Delete custom_model.py

custom_model.py

@@ -1,35 +0,0 @@
-import torch
-import torch.nn as nn
-from transformers import PreTrainedModel, AutoConfig, AutoModel
-
-class CustomModel(PreTrainedModel):
-    config_class = AutoConfig  # Use AutoConfig to dynamically load the configuration class
-
-    def __init__(self, config):
-        super().__init__(config)
-        # Implement your model architecture here
-        self.encoder = AutoModel.from_config(config)  # Load the base model
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-    def forward(self, input_ids, attention_mask=None):
-        # Pass inputs through the encoder
-        outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
-        # Get the pooled output (e.g., CLS token for classification tasks)
-        pooled_output = outputs.last_hidden_state[:, 0, :]
-        # Pass through the classifier
-        logits = self.classifier(pooled_output)
-        return logits
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
-        try:
-            # Load the configuration
-            config = cls.config_class.from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
-            # Initialize the model with the configuration
-            model = cls(config)
-            # Optionally, you can load the state_dict here if needed
-            # model.load_state_dict(torch.load(os.path.join(pretrained_model_name_or_path, "pytorch_model.bin")))
-            return model
-        except Exception as e:
-            print(f"Failed to load model from {pretrained_model_name_or_path}. Error: {e}")
-            return None