Update training_bertFineTuning.py

training_bertFineTuning.py

@@ -60,7 +60,7 @@ def format_time(elapsed):
     return str(datetime.timedelta(seconds=elapsed_rounded))
 
 
-def training_bertFineTuning(chosen_model,  sentences, labels, max_len,  batch_size, epochs = 4):
+def training_bertFineTuning(chosen_model, model_path,  sentences, labels, max_len,  batch_size, epochs = 4):
 
     # If there's a GPU available...
     if torch.cuda.is_available():
@@ -82,12 +82,12 @@ def training_bertFineTuning(chosen_model,  sentences, labels, max_len,  batch_si
 ###########################################################################################################
 
 
-    if chosen_model == 'bert-base-multilingual-cased' :
+    if chosen_model == 'bert' :
         print('Loading Bert Tokenizer...')
-        tokenizer = BertTokenizer.from_pretrained(chosen_model, do_lower_case=True)
-    elif chosen_model == 'camembert-base':
+        tokenizer = BertTokenizer.from_pretrained(model_path, do_lower_case=True)
+    elif chosen_model == 'camembert':
         print('Loading Camembert Tokenizer...')
-        tokenizer = CamembertTokenizer.from_pretrained(chosen_model , do_lower_case=True)
+        tokenizer = CamembertTokenizer.from_pretrained(model_path , do_lower_case=True)
 
 
 
@@ -192,18 +192,18 @@ def training_bertFineTuning(chosen_model,  sentences, labels, max_len,  batch_si
 
     # Load BertForSequenceClassification, the pretrained BERT model with a single
     # linear classification layer on top.
-    if chosen_model == 'bert-base-multilingual-cased':
+    if chosen_model == 'bert':
         model = BertForSequenceClassification.from_pretrained(
-            chosen_model, # Use the 12-layer BERT model, with an uncased vocab.
+            model_path, # Use the 12-layer BERT model, with an uncased vocab.
             num_labels = numberOfClasses, # The number of output labels--2 for binary classification.
             # You can increase this for multi-class tasks.
             output_attentions = False, # Whether the model returns attentions weights.
             output_hidden_states = False, # Whether the model returns all hidden-states.
             )
-    elif chosen_model == 'camembert-base':
+    elif chosen_model == 'camembert':
 
         model = CamembertForSequenceClassification.from_pretrained(
-            chosen_model, # Use the 12-layer BERT model, with an uncased vocab.
+            model_path, # Use the 12-layer BERT model, with an uncased vocab.
             num_labels = numberOfClasses, # The number of output labels--2 for binary classification.
             # You can increase this for multi-class tasks.
             output_attentions = False, # Whether the model returns attentions weights.
@@ -456,7 +456,7 @@ if __name__ == "__main__":
     minOfInstancePerClass = int(config.get('general','minOfInstancePerClass'))
     maxOfInstancePerClass = int(config.get('general','maxOfInstancePerClass'))
 
-    chosen_tokeniser = config.get('model','tokeniser')
+    model_path = config.get('model','path')
     chosen_model = config.get('model','model')
 
     max_len = int(config.get('model','max_len_sequences'))
@@ -484,7 +484,7 @@ if __name__ == "__main__":
 
     #call train method
 
-    model = training_bertFineTuning(chosen_model, sentences, labels, max_len, batch_size, epochs)
+    model = training_bertFineTuning(chosen_model,model_path, sentences, labels, max_len, batch_size, epochs)
 
 
     #save the model