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)