def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(
TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFOpenAIGPTModel.from_pretrained(model_name,
cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
def create_and_check_openai_gpt_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = TFOpenAIGPTModel(config=config)
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))
def create_and_check_openai_gpt_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = TFOpenAIGPTModel(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
sequence_output = model(inputs)[0]
inputs = [input_ids, input_mask]
sequence_output = model(inputs)[0]
sequence_output = model(input_ids)[0]
result = {
"sequence_output": sequence_output.numpy(),
}
self.parent.assertListEqual(
list(result["sequence_output"].shape), [self.batch_size, self.seq_length, self.hidden_size]
)
def test_model_from_pretrained(self):
for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFOpenAIGPTModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_model_from_pretrained(self):
for model_name in list(
TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFOpenAIGPTModel.from_pretrained(model_name,
cache_dir=CACHE_DIR)
self.assertIsNotNone(model)