make model importable

train_bilal_baseline.py

@@ -16,6 +16,37 @@
 MAX_LENGTH = 320
 
 
+def bilal_full_yelp_finetune():
+    """Create a BERT model using the model and parameters specified in the Bilal paper:
+    https://link.springer.com/article/10.1007/s10660-022-09560-w/tables/2
+
+        - model: TFBertForSequenceClassification
+        - learning rate: 2e-5
+        - epsilon: 1e-8
+    """
+    # Using the TFBertForSequenceClassification as specified in the paper:
+    bert_model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
+
+    # Don't freeze any layers:
+    untrainable = []
+    trainable = [w.name for w in bert_model.weights]
+
+    for w in bert_model.weights:
+        if w.name in untrainable:
+            w._trainable = False
+        elif w.name in trainable:
+            w._trainable = True
+
+    # Compile the model:
+    bert_model.compile(
+        optimizer = tf.keras.optimizers.Adam(learning_rate=2e-5,epsilon=1e-08),
+        loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+        metrics = [tf.keras.metrics.SparseCategoricalAccuracy("accuracy")]
+    )
+
+    return bert_model
+
+
 if __name__ == "__main__":
 
     # Code for helping save models locally after training:
@@ -109,38 +140,6 @@ def get_google_drive_download_url(raw_url: str):
 
     print("Training ...")
 
-
-    def bilal_full_yelp_finetune():
-        """Create a BERT model using the model and parameters specified in the Bilal paper:
-        https://link.springer.com/article/10.1007/s10660-022-09560-w/tables/2
-
-            - model: TFBertForSequenceClassification
-            - learning rate: 2e-5
-            - epsilon: 1e-8
-        """
-        # Using the TFBertForSequenceClassification as specified in the paper:
-        bert_model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
-
-        # Don't freeze any layers:
-        untrainable = []
-        trainable = [w.name for w in bert_model.weights]
-
-        for w in bert_model.weights:
-            if w.name in untrainable:
-                w._trainable = False
-            elif w.name in trainable:
-                w._trainable = True
-
-        # Compile the model:
-        bert_model.compile(
-            optimizer = tf.keras.optimizers.Adam(learning_rate=2e-5,epsilon=1e-08),
-            loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
-            metrics = [tf.keras.metrics.SparseCategoricalAccuracy("accuracy")]
-        )
-
-        return bert_model
-
-
     model = bilal_full_yelp_finetune()
     print(model.summary())