Update README.md

README.md

@@ -73,9 +73,9 @@ pip install -U sentence-transformers
 Then you can use the model like this:
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["Cat scratch injury", "Cat scratch disease", "Bartonellosis"]
+sentences = ["wond door kattenscrab", "kattenkrabziekte", "bartonellosis"]
 
-model = SentenceTransformer('FremyCompany/BioLORD-2023-M')
+model = SentenceTransformer('FremyCompany/BioLORD-2023-M-Dutch-InContext-v1 ')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -94,10 +94,10 @@ def mean_pooling(model_output, attention_mask):
     return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
 
 # Sentences we want sentence embeddings for
-sentences = ["Cat scratch injury", "Cat scratch disease", "Bartonellosis"]
+sentences = ["wond door kattenscrab", "kattenkrabziekte", "bartonellosis"]
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('FremyCompany/BioLORD-2023-M')
+tokenizer = AutoTokenizer.from_pretrained('FremyCompany/BioLORD-2023-M-Dutch-InContext-v1 ')
 model = AutoModel.from_pretrained('FremyCompany/BioLORD-2023-M')
 
 # Tokenize sentences