forked from mindspore-Ecosystem/mindspore
98 lines
3.2 KiB
Python
98 lines
3.2 KiB
Python
# 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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"""Tokenizer."""
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import os
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import argparse
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from typing import Callable
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from multiprocessing import Pool
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parser = argparse.ArgumentParser(description='Corpus tokenizer which text file must end with `.txt`.')
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parser.add_argument("--corpus_folder", type=str, default="", required=True,
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help="Corpus folder path, if multi-folders are provided, use ',' split folders.")
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parser.add_argument("--output_folder", type=str, default="", required=True,
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help="Output folder path.")
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parser.add_argument("--tokenizer", type=str, default="nltk", required=False,
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help="Tokenizer to be used, nltk or jieba, if nltk is not installed fully, "
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"use jieba instead.")
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parser.add_argument("--pool_size", type=int, default=2, required=False,
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help="Processes pool size.")
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TOKENIZER = Callable
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def create_tokenized_sentences(file_path, tokenized_file):
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"""
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Create tokenized sentences.
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Args:
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file_path (str): Text file.
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tokenized_file (str): Output file.
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"""
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global TOKENIZER
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print(f" | Processing {file_path}.")
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tokenized_sen = []
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with open(file_path, "r") as file:
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for sen in file:
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tokens = TOKENIZER(sen)
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tokens = [t for t in tokens if t != " "]
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if len(tokens) > 175:
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continue
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tokenized_sen.append(" ".join(tokens) + "\n")
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with open(tokenized_file, "w") as file:
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file.writelines(tokenized_sen)
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print(f" | Wrote to {tokenized_file}.")
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def tokenize():
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"""Tokenizer."""
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global TOKENIZER
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args, _ = parser.parse_known_args()
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src_folder = args.corpus_folder.split(",")
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try:
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from nltk.tokenize import word_tokenize
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TOKENIZER = word_tokenize
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except (ImportError, ModuleNotFoundError, LookupError):
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try:
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import jieba
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except Exception as e:
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raise e
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print(" | NLTK is not found, use jieba instead.")
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TOKENIZER = jieba.cut
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if args.tokenizer == "jieba":
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import jieba
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TOKENIZER = jieba.cut
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pool = Pool(args.pool_size)
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for folder in src_folder:
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for file in os.listdir(folder):
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if not file.endswith(".txt"):
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continue
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file_path = os.path.join(folder, file)
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out_path = os.path.join(args.output_folder, file.replace(".txt", "_tokenized.txt"))
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pool.apply_async(create_tokenized_sentences, (file_path, out_path,))
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pool.close()
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pool.join()
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if __name__ == '__main__':
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tokenize()
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