Create custom_model.py

custom_model.py

@@ -0,0 +1,20 @@
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel, PreTrainedTokenizer, AutoTokenizer, AutoConfig
+
+# Define a custom model class
+class CustomModel(PreTrainedModel):
+    config_class = None  # Set this to the custom configuration class if available
+
+    def __init__(self, config):
+        super().__init__(config)
+        # Implement your model architecture here
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
+        config = cls.config_class.from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
+        model = cls(config)
+        # Load the weights from the pretrained model
+        model.load_state_dict(torch.load(pretrained_model_name_or_path))
+        return model