Update README.md

README.md

@@ -10,6 +10,10 @@ Language model of the pre-print arXiv paper titled: "_**miCSE**: Mutual Informat
 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. Sentence representations correspond to the embedding of the _**[CLS]**_ token.
 
 
+# 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.
+
+
 # Model Usage
 
 ```shell
@@ -20,7 +24,6 @@ tokenizer = AutoTokenizer.from_pretrained("sap-ai-research/miCSE")
 
 model = AutoModel.from_pretrained("sap-ai-research/miCSE")
 
-
 # Encoding of sentences in a list with a predefined maximum lengths of tokens (max_length)
 
 max_length = 32