Update README.md

README.md

@@ -34,7 +34,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["Una ragazza si acconcia i capelli.", "Una ragazza si sta spazzolando i capelli."]
 
-model = SentenceTransformer('nickprock/sentence-bert-base-italian-xxl-cased')
+model = SentenceTransformer('nickprock/sentence-bert-base-italian-xxl-uncased')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -60,8 +60,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['Una ragazza si acconcia i capelli.', 'Una ragazza si sta spazzolando i capelli.']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('nickprock/sentence-bert-base-italian-xxl-cased')
-model = AutoModel.from_pretrained('nickprock/sentence-bert-base-italian-xxl-cased')
+tokenizer = AutoTokenizer.from_pretrained('nickprock/sentence-bert-base-italian-xxl-uncased')
+model = AutoModel.from_pretrained('nickprock/sentence-bert-base-italian-xxl-uncased')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')