Merge pull request #32 from KOSASIH/deepsource-transform-aa7c6314

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natural_language_processing/entity_recognition.py

@@ -1,19 +1,30 @@
 import tensorflow as tf
 
+
 def train_entity_recognition_model(data, labels, config):
     # Define the model architecture
-    model = tf.keras.Sequential([
-        tf.keras.layers.Embedding(config['vocab_size'], config['embedding_dim'], input_length=config['max_sequence_length']),
-        tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(config['hidden_units'], return_sequences=True)),
-        tf.keras.layers.Dense(config['output_units'], activation=config['output_activation'])
-    ])
+    model = tf.keras.Sequential(
+        [
+            tf.keras.layers.Embedding(
+                config["vocab_size"],
+                config["embedding_dim"],
+                input_length=config["max_sequence_length"],
+            ),
+            tf.keras.layers.Bidirectional(
+                tf.keras.layers.LSTM(config["hidden_units"], return_sequences=True)
+            ),
+            tf.keras.layers.Dense(
+                config["output_units"], activation=config["output_activation"]
+            ),
+        ]
+    )
 
     # Compile the model
-    model.compile(optimizer=config['optimizer'],
-                  loss=config['loss'],
-                  metrics=[config['metric']])
+    model.compile(
+        optimizer=config["optimizer"], loss=config["loss"], metrics=[config["metric"]]
+    )
 
     # Train the model
-    model.fit(data, labels, epochs=config['epochs'], batch_size=config['batch_size'])
+    model.fit(data, labels, epochs=config["epochs"], batch_size=config["batch_size"])
 
     return model