Update app.py

app.py

@@ -93,9 +93,9 @@ if (should_train_model=='1'): #train model
 
     repo_name = "Reyad-Ahmmed/hf-data-timeframe"
     
-    #tokenizer = BertTokenizer.from_pretrained(repo_name, subfolder="bert_embeddings_finetune
+    tokenizer = BertTokenizer.from_pretrained(repo_name, subfolder="bert_embeddings_finetune")
     
-    tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
+    #tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
                                               
     # I made sure to add all the ones in the training and eval data to this list
     # since we are training using data that only contains the left tag - we don't need right tags added to this list
@@ -104,9 +104,9 @@ if (should_train_model=='1'): #train model
 
     
     # Model
-    #model = BertForSequenceClassification.from_pretrained(repo_name, subfolder="bert_embeddings_finetune", output_attentions=True, num_labels=len(label_mapping), output_hidden_states=True).to('cpu')
+    model = BertForSequenceClassification.from_pretrained(repo_name, subfolder="bert_embeddings_finetune", output_attentions=True, num_labels=len(label_mapping), output_hidden_states=True).to('cpu')
 
-    model = BertForSequenceClassification.from_pretrained("roberta-base", output_attentions=True, num_labels=len(label_mapping), output_hidden_states=True).to('cpu')
+    #model = BertForSequenceClassification.from_pretrained("roberta-base", output_attentions=True, num_labels=len(label_mapping), output_hidden_states=True).to('cpu')
 
     # Reset tokenizer size to include the new size after adding the tags to the tokenizer's tokens
     model.resize_token_embeddings(len(tokenizer))