Create model.py

model.py

@@ -0,0 +1,66 @@
+from transformers import PretrainedConfigfrom abc import ABCMeta
+import torch
+from transformers.pytorch_utils import nn
+from transformers import BertModel, BertConfig
+import torch
+import torch.nn.functional as F
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import SequenceClassifierOutput
+from transformers import PretrainedConfig
+
+class BertLSTMConfig(PretrainedConfig):
+    model_type = "bertLSTMForSequenceClassification"
+
+    def __init__(self,
+                 num_classes=2,
+                 embed_dim=768,
+                 num_layers=12,
+                 hidden_dim_lstm=256,  # New parameter for LSTM
+                 dropout_rate=0.1,
+                 **kwargs):
+        super().__init__(**kwargs)
+        self.num_classes = num_classes
+        self.embed_dim = embed_dim
+        self.num_layers = num_layers
+        self.hidden_dim_lstm = hidden_dim_lstm  # Assign LSTM hidden dimension
+        self.dropout_rate = dropout_rate
+        self.id2label = {
+            0: "fake",
+            1: "true",
+        }
+        self.label2id = {
+            "fake": 0,
+            "true": 1,
+        }
+
+
+class BertLSTMForSequenceClassification(PreTrainedModel, metaclass=ABCMeta):
+    config_class = BertLSTMConfig
+    def __init__(self, config):
+        super(BertLSTMForSequenceClassification, self).__init__(config)
+        self.num_classes = config.num_classes
+        self.embed_dim = config.embed_dim
+        self.num_layers = config.num_layers
+        self.hidden_dim_lstm = config.hidden_dim_lstm
+        self.dropout = nn.Dropout(config.dropout_rate)
+        self.bert = BertModel.from_pretrained('bert-base-uncased')
+        print("BERT Model Loaded")
+        self.lstm = nn.LSTM(self.embed_dim, self.hidden_dim_lstm, batch_first=True, num_layers=3)
+        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)
+        pooled_output = bert_output.pooler_output  # Use the pooled output for classification
+        out, _ = self.lstm(pooled_output.unsqueeze(1))
+        out = self.dropout(out[:, -1, :])
+        logits = self.fc(out)
+        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