add the link to Github

README.md

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 Mutual Implication Score is a symmetric measure of text semantic similarity
 based on a RoBERTA model pretrained for natural language inference
-and fine-tuned on paraphrases dataset. 
+and fine-tuned on a paraphrase detection dataset. 
 
-It is **particularly useful for paraphrases detection**, but can also be applied to other semantic similarity tasks, such as content similarity scoring in text style transfer.
+The code for inference and evaluation of the model is available [here](https://github.com/skoltech-nlp/mutual_implication_score).
+
+This measure is **particularly useful for paraphrase detection**, but can also be applied to other semantic similarity tasks, such as content similarity scoring in text style transfer.
 
 ## How to use
 The following snippet illustrates code usage:
@@ -35,21 +37,21 @@ print(scores)
 
 ## Model details
 
-We slightly modify [RoBERTa-Large NLI](https://huggingface.co/ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli) model architecture (see the scheme below) and fine-tune it with [QQP](https://www.kaggle.com/c/quora-question-pairs) paraphrases dataset.
+We slightly modify the [RoBERTa-Large NLI](https://huggingface.co/ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli) model architecture (see the scheme below) and fine-tune it with [QQP](https://www.kaggle.com/c/quora-question-pairs) paraphrase dataset.
 
 ![alt text](https://huggingface.co/SkolkovoInstitute/Mutual_Implication_Score/raw/main/MIS.jpg)
 
 ## Performance on Text Style Transfer and Paraphrase Detection tasks
 
-This measure was developed in terms of large scale comparison of different measures on text style transfer and paraphrases datasets.
+This measure was developed in terms of large scale comparison of different measures on text style transfer and paraphrase datasets.
 
 <img src="https://huggingface.co/SkolkovoInstitute/Mutual_Implication_Score/raw/main/corr_main.jpg" alt="drawing" width="1000"/>
 
 The scheme above shows the correlations of measures of different classes with human judgments on paraphrase and text style transfer datasets. The text above each dataset indicates the best-performing measure. The rightmost columns show the mean performance of measures across the datasets.
 
-MIS outperforms all measures on paraphrases detection task and performs on par with top measures on text style transfer task. 
+MIS outperforms all measures on the paraphrase detection task and performs on par with top measures on the text style transfer task. 
 
-To learn more refer to our article: [A large-scale computational study of content preservation measures for text style transfer and paraphrase generation](https://aclanthology.org/2022.acl-srw.23/)
+To learn more, refer to our article: [A large-scale computational study of content preservation measures for text style transfer and paraphrase generation](https://aclanthology.org/2022.acl-srw.23/)
 
 ## Citations
 If you find this repository helpful, feel free to cite our publication: