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.gitattributes

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+BERT_MODEL.pth filter=lfs diff=lfs merge=lfs -text

All_Model.py

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+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))

BERT_MODEL.pth

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+oid sha256:57c9356e403f57cc872c6b1110530816b52b4ff08b95cf2cabad9b6d0d754ceb
+size 438026970