Update app.py

app.py

@@ -131,14 +131,14 @@ def train_function_no_sweeps(base_model_path):   #, train_dataset, test_dataset)
         # Add other hyperparameters as needed
     }
     # The base model you will train a LoRA on top of
-    #base_model_path = "facebook/esm2_t12_35M_UR50D"  
+    base_model_path = "facebook/esm2_t12_35M_UR50D"  
     
     # Define labels and model
     #id2label = {0: "No binding site", 1: "Binding site"}
     #label2id = {v: k for k, v in id2label.items()}
 
     
-    base_model = AutoModelForTokenClassification.from_pretrained(base_model_path, num_labels=len(id2label), id2label=id2label, label2id=label2id, token=HF_TOKEN)
+    base_model = AutoModelForTokenClassification.from_pretrained(base_model_path, num_labels=len(id2label), id2label=id2label, label2id=label2id)
 
     '''
     # Load the data from pickle files (replace with your local paths)
@@ -156,7 +156,7 @@ def train_function_no_sweeps(base_model_path):   #, train_dataset, test_dataset)
     '''
     
     # Tokenization
-    tokenizer = AutoTokenizer.from_pretrained(base_model_path, token=HF_TOKEN) #("facebook/esm2_t12_35M_UR50D")
+    tokenizer = AutoTokenizer.from_pretrained(base_model_path) #("facebook/esm2_t12_35M_UR50D")
     #max_sequence_length = 1000
     
     train_tokenized = tokenizer(train_sequences, padding=True, truncation=True, max_length=max_sequence_length, return_tensors="pt", is_split_into_words=False)