def construct_flat_datasets(args1, subwords_path):
global tokenizer_bert, tokenizer_ro, args
args = args1
if args.bert:
tokenizer_bert = FullTokenizer(
vocab_file=join(args.bert_model_dir, "vocab.vocab"))
tokenizer_bert.vocab_size = len(tokenizer_bert.vocab)
samples = get_text_samples(args)
if os.path.isfile(subwords_path + '.subwords'):
tokenizer_ro = construct_tokenizer(None, subwords_path, args)
else:
tokenizer_ro = construct_tokenizer(list(samples), subwords_path, args)
sample_train = int(args.total_samples * args.train_dev_split)
if args.records:
dataset = tf.data.Dataset.from_generator(
generator_tensors_ids_and_segs, ((tf.int64, tf.int64), tf.int64),
((tf.TensorShape([None]), tf.TensorShape(
[None])), tf.TensorShape([None])))
if args.separate:
train_dataset = dataset
dev_dataset = tf.data.Dataset.from_generator(
generator_tensors_ids_and_segs_dev,
((tf.int64, tf.int64), tf.int64), ((tf.TensorShape(
[None]), tf.TensorShape([None])), tf.TensorShape([None])))
return train_dataset, dev_dataset
else:
gen_dataset = generator_tensors_ids()
dataset = list(gen_dataset)
nr_samples = len(dataset)
sample_train = int(args.train_dev_split * nr_samples)
# dataset = tf.convert_to_tensor(dataset, dtype=tf.int64)
dataset = tf.data.Dataset.from_generator(
generator_tensors_ids, (tf.int64, tf.int64), (tf.TensorShape(
[2, args.seq_length]), tf.TensorShape([args.seq_length])))
if args.separate:
gen_dataset = generator_tensors_ids_dev()
dev_dataset = list(gen_dataset)
# dataset = tf.convert_to_tensor(dataset, dtype=tf.int64)
dev_dataset = tf.data.Dataset.from_generator(
generator_tensors_ids_dev, (tf.int64, tf.int64),
(tf.TensorShape([2, args.seq_length
]), tf.TensorShape([args.seq_length])))
return dataset, dev_dataset
train_dataset = dataset.take(sample_train)
dev_dataset = dataset.skip(sample_train)
return train_dataset, dev_dataset
def get_tokenizers_ckeckpoint(args1):
global args
args = args1
tokenizer_ro_path = join(args.checkpoint, 'tokenizer_ro')
tokenizer_ro = tfds.features.text.SubwordTextEncoder.load_from_file(tokenizer_ro_path)
tf.compat.v1.logging.info('restoring ro tokenizer from {}'.format(tokenizer_ro_path))
tokenizer_bert = None
if args.bert:
tokenizer_bert_path = join(args.checkpoint, 'tokenizer_bert.vocab')
tokenizer_bert = FullTokenizer(vocab_file=tokenizer_bert_path)
tokenizer_bert.vocab_size = len(tokenizer_bert.vocab)
tf.compat.v1.logging.info('restoring bert tokenizer from {}'.format(tokenizer_bert_path))
tf.compat.v1.logging.info('tokenizers restored')
return tokenizer_ro, tokenizer_bert
