根據GB11643-1999公民身份證號碼是特征組合碼，由十七位數字本體碼和一位數字校驗碼組成，排列順序從左至右依次為：

六位數字地址碼八位數字出生日期碼三位數字順序碼一位數字校驗碼（數字10用羅馬X表示）

校驗系統：

校驗碼采用ISO7064:1983，MOD11-2校驗碼系統（圖為校驗規則樣例）

用身份證號的前17位的每一位號碼字符值分別乘上對應的加權因子值，得到的結果求和后對11進行取余，最后的結果放到表2檢驗碼字符值..換算關系表中得出最后的一位身份證號碼

代碼：

# coding=utf-8# Copyright 2018 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.'''Convert BERT checkpoint.''' import argparse import torch from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bertfrom transformers.utils import logging logging.set_verbosity_info() def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path): # Initialise PyTorch model config = BertConfig.from_json_file(bert_config_file) print('Building PyTorch model from configuration: {}'.format(str(config))) model = BertForPreTraining(config) # Load weights from tf checkpoint load_tf_weights_in_bert(model, config, tf_checkpoint_path) # Save pytorch-model print('Save PyTorch model to {}'.format(pytorch_dump_path)) torch.save(model.state_dict(), pytorch_dump_path) if __name__ == '__main__': parser = argparse.ArgumentParser() # Required parameters parser.add_argument( '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.' ) parser.add_argument( '--bert_config_file', default=None, type=str, required=True, help='The config json file corresponding to the pre-trained BERT model. n' 'This specifies the model architecture.', ) parser.add_argument( '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.' ) args = parser.parse_args() convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

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