def main():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"data", nargs="+",
help="Source text file.")
parser.add_argument(
"--save_vocab", required=True,
help="Output vocabulary file.")
parser.add_argument(
"--min_frequency", type=int, default=1,
help="Minimum word frequency.")
parser.add_argument(
"--size", type=int, default=0,
help="Maximum vocabulary size. If = 0, do not limit vocabulary.")
parser.add_argument(
"--without_sequence_tokens", default=False, action="store_true",
help="If set, do not add special sequence tokens (start, end) in the vocabulary.")
tokenizers.add_command_line_arguments(parser)
args = parser.parse_args()
tokenizer = tokenizers.build_tokenizer(args)
special_tokens = [constants.PADDING_TOKEN]
if not args.without_sequence_tokens:
special_tokens.append(constants.START_OF_SENTENCE_TOKEN)
special_tokens.append(constants.END_OF_SENTENCE_TOKEN)
vocab = utils.Vocab(special_tokens=special_tokens)
for data_file in args.data:
vocab.add_from_text(data_file, tokenizer=tokenizer)
vocab = vocab.prune(max_size=args.size, min_frequency=args.min_frequency)
vocab.serialize(args.save_vocab)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("data", nargs="*", help="Source text file.")
parser.add_argument(
"--from_vocab",
default=None,
help="Build from a saved vocabulary (see also --from_format).")
parser.add_argument(
"--from_format",
default="default",
choices=["default", "sentencepiece"],
help="The format of the saved vocabulary (see also --from_vocab).")
parser.add_argument("--save_vocab",
required=True,
help="Output vocabulary file.")
parser.add_argument("--min_frequency",
type=int,
default=1,
help="Minimum word frequency.")
parser.add_argument(
"--size",
type=int,
default=0,
help="Maximum vocabulary size. If = 0, do not limit vocabulary.")
parser.add_argument(
"--size_multiple",
type=int,
default=1,
help=
("Ensure that the vocabulary size + 1 is a multiple of this value "
"(+ 1 represents the <unk> token that will be added during the training."
))
parser.add_argument(
"--without_sequence_tokens",
default=False,
action="store_true",
help=
"If set, do not add special sequence tokens (start, end) in the vocabulary."
)
tokenizers.add_command_line_arguments(parser)
args = parser.parse_args()
tokenizer = tokenizers.build_tokenizer(args)
special_tokens = [constants.PADDING_TOKEN]
if not args.without_sequence_tokens:
special_tokens.append(constants.START_OF_SENTENCE_TOKEN)
special_tokens.append(constants.END_OF_SENTENCE_TOKEN)
vocab = utils.Vocab(special_tokens=special_tokens,
from_file=args.from_vocab,
from_format=args.from_format)
for data_file in args.data:
vocab.add_from_text(data_file, tokenizer=tokenizer)
vocab = vocab.prune(max_size=args.size, min_frequency=args.min_frequency)
vocab.pad_to_multiple(args.size_multiple, num_oov_buckets=1)
vocab.serialize(args.save_vocab)
def build_vocab_from_file(src_file, save_path, min_frequency=5, size=0, without_sequence_tokens=False):
"""
Generate word vocabularies from monolingual corpus.
:param src_file: Source text file.
:param save_path: Output vocabulary file.
:param min_frequency: Minimum word frequency. # for yelp and amazon, min_frequency=5
:param size: Maximum vocabulary size. If = 0, do not limit vocabulary.
:param without_sequence_tokens: If set, do not add special sequence tokens (start, end) in the vocabulary.
:return: No return.
"""
special_tokens = [constants.PADDING_TOKEN]
if not without_sequence_tokens:
special_tokens.append(constants.START_OF_SENTENCE_TOKEN)
special_tokens.append(constants.END_OF_SENTENCE_TOKEN)
vocab = utils.Vocab(special_tokens=special_tokens)
if isinstance(src_file, list):
for data_file in src_file:
vocab.add_from_text(data_file)
else:
vocab.add_from_text(src_file)
vocab = vocab.prune(max_size=size, min_frequency=min_frequency)
vocab.serialize(save_path)