def train_dataset(dataset, config): config = configurations_qa[config](dataset) n_iters = dataset.n_iters if hasattr(dataset, "n_iters") else 25 trainer = Trainer(dataset, config=config, _type=dataset.trainer_type) trainer.train(dataset.train_data, dataset.dev_data, n_iters=n_iters, save_on_metric=dataset.save_on_metric) return trainer
def train_dataset_and_get_atn_map(dataset, encoders, num_iters=15): for e in encoders: config = configurations_qa[e](dataset) trainer = Trainer(dataset, config=config, _type=dataset.trainer_type) trainer.train(dataset.train_data, dataset.dev_data, n_iters=num_iters, save_on_metric=dataset.save_on_metric) # Get train losses as well? evaluator = Evaluator(dataset, trainer.model.dirname) _, attentions, scores = evaluator.evaluate(dataset.test_data, save_results=True) return scores, attentions
def train_dataset(dataset, config): try: config = configurations_qa[config](dataset) n_iters = dataset.n_iters if hasattr(dataset, "n_iters") else 25 trainer = Trainer(dataset, config=config, _type=dataset.trainer_type) trainer.train(dataset.train_data, dataset.dev_data, n_iters=n_iters, save_on_metric=dataset.save_on_metric) evaluator = Evaluator(dataset, trainer.model.dirname) _ = evaluator.evaluate(dataset.test_data, save_results=True) return trainer, evaluator except Exception as e: print(e) return