Fix the finetuning script's loss and metric config (#176)

* Fix the finetuning script

* change softmax layer to return logits

Author: chenmoneygithub

examples/bert/run_glue_finetuning.py

@@ -18,7 +18,7 @@
 import datasets
 import keras_tuner
 import tensorflow as tf
-import tensorflow_text as tftext
+import tensorflow_text as tf_text
 from absl import app
 from absl import flags
 from tensorflow import keras
@@ -81,20 +81,20 @@ def pack_inputs(
 ):
     # In case inputs weren't truncated (as they should have been),
     # fall back to some ad-hoc truncation.
-    trimmed_segments = tftext.RoundRobinTrimmer(
+    trimmed_segments = tf_text.RoundRobinTrimmer(
         seq_length - len(inputs) - 1
     ).trim(inputs)
     # Combine segments.
-    segments_combined, segment_ids = tftext.combine_segments(
+    segments_combined, segment_ids = tf_text.combine_segments(
         trimmed_segments,
         start_of_sequence_id=start_of_sequence_id,
         end_of_segment_id=end_of_segment_id,
     )
     # Pad to dense Tensors.
-    input_word_ids, _ = tftext.pad_model_inputs(
+    input_word_ids, _ = tf_text.pad_model_inputs(
         segments_combined, seq_length, pad_value=padding_id
     )
-    input_type_ids, input_mask = tftext.pad_model_inputs(
+    input_type_ids, input_mask = tf_text.pad_model_inputs(
         segment_ids, seq_length, pad_value=0
     )
     # Assemble nest of input tensors as expected by BERT model.
@@ -184,8 +184,8 @@ def build(self, hp):
             optimizer=keras.optimizers.Adam(
                 learning_rate=hp.Choice("lr", [5e-5, 4e-5, 3e-5, 2e-5])
             ),
-            loss="sparse_categorical_crossentropy",
-            metrics=["accuracy"],
+            loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+            metrics=[keras.metrics.SparseCategoricalAccuracy()],
         )
         return finetuning_model
 
@@ -197,7 +197,7 @@ def main(_):
     with open(FLAGS.vocab_file, "r") as vocab_file:
         for line in vocab_file:
             vocab.append(line.strip())
-    tokenizer = tftext.BertTokenizer(
+    tokenizer = tf_text.BertTokenizer(
         FLAGS.vocab_file,
         lower_case=FLAGS.do_lower_case,
         token_out_type=tf.int32,