Update README.md

README.md

@@ -35,7 +35,7 @@ import torch
 from transformers import AutoModel, AutoTokenizer
 
 # Load swissBERT for sentence embeddings model
-model_name = "jgrosjean-mathesis/swissbert-for-sentence-embeddings"
+model_name = "jgrosjean-mathesis/sentence-swissbert"
 model = AutoModel.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
@@ -161,7 +161,7 @@ Note: For French, Italian and Romansh, the training data remains in German, whil
 
 ### Results
 
-Sentence SwissBERT achieves comparable results as the best-performing multilingual Sentence-BERT model (distiluse-base-multilingual-cased) and always outperforms it for German and Romansh.
+Sentence SwissBERT achieves comparable results as the best-performing multilingual Sentence-BERT model (distiluse-base-multilingual-cased) and outperforms it for German and Romansh.
 
 | Evaluation task        |Swissbert |           |Sentence Swissbert|           |Sentence-BERT|           |
 |------------------------|----------|-----------|------------------|-----------|-------------|-----------|