Create model.py

model.py

@@ -0,0 +1,87 @@
+from transformers import BertModel, BertConfig
+from transformers.modeling_outputs import BaseModelOutputWithPooling
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import PreTrainedModel, SequenceClassifierOutput
+from transformers import PretrainedConfig
+
+class BertLSTMConfig(PretrainedConfig):
+    model_type = "bertLSTMForSequenceClassification"
+
+    def __init__(self,
+                 num_classes=2,
+                 hidden_size=768,  # BERT hidden size
+                 num_layers=12,
+                 hidden_dim_lstm=256,  # New parameter for LSTM
+                 hidden_dropout_prob=0.1,  # Changed from dropout_rate to hidden_dropout_prob
+                 **kwargs):
+        super().__init__(**kwargs)
+        self.num_classes = num_classes
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.hidden_dim_lstm = hidden_dim_lstm  # Assign LSTM hidden dimension
+        self.hidden_dropout_prob = hidden_dropout_prob  # Adjusted to BERT parameter name
+        self.id2label = {
+            0: "fake",
+            1: "true",
+        }
+        self.label2id = {
+            "fake": 0,
+            "true": 1,
+        }
+
+
+class BertLSTMForSequenceClassification(PreTrainedModel):
+    config_class = BertLSTMConfig
+
+    def __init__(self, config):
+        super(BertLSTMForSequenceClassification, self).__init__(config)
+        self.num_classes = config.num_classes
+        self.embed_dim = config.hidden_size  # BERT hidden size is used as the embedding dimension
+        self.num_layers = config.num_layers
+        self.hidden_dim_lstm = config.hidden_dim_lstm
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        # Use BertModel instead of AlbertModel
+        self.bert = BertModel.from_pretrained('bert-base-uncased', output_hidden_states=True, output_attentions=False)
+        print("BERT Model Loaded")
+
+        # Adjust the input dimension for LSTM
+        self.lstm = nn.LSTM(self.embed_dim, self.hidden_dim_lstm, batch_first=True, num_layers=self.num_layers)
+
+        # Adjust the output dimension for the linear layer
+        self.fc = nn.Linear(self.hidden_dim_lstm, self.num_classes)
+
+    def forward(self, input_ids, attention_mask, token_type_ids, labels=None):
+        bert_output = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
+        hidden_states = bert_output["hidden_states"]
+
+        # Extract the embeddings from the last layer of BERT
+        last_hidden_states = hidden_states[-1]
+
+        # Apply dropout
+        last_hidden_states = self.dropout(last_hidden_states)
+
+        # Pass through LSTM
+        lstm_output, _ = self.lstm(last_hidden_states, None)
+
+        # Take the output from the last time step
+        lstm_output = lstm_output[:, -1, :]
+
+        # Apply dropout
+        lstm_output = self.dropout(lstm_output)
+
+        # Linear layer for classification
+        logits = self.fc(lstm_output)
+
+        loss = None
+        if labels is not None:
+            loss = F.cross_entropy(logits, labels)
+
+        out = SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=bert_output.hidden_states,
+            attentions=bert_output.attentions,
+        )
+        return out