def test_model_for_pretraining_from_pretrained(self):
model_name = "bert-base-cased"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForPreTraining.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForPreTraining)
def test_model_for_pretraining_from_pretrained(self):
import h5py
self.assertTrue(h5py.version.hdf5_version.startswith("1.10"))
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForPreTraining.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForPreTraining)
def test_model_for_pretraining_from_pretrained(self):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForPreTraining.from_pretrained(model_name,
from_pt=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForPreTraining)
model = AutoModelForPreTraining.from_pretrained(model_name,
from_tf=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForPreTraining)
def model(self):
from transformers import TFAutoModelForPreTraining
model = TFAutoModelForPreTraining.from_pretrained(
self.pretrained_model_name_or_path)
self.to_device()
return model
def get_model(self):
from transformers import TFAutoModelForPreTraining
_model = TFAutoModelForPreTraining.from_pretrained(
self.pretrained_model_name_or_path)
return _model