KoBert 기반 Chatbot test.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EEHZPVwg1SQm",
        "outputId": "0e35847e-772d-457a-8c1b-e9a68fdd706f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting mxnet\n",
            "  Downloading mxnet-1.9.1-py3-none-manylinux2014_x86_64.whl (49.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.1/49.1 MB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting graphviz<0.9.0,>=0.8.1\n",
            "  Downloading graphviz-0.8.4-py2.py3-none-any.whl (16 kB)\n",
            "Requirement already satisfied: numpy<2.0.0,>1.16.0 in /usr/local/lib/python3.8/dist-packages (from mxnet) (1.21.6)\n",
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            "Installing collected packages: graphviz, mxnet\n",
            "  Attempting uninstall: graphviz\n",
            "    Found existing installation: graphviz 0.10.1\n",
            "    Uninstalling graphviz-0.10.1:\n",
            "      Successfully uninstalled graphviz-0.10.1\n",
            "Successfully installed graphviz-0.8.4 mxnet-1.9.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting gluonnlp\n",
            "  Downloading gluonnlp-0.10.0.tar.gz (344 kB)\n",
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            "Building wheels for collected packages: gluonnlp\n",
            "  Building wheel for gluonnlp (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gluonnlp: filename=gluonnlp-0.10.0-cp38-cp38-linux_x86_64.whl size=688989 sha256=1b04ce5075d862eb384ce90fb30a31451c79e1f6ce34d27683aa681580c67b7b\n",
            "  Stored in directory: /root/.cache/pip/wheels/b6/93/9d/2237550c409eb3ed725d6302b7897ddd9a037b40cef66dcd9c\n",
            "Successfully built gluonnlp\n",
            "Installing collected packages: gluonnlp\n",
            "Successfully installed gluonnlp-0.10.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.97-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.97\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers==3.0.2\n",
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            "  Downloading sacremoses-0.0.53.tar.gz (880 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m880.6/880.6 KB\u001b[0m \u001b[31m70.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: joblib in /usr/local/lib/python3.8/dist-packages (from sacremoses->transformers==3.0.2) (1.2.0)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895260 sha256=1300a8f4afd7e1cd3751f3714ec5bed42376c4df3f062221142dedf734cae317\n",
            "  Stored in directory: /root/.cache/pip/wheels/82/ab/9b/c15899bf659ba74f623ac776e861cf2eb8608c1825ddec66a4\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: tokenizers, sacremoses, transformers\n",
            "Successfully installed sacremoses-0.0.53 tokenizers-0.8.1rc1 transformers-3.0.2\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (1.13.1+cu116)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch) (4.4.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install mxnet\n",
        "!pip install gluonnlp pandas tqdm\n",
        "!pip install sentencepiece\n",
        "!pip install transformers==3.0.2\n",
        "!pip install torch"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://git@github.com/SKTBrain/KoBERT.git@master"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9kogGnih1mrb",
        "outputId": "1b7cc6e5-c385-4da1-bb2f-a50e6729d1a5"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://****@github.com/SKTBrain/KoBERT.git@master\n",
            "  Cloning https://****@github.com/SKTBrain/KoBERT.git (to revision master) to /tmp/pip-req-build-98hqkycu\n",
            "  Running command git clone --filter=blob:none --quiet 'https://****@github.com/SKTBrain/KoBERT.git' /tmp/pip-req-build-98hqkycu\n",
            "  Resolved https://****@github.com/SKTBrain/KoBERT.git to commit 47a69af87928fc24e20f571fe10c3cc9dd9af9a3\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting boto3<=1.15.18\n",
            "  Downloading boto3-1.15.18-py2.py3-none-any.whl (129 kB)\n",
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            "Collecting mxnet<=1.7.0.post2,>=1.4.0\n",
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            "\u001b[?25hCollecting onnxruntime<=1.8.0,==1.8.0\n",
            "  Downloading onnxruntime-1.8.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5 MB)\n",
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            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from gluonnlp<=0.10.0,>=0.6.0->kobert==0.2.3) (23.0)\n",
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            "Requirement already satisfied: graphviz<0.9.0,>=0.8.1 in /usr/local/lib/python3.8/dist-packages (from mxnet<=1.7.0.post2,>=1.4.0->kobert==0.2.3) (0.8.4)\n",
            "Requirement already satisfied: requests<3,>=2.20.0 in /usr/local/lib/python3.8/dist-packages (from mxnet<=1.7.0.post2,>=1.4.0->kobert==0.2.3) (2.25.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch<=1.10.1,>=1.7.0->kobert==0.2.3) (4.4.0)\n",
            "Collecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.3/3.3 MB\u001b[0m \u001b[31m101.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting huggingface-hub==0.0.12\n",
            "  Downloading huggingface_hub-0.0.12-py3-none-any.whl (37 kB)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (4.64.1)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.8/dist-packages (from transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (0.0.53)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (3.9.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (2022.6.2)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.8/dist-packages (from transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (6.0)\n",
            "Requirement already satisfied: urllib3<1.26,>=1.20 in /usr/local/lib/python3.8/dist-packages (from botocore<1.19.0,>=1.18.18->boto3<=1.15.18->kobert==0.2.3) (1.24.3)\n",
            "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.8/dist-packages (from botocore<1.19.0,>=1.18.18->boto3<=1.15.18->kobert==0.2.3) (2.8.2)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.20.0->mxnet<=1.7.0.post2,>=1.4.0->kobert==0.2.3) (2022.12.7)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.20.0->mxnet<=1.7.0.post2,>=1.4.0->kobert==0.2.3) (4.0.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.20.0->mxnet<=1.7.0.post2,>=1.4.0->kobert==0.2.3) (2.10)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from sacremoses->transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (1.15.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.8/dist-packages (from sacremoses->transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (1.2.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.8/dist-packages (from sacremoses->transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (7.1.2)\n",
            "Building wheels for collected packages: kobert\n",
            "  Building wheel for kobert (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kobert: filename=kobert-0.2.3-py3-none-any.whl size=15708 sha256=d6c1a99c2a2819a11c15f77e007b96670962f669595c413050489b9206c331d1\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-0fhavkv2/wheels/bf/5f/74/81bf3a1332130eb6629ecf58876a8746b77021e7d7b0638e91\n",
            "Successfully built kobert\n",
            "Installing collected packages: tokenizers, sentencepiece, torch, onnxruntime, jmespath, mxnet, huggingface-hub, botocore, transformers, s3transfer, boto3, kobert\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.8.1rc1\n",
            "    Uninstalling tokenizers-0.8.1rc1:\n",
            "      Successfully uninstalled tokenizers-0.8.1rc1\n",
            "  Attempting uninstall: sentencepiece\n",
            "    Found existing installation: sentencepiece 0.1.97\n",
            "    Uninstalling sentencepiece-0.1.97:\n",
            "      Successfully uninstalled sentencepiece-0.1.97\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 1.13.1+cu116\n",
            "    Uninstalling torch-1.13.1+cu116:\n",
            "      Successfully uninstalled torch-1.13.1+cu116\n",
            "  Attempting uninstall: mxnet\n",
            "    Found existing installation: mxnet 1.9.1\n",
            "    Uninstalling mxnet-1.9.1:\n",
            "      Successfully uninstalled mxnet-1.9.1\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 3.0.2\n",
            "    Uninstalling transformers-3.0.2:\n",
            "      Successfully uninstalled transformers-3.0.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchvision 0.14.1+cu116 requires torch==1.13.1, but you have torch 1.10.1 which is incompatible.\n",
            "torchtext 0.14.1 requires torch==1.13.1, but you have torch 1.10.1 which is incompatible.\n",
            "torchaudio 0.13.1+cu116 requires torch==1.13.1, but you have torch 1.10.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed boto3-1.15.18 botocore-1.18.18 huggingface-hub-0.0.12 jmespath-0.10.0 kobert-0.2.3 mxnet-1.7.0.post2 onnxruntime-1.8.0 s3transfer-0.3.7 sentencepiece-0.1.96 tokenizers-0.10.3 torch-1.10.1 transformers-4.8.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import gluonnlp as nlp\n",
        "import numpy as np\n",
        "from tqdm import tqdm, tqdm_notebook\n",
        "#kobert\n",
        "from kobert.utils import get_tokenizer\n",
        "from kobert.pytorch_kobert import get_pytorch_kobert_model\n",
        "\n",
        "#transformers\n",
        "from transformers import AdamW\n",
        "from transformers.optimization import get_cosine_schedule_with_warmup"
      ],
      "metadata": {
        "id": "5LurDS3q1qLg"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#GPU 사용\n",
        "device = torch.device(\"cuda:0\")"
      ],
      "metadata": {
        "id": "V8T5DhqH2PDA"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#BERT 모델, Vocabulary 불러오기\n",
        "bertmodel, vocab = get_pytorch_kobert_model()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IJkxWfrM2Vfh",
        "outputId": "3a8bf54d-f2f2-4b7c-bc12-35abed075aa2"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/.cache/kobert_v1.zip[██████████████████████████████████████████████████]\n",
            "/content/.cache/kobert_news_wiki_ko_cased-1087f8699e.spiece[██████████████████████████████████████████████████]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#구글드라이브 연동\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2PQMhl72hP2",
        "outputId": "a0b58128-c079-43ae-fc33-1495b049f567"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "chatbot_data = pd.read_csv('/content/drive/MyDrive/ChatbotData.csv')"
      ],
      "metadata": {
        "id": "MjSg0o7K2pcR"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chatbot_data.sample(n=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "_RXuB2-G3Kes",
        "outputId": "7b086c17-3580-499a-e071-3e8958c8d825"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           Q                          A  label\n",
              "7119            오늘 그 사람 생일이네         생각날 수 밖에 없다고 생각해요.      1\n",
              "7024           언젠간 먼저 연락오겠죠?         붙잡고 싶다면 먼저 연락해보세요.      1\n",
              "11707     친구의 남친 좋아하게 된 거 같아              사랑은 쟁취하는 거예요.      2\n",
              "3898     자기도 해결방안이 없으면서 불만만해             자기도 알고 있을 거예요.      0\n",
              "3554            울 남친은 애교가 많아                     귀엽겠네요.      0\n",
              "3407                와플 먹고 싶어             근처 카페에서 드셔보세요.      0\n",
              "2779          신혼여행 한달쯤 가고 싶다                 휴가를 붙여보세요.      0\n",
              "4994           학생만 평생하면 좋겠다.                이제 취업 하셔야죠.      0\n",
              "5717                긴연애의 끝인가       긴 연애였던 만큼 아플까 걱정이에요.      1\n",
              "286    군대 갔다 올 때까지 기다릴 수 있을까  자신의 삶을 살다보면 기다릴 수 있을 거예요.      0"
            ],
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Q</th>\n",
              "      <th>A</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7119</th>\n",
              "      <td>오늘 그 사람 생일이네</td>\n",
              "      <td>생각날 수 밖에 없다고 생각해요.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7024</th>\n",
              "      <td>언젠간 먼저 연락오겠죠?</td>\n",
              "      <td>붙잡고 싶다면 먼저 연락해보세요.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11707</th>\n",
              "      <td>친구의 남친 좋아하게 된 거 같아</td>\n",
              "      <td>사랑은 쟁취하는 거예요.</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3898</th>\n",
              "      <td>자기도 해결방안이 없으면서 불만만해</td>\n",
              "      <td>자기도 알고 있을 거예요.</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3554</th>\n",
              "      <td>울 남친은 애교가 많아</td>\n",
              "      <td>귀엽겠네요.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3407</th>\n",
              "      <td>와플 먹고 싶어</td>\n",
              "      <td>근처 카페에서 드셔보세요.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2779</th>\n",
              "      <td>신혼여행 한달쯤 가고 싶다</td>\n",
              "      <td>휴가를 붙여보세요.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4994</th>\n",
              "      <td>학생만 평생하면 좋겠다.</td>\n",
              "      <td>이제 취업 하셔야죠.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5717</th>\n",
              "      <td>긴연애의 끝인가</td>\n",
              "      <td>긴 연애였던 만큼 아플까 걱정이에요.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>286</th>\n",
              "      <td>군대 갔다 올 때까지 기다릴 수 있을까</td>\n",
              "      <td>자신의 삶을 살다보면 기다릴 수 있을 거예요.</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chatbot_data.loc[(chatbot_data['label'] == \"0\"), 'label'] = 0  #일상 => 0\n",
        "chatbot_data.loc[(chatbot_data['label'] == \"1\"), 'label'] = 1  #이별 => 1\n",
        "chatbot_data.loc[(chatbot_data['label'] == \"2\"), 'label'] = 2  #사랑 => 2\n",
        "\n",
        "data_list = []\n",
        "for q, label in zip(chatbot_data['Q'], chatbot_data['label'])  :\n",
        "    data = []\n",
        "    data.append(q)\n",
        "    data.append(str(label))\n",
        "\n",
        "    data_list.append(data)"
      ],
      "metadata": {
        "id": "ueuOcG6n3WxZ"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data_list[0])\n",
        "print(data_list[6000])\n",
        "print(data_list[-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8xo_OLD34EK0",
        "outputId": "c6c627d1-4cdb-4d01-b50a-0182c36f752e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['12시 땡!', '0']\n",
            "['눈물이 많터냐', '1']\n",
            "['힘들어서 결혼할까봐', '2']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#train & test 데이터로 나누기\n",
        "from sklearn.model_selection import train_test_split\n",
        "                                                         \n",
        "dataset_train, dataset_test = train_test_split(data_list, test_size=0.2, random_state=0)"
      ],
      "metadata": {
        "id": "v9uW6UrD5fkx"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(dataset_train))\n",
        "print(len(dataset_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oOzpabxk5iF_",
        "outputId": "dd90069b-9eeb-43f3-8e49-ede933843550"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9458\n",
            "2365\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class BERTDataset(Dataset):\n",
        "    def __init__(self, dataset, sent_idx, label_idx, bert_tokenizer, max_len,\n",
        "                 pad, pair):\n",
        "        transform = nlp.data.BERTSentenceTransform(\n",
        "            bert_tokenizer, max_seq_length=max_len, pad=pad, pair=pair)\n",
        "\n",
        "        self.sentences = [transform([i[sent_idx]]) for i in dataset]\n",
        "        self.labels = [np.int32(i[label_idx]) for i in dataset]\n",
        "\n",
        "    def __getitem__(self, i):\n",
        "        return (self.sentences[i] + (self.labels[i], ))\n",
        "\n",
        "    def __len__(self):\n",
        "        return (len(self.labels))"
      ],
      "metadata": {
        "id": "WhUjfmWB5lcw"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting parameters\n",
        "max_len = 64\n",
        "batch_size = 64\n",
        "warmup_ratio = 0.1\n",
        "num_epochs = 5\n",
        "max_grad_norm = 1\n",
        "log_interval = 200\n",
        "learning_rate =  5e-5"
      ],
      "metadata": {
        "id": "ZAe6x5FG5nwH"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#토큰화\n",
        "tokenizer = get_tokenizer()\n",
        "tok = nlp.data.BERTSPTokenizer(tokenizer, vocab, lower=False)\n",
        "\n",
        "data_train = BERTDataset(dataset_train, 0, 1, tok, max_len, True, False)\n",
        "data_test = BERTDataset(dataset_test, 0, 1, tok, max_len, True, False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "axMqoJ8g5q2-",
        "outputId": "5bb92029-6732-42b4-dea6-1abda46fb9f0"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "using cached model. /content/.cache/kobert_news_wiki_ko_cased-1087f8699e.spiece\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LfIQUfh05yxt",
        "outputId": "58fbd9d6-ec64-405e-a4fe-5ddae7f533c9"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([   2, 1406, 3466, 3647, 6213, 6853, 7126, 5591,    3,    1,    1,\n",
              "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
              "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
              "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
              "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
              "           1,    1,    1,    1,    1,    1,    1,    1,    1], dtype=int32),\n",
              " array(9, dtype=int32),\n",
              " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       dtype=int32),\n",
              " 0)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataloader = torch.utils.data.DataLoader(data_train, batch_size=batch_size, num_workers=5)\n",
        "test_dataloader = torch.utils.data.DataLoader(data_test, batch_size=batch_size, num_workers=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HLx-mQiR51RT",
        "outputId": "c58e6fc2-ad63-4b9b-ba51-269191283154"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:478: UserWarning: This DataLoader will create 5 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class BERTClassifier(nn.Module):\n",
        "    def __init__(self,\n",
        "                 bert,\n",
        "                 hidden_size = 768,\n",
        "                 num_classes=3,   ##클래스 수 조정##\n",
        "                 dr_rate=None,\n",
        "                 params=None):\n",
        "        super(BERTClassifier, self).__init__()\n",
        "        self.bert = bert\n",
        "        self.dr_rate = dr_rate\n",
        "                 \n",
        "        self.classifier = nn.Linear(hidden_size , num_classes)\n",
        "        if dr_rate:\n",
        "            self.dropout = nn.Dropout(p=dr_rate)\n",
        "    \n",
        "    def gen_attention_mask(self, token_ids, valid_length):\n",
        "        attention_mask = torch.zeros_like(token_ids)\n",
        "        for i, v in enumerate(valid_length):\n",
        "            attention_mask[i][:v] = 1\n",
        "        return attention_mask.float()\n",
        "\n",
        "    def forward(self, token_ids, valid_length, segment_ids):\n",
        "        attention_mask = self.gen_attention_mask(token_ids, valid_length)\n",
        "        \n",
        "        _, pooler = self.bert(input_ids = token_ids, token_type_ids = segment_ids.long(), attention_mask = attention_mask.float().to(token_ids.device))\n",
        "        if self.dr_rate:\n",
        "            out = self.dropout(pooler)\n",
        "        return self.classifier(out)"
      ],
      "metadata": {
        "id": "hxqZpKK_53dP"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#BERT 모델 불러오기\n",
        "model = BERTClassifier(bertmodel,  dr_rate=0.5).to(device)\n",
        "\n",
        "#optimizer와 schedule 설정\n",
        "no_decay = ['bias', 'LayerNorm.weight']\n",
        "optimizer_grouped_parameters = [\n",
        "    {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},\n",
        "    {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
        "]\n",
        "\n",
        "optimizer = AdamW(optimizer_grouped_parameters, lr=learning_rate)\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "\n",
        "t_total = len(train_dataloader) * num_epochs\n",
        "warmup_step = int(t_total * warmup_ratio)\n",
        "\n",
        "scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=warmup_step, num_training_steps=t_total)\n",
        "\n",
        "#정확도 측정을 위한 함수 정의\n",
        "def calc_accuracy(X,Y):\n",
        "    max_vals, max_indices = torch.max(X, 1)\n",
        "    train_acc = (max_indices == Y).sum().data.cpu().numpy()/max_indices.size()[0]\n",
        "    return train_acc\n",
        "    \n",
        "train_dataloader"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S8WB8Kwu57di",
        "outputId": "1723dabd-d302-4ca7-df19-4468e1c7390f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch.utils.data.dataloader.DataLoader at 0x7f3394fb2dc0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for e in range(num_epochs):\n",
        "    train_acc = 0.0\n",
        "    test_acc = 0.0\n",
        "    model.train()\n",
        "    for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(tqdm_notebook(train_dataloader)):\n",
        "        optimizer.zero_grad()\n",
        "        token_ids = token_ids.long().to(device)\n",
        "        segment_ids = segment_ids.long().to(device)\n",
        "        valid_length= valid_length\n",
        "        label = label.long().to(device)\n",
        "        out = model(token_ids, valid_length, segment_ids)\n",
        "        loss = loss_fn(out, label)\n",
        "        loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)\n",
        "        optimizer.step()\n",
        "        scheduler.step()  # Update learning rate schedule\n",
        "        train_acc += calc_accuracy(out, label)\n",
        "        if batch_id % log_interval == 0:\n",
        "            print(\"epoch {} batch id {} loss {} train acc {}\".format(e+1, batch_id+1, loss.data.cpu().numpy(), train_acc / (batch_id+1)))\n",
        "    print(\"epoch {} train acc {}\".format(e+1, train_acc / (batch_id+1)))\n",
        "    \n",
        "    model.eval()\n",
        "    for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(tqdm_notebook(test_dataloader)):\n",
        "        token_ids = token_ids.long().to(device)\n",
        "        segment_ids = segment_ids.long().to(device)\n",
        "        valid_length= valid_length\n",
        "        label = label.long().to(device)\n",
        "        out = model(token_ids, valid_length, segment_ids)\n",
        "        test_acc += calc_accuracy(out, label)\n",
        "    print(\"epoch {} test acc {}\".format(e+1, test_acc / (batch_id+1)))"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "aYClsEpf7msj",
        "outputId": "e1200a14-9ac1-4990-d5df-cb49551d41f8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-20-480b6a139979>:5: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(tqdm_notebook(train_dataloader)):\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/148 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "91a8bff42c6b419dbde69bc0f091b537"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 batch id 1 loss 1.1355516910552979 train acc 0.359375\n",
            "epoch 1 train acc 0.6683952702702702\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-20-480b6a139979>:23: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(tqdm_notebook(test_dataloader)):\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/37 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc8c43dd391f448882136b9af4a8f6c3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 test acc 0.8257019827204254\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/148 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "619b6c287f834b50a3617821dbf5939b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 batch id 1 loss 0.3179725408554077 train acc 0.90625\n",
            "epoch 2 train acc 0.8485472972972973\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/37 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6fe2238802f5477ba480b5504c21dc59"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 test acc 0.8655017722640673\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/148 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "708a4de69df64ae3a1dfca516de29bc7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 batch id 1 loss 0.21871399879455566 train acc 0.9375\n",
            "epoch 3 train acc 0.9073057432432432\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/37 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ea6aa5089e0241b28a7dee14104f5f0f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 test acc 0.8743907842268497\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/148 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5b8004ca01db4cdf87de5fce5a2a1b97"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 4 batch id 1 loss 0.07919886708259583 train acc 0.96875\n",
            "epoch 4 train acc 0.9441216216216215\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/37 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "24eb4b64edc84f1b80d2e143c2a2f117"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 4 test acc 0.8794998892334958\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/148 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2c6763bc702e42a7b8f2c79af2519df1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 5 batch id 1 loss 0.05995326116681099 train acc 0.96875\n",
            "epoch 5 train acc 0.9634712837837838\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/37 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a627e6037f0c4ee89870e6a4b432e29a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 5 test acc 0.8769245680106335\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#토큰화\n",
        "tokenizer = get_tokenizer()\n",
        "tok = nlp.data.BERTSPTokenizer(tokenizer, vocab, lower=False)\n",
        "\n",
        "def predict(predict_sentence):\n",
        "\n",
        "    data = [predict_sentence, '0']\n",
        "    dataset_another = [data]\n",
        "\n",
        "    another_test = BERTDataset(dataset_another, 0, 1, tok, max_len, True, False)\n",
        "    test_dataloader = torch.utils.data.DataLoader(another_test, batch_size=batch_size, num_workers=5)\n",
        "    \n",
        "    model.eval()\n",
        "\n",
        "    for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(test_dataloader):\n",
        "        token_ids = token_ids.long().to(device)\n",
        "        segment_ids = segment_ids.long().to(device)\n",
        "\n",
        "        valid_length= valid_length\n",
        "        label = label.long().to(device)\n",
        "\n",
        "        out = model(token_ids, valid_length, segment_ids)\n",
        "\n",
        "\n",
        "        test_eval=[]\n",
        "        for i in out:\n",
        "            logits=i\n",
        "            logits = logits.detach().cpu().numpy()\n",
        "\n",
        "            if np.argmax(logits) == 0:\n",
        "                test_eval.append(\"일상\")\n",
        "            elif np.argmax(logits) == 1:\n",
        "                test_eval.append(\"부정\")\n",
        "            elif np.argmax(logits) == 2:\n",
        "                test_eval.append(\"긍정\")\n",
        "\n",
        "        print(\">> 입력하신 내용에서 \" + test_eval[0] + \" 느껴집니다.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "44uht3j1_t-f",
        "outputId": "11d318b8-a914-416c-d4ec-582838210f75"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "using cached model. /content/.cache/kobert_news_wiki_ko_cased-1087f8699e.spiece\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#질문 무한반복하기! 0 입력시 종료\n",
        "end = 1\n",
        "while end == 1 :\n",
        "    sentence = input(\"하고싶은 말을 입력해주세요 : \")\n",
        "    if sentence == 0 :\n",
        "        break\n",
        "    predict(sentence)\n",
        "    print(\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "id": "jHcEAcOH_zJd",
        "outputId": "964452cf-e432-4865-8f79-00c5c6dacb05"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "하고싶은 말을 입력해주세요 : 사랑해\n",
            ">> 입력하신 내용에서 긍정 느껴집니다.\n",
            "\n",
            "\n",
            "하고싶은 말을 입력해주세요 : 안뇽\n",
            ">> 입력하신 내용에서 일상 느껴집니다.\n",
            "\n",
            "\n",
            "하고싶은 말을 입력해주세요 : 하이하이\n",
            ">> 입력하신 내용에서 일상 느껴집니다.\n",
            "\n",
            "\n",
            "하고싶은 말을 입력해주세요 : 0\n",
            ">> 입력하신 내용에서 부정 느껴집니다.\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-7e05f0054d59>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0msentence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"하고싶은 말을 입력해주세요 : \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msentence\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    858\u001b[0m                 \u001b[0;34m\"raw_input was called, but this frontend does not support input requests.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    859\u001b[0m             )\n\u001b[0;32m--> 860\u001b[0;31m         return self._input_request(str(prompt),\n\u001b[0m\u001b[1;32m    861\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    862\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    902\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    903\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 904\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Interrupted by user\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    905\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    906\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid Message:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
          ]
        }
      ]
    }
  ]
}