nlp_DL_Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "22a77ecf",
   "metadata": {},
   "source": [
    "# basic of nlp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73e77262",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 文本分类"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "d8de481e",
   "metadata": {
    "hidden": true
   },
   "source": [
    "https://www.zhihu.com/question/50888062/answer/1714628626\n",
    "不同模型的适用场景不同，常用的模型有Fasttext/TextCNN/DPCNN/TextRCNN/BiLSTM+Attention/HAN/BERT/Capsule/TextGCN\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2e7fe4e",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e9149e9f",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Seq2seq\n",
    "\n",
    "seq2seq是一种框架结构，它接受一个序列，再输出一个序列，由编码器和解码器两部分构成。 具体地，编码器处理输入序列中的每一项，将捕获的信息编译成一个向量（encoder）。处理完整个输入序列后，编码器将编码后的信息发送给解码器，解码器开始生成输出序列"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a158561",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Attention机制\n",
    "在attention机制出来之前，大多数人用的是rnn来捕捉输入序列的语义信息，但rnn存在长距离信息难以捕捉的问题。 attention机制源于人脑的认知原理，人类会选择性关注自己想关注的内容而忽略另外一部分，将attention机制融入NLP任务，就可以在适当的位置捕捉适当的信息。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd38ce8d",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## torch构建seq2seq + attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8a779a42",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-13T08:19:57.183132Z",
     "start_time": "2021-04-13T08:19:57.170129Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# 构建seq2seq + attention\n",
    "# encoder编码器\n",
    "\n",
    "import torch.nn as nn\n",
    "# 编码器\n",
    "class EncoderRNN(nn.Module):\n",
    "    def __init__(self,input_size,hidden_size):\n",
    "        super(EncoderRNN,self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.embedding = nn.Embedding(input_size,hidden_size)\n",
    "        self.gru = nn.GRU(hidden_size,hidden_size)\n",
    "    \n",
    "    def forward(self,input,hidden):\n",
    "        embedded = self.embedding(input).view(1,1,-1)\n",
    "        output,hidden = self.gru(embedded,hidden)\n",
    "        return output,hidden\n",
    "    \n",
    "    # 初始化隐状态\n",
    "    def initHidden(self):\n",
    "        return torch.zeros(1,1,self.hidden_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c8602211",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-13T08:21:07.291296Z",
     "start_time": "2021-04-13T08:21:07.278315Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "# 解码器\n",
    "class AttnDecoderRNN(nn.Module):\n",
    "    def __init__(self,hidden_size,output_size):\n",
    "        super(AttnDecoderRNN,self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.output_size = output_size\n",
    "        \n",
    "        self.embedding = nn.Embedding(\n",
    "        self.output_size,self.hidden_size)\n",
    "        self.gru = nn.GRU(self.hidden_size,self.hidden_size)\n",
    "        self.out = nn.Linear(self.hidden_size*2,self.output_size)\n",
    "        \n",
    "    def forward(self,input,hidden,encoder_outputs):\n",
    "        embedded = self.embedding(input).view(1,1,-1)\n",
    "        embedded = F.relu(embedded)\n",
    "        output,hidden = self.gru(embedded,hidden)\n",
    "        \n",
    "        # 注意力运算，由encoder_outputs和hidden进行内积\n",
    "        attn_weights = F.softmax(torch.bmm(encoder_outputs.unsqueeze(\n",
    "        0),hidden.view(1,self.hidden_size,-1)),dim=1)\n",
    "        # 添加注意力机制的原文信息\n",
    "        weighted_context = torch.matmul(\n",
    "        attn_weights.squeeze(2),encoder_outputs)\n",
    "        # 拼接当前信息以及添加注意力机制的原文信息\n",
    "        output = torch.cat([output.squeeze(0),weighted_context],dim=1)\n",
    "        # 对输出进行 softmax 计算\n",
    "        output = F.softmax(self.out(output),dim=1)\n",
    "        return output,hidden,attn_weights\n",
    "    \n",
    "    def initHidden(self):\n",
    "        return torch.zeros(1,1,self.hidden_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a74651ab",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-13T08:21:52.706252Z",
     "start_time": "2021-04-13T08:21:52.652507Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 1000])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对于编码器与解码器的尝试\n",
    "import torch\n",
    "INPUT_SIZE = 1000\n",
    "HIDDEN_SIZE = 60\n",
    "OUTPUT_SIZE = 1000\n",
    "\n",
    "my_encoder = EncoderRNN(input_size = INPUT_SIZE,hidden_size=HIDDEN_SIZE)\n",
    "my_decoder = AttnDecoderRNN(hidden_size=HIDDEN_SIZE,output_size=OUTPUT_SIZE)\n",
    "\n",
    "# 编码过程\n",
    "input = torch.tensor([[0],[4],[3],[1]])\n",
    "encoder_hidden = my_encoder.initHidden()\n",
    "input_length = input.size(0)\n",
    "encoder_outputs = torch.zeros(input_length,my_encoder.hidden_size)\n",
    "for i in range(input_length):\n",
    "    encoder_output,encoder_hidden = my_encoder(input[i],encoder_hidden)\n",
    "    encoder_outputs[i] = encoder_output[0,0]\n",
    "encoder_outputs.shape\n",
    "\n",
    "# 解码过程\n",
    "decoder_input = torch.tensor([[0]])\n",
    "decoder_hidden = encoder_hidden\n",
    "decoder_output,decoder_hidden,decoder_attention = my_decoder(\n",
    "decoder_input,decoder_hidden,encoder_outputs)\n",
    "decoder_output.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "298f7ecb",
   "metadata": {},
   "source": [
    "# Transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "022cb645",
   "metadata": {},
   "source": [
    "## Transformer的hugging face简易调包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6d08f107",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-13T08:24:29.242722Z",
     "start_time": "2021-04-13T08:24:26.831573Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json (Caused by ProxyError('Cannot connect to proxy.', RemoteDisconnected('Remote end closed connection without response')))\n",
      "HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json (Caused by ProxyError('Cannot connect to proxy.', RemoteDisconnected('Remote end closed connection without response')))\n"
     ]
    },
    {
     "ename": "OSError",
     "evalue": "Can't load config for 'distilbert-base-uncased-finetuned-sst-2-english'. Make sure that:\n\n- 'distilbert-base-uncased-finetuned-sst-2-english' is a correct model identifier listed on 'https://huggingface.co/models'\n\n- or 'distilbert-base-uncased-finetuned-sst-2-english' is the correct path to a directory containing a config.json file\n\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRemoteDisconnected\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    666\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_new_proxy_conn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 667\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prepare_proxy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    668\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_prepare_proxy\u001b[1;34m(self, conn)\u001b[0m\n\u001b[0;32m    931\u001b[0m         \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_tunnel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_proxy_host\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproxy_headers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 932\u001b[1;33m         \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    933\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    316\u001b[0m             \u001b[1;31m# self._tunnel_host below.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 317\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tunnel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    318\u001b[0m             \u001b[1;31m# Mark this connection as not reusable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_tunnel\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    905\u001b[0m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresponse_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msock\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 906\u001b[1;33m         \u001b[1;33m(\u001b[0m\u001b[0mversion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    907\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    264\u001b[0m             \u001b[1;31m# sending a valid response.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 265\u001b[1;33m             raise RemoteDisconnected(\"Remote end closed connection without\"\n\u001b[0m\u001b[0;32m    266\u001b[0m                                      \" response\")\n",
      "\u001b[1;31mRemoteDisconnected\u001b[0m: Remote end closed connection without response",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    448\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 449\u001b[1;33m                     \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    450\u001b[0m                 )\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    726\u001b[0m             retries = retries.increment(\n\u001b[1;32m--> 727\u001b[1;33m                 \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_pool\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_stacktrace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    728\u001b[0m             )\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\util\\retry.py\u001b[0m in \u001b[0;36mincrement\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    445\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 446\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    447\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json (Caused by ProxyError('Cannot connect to proxy.', RemoteDisconnected('Remote end closed connection without response')))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mProxyError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\transformers\\configuration_utils.py\u001b[0m in \u001b[0;36mget_config_dict\u001b[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[0;32m    464\u001b[0m                 \u001b[0muse_auth_token\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_auth_token\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 465\u001b[1;33m                 \u001b[0muser_agent\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muser_agent\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    466\u001b[0m             )\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\transformers\\file_utils.py\u001b[0m in \u001b[0;36mcached_path\u001b[1;34m(url_or_filename, cache_dir, force_download, proxies, resume_download, user_agent, extract_compressed_file, force_extract, use_auth_token, local_files_only)\u001b[0m\n\u001b[0;32m   1172\u001b[0m             \u001b[0muse_auth_token\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_auth_token\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1173\u001b[1;33m             \u001b[0mlocal_files_only\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlocal_files_only\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1174\u001b[0m         )\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\transformers\\file_utils.py\u001b[0m in \u001b[0;36mget_from_cache\u001b[1;34m(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, use_auth_token, local_files_only)\u001b[0m\n\u001b[0;32m   1334\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1335\u001b[1;33m             \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_redirects\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0metag_timeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1336\u001b[0m             \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mhead\u001b[1;34m(url, **kwargs)\u001b[0m\n\u001b[0;32m    103\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'allow_redirects'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 104\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'head'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    105\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     60\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    529\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 530\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    531\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    642\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 643\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    509\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_ProxyError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 510\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mProxyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    511\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mProxyError\u001b[0m: HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json (Caused by ProxyError('Cannot connect to proxy.', RemoteDisconnected('Remote end closed connection without response')))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\transformers\\pipelines\\base.py\u001b[0m in \u001b[0;36minfer_framework_from_model\u001b[1;34m(model, model_classes, revision, task)\u001b[0m\n\u001b[0;32m     91\u001b[0m                 \u001b[0mmodel_class\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_classes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pt\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mAutoModel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 92\u001b[1;33m                 \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_class\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrevision\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrevision\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_from_pipeline\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     93\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\transformers\\models\\auto\\auto_factory.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[0;32m    354\u001b[0m             config, kwargs = AutoConfig.from_pretrained(\n\u001b[1;32m--> 355\u001b[1;33m                 \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_unused_kwargs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    356\u001b[0m             )\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\transformers\\models\\auto\\configuration_auto.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[0;32m    397\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"_from_auto\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 398\u001b[1;33m         \u001b[0mconfig_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPretrainedConfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_config_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    399\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34m\"model_type\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mconfig_dict\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\transformers\\configuration_utils.py\u001b[0m in \u001b[0;36mget_config_dict\u001b[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[0;32m    476\u001b[0m             )\n\u001b[1;32m--> 477\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mEnvironmentError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    478\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOSError\u001b[0m: Can't load config for 'distilbert-base-uncased-finetuned-sst-2-english'. Make sure that:\n\n- 'distilbert-base-uncased-finetuned-sst-2-english' is a correct model identifier listed on 'https://huggingface.co/models'\n\n- or 'distilbert-base-uncased-finetuned-sst-2-english' is the correct path to a directory containing a config.json file\n\n",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mRemoteDisconnected\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    666\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_new_proxy_conn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 667\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prepare_proxy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    668\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_prepare_proxy\u001b[1;34m(self, conn)\u001b[0m\n\u001b[0;32m    931\u001b[0m         \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_tunnel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_proxy_host\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproxy_headers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 932\u001b[1;33m         \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    933\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    316\u001b[0m             \u001b[1;31m# self._tunnel_host below.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 317\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tunnel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    318\u001b[0m             \u001b[1;31m# Mark this connection as not reusable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_tunnel\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    905\u001b[0m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresponse_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msock\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 906\u001b[1;33m         \u001b[1;33m(\u001b[0m\u001b[0mversion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    907\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    264\u001b[0m             \u001b[1;31m# sending a valid response.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 265\u001b[1;33m             raise RemoteDisconnected(\"Remote end closed connection without\"\n\u001b[0m\u001b[0;32m    266\u001b[0m                                      \" response\")\n",
      "\u001b[1;31mRemoteDisconnected\u001b[0m: Remote end closed connection without response",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    448\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 449\u001b[1;33m                     \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    450\u001b[0m                 )\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    726\u001b[0m             retries = retries.increment(\n\u001b[1;32m--> 727\u001b[1;33m                 \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_pool\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_stacktrace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    728\u001b[0m             )\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\util\\retry.py\u001b[0m in \u001b[0;36mincrement\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    445\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 446\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    447\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json (Caused by ProxyError('Cannot connect to proxy.', RemoteDisconnected('Remote end closed connection without response')))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mProxyError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\transformers\\configuration_utils.py\u001b[0m in \u001b[0;36mget_config_dict\u001b[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[0;32m    464\u001b[0m                 \u001b[0muse_auth_token\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_auth_token\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 465\u001b[1;33m                 \u001b[0muser_agent\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muser_agent\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    466\u001b[0m             )\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\transformers\\file_utils.py\u001b[0m in \u001b[0;36mcached_path\u001b[1;34m(url_or_filename, cache_dir, force_download, proxies, resume_download, user_agent, extract_compressed_file, force_extract, use_auth_token, local_files_only)\u001b[0m\n\u001b[0;32m   1172\u001b[0m             \u001b[0muse_auth_token\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_auth_token\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1173\u001b[1;33m             \u001b[0mlocal_files_only\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlocal_files_only\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1174\u001b[0m         )\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\transformers\\file_utils.py\u001b[0m in \u001b[0;36mget_from_cache\u001b[1;34m(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, use_auth_token, local_files_only)\u001b[0m\n\u001b[0;32m   1334\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1335\u001b[1;33m             \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_redirects\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0metag_timeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1336\u001b[0m             \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mhead\u001b[1;34m(url, **kwargs)\u001b[0m\n\u001b[0;32m    103\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'allow_redirects'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 104\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'head'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    105\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     60\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    529\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 530\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    531\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    642\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 643\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    509\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_ProxyError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 510\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mProxyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    511\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mProxyError\u001b[0m: HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json (Caused by ProxyError('Cannot connect to proxy.', RemoteDisconnected('Remote end closed connection without response')))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-e7eea0b9fae2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtransformers\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# 情感分类\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0memotion_classifier\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'sentiment-analysis'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0memotion_classifier\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'what a fucking bad bitch'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\transformers\\pipelines\\__init__.py\u001b[0m in \u001b[0;36mpipeline\u001b[1;34m(task, model, config, tokenizer, framework, revision, use_fast, model_kwargs, **kwargs)\u001b[0m\n\u001b[0;32m    364\u001b[0m     \u001b[1;31m# Infer the framework form the model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    365\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mframework\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 366\u001b[1;33m         \u001b[0mframework\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minfer_framework_from_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtargeted_task\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrevision\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrevision\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtask\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    367\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    368\u001b[0m     \u001b[0mtask_class\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel_class\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtargeted_task\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"impl\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtargeted_task\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mframework\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\transformers\\pipelines\\base.py\u001b[0m in \u001b[0;36minfer_framework_from_model\u001b[1;34m(model, model_classes, revision, task)\u001b[0m\n\u001b[0;32m     93\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m                 \u001b[0mmodel_class\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_classes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"tf\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTFAutoModel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 95\u001b[1;33m                 \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_class\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrevision\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrevision\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_from_pipeline\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     96\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     97\u001b[0m     \u001b[0mframework\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"tf\"\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"TF\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m\"pt\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\transformers\\models\\auto\\auto_factory.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[0;32m    353\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mPretrainedConfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    354\u001b[0m             config, kwargs = AutoConfig.from_pretrained(\n\u001b[1;32m--> 355\u001b[1;33m                 \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_unused_kwargs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    356\u001b[0m             )\n\u001b[0;32m    357\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\transformers\\models\\auto\\configuration_auto.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[0;32m    396\u001b[0m         \"\"\"\n\u001b[0;32m    397\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"_from_auto\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 398\u001b[1;33m         \u001b[0mconfig_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPretrainedConfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_config_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    399\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34m\"model_type\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mconfig_dict\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    400\u001b[0m             \u001b[0mconfig_class\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCONFIG_MAPPING\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mconfig_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"model_type\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\transformers\\configuration_utils.py\u001b[0m in \u001b[0;36mget_config_dict\u001b[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[0;32m    475\u001b[0m                 \u001b[1;34mf\"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a {CONFIG_NAME} file\\n\\n\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    476\u001b[0m             )\n\u001b[1;32m--> 477\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mEnvironmentError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    478\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    479\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mJSONDecodeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOSError\u001b[0m: Can't load config for 'distilbert-base-uncased-finetuned-sst-2-english'. Make sure that:\n\n- 'distilbert-base-uncased-finetuned-sst-2-english' is a correct model identifier listed on 'https://huggingface.co/models'\n\n- or 'distilbert-base-uncased-finetuned-sst-2-english' is the correct path to a directory containing a config.json file\n\n"
     ]
    }
   ],
   "source": [
    "# hugging face 调包\n",
    "from transformers import pipeline\n",
    "# 没一个用的时候要下载一个pipeline model\n",
    "\n",
    "# 情感分类\n",
    "emotion_classifier = pipeline('sentiment-analysis')\n",
    "emotion_classifier ('what a fucking bad bitch')\n",
    "\n",
    "# NER\n",
    "ner = pipeline('ner')\n",
    "ner('Fudan University is a very good college')\n",
    "\n",
    "# 翻译\n",
    "translator = pipeline('translation_en_to_fr')\n",
    "translator = ('please fuck me,use your big old cock')\n",
    "\n",
    "# 文本摘要\n",
    "TEXT = ''\n",
    "summarizer = pipeline('summarization')\n",
    "summarizer(TEXT)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bee3b4f5",
   "metadata": {},
   "source": [
    "# BERT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3bbc10c",
   "metadata": {},
   "source": [
    "## BERT的hugging face简易调包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1477902f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-13T08:35:27.342181Z",
     "start_time": "2021-04-13T08:35:27.254095Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. <pytorch_transformers.tokenization_bert.BertTokenizer object at 0x0000012CFB0BBEB8> \n",
      "\n",
      "2. [['[CLS]', '中', '国', '的', '首', '都', '是', '哪', '里', '?', '[SEP]', '北', '京', '是', '[MASK]', '国', '的', '首', '都', '。', '[SEP]']] \n",
      "\n",
      "3. [[101, 704, 1744, 4638, 7674, 6963, 3221, 1525, 7027, 136, 102, 1266, 776, 3221, 103, 1744, 4638, 7674, 6963, 511, 102]] \n",
      "\n",
      "4. tensor([[ 101,  704, 1744, 4638, 7674, 6963, 3221, 1525, 7027,  136,  102, 1266,\n",
      "          776, 3221,  103, 1744, 4638, 7674, 6963,  511,  102]])\n"
     ]
    }
   ],
   "source": [
    "# hugging face 调包\n",
    "import torch\n",
    "from pytorch_transformers import BertTokenizer\n",
    "\n",
    "# 指定实验需下载的预训练模型\n",
    "model_name = 'bert-base-chinese'\n",
    "\n",
    "# BERT在预训练中引入了[CLS]和[SEP]标记句子的开头和结尾\n",
    "# 准备输入模型的语句\n",
    "samples = ['[CLS]中国的首都是哪里?[SEP]北京是[MASK]国的首都。[SEP]']\n",
    "\n",
    "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
    "print('''1.''',tokenizer,'\\n')\n",
    "\n",
    "tokenized_text = [tokenizer.tokenize(i) for i in samples]\n",
    "print('''2.''',tokenized_text,'\\n')\n",
    "\n",
    "input_ids = [tokenizer.convert_tokens_to_ids(i) for i in tokenized_text]\n",
    "print('''3.''',input_ids,'\\n')\n",
    "\n",
    "input_ids = torch.LongTensor(input_ids)\n",
    "print('''4.''',input_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b915e7df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-13T08:36:57.358321Z",
     "start_time": "2021-04-13T08:36:57.167285Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Couldn't reach server at 'https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-config.json' to download pretrained model configuration file.\n"
     ]
    },
    {
     "ename": "ProxyError",
     "evalue": "HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /models.huggingface.co/bert/bert-base-chinese-config.json (Caused by ProxyError('Cannot connect to proxy.', RemoteDisconnected('Remote end closed connection without response')))",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRemoteDisconnected\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    666\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_new_proxy_conn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 667\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prepare_proxy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    668\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_prepare_proxy\u001b[1;34m(self, conn)\u001b[0m\n\u001b[0;32m    931\u001b[0m         \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_tunnel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_proxy_host\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproxy_headers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 932\u001b[1;33m         \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    933\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    316\u001b[0m             \u001b[1;31m# self._tunnel_host below.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 317\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tunnel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    318\u001b[0m             \u001b[1;31m# Mark this connection as not reusable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_tunnel\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    905\u001b[0m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresponse_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msock\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 906\u001b[1;33m         \u001b[1;33m(\u001b[0m\u001b[0mversion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    907\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    264\u001b[0m             \u001b[1;31m# sending a valid response.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 265\u001b[1;33m             raise RemoteDisconnected(\"Remote end closed connection without\"\n\u001b[0m\u001b[0;32m    266\u001b[0m                                      \" response\")\n",
      "\u001b[1;31mRemoteDisconnected\u001b[0m: Remote end closed connection without response",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    448\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 449\u001b[1;33m                     \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    450\u001b[0m                 )\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    726\u001b[0m             retries = retries.increment(\n\u001b[1;32m--> 727\u001b[1;33m                 \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_pool\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_stacktrace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    728\u001b[0m             )\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\util\\retry.py\u001b[0m in \u001b[0;36mincrement\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    445\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 446\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    447\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /models.huggingface.co/bert/bert-base-chinese-config.json (Caused by ProxyError('Cannot connect to proxy.', RemoteDisconnected('Remote end closed connection without response')))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mProxyError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-5adb684c879c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mbert_saving_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'./'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# 读取预训练模型\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBertForMaskedLM\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_name\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcache_dir\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbert_saving_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\pytorch_transformers\\modeling_utils.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[0;32m    490\u001b[0m                 \u001b[0mcache_dir\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcache_dir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_unused_kwargs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    491\u001b[0m                 \u001b[0mforce_download\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mforce_download\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 492\u001b[1;33m                 \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    493\u001b[0m             )\n\u001b[0;32m    494\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\pytorch_transformers\\modeling_utils.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[0;32m    192\u001b[0m                         \u001b[1;34m', '\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpretrained_config_archive_map\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    193\u001b[0m                         config_file))\n\u001b[1;32m--> 194\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    195\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mresolved_config_file\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mconfig_file\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    196\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"loading configuration file {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconfig_file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\pytorch_transformers\\modeling_utils.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[0;32m    178\u001b[0m         \u001b[1;31m# redirect to the cache, if necessary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    179\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 180\u001b[1;33m             \u001b[0mresolved_config_file\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcached_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconfig_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcache_dir\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcache_dir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mforce_download\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mforce_download\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    181\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mEnvironmentError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    182\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mpretrained_model_name_or_path\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpretrained_config_archive_map\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\pytorch_transformers\\file_utils.py\u001b[0m in \u001b[0;36mcached_path\u001b[1;34m(url_or_filename, cache_dir, force_download, proxies)\u001b[0m\n\u001b[0;32m    116\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mparsed\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscheme\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'http'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'https'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m's3'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    117\u001b[0m         \u001b[1;31m# URL, so get it from the cache (downloading if necessary)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 118\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mget_from_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl_or_filename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcache_dir\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcache_dir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mforce_download\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mforce_download\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    119\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl_or_filename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    120\u001b[0m         \u001b[1;31m# File, and it exists.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\pytorch_transformers\\file_utils.py\u001b[0m in \u001b[0;36mget_from_cache\u001b[1;34m(url, cache_dir, force_download, proxies)\u001b[0m\n\u001b[0;32m    242\u001b[0m                 \u001b[0ms3_get\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtemp_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    243\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m                 \u001b[0mhttp_get\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtemp_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    245\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    246\u001b[0m             \u001b[1;31m# we are copying the file before closing it, so flush to avoid truncation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\pytorch_transformers\\file_utils.py\u001b[0m in \u001b[0;36mhttp_get\u001b[1;34m(url, temp_file, proxies)\u001b[0m\n\u001b[0;32m    178\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    179\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mhttp_get\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtemp_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 180\u001b[1;33m     \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    181\u001b[0m     \u001b[0mcontent_length\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Content-Length'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    182\u001b[0m     \u001b[0mtotal\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcontent_length\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcontent_length\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(url, params, **kwargs)\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'allow_redirects'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 76\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'get'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     77\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     78\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     59\u001b[0m     \u001b[1;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    528\u001b[0m         }\n\u001b[0;32m    529\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 530\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    531\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    532\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    641\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    642\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 643\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    645\u001b[0m         \u001b[1;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    508\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    509\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_ProxyError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 510\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mProxyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    511\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    512\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_SSLError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mProxyError\u001b[0m: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /models.huggingface.co/bert/bert-base-chinese-config.json (Caused by ProxyError('Cannot connect to proxy.', RemoteDisconnected('Remote end closed connection without response')))"
     ]
    }
   ],
   "source": [
    "from pytorch_transformers import BertForMaskedLM\n",
    "bert_saving_path = './'\n",
    "# 读取预训练模型\n",
    "model = BertForMaskedLM.from_pretrained(model_name,cache_dir=bert_saving_path)\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99fa6d09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 此时已经准备好了语句和待训练模型，接下来要做的就是让模型去预测覆盖词的序号\n",
    "outputs = model(input_ids)\n",
    "prediction_scores = outputs[0]\n",
    "prediction_scores.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2b4e7fc4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-13T08:38:02.384439Z",
     "start_time": "2021-04-13T08:38:02.334920Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'prediction_scores' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-3af5b6d23b5a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 找到预测值中最大值对应的序号，通过tokenizer.convert_ids_to_tokens()在词表中查找，转换成对应的字\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0msample\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprediction_scores\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mpred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'prediction_scores' is not defined"
     ]
    }
   ],
   "source": [
    "# 找到预测值中最大值对应的序号，通过tokenizer.convert_ids_to_tokens()在词表中查找，转换成对应的字\n",
    "import numpy as np\n",
    "sample = prediction_scores[0].detach().numpy()\n",
    "pred = np.argmax(sample,axis=1)\n",
    "\n",
    "tokenizer.convert_ids_to_tokens(pred)[14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8daaa39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 句子预测任务，训练数据的构成是由语料库中的句子组成句子对\n",
    "samples = [\"[CLS]今天天气怎么样？[SEP]今天天气很好。[SEP]\",\"[CLS]小明今年几岁了？[SEP]小明爱吃西瓜。[SEP]\"]\n",
    "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
    "tokenized_text = [tokenizer.tokenize(i) for i in samples]\n",
    "input_ids = [tokenizer.convert_tokens_to_ids(i) for i in tokenized_text]\n",
    "input_ids = torch.LongTensor(input_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3995aade",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-13T08:39:16.927335Z",
     "start_time": "2021-04-13T08:39:16.757325Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Couldn't reach server at 'https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-config.json' to download pretrained model configuration file.\n"
     ]
    },
    {
     "ename": "ProxyError",
     "evalue": "HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /models.huggingface.co/bert/bert-base-chinese-config.json (Caused by ProxyError('Cannot connect to proxy.', RemoteDisconnected('Remote end closed connection without response')))",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRemoteDisconnected\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    666\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_new_proxy_conn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 667\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prepare_proxy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    668\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_prepare_proxy\u001b[1;34m(self, conn)\u001b[0m\n\u001b[0;32m    931\u001b[0m         \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_tunnel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_proxy_host\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproxy_headers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 932\u001b[1;33m         \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    933\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    316\u001b[0m             \u001b[1;31m# self._tunnel_host below.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 317\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tunnel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    318\u001b[0m             \u001b[1;31m# Mark this connection as not reusable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_tunnel\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    905\u001b[0m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresponse_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msock\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 906\u001b[1;33m         \u001b[1;33m(\u001b[0m\u001b[0mversion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    907\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    264\u001b[0m             \u001b[1;31m# sending a valid response.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 265\u001b[1;33m             raise RemoteDisconnected(\"Remote end closed connection without\"\n\u001b[0m\u001b[0;32m    266\u001b[0m                                      \" response\")\n",
      "\u001b[1;31mRemoteDisconnected\u001b[0m: Remote end closed connection without response",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    448\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 449\u001b[1;33m                     \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    450\u001b[0m                 )\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    726\u001b[0m             retries = retries.increment(\n\u001b[1;32m--> 727\u001b[1;33m                 \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_pool\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_stacktrace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    728\u001b[0m             )\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\util\\retry.py\u001b[0m in \u001b[0;36mincrement\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    445\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 446\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    447\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /models.huggingface.co/bert/bert-base-chinese-config.json (Caused by ProxyError('Cannot connect to proxy.', RemoteDisconnected('Remote end closed connection without response')))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mProxyError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-57e4922a1602>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 使用BertForNextSentencePrediction()初始化模型\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpytorch_transformers\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mBertForNextSentencePrediction\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBertForNextSentencePrediction\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_name\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcache_dir\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbert_saving_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\pytorch_transformers\\modeling_utils.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[0;32m    490\u001b[0m                 \u001b[0mcache_dir\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcache_dir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_unused_kwargs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    491\u001b[0m                 \u001b[0mforce_download\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mforce_download\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 492\u001b[1;33m                 \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    493\u001b[0m             )\n\u001b[0;32m    494\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\pytorch_transformers\\modeling_utils.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[0;32m    192\u001b[0m                         \u001b[1;34m', '\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpretrained_config_archive_map\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    193\u001b[0m                         config_file))\n\u001b[1;32m--> 194\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    195\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mresolved_config_file\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mconfig_file\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    196\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"loading configuration file {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconfig_file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\pytorch_transformers\\modeling_utils.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[0;32m    178\u001b[0m         \u001b[1;31m# redirect to the cache, if necessary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    179\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 180\u001b[1;33m             \u001b[0mresolved_config_file\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcached_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconfig_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcache_dir\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcache_dir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mforce_download\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mforce_download\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    181\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mEnvironmentError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    182\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mpretrained_model_name_or_path\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpretrained_config_archive_map\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\pytorch_transformers\\file_utils.py\u001b[0m in \u001b[0;36mcached_path\u001b[1;34m(url_or_filename, cache_dir, force_download, proxies)\u001b[0m\n\u001b[0;32m    116\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mparsed\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscheme\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'http'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'https'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m's3'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    117\u001b[0m         \u001b[1;31m# URL, so get it from the cache (downloading if necessary)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 118\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mget_from_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl_or_filename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcache_dir\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcache_dir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mforce_download\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mforce_download\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    119\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl_or_filename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    120\u001b[0m         \u001b[1;31m# File, and it exists.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\pytorch_transformers\\file_utils.py\u001b[0m in \u001b[0;36mget_from_cache\u001b[1;34m(url, cache_dir, force_download, proxies)\u001b[0m\n\u001b[0;32m    242\u001b[0m                 \u001b[0ms3_get\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtemp_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    243\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m                 \u001b[0mhttp_get\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtemp_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    245\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    246\u001b[0m             \u001b[1;31m# we are copying the file before closing it, so flush to avoid truncation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\pytorch_transformers\\file_utils.py\u001b[0m in \u001b[0;36mhttp_get\u001b[1;34m(url, temp_file, proxies)\u001b[0m\n\u001b[0;32m    178\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    179\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mhttp_get\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtemp_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 180\u001b[1;33m     \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    181\u001b[0m     \u001b[0mcontent_length\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Content-Length'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    182\u001b[0m     \u001b[0mtotal\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcontent_length\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcontent_length\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(url, params, **kwargs)\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'allow_redirects'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 76\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'get'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     77\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     78\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     59\u001b[0m     \u001b[1;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    528\u001b[0m         }\n\u001b[0;32m    529\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 530\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    531\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    532\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    641\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    642\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 643\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    645\u001b[0m         \u001b[1;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    508\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    509\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_ProxyError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 510\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mProxyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    511\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    512\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_SSLError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mProxyError\u001b[0m: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /models.huggingface.co/bert/bert-base-chinese-config.json (Caused by ProxyError('Cannot connect to proxy.', RemoteDisconnected('Remote end closed connection without response')))"
     ]
    }
   ],
   "source": [
    "# 使用BertForNextSentencePrediction()初始化模型\n",
    "from pytorch_transformers import BertForNextSentencePrediction\n",
    "model = BertForNextSentencePrediction.from_pretrained(model_name,cache_dir=bert_saving_path)\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a45d73bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 最后将样本输入模型进行预测，输出模型的预测结果\n",
    "outputs = model(input_ids)\n",
    "seq_relationship_scores = outputs[0]\n",
    "seq_relationship_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "030830a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = seq_relationship_scores.detach().numpy()\n",
    "pred = np.argmax(sample,axis=1)\n",
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d5b1cc0",
   "metadata": {},
   "source": [
    "## 基于pytorch的bert文本分类（基于transformers）\n",
    "用的是BERT之恶接一层fc直接进行分类\n",
    "下载预训练模型权重，这里下载的是 chinese_roberta_wwm_ext_pytorch。\n",
    "数据集选择的THUCNews，自行下载并整理出10w条数据，内容是10类新闻文本标题的中文分类问题（10分类），每类新闻标题数据量相等，为1w条"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9115b9b9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-13T08:46:50.422194Z",
     "start_time": "2021-04-13T08:46:50.274204Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ProxyError",
     "evalue": "HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /bert_model//resolve/main/vocab.txt (Caused by ProxyError('Cannot connect to proxy.', RemoteDisconnected('Remote end closed connection without response')))",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRemoteDisconnected\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    666\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_new_proxy_conn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 667\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prepare_proxy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    668\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_prepare_proxy\u001b[1;34m(self, conn)\u001b[0m\n\u001b[0;32m    931\u001b[0m         \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_tunnel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_proxy_host\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproxy_headers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 932\u001b[1;33m         \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    933\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    316\u001b[0m             \u001b[1;31m# self._tunnel_host below.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 317\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tunnel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    318\u001b[0m             \u001b[1;31m# Mark this connection as not reusable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_tunnel\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    905\u001b[0m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresponse_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msock\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 906\u001b[1;33m         \u001b[1;33m(\u001b[0m\u001b[0mversion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    907\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    264\u001b[0m             \u001b[1;31m# sending a valid response.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 265\u001b[1;33m             raise RemoteDisconnected(\"Remote end closed connection without\"\n\u001b[0m\u001b[0;32m    266\u001b[0m                                      \" response\")\n",
      "\u001b[1;31mRemoteDisconnected\u001b[0m: Remote end closed connection without response",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    448\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 449\u001b[1;33m                     \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    450\u001b[0m                 )\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    726\u001b[0m             retries = retries.increment(\n\u001b[1;32m--> 727\u001b[1;33m                 \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_pool\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_stacktrace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    728\u001b[0m             )\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\urllib3\\util\\retry.py\u001b[0m in \u001b[0;36mincrement\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    445\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 446\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    447\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /bert_model//resolve/main/vocab.txt (Caused by ProxyError('Cannot connect to proxy.', RemoteDisconnected('Remote end closed connection without response')))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mProxyError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-867e145bd197>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[0mbert_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"bert_model/\"\u001b[0m    \u001b[1;31m# 该文件夹下存放三个文件（'vocab.txt', 'pytorch_model.bin', 'config.json'）\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m \u001b[0mtokenizer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBertTokenizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbert_path\u001b[0m\u001b[1;33m)\u001b[0m   \u001b[1;31m# 初始化分词器\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\transformers\\tokenization_utils_base.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, *init_inputs, **kwargs)\u001b[0m\n\u001b[0;32m   1668\u001b[0m                         \u001b[0mlocal_files_only\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlocal_files_only\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1669\u001b[0m                         \u001b[0muse_auth_token\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_auth_token\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1670\u001b[1;33m                         \u001b[0muser_agent\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muser_agent\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1671\u001b[0m                     )\n\u001b[0;32m   1672\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\transformers\\file_utils.py\u001b[0m in \u001b[0;36mcached_path\u001b[1;34m(url_or_filename, cache_dir, force_download, proxies, resume_download, user_agent, extract_compressed_file, force_extract, use_auth_token, local_files_only)\u001b[0m\n\u001b[0;32m   1171\u001b[0m             \u001b[0muser_agent\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muser_agent\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1172\u001b[0m             \u001b[0muse_auth_token\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_auth_token\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1173\u001b[1;33m             \u001b[0mlocal_files_only\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlocal_files_only\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1174\u001b[0m         )\n\u001b[0;32m   1175\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl_or_filename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\transformers\\file_utils.py\u001b[0m in \u001b[0;36mget_from_cache\u001b[1;34m(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, use_auth_token, local_files_only)\u001b[0m\n\u001b[0;32m   1333\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mlocal_files_only\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1334\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1335\u001b[1;33m             \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_redirects\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0metag_timeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1336\u001b[0m             \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1337\u001b[0m             \u001b[0metag\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"X-Linked-Etag\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"ETag\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mhead\u001b[1;34m(url, **kwargs)\u001b[0m\n\u001b[0;32m    102\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    103\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'allow_redirects'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 104\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'head'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    105\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    106\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     59\u001b[0m     \u001b[1;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    528\u001b[0m         }\n\u001b[0;32m    529\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 530\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    531\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    532\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    641\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    642\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 643\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    645\u001b[0m         \u001b[1;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\envs\\Deep\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    508\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    509\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_ProxyError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 510\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mProxyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    511\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    512\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_SSLError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mProxyError\u001b[0m: HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /bert_model//resolve/main/vocab.txt (Caused by ProxyError('Cannot connect to proxy.', RemoteDisconnected('Remote end closed connection without response')))"
     ]
    }
   ],
   "source": [
    "# 导入必要的库\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "import json, time \n",
    "from tqdm import tqdm \n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
    "from transformers import BertModel, BertConfig, BertTokenizer, AdamW, get_cosine_schedule_with_warmup\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "bert_path = \"bert_model/\"    # 该文件夹下存放三个文件（'vocab.txt', 'pytorch_model.bin', 'config.json'）\n",
    "tokenizer = BertTokenizer.from_pretrained(bert_path)   # 初始化分词器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2da93774",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2 预处理数据集\n",
    "\n",
    "input_ids, input_masks, input_types,  = [], [], []  # input char ids, segment type ids,  attention mask\n",
    "labels = []         # 标签\n",
    "maxlen = 30      # 取30即可覆盖99%\n",
    "\n",
    "# 数据为每一行(text)\\t(label)的csv文件\n",
    "with open(\"news_title_dataset.csv\", encoding='utf-8') as f:\n",
    "    for i, line in tqdm(enumerate(f)): \n",
    "        title, y = line.strip().split('\\t')\n",
    "\n",
    "        # encode_plus会输出一个字典，分别为'input_ids', 'token_type_ids', 'attention_mask'对应的编码\n",
    "        # 根据参数会短则补齐，长则切断\n",
    "        encode_dict = tokenizer.encode_plus(text=title, max_length=maxlen, \n",
    "                                            padding='max_length', truncation=True)\n",
    "        \n",
    "        input_ids.append(encode_dict['input_ids'])\n",
    "        input_types.append(encode_dict['token_type_ids'])\n",
    "        input_masks.append(encode_dict['attention_mask'])\n",
    "\n",
    "        labels.append(int(y))\n",
    "\n",
    "input_ids, input_types, input_masks = np.array(input_ids), np.array(input_types), np.array(input_masks)\n",
    "labels = np.array(labels)\n",
    "print(input_ids.shape, input_types.shape, input_masks.shape, labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fd3ae62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3 切分训练集、验证集和测试集\n",
    "\n",
    "# 随机打乱索引\n",
    "idxes = np.arange(input_ids.shape[0])\n",
    "np.random.seed(2019)   # 固定种子\n",
    "np.random.shuffle(idxes)\n",
    "print(idxes.shape, idxes[:10])\n",
    "\n",
    "\n",
    "# 8:1:1 划分训练集、验证集、测试集\n",
    "input_ids_train, input_ids_valid, input_ids_test = input_ids[idxes[:80000]], input_ids[idxes[80000:90000]], input_ids[idxes[90000:]]\n",
    "input_masks_train, input_masks_valid, input_masks_test = input_masks[idxes[:80000]], input_masks[idxes[80000:90000]], input_masks[idxes[90000:]] \n",
    "input_types_train, input_types_valid, input_types_test = input_types[idxes[:80000]], input_types[idxes[80000:90000]], input_types[idxes[90000:]]\n",
    "\n",
    "y_train, y_valid, y_test = labels[idxes[:80000]], labels[idxes[80000:90000]], labels[idxes[90000:]]\n",
    "\n",
    "print(input_ids_train.shape, y_train.shape, input_ids_valid.shape, y_valid.shape, \n",
    "      input_ids_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b97e2bb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4加载到高效的DataLoader\n",
    "\n",
    "BATCH_SIZE = 64  # 如果会出现OOM问题，减小它\n",
    "# 训练集\n",
    "train_data = TensorDataset(torch.LongTensor(input_ids_train), \n",
    "                           torch.LongTensor(input_masks_train), \n",
    "                           torch.LongTensor(input_types_train), \n",
    "                           torch.LongTensor(y_train))\n",
    "train_sampler = RandomSampler(train_data)  \n",
    "train_loader = DataLoader(train_data, sampler=train_sampler, batch_size=BATCH_SIZE)\n",
    "\n",
    "# 验证集\n",
    "valid_data = TensorDataset(torch.LongTensor(input_ids_valid), \n",
    "                          torch.LongTensor(input_masks_valid),\n",
    "                          torch.LongTensor(input_types_valid), \n",
    "                          torch.LongTensor(y_valid))\n",
    "valid_sampler = SequentialSampler(valid_data)\n",
    "valid_loader = DataLoader(valid_data, sampler=valid_sampler, batch_size=BATCH_SIZE)\n",
    "\n",
    "# 测试集（是没有标签的）\n",
    "test_data = TensorDataset(torch.LongTensor(input_ids_test), \n",
    "                          torch.LongTensor(input_masks_test),\n",
    "                          torch.LongTensor(input_types_test))\n",
    "test_sampler = SequentialSampler(test_data)\n",
    "test_loader = DataLoader(test_data, sampler=test_sampler, batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f6b456e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5 定义bert模型\n",
    "\n",
    "# 定义model\n",
    "class Bert_Model(nn.Module):\n",
    "    def __init__(self, bert_path, classes=10):\n",
    "        super(Bert_Model, self).__init__()\n",
    "        self.config = BertConfig.from_pretrained(bert_path)\n",
    "        self.bert = BertModel.from_pretrained(bert_path)\n",
    "        self.fc = nn.Linear(self.config.hidden_size, classes)  # 直接分类\n",
    "        \n",
    "        \n",
    "    def forward(self, input_ids, attention_mask=None, token_type_ids=None):\n",
    "        outputs = self.bert(input_ids, attention_mask, token_type_ids)\n",
    "        out_pool = outputs[1]   # 池化后的输出 \n",
    "        logit = self.fc(out_pool)\n",
    "        return logit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c56536b5",
   "metadata": {},
   "source": [
    "可以发现，bert模型的定义由于高效简易的封装库存在，使得定义模型较为容易，如果想要在bert之后加入cnn/rnn等层，可在这里定义。（NER则为加CRF）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a248a7cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6 实例化bert模型\n",
    "\n",
    "def get_parameter_number(model):\n",
    "    #  打印模型参数\n",
    "    total_num = sum(p.numel() for p in model.parameters())\n",
    "    trainable_num = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "    return 'Total parameters: {}, Trainable parameters: {}'.format(total_num, trainable_num)\n",
    "\n",
    "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "EPOCHS = 5\n",
    "model = Bert_Model(bert_path).to(DEVICE)\n",
    "print(get_parameter_number(model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77a320c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 7 定义优化器\n",
    "\n",
    "optimizer = AdamW(model.parameters(), lr=2e-5, weight_decay=1e-4) #AdamW优化器\n",
    "scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=len(train_loader),\n",
    "                                            num_training_steps=EPOCHS*len(train_loader))\n",
    "# 学习率先线性warmup一个epoch，然后cosine式下降。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49fd4d00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 8 定义训练函数和验证测试函数\n",
    "\n",
    "# 评估模型性能，在验证集上\n",
    "def evaluate(model, data_loader, device):\n",
    "    model.eval()\n",
    "    val_true, val_pred = [], []\n",
    "    with torch.no_grad():\n",
    "        for idx, (ids, att, tpe, y) in (enumerate(data_loader)):\n",
    "            y_pred = model(ids.to(device), att.to(device), tpe.to(device))\n",
    "            y_pred = torch.argmax(y_pred, dim=1).detach().cpu().numpy().tolist()\n",
    "            val_pred.extend(y_pred)\n",
    "            val_true.extend(y.squeeze().cpu().numpy().tolist())\n",
    "    \n",
    "    return accuracy_score(val_true, val_pred)  #返回accuracy\n",
    "\n",
    "\n",
    "# 测试集没有标签，需要预测提交\n",
    "def predict(model, data_loader, device):\n",
    "    model.eval()\n",
    "    val_pred = []\n",
    "    with torch.no_grad():\n",
    "        for idx, (ids, att, tpe) in tqdm(enumerate(data_loader)):\n",
    "            y_pred = model(ids.to(device), att.to(device), tpe.to(device))\n",
    "            y_pred = torch.argmax(y_pred, dim=1).detach().cpu().numpy().tolist()\n",
    "            val_pred.extend(y_pred)\n",
    "    return val_pred\n",
    "\n",
    "\n",
    "def train_and_eval(model, train_loader, valid_loader, \n",
    "                   optimizer, scheduler, device, epoch):\n",
    "    best_acc = 0.0\n",
    "    patience = 0\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    for i in range(epoch):\n",
    "        \"\"\"训练模型\"\"\"\n",
    "        start = time.time()\n",
    "        model.train()\n",
    "        print(\"***** Running training epoch {} *****\".format(i+1))\n",
    "        train_loss_sum = 0.0\n",
    "        for idx, (ids, att, tpe, y) in enumerate(train_loader):\n",
    "            ids, att, tpe, y = ids.to(device), att.to(device), tpe.to(device), y.to(device)  \n",
    "            y_pred = model(ids, att, tpe)\n",
    "            loss = criterion(y_pred, y)\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            scheduler.step()   # 学习率变化\n",
    "            \n",
    "            train_loss_sum += loss.item()\n",
    "            if (idx + 1) % (len(train_loader)//5) == 0:    # 只打印五次结果\n",
    "                print(\"Epoch {:04d} | Step {:04d}/{:04d} | Loss {:.4f} | Time {:.4f}\".format(\n",
    "                          i+1, idx+1, len(train_loader), train_loss_sum/(idx+1), time.time() - start))\n",
    "                # print(\"Learning rate = {}\".format(optimizer.state_dict()['param_groups'][0]['lr']))\n",
    "\n",
    "        \"\"\"验证模型\"\"\"\n",
    "        model.eval()\n",
    "        acc = evaluate(model, valid_loader, device)  # 验证模型的性能\n",
    "        if acc > best_acc:\n",
    "            best_acc = acc\n",
    "            torch.save(model.state_dict(), \"best_bert_model.pth\") \n",
    "        \n",
    "        print(\"current acc is {:.4f}, best acc is {:.4f}\".format(acc, best_acc))\n",
    "        print(\"time costed = {}s \\n\".format(round(time.time() - start, 5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da7a0335",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 9 开始训练和验证模型\n",
    "\n",
    "# 训练和验证评估\n",
    "train_and_eval(model, train_loader, valid_loader, optimizer, scheduler, DEVICE, EPOCHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a722671",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 10 加载最优模型进行测试\n",
    "\n",
    "# 加载最优权重对测试集测试\n",
    "model.load_state_dict(torch.load(\"best_bert_model.pth\"))\n",
    "pred_test = predict(model, test_loader, DEVICE)\n",
    "print(\"\\n Test Accuracy = {} \\n\".format(accuracy_score(y_test, pred_test)))\n",
    "print(classification_report(y_test, pred_test, digits=4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb21f0be",
   "metadata": {},
   "source": [
    "综上，10步可以搭建一个基于pytorch的bert模型解决下游任务，吃透了即可来完成任何子任务"
   ]
  }
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