Replace boilerplate example code with actual huggingface repo model paths

README.md

@@ -25,7 +25,7 @@ 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 = ["Det här är en exempelmening", "Varje exempel blir konverterad"]
 
 model = SentenceTransformer('KBLab/sentence-bert-swedish-cased')
 embeddings = model.encode(sentences)
@@ -50,11 +50,11 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
+sentences = ['Det här är en exempelmening', 'Varje exempel blir konverterad']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('KBLab/sentence-bert-swedish-cased')
+model = AutoModel.from_pretrained('KBLab/sentence-bert-swedish-cased')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')