{
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  {
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   "source": [
    "## tokenizer\n",
    "\n",
    "tokenizer负责将输入的字符串，转换为token，或者转换为ids序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入模型\n",
    "from transformers import AutoTokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"liam168/c2-roberta-base-finetuned-dianping-chinese\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['这', '是', '中', '英', '文', 'test', '语', '句', '，', 'mix', '中', '英', '文', '及', '标', '点', '符', '号']\n",
      "['测', '试', 'mix', '中', '英', '文', '及', '标', '点', '符', '号']\n"
     ]
    }
   ],
   "source": [
    "# 转换为token\n",
    "input = \"这是中英文test语句，mix中英文及标点符号\"\n",
    "input_array = [\"测试\",\"mix中英文及标点符号\"]\n",
    "print(tokenizer.tokenize(input))\n",
    "print(tokenizer.tokenize(input_array))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[  101,  6821,  3221,   704,  5739,  3152, 10060,  6427,  1368,  8024,\n",
      "          9678,   704,  5739,  3152,  1350,  3403,  4157,  5016,  1384,   102]])\n",
      "tensor([[ 101, 3844, 6407,  102, 9678,  704, 5739, 3152, 1350, 3403, 4157, 5016,\n",
      "         1384,  102]])\n",
      "['[CLS]', '这', '是', '中', '英', '文', 'test', '语', '句', '，', 'mix', '中', '英', '文', '及', '标', '点', '符', '号', '[SEP]']\n",
      "['[CLS]', '测', '试', '[SEP]', 'mix', '中', '英', '文', '及', '标', '点', '符', '号', '[SEP]']\n"
     ]
    }
   ],
   "source": [
    "#转换为id序列\n",
    "ids = tokenizer(input,padding=True,truncation=True,max_length=512,return_tensors=\"pt\")\n",
    "print(ids.input_ids)\n",
    "idss = tokenizer([input_array],padding=True,truncation=True,max_length=512,return_tensors=\"pt\")\n",
    "print(idss.input_ids)\n",
    "\n",
    "#将ids序列，又解码成为token\n",
    "print(tokenizer.convert_ids_to_tokens(ids.input_ids[0]))\n",
    "print(tokenizer.convert_ids_to_tokens(idss.input_ids[0]))"
   ]
  }
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