updateded size

src/model/train.py

@@ -1,16 +1,39 @@
 from sklearn.model_selection import train_test_split
-from transformers import BertTokenizer, BertForSequenceClassification, AdamW
+from transformers import BertTokenizer, BertForSequenceClassification, BertConfig, AdamW
 from torch.utils.data import DataLoader, TensorDataset
 import torch
 from tqdm import tqdm
 import pyarrow.parquet as pq
 
 class FakeNewsModelTrainer:
-    def __init__(self, model_name='google-bert/bert-base-multilingual-cased', max_length=512):
+    def __init__(self, model_name='google-bert/bert-base-multilingual-cased', max_length=512, size_factor=0.5):
         self.model_name = model_name
         self.max_length = max_length
+        self.size_factor = size_factor
         self.tokenizer = BertTokenizer.from_pretrained(model_name)
-        self.model = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)
+        
+        # Load the original config
+        original_config = BertConfig.from_pretrained(model_name)
+        
+        # Calculate new dimensions
+        new_hidden_size = max(int(original_config.hidden_size * size_factor ** 0.5), 16)
+        new_num_hidden_layers = max(int(original_config.num_hidden_layers * size_factor ** 0.5), 1)
+        new_num_attention_heads = max(int(original_config.num_attention_heads * size_factor ** 0.5), 1)
+        
+        # Create a new config with reduced size
+        config = BertConfig(
+            vocab_size=original_config.vocab_size,
+            hidden_size=new_hidden_size,
+            num_hidden_layers=new_num_hidden_layers,
+            num_attention_heads=new_num_attention_heads,
+            intermediate_size=new_hidden_size * 4,
+            max_position_embeddings=original_config.max_position_embeddings,
+            num_labels=2
+        )
+        
+        # Initialize the model with the new config
+        self.model = BertForSequenceClassification(config)
+        
         self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
         self.model.to(self.device)
 
@@ -91,10 +114,10 @@ if __name__ == '__main__':
     train_df, val_df = train_test_split(df, test_size=0.35, random_state=42)
 
     # Initialize and train the model
-    trainer = FakeNewsModelTrainer()
+    trainer = FakeNewsModelTrainer(size_factor=0.25)
     train_data = trainer.prepare_data(train_df)
     val_data = trainer.prepare_data(val_df)
     trainer.train(train_data, val_data)
 
     # Save the model
-    trainer.save_model('VeriMind')
+    trainer.save_model('VeriMindSmall')