Update README.md

README.md

@@ -60,19 +60,6 @@ sentences = ['This is an example sentence', 'Each sentence is converted']
 # Load model from HuggingFace Hub
 tokenizer = AutoTokenizer.from_pretrained('BlackKakapo/stsb-xlm-r-multilingual-ro')
 model = AutoModel.from_pretrained('BlackKakapo/stsb-xlm-r-multilingual-ro')
-
-# Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
-
-# Compute token embeddings
-with torch.no_grad():
-    model_output = model(**encoded_input)
-
-# Perform pooling. In this case, mean pooling.
-sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-
-print("Sentence embeddings:")
-print(sentence_embeddings)
 ```
 
 ## Training