# coding=utf-8 # Copyright 2019-present, the HuggingFace Inc. team. # # 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. """ Preprocessing script before training DistilBERT. """ from collections import Counter import argparse import pickle import logging logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt = '%m/%d/%Y %H:%M:%S', level = logging.INFO) logger = logging.getLogger(__name__) if __name__ == '__main__': parser = argparse.ArgumentParser(description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)") parser.add_argument("--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset.") parser.add_argument("--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file.") parser.add_argument("--vocab_size", default=30522, type=int) args = parser.parse_args() logger.info(f'Loading data from {args.data_file}') with open(args.data_file, 'rb') as fp: data = pickle.load(fp) logger.info('Counting occurences for MLM.') counter = Counter() for tk_ids in data: counter.update(tk_ids) counts = [0]*args.vocab_size for k, v in counter.items(): counts[k] = v logger.info(f'Dump to {args.token_counts_dump}') with open(args.token_counts_dump, 'wb') as handle: pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)