fix: lstm model

ners/gender/models/lstm.py

@@ -111,8 +111,9 @@ def build_model(cfg: Config, vocab_size: int) -> Sequential:
     logging.info("Building LSTM model")
     model = Sequential([
         Embedding(input_dim=vocab_size, output_dim=cfg.embedding_dim),
+        Bidirectional(LSTM(cfg.lstm_units, return_sequences=True)),
         Bidirectional(LSTM(cfg.lstm_units)),
-        Dense(32, activation="relu"),
+        Dense(64, activation="relu"),
         Dense(2, activation="softmax")
     ])
     model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
@@ -138,7 +139,7 @@ def evaluate_proba(y_true, y_proba, threshold, class_names):
         classification report.
     :return: None
     """
-    y_pred = 1 if y_proba[:, 1] >= threshold else 0
+    y_pred = (y_proba[:, 1] >= threshold).astype(int)
     acc = accuracy_score(y_true, y_pred)
     pr, rc, f1, _ = precision_recall_fscore_support(y_true, y_pred, average='binary')
     cm = confusion_matrix(y_true, y_pred)
@@ -213,9 +214,9 @@ def save_artifacts(model, tokenizer, encoder):
 
     :return: None
     """
-    model_path = os.path.join(GENDER_MODELS_DIR, "BiLSTM_model.h5")
-    tokenizer_path = os.path.join(GENDER_MODELS_DIR, "BiLSTM_tokenizer.pkl")
-    encoder_path = os.path.join(GENDER_MODELS_DIR, "BiLSTM_label_encoder.pkl")
+    model_path = os.path.join(GENDER_MODELS_DIR, "lstm_model.keras")
+    tokenizer_path = os.path.join(GENDER_MODELS_DIR, "lstm_tokenizer.pkl")
+    encoder_path = os.path.join(GENDER_MODELS_DIR, "lstm_label_encoder.pkl")
 
     model.save(model_path)
     with open(tokenizer_path, "wb") as f: