Replace the usage of FLAGS.bert_hub_module_handle with function argum…

…ent to faciliate code reuse in colabs.

run_classifier_with_tfhub.py

@@ -35,15 +35,12 @@
 
 
 def create_model(is_training, input_ids, input_mask, segment_ids, labels,
-                 num_labels):
+                 num_labels, bert_hub_module_handle):
   """Creates a classification model."""
   tags = set()
   if is_training:
     tags.add("train")
-  bert_module = hub.Module(
-      FLAGS.bert_hub_module_handle,
-      tags=tags,
-      trainable=True)
+  bert_module = hub.Module(bert_hub_module_handle, tags=tags, trainable=True)
   bert_inputs = dict(
       input_ids=input_ids,
       input_mask=input_mask,
@@ -87,7 +84,7 @@ def create_model(is_training, input_ids, input_mask, segment_ids, labels,
 
 
 def model_fn_builder(num_labels, learning_rate, num_train_steps,
-                     num_warmup_steps, use_tpu):
+                     num_warmup_steps, use_tpu, bert_hub_module_handle):
   """Returns `model_fn` closure for TPUEstimator."""
 
   def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
@@ -105,7 +102,8 @@ def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
     is_training = (mode == tf.estimator.ModeKeys.TRAIN)
 
     (total_loss, per_example_loss, logits) = create_model(
-        is_training, input_ids, input_mask, segment_ids, label_ids, num_labels)
+        is_training, input_ids, input_mask, segment_ids, label_ids, num_labels,
+        bert_hub_module_handle)
 
     output_spec = None
     if mode == tf.estimator.ModeKeys.TRAIN:
@@ -140,10 +138,10 @@ def metric_fn(per_example_loss, label_ids, logits):
   return model_fn
 
 
-def create_tokenizer_from_hub_module():
+def create_tokenizer_from_hub_module(bert_hub_module_handle):
   """Get the vocab file and casing info from the Hub module."""
   with tf.Graph().as_default():
-    bert_module = hub.Module(FLAGS.bert_hub_module_handle)
+    bert_module = hub.Module(bert_hub_module_handle)
     tokenization_info = bert_module(signature="tokenization_info", as_dict=True)
     with tf.Session() as sess:
       vocab_file, do_lower_case = sess.run([tokenization_info["vocab_file"],
@@ -175,7 +173,7 @@ def main(_):
 
   label_list = processor.get_labels()
 
-  tokenizer = create_tokenizer_from_hub_module()
+  tokenizer = create_tokenizer_from_hub_module(FLAGS.bert_hub_module_handle)
 
   tpu_cluster_resolver = None
   if FLAGS.use_tpu and FLAGS.tpu_name:
@@ -207,7 +205,8 @@ def main(_):
       learning_rate=FLAGS.learning_rate,
       num_train_steps=num_train_steps,
       num_warmup_steps=num_warmup_steps,
-      use_tpu=FLAGS.use_tpu)
+      use_tpu=FLAGS.use_tpu,
+      bert_hub_module_handle=FLAGS.bert_hub_module_handle)
 
   # If TPU is not available, this will fall back to normal Estimator on CPU
   # or GPU.