Update README.md

README.md

@@ -7,11 +7,11 @@ Language model of the pre-print arXiv paper titled: "_**miCSE**: Mutual Informat
 
 
 # Brief Model Description
-The **miCSE** language model is trained for sentence similarity computation. Training the model imposes alignment between the attention pattern of different views (embeddings of augmentations) during contrastive learning. Learning sentence embeddings with **miCSE** entails enforcing the syntactic consistency across augmented views for every single sentence, making contrastive self-supervised learning more sample efficient. This is achieved by regularizing the attention distribution. Regularizing the attention space enables learning representation in self-supervised fashion even when the training corpus is comparatively small. 
+The **miCSE** language model is trained for sentence similarity computation. Training the model imposes alignment between the attention pattern of different views (embeddings of augmentations) during contrastive learning. Learning sentence embeddings with **miCSE** entails enforcing the syntactic consistency across augmented views for every single sentence, making contrastive self-supervised learning more sample efficient. This is achieved by regularizing the attention distribution. Regularizing the attention space enables learning representation in self-supervised fashion even when the training corpus is comparatively small. This is particularly interesting for real-world NLP applications, where training data is significantly smaller thank Wikipedia.
 
 
 # Intended Use
-The model intended to be used for encoding sentences or short paragraphs. Given an input text, the model produces a vector embedding, which captures the semantics. The embedding can be used for numerous tasks, e.g., **retrieval**, **clustering** or **sentence similarity** comparison (see example below) .Sentence representations correspond to the embedding of the _**[CLS]**_ token.
+The model intended to be used for encoding sentences or short paragraphs. Given an input text, the model produces a vector embedding, which captures the semantics. The embedding can be used for numerous tasks, e.g., **retrieval**, **clustering** or **sentence similarity** comparison (see example below). Sentence representations correspond to the embedding of the _**[CLS]**_ token.
 
 
 # Model Usage