Create README.md

README.md

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+---
+language: 
+  - ru
+tags:
+- sentence-similarity
+- text-classification
+datasets:
+- merionum/ru_paraphraser
+- RuPAWS
+---
+
+
+This is a cross-encoder model trained to predict semantic equivalence of two Russian sentences. 
+
+It classifies text pairs as paraphrases (class 1) or non-paraphrases (class 0). Its scores can be used as a metric of content preservation for paraphrasing or text style transfer.
+
+It is a [sberbank-ai/ruRoberta-large](https://huggingface.co/sberbank-ai/ruRoberta-large) model fine-tuned on a union of 3 datasets: 
+1. `RuPAWS`: https://github.com/ivkrotova/rupaws_dataset based on Quora and QQP;
+2. `ru_paraphraser`: https://huggingface.co/merionum/ru_paraphraser;
+3. Results of the manual check of content preservation for the [RUSSE-2022](https://www.dialog-21.ru/media/5755/dementievadplusetal105.pdf) text detoxification dataset collection  (`content_5.tsv`). 
+
+The task was formulated as binary classification: whether the two sentences have the same meaning (1) or different (0). 
+
+The table shows the training dataset size after duplication (joining `text1 + text2` and `text2 + text1` pairs): 
+
+source \ label	| 0	| 1
+-- | -- | --
+detox |	1412|	3843
+paraphraser	|5539	| 1688
+rupaws_qqp	|1112 |	792
+rupaws_wiki	|3526	| 2166
+
+The model was trained with Adam optimizer and the following hyperparameters: 
+
+```
+learning_rate = 1e-5
+batch_size = 8
+gradient_accumulation_steps = 4
+n_epochs = 3
+max_grad_norm = 1.0
+```
+
+After training, the model had the following ROC AUC scores on the test sets:
+set | ROC AUC
+- | -
+detox         | 0.857112
+paraphraser   | 0.858465
+rupaws_qqp    | 0.859195
+rupaws_wiki   | 0.906121