def train_nn_wavg_pr_model(embedding_method, dataset_id, layer, device_id): data = load_train_data(dataset_id, 'classification') train, val = stratified_sampling(data) print(len(train), len(val)) trainer = ModelTrainer(embedding_method, dataset_id, layer, device_id) model = trainer.train_nn_wavg_pr_model(train, val) save_model(embedding_method, dataset_id, layer, 'nn_wavg_pr_model', model)
def train_nn_rouge_reg_model(embedding_method, dataset_id, layer, device_id): data = load_train_data(dataset_id, 'regression_rouge') train, val = stratified_sampling(data) print(len(train), len(val)) trainer = ModelTrainer(embedding_method, dataset_id, layer, device_id) model = trainer.train_nn_rouge_reg_model(train, val) save_model(embedding_method, dataset_id, layer, 'nn_rouge_reg_model', model)
def train_nn_sinkhorn_pr_model(embedding_method, dataset_id, layer, device_id): data = load_train_data(dataset_id, 'classification') for i, train, val in cross_validation_sampling(data): print(len(train), len(val)) print(f'Model {i + 1}') trainer = ModelTrainer(embedding_method, dataset_id, layer, device_id) model = trainer.train_nn_sinkhorn_pr_model(train, val) save_model(embedding_method, dataset_id, layer, f'nn_sinkhorn_pr_model_{i}', model)
def train_cond_nn_wavg_pr_model(embedding_method, dataset_id, layer, device_id): data = load_train_data(dataset_id, 'classification') for i, train, val in cross_validation_sampling(data): print(len(train), len(val)) print(f'Model {i + 1}') trainer = ModelTrainer(embedding_method, dataset_id, layer, device_id) model = trainer.train_cond_nn_wavg_pr_model(train, val) save_model(embedding_method, dataset_id, layer, f'cond_nn_wavg_pr_model_{i}', model) import torch torch.cuda.empty_cache()