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  - text: "Ти неймовірна!"
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  ---
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- Binary toxicity classifier for Ukrainian.
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  This is the fine-tuned on the downstream task ["Geotrend/distilbert-base-uk-cased"](https://huggingface.co/Geotrend/distilbert-base-uk-cased) instance.
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- The evaluation metrics are:
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  **Precision**: 0.9242
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  **Recall**: 0.9225
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  **F1**: 0.9224
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- The training and evaluation data will be clarified later.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - text: "Ти неймовірна!"
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  ---
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+ ## Binary toxicity classifier for Ukrainian.
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  This is the fine-tuned on the downstream task ["Geotrend/distilbert-base-uk-cased"](https://huggingface.co/Geotrend/distilbert-base-uk-cased) instance.
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+ The evaluation metrics for binary toxicity classification are:
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  **Precision**: 0.9242
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  **Recall**: 0.9225
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  **F1**: 0.9224
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+ The training and evaluation data will be clarified later.
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+
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+ ## How to use
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
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+
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+ # load tokenizer and model weights
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+ tokenizer = AutoTokenizer.from_pretrained('dardem/distilbert-base-uk-cased-toxicity')
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+ model = AutoModelForSequenceClassification.from_pretrained('dardem/distilbert-base-uk-cased-toxicity')
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+
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+ # prepare the input
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+ batch = tokenizer.encode('Ти неймовірна!', return_tensors='pt')
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+
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+ # inference
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+ model(batch)
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+ ```