Update README.md

README.md

@@ -33,8 +33,8 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 psmiles_strings = ["[*]CC[*]", "[*]COC[*]"]
 
-model = SentenceTransformer('kuelumbus/polyBERT')
-embeddings = model.encode(psmiles_strings)
+polyBERT = SentenceTransformer('kuelumbus/polyBERT')
+embeddings = polyBERT.encode(psmiles_strings)
 print(embeddings)
 ```
 
@@ -60,14 +60,14 @@ psmiles_strings = ["[*]CC[*]", "[*]COC[*]"]
 
 # Load model from HuggingFace Hub
 tokenizer = AutoTokenizer.from_pretrained('kuelumbus/polyBERT')
-model = AutoModel.from_pretrained('kuelumbus/polyBERT')
+polyBERT = AutoModel.from_pretrained('kuelumbus/polyBERT')
 
 # Tokenize sentences
 encoded_input = tokenizer(psmiles_strings, padding=True, truncation=True, return_tensors='pt')
 
 # Compute token embeddings
 with torch.no_grad():
-    model_output = model(**encoded_input)
+    model_output = polyBERT(**encoded_input)
 
 # Perform pooling. In this case, mean pooling.
 fingerprints = mean_pooling(model_output, encoded_input['attention_mask'])