依据GB11643-1999中国公民身份证号是特点组成码，由十七位数字本身码和一位数字校验码构成 ，顺序排列从左至右先后为：

  1. 六位数字地址码
  2. 八位数字出生年月码
  3. 三位数字次序码
  4. 一位数字校验码（数字10用罗马帝国X表明）

校检系统软件：

     校验码选用ISO7064:1983，MOD11-2校验码系统软件（图为校检标准示例）

用身份证号码的前17位的每一位号标识符值各自乘上相匹配的权重计算因素值，获得的結果求饶后对11开展取余 ，最终的結果放进表2检验码标识符值..计算关系表中得到最终的一位身份证号

编码：

# 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_bert from 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) 文章来源于网络，如有侵权请联系站长QQ61910465删除
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