Update README.md

README.md

@@ -1,36 +1,7 @@
----
-language:
-- en
-tags:
-- toxic comments classification
-licenses:
-- cc-by-nc-sa
----
+This model is trained by NLP_team for the Advanced NLP course, 2022.
+The model was trained for the paper [Text Detoxification using Large Pre-trained Neural Models](https://arxiv.org/abs/1911.00536).
 
 ## Toxicity Classification Model
 
 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.
 
-## How to use
-```python
-from transformers import RobertaTokenizer, RobertaForSequenceClassification
-
-# load tokenizer and model weights
-tokenizer = RobertaTokenizer.from_pretrained('SkolkovoInstitute/roberta_toxicity_classifier')
-model = RobertaForSequenceClassification.from_pretrained('SkolkovoInstitute/roberta_toxicity_classifier')
-
-# 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