diff --git a/flexnlp/notebooks/FederatedSS.py b/flexnlp/notebooks/FederatedSS.py
index f595657..c3ee55a 100644
--- a/flexnlp/notebooks/FederatedSS.py
+++ b/flexnlp/notebooks/FederatedSS.py
@@ -96,7 +96,7 @@ def copy_server_model_to_clients(server_flex_model: FlexModel):
 
 # Prepare data for training phase
 
-def create_input_examples_for_training(X_data_as_list):
+def create_input_examples_for_training(X_data_as_list, X_test_as_list):
     """Function to create a DataLoader to train/finetune the model at client level
 
     Args:
@@ -104,13 +104,17 @@ def create_input_examples_for_training(X_data_as_list):
         with the following keys: query, pos, neg.
     """
     train_examples = [InputExample(texts=[example['query'], example['pos'][0], example['neg'][0]]) for example in X_data_as_list]
-    return DataLoader(train_examples, shuffle=True, batch_size=16), train_examples
+    dev_examples = [InputExample(texts=[example['query'], example['pos'][0], example['neg'][0]]) for example in X_test_as_list]
+    return DataLoader(train_examples, shuffle=True, batch_size=16), dev_examples
 
 def train(client_flex_model: FlexModel, client_data: Dataset):
     print("Training client")
     model = client_flex_model['model']
     # client_train_dataset = client_data.to_numpy()
-    train_dataloader, dev_examples = create_input_examples_for_training(X_data_as_list=client_data.X_data.tolist())
+    X_data = client_data.X_data.tolist()
+    tam_train = int(len(X_data) * 0.75)
+    X_data, X_test = X_data[:tam_train], X_data[tam_train:]
+    train_dataloader, dev_examples = create_input_examples_for_training(X_data_as_list=X_data, X_test_as_list=X_test)
     train_loss = losses.TripletLoss(model=model)
     evaluator = TripletEvaluator.from_input_examples(dev_examples)
     warmup_steps = int(len(train_dataloader) * 1 * 0.1) #10% of train data
@@ -120,6 +124,7 @@ def train(client_flex_model: FlexModel, client_data: Dataset):
         evaluator=evaluator,
         evaluation_steps=1000,
     )
+    model.evaluate(evaluator, 'model_evaluation')
     sentences = ['This is an example sentence', 'Each sentence is converted']
     encodings = model.encode(sentences)
     print(f"New encodings: {encodings}")