def evaluate(args):
"""
evaluate the trained model on dev files
"""
logger = logging.getLogger("brc")
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert len(args.dev_files) > 0, 'No dev files are provided.'
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len, dev_files=args.dev_files)
steps_per_epoch = brc_data.size('dev') // args.batch_size
args.decay_steps = args.decay_epochs * steps_per_epoch
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Restoring the model...')
RCModel = choose_model_by_gpu_setting(args)
rc_model = RCModel(vocab, args)
logger.info('Restoring the model...{}'.format(RCModel.__name__))
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
logger.info('Evaluating the model on dev set...')
dev_batches = brc_data.gen_mini_batches('dev', args.batch_size,
pad_id=vocab.get_id(vocab.pad_token), shuffle=False)
dev_loss, dev_bleu_rouge = rc_model.evaluate(
dev_batches, result_dir=args.result_dir, result_prefix='dev.predicted', save_full_info=False)
logger.info('Loss on dev set: {}'.format(dev_loss))
logger.info('Result on dev set: {}'.format(dev_bleu_rouge))
logger.info('Predicted answers are saved to {}'.format(os.path.join(args.result_dir)))
def train(args):
"""
trains the reading comprehension model
"""
logger = logging.getLogger("brc")
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
if args.word2vec_path:
logger.info('learn_word_embedding:{}'.format(args.learn_word_embedding))
logger.info('loadding %s \n' % args.word2vec_path)
word2vec = gensim.models.Word2Vec.load(args.word2vec_path)
vocab.load_pretrained_embeddings_from_w2v(word2vec.wv)
logger.info('load pretrained embedding from %s done\n' % args.word2vec_path)
if args.use_char_embed:
with open(os.path.join(args.vocab_dir, 'char_vocab.data'), 'rb') as fin:
char_vocab = pickle.load(fin)
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len,
args.train_files, args.dev_files)
steps_per_epoch = brc_data.size('train') // args.batch_size
args.decay_steps = args.decay_epochs * steps_per_epoch
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
if args.use_char_embed:
logger.info('Converting text into char ids...')
brc_data.convert_to_char_ids(char_vocab)
logger.info('Binding char_vocab to args to pass to RCModel')
args.char_vocab = char_vocab
RCModel = choose_model_by_gpu_setting(args)
logger.info('Initialize the model...')
rc_model = RCModel(vocab, args)
logger.info('Training the model...{}'.format(RCModel.__name__))
rc_model.train(brc_data, args.epochs, args.batch_size, save_dir=args.model_dir,
save_prefix=args.algo,
dropout_keep_prob=args.dropout_keep_prob)
logger.info('Done with model training!')
def predict(args):
"""
predicts answers for test files
"""
logger = logging.getLogger("brc")
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert len(args.test_files) > 0, 'No test files are provided.'
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len,
test_files=args.test_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Restoring the model...')
steps_per_epoch = brc_data.size('train') // args.batch_size
args.decay_steps = args.decay_epochs * steps_per_epoch
RCModel = choose_model_by_gpu_setting(args)
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
logger.info('Predicting answers for test set...')
test_batches = brc_data.gen_mini_batches('test', args.batch_size,
pad_id=vocab.get_id(vocab.pad_token), shuffle=False)
rc_model.evaluate(test_batches,
result_dir=args.result_dir, result_prefix='test.predicted')
