def create_and_check_xlnet_base_model(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
model = TFXLNetModel(config)
inputs = {'input_ids': input_ids_1,
'input_mask': input_mask,
'token_type_ids': segment_ids}
_, _ = model(inputs)
inputs = [input_ids_1, input_mask]
outputs, mems_1 = model(inputs)
result = {
"mems_1": [mem.numpy() for mem in mems_1],
"outputs": outputs.numpy(),
}
config.mem_len = 0
model = TFXLNetModel(config)
no_mems_outputs = model(inputs)
self.parent.assertEqual(len(no_mems_outputs), 1)
self.parent.assertListEqual(
list(result["outputs"].shape),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(
list(list(mem.shape) for mem in result["mems_1"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFXLNetModel.from_pretrained(model_name,
cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
def create_and_check_xlnet_base_model(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
):
model = TFXLNetModel(config)
inputs = {
"input_ids": input_ids_1,
"input_mask": input_mask,
"token_type_ids": segment_ids
}
result = model(inputs)
inputs = [input_ids_1, input_mask]
result = model(inputs)
config.mem_len = 0
model = TFXLNetModel(config)
no_mems_outputs = model(inputs)
self.parent.assertEqual(len(no_mems_outputs), 1)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertListEqual(
[mem.shape for mem in result.mems],
[(self.seq_length, self.batch_size, self.hidden_size)] *
self.num_hidden_layers,
)
def test_model_from_pretrained(self):
for model_name in TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFXLNetModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_model_from_pretrained(self):
for model_name in list(
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFXLNetModel.from_pretrained(model_name,
cache_dir=CACHE_DIR)
self.assertIsNotNone(model)
