Update All_Model.py

All_Model.py

@@ -1,36 +1,19 @@
-from transformers import RobertaTokenizer, RobertaModel
-from transformers import BertModel, BertTokenizer
-from torch import nn
-
-#=============
-#  RO-BERTA MODEL
-#=============
-RO_BERTA_MODEL_PATH = "./Models/roberta_model.pth"
-roberta_tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
-class RoBertaForMultiLabel(nn.Module):
-    def __init__(self, pretrained_model='roberta-base', num_labels=5):
-        super().__init__()
-        self.bert = RobertaModel.from_pretrained(pretrained_model)
-        self.dropout = nn.Dropout(0.3)
-        self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels)
-
-    def forward(self, input_ids, attention_mask):
-        pooled_output = self.bert(input_ids=input_ids, attention_mask=attention_mask).pooler_output
-        return self.classifier(self.dropout(pooled_output))
-
-    
-#================
-# BERT MODEL
-#================
-BERT_MODEL_PATH = "./Models/BERT_MODEL.pth"
-bert_tokenizer=BertTokenizer.from_pretrained('bert-base-uncased')
-class BertForMultiLabel(nn.Module):
-    def __init__(self, pretrained_model='bert-base-uncased', num_labels=5):
-        super().__init__()
-        self.bert = BertModel.from_pretrained(pretrained_model)
-        self.dropout = nn.Dropout(0.3)
-        self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels)
-
-    def forward(self, input_ids, attention_mask):
-        pooled_output = self.bert(input_ids=input_ids, attention_mask=attention_mask).pooler_output
-        return self.classifier(self.dropout(pooled_output))
+from transformers import RobertaTokenizer, RobertaModel
+from transformers import BertModel, BertTokenizer
+from torch import nn
+    
+#================
+# BERT MODEL
+#================
+BERT_MODEL_PATH = "./BERT_MODEL.pth"
+bert_tokenizer=BertTokenizer.from_pretrained('bert-base-uncased')
+class BertForMultiLabel(nn.Module):
+    def __init__(self, pretrained_model='bert-base-uncased', num_labels=5):
+        super().__init__()
+        self.bert = BertModel.from_pretrained(pretrained_model)
+        self.dropout = nn.Dropout(0.3)
+        self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels)
+
+    def forward(self, input_ids, attention_mask):
+        pooled_output = self.bert(input_ids=input_ids, attention_mask=attention_mask).pooler_output
+        return self.classifier(self.dropout(pooled_output))