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+ ---
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+ language:
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+ - en
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+ tags:
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+ - toxic comments classification
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+ licenses:
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+ - cc-by-nc-sa
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+ ---
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+
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+ ## Toxicity Classification Model
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+
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+ This model is trained for toxicity classification task. The dataset used for training is the merge of the English parts of the three datasets by **Jigsaw** ([Jigsaw 2018](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge), [Jigsaw 2019](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification), [Jigsaw 2020](https://www.kaggle.com/c/jigsaw-multilingual-toxic-comment-classification)), containing around 2 million examples. We split it into two parts and fine-tune a RoBERTa model ([RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692)) on it. The classifiers perform closely on the test set of the first Jigsaw competition, reaching the **AUC-ROC** of 0.98 and **F1-score** of 0.76.
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+
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+ ## How to use
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+ ```python
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+ from transformers import RobertaTokenizer, RobertaForSequenceClassification
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+
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+ # load tokenizer and model weights
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+ tokenizer = RobertaTokenizer.from_pretrained('SkolkovoInstitute/roberta_toxicity_classifier')
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+ model = RobertaForSequenceClassification.from_pretrained('SkolkovoInstitute/roberta_toxicity_classifier')
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+
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+ # prepare the input
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+ batch = tokenizer.encode('you are amazing', return_tensors='pt')
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+
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+ # inference
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+ model(batch)
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+ ```
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+
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+ ## Licensing Information
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+
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+ [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
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+
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+ [![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
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+
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+ [cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
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+ [cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png