def test_albert_get_pretrained(model_name):
assert len(list_pretrained_albert()) > 0
with tempfile.TemporaryDirectory() as root:
cfg, tokenizer, backbone_params_path, mlm_params_path =\
get_pretrained_albert(model_name, load_backbone=True, load_mlm=True, root=root)
assert cfg.MODEL.vocab_size == len(tokenizer.vocab)
albert_model = AlbertModel.from_cfg(cfg)
albert_model.load_parameters(backbone_params_path)
albert_mlm_model = AlbertForMLM(cfg)
if mlm_params_path is not None:
albert_mlm_model.load_parameters(mlm_params_path)
# Just load the backbone
albert_mlm_model = AlbertForMLM(cfg)
albert_mlm_model.backbone_model.load_parameters(backbone_params_path)
def test_list_pretrained_albert():
assert len(list_pretrained_albert()) > 0
assert_allclose(np.swapaxes(contextual_embeddings_tn.asnumpy(), 0, 1),
contextual_embeddings.asnumpy(), 1E-4, 1E-4)
assert_allclose(pooled_out_tn.asnumpy(), pooled_out.asnumpy(), 1E-4, 1E-4)
assert_allclose(sop_score.asnumpy(), sop_score_tn.asnumpy(), 1E-4, 1E-4)
assert_allclose(mlm_scores.asnumpy(), mlm_scores_tn.asnumpy(), 1E-4, 1E-4)
assert mlm_scores.shape == (batch_size, num_mask, cfg.MODEL.vocab_size)
assert sop_score.shape == (batch_size, 2)
def test_list_pretrained_albert():
assert len(list_pretrained_albert()) > 0
@pytest.mark.slow
@pytest.mark.remote_required
@pytest.mark.parametrize('model_name', list_pretrained_albert())
def test_albert_get_pretrained(model_name):
assert len(list_pretrained_albert()) > 0
with tempfile.TemporaryDirectory() as root:
cfg, tokenizer, backbone_params_path, mlm_params_path =\
get_pretrained_albert(model_name, load_backbone=True, load_mlm=True, root=root)
assert cfg.MODEL.vocab_size == len(tokenizer.vocab)
albert_model = AlbertModel.from_cfg(cfg)
albert_model.load_parameters(backbone_params_path)
albert_mlm_model = AlbertForMLM(cfg)
if mlm_params_path is not None:
albert_mlm_model.load_parameters(mlm_params_path)
# Just load the backbone
albert_mlm_model = AlbertForMLM(cfg)
albert_mlm_model.backbone_model.load_parameters(backbone_params_path)