Update README.md

README.md

@@ -41,7 +41,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 phrases = ["information retrieval", "text mining", "natural language processing"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('uclanlp/keyphrase-mpnet-v1')
 embeddings = model.encode(phrases)
 print(embeddings)
 ```
@@ -65,8 +65,8 @@ def mean_pooling(model_output, attention_mask):
 phrases = ["information retrieval", "text mining", "natural language processing"]
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('uclanlp/keyphrase-mpnet-v1')
+model = AutoModel.from_pretrained('uclanlp/keyphrase-mpnet-v1')
 
 # Tokenize sentences
 encoded_input = tokenizer(phrases, padding=True, truncation=True, return_tensors='pt')
@@ -78,7 +78,7 @@ with torch.no_grad():
 # Perform pooling. In this case, mean pooling.
 sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
 
-print("Sentence embeddings:")
+print("Phrase embeddings:")
 print(sentence_embeddings)
 ```