fix: correct examples configuration and fix validation data never being checked

examples/advanced_training.py

@@ -122,6 +122,7 @@ def main():
         patience_early_stopping=5,
         num_workers=0,
         trainer_params={"deterministic": True},
+        raw_labels=False, # no encoding needed, labels are already integers
     )
 
     classifier.train(
@@ -162,6 +163,8 @@ def main():
         patience_early_stopping=7,
         num_workers=0,
         trainer_params=advanced_trainer_params,
+        raw_labels=False, # no encoding needed, labels are already integers
+
     )
 
     advanced_classifier.train(
@@ -196,6 +199,8 @@ def main():
         patience_early_stopping=3,
         num_workers=0,  # No multiprocessing for CPU
         trainer_params={"deterministic": True, "accelerator": "cpu"},
+        raw_labels=False, # no encoding needed, labels are already integers
+
     )
 
     cpu_classifier.train(
@@ -225,7 +230,6 @@ def main():
         "max_epochs": 25,
         "enable_progress_bar": True,
         "log_every_n_steps": 1,
-        "check_val_every_n_epoch": 2,  # Validate every 2 epochs
         "enable_checkpointing": True,
         "enable_model_summary": True,
         "deterministic": True,
@@ -238,6 +242,7 @@ def main():
         patience_early_stopping=8,
         num_workers=0,
         trainer_params=custom_trainer_params,
+        raw_labels=False, # no encoding needed, labels are already integers
     )
 
     custom_classifier.train(

examples/basic_classification.py

@@ -125,6 +125,7 @@ def main():
         lr=1e-3,
         patience_early_stopping=5,
         num_workers=0,  # Use 0 for simple examples to avoid multiprocessing issues
+        raw_labels=False # no encoding needed, labels are already integers
     )
     classifier.train(
         X_train, y_train, training_config=training_config, X_val=X_val, y_val=y_val, verbose=True

examples/multiclass_classification.py

@@ -142,6 +142,7 @@ def main():
         patience_early_stopping=7,
         num_workers=0,
         trainer_params={"deterministic": True},
+        raw_labels=False, # no encoding needed, labels are already integers 
     )
     classifier.train(
         X_train, y_train, training_config=training_config, X_val=X_val, y_val=y_val, verbose=True

examples/simple_explainability_example.py

@@ -150,6 +150,7 @@ def main():
         patience_early_stopping=5,
         num_workers=0,
         trainer_params={"deterministic": True},
+        raw_labels=False # no encoding needed, labels are already integers
     )
     classifier.train(
         X_train, y_train, training_config=training_config, X_val=X_val, y_val=y_val, verbose=True
@@ -279,7 +280,6 @@ def main():
                     # Extract attributions and mapping info
                     attributions = result["attributions"][0][0]  # shape: (seq_len,)
                     offset_mapping = result["offset_mapping"][0]  # List of (start, end) tuples
-                    word_ids = result["word_ids"][0]  # List of word IDs for each token
 
                     # Map token-level attributions to character-level (for ASCII visualization)
                     char_attributions = map_attributions_to_char(

examples/using_additional_features.py

@@ -156,6 +156,8 @@ def train_and_evaluate_model(X, y, model_name, use_categorical=False, use_simple
         patience_early_stopping=3,
         num_workers=0,
         trainer_params={"enable_progress_bar": True, "deterministic": True},
+        raw_labels=False,  # no encoding needed, labels are already integers
+        raw_categorical_inputs=False,  # no encoding needed, categorical inputs are already integers
     )
 
     # Create and build model
@@ -172,7 +174,7 @@ def train_and_evaluate_model(X, y, model_name, use_categorical=False, use_simple
     if use_categorical:
         print("   ✅ Running validation for text-with-categorical-variables model...")
         try:
-            result = classifier.predict(X_test)
+            result = classifier.predict(X_test, raw_categorical_inputs=False)
             predictions = result["prediction"].squeeze().numpy()
             test_accuracy = (predictions == y_test).mean()
             print(f"   Test accuracy: {test_accuracy:.3f}")

torchTextClassifiers/torchTextClassifiers.py

@@ -375,9 +375,10 @@ def train(
         if y_val is not None:
             assert X_val is not None, "X_val must be provided if y_val is provided."
 
-        X_val: Optional[Dict[str, Any]] = None
+        X_val_checked: Optional[Dict[str, Any]] = None
         if X_val is not None and y_val is not None:
-            X_val, y_val = self._check_XY(X_val, y_val)
+            X_val_checked, y_val = self._check_XY(X_val, y_val, training_config.raw_categorical_inputs, training_config.raw_labels)
+        X_val = X_val_checked
 
         if (
             (X_train["categorical_variables"] is not None)