def test_model2model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = Model2Model.from_pretrained(model_name)
self.assertIsInstance(model.encoder, BertModel)
self.assertIsInstance(model.decoder, BertForMaskedLM)
self.assertEqual(model.decoder.config.is_decoder, True)
self.assertEqual(model.encoder.config.is_decoder, False)
def test_question_answering_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
model, loading_info = AutoModelForQuestionAnswering.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForQuestionAnswering)
def test_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModel.from_pretrained(model_name)
model, loading_info = AutoModel.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertModel)
for value in loading_info.values():
self.assertEqual(len(value), 0)
def test_model_for_pretraining_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForPreTraining.from_pretrained(model_name)
model, loading_info = AutoModelForPreTraining.from_pretrained(
model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForPreTraining)
for key, value in loading_info.items():
# Only one value should not be initialized and in the missing keys.
self.assertEqual(len(value), 1 if key == "missing_keys" else 0)
def test_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = BertConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, PretrainedConfig)
model = BertModel.from_pretrained(model_name)
model, loading_info = BertModel.from_pretrained(
model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, PreTrainedModel)
for value in loading_info.values():
self.assertEqual(len(value), 0)
config = BertConfig.from_pretrained(model_name,
output_attentions=True,
output_hidden_states=True)
model = BertModel.from_pretrained(model_name,
output_attentions=True,
output_hidden_states=True)
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(model.config, config)
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = BertModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
def test_model_from_pretrained(self):
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = BertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
self.assertIsNotNone(model)
vocab = Vocab(
list_of_tokens=idx_to_token,
unknown_token="[UNK]",
padding_token="[PAD]",
bos_token=None,
eos_token=None,
reserved_tokens=["[CLS]", "[SEP]", "[MASK]"],
token_to_idx=token_to_idx
)
vocab_filename = "{}-vocab.pkl".format(args.type)
vocab_filepath = ptr_dir / vocab_filename
if not vocab_filepath.exists():
with open(vocab_filepath, mode="wb") as io:
pickle.dump(vocab, io)
else:
print("Already you have {}".format(vocab_filename))
print("Saving the vocab of {} is done".format(args.type))
# Saving weights of pretrained model
weight_url = BERT_PRETRAINED_MODEL_ARCHIVE_MAP.get(args.type)
weight_filename = weight_url.split("/")[-1]
weight_filepath = ptr_dir / weight_filename
if not weight_filepath.exists():
urlretrieve(weight_url, weight_filepath)
else:
print("Already you have {}".format(weight_filename))
print("Saving weights of {} is done".format(args.type))
