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 config(*args, **kwargs):
r"""
# Using torch.hub !
import torch
config = torch.hub.load('huggingface/pytorch-transformers', 'config', 'bert-base-uncased') # Download configuration from S3 and cache.
config = torch.hub.load('huggingface/pytorch-transformers', 'config', './test/bert_saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
config = torch.hub.load('huggingface/pytorch-transformers', 'config', './test/bert_saved_model/my_configuration.json')
config = torch.hub.load('huggingface/pytorch-transformers', 'config', 'bert-base-uncased', output_attention=True, foo=False)
assert config.output_attention == True
config, unused_kwargs = torch.hub.load('huggingface/pytorch-transformers', 'config', 'bert-base-uncased', output_attention=True, foo=False, return_unused_kwargs=True)
assert config.output_attention == True
assert unused_kwargs == {'foo': False}
"""
return AutoConfig.from_pretrained(*args, **kwargs)
def _get_config(self):
config = AutoConfig.from_pretrained(self.base_model,
num_labels=self.num_labels)
return config