src/model/train.py

@@ -1,7 +1,7 @@
 import pandas as pd
 import numpy as np
 from sklearn.model_selection import train_test_split
-from transformers import BertTokenizer, BertForSequenceClassification, AdamW, get_linear_schedule_with_warmup
+from transformers import BertTokenizer, BertConfig, BertForSequenceClassification, AdamW, get_linear_schedule_with_warmup
 from torch.utils.data import DataLoader, TensorDataset, WeightedRandomSampler
 import torch
 from tqdm import tqdm
@@ -9,7 +9,7 @@ import pyarrow.parquet as pq
 from sklearn.metrics import classification_report, confusion_matrix
 
 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.tokenizer = BertTokenizer.from_pretrained(model_name)
@@ -17,6 +17,28 @@ class FakeNewsModelTrainer:
         self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
         self.model.to(self.device)
 
+        # 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)
+
     def prepare_data(self, df):
         texts = df.apply(lambda row: f"{row['title'] or ''} {row['text'] or ''}".strip(), axis=1).tolist()
         labels = df['label'].tolist()