Update README.md

README.md

@@ -12,12 +12,20 @@ Full credits go to [Mohamad Merchant](https://twitter.com/mohmadmerchant1)
 
 Reproduced by [Vu Minh Chien](https://www.linkedin.com/in/vumichien/)
 
-Motivation: Semantic Similarity determines how similar two sentences are, in terms of what they mean.  The fine-tuned BERT model takes two sentences as inputs and outputs a similarity score for these two sentences.
+Motivation: Semantic Similarity determines how similar two sentences are, in terms of their meaning. In this tutorial, we can fine-tune BERT model and use it to predict the similarity score for two sentences.
 
 ## Training and evaluation data
 
 This example demonstrates the use of SNLI (Stanford Natural Language Inference) Corpus to predict semantic sentence similarity with Transformers.
 
+Here are the "similarity" label values in SNLI dataset:
+
+- Contradiction: The sentences share no similarity.
+
+- Entailment: The sentences have a similar meaning.
+
+- Neutral: The sentences are neutral.
+
 ## Training procedure
 
 ### Training hyperparameters