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Update README.md

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