def test_bart(model_name):
# test from pretrained
assert len(list_pretrained_bart()) > 0
with tempfile.TemporaryDirectory() as root:
cfg, tokenizer, params_path, _ =\
get_pretrained_bart(model_name, load_backbone=True, root=root)
assert cfg.MODEL.vocab_size == len(tokenizer.vocab)
# test standard bart encoder and decoder
bart_model = BartModel.from_cfg(cfg)
bart_model.load_parameters(params_path)
# test bart encoder and decoder with pooler
bart_model_with_pooler = BartModel.from_cfg(
cfg, use_pooler=True, classifier_activation=False)
bart_model_with_pooler.load_parameters(params_path)
def test_list_pretrained_bart():
assert len(list_pretrained_bart()) > 0
import numpy as np
import numpy.testing as npt
from gluonnlp.models.bart import BartModel, \
list_pretrained_bart, get_pretrained_bart, bart_cfg_reg
from gluonnlp.utils.testing import verify_backbone_fp16
mx.npx.set_np()
def test_list_pretrained_bart():
assert len(list_pretrained_bart()) > 0
@pytest.mark.slow
@pytest.mark.remote_required
@pytest.mark.parametrize('model_name', list_pretrained_bart())
def test_bart(model_name):
# test from pretrained
assert len(list_pretrained_bart()) > 0
with tempfile.TemporaryDirectory() as root:
cfg, tokenizer, params_path, _ =\
get_pretrained_bart(model_name, load_backbone=True, root=root)
assert cfg.MODEL.vocab_size == len(tokenizer.vocab)
# test standard bart encoder and decoder
bart_model = BartModel.from_cfg(cfg)
bart_model.load_parameters(params_path)
# test bart encoder and decoder with pooler
bart_model_with_pooler = BartModel.from_cfg(
cfg, use_pooler=True, classifier_activation=False)
bart_model_with_pooler.load_parameters(params_path)