def main():
config = get_config()
with config:
config.logging_steps = 400
config.train_epochs = 2
config.lr = 4e-5
# config.lr = 1e-4
config.model_type = 'roberta'
config.model_path = util.models_path('StackOBERTflow-comments-small-v1')
# config.train_head_only = True
ds = TDDataset(config, binary=True)
tokenizer = tu.load_tokenizer(config)
model_cls = tu.get_model_cls(config)
train_dataloader = ds.get_complete_train_dataloader(tokenizer)
model = tu.load_model(config)
model.to(config.device)
util.set_seed(config)
experiment = Experiment(config, model, tokenizer)
global_step, tr_loss = experiment.train(train_dataloader)
experiment.save_model(util.models_path('satd_complete_binary'))
def main(config, results):
model_config = tu.load_model_config(config)
tokenizer = tu.load_tokenizer(config, model_config)
ds = Dataset(config, tokenizer)
label_names = ds.label_names
train_dataloader, valid_dataloader = ds.get_train_valid_dataloaders()
model = tu.load_model(config, model_config)
model.to(config.device)
util.set_seed(config)
experiment = Experiment(config,
model,
tokenizer,
label_names=label_names,
results=results)
global_step, tr_loss = experiment.train(train_dataloader,
valid_dataloader=valid_dataloader)
results = experiment.results
experiment.save_model(util.models_path('comment_code_shuffle'))
return results
def main(config, results):
model_config = tu.load_model_config(config)
tokenizer = tu.load_tokenizer(config, model_config)
ds = Dataset(config, tokenizer)
label_names = ds.label_names
train_dataloader = ds.get_train_dataloader()
fake_valid_dataloader = ds.get_fake_valid_dataloader()
# with config:
# config.max_steps=100
model = tu.load_model(config, model_config)
model.to(config.device)
util.set_seed(config)
experiment = Experiment(config, model, tokenizer, label_names=label_names, results=results)
global_step, tr_loss = experiment.train(train_dataloader, valid_dataloader=fake_valid_dataloader) #test_dataloader=test_dataloader)
valid_dataloader = ds.get_valid_dataloader()
test_dataloader = ds.get_test_dataloader()
experiment.evaluate('test_final', test_dataloader)
experiment.evaluate('valid_final', valid_dataloader)
experiment.save_model('test_model_complexity')
with config:
config.model_path = 'test_model_complexity'
model = tu.load_model(config, model_config)
model.to(config.device)
logger.warn('#################################### =========================')
experiment = Experiment(config, model, tokenizer, label_names=label_names, results=results)
experiment.evaluate('test_final_reloaded', test_dataloader)
experiment.evaluate('valid_final_reloaded', valid_dataloader)
results = experiment.results
return results