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

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

校驗(yàn)系統(tǒng)：

校驗(yàn)碼采用ISO7064:1983，MOD11-2校驗(yàn)碼系統(tǒng)（圖為校驗(yàn)規(guī)則樣例）

用身份證號(hào)的前17位的每一位號(hào)碼字符值分別乘上對(duì)應(yīng)的加權(quán)因子值，得到的結(jié)果求和后對(duì)11進(jìn)行取余，最后的結(jié)果放到表2檢驗(yàn)碼字符值..換算關(guān)系表中得出最后的一位身份證號(hào)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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