Update README.md

README.md

@@ -43,6 +43,13 @@ model_name = 'vamsibanda/sbert-onnx-all-roberta-large-v1'
 cache_folder = './'
 model_path =  os.path.join(cache_folder, model_name.replace("/", "_"))
 
+def generate_embedding(text):
+    token = tokenizer(text, return_tensors='pt')
+    embeddings = model(input_ids=token['input_ids'], attention_mask=token['attention_mask'])
+    sbert_embeddings = mean_pooling(embeddings, token['attention_mask'])
+    sbert_embeddings = F.normalize(sbert_embeddings, p=2, dim=1)
+    return sbert_embeddings.tolist()[0]
+    
 def mean_pooling(model_output, attention_mask):
     token_embeddings = model_output[0] #First element of model_output contains all token embeddings
     input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
@@ -65,10 +72,6 @@ tokenizer = AutoTokenizer.from_pretrained(model_path)
 model = ORTModelForFeatureExtraction.from_pretrained(model_path, force_download=False)
 pooling_layer = Pooling.load(f"{model_path}/1_Pooling")
 
-token = tokenizer('That is a happy person', return_tensors='pt')
-embeddings = model(input_ids=token['input_ids'], attention_mask=token['attention_mask'])
-sbert_embeddings = mean_pooling(embeddings, token['attention_mask'])
-sbert_embeddings = F.normalize(sbert_embeddings, p=2, dim=1)
-sbert_embeddings.tolist()[0]
+generate_embedding('That is a happy person')
                     
 ```