textdetox
/

bert-multilingual-toxicity-classifier

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 ---
 library_name: transformers
-tags: []
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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
 ---
+## 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   | 0.9225  |
+| Russian   | ru   | 0.9525  |
+| Ukrainian | uk   | 0.96    |
+| German    | de   | 0.7325  |
+| Spanish   | es   | 0.7125  |
+| Arabic    | ar   | 0.6625  |
+| Amharic   | am   | 0.5575  |
+| Hindi     | hi   | 0.9725  |
+| Chinese   | zh   | 0.9175  |
+| Italian   | it   | 0.5864  |
+| French    | fr   | 0.9235  |
+| Hinglish  | hin  | 0.61    |
+| Hebrew    | he   | 0.8775  |
+| Japanese  | ja   | 0.8773  |
+| Tatar     | tt   | 0.5744  |
+## 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.
+Citation TBD soon.