"The input data dir. Should contain the TFRecord files. "
"Can be TF Dataset with prefix tfds://")
flags.DEFINE_string(
"output_dir", "/tmp/bigb",
"The output directory where the model checkpoints will be written.")
## Other parameters
flags.DEFINE_string(
"init_checkpoint", None,
"Initial checkpoint (usually from a pre-trained BigBird model).")
flags.DEFINE_integer(
"max_encoder_length", 512,
"The maximum total input sequence length after SentencePiece tokenization. "
"Sequences longer than this will be truncated, and sequences shorter "
"than this will be padded.")
flags.DEFINE_string(
"substitute_newline", None,
"Replace newline charachter from text with supplied string.")
flags.DEFINE_bool(
"do_train", True,
"Whether to run training.")
flags.DEFINE_bool(
"do_eval", False,
"Whether to run eval on the dev set.")
"data_dir", "tfds://wiki40b/en",
"The input data dir. Should contain the TFRecord files. "
"Can be TF Dataset with prefix tfds://")
flags.DEFINE_string(
"output_dir", "/tmp/bigb",
"The output directory where the model checkpoints will be written.")
## Other parameters
flags.DEFINE_string(
"init_checkpoint", None,
"Initial checkpoint (usually from a pre-trained BigBird model).")
flags.DEFINE_integer(
"max_encoder_length", 512,
"The maximum total input sequence length after SentencePiece tokenization. "
"Sequences longer than this will be truncated, and sequences shorter "
"than this will be padded. Must match data generation.")
flags.DEFINE_integer(
"max_predictions_per_seq", 75,
"Maximum number of masked LM predictions per sequence. "
"Must match data generation.")
flags.DEFINE_float("masked_lm_prob", 0.15, "Masked LM probability.")
flags.DEFINE_string(
"substitute_newline", " ",
"Replace newline charachter from text with supplied string.")
flags.DEFINE_bool("do_train", True, "Whether to run training.")
import tokenization
import numpy as np
FLAGS = flags.FLAGS
flags.DEFINE_string("input_file", None,
"Input raw text file (or comma-separated list of files).")
flags.DEFINE_string(
"output_file", None,
"Output TF example file (or comma-separated list of files).")
flags.DEFINE_string("vocab_file", None,
"The vocabulary file that the BERT model was trained on.")
flags.DEFINE_integer("max_seq_length", 2048, "Maximum sequence length.")
flags.DEFINE_integer("max_predictions_per_seq", 75,
"Maximum number of masked LM predictions per sequence.")
flags.DEFINE_integer("random_seed", 12345, "Random seed for data generation.")
flags.DEFINE_float("masked_lm_prob", 0.15, "Masked LM probability.")
flags.DEFINE_integer("split_output_data_len", 6, "split output data len")
flags.DEFINE_bool("is_train", True, "training data를 생성할 것인지")
class TrainingInstance(object):
"""A single training instance (sentence pair)."""
