def create_and_check_albert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = TFAlbertModel(config=config)
# inputs = {'input_ids': input_ids,
# 'attention_mask': input_mask,
# 'token_type_ids': token_type_ids}
# sequence_output, pooled_output = model(**inputs)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
sequence_output, pooled_output = model(inputs)
inputs = [input_ids, input_mask]
sequence_output, pooled_output = model(inputs)
sequence_output, pooled_output = model(input_ids)
result = {
"sequence_output": sequence_output.numpy(),
"pooled_output": pooled_output.numpy(),
}
self.parent.assertListEqual(
list(result["sequence_output"].shape),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(list(result["pooled_output"].shape), [
self.batch_size, self.hidden_size])
def create_and_check_albert_model(self, config, input_ids, token_type_ids,
input_mask, sequence_labels,
token_labels, choice_labels):
model = TFAlbertModel(config=config)
# inputs = {'input_ids': input_ids,
# 'attention_mask': input_mask,
# 'token_type_ids': token_type_ids}
# sequence_output, pooled_output = model(**inputs)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids
}
result = model(inputs)
inputs = [input_ids, input_mask]
result = model(inputs)
result = model(input_ids)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape,
(self.batch_size, self.hidden_size))
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
# for model_name in list(TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in ['albert-base-uncased']:
model = TFAlbertModel.from_pretrained(
model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
def __init__(self, config, *inputs, **kwargs):
super(TFAlbertForMultipleChoice,
self).__init__(config, *inputs, **kwargs)
self.albert = TFAlbertModel(config, name='albert')
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
1,
kernel_initializer=get_initializer(config.initializer_range),
name='classifier')
def test_model_from_pretrained(self):
for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFAlbertModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_model_from_pretrained(self):
for model_name in list(TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFAlbertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
self.assertIsNotNone(model)