Create README.md

README.md

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+--
+language:
+- en
+tags:
+- toxic comments classification
+licenses:
+- cc-by-nc-sa
+---
+## Toxicity Classification Model (but for the first part of the data)
+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. THIS MODEL WAS FINE-TUNED ON THE FIRST PART. 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.
+## How to use
+```python
+from transformers import RobertaTokenizer, RobertaForSequenceClassification
+# load tokenizer and model weights, but be careful, here we need to use auth token
+tokenizer = RobertaTokenizer.from_pretrained('SkolkovoInstitute/roberta_toxicity_classifier', use_auth_token=True)
+model = RobertaForSequenceClassification.from_pretrained('SkolkovoInstitute/roberta_toxicity_classifier', use_auth_token=True)
+# prepare the input
+batch = tokenizer.encode('you are amazing', return_tensors='pt')
+# inference
+model(batch)
+```
+## Licensing Information
+[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
+[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
+[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
+[cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png