def create_train_dataset(batch_size):
"""create train dataset"""
# apply repeat operations
repeat_count = bert_train_cfg.epoch_size
ds = de.StorageDataset([bert_train_cfg.DATA_DIR], bert_train_cfg.SCHEMA_DIR,
columns_list=["input_ids", "input_mask", "segment_ids", "next_sentence_labels",
"masked_lm_positions", "masked_lm_ids", "masked_lm_weights"])
type_cast_op = deMap.TypeCastOp("int32")
ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op)
ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op)
ds = ds.map(input_columns="next_sentence_labels", operations=type_cast_op)
ds = ds.map(input_columns="segment_ids", operations=type_cast_op)
ds = ds.map(input_columns="input_mask", operations=type_cast_op)
ds = ds.map(input_columns="input_ids", operations=type_cast_op)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
ds = ds.repeat(repeat_count)
return ds
def me_de_train_dataset():
"""test me de train dataset"""
# apply repeat operations
repeat_count = 1
ds = de.StorageDataset(DATA_DIR, SCHEMA_DIR, columns_list=["input_ids", "input_mask", "segment_ids",
"next_sentence_labels", "masked_lm_positions",
"masked_lm_ids", "masked_lm_weights"])
type_cast_op = deMap.TypeCastOp("int32")
ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op)
ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op)
ds = ds.map(input_columns="next_sentence_labels", operations=type_cast_op)
ds = ds.map(input_columns="segment_ids", operations=type_cast_op)
ds = ds.map(input_columns="input_mask", operations=type_cast_op)
ds = ds.map(input_columns="input_ids", operations=type_cast_op)
# apply batch operations
batch_size = 16
ds = ds.batch(batch_size, drop_remainder=True)
ds = ds.repeat(repeat_count)
return ds