Update README.md

README.md

@@ -778,7 +778,11 @@ pip install -U sentence-transformers
 Then you can use the model like this:
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+
+sentences = [
+    "I like rainy days because they make me feel relaxed.",
+    "I don't like rainy days because they don't make me feel relaxed."
+]
 
 model = SentenceTransformer('dmlls/all-mpnet-base-v2-negation')
 embeddings = model.encode(sentences)
@@ -793,7 +797,7 @@ from transformers import AutoTokenizer, AutoModel
 import torch
 import torch.nn.functional as F
 
-#Mean Pooling - Take attention mask into account for correct averaging
+# Mean Pooling - Take attention mask into account for correct averaging
 def mean_pooling(model_output, attention_mask):
     token_embeddings = model_output[0] #First element of model_output contains all token embeddings
     input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
@@ -801,7 +805,10 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
+sentences = [
+    "I like rainy days because they make me feel relaxed.",
+    "I don't like rainy days because they don't make me feel relaxed."
+]
 
 # Load model from HuggingFace Hub
 tokenizer = AutoTokenizer.from_pretrained('dmlls/all-mpnet-base-v2-negation')
@@ -820,7 +827,6 @@ sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']
 # Normalize embeddings
 sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
 
-print("Sentence embeddings:")
 print(sentence_embeddings)
 ```