def write_question_answering(task,
model,
input_file,
output_file,
predict_batch_size,
seq_length,
tokenization,
vocab_file,
do_lower_case,
version_2_with_negative=False):
"""Makes question answering predictions and writes to output file."""
data_config = question_answering_dataloader.QADataConfig(
do_lower_case=do_lower_case,
doc_stride=128,
drop_remainder=False,
global_batch_size=predict_batch_size,
input_path=input_file,
is_training=False,
query_length=64,
seq_length=seq_length,
tokenization=tokenization,
version_2_with_negative=version_2_with_negative,
vocab_file=vocab_file)
all_predictions, _, _ = question_answering.predict(task, data_config, model)
with tf.io.gfile.GFile(output_file, 'w') as writer:
writer.write(json.dumps(all_predictions, indent=4) + '\n')
def test_predict(self, version_2_with_negative):
validation_data = self._get_validation_data_config(
version_2_with_negative=version_2_with_negative)
config = question_answering.QuestionAnsweringConfig(
model=question_answering.ModelConfig(encoder=self._encoder_config),
train_data=self._train_data_config,
validation_data=validation_data)
task = question_answering.QuestionAnsweringTask(config)
model = task.build_model()
all_predictions, all_nbest, scores_diff = question_answering.predict(
task, validation_data, model)
self.assertLen(all_predictions, 1)
self.assertLen(all_nbest, 1)
if version_2_with_negative:
self.assertLen(scores_diff, 1)
else:
self.assertEmpty(scores_diff)