Update README.md

README.md

@@ -10,14 +10,42 @@ 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.
 
 
-# Usage
-
+# Model Usage
 
 ```shell
-tokenizer = AutoTokenizer.from_pretrained("sap-ai-research/<----Enter Model Name---->")
+from transformers import AutoTokenizer, AutoModel
+
+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
+
+sentences = [
+    "This is a sentence for testing miCSE.", 
+    "This is yet another test sentence for the mutual information Contrastive Sentence Embeddings model."
+]
+
+batch = tokenizer.batch_encode_plus(
+                sentences,
+                return_tensors='pt',
+                padding=True,
+                max_length=max_length,
+                truncation=True
+            )
+
+# Compute the embeddings
+
+outputs = model(**batch, output_hidden_states=True, return_dict=True)
+
+embeddings = outputs.last_hidden_state[:,0]
 
-model = AutoModelWithLMHead.from_pretrained("sap-ai-research/<----Enter Model Name---->")
 ```
+
+
 # Benchmark
 
 Model results on SentEval Benchmark: