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59 lines
2.4 KiB
59 lines
2.4 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Generate News Crawl corpus dataset."""
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import argparse
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from src.utils import Dictionary
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from src.utils.preprocess import create_pre_training_dataset
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parser = argparse.ArgumentParser(description='Create News Crawl Pre-Training Dataset.')
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parser.add_argument("--src_folder", type=str, default="", required=True,
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help="Raw corpus folder.")
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parser.add_argument("--existed_vocab", type=str, default="", required=True,
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help="Existed vocab path.")
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parser.add_argument("--mask_ratio", type=float, default=0.4, required=True,
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help="Mask ratio.")
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parser.add_argument("--output_folder", type=str, default="", required=True,
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help="Dataset output path.")
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parser.add_argument("--max_len", type=int, default=32, required=False,
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help="Max length of sentences.")
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parser.add_argument("--suffix", type=str, default="", required=False,
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help="Add suffix to output file.")
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parser.add_argument("--processes", type=int, default=2, required=False,
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help="Size of processes pool.")
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if __name__ == '__main__':
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args, _ = parser.parse_known_args()
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if not (args.src_folder and args.output_folder):
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raise ValueError("Please enter required params.")
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if not args.existed_vocab:
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raise ValueError("`--existed_vocab` is required.")
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vocab = Dictionary.load_from_persisted_dict(args.existed_vocab)
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create_pre_training_dataset(
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folder_path=args.src_folder,
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output_folder_path=args.output_folder,
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vocabulary=vocab,
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prefix="news.20", suffix=args.suffix,
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mask_ratio=args.mask_ratio,
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min_sen_len=10,
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max_sen_len=args.max_len,
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dataset_type="tfrecord",
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cores=args.processes
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)
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print(f" | Vocabulary size: {vocab.size}.")
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