You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
85 lines
3.6 KiB
85 lines
3.6 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ============================================================================
|
|
"""Generate Gigaword dataset."""
|
|
import os
|
|
import argparse
|
|
|
|
from src.dataset import BiLingualDataLoader
|
|
from src.language_model import NoiseChannelLanguageModel
|
|
from src.utils import Dictionary
|
|
|
|
parser = argparse.ArgumentParser(description='Create Gigaword fine-tune Dataset.')
|
|
parser.add_argument("--train_src", type=str, default="", required=False,
|
|
help="train dataset source file path.")
|
|
parser.add_argument("--train_ref", type=str, default="", required=False,
|
|
help="train dataset reference file path.")
|
|
parser.add_argument("--test_src", type=str, default="", required=False,
|
|
help="test dataset source file path.")
|
|
parser.add_argument("--test_ref", type=str, default="", required=False,
|
|
help="test dataset reference file path.")
|
|
parser.add_argument("--noise_prob", type=float, default=0., required=False,
|
|
help="add noise prob.")
|
|
parser.add_argument("--existed_vocab", type=str, default="", required=False,
|
|
help="existed vocab path.")
|
|
parser.add_argument("--max_len", type=int, default=64, required=False,
|
|
help="max length of sentences.")
|
|
parser.add_argument("--output_folder", type=str, default="", required=True,
|
|
help="dataset output path.")
|
|
parser.add_argument("--format", type=str, default="tfrecord", required=False,
|
|
help="dataset format.")
|
|
|
|
if __name__ == '__main__':
|
|
args, _ = parser.parse_known_args()
|
|
|
|
vocab = Dictionary.load_from_persisted_dict(args.existed_vocab)
|
|
|
|
if args.train_src and args.train_ref:
|
|
train = BiLingualDataLoader(
|
|
src_filepath=args.train_src,
|
|
tgt_filepath=args.train_ref,
|
|
src_dict=vocab, tgt_dict=vocab,
|
|
src_lang="en", tgt_lang="en",
|
|
language_model=NoiseChannelLanguageModel(add_noise_prob=args.noise_prob),
|
|
max_sen_len=args.max_len
|
|
)
|
|
if "tf" in args.format.lower():
|
|
train.write_to_tfrecord(
|
|
path=os.path.join(args.output_folder, "gigaword_train_dataset.tfrecord")
|
|
)
|
|
else:
|
|
train.write_to_mindrecord(
|
|
path=os.path.join(args.output_folder, "gigaword_train_dataset.mindrecord")
|
|
)
|
|
|
|
if args.test_src and args.test_ref:
|
|
test = BiLingualDataLoader(
|
|
src_filepath=args.test_src,
|
|
tgt_filepath=args.test_ref,
|
|
src_dict=vocab, tgt_dict=vocab,
|
|
src_lang="en", tgt_lang="en",
|
|
language_model=NoiseChannelLanguageModel(add_noise_prob=0),
|
|
max_sen_len=args.max_len
|
|
)
|
|
if "tf" in args.format.lower():
|
|
test.write_to_tfrecord(
|
|
path=os.path.join(args.output_folder, "gigaword_test_dataset.tfrecord")
|
|
)
|
|
else:
|
|
test.write_to_mindrecord(
|
|
path=os.path.join(args.output_folder, "gigaword_test_dataset.mindrecord")
|
|
)
|
|
|
|
print(f" | Vocabulary size: {vocab.size}.")
|