newsqa_predictor_test_verb.py

import torch 
from helpers import torch_utils, utils
from torch.autograd import variable
from data_loaders.language_model_loader import LanguageModelLoader 
from models.language_model import LanguageModel
from models.language_trainer import LanguageTrainer
from models.language_wrapper import LanguageWrapper 
from helpers import constants 


dataset_path = 'datasets/newsqa_unsupervised_verb_filtered'
load_path = 'logs/squad_saved_data/model_14.pyt7'

language_model_loader = LanguageModelLoader(dataset_path, tokenizer_type=constants.TOKENIZER_TAB)
language_model = torch_utils.load_model(load_path).cuda()
language_model.config['save_directory'] = 'logs/newsqa_unsupervised_verb_filtered'

language_wrapper = LanguageWrapper(language_model, language_model_loader.get_vocab())
language_trainer = LanguageTrainer(language_model.config, language_wrapper, language_model_loader)

#test_predictions = language_trainer.predict(dataset_type=constants.DATASET_TEST, 
#            epoch_num=10, max_length=20)
#dev_predictions = language_trainer.predict(dataset_type=constants.DATASET_VALIDATION, 
#            epoch_num=10, max_length=10)
train_predictions = language_trainer.predict(dataset_type=constants.DATASET_TRAIN, 
            epoch_num=12, max_length=15,
            beam_size=5)

utils.save_lines(train_predictions, 'logs/newsqa_saved_data/train_predictions_epoch_6_verb_filtered.txt')
#utils.save_lines(dev_predictions, 'logs/newsqa_saved_data/validation_predictions_epoch_6.txt')
#utils.save_lines(test_predictions, 'logs/newsqa_saved_data/test_predictions_epoch_6.txt')