Fix the dense embedding dimension slice

README.md

@@ -4684,7 +4684,7 @@ batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=Tr
 outputs = model(**batch_dict)
 
 dimension=768 # The output dimension of the output embedding, should be in [128, 768]
-embeddings = outputs.last_hidden_state[:, 0][:dimension]
+embeddings = outputs.last_hidden_state[:, 0, :dimension]
 
 embeddings = F.normalize(embeddings, p=2, dim=1)
 scores = (embeddings[:1] @ embeddings[1:].T) * 100