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--- |
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library_name: transformers |
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language: |
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- en |
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- fr |
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- it |
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- es |
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- ru |
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- uk |
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- tt |
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- ar |
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- hi |
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- ja |
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- zh |
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- he |
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- am |
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- de |
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license: openrail++ |
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datasets: |
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- textdetox/multilingual_toxicity_dataset |
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metrics: |
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- f1 |
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base_model: |
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- google-bert/bert-base-multilingual-cased |
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pipeline_tag: text-classification |
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tags: |
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- toxic |
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--- |
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## Multilingual Toxicity Classifier for 15 Languages (2025) |
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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). |
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Now, the models covers 15 languages from various language families: |
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| Language | Code | F1 Score | |
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|-----------|------|---------| |
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| English | en | 0.9035 | |
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| Russian | ru | 0.9224 | |
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| Ukrainian | uk | 0.9461 | |
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| German | de | 0.5181 | |
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| Spanish | es | 0.7291 | |
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| Arabic | ar | 0.5139 | |
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| Amharic | am | 0.6316 | |
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| Hindi | hi | 0.7268 | |
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| Chinese | zh | 0.6703 | |
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| Italian | it | 0.6485 | |
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| French | fr | 0.9125 | |
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| Hinglish | hin | 0.6850 | |
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| Hebrew | he | 0.8686 | |
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| Japanese | ja | 0.8644 | |
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| Tatar | tt | 0.6170 | |
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## How to use |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained('textdetox/bert-multilingual-toxicity-classifier') |
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model = AutoModelForSequenceClassification.from_pretrained('textdetox/bert-multilingual-toxicity-classifier') |
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batch = tokenizer.encode("You are amazing!", return_tensors="pt") |
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output = model(batch) |
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# idx 0 for neutral, idx 1 for toxic |
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``` |
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## Citation |
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The model is prepared for [TextDetox 2025 Shared Task](https://pan.webis.de/clef25/pan25-web/text-detoxification.html) evaluation. |
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Citation TBD soon. |
