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NAUSICAA\Julian
2019-04-13 16:38:58 -03:00
parent 7e0a8517b6
commit 65397d7ae4
1 changed files with 36 additions and 36 deletions
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tests/test_end_to_end.py
+36 -36
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@@ -137,45 +137,45 @@ def test_ulmfit_fastai_end_to_end_label_smoothing():
exp2.train_cls(num_lm_epochs=0, unfreeze=False, bs=4, label_smoothing_eps=0.1 )
def test_ulmfit_fastai_bidir_end_to_end():
""" Test ulmfit with sentencepiece tokenizer on small wikipedia dataset.
"""
test_data, wt2 = get_test_data()
lm_name = 'end-to-end-test-fastai'
# def test_ulmfit_fastai_bidir_end_to_end():
# """ Test ulmfit with sentencepiece tokenizer on small wikipedia dataset.
# """
# test_data, wt2 = get_test_data()
# lm_name = 'end-to-end-test-fastai'
exp = ulmfit.pretrain_lm.LMHyperParams(
dataset_path=wt2,
lang='en',
cuda_id=cuda_id,
qrnn=False,
bidir=True,
tokenizer='f',
max_vocab=100,
name=lm_name,
)
exp.train_lm(num_epochs=1, bs=2)
exp2 = ulmfit.train_clas.CLSHyperParams.from_lm(str(test_data / 'imdb'), str(exp.model_dir))
exp2.train_cls(num_lm_epochs=0, unfreeze=False, bs=4, )
# exp = ulmfit.pretrain_lm.LMHyperParams(
# dataset_path=wt2,
# lang='en',
# cuda_id=cuda_id,
# qrnn=False,
# bidir=True,
# tokenizer='f',
# max_vocab=100,
# name=lm_name,
# )
# exp.train_lm(num_epochs=1, bs=2)
# exp2 = ulmfit.train_clas.CLSHyperParams.from_lm(str(test_data / 'imdb'), str(exp.model_dir))
# exp2.train_cls(num_lm_epochs=0, unfreeze=False, bs=4, )
def test_ulmfit_moses_fa_bidir_end_to_end():
""" Test ulmfit with sentencepiece tokenizer on small wikipedia dataset.
"""
test_data, wt2 = get_test_data()
lm_name = 'end-to-end-test-fastai'
# def test_ulmfit_moses_fa_bidir_end_to_end():
# """ Test ulmfit with sentencepiece tokenizer on small wikipedia dataset.
# """
# test_data, wt2 = get_test_data()
# lm_name = 'end-to-end-test-fastai'
exp = ulmfit.pretrain_lm.LMHyperParams(
dataset_path=wt2,
lang='en',
cuda_id=cuda_id,
qrnn=False,
bidir=True,
tokenizer='vf',
max_vocab=100,
name=lm_name,
)
exp.train_lm(num_epochs=1, bs=2)
exp2 = ulmfit.train_clas.CLSHyperParams.from_lm(test_data / 'imdb', exp.model_dir)
exp2.train_cls(num_lm_epochs=0, unfreeze=False, bs=4, )
# exp = ulmfit.pretrain_lm.LMHyperParams(
# dataset_path=wt2,
# lang='en',
# cuda_id=cuda_id,
# qrnn=False,
# bidir=True,
# tokenizer='vf',
# max_vocab=100,
# name=lm_name,
# )
# exp.train_lm(num_epochs=1, bs=2)
# exp2 = ulmfit.train_clas.CLSHyperParams.from_lm(test_data / 'imdb', exp.model_dir)
# exp2.train_cls(num_lm_epochs=0, unfreeze=False, bs=4, )
# def test_classification_model_work_with_different_dropmul():
# learn = self.create_cls_learner(data_clas, drop_mult=0.1)