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

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Merged
merged 2 commits into from
May 11, 2022
Merged

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

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d75844b
Fix the finetuning script
chenmoneygithub May 10, 2022
2bfef5d
change softmax layer to return logits
chenmoneygithub May 11, 2022
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16 changes: 8 additions & 8 deletions examples/bert/run_glue_finetuning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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.
Expand Down Expand Up @@ -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

Expand All @@ -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,
Expand Down