Update README.md

README.md

@@ -11,7 +11,7 @@ tags:
 - transformers
 ---
 
-# sentence-BERT-IT-base
+# sentence-bert-base
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It was trained on [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/it/train). 
 
@@ -31,7 +31,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["Questo è un esempio di frase", "Questo è un ulteriore esempio"]
 
-model = SentenceTransformer('efederici/sentence-BERT-IT-base')
+model = SentenceTransformer('efederici/sentence-bert-base')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -57,8 +57,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ["Questo è un esempio di frase", "Questo è un ulteriore esempio"]
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('efederici/sentence-BERT-IT-base')
-model = AutoModel.from_pretrained('efederici/sentence-BERT-IT-base')
+tokenizer = AutoTokenizer.from_pretrained('efederici/sentence-bert-base')
+model = AutoModel.from_pretrained('efederici/sentence-bert-base')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')