diff --git a/src/model/train.py b/src/model/train.py
index 14f00dc..97fa4da 100644
--- a/src/model/train.py
+++ b/src/model/train.py
@@ -1,5 +1,3 @@
-import pandas as pd
-import numpy as np
 from sklearn.model_selection import train_test_split
 from transformers import BertTokenizer, BertForSequenceClassification, AdamW
 from torch.utils.data import DataLoader, TensorDataset
@@ -17,13 +15,25 @@ class FakeNewsModelTrainer:
         self.model.to(self.device)
 
     def prepare_data(self, df):
-        texts = df['text'].tolist()
+        # Combine title and text, handling potential empty values
+        texts = df.apply(lambda row: f"{row['title'] or ''} {row['text'] or ''}".strip(), axis=1).tolist()
         labels = df['label'].tolist()
 
-        encoded_texts = self.tokenizer(texts, padding=True, truncation=True, max_length=self.max_length, return_tensors='pt')
+        # Filter out empty texts
+        valid_texts = []
+        valid_labels = []
+        for text, label in zip(texts, labels):
+            if text.strip():  # Check if the text is not empty after stripping whitespace
+                valid_texts.append(text)
+                valid_labels.append(label)
+
+        if not valid_texts:
+            raise ValueError("No valid texts found in the dataset after filtering empty entries.")
+
+        encoded_texts = self.tokenizer(valid_texts, padding=True, truncation=True, max_length=self.max_length, return_tensors='pt')
         input_ids = encoded_texts['input_ids']
         attention_mask = encoded_texts['attention_mask']
-        labels = torch.tensor(labels)
+        labels = torch.tensor(valid_labels)
 
         return TensorDataset(input_ids, attention_mask, labels)
 
@@ -75,15 +85,13 @@ class FakeNewsModelTrainer:
         self.model.save_pretrained(path)
         self.tokenizer.save_pretrained(path)
 
-
 # Usage example
 if __name__ == '__main__':
     # Load and preprocess the data
     df = pq.read_table('dataset.parquet').to_pandas()
-    df['text'] = df['title'] + ' ' + df['text']  # Combine title and text
     
     # Split the data
-    train_df, val_df = train_test_split(df, test_size=0.3, random_state=42)
+    train_df, val_df = train_test_split(df, test_size=0.35, random_state=42)
 
     # Initialize and train the model
     trainer = FakeNewsModelTrainer()