fix: normalize hyper params

research/models/bigru_model.py

@@ -13,7 +13,7 @@ from research.neural_network_model import NeuralNetworkModel
 class BiGRUModel(NeuralNetworkModel):
     """Bidirectional GRU model for name classification"""
 
-    def build_model_with_vocab(self, vocab_size: int, max_len: int = 6, **kwargs) -> Any:
+    def build_model_with_vocab(self, vocab_size: int, **kwargs) -> Any:
         params = kwargs
         model = Sequential(
             [
@@ -22,7 +22,6 @@ class BiGRUModel(NeuralNetworkModel):
                 Embedding(
                     input_dim=vocab_size,
                     output_dim=params.get("embedding_dim", 64),
-                    input_length=max_len,
                     mask_zero=True,
                 ),
                 # First recurrent block returns full sequences to allow stacking.

research/models/cnn_model.py

@@ -21,7 +21,7 @@ from research.neural_network_model import NeuralNetworkModel
 class CNNModel(NeuralNetworkModel):
     """1D Convolutional Neural Network for character patterns"""
 
-    def build_model_with_vocab(self, vocab_size: int, max_len: int = 20, **kwargs) -> Any:
+    def build_model_with_vocab(self, vocab_size: int, **kwargs) -> Any:
         """Build CNN model with known vocabulary size"""
 
         params = kwargs

research/models/lstm_model.py

@@ -13,7 +13,7 @@ from research.neural_network_model import NeuralNetworkModel
 class LSTMModel(NeuralNetworkModel):
     """LSTM model for sequence learning"""
 
-    def build_model_with_vocab(self, vocab_size: int, max_len: int = 6, **kwargs) -> Any:
+    def build_model_with_vocab(self, vocab_size: int, **kwargs) -> Any:
         params = kwargs
         model = Sequential(
             [
@@ -21,7 +21,6 @@ class LSTMModel(NeuralNetworkModel):
                 Embedding(
                     input_dim=vocab_size,
                     output_dim=params.get("embedding_dim", 64),
-                    input_length=max_len,
                     mask_zero=True,
                 ),
                 # Stacked bidirectional LSTMs: first returns sequences to feed the next.

research/models/naive_bayes_model.py

@@ -17,7 +17,7 @@ class NaiveBayesModel(TraditionalModel):
         # includes unigrams for coverage and higher n for suffix/prefix cues.
         vectorizer = CountVectorizer(
             analyzer="char",
-            ngram_range=params.get("ngram_range", (1, 4)),
+            ngram_range=params.get("ngram_range", (2, 5)),
             max_features=params.get("max_features", 8000),
         )
 

research/models/transformer_model.py

@@ -22,21 +22,21 @@ from research.neural_network_model import NeuralNetworkModel
 class TransformerModel(NeuralNetworkModel):
     """Transformer-based model"""
 
-    def build_model_with_vocab(self, vocab_size: int, max_len: int = 6, **kwargs) -> Any:
+    def build_model_with_vocab(self, vocab_size: int, **kwargs) -> Any:
         params = kwargs
 
         # Build Transformer model
-        inputs = Input(shape=(max_len,))
+        inputs = Input(shape=(params.get("max_len", 8),))
         x = Embedding(
             input_dim=vocab_size,
             output_dim=params.get("embedding_dim", 64),
-            input_length=max_len,
+            input_length=params.get("max_len", 8),
             mask_zero=True,
         )(inputs)
 
         # Add positional encoding
-        positions = tf.range(start=0, limit=max_len, delta=1)
-        pos_embedding = Embedding(input_dim=max_len, output_dim=params.get("embedding_dim", 64))(
+        positions = tf.range(start=0, limit=params.get("max_len", 8), delta=1)
+        pos_embedding = Embedding(input_dim=params.get("max_len", 8), output_dim=params.get("embedding_dim", 64))(
             positions
         )
         x = x + pos_embedding