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基于Python的身份证验证识别和数据处理

发布于2020-11-18 06:39     阅读(460)     评论(0)     点赞(7)     收藏(0)


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根据GB11643-1999公民身份证号码是特征组合码,由十七位数字本体码和一位数字校验码组成,排列顺序从左至右依次为:

  1. 六位数字地址码
  2. 八位数字出生日期码
  3. 三位数字顺序码
  4. 一位数字校验码(数字10用罗马X表示)

校验系统:

     校验码采用ISO7064:1983,MOD11-2校验码系统(图为校验规则样例)

用身份证号的前17位的每一位号码字符值分别乘上对应的加权因子值,得到的结果求和后对11进行取余,最后的结果放到表2检验码字符值..换算关系表中得出最后的一位身份证号码

代码:

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# 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)

原文链接:https://www.cnblogs.com/langzhig/p/13969958.html

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所属网站分类: 技术文章 > 博客

作者:sdhjsdh

链接: https://www.pythonheidong.com/blog/article/618136/4983dc0caaeb422fca9d/

来源: python黑洞网

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