def run():
args = training.Args(experiment_name=__name__.split('.')[-1],
bert_model='bert-base-uncased',
max_seq_length=80,
annealing_factor=0.1,
learning_rate=8e-05,
num_train_epochs=30)
grid_space = {'learning_rate': [1e-4, 9e-5, 8e-5, 7e-5, 6e-5, 5e-5]}
experiments.run(args=args,
model_constructor=bert.BERT.from_args,
data_loaders_constructor=bert.DataLoadersAdvOriginalRW,
grid_space=grid_space,
n_experiments=20)
def run():
args = training.Args(experiment_name=__name__.split('.')[-1],
bert_model='bert-large-uncased',
max_seq_length=80,
num_train_epochs=20,
annealing_factor=0.1)
model_constructor = bert.BERT.from_args
grid_space = {'learning_rate': [6e-5, 5e-5, 4e-5, 3e-5, 2e-5]}
experiments.run(args=args,
model_constructor=model_constructor,
data_loaders_constructor=bert.DataLoadersAdvOriginal,
grid_space=grid_space,
n_experiments=20)
def test_run_with_no_existing_data(self):
args = training.Args(experiment_name='test')
model_constructor = lambda x: None
data_loaders = None
grid_space = None
n_experiments = 3
train_fn = FakeTrainFunction()
experiments.run(args, model_constructor, data_loaders, grid_space,
n_experiments, train_fn)
accs = pd.read_csv(self.accs_path)
preds = pd.read_csv(self.preds_path)
self.assertEqual(3, len(accs))
self.assertEqual(18, len(preds))
def run():
args = training.Args(experiment_name=__name__.split('.')[-1],
bert_model='bert-large-uncased',
annealing_factor=0.1,
num_train_epochs=20,
max_seq_length=80,
learning_rate=2e-5)
model_constructor = bert.BERT.from_args
experiments.run(args=args,
model_constructor=model_constructor,
data_loaders_constructor=bert.DataLoadersAdv,
grid_space=None,
n_experiments=20,
do_grid=False)
def run():
args = training.Args(
experiment_name=__name__.split('.')[-1],
bert_model='bert-base-uncased',
max_seq_length=80,
annealing_factor=0.1)
model_constructor = bert.BERT.from_args
grid_space = {
'learning_rate': [1e-4, 9e-5, 8e-5, 7e-5, 6e-5, 5e-5],
'num_train_epochs': [3, 5, 10, 20, 30]}
experiments.run(
args=args,
model_constructor=model_constructor,
data_loaders_constructor=bert.DataLoaders,
grid_space=grid_space,
n_experiments=20)
def run():
args = training.Args(experiment_name=__name__.split('.')[-1],
use_bert=False,
tune_embeds=True,
annealing_factor=0.1,
num_train_epochs=20,
train_batch_size=32,
hidden_size=512,
dropout_prob=0.1)
model_constructor = bilstm.BiLSTM_CW
grid_space = {'learning_rate': [0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03]}
experiments.run(args=args,
model_constructor=model_constructor,
data_loaders_constructor=bilstm.DataLoadersAdvOriginal,
grid_space=grid_space,
n_experiments=20)
def run():
args = training.Args(experiment_name=__name__.split('.')[-1],
use_bert=False,
num_train_epochs=3,
hidden_size=300,
train_batch_size=32,
tune_embeds=True)
model_constructor = bov.BOV_CW
grid_space = {
'learning_rate': [0.2, 0.1, 0.09, 0.08],
'dropout_prob': [0., 0.1]
}
experiments.run(args=args,
model_constructor=model_constructor,
data_loaders_constructor=bov.DataLoaders,
grid_space=grid_space,
n_experiments=20)
def run():
args = training.Args(experiment_name=__name__.split('.')[-1],
use_bert=False,
n_train_epochs=3,
dropout_prob=0.,
train_batch_size=32,
tune_embeds=True)
grid_space = {
'learning_rate': [0.1, 0.09, 0.08],
'n_train_epochs': [3, 5],
'dropout_prob': [0., 0.1],
'train_batch_size': [16, 32, 64]
}
experiments.run(args=args,
model_constructor=bov.BOV_CW,
data_loaders_constructor=bov.DataLoadersAdvOriginal,
grid_space=grid_space,
n_experiments=20)
def run():
args = training.Args(experiment_name=__name__.split('.')[-1],
use_bert=False,
tune_embeds=True,
annealing_factor=0.1,
num_train_epochs=20,
train_batch_size=32)
model_constructor = bilstm.BiLSTM
data_loaders = bilstm.DataLoaders()
grid_space = {
'learning_rate':
[0.3, 0.2, 0.1, 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03],
'dropout_prob': [0., 0.1],
'hidden_size': [128, 256, 512]
}
experiments.run(args=args,
model_constructor=model_constructor,
data_loaders=data_loaders,
grid_space=grid_space,
n_experiments=20)
