0.7051 |
| Hebrew | he | 0.8911 |
| Japanese | ja | 0.8725 |
| Tatar | tt | 0.6542 |
How to use
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('textdetox/twitter-xlmr-toxicity-classifier')
model = AutoModelForSequenceClassification.from_pretrained('textdetox/twitter-xlmr-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.